But Prequal challenges that intuition. Instead of balancing traffic evenly, it focuses on balancing wait time, routing requests based on how quickly they can actually be served rather than just spreading them uniformly.

According to Google, this approach is already deployed across 20+ services, including YouTube’s serving stack (NSDI ‘24 paper).

Over the past few weeks, I’ve been reimplementing this algorithm in Go partly to understand it deeply, and partly for the bragging rights of building my own load balancer from scratch.

This post is a technical walkthrough of both the paper and codebase:

I believe the most interesting part of this repo is not just that it implements Prequal. It is that the repo preserves the engineering process of getting to a result you can trust. There are wrong runs, methodological mistakes, a regime pivot, overhead profiling, and a final bounded claim rather than a “it worked on my machine”.

Key Takeaways

• Prequal is a load-balancing algorithm Google reports deploying across 20+ services, including YouTube’s serving stack (NSDI ‘24 paper).
• This Go reimplementation, packaged as a Kubernetes ingress controller, cuts p99 tail latency by 8.6x vs round-robin in a paper-aligned heterogeneous regime (16 backends, 16x service-time skew, I/O-bound).
• In a small CPU-bound regime (4 backends), the same implementation is roughly 25% slower than round-robin. The negative case is published alongside the positive one.
• The interesting engineering story is not the algorithm itself. It is the benchmark protocol, investigation trail, and regime pivot that turned a 10x-worse false negative into a bounded, defensible claim.

What problem does Prequal solve?

Prequal, introduced at NSDI ‘24 by Wydrowski et al., replaces CPU-based balancing with active probing of two per-backend signals: requests-in-flight (RIF) and recent latency. A hot-cold lexicographic rule picks the lowest-latency backend below an RIF quantile threshold (default 0.75), falling back to lowest-RIF when every candidate is congested.

Prequal’s central claim, from the paper is that the right signal for load balancing is not CPU utilization but expected wait time, and the paper reports that Google runs this approach across 20+ services including YouTube. The algorithm replaces smoothed load metrics with active probes of requests-in-flight and latency, then uses a hot-cold lexicographic rule on those two signals to pick a backend.

The paper starts from a real production observation inside Google: in large multi-tenant systems, balancing CPU evenly across replicas is not the same thing as minimizing latency. A backend can look “lightly loaded” according to a smoothed resource metric and still be a bad place to send the next request because it is on a noisy host, has a growing queue, or has just crossed into a regime where service time gets ugly.

That is part of what makes the paper compelling. The authors are not proposing a clever synthetic algorithm in the abstract. They are describing the load-balancing approach Google says it uses in production, especially in YouTube’s serving stack, after living with the failure modes of more conventional strategies.

Prequal’s answer is to use two signals:

• RIF: requests in flight
• latency: a backend-reported estimate of recent service latency

And then to sample those signals by probing backends asynchronously.

The selection rule from the paper is the part worth remembering. Prequal does not combine latency and RIF into one score by default. It uses a lexicographic rule:

1. Split candidates into “cold” and “hot” using an RIF quantile threshold.
2. If any cold candidates exist, pick the one with the lowest latency.
3. If every candidate is hot, pick the one with the lowest RIF.

That rule matters because it captures something simple and useful:

• latency is the best tie-breaker among backends that are not yet visibly congested
• once everything is congested, queue depth wins and you should pick the least loaded one

The paper calls this the hot-cold lexicographic rule, or HCL. In this repo, that is the heart of the algorithm.

If you have read about the power of two choices algorithm before, Prequal lives in the same family. Power of two picks two random backends and sends the request to whichever has fewer in-flight connections. It is cheap, surprisingly close to optimal, and widely deployed. HAProxy’s own benchmark (Power of Two Load Balancing) shows it beating round-robin on peak connection skew but still losing a few percent to a full least-connections scan. Prequal generalizes the idea: instead of two random picks checked synchronously, it keeps a small pool of asynchronous probe results and selects from that pool using both RIF and latency, not just connection count.

The other big paper idea is async probing. Synchronous probing would put an extra network hop in the critical path of every request. Prequal instead probes off the request path, stores recent probe observations in a bounded pool, and reuses them enough to be cheap without letting them go stale.

What I’ve actually built

At runtime this project is one Go binary with two jobs:

• a Kubernetes controller that watches Ingress and EndpointSlice
• an HTTP reverse proxy that receives requests and selects backends

Around that, the repo includes:

• a Rust backend used for controlled benchmarks
• benchmark manifests for uniform and heterogeneous workloads
• k6 scripts for open-loop, ramp, burst, overload, multi-route, and long-duration tests
• Prometheus and Grafana assets
• frozen benchmark reports and investigation logs

The top-level structure is clean and maps well to the architecture:

controller/      Kubernetes reconciliation and route state
server/          Reverse proxy and request-path selection
loadbalancer/    Prequal, least-connections, round-robin, probe logic, RIF, latency, pools
backend/         Rust benchmark backend exposing /work and /prequal/probe
observability/   Prometheus metrics
tree/            Host/path trie for ingress routing
benchmark/       Manifests, k6 scripts, dashboards, reports, investigations, raw results

The entrypoint in main.go wires all of that together:

store := controller.NewBackendIPStore()
ctrl := controller.NewController(factory, store, queue)

tracker := &loadbalancer.RIFTracker{}
latencyTracker := loadbalancer.NewLatencyTracker()

cfg := loadbalancer.DefaultProbeConfig()
cfg.ApplyEnv()

pools := pool.NewRoutePools(pool.PoolConfig{
    MaxSize:     cfg.PoolMaxSize,
    MaxAge:      cfg.PoolMaxAge,
    ReuseLimit:  cfg.PoolReuseLimit,
    QRIF:        cfg.QRIF,
    MaxProbeAge: cfg.MaxProbeAge,
}, cfg.PoolMaintenanceInterval)

prober := loadbalancer.NewProber(pools, store, cfg, stop)
proxyServer := server.NewProxyServerWithConfig(
    ctrl.GetRouter(), store, selectors, tracker, latencyTracker, pools, prober, serverCfg,
)

That composition is the design of the loadbalancer:

• the controller owns route and endpoint discovery
• the proxy owns request forwarding
• the load balancer owns route-local state and selection policy

Control plane: translating Kubernetes into route state

The control plane lives in controller/. It uses shared informers to watch Ingress and EndpointSlice, then builds two in-memory structures:

• a host/path router
• a route-key to endpoint list store

The important point is that the controller does not directly configure nginx or write files. It builds local state for the in-process proxy.

Watching ingress and endpoints

controller.NewController registers event handlers for both resource types:

• ingress add, update, delete
• endpointslice add, update, delete

On an ingress event, the controller queues the ingress key for reconciliation. On an endpoint event, it finds which ingresses depend on the service and requeues those.

That dependency mapping is stored in serviceToIngress, which is what lets endpoint churn trigger only the routes that care about it.

The ingress reconciliation path in controller/controller.go does four things:

1. filters to this controller’s class
2. parses rules into route specs
3. updates the router
4. refreshes the endpoint store for each referenced service/port

The filtering logic accepts:

• spec.ingressClassName == "prequal"
• legacy kubernetes.io/ingress.class: prequal
• a fallback label ingress.class=prequal

Route matching with a trie

The router in controller/router.go delegates path matching to tree/, which implements a segment trie. Each host gets a HostConfig with a list of paths plus a trie built from those paths.

tree.Match supports:

• exact paths
• prefix paths
• longest-prefix semantics
• default-host fallback when a specific host is missing

That means route resolution is:

1. match host
2. walk the trie by URL segments
3. prefer exact matches, otherwise keep the best prefix match

Kubernetes objects are converted once into RouteSpec, and the request path never has to understand Kubernetes types.

Endpoint storage is route-local

The controller stores endpoints in BackendIPStore, keyed by a route key derived from namespace, service, and port:

• namespace/service
• namespace/service:port
• namespace/service:portName

That matters because the load-balancing state is also route-local. If two ingress routes point at different services, their probe history does not mix. If two routes point at the same service but different ports, their state stays separate too.

Dataplane: the reverse proxy request path

The dataplane lives in server/server.go. This is where a request enters the proxy, gets matched to a route, resolves candidate backends, triggers async probes, selects one backend, and is forwarded with httputil.ReverseProxy. For the full upstream context of how a request reaches this point (DNS, service mesh, kube-proxy, endpoints), see my earlier walkthrough on the request flow from a user to a Kubernetes pod.

The flow is:

1. normalize host
2. match route from the trie
3. fetch candidate backends from the endpoint store
4. trigger async probes for that route
5. pick a backend using the requested algorithm
6. increment RIF counters
7. proxy the request
8. record observed latency locally

That is all in one request handler, which makes the architecture easy to follow.

The selection branch is especially important:

func (p *ProxyServer) selectBackend(routeKey, algo string, backends []*controller.Endpoint) (*controller.Endpoint, error) {
    switch algo {
    case "prequal", "":
        entry, err := p.pools.Select(routeKey, backends)
        if err != nil {
            return nil, err
        }
        p.pools.IncrementRIF(routeKey, entry.Backend)
        return entry.Endpoint, nil
    default:
        sel, exists := p.selectors[algo]
        if !exists {
            return p.selectBackend(routeKey, "prequal", backends)
        }
        return sel.Select(backends)
    }
}

A few design choices here that i’ve made are:

First, Prequal is the default. If the ingress annotation lb/algo is empty, the proxy uses Prequal.

Second, round-robin algo has been included as well.

This makes the benchmark harness clean. The same controller, proxy, transport, and backend stack can be benchmarked with different selection rules by patching one ingress annotation.

Third, the proxy increments both a global RIF tracker and the selected pool entry’s RIF view. That keeps the request path’s immediate state and the pool’s sampled state reasonably aligned.

How is Prequal implemented in Go?

The implementation is split across:

• loadbalancer/prober.go
• loadbalancer/pool/pool.go
• loadbalancer/pool/pools.go
• loadbalancer/rif.go
• loadbalancer/latency.go
• loadbalancer/config.go

This is the core of the repo.

Route-local probe pools

The paper’s async probing design only works if sampled state is bounded and per-route. That is what RoutePools does: it owns one ProbePool per route key.

Each ProbeEntry holds:

• backend address
• endpoint pointer
• RIF
• latency
• probe timestamp
• UsesLeft

UsesLeft is the local implementation of probe reuse. A probe can be selected a limited number of times before it is evicted.

The pool is bounded by both size and age:

• MaxSize
• MaxAge
• MaxProbeAge

That gives the lb three protection mechanisms against stale decisions:

• cap how many probe samples are retained
• remove entries that are too old in wall-clock terms
• remove entries once they have been reused enough

HCL in code

The HCL selection rule is implemented directly in loadbalancer/pool/pool.go. This is the most important code in the project.

The key selection logic looks like this:

threshold := rifs[idx]

var bestCold *ProbeEntry
var bestHot *ProbeEntry
allHot := true

for i, e := range pool.entries {
    if e.RIF <= threshold {
        allHot = false
        if bestCold == nil || e.Latency < bestCold.Latency {
            bestCold = e
            bestColdIndex = i
        }
        continue
    }
    if bestHot == nil || e.RIF < bestHot.RIF {
        bestHot = e
        bestHotIndex = i
    }
}

selected := bestCold
if allHot {
    selected = bestHot
}

That is a direct implementation of the paper’s idea:

• compute the RIF quantile threshold
• treat RIF <= threshold as cold
• among cold entries, minimize latency
• if nothing is cold, minimize RIF

It is not trying to be clever, which I think is the right call here. Algorithm code gets dangerous when it becomes hard to explain. This one stays direct.

The fallback behavior is also worth noting. If the pool has fewer than two entries, the code falls back to a random backend from the full backend list. That is how the system behaves before warmup or after starvation:

if len(pool.entries) < 2 {
    ep := allBackends[rand.Intn(len(allBackends))]
    return &ProbeEntry{Backend: ep.String(), Endpoint: ep}, len(pool.entries), nil
}

The benchmark campaign explicitly tracks how often that fallback happens. In the decisive Prequal runs, it is zero, which matters because otherwise a “Prequal win” might secretly be a random-selection run.

Async probing

loadbalancer.Prober is the other half of the design. It is responsible for:

• sending HTTP probes to /prequal/probe
• decoding backend-reported RIF and latency
• rejecting stale probe responses
• feeding entries into the route-local pool
• keeping pools warm in the background

The request path never blocks on probe completion. Instead, TriggerProbes(routeKey) enqueues work:

func (pr *Prober) TriggerProbes(routeKey string) {
    if routeKey == "" {
        return
    }
    n := pr.probesForQuery()
    for i := 0; i < n; i++ {
        pr.enqueueProbe(routeKey)
    }
}

Workers consume those route keys, sample a backend for the route, call the probe endpoint, and add a ProbeEntry to the right pool.

The configuration lives in loadbalancer/config.go, and the defaults are important because they define the repo’s behavior:

• pool size: 16
• pool max age: 1s
• reuse limit: 3
• QRIF: 0.75
• probes per query: 1.0
• probe workers: 8
• background interval: 100ms
• probe timeout: 100ms
• max probe age: 2s

Those values are not arbitrary, but they are also not identical to the paper’s defaults. More on this later

RIF and latency tracking

Besides backend-reported probe data, the proxy maintains its own local trackers:

• RIFTracker uses sync.Map and atomic.Int64
• LatencyTracker keeps a per-backend circular buffer and reports a local median

These are used for two things:

• supporting least-connections
• optionally seeding the probe pool when the async prober is disabled

The pool can be bootstrap-seeded from local observations if the async prober is absent, but once the prober exists, backend probes become the authoritative source.

The benchmark backend

The Rust backend in backend/src/main.rs is part of the implementation model.

It exposes three endpoints:

• POST /work
• GET /health
• GET /prequal/probe

/work simulates the backend’s actual service time. /prequal/probe exposes the two signals Prequal needs.

The backend’s internal state is visible in exactly the way the algorithm expects. Which makes controlled experiments possible at all.

Work mode: CPU-bound or I/O-bound

The backend has two modes:

• CPU-bound SHA256 loop
• I/O-bound sleep mode

That switch is controlled by IO_BOUND_MODE.

In CPU-bound mode, each request burns CPU with repeated hashing. In I/O-bound mode, each request sleeps for iterations * IO_BOUND_BASE_US.

That one switch ends up being central to the benchmark story. On small CPU-bound fleets, Prequal loses. In the paper-aligned I/O-bound skewed regime, it wins decisively.

Probe responses are RIF-conditioned

The backend tracks current RIF with an atomic counter and stores recent latency samples in five buckets:

• 0
• 1
• 2..3
• 4..7
• 8+

The probe handler looks at the current RIF, chooses the matching bucket, and returns the median latency from that bucket or the nearest non-empty bucket.

This means the probe latency signal is not a raw median across all recent requests. It is conditioned on queue depth.

The probe response shape is simple:

{
  "rif": 3,
  "latency_median_ms": 12.5,
  "timestamp_ms": 1710000000000
}

The Go prober then turns that into a ProbeEntry, rejects it if the timestamp is too stale, and inserts it into the right pool.

Fault injection is built in

The backend also supports probe-fault modes:

• timeout
• HTTP 500
• malformed JSON
• stale timestamp

This makes it possible to test whether the prober correctly records failures, drops bad data, and avoids poisoning the pool with stale observations.

The observability path

The controller and proxy expose Prometheus metrics for request latency, per-algorithm selections, probe success and failure counts, probe queue depth, pool occupancy, and active backend count. A debug server exposes /metrics, /routes, /healthz, /readyz, and pprof endpoints. That is enough to explain benchmark results, not just report them.

This repo instruments the controller and proxy heavily enough that you can explain a benchmark result rather than just report it.

The Prometheus metrics in observability/metrics.go cover:

• total requests and request latency
• backend selections by route and algorithm
• no-route and no-backend events
• reconciliation counts and durations
• probes sent, succeeded, failed, dropped
• probe queue depth
• pool occupancy
• selection algorithm usage
• active backend count

And the debug server in debug.go exposes:

• /metrics
• /routes
• /healthz
• /readyz
• pprof endpoints

That instrumentation is what makes the benchmark investigation credible. The repo can answer questions like:

• Did Prequal actually avoid the slow replicas?
• Was the pool starving?
• Were requests falling back to random?
• Was the controller CPU-bound?
• Was mutex contention the problem?

The observability here is how the benchmark results got debugged, not dashboard decoration.

Benchmarking

A controlled protocol (interleaved algorithm order, controller rollout-restart per run, warmup period, full metadata capture) turned an early 10x-worse false negative into a reproducible 8.6x p99 improvement. The methodology fix alone, with zero algorithm code changed, produced a 56x p99 reduction in the heterogeneous run.

The benchmark harness in benchmark/ is extensive enough that it deserves to be treated as part of the software, not just support files.

There are several traffic models:

• steady_state.js
• open_loop.js
• rate_ramp.js
• burst.js
• overload.js
• long_duration.js
• multi_route.js

And there are matching manifests for:

• uniform workloads
• heterogeneous workloads
• multi-route workloads
• route-scale
• long-duration
• fault injection

The comparisons are made by holding everything constant except the algorithm annotation on the ingress.

That gives a fair comparison:

• same controller binary
• same route matching
• same transport
• same backend images
• same cluster
• same load script
• same observability stack

Only the backend selection rule changes.

The controlled protocol

The methodology fix alone, with zero algorithm code changed, produced a 56x p99 reduction in the E-B heterogeneous run. The seven competing hypotheses are walked in here

An early heterogeneous run made Prequal look dramatically worse than the baselines, but it turned out the main problem was protocol:

• algorithms were run sequentially instead of interleaved
• controller state leaked across runs
• probe pools were not reset cleanly between repetitions

The fix became the canonical benchmark protocol:

• interleaved algorithm order
• controller rollout restart before every run
• warmup period before measurement
• full metadata capture per run

That protocol is encoded in benchmark/scripts/run_interleaved_campaign.sh and benchmark/scripts/run_campaign.sh.

Why open-loop matters

The decisive C2 benchmark uses k6 constant-arrival-rate mode:

export const options = {
  scenarios: {
    open_loop: {
      executor: 'constant-arrival-rate',
      rate,
      timeUnit: '1s',
      duration,
      preAllocatedVUs,
      maxVUs,
    },
  },
}

This is a better choice than closed-loop when the point is to expose queueing behavior. Closed-loop traffic self-throttles when latency rises. Open-loop keeps pushing at the target rate and makes tail failures visible.

The ramp benchmark does the complementary thing: it increases arrival rate in stages until the system crosses its comfortable regime.

Together, those two tests are enough to answer the important question: does Prequal help under skew and near saturation, which is exactly where the paper says it should?

How much faster is Prequal than round-robin?

On a 16-backend cluster with 16x service-time skew (I/O-bound, open-loop), this Go Prequal implementation cut p99 tail latency by 8.6x versus round-robin and 8.5x versus least-connections. On a 4-backend CPU-bound cluster, the same implementation was roughly 25% slower than round-robin on throughput. Prequal is a tail-latency tool, not a small-fleet tool.

Across five-run interleaved campaigns on a 16-backend cluster with 16x service-time skew, this Prequal implementation cut p99 tail latency by 8.6x against round-robin and 8.5x against least-connections on an open-loop workload, and by 6.8x against round-robin on a rate ramp. On a small 4-backend CPU-bound workload, the same implementation was roughly 25% slower than round-robin on throughput. The repo’s final claim is deliberately bounded, and I think it is the right one.

Small CPU-bound fleet: Prequal loses

In the small-fleet CPU-bound regime, this implementation does not win.

The report’s C1 numbers are:

That is roughly a 25% throughput deficit versus round-robin.

I profiled it and the overhead investigation concludes that:

• there is no hot path dominating controller CPU
• mutex contention is negligible
• heap use is tiny
• the measurable cost is mostly diffuse probe/network competition in a regime where the algorithm does not have enough diversity or skew to pay for itself

Paper-aligned skewed I/O-bound regime: Prequal wins hard

The story changes once the benchmark is moved closer to the paper’s assumptions:

• 16 backends instead of 4
• 14 fast, 2 slow
• 16x service-time skew
• I/O-bound backend mode
• open-loop and ramp traffic

In the E-B heterogeneous open-loop campaign, the median p99 numbers are:

That is an 8.6x p99 improvement versus the best baseline.

In the E-B ramp campaign:

That is still a 6.8x p99 improvement.

The selection-rate data explains why. Prequal pushes traffic almost entirely to the fast backends and drives the two slow replicas down to nearly zero selections per second. Round-robin, by definition, keeps giving the slow pair their fair share. Least-connections improves the p95, but still reacts too slowly to avoid queueing at the slow replicas, so the tail remains pinned near their service time.

This mechanism lines up with the result

The win is in the tail, not the center

One subtle but important point from the data is that p50 is basically the same across algorithms in the winning regime. The advantage is almost entirely in p99 and p99.9.

That is exactly what you would expect if the algorithm is avoiding pathological queueing rather than making the median request faster.

It is also why Prequal is interesting. If your median is already fine, the only remaining reason to build a more sophisticated load balancer is to keep a minority of requests from getting stuck behind bad backend choices.

How faithful is this Go implementation to the Prequal paper?

This Go implementation defaults to Q_RIF = 0.75 versus the paper’s 0.84, probes-per-query = 1.0 versus the paper’s 3 (testbed) and 5 (YouTube production), and hardcodes probe reuse at 3 rather than deriving it from pool size and fleet size. The frozen benchmark report documents every divergence explicitly.

This repo is faithful to the paper’s central ideas, but it is not a line-by-line reproduction. The frozen report documents the differences clearly, and they matter.

The most important divergences are:

QRIF default is 0.75, not the paper’s ~0.84

The paper’s baseline uses Q_RIF = 2^(-0.25) ≈ 0.84. This repo defaults to 0.75.

That is within the paper’s recommended band and probably a minor difference, but it is still a difference.

Probes per query is 1.0, not 3 or 5

The paper uses 3 probes per query in testbed experiments and mentions 5 in YouTube production. This repo defaults to 1.0.

That choice was motivated by keeping probe overhead reasonable on a small local cluster, but it is a substantial departure.

Probe reuse is a fixed constant

The paper derives reuse behavior from a formula involving pool size, fleet size, probe rate, and removal rate. This repo hardcodes PoolReuseLimit = 3.

That is a reasonable engineering choice for a local implementation, but it means the repo is approximating one part of the paper’s mechanics rather than reproducing it exactly.

Probe removal is maintenance-driven

The paper frames removal as a per-query process. This repo performs cleanup and “remove worst” behavior on a maintenance tick plus reuse depletion.

That keeps work off the request hot path, which is sensible for Go code in a proxy, but it is another behavioral difference.

Backend probing is not yet sampling without replacement

The report points out a latent issue: ProbeRandom picks one backend at a time with rand.Intn, so if probes-per-query were raised above 1, the implementation would not yet match the paper’s “sample without replacement” requirement.

Final thoughts

Writing this was a lot of fun—and watching Claude run tests and benchmarks made it even better. There were a couple of ideas I wanted to explore further but didn’t get to, mainly because I was short on time and wanted to move on to the next project.

One idea was to build a sidecar container or probe that uses eBPF to attach to the main backend container, collect both latency and RIF, and expose that data through a path that the ingress controller could use to make smarter routing decisions. Another was to design a centralized probe pool that multiple ingress controller pods could read from, or to implement a gossip protocol between controller pods to share this information in a decentralized way.

But eventually, it was time to move on. As always, the next project always tends to pull more than the last.

Until next time.

References

• Wydrowski, Kleinberg, Rumble, Archer. Load is not what you should balance: Introducing Prequal, NSDI 2024.
• Repo benchmark report: benchmark/REPORT.md
• Tail-spike investigation: benchmark/investigations/2026-04-19-c2-tail-spike.md
• Regime pivot investigation: benchmark/investigations/2026-04-20-regime-pivot.md
• Overhead profiling investigation: benchmark/investigations/2026-04-20-prequal-overhead-profiling.md
• HAProxy Technologies. Power of Two Load Balancing — context for why Prequal’s quantile-over-pool design is a more sophisticated variant of the same “sample a subset, don’t score every backend” idea.
• Full source code: github.com/sathwick-p/prequal — the controller, reverse proxy, Rust benchmark backend, k6 scripts, Prometheus/Grafana assets, and frozen benchmark reports all live here.

1. Introduction: What is Claude Code?
2. High-Level Architecture
3. Startup: The Race Against Time
4. The Query Engine: Brains of the Operation
5. The Tool System: 60+ Tools Behind a Single Interface
6. The Permission System: Safety at Every Layer
7. Terminal UI: React, but for Your Terminal
8. The Command System: 100+ Slash Commands
9. Skills, Plugins, and MCP: The Extensibility Trifecta
10. Context Management: Fighting the Token Limit
11. State Management: Immutable Store for a Mutable World
12. Session Persistence and History
13. Multi-Agent Architecture: Subagents, Swarms, and Worktrees
14. Error Recovery: A System That Refuses to Crash
15. Cost Tracking and Telemetry
16. Execution Modes: One Codebase, Many Faces
17. BUDDY: A Tamagotchi-Style AI Pet
18. KAIROS: Persistent Assistant Mode and Auto-Dreaming
19. ULTRAPLAN: Remote Planning Sessions
20. Coordinator Mode: Multi-Agent Orchestrator
21. The Memory System: Persistent AI Memory
22. Hooks: User-Defined Automation
23. Voice Mode, Bridge, and Infrastructure
24. Vim Mode, Keybindings, and Developer Ergonomics
25. Key Engineering Patterns and Takeaways
26. Conclusion

1. Introduction: What is Claude Code?

Claude Code is Anthropic’s official CLI tool — an interactive, AI-powered development assistant that lives in your terminal. It lets developers have natural-language conversations with Claude to edit files, run shell commands, search codebases, manage Git workflows, create pull requests, debug issues, and much more.

But underneath the conversational interface lies a remarkably sophisticated piece of software engineering: a custom React-based terminal renderer, a multi-layered permission system, an elastic tool discovery mechanism, a self-healing query loop with automatic context compression, and an extensibility framework spanning skills, plugins, and the Model Context Protocol (MCP).

This article is a deep technical analysis of the Claude Code source code — approximately 330+ utility files, 45+ tool implementations, 100+ slash commands, 146 UI components, and a custom terminal rendering framework — all written in TypeScript with React, running on Bun.

Let’s take it apart, piece by piece.

2. High-Level Architecture

Claude Code follows a layered architecture where each layer has clear responsibilities:

Tech Stack

Directory Structure

src/
├── main.tsx                 # Application entry (~800KB, bootstraps everything)
├── QueryEngine.ts           # Conversation management & API orchestration
├── query.ts                 # Query loop state machine (retries, compaction, recovery)
├── Tool.ts                  # Unified tool interface (generic over Input/Output/Progress)
├── tools.ts                 # Tool registry with feature-gated loading
├── commands.ts              # Command registry with lazy dispatch
├── context.ts               # System/user context builder (git, CLAUDE.md, date)
├── cost-tracker.ts          # Per-model usage accumulation and display
├── history.ts               # Session history (JSONL, dedup, paste refs)
├── setup.ts                 # Pre-action configuration and auth
├── entrypoints/             # CLI, SDK, MCP entry points
├── tools/                   # 45+ tool implementations (Bash, FileRead, Agent, etc.)
├── commands/                # 100+ slash command implementations
├── components/              # 146 React terminal components
├── ink/                     # Custom terminal rendering framework (~90 files)
├── services/                # API, analytics, MCP, compact, plugins
├── hooks/                   # 85+ hook implementations
├── state/                   # AppState store (Zustand-like)
├── utils/                   # 330+ utilities (git, config, permissions, etc.)
├── skills/                  # Skill loading, bundled skills
├── keybindings/             # Dynamic keybinding system
├── vim/                     # Full vi/vim mode
├── bridge/                  # CCR bridge (WebSocket to claude.ai)
├── coordinator/             # Multi-agent coordination
├── remote/                  # Remote session management
├── tasks/                   # Background task system
├── migrations/              # Versioned data migrations
└── types/                   # Shared type definitions

3. Startup: The Race Against Time

Claude Code’s startup is aggressively optimized. The goal: minimize time-to-first-render so the developer is never left staring at a blank terminal.

3.1 Parallelized Prefetching

Before any module imports happen, three critical operations fire in parallel:

// main.tsx — lines 1-20, before any other imports
profileCheckpoint('main_tsx_entry')
startMdmRawRead()        // macOS MDM policy read (subprocess)
startKeychainPrefetch()   // OAuth + API key keychain reads (2 subprocesses)

This exploits a clever insight: TypeScript module evaluation takes ~135ms anyway (sequential by nature). By spawning subprocesses immediately, macOS keychain reads (~65ms total) run entirely in parallel with import resolution, becoming effectively free.

3.2 Initialization Sequence

The init() function (memoized to prevent re-entrancy) orchestrates 16 setup stages:

1. Config validation — Parse and validate all JSON config files
2. Safe environment variables — Apply non-sensitive env vars before trust dialog
3. CA certificates — Load extra root CAs before first TLS handshake
4. Graceful shutdown handlers — Register SIGINT/SIGTERM handlers
5. OAuth population — Async account info fetch
6. IDE detection — JetBrains, VS Code identification
7. Remote settings — Fetch managed settings from server (async, awaited later)
8. Policy limits — Load org-enforced limits (async)
9. First-start timestamp — Analytics marker
10. mTLS configuration — Client certificate setup
11. Proxy agents — Configure HTTP/HTTPS proxies
12. API preconnection — TCP+TLS handshake overlaps with remaining init
13. Upstream proxy (CCR) — CONNECT relay for organization credentials
14. Shell detection — Windows-specific shell resolution
15. LSP manager — Language Server Protocol cleanup handlers
16. Team cleanup — Multi-agent swarm cleanup on shutdown

3.3 Fast Paths

Before full initialization, fast paths handle quick-exit commands:

• --version — Print version and exit (no init, no React)
• --dump-system-prompt — Output the system prompt and exit
• mcp serve — Start MCP server mode (different init path)

3.4 Startup Profiling

A sampled profiler (startupProfiler.ts) measures every phase:

• 100% of internal builds get sampled
• 0.5% of external users are sampled
• CLAUDE_CODE_PROFILE_STARTUP=1 forces full profiling with memory snapshots

The decision is made once at module load time — non-sampled users pay zero profiling overhead.

3.5 Entrypoint Resolution

The system identifies its execution context early and sets CLAUDE_CODE_ENTRYPOINT:

This gates feature loading — for example, REPL components only load in interactive mode.

4. The Query Engine: Brains of the Operation

The query engine is the core loop that manages conversations with Claude. It’s split across two files: QueryEngine.ts (session-level orchestration) and query.ts (per-turn state machine).

4.1 QueryEngine: The Session Coordinator

The QueryEngine class is a singleton per conversation. It persists state across turns and coordinates:

• System context building (git status, CLAUDE.md files, date)
• Message management (accumulation, normalization, persistence)
• API calls (streaming, retries, fallback)
• Permission tracking (denial counts for SDK reporting)
• Cost accumulation (per-model token tracking)

Key method: submitMessage(prompt, options) — an AsyncGenerator that yields SDK messages throughout the turn. Before entering the query loop, it:

1. Creates a file history snapshot (for undo/restore)
2. Records the transcript to disk before the API call (even if the process is killed mid-request, the conversation is resumable)
3. Wraps canUseTool to track permission denials

4.2 The Query Loop: A Resilient State Machine

The query() function in query.ts is where the magic happens. It’s a while(true) loop managing a mutable state object:

queryLoop():
  while(true):
    1. Prefetch memory + skills (parallel)
    2. Apply message compaction (snip, microcompact, context collapse)
    3. Call API with streaming
    4. Handle streaming errors (fallback, retry)
    5. Execute tools (concurrent or serial)
    6. Check recovery paths (compact, collapse drain, token escalation)
    7. Continue loop or return

The state object tracks everything needed across iterations:

type State = {
  messages: Message[]
  toolUseContext: ToolAvailabilityContext
  maxOutputTokensRecoveryCount: number  // 0–3 limit
  autoCompactTracking: CompactState     // Compaction state + failure count
  pendingToolUseSummary: Promise<...>   // Async tool summaries
  transition: TransitionReason          // Why the loop didn't terminate
}

4.3 Streaming and Tool Execution

The query loop streams API responses and processes them incrementally:

1. Stream start — Yields stream_request_start event
2. Accumulation — Collects assistantMessages, toolUseBlocks, toolResults
3. Usage tracking — Tracks currentMessageUsage and lastStopReason
4. Tool dispatch — Routes tool calls to the orchestrator

Tool execution uses a sophisticated concurrency model:

partitionToolCalls(blocks[]):
  ├─ Batch 1: Read-only tools A, B, C  → runConcurrently(max=10)
  ├─ Batch 2: Write tool D              → runSerially()
  ├─ Batch 3: Read-only tools E, F      → runConcurrently(max=10)
  └─ ...

Each tool’s isConcurrencySafe() method determines if it can run in parallel. Read-only tools (glob, grep, file reads) run concurrently; write tools (edits, bash with side effects) run serially with context propagation between batches.

A streaming tool executor can even begin executing tools while the model is still streaming, reducing latency by overlapping computation and I/O.

4.4 Token Budget Continuation

When the model’s output budget is approaching exhaustion but the task isn’t complete, the engine:

1. Injects an invisible meta-message: “Resume directly — no apology, no recap”
2. Continues the loop with a token_budget_continuation transition
3. Tracks cumulative tokens without interrupting the user
4. Detects diminishing returns to avoid infinite loops

Maximum 3 consecutive output-token recovery attempts before surfacing the stop reason.

5. The Tool System: 60+ Tools Behind a Single Interface

Every tool in Claude Code conforms to a single generic interface:

interface Tool<Input, Output, Progress> {
  name: string
  description(): string          // Dynamic, permission-context-aware
  prompt(): string               // System prompt additions
  inputSchema: ZodSchema<Input>  // Zod → JSON Schema for API

  call(input: Input, context: ToolContext): Promise<ToolResult<Output>>
  checkPermissions(input: Input): PermissionResult
  validateInput(input: Input): ValidationResult
  isConcurrencySafe(input: Input): boolean

  // 4-tier rendering
  renderToolUseMessage(input: Input): ReactNode
  renderToolUseProgressMessage(input: Input, progress: Progress): ReactNode
  renderToolResultMessage(output: Output): ReactNode
  renderToolUseErrorMessage(error: Error): ReactNode

  mapToolResultToToolResultBlockParam(output: Output, id: string): ToolResultBlockParam
}

5.1 The Tool Registry

Tools are loaded through a feature-gated registry:

assembleToolPool(permissionContext, mcpTools):
  1. getTools(permissionContext)        // Filter built-ins by deny rules
  2. filterToolsByDenyRules()           // Remove blanket-denied MCP tools
  3. uniqBy(name)                       // Deduplicate (built-ins win)
  4. sort(name)                         // Alphabetical for prompt cache stability

Sorting by name is a subtle but important optimization: it keeps the tool list in the same order across requests, maximizing prompt cache hit rates on the API side.

5.2 Deferred Tool Discovery

Not all 60+ tools are sent to the model in every request. Tools marked shouldDefer: true are hidden until the model explicitly searches for them via ToolSearchTool:

Model: "I need to create a task..."
  → Calls ToolSearchTool("task create")
  → Returns TaskCreateTool schema
  → Model calls TaskCreateTool in the same turn

~18 tools are deferred: LSP, TaskCreate, MCPTool, SkillTool, EnterPlanMode, etc. This keeps the base prompt under 200K tokens while allowing elastic discovery.

5.3 Key Tool Implementations

BashTool — Command Execution with Guardrails

The most frequently used tool runs shell commands with extensive safety:

• 30K character result limit — Large outputs persist to disk with a preview
• Sandbox awareness — Detects containerized vs. native execution
• Background tasks — Auto-backgrounds commands exceeding 15 seconds
• Search classification — Marks ls, grep, cat output as collapsible in the UI
• Permission dialogs — sed edits show a preview before execution

FileEditTool — Precision String Replacement

Rather than rewriting entire files, the edit tool does surgical string replacement:

• Old/new string matching — Finds exact occurrences, replaces one or all
• 1 GiB size limit — Prevents OOM on massive files
• Git-aware diffing — Shows before/after diff via gitDiff()
• Undo integration — Plugs into FileHistory for one-click undo

AgentTool — Subagent Spawning

Claude Code can spawn child agents for parallel work:

• Isolation modes — Worktree (isolated git branch) or remote (CCR)
• Model selection — Override with opus | sonnet | haiku
• Background execution — Agents run async with notification on completion
• Named addressing — SendMessage to named agents for multi-agent coordination
• Permission inheritance — Child agents inherit or restrict parent permissions

GrepTool — Content Search (Ripgrep Wrapper)

Wraps rg with sensible defaults for LLM use:

• 250-line default limit — Prevents context flooding
• Multiline mode — rg -U --multiline-dotall for cross-line patterns
• VCS exclusion — Auto-skips .git, .svn, .hg
• Three output modes — Content, file paths only, or match counts

LSPTool — Language Intelligence

9 operations powered by Language Server Protocol:

• goToDefinition, findReferences, hover
• documentSymbol, workspaceSymbol
• goToImplementation, prepareCallHierarchy
• incomingCalls, outgoingCalls

Only loaded when an LSP server is connected. Deferred by default.

WebSearchTool — Native Web Search

Server-side web search (beta feature):

• Max 8 searches per invocation
• Domain filtering — allowed_domains and blocked_domains parameters
• Streaming results — Interleaves text and citation blocks

5.4 Tool Result Budgeting

Every tool has a maxResultSizeChars limit:

When output exceeds the limit, it’s saved to ~/.claude/tool-results/{uuid}/output.txt and the model receives a preview with a file reference. FileReadTool is exempt because persisting its output would create a circular dependency (Read → persist → model reads persisted file → …).

5.5 Lazy Schemas

Tool input schemas use a lazySchema() factory that defers Zod instantiation:

const schema = lazySchema(() => z.object({
  command: z.string(),
  timeout: z.number().optional(),
}))

This prevents circular import cycles (Tool.ts ← tools/ ← Tool.ts) and enables mid-session schema changes when feature flags flip.

6. The Permission System: Safety at Every Layer

Claude Code’s permission system is one of its most sophisticated subsystems — a multi-layered defense that balances safety with developer productivity.

6.1 Permission Modes

Five public modes control the default behavior:

Plus two internal modes:

• auto — ML classifier evaluates each command
• bubble — Internal delegation to parent agent

6.2 Rule System

Permission rules form a priority cascade:

type PermissionRule = {
  source: 'userSettings' | 'projectSettings' | 'localSettings' | 'cliArg' | 'session'
  ruleBehavior: 'allow' | 'deny' | 'ask'
  ruleValue: { toolName: string, ruleContent?: string }
}

Rules support glob patterns: Bash(git push*) allows any git push command, Bash(python:*) allows all Python commands.

6.3 Decision Pipeline

For every tool call:

1. validateInput()        → Tool-specific validation (size limits, blocked patterns)
2. checkPermissions()     → Rule matching + classifier + hooks
3. Decision:
   ├─ allow  → Execute immediately
   ├─ deny   → Return error to model
   └─ ask    → Show permission dialog to user
4. Pre/Post hooks         → Can modify input or block execution

6.4 Dangerous Pattern Detection

The system identifies permission rules that are too broad to auto-allow:

• Tool-level allow (no content restriction) — Would allow ALL commands
• Interpreter prefixes — python:*, node:*, ruby:* (arbitrary code execution)
• Wildcards — *, python* (too permissive)

6.5 Three-Way Permission Result

Every permission check returns a typed union:

type PermissionResult =
  | { behavior: 'allow', updatedInput?: Input }   // Hooks can modify input
  | { behavior: 'ask', message: string }           // Prompt user
  | { behavior: 'deny', message: string }          // Block with explanation

The updatedInput field is powerful: pre-execution hooks can transparently modify tool parameters (e.g., adding safety flags to shell commands).

7. Terminal UI: React, but for Your Terminal

Perhaps the most impressive subsystem in Claude Code is its custom terminal rendering framework — a complete reimplementation of React rendering for terminal environments, rivaling web browsers in sophistication.

7.1 The Rendering Pipeline

React Components
    ↓
Custom React Reconciler (createReconciler API)
    ↓
Virtual DOM Tree (ink-box, ink-text, ink-root, ink-link)
    ↓
Yoga Layout Engine (flexbox calculations)
    ↓
Output Builder (write / blit / clip / clear / shift operations)
    ↓
Screen Buffer (2D cell array with interned styles + hyperlinks)
    ↓
Diff Engine (compare with previous frame)
    ↓
ANSI Escape Sequences → TTY

7.2 Custom React Reconciler

Claude Code implements a custom React host configuration using createReconciler:

Element types:

• ink-root — Root container
• ink-box — Flexbox layout container (like <div>)
• ink-text — Text content
• ink-virtual-text — Nested text (layout optimization)
• ink-link — OSC 8 hyperlinks
• ink-progress — Progress indicators
• ink-raw-ansi — Raw ANSI passthrough (bypasses measurement)

The reconciler tracks three categories of changes separately:

• Styles — Passed to Yoga for layout recalculation
• Text styles — Colorization, bold, italic, etc.
• Event handlers — Stored separately to prevent handler identity changes from invalidating the dirty flag

7.3 Yoga Layout Engine

Rather than manual ANSI cursor positioning, Claude Code uses Yoga — Facebook’s cross-platform flexbox implementation — for layout:

<Box flexDirection="row" gap={1} paddingX={2}>
  <Box flexGrow={1}>
    <Text>Left panel</Text>
  </Box>
  <Box width={30}>
    <Text>Right sidebar</Text>
  </Box>
</Box>

This brings responsive, declarative layouts to the terminal. Text nodes register measure functions with Yoga:

node.yogaNode.setMeasureFunc((width, measureMode) => {
  const wrapped = wrapText(text, width)
  return { width: actualWidth, height: numLines }
})

A generational reset pattern prevents memory leaks from native Yoga bindings:

if (now - lastPoolResetTime > SESSION_POOL_RESET_MS) {
  migrateScreenPools()  // Free and recreate all Yoga nodes
}

7.4 The Dirty Flag Cascade

Nodes track a dirty flag that cascades upward:

function markDirty(node: DOMElement) {
  node.dirty = true
  if (node.parentNode) markDirty(node.parentNode)
}

Only subtrees with dirty ancestors are re-laid out, providing incremental performance.

7.5 Double Buffering and Blitting

The renderer uses classic graphics techniques:

Double buffering:

private frontFrame: Frame   // Currently displayed
private backFrame: Frame    // Being rendered into
// After render: swap pointers
[this.frontFrame, this.backFrame] = [this.backFrame, this.frontFrame]

Blitting (copy unchanged regions):

blit(src: Screen, x, y, width, height)
// If a region hasn't changed, copy from previous frame
// instead of re-rendering — the "GPU blit" technique for terminals

When a selection overlay is applied, it “contaminates” the frame, disabling blit for the next render to prevent visual artifacts.

7.6 Screen Buffer: The 2D Cell Model

The screen is a 2D array of cells:

type Cell = {
  char: string          // Interned via CharPool
  width: CellWidth      // 1 (normal), 2 (wide/CJK/emoji), -1 (tail of wide char)
  styleId: number       // Interned via StylePool
  hyperlink?: number    // Interned via HyperlinkPool
}

Three interning pools minimize memory and enable O(1) comparisons:

• CharPool — Deduplicates character strings, returns integer IDs
• StylePool — Deduplicates ANSI style combinations, pre-computes transition sequences
• HyperlinkPool — Deduplicates OSC 8 URLs (reset every 5 minutes to bound growth)

The style pool’s transition() method is especially clever:

// Pre-computed: "how to go from style A to style B"
transition(fromId: number, toId: number): string {
  const key = fromId * 0x100000 + toId
  return transitionCache.get(key)  // O(1) vs. diffing AnsiCode arrays
}

7.7 Scroll Optimization

ScrollBox uses hardware scroll regions when available:

CSI top;bottom r    → Set scroll region
CSI n S             → Scroll up n lines (DECSTBM)

This is dramatically faster than rewriting 50+ rows of content. For smooth animation, scroll deltas accumulate and drain at terminal-specific rates:

// Native terminals: proportional drain (~3/4 per frame)
const step = Math.max(MIN, (abs * 3) >> 2)

// xterm.js: adaptive (instant for ≤5, smaller steps for fast scrolls)
const step = abs <= 5 ? abs : abs < 12 ? 2 : 3

7.8 Event System

Events follow DOM semantics with capture and bubble phases:

function collectListeners(target, event): DispatchListener[] {
  // Walk from target to root
  // Capture handlers: root-first
  // Bubble handlers: target-first
}

Event priority mirrors web browsers:

7.9 Text Selection

Full text selection with word and line modes:

• Character mode — Drag selects character by character
• Word mode — Double-click selects word; subsequent drag extends by word boundaries
• Line mode — Triple-click selects line; drag extends by lines
• Scroll tracking — Text that scrolls off-screen is accumulated for correct copy
• Soft-wrap handling — Wrapped lines are joined into logical lines when copying

7.10 Keyboard Input Parsing

Terminal keyboard input is notoriously ambiguous. The parser handles multiple protocols:

• Kitty Keyboard Protocol — CSI u with codepoint + modifiers
• xterm modifyOtherKeys — CSI 27; modifier; keycode ~
• Legacy function keys — F1-F12 with their many escape sequence variants
• SGR mouse events — CSI < button; col; row M/m
• Terminal identity detection — XTVERSION response parsing for feature detection

8. The Command System: 100+ Slash Commands

8.1 Architecture

Commands use a declarative registration model with three types:

The command registry is memoized and lazy-loaded:

const COMMANDS = memoize(() => [
  // Static commands array — module imports deferred until first call
])

const loadAllCommands = memoize((cwd: string) => {
  // Merges: COMMANDS() + skills + plugins + workflows + MCP commands
})

8.2 Command Discovery Pipeline

getCommands(cwd)
  ├─ loadAllCommands(cwd) [memoized by CWD]
  │   ├─ getSkills()          → Disk, bundled, plugin, MCP skills
  │   ├─ getPluginCommands()  → Marketplace + built-in plugins
  │   ├─ getWorkflowCommands()→ Automation workflows [feature-gated]
  │   └─ COMMANDS()           → Static built-in commands
  ├─ getDynamicSkills()       → Session-discovered skills
  ├─ Filter by availability   → Auth provider gating
  ├─ Filter by isEnabled()    → Feature flag gating
  └─ Dedupe + sort

8.3 Remote and Bridge Filtering

Commands are pre-filtered based on execution context:

• Remote mode — Only REMOTE_SAFE_COMMANDS (session, exit, clear, help, theme, cost…)
• Bridge mode — Only BRIDGE_SAFE_COMMANDS (prompt-type skills, plus text-output locals like clear, cost, summary)
• Local JSX commands — Always blocked over bridge (can’t render React over WebSocket)

8.4 Notable Command Implementations

/commit — Git Safety Protocol

The commit command enforces strict safety rules:

• Never git commit --amend (only create new commits)
• Never skip hooks (--no-verify, --no-gpg-sign)
• Never use -i flags (interactive mode unsupported)
• Warn on secrets (.env, credentials.json)
• Restricted tool access: only Bash(git add:*), Bash(git status:*), Bash(git commit:*)

/init — Interactive Project Setup

Multi-phase onboarding:

1. Ask what to set up (CLAUDE.md, skills, hooks)
2. Survey codebase (manifest files, README, CI, existing config)
3. Interview user on gaps
4. Synthesize proposal and create artifacts

/doctor — Self-Diagnostics

Checks system health: API connectivity, auth status, model availability, MCP server connections, permission configuration.

9. Skills, Plugins, and MCP: The Extensibility Trifecta

9.1 Skills

Skills are markdown-based prompt templates with frontmatter metadata:

---
name: my-skill
description: What this skill does
whenToUse: When Claude should invoke it
allowedTools: [Bash, Read, Edit]
model: claude-sonnet-4-6
userInvocable: true
---

Skill prompt content here...

Discovery sources (5):

1. .claude/skills/ — Project-level skills
2. ~/.claude/skills/ — User-level skills
3. Bundled skills — Compiled into the binary
4. Plugin skills — From installed plugins
5. MCP skill builders — Auto-generated from MCP servers with Prompt capability

Forked execution: Skills with context: 'fork' run in isolated subagents with their own token budgets, preventing large skills from consuming session context.

Bundled skills support lazy extraction of reference files to disk with per-process nonce-based path protection (defends against symlink/TOCTOU attacks).

9.2 Plugins

Plugins bundle skills, hooks, and MCP servers:

Plugin
├─ Skills (markdown files)
├─ Hooks (pre/post tool execution)
├─ MCP Servers (tool providers)
└─ Options (user-configurable variables)

Types:

• Built-in plugins — Pre-installed, togglable, {name}@builtin
• Marketplace plugins — Installed to ~/.claude/plugins, versioned
• Project plugins — --plugin-dir for session-only plugins

Plugin variables are substituted into prompts at invocation time via substitutePluginVariables().

9.3 Model Context Protocol (MCP)

MCP is the primary extensibility mechanism for bringing external tools into Claude Code.

Supported transports:

• stdio — Local subprocess
• sse / http / ws — Network-based (with optional OAuth/XAA)
• sdk — Embedded SDK
• claudeai-proxy — Claude.ai tunnel

Config scopes (priority order):

1. local — .mcp.json in project root
2. project — .claude/.mcp.json
3. user — ~/.claude/.mcp.json
4. userSettings — settings.json mcpServers
5. policySettings — Managed organizational policy
6. enterprise — Enterprise-managed
7. claudeai — Claude.ai-managed
8. dynamic — Runtime-injected

Connection lifecycle:

MCPServerConnection =
  | ConnectedMCPServer     → Ready to use
  | FailedMCPServer        → Connection error
  | NeedsAuthMCPServer     → Awaiting OAuth
  | PendingMCPServer       → Reconnecting (max attempts)
  | DisabledMCPServer      → Explicitly disabled

MCP tools are normalized and prefixed: mcp__server__toolname. They receive the same permission checks, deny rules, and analytics as built-in tools.

10. Context Management: Fighting the Token Limit

With conversations that can last hours and generate hundreds of tool calls, managing the context window is critical. Claude Code uses a multi-strategy approach.

10.1 Auto-Compaction

When token count exceeds context_window - 13,000:

1. Strip images/documents from older messages (replace with [image] markers)
2. Group messages by API round (assistant + tool results)
3. Call the compaction model to generate a summary
4. Replace old messages with a CompactBoundaryMessage
5. Re-inject up to 5 files + skills post-compaction (50K token budget for files, 25K for skills)

A circuit breaker prevents thrashing: max 3 consecutive compaction failures before giving up.

10.2 Microcompaction

Lighter-weight compression for tool results:

• Time-based — Clear tool results older than a TTL
• Size-based — Truncate when accumulated tool result tokens exceed threshold
• Tool-specific — Only compacts: FileRead, Bash, Grep, Glob, WebSearch, WebFetch, FileEdit, FileWrite
• Cache-aware — A “cached” variant preserves prompt cache integrity via CacheEditsBlock

10.3 Snip Compaction

A history truncation strategy (feature-gated):

• Remove old messages beyond a snip boundary
• Preserve the assistant’s “protected tail” for context continuity
• Track tokens freed for accurate token budget calculations
• Full history preserved in REPL for UI scrollback (non-destructive)

10.4 Context Collapse

Staged collapses are committed lazily — only when the API returns a 413 (prompt too long):

API 413 → Collapse drain (commit staged collapses)
        → If insufficient → Reactive compact (full summarization)
        → If still insufficient → Surface error to user

10.5 System Context

Two tiers of context are injected into every request:

System context (memoized per session):

• Git status (branch, recent commits, file status — truncated at 2000 chars)
• Cache breaker (optional debug injection)

User context (memoized per session):

• CLAUDE.md file contents (auto-discovered from project + parent directories)
• Current date (ISO format)

11. State Management: Immutable Store for a Mutable World

11.1 The Store

Claude Code uses a minimal, Zustand-like store:

type Store<T> = {
  getState: () => T
  setState: (updater: (prev: T) => T) => void
  subscribe: (listener: Listener) => () => void
}

• No middleware
• Synchronous updates
• Identity comparison (Object.is) gates listener invocation
• React integration via useSyncExternalStore

11.2 AppState: The Unified State Object

The AppState object contains everything:

Core settings:

• settings — User preferences (theme, model, etc.)
• mainLoopModel — Current AI model for the session
• toolPermissionContext — Safety mode and rules

• expandedView — ‘none’

  ‘tasks’

  ‘teammates’

Bridge state (Claude.ai integration):

• replBridgeEnabled / replBridgeConnected / replBridgeSessionActive
• replBridgeConnectUrl / replBridgeError

Multi-agent state:

• tasks: { [taskId: string]: TaskState }
• agentNameRegistry: Map<string, AgentId>
• foregroundedTaskId / viewingAgentTaskId

MCP state:

• mcp.clients: MCPServerConnection[]
• mcp.tools, mcp.commands, mcp.resources

Speculation state (parallel model execution):

type SpeculationState =
  | { status: 'idle' }
  | { status: 'active', messagesRef, writtenPathsRef, boundary, isPipelined }

Speculation is a latency optimization: while the user is still typing, the model begins generating a response speculatively. File writes go to an overlay filesystem (writtenPathsRef), and on completion, the overlay is either committed (if the user’s actual input matches the speculation boundary) or discarded. isPipelined indicates whether a suggestion was already generated and is queued for display.

11.3 Centralized Side Effects

All state mutations that affect external systems flow through onChangeAppState():

• Permission mode changes → Notify CCR bridge
• Model changes → Persist to user settings
• Settings mutations → Clear auth caches
• View changes → Persist UI state

One choke point, no scattered side effects.

12. Session Persistence and History

12.1 Transcript Recording

The engine records transcripts with ordering guarantees:

• Assistant messages — Fire-and-forget (lazy JSON stringify with 100ms drain)
• User/boundary messages — Blocking await (ordering guarantee)
• Pre-compact flush — Writes preserved segment before compaction boundary

Even if the process is killed mid-request, the conversation is resumable from the last recorded transcript.

12.2 History System

Two-level history with deduplication:

In-memory: pendingEntries[] — Queue before flush to disk

On-disk: ~/.claude/history.jsonl — Append-only log

type LogEntry = {
  display: string                    // Formatted prompt for Ctrl+R picker
  project: string                    // Current project root
  sessionId: SessionId
  timestamp: number
  pastedContents?: Record<number, StoredPastedContent>
}

Key algorithms:

• Dedup by display text (newest first) for Ctrl+R
• Current-session-first ordering (up-arrow doesn’t interleave sessions)
• Small pastes (<1KB) inlined; large pastes stored with hash references

12.3 Cost State Persistence

Session costs survive process restarts:

getStoredSessionCosts()       // Retrieve if session ID matches
saveCurrentSessionCosts()     // Persist before session switch
restoreCostStateForSession()  // Restore on resume (validates session ID)

13. Multi-Agent Architecture: Subagents, Swarms, and Worktrees

13.1 Agent Spawning

The AgentTool spawns child agents with configurable isolation:

• Default — Shared filesystem, separate conversation context
• Worktree — Isolated git branch copy, changes merged on exit
• Remote (CCR) — Runs on a separate machine

Agents are addressable by name:

Model: "Ask the test-runner agent to run the suite"
  → SendMessage(to: "test-runner", message: "Run the test suite")

13.2 Task System

Background tasks use file-based IPC with concurrent-session locking:

type TaskType = 'local_bash' | 'local_agent' | 'remote_agent'
              | 'in_process_teammate' | 'local_workflow' | 'monitor_mcp' | 'dream'

type TaskStatus = 'pending' | 'running' | 'completed' | 'failed' | 'killed'

Task IDs use base-36 encoding with type prefixes (b=bash, a=agent, r=remote, etc.).

Lock retries use 30 attempts with 5-100ms backoff (~2.6s max wait) for swarm coordination across tmux/iTerm2 panes.

13.3 Worktree Isolation

EnterWorktreeTool / ExitWorktreeTool provide git-level isolation:

1. Create a temporary git worktree on a new branch
2. Agent works in the worktree (safe to make destructive changes)
3. On exit: keep changes (merge) or discard (clean up)

14. Error Recovery: A System That Refuses to Crash

14.1 API Error Recovery

The retry system handles transient and permanent errors differently:

Transient (retryable): | Error | Strategy | |——-|———-| | 529 (Overloaded) | Max 3 retries for foreground queries | | 429 (Rate limit) | Exponential backoff, persistent mode available | | ECONNRESET/EPIPE | Stale connection retry |

Permanent (fail fast): | Error | Strategy | |——-|———-| | 401 | OAuth refresh → retry once → clear credentials | | 400 | Invalid request, no retry | | 403 | Permission denied, no retry |

Persistent retry mode (for unattended operation):

• Env var: CLAUDE_CODE_UNATTENDED_RETRY
• Indefinite 429/529 retries with max 5-minute backoff
• 30-second heartbeat keep-alive messages

14.2 Prompt-Too-Long Recovery

When the API returns 413:

413 Prompt Too Long
  ├─ 1. Collapse drain (commit staged context collapses)
  ├─ 2. Reactive compact (generate full conversation summary)
  └─ 3. Surface error if all paths exhausted

The error is withheld from the SDK until recovery paths are exhausted — the user never sees a 413 if compaction can resolve it.

14.3 Max Output Tokens Recovery

max_output_tokens stop reason
  ├─ 1. Escalate to 64K tokens (once per turn)
  ├─ 2. Inject meta recovery message ("Resume directly")
  ├─ 3. Max 3 attempts before surfacing
  └─ 4. Withhold intermediate errors

14.4 Model Fallback

On persistent 529 errors:

1. Switch to fallback model (e.g., Sonnet when Opus is overloaded)
2. Strip thinking blocks (model-bound signatures)
3. Log fallback event with chain ID
4. Yield system message about the switch

14.5 Streaming Fallback

If streaming fails mid-response:

1. Retry with non-streaming request
2. Tombstone orphaned messages
3. Clear assistant messages to restart the turn
4. Fresh tool executor to prevent orphan results

15. Cost Tracking and Telemetry

15.1 Usage Accumulation

Per-model tracking:

• Input tokens, output tokens
• Cache read/write tokens
• Web search requests
• USD cost (calculated via calculateUSDCost())

Advisor model costs are recursively accumulated from getAdvisorUsage().

15.2 Display

formatTotalCost() produces a multi-line report:

• Total cost
• Per-model breakdown
• API/wall-clock duration
• Lines of code changed
• Unknown model cost disclaimer

15.3 Telemetry

Analytics use a decoupled sink pattern:

• attachAnalyticsSink() called during startup
• Events queued until sink is available (prevents import cycles)
• Datadog fanout + first-party event logging
• PII-tagged fields for compliance
• OpenTelemetry spans for LLM request tracing

Gateway detection identifies proxy infrastructure from response headers: LiteLLM, Helicone, Portkey, Cloudflare AI Gateway, Kong, Braintrust, Databricks.

16. Execution Modes: One Codebase, Many Faces

Claude Code runs in multiple modes from a single codebase:

Interactive CLI (Default)

Full React terminal UI with REPL loop, text selection, mouse support, and rich rendering.

Non-Interactive / Headless

--print mode outputs the response to stdout. --output saves to a file. No user interaction — suitable for scripts, CI/CD, and piping.

MCP Server Mode

claude mcp serve runs Claude Code as an MCP server, exposing its tools to other MCP clients.

Bridge Mode (Claude.ai Integration)

WebSocket connection to claude.ai for remote control:

• CLI sends status updates to the web UI
• Web UI sends control commands back
• Bidirectional message adaptation (SDK format ↔ local format)
• Viewer-only mode for read-only clients

Remote / Teleport

claude remote-control exposes the CLI as a WebSocket server. Users can connect via claude.ai’s web interface or QR code.

Local Agent Mode

Subprocesses spawned for multi-agent swarms. Each agent gets its own session, AppState, and task directory. Communication via file I/O.

Coordinator Mode

Orchestrates multiple agents working in parallel on different aspects of a task. (See dedicated section below.)

17. BUDDY: A Tamagotchi-Style AI Pet

One of the most surprising finds in the codebase: a fully implemented Tamagotchi-style virtual companion that lives beside the user’s input box. What started as an April Fools feature (teaser window: April 1-7, 2026) became a real, permanent feature.

17.1 How Your Buddy Is Born

Every companion is deterministically generated from the user’s account ID using a Mulberry32 seeded PRNG:

// Mulberry32 — tiny seeded PRNG, good enough for picking ducks
function mulberry32(seed: number): () => number {
  let a = seed >>> 0
  return function () {
    a |= 0
    a = (a + 0x6d2b79f5) | 0
    let t = Math.imul(a ^ (a >>> 15), 1 | a)
    t = (t + Math.imul(t ^ (t >>> 7), 61 | t)) ^ t
    return ((t ^ (t >>> 14)) >>> 0) / 4294967296
  }
}

The seed is hash(userId + 'friend-2026-401'). This means your companion is unique to you but identical across devices and sessions — you always get the same one.

17.2 Species, Rarity, and Cosmetics

18 species: duck, goose, blob, cat, dragon, octopus, owl, penguin, turtle, snail, ghost, axolotl, capybara, cactus, robot, rabbit, mushroom, chonk

Species names are encoded as String.fromCharCode(0x64,0x75,0x63,0x6b) rather than string literals to avoid tripping an excluded-strings build check (one species name collides with a model codename).

Rarity tiers (weighted random):

Cosmetics:

• 6 eye styles: ·, +, x, @, °, and a special star eye
• 8 hats: none, crown, tophat, propeller, halo, wizard, beanie, tinyduck
• 1% shiny chance — independent of rarity
• 5 stats: DEBUGGING, PATIENCE, CHAOS, WISDOM, SNARK — one peak stat, one dump stat, rest scattered. Higher rarity = higher stat floors.

Each species star rating displays with themed colors: common (inactive), uncommon (green), rare (permission blue), epic (auto-accept purple), legendary (warning gold).

17.3 Soul Generation

On first “hatch,” Claude generates a unique name and personality for the companion. This is stored permanently in the user’s global config as StoredCompanion:

type StoredCompanion = CompanionSoul & { hatchedAt: number }
type CompanionSoul = { name: string; personality: string }

Critically, only the soul persists — bones (species, rarity, stats) are regenerated from the userId hash every time. This prevents users from editing their config to fake a legendary, and allows species renames without breaking stored companions.

17.4 Sprite Animation System

Each species has 3 animation frames as 5-line, 12-character-wide ASCII art:

Frame 0 (idle):     Frame 1 (fidget):   Frame 2 (rare):
    __                   __               __
  <(· )___             <(· )___         <(· )___
   (  ._>               (  ._>           (  .__>
    `--´                 `--´~            `--´

Eye placeholders {E} are replaced with the companion’s assigned eye character at render time. Hat lines overlay the top row (only when the species’ top row is blank).

The idle sequence cycles at 500ms per tick:

const IDLE_SEQUENCE = [0, 0, 0, 0, 1, 0, 0, 0, -1, 0, 0, 2, 0, 0, 0]
// -1 = "blink on frame 0" (eyes temporarily replaced)

This creates a natural feel: mostly still, occasional fidgets, rare blinks.

17.5 Speech Bubbles and Interaction

The companion renders as a CompanionSprite React component positioned beside the prompt input. It features:

• Speech bubbles with a SpeechBubble component using rounded borders
• Bubbles display for ~10 seconds (20 ticks) then fade over the last 3 seconds
• /buddy pet triggers a floating heart animation (2.5 seconds) with hearts drifting upward
• The companion can react to conversation events via companionReaction in AppState
• When terminal is too narrow (<100 cols), the full sprite is hidden and replaced with a compact face-only rendering

A companion intro is injected as a special attachment into the conversation, informing Claude that a small creature named X sits beside the input box and occasionally comments in bubbles.

17.6 Teaser and Release Strategy

export function isBuddyTeaserWindow(): boolean {
  const d = new Date()
  return d.getFullYear() === 2026 && d.getMonth() === 3 && d.getDate() <= 7
}

The teaser uses local dates, not UTC — creating a rolling 24-hour wave across timezones for sustained social media buzz rather than a single UTC-midnight spike. During the teaser window, users who haven’t hatched a companion see a rainbow-colored /buddy notification.

18. KAIROS: Persistent Assistant Mode and Auto-Dreaming

KAIROS (feature-flagged as KAIROS) is a complete alternate UX where Claude becomes a long-lived autonomous agent that persists across sessions — the “Always-On Claude.”

18.1 Auto-Dreaming: Memory Consolidation

The most concrete KAIROS subsystem in the codebase is the auto-dream system (services/autoDream/). This is a background memory consolidation agent that runs as a forked subagent.

Gate order (cheapest checks first):

1. Time gate: Hours since last consolidation >= minHours (default: 24h)
2. Session gate: Number of transcript sessions since last consolidation >= minSessions (default: 5)
3. Lock gate: No other process is mid-consolidation (file lock with mtime-based conflict detection)
4. Scan throttle: Even when the time gate passes, session scanning is throttled to every 10 minutes

The 4-phase dream prompt:

Phase 1 — Orient
  └─ ls the memory directory, read the index, skim existing topic files

Phase 2 — Gather recent signal
  └─ Check daily logs, find drifted memories, grep transcripts narrowly

Phase 3 — Consolidate
  └─ Write/update memory files, merge duplicates, convert relative dates

Phase 4 — Prune and index
  └─ Update the entrypoint index (max ~25KB), remove stale pointers

Tool constraints for dream runs: Bash is restricted to read-only commands only — ls, find, grep, cat, stat, wc, head, tail. Write operations are denied. File edits go through the normal Edit/Write tools with permission via createAutoMemCanUseTool().

Dream task lifecycle:

type DreamTaskState = {
  type: 'dream'
  phase: 'starting' | 'updating'    // Flips when first Edit/Write lands
  sessionsReviewing: number
  filesTouched: string[]             // Paths observed in Edit/Write tool_use
  turns: DreamTurn[]                 // Last 30 assistant turns (rolling window)
  abortController?: AbortController
  priorMtime: number                 // For lock rollback on failure
}

Users can kill a running dream from the background tasks dialog (Shift+Down). On kill, the lock mtime is rewound so the next session can retry.

18.2 KAIROS Integration Points

KAIROS is referenced throughout the codebase:

• getKairosActive() in bootstrap state — gates whether KAIROS mode is active
• Auto-dream is disabled in KAIROS mode (KAIROS uses its own disk-skill dream variant)
• Brief mode (BriefTool) — all output goes through SendUserMessage tool (structured markdown + attachments + status)
• Proactive <tick> prompts — periodic check-ins where Claude decides what to do next
• 15-second blocking budget — commands exceeding 15s are auto-backgrounded
• Exclusive tools: SendUserFile, PushNotification, SubscribePR (GitHub webhook subscriptions), SleepTool
• Append-only daily logs at ~/.claude/projects/<slug>/memory/logs/YYYY/MM/YYYY-MM-DD.md
• Midnight boundary handling — flushes yesterday’s transcript on date change so the dream process can find it

18.3 Session History (Assistant Mode)

assistant/sessionHistory.ts provides paginated session event retrieval for KAIROS:

type HistoryPage = {
  events: SDKMessage[]
  firstId: string | null    // Cursor for next-older page
  hasMore: boolean
}

Uses OAuth-authenticated API calls to CCR (Claude Code Remote) to fetch session transcripts. Pagination goes backwards (newest → oldest) with 100 events per page and 15-second timeout per request.

19. ULTRAPLAN: Remote Planning Sessions

ULTRAPLAN is an interactive planning system that farms out complex exploration to a remote Claude Code instance (CCR) for up to 30 minutes.

19.1 How It Works

1. User types “ultraplan” (keyword detection, not slash command) or uses /ultraplan
2. A remote CCR session is created with plan mode pre-configured
3. The CLI polls the remote session every 3 seconds for up to 30 minutes
4. Remote Claude explores, plans, and calls ExitPlanMode when ready
5. User approves or rejects the plan in the browser (claude.ai)
6. Rejected plans loop back for iteration

19.2 Keyword Detection

The keyword trigger system (utils/ultraplan/keyword.ts) is remarkably sophisticated. It finds “ultraplan” in user input while avoiding false positives:

Skipped contexts:

• Inside paired delimiters (backticks, quotes, brackets, angle brackets)
• Path-like context (src/ultraplan/foo.ts, ultraplan.tsx)
• Identifier-like context (--ultraplan-mode, ultraplan-s)
• Followed by ? (questions about the feature shouldn’t invoke it)
• Slash command input (/rename ultraplan foo runs /rename, not ultraplan)

When triggered, “ultraplan” is replaced with “plan” in the forwarded prompt to keep it grammatical: "please ultraplan this" → "please plan this".

19.3 Two Execution Paths on Approval

On approval, the user chooses one of two paths:

The teleport path uses a sentinel string __ULTRAPLAN_TELEPORT_LOCAL__ embedded in the browser’s rejection feedback. The rejection keeps the remote in plan mode, but the plan text is extracted from the feedback and executed locally.

19.4 Event Stream Scanning

The ExitPlanModeScanner class is a pure stateful classifier for the CCR event stream:

type ScanResult =
  | { kind: 'approved'; plan: string }
  | { kind: 'teleport'; plan: string }
  | { kind: 'rejected'; id: string }
  | { kind: 'pending' }
  | { kind: 'terminated'; subtype: string }
  | { kind: 'unchanged' }

Phase tracking for the UI pill:

running → (turn ends, no ExitPlanMode) → needs_input
needs_input → (user replies in browser) → running
running → (ExitPlanMode emitted) → plan_ready
plan_ready → (rejected) → running
plan_ready → (approved) → poll resolves, pill removed

Resilience: The poller tolerates up to 5 consecutive network failures before aborting (a 30-minute poll makes ~600 API calls — at any nonzero failure rate, one blip is inevitable).

20. Coordinator Mode: Multi-Agent Orchestrator

Coordinator Mode (CLAUDE_CODE_COORDINATOR_MODE=1) transforms Claude Code from a single-agent assistant into a multi-agent orchestrator where a master coordinator directs multiple parallel workers.

20.1 Architecture

Coordinator (you)
  ├─ AgentTool → Worker A (research)     ─┐
  ├─ AgentTool → Worker B (research)     ─┤ Run in parallel
  ├─ AgentTool → Worker C (implement)    ─┘
  └─ SendMessage → Continue Worker A with synthesized spec

The coordinator’s system prompt enforces a strict discipline:

• “Never write ‘based on your findings’“ — the coordinator must synthesize worker research into specific specs with file paths, line numbers, and exactly what to change
• Workers report back as XML <task-notification> messages with status, summary, result, and usage
• The coordinator never polls — workers push completion notifications
• Workers get isolated scratch directories (via tengu_scratch feature gate) for durable cross-worker knowledge

20.2 Worker Capabilities

Workers spawned via AgentTool have access to standard tools (or a simplified Bash/Read/Edit set in CLAUDE_CODE_SIMPLE mode), plus MCP tools from configured servers. The coordinator injects a workerToolsContext into the system prompt listing exactly which tools workers can use.

20.3 Task Workflow

The coordinator system prompt defines four phases:

Concurrency rules:

• Read-only tasks (research) — run in parallel freely
• Write-heavy tasks (implementation) — one at a time per set of files
• Verification — can run alongside implementation on different file areas

20.4 Continue vs. Spawn

The system provides explicit guidance on when to continue an existing worker vs. spawn fresh:

20.5 Session Mode Matching

When resuming a session, coordinator mode is automatically matched to the stored session mode:

export function matchSessionMode(sessionMode: 'coordinator' | 'normal' | undefined): string | undefined {
  // If current mode doesn't match the resumed session, flip the env var
  if (sessionIsCoordinator) {
    process.env.CLAUDE_CODE_COORDINATOR_MODE = '1'
  } else {
    delete process.env.CLAUDE_CODE_COORDINATOR_MODE
  }
}

This prevents a normal session from being resumed in coordinator mode (or vice versa), which would cause confusion.

21. The Memory System: Persistent AI Memory

Claude Code has a sophisticated file-based memory system (memdir/) that allows it to remember context across conversations — user preferences, project knowledge, feedback, and reference pointers.

21.1 Memory Architecture

Memories are stored as individual markdown files with YAML frontmatter at ~/.claude/projects/<sanitized-project-root>/memory/:

---
name: user_role
description: User is a senior backend engineer focused on Rust
type: user
---

User is a senior backend engineer at Acme Corp, primarily works in Rust...

An index file MEMORY.md (max 200 lines / 25KB) serves as a table of contents — it’s loaded into every conversation’s system prompt so Claude knows what memories exist without reading them all.

21.2 Four Memory Types

The system explicitly does NOT save: code patterns, architecture, git history, debugging solutions, or anything derivable from the current project state.

21.3 Intelligent Memory Recall

Not all memories are loaded every turn. Instead, a Sonnet-powered relevance selector (findRelevantMemories.ts) runs as a side query:

1. Scan all .md files in the memory directory (max 200, newest-first)
2. Parse frontmatter headers (name, description, type) from the first 30 lines
3. Send the user’s query + memory manifest to Sonnet with structured JSON output
4. Sonnet returns up to 5 most relevant filenames
5. Those files are injected into the conversation context

A clever optimization: recently-used tools are passed to the selector so it skips reference docs for tools Claude is already exercising (e.g., don’t surface MCP spawn docs when Claude is actively using the spawn tool).

21.4 Path Security

The memory path system includes robust security validation:

• Rejects relative paths, root paths, UNC paths, null bytes
• projectSettings (committed to repo) is intentionally excluded from autoMemoryDirectory — a malicious repo could otherwise set autoMemoryDirectory: "~/.ssh" and gain write access to sensitive directories
• All worktrees of the same git repo share one memory directory (via findCanonicalGitRoot)
• CLAUDE_COWORK_MEMORY_PATH_OVERRIDE for SDK/Cowork integration

21.5 Team Memory

When the TEAMMEM feature is enabled, memories split into private (per-user) and team (shared) directories. User preferences stay private; project conventions and reference pointers default to team scope. A conflict rule prevents private feedback memories from contradicting team-level ones.

22. Hooks: User-Defined Automation

The hooks system (schemas/hooks.ts) lets users attach automated behaviors to Claude Code events — shell commands, LLM prompts, HTTP calls, or agent verifiers that fire before/after tool use, message submission, and more.

22.1 Four Hook Types

type HookCommand =
  | { type: 'command'; command: string; shell?: 'bash' | 'powershell' }
  | { type: 'prompt'; prompt: string; model?: string }
  | { type: 'http'; url: string; headers?: Record<string, string> }
  | { type: 'agent'; prompt: string; model?: string }

Command hooks run shell commands with optional timeout, async/background execution, and one-shot mode (once: true — runs once then auto-removes).

Prompt hooks evaluate an LLM prompt with $ARGUMENTS placeholder for the hook input JSON.

HTTP hooks POST the hook input to a URL with configurable headers and env var interpolation (only explicitly-allowed env vars are resolved — prevents leaking secrets).

Agent hooks run a full agentic verification loop (“Verify that unit tests ran and passed”) with configurable model and timeout.

22.2 Event-Matcher-Hook Pipeline

Hooks are configured in settings.json as a three-level structure:

Event → Matcher[] → Hook[]

Each event (PreToolUse, PostToolUse, PreMessage, PostMessage, etc.) has an array of matchers with optional permission-rule-syntax patterns (e.g., "Bash(git *)" — only fires for git commands). Each matcher has an array of hooks to execute.

The if condition field uses the same permission rule syntax as the tool permission system, evaluated against tool_name and tool_input — so hooks can fire selectively without spawning a process for every tool call.

22.3 Advanced Hook Features

• async: true — Hook runs in background without blocking the model
• asyncRewake: true — Runs in background but wakes the model on exit code 2 (blocking error)
• once: true — Auto-removes after first execution (useful for one-time setup)
• statusMessage — Custom spinner text while the hook runs
• Environment variable interpolation in HTTP headers with explicit allowlist

23. Voice Mode, Bridge, and Infrastructure

23.1 Voice Mode

Claude Code includes a voice input mode (feature-flagged as VOICE_MODE) that allows voice-to-text interaction:

• Requires Anthropic OAuth (not API keys, Bedrock, or Vertex) — uses the voice_stream endpoint on claude.ai
• Protected by a GrowthBook kill-switch (tengu_amber_quartz_disabled) for emergency off
• Auth check uses memoized keychain reads (~20-50ms first call, cache hit thereafter)
• The /voice command, ConfigTool, and a VoiceModeNotice component all gate on isVoiceModeEnabled()

23.2 The Bridge System (31 Files)

The bridge (bridge/) is the most substantial networking subsystem — a persistent WebSocket connection between the local CLI and claude.ai’s web interface (CCR). It enables using Claude Code from a browser while the actual tools execute locally.

Key components:

• bridgeMain.ts — Main bridge loop with exponential backoff (2s initial → 2min cap → 10min give-up)
• replBridge.ts / replBridgeTransport.ts — REPL-side bridge handle, message framing
• bridgeApi.ts — API client with JWT refresh, trusted device tokens, session validation
• bridgeMessaging.ts / inboundMessages.ts — Message adaptation (SDK format ↔ local format)
• bridgePermissionCallbacks.ts — Permission request mediation between web UI and local CLI
• sessionRunner.ts — Spawns agent sessions per work item, manages worktrees
• capacityWake.ts — Wakes idle bridge when capacity becomes available
• workSecret.ts — Encrypted work routing between bridge workers

The bridge handles session lifecycle, token refresh, trusted device enrollment, and graceful reconnection — essentially a mini-RPC framework over WebSocket.

23.3 Direct Connect

The server/ directory implements Direct Connect — a WebSocket-based protocol for external clients to connect to a running Claude Code instance:

class DirectConnectSessionManager {
  connect(): void                    // Open WebSocket
  sendMessage(content): boolean      // Send user message
  respondToPermissionRequest(...)    // Handle tool permission prompts
  sendInterrupt(): void              // Cancel current request
  disconnect(): void                 // Close connection
}

Messages are JSON-over-WebSocket using the SDK message format. Control requests (permission prompts) are forwarded to the client, which responds with allow/deny decisions. This enables IDE integrations and custom frontends.

23.4 Upstream Proxy (CCR Security)

When running inside a CCR container, the upstream proxy system (upstreamproxy/) provides secure network access:

1. Read session token from /run/ccr/session_token
2. Set prctl(PR_SET_DUMPABLE, 0) — blocks same-UID ptrace (prevents prompt-injected gdb -p $PPID from scraping the token off the heap)
3. Download CA certificate and concatenate with system bundle for MITM proxy trust
4. Start local CONNECT→WebSocket relay on a random port
5. Unlink the token file (token stays heap-only; file is gone before the agent loop can access it)
6. Inject HTTPS_PROXY / SSL_CERT_FILE env vars for all subprocesses

Every step fails open — a broken proxy never breaks an otherwise-working session. The NO_PROXY list covers loopback, RFC1918, IMDS, Anthropic API, GitHub, and package registries.

23.5 Output Styles

The outputStyles/ system lets users customize Claude’s response format via markdown files:

• Project styles: .claude/output-styles/*.md
• User styles: ~/.claude/output-styles/*.md
• Plugin styles: provided by installed plugins

Each style file has frontmatter (name, description, keep-coding-instructions) and a prompt body that shapes how Claude formats its responses.

23.6 Native TypeScript Modules

native-ts/ contains TypeScript bindings for performance-critical native code:

• yoga-layout/ — TypeScript interface to the Yoga layout engine (flexbox calculations)
• file-index/ — Native file indexing for fast codebase search
• color-diff/ — Native color difference calculations (for theme/styling)

23.7 Moreright (Internal-Only)

The moreright/ directory contains a stub for an internal-only feature. The external build ships a no-op implementation with onBeforeQuery, onTurnComplete, and render all returning trivially. The real implementation is internal to Anthropic.

24. Vim Mode, Keybindings, and Developer Ergonomics

24.1 Vim Mode

A full vi command system:

• Motions — h, j, k, l, w, b, e, 0, $, gg, G
• Operators — d (delete), c (change), y (yank)
• Text Objects — iw (inner word), ap (a paragraph)
• Modal State Machine — Insert, Normal, Visual modes

All compiled to a single-pass command matcher for low-latency input processing.

24.2 Dynamic Keybindings

Context-aware keybinding resolution:

type KeybindingContext = {
  focus?: 'prompt' | 'file' | 'terminal'
  isRecording?: boolean
  vimMode?: boolean
  mode?: 'insert' | 'normal' | 'visual'
}

Users can define chord bindings: ctrl+k ctrl+o maps to custom actions via ~/.claude/keybindings.json.

24.3 Debug Tools

• CLAUDE_CODE_DEBUG_REPAINTS=1 — Shows component owner chain for every repaint
• CLAUDE_CODE_COMMIT_LOG=/tmp/commits.log — Logs slow renders for profiling
• CLAUDE_CODE_PROFILE_STARTUP=1 — Full startup profiling with memory snapshots

25. Key Engineering Patterns and Takeaways

Pattern 1: Lazy Everything

Claude Code is aggressive about deferral:

• Lazy schemas — Zod instantiation deferred via lazySchema()
• Lazy commands — Module imports via load() functions
• Lazy tools — 18 tools deferred to ToolSearchTool
• Lazy modules — Dynamic imports for OpenTelemetry, analytics, heavy components
• Lazy bundled skills — Reference files extracted on first use

Pattern 2: Memoization by Identity

Key functions are memoized to prevent redundant work:

• COMMANDS() — Memoized, cleared by clearCommandMemoizationCaches()
• loadAllCommands(cwd) — Memoized by working directory
• init() — Memoized to prevent re-entrancy

Pattern 3: Feature Flags for Dead Code Elimination

Bun’s feature() function enables compile-time dead code elimination:

if (feature('COORDINATOR_MODE')) {
  // This entire block is removed from the binary when the flag is off
  const { CoordinatorUI } = await import('./coordinator/index.js')
}

Pattern 4: Interning for Performance

Three interning pools (chars, styles, hyperlinks) reduce memory and enable O(1) comparison by integer ID instead of string equality. The style pool even pre-computes ANSI transition sequences.

Pattern 5: Fail-Closed Security

The buildTool() factory provides safe defaults for 7 commonly-stubbed methods. Permissions default to “ask” — a tool must explicitly opt into auto-approval.

Pattern 6: Centralized Side Effects

onChangeAppState() is the single choke point for all state mutations that affect external systems. No scattered useEffect side effects.

Pattern 7: File-Based IPC

Multi-agent coordination uses files, not sockets:

• Task outputs in ~/.claude/
• History in ~/.claude/history.jsonl
• Session transcripts for resume
• Lock files with retry backoff for concurrent access

Pattern 8: Prompt Cache Stability

Tools are sorted alphabetically before being sent to the API. This keeps the tool list in the same order across requests, maximizing prompt cache hit rates.

Pattern 9: Progressive Disclosure

The deferred tool system implements progressive disclosure at the API level:

• Base prompt stays under 200K tokens
• Model discovers additional tools on demand via ToolSearchTool
• Discovered tools are callable in the same turn

Pattern 10: Three-Tier Configuration

Settings are resolved from multiple sources with clear precedence:

MDM Policy (highest) → Remote Managed → User Settings
→ Project Config → Global Config → Defaults (lowest)

26. Conclusion

Claude Code is a remarkable piece of engineering. What appears to the user as a simple chat interface in the terminal is backed by:

• A custom React reconciler with Yoga layout, double-buffered rendering, and hardware scroll optimization
• A resilient query engine with automatic context compression, multi-strategy error recovery, and token budget continuation
• A 60+ tool ecosystem unified under a single generic interface with Zod validation, lazy schemas, and elastic discovery
• A multi-layered permission system balancing security and developer productivity across 5 modes, rule patterns, and ML classifiers
• An extensibility framework spanning skills, plugins, and MCP with 8 configuration scopes and 5 transport types
• Production-grade infrastructure: interned style pools, file-based IPC, sampled profiling, parallelized startup, and comprehensive telemetry

The codebase demonstrates that a CLI tool can be as architecturally sophisticated as any web application — perhaps more so, given the unique constraints of terminal rendering, keyboard input ambiguity, and the need to coordinate an AI model, file system, shell, and git repository all within a single conversation loop.

For developers building similar tools, the key lessons are:

1. Invest in the rendering layer. Claude Code’s custom Ink framework is its competitive advantage for terminal UX.
2. Design for failure. The multi-strategy error recovery (compaction → collapse → fallback → surface) means users almost never see raw API errors.
3. Defer aggressively. Lazy loading at every level — schemas, modules, tools, skills — keeps startup fast and memory bounded.
4. Intern everything. Style pools, character pools, and hyperlink pools turn O(n) string comparisons into O(1) integer comparisons.
5. Make safety the default. Fail-closed permissions, dangerous pattern detection, and mandatory confirmation for destructive operations build user trust.

Claude Code isn’t just a wrapper around an API. It’s a complete development environment that happens to run in your terminal.

This analysis is based on examination of the Claude Code source code. All technical details reflect the codebase as observed at the time of analysis.

Based on direct source inspection. “Hidden” = hidden from --help, feature-flagged, or dependent on non-public backends. Source presence does not guarantee your build/account has access.

Table of Contents

• 1. Hidden CLI Flags
• 2. Feature-Gated Slash Commands
• 3. The Buddy System — A Full Tamagotchi Pet
• 4. KAIROS — Persistent Autonomous Assistant Mode
• 5. Auto-Dream — Background Memory Consolidation
• 6. Magic Docs — Self-Maintaining Documentation
• 7. ULTRAPLAN — Remote 30-Minute Planning Sessions
• 8. Coordinator Mode — Multi-Agent Swarms
• 9. Speculation — Predictive Response Generation
• 10. The Advisor Model System
• 11. Voice Mode
• 12. Team Memory Sync
• 13. Remote Triggers — Scheduled Cloud Agents
• 14. Direct Connect — cc:// Session URLs
• 15. Bridge Mode & Remote Control
• 16. SSH Remote Execution
• 17. MCP Channels — Inbound Push Notifications
• 18. AFK / Auto-Permission Mode
• 19. Background Sessions (Detached)
• 20. “While You Were Away” Session Recaps
• 21. Tool-Use Summary Generation
• 22. Auto-Memory Extraction
• 23. Prompt Suggestions & Follow-Up Generation
• 24. Deferred Tool Discovery
• 25. Hidden Keybindings
• 26. Lesser-Known Environment Variables
• 27. Lesser-Known Settings Keys
• 28. CLAUDE.md Loading — Hidden Discovery Paths
• 29. Internal-Only Commands
• 30. Build-Time Feature Flags

1. Hidden CLI Flags

These flags are registered with .hideHelp() — they work but won’t appear in claude --help:

Deprecated aliases (still work): --afk and --dangerously-skip-permissions-with-classifiers → map to --enable-auto-mode

Correction: --voice is NOT a CLI flag. Voice is activated via /voice slash command or the voiceEnabled setting. --brief and --proactive are not hidden — they appear in help when their feature flags are on.

2. Feature-Gated Slash Commands

These commands exist but are conditionally registered or hidden:

3. The Buddy System — A Full Tamagotchi Pet

A fully implemented virtual companion that lives beside your input box.

Activate: /buddy

• Deterministic generation from your userId via Mulberry32 PRNG — same user = same companion across all devices
• 18 species: duck, goose, blob, cat, dragon, octopus, owl, penguin, turtle, snail, ghost, axolotl, capybara, cactus, robot, rabbit, mushroom, chonk
• 5 rarity tiers: common (60%), uncommon (25%), rare (10%), epic (4%), legendary (1%)
• 6 eye styles, 8 hats (commons get no hat), 1% shiny chance
• 5 stats: DEBUGGING, PATIENCE, CHAOS, WISDOM, SNARK — one peak stat, one dump stat, floors scale with rarity
• Soul generation: On first hatch, Claude generates a unique name and personality, stored permanently
• ASCII sprite animation: 3 frames per species at 500ms tick — idle, fidget, and rare blink frames
• Speech bubbles (10 seconds, 3-second fade) and /buddy pet hearts animation (2.5 seconds)
• Anti-cheat: Only the soul persists — bones (species/rarity/stats) are regenerated from userId hash every load

Release window: Teaser April 1-7 2026 (local dates for rolling timezone buzz). Live permanently from April 2026 onward. Always on for internal builds.

4. KAIROS — Persistent Autonomous Assistant Mode

Activate: --assistant flag (feature-gated: KAIROS)

A complete alternate UX where Claude becomes a long-lived autonomous agent persisting across sessions:

• Append-only daily logs at ~/.claude/projects/<slug>/memory/logs/YYYY/MM/YYYY-MM-DD.md
• 15-second blocking budget — any command exceeding 15s is auto-backgrounded
• Proactive <tick> prompts — periodic check-ins where Claude decides what to do next or calls Sleep
• Brief mode — all output through SendUserMessage tool (structured markdown + attachments + status), not free-form text
• Exclusive tools: SendUserFile, PushNotification, SubscribePR (GitHub webhook subscriptions), SleepTool
• Midnight boundary handling — flushes transcript on date change so the dream process can find it
• Nightly dreaming uses a separate disk-skill variant (distinct from the auto-dream system below)

5. Auto-Dream — Background Memory Consolidation

Runs automatically in the background — no user action needed.

A forked subagent reviews your recent sessions and consolidates learnings into structured memory files.

4-phase process:

1. Orient — ls memory dir, read index, skim existing topics
2. Gather — Check daily logs, find drifted memories, grep transcripts narrowly
3. Consolidate — Write/update memory files, merge duplicates, convert relative dates to absolute
4. Prune — Update MEMORY.md index (max ~25KB), remove stale pointers

Gates (cheapest checks first):

• Time: 24+ hours since last consolidation
• Sessions: 5+ sessions since last consolidation
• Lock: no other process mid-consolidation
• Scan throttle: session scanning limited to every 10 minutes

Safety: Bash restricted to read-only commands. Users can kill from the background tasks dialog (Shift+Down).

User control: autoDreamEnabled setting overrides the GrowthBook gate tengu_onyx_plover.

6. Magic Docs — Self-Maintaining Documentation

Files with a # MAGIC DOC: <title> first line are automatically updated by a background agent.

How to use:

1. Create a markdown file with # MAGIC DOC: My Topic as the first line
2. Optionally add italic instructions on the next line
3. Make sure Claude reads that file during a session
4. A constrained background agent will update it with new learnings

The agent can only edit that specific file — it cannot modify other files. This is triggered by file format, not a command.

7. ULTRAPLAN — Remote 30-Minute Planning Sessions

Activate: Type “ultraplan” in your message (keyword detection) or use /ultraplan

Farms out complex exploration to a remote Claude Code instance (CCR):

1. Remote session created with plan mode pre-configured
2. CLI polls every 3 seconds for up to 30 minutes
3. Remote Claude explores, plans, and calls ExitPlanMode when ready
4. You approve or reject the plan in the browser (claude.ai)
5. Rejected plans loop back for iteration
6. On approval: choose “remote” (execute in cloud) or “teleport to terminal” (execute locally)

Smart keyword detection avoids false positives — skips occurrences inside quotes, paths, identifiers, and questions.

8. Coordinator Mode — Multi-Agent Swarms

Activate: CLAUDE_CODE_COORDINATOR_MODE=1

Transforms Claude into a multi-agent orchestrator:

• Master coordinator spawns workers via AgentTool in parallel
• Workers report back as XML <task-notification> messages with status, summary, result, and token usage
• Coordinator never polls — push-based completion notifications
• Workers get isolated scratch directories (via tengu_scratch gate) for cross-worker knowledge
• System prompt enforces: “Never write ‘based on your findings’ — synthesize yourself”
• 4-phase workflow: Research (parallel) → Synthesis (coordinator) → Implementation → Verification
• Explicit continue-vs-spawn guidance based on context overlap

9. Speculation — Predictive Response Generation

While you’re still typing, Claude Code speculatively starts generating a response.

• File writes go to an overlay filesystem (not your real files)
• If your actual input matches the speculation boundary → overlay committed instantly
• If it doesn’t match → discarded silently
• Feature gate: tengu_chomp_inflection (GrowthBook)
• Result: noticeably lower perceived latency for predictable follow-ups

10. The Advisor Model System

Activate: /advisor <model> or --advisor <model>

Attaches a secondary reviewer/advisor model as a server-side tool:

• Main model (e.g., Sonnet) can call a stronger model (e.g., Opus) for review
• Full conversation history is forwarded when the advisor is invoked
• Beta header: advisor-tool-2026-03-01
• Does NOT work on Bedrock/Vertex (they don’t support the advisor beta header)
• Disable with /advisor off or CLAUDE_CODE_DISABLE_ADVISOR_TOOL
• GrowthBook gate: tengu_sage_compass

11. Voice Mode

Activate: /voice slash command or voiceEnabled setting

• Hold-to-talk dictation (default keybinding: hold space)
• Requires Anthropic OAuth — not available with API keys, Bedrock, or Vertex
• Uses the voice_stream endpoint on claude.ai
• Multiple audio backends: native audio, SoX, arecord
• Protected by GrowthBook kill-switch (tengu_amber_quartz_disabled) for emergency off

12. Team Memory Sync

Feature-gated: TEAMMEM

Memories split into private (per-user) and team (shared) directories:

• Team memory synced to server APIs across authenticated org members
• Secret scanning prevents leaking sensitive data into shared memory
• Optimistic locking for conflict resolution
• Team memory lives at .../memory/team/MEMORY.md
• Requires first-party OAuth and org-scoped server APIs

13. Remote Triggers — Scheduled Cloud Agents

Tool: RemoteTrigger (deferred, discovered via ToolSearchTool)

Create and manage scheduled remote Claude Code agents via CCR API:

• create — Schedule a trigger with cron expression
• list / get — View triggers
• update — Modify a trigger
• run — Manually fire a trigger

Requires Claude.ai OAuth. Feature gate: tengu_surreal_dali. Beta: ccr-triggers-2026-01-30.

14. Direct Connect — cc:// Session URLs

Activate: claude server (when DIRECT_CONNECT is enabled)

Creates shareable session URLs that external clients can connect to:

• WebSocket-based protocol with SDK message format
• Permission requests forwarded to connecting client
• Supports interrupt/cancel signals
• claude open cc://... connects to an existing session (described as internal)
• Enables custom IDE integrations and frontends

15. Bridge Mode & Remote Control

Activate: --remote-control / --rc (when BRIDGE_MODE compiled in)

Persistent WebSocket connection between local CLI and claude.ai web interface:

• Use Claude Code from a browser while tools execute locally
• Exponential backoff reconnection (2s → 2min cap → 10min give-up)
• JWT token refresh, trusted device enrollment
• Permission request mediation between web UI and local CLI
• 31 files in the bridge subsystem — essentially a full RPC framework

Related settings:

• remoteControlAtStartup — auto-start bridge
• taskCompleteNotifEnabled, inputNeededNotifEnabled, agentPushNotifEnabled — push notification controls

16. SSH Remote Execution

Activate: claude ssh <host> [dir] (when SSH_REMOTE is enabled)

• Deploys Claude Code binary to a remote Linux host over SSH
• API auth tunnels back through the local machine — no separate remote auth setup
• Build-time feature-gated

17. MCP Channels — Inbound Push Notifications

Activate: --channels plugin:name@marketplace

Register sessions for real-time event delivery from approved MCP servers/plugins:

• Allowlist controlled by GrowthBook
• --dangerously-load-development-channels bypasses the allowlist
• Enables event-driven workflows (e.g., GitHub events, CI notifications)

18. AFK / Auto-Permission Mode

Activate: --enable-auto-mode (hidden flag, requires TRANSCRIPT_CLASSIFIER)

Classifier-assisted automatic permission decisions when the user is away:

• AI model evaluates each permission request based on conversation context
• Beta header: afk-mode-2026-01-31
• Deprecated aliases: --afk, --dangerously-skip-permissions-with-classifiers
• Designed for unattended, long-running agent workflows

19. Background Sessions (Detached)

Feature-gated: BG_SESSIONS

Run Claude Code sessions in the background with management commands:

• claude --bg / claude --background — Start a detached session
• claude ps — List running background sessions
• claude logs <id> — View session logs
• claude attach <id> — Reattach to a session
• claude kill <id> — Stop a session

20. “While You Were Away” Session Recaps

When you return to a session after being away, Claude can generate a short recap card summarizing what happened.

• Implemented in services/awaySummary.ts
• Produces short re-entry summaries automatically
• UI-side feature, not a slash command

21. Tool-Use Summary Generation

For SDK/mobile surfaces, raw tool batches are automatically converted into compact high-level progress summaries:

• Implemented in services/toolUseSummary/toolUseSummaryGenerator.ts
• Generates short labels for completed tool batches
• Used by SDK to provide progress updates to clients and mobile-like single-line rows

22. Auto-Memory Extraction

A background agent automatically extracts memories from your conversations:

• Runs at end of each complete query loop (when EXTRACT_MEMORIES is enabled)
• Uses a forked agent that shares the prompt cache
• Scans existing memory files first to avoid duplicates
• When the main agent writes memories directly, the background extractor skips that range
• Writes to ~/.claude/projects/<path>/memory/
• Auto-memory is enabled by default unless disabled via CLAUDE_CODE_DISABLE_AUTO_MEMORY, --bare, or settings

23. Prompt Suggestions & Follow-Up Generation

Activate: CLAUDE_CODE_ENABLE_PROMPT_SUGGESTION=1

After Claude finishes a response, it can suggest what to ask next:

• Feature gate: tengu_chomp_inflection (GrowthBook)
• Env var can force on/off, otherwise GrowthBook + interactive-session checks apply
• Tied to the speculation system — may pre-generate the speculative response too

24. Deferred Tool Discovery

~18 of Claude Code’s 60+ tools are not sent to the model in every request. They’re discovered on-demand:

1. Model calls ToolSearchTool with a keyword query
2. Matching deferred tools’ schemas are returned
3. Model calls the discovered tool in the same turn

Query syntax:

• select:TaskCreate,LSP — Direct selection by name
• task create — Keyword search against names, descriptions, and hints
• +slack send — Require “slack” in tool name

Why it matters: Keeps the base prompt under 200K tokens. Without this, 60+ tool schemas would consume too much context.

25. Hidden Keybindings

Feature-gated keybindings that only appear when their features are enabled:

Always-available but often unknown:

All keybindings are overridable via ~/.claude/keybindings.json.

26. Lesser-Known Environment Variables

Debug & profiling:

Behavioral overrides:

Provider switching:

Prompt caching control:

27. Lesser-Known Settings Keys

In ~/.claude/settings.json — things most people don’t know you can set:

28. CLAUDE.md Loading — Hidden Discovery Paths

Beyond the standard project CLAUDE.md, Claude Code loads instructions from:

• ~/.claude/CLAUDE.md — User-level global instructions
• Parent directories up to git root — all CLAUDE.md files in parent dirs are included
• .claude/CLAUDE.md — Inside the .claude directory
• .claude/rules/*.md — Per-project rule files (all .md files in this directory)
• @include-style references inside memory files

29. Internal-Only Commands

Registered only when USER_TYPE === 'ant' — not in public builds:

backfill-sessions, break-cache, bughunter, ctx_viz, good-claude, init-verifiers, force-snip, mock-limits, bridge-kick, subscribe-pr, reset-limits, share, ant-trace, perf-issue, env, oauth-refresh, debug-tool-call, agents-platform, autofix-pr, onboarding

30. Build-Time Feature Flags

Compile-time flags via feature() from bun:bundle. When off, code is eliminated entirely from the binary:

Compiled from static analysis of the Claude Code source. Corrections applied from the audited version. Features behind flags may not be in your build.

Over time, this question started to feel dry and repetitive. I often found myself skipping it whenever I saw it again.

Recently, though, I came across a very interesting variation of the problem. I tried designing a solution for it, and that is what inspired me to write this post.

The Challenge

The challenge is to design a URL shortener that supports 1 trillion URLs.

At first glance, this sounds like a simple scaling problem, but it actually changes a lot of things and breaks many of the naive assumptions that usually work for the standard Bitly-style system design question.

Even if we assume a base-62 encoding (0-9, a-z, A-Z), a 7-character string gives us roughly 3.5 trillion possible combinations.

That sounds like plenty for 1 trillion URLs, but things get complicated once we start thinking about distribution, storage, and collision guarantees.

Also, 62^7 is only the raw theoretical namespace. In practice, the usable space is smaller because some codes will be reserved for custom aliases, blocked words, internal testing, and occasionally wasted ranges from crashed allocators. The margin is still comfortable, but it is not infinite.

Constraints

1. The shortened URL can have at most 7 characters.
2. The system must guarantee unique URLs with no collisions.
3. The system must support 1 trillion URLs over its entire lifetime.

Before diving into the trillion-URL challenge, let’s first revisit the standard approach used to solve the traditional URL shortener design problem.

The Traditional Approach

Functional Requirements

Core requirements

• Users should be able to submit a long URL and receive a shortened version.
• Optionally, users should be able to specify a custom alias for their shortened URL, i.e. www.short.ly/my-custom-alias.
• Optionally, users should be able to specify an expiration date for their shortened URL.
• Users should be able to access the original URL by using the shortened URL.

Below the line (out of scope)

• User authentication and account management.
• Analytics on link clicks, such as click counts or geographic data.

Non-Functional Requirements

Core requirements

• The system should ensure uniqueness for short codes, where each short code maps to exactly one long URL.
• Redirection should occur with minimal delay, ideally under 100 ms.
• The system should be reliable and available 99.99% of the time, with availability prioritized over consistency.
• The system should scale to support 1 billion shortened URLs and 100 million DAU.

Below the line (out of scope)

• Real-time consistency for analytics.
• Advanced security features such as spam detection and malicious URL filtering.

High-Level Design

We’ll go through the functional requirements one by one and design a system that satisfies them.

1. Submitting a long URL and receiving a short URL

When a user submits a long URL, the client sends a POST request to /urls with the long URL, custom alias, and expiration date.

The flow looks like this:

1. The primary server receives the request and validates the long URL format using libraries like is-url or simple validation logic.
2. Optionally, we can check whether this exact long URL was already shortened and return the existing short code as a deduplication optimization.
3. In practice, most URL shorteners allow multiple short codes for the same long URL, since different users may want different expiration dates, separate analytics, or different custom aliases.
4. If the URL is valid, we generate a short code and store the mapping.

Generating the short code

The standard answer is to use a hash function that produces enough randomness to make collisions unlikely. A hash function like MD5 takes an input and produces a deterministic fixed-size output. That means the same long URL would always map to the same hash, which is useful if you want deterministic code generation.

But the system still needs to store the redirect mapping, because a hash is not reversible and we still need metadata such as expiration dates, ownership, and custom aliases.

It is also not desirable if you need multiple short codes for the same URL.

Hash outputs also have high entropy, which makes them appear random. We can encode that output using base-62 and take the first 7 characters as our shortcode.

Here, encoding simply means converting binary hash output into a sequence of readable characters from a chosen alphabet so it can be used as a short, URL-friendly code.

This gives us approximately 62^7 = 3.52 trillion possible values, which is a large namespace.

But a large namespace does not make random or truncated-hash generation safe. If the code space has size |S| and n codes are already in use, the probability that the next random code collides is n / |S|. That means you still need retries and database checks to enforce uniqueness. In a space of this size, even a system that creates around 1 billion links would expect on the order of 10^5 colliding pairs unless it performs uniqueness checks.

At more ordinary scale, a more production-friendly baseline is to stop relying on random hashes and use a centralized counter instead. Redis is a good fit here because INCR is atomic and hands out unique integers efficiently. We can base-62-encode each integer into a short code and guarantee uniqueness without a retry loop.

Even if links expire, we usually do not recycle short codes. Reusing codes creates ugly edge cases with stale caches, delayed clients, and old analytics data, so it is safer to treat the namespace as append-only over the system’s lifetime.

Once we have the short URL, we can insert it into the database along with the long URL, optional custom alias, and expiration date. Finally, we return the shortened URL to the client.

2. Accessing the original URL from the shortened URL

Once the short URL is live, users can use it to reach the original URL. Importantly, that shortened URL exists under a domain we own.

When a user accesses a shortened URL, the flow looks like this:

1. The browser sends a GET request with the short code, for example GET /abc123.
2. The primary server looks up the short code in the database.
3. If the short code exists and has not expired, the server retrieves the long URL. If it has expired, the server returns 410 Gone.
4. The server responds with an HTTP redirect, usually either 301 or 302.

For a URL shortener, a 302 redirect is often preferred because:

• It gives us more control over the redirection process, allowing us to update or expire links later.
• It prevents browsers from aggressively caching the redirect.
• It still allows us to track click statistics, even though analytics are out of scope for this design.

How do we make redirects fast?

A naive database lookup could devolve into a full table scan, which is clearly too slow.

A better baseline is to add an index, or simply make the shortened URL the primary key. That gives us indexed lookups and also enforces uniqueness.

The remaining problem is SSD IOPS. A single database instance would still struggle to keep up with heavy traffic, leading to slower response times and possible timeouts.

A much better solution is to place an in-memory cache like Redis or Memcached between the server and the database. Frequently accessed mappings from short code to long URL can live in memory.

The read path then becomes:

• On a cache hit, return the long URL in milliseconds.
• On a cache miss, query the database, populate the cache, and return the result.

The difference in speed is significant:

• Memory access time: about 100 nanoseconds (0.0001 ms)
• SSD access time: about 0.1 ms
• HDD access time: about 10 ms

That means memory access is roughly 1,000 times faster than SSD and 100,000 times faster than HDD.

In terms of operations per second:

• Memory can support millions of reads per second.
• SSDs can support roughly 100,000 IOPS.
• HDDs typically support around 100 to 200 IOPS.

The only real challenge here is cache invalidation, which can get complicated when updates or deletions happen. In this system, though, the problem is smaller because shortened URLs are mostly read-heavy and rarely change.

The cache also needs time to warm up, so the first few requests for a URL may still hit the database. Since memory is limited, we also need to think carefully about cache size, eviction policies such as LRU, and which entries are worth storing.

3. Scaling the standard design to 1 billion URLs

Let’s do some rough sizing.

Each row in the database contains:

• A short code, roughly 8 bytes
• A long URL, roughly 100 bytes
• creationTime, roughly 8 bytes
• An optional custom alias, roughly 100 bytes
• An expiration date, roughly 8 bytes

That totals around 200 bytes per row. If we round up to 500 bytes to account for metadata such as creator ID, analytics ID, and internal overhead, then 1 billion mappings would require:

500 bytes * 1 billion rows = 500 GB

That is still within the capabilities of modern SSDs, and if we need more headroom, we can shard data across multiple servers.

Reads are much more frequent than writes, so we can separate the system into reader and writer services and scale them independently. We can then add more server instances behind a load balancer to handle higher RPS without concentrating load on a single machine.

Here is the high-level difference between the two versions of the problem:

All of this works well for the standard problem. Now let’s go back to the trillion-URL version and look at why those answers stop working.

The core shift is from generating IDs with local randomness to treating the shortcode space as a globally allocated namespace.

Why the Usual Approaches Fail at 1 Trillion URLs

The constraints change the problem enough that several standard answers break down.

1. Truncated hashes stop being safe

The assumption that MD5 plus base-62 gives us enough entropy fails here. Once we truncate the hash to just 7 characters, we are throwing away most of the hash space, and the birthday paradox tells us that collisions become mathematically unavoidable.

The birthday paradox says that in a room of just 23 people, there is about a 50% chance that two people share the same birthday, even though there are 365 possible days.

That feels surprising because 23 is much smaller than 365.

The reason is that we are comparing many pairs, not just one.

The number of comparisons among k items is:

So the probability of collision grows quadratically as the number of generated IDs increases.

After base-62 encoding, the total possible ID space is approximately 3.5 trillion. The rough intuition is that collisions start becoming noticeable around $\sqrt{N}$, but the more precise 50% threshold is:

Where:

• k_{50} is the point where the chance of at least one collision is about 50%
• N is the size of the total ID space

For 7 base-62 characters:

So:

So by about 2.2 million generated IDs, the system already has roughly a 50% chance of at least one collision. Even at 2 million IDs, the probability is already significant. In this system, that is disastrous, because a user could be redirected to the wrong site.

2. Retry-on-collision becomes too expensive

One way to patch the problem is to salt the URL and keep rehashing until you find a unique value.

That creates a loop like this:

hash -> DB check -> collision? -> rehash -> check again

At trillion-record scale, the database index will itself be several terabytes. Every collision check becomes a random lookup against that massive index, and many of those lookups will fall out of cache.

If you have heavy write traffic, you end up spending a large chunk of your compute budget just proving that a string has not been used before. In the worst case, that becomes extremely expensive and operationally ugly.

We need to stop searching for uniqueness and start generating uniqueness.

Generating Uniqueness Instead of Searching for It

The simplest way to guarantee uniqueness is to use a counter again. That mathematically guarantees a unique value for every URL.

But now we hit the next problem: we cannot hand users a giant integer because the short URL is constrained to at most 7 characters.

Turning a large integer into a 7-character code

The solution is to map the large integer into a base-62 string.

A base-62 alphabet can look like this:

• 0-9 (10 characters)
• a-z (26 characters)
• A-Z (26 characters)

That gives us:

Here is a simple example. Assume our counter is 500.

The remainder 4 maps to the character at index 4 in the alphabet, and the quotient 8 becomes the next digit in the conversion.

This gives us a one-to-one mapping where every integer in the usable range maps to a unique short string, and we can decode it back correctly if needed. There are no collision checks and no guesswork. The mapping itself is O(1).

Avoiding a global counter bottleneck

A single counter is a disaster in a distributed system, because every writer would need to talk to the same coordinator.

To solve that, we introduce a coordinator service like ZooKeeper. It acts as a distributed, highly available source of truth and hands out ranges of IDs to each server.

For example:

• Server A gets 1 to 1,000,000
• Server B gets 1,000,001 to 2,000,000

Now each server can allocate IDs locally in memory by incrementing a local integer. That means:

• No network calls for every URL creation
• No coordination on every write
• No locks
• No database uniqueness checks

Once a server exhausts its range, it goes back to ZooKeeper and requests another one.

If Server A crashes, the worst case is that we lose some unused IDs from its range. That is fine. We have roughly 3.5 trillion total combinations and only need 1 trillion of them, so wasting some IDs is acceptable.

This makes ID generation solid. We are no longer hoping collisions do not happen. We have mathematically eliminated them.

The Storage Wall

Now we get to the storage problem.

If each URL takes roughly 100 bytes, then 100B * 1T = 100 TB of raw URL data alone, and that is before timestamps, indexes, metadata, replication, and everything else.

That is not something a single PostgreSQL instance should handle. Even if it could, a deep B-tree index at this scale would translate into a cascade of disk lookups.

So the obvious answer is to distribute and shard the data across multiple nodes. For example, if 100 nodes each handle 5 TB, the problem becomes much more manageable.

Routing requests to the right shard

The next question is how to know which node holds which URL.

The simplest approach is modulo-based sharding. The routing rule can be as simple as:

shard = hash(short_url) % 100

That gives us O(1) routing without a central directory.

If we expect the number of shards to change frequently, true consistent hashing or rendezvous hashing is a better choice because it minimizes remapping when nodes are added or removed.

Since this workload is mostly key-value lookups with no major joins or cross-node transactions, databases like Cassandra or DynamoDB are a much better fit than a monolithic relational database.

They are built for exactly this type of access pattern, and their LSM-tree storage engines handle write-heavy workloads much better while also simplifying sharding and replication.

The read path at trillion scale

The Pareto principle applies strongly here: a small fraction of URLs will receive most of the traffic.

So treating every URL equally is wasteful. Instead, we place a distributed cache such as Redis or Memcached in front of the storage layer.

The read path becomes:

• Cache hit: return the URL in under a millisecond.
• Cache miss: look up the correct shard in Cassandra, fetch the URL, store it in Redis, and then return the redirect.

Security, Global UX, and the Reductionist Mindset

There is one important issue that usually does not come up early in system design interviews: security.

Making the URLs unpredictable

If the system simply uses a visible counter, then the short URLs become predictable. If someone knows one URL, they can often guess the previous or next one.

That means an attacker could scrape the namespace, discover private links, or infer business intelligence like usage growth.

So even though we still want a counter under the hood, we need the outward-facing short codes to look random.

That is where a Feistel cipher helps. More precisely, we use it as a reversible permutation over the fixed ID space: the counter value is scrambled into another integer of the same size.

Every input still maps to a unique output, and we can still reverse it back to the original value. In other words, it behaves like format-preserving scrambling. But from the outside, the output looks random.

That gives us the best of both worlds: guaranteed uniqueness underneath and non-predictable public URLs on top.

Global reads and disaster scenarios

Now imagine a regional failure, or simply a user very far away from the region where the system is deployed.

If all redirects are served from, say, Northern Virginia, then a user in Tokyo has to make a full round trip to Virginia before being redirected. That can easily add around 300 ms, which is a poor user experience for something as simple as a redirect.

To avoid that, we can push reads and redirects closer to the user with a CDN like Cloudflare or Akamai, but that only helps if the short-code mapping is also available near the edge. That can be done by caching redirect responses, storing hot mappings in edge KV, or running edge compute backed by nearby replicas. Otherwise, the edge still has to call back to origin.

With edge-local data, the Tokyo user can be served from the Tokyo edge in something closer to 10 ms.

Writes are different. If a user in New York creates a link, it gets written to Cassandra and then replicated asynchronously to Tokyo.

That brings us to the CAP theorem.

Consistency, availability, and partition tolerance cannot all be maximized at the same time. In this case, we are willing to give up immediate consistency so we can preserve high availability and partition tolerance.

That means eventual consistency is acceptable. If a user in Tokyo cannot immediately access a link that was just created in New York because replication is still catching up, that is not a fatal problem. A refresh a few seconds later should resolve it.

So we are okay with eventual consistency, but we are not willing to compromise on availability or partition tolerance.

The one thing we absolutely cannot compromise on is uniqueness. That is the beauty of the range-manager approach. Even if New York and Tokyo are completely isolated from each other, as long as they were assigned non-overlapping ranges before the split, they can continue generating unique URLs independently with no risk of overlap.

Final Takeaway

Let’s step back and trace what we built here.

We did not approach this as a simple URL-shortening coding task. We approached it as a namespace-management problem.

1. We determined the size of the namespace: 62^7, or about 3.5 trillion possible short codes.
2. We reduced uniqueness to a sequential counter.
3. We handled distributed coordination through range allocation with ZooKeeper.
4. We scaled storage with sharded key-value data in Cassandra.
5. We reduced read latency by caching the hot set with Redis.
6. We addressed predictability and security with a Feistel cipher.

That is the reductionist mindset.

At this scale, you cannot brute-force your way through the problem with more code. You have to find the right abstraction that makes the problem manageable again.

If you walk into an interview and say, “I’ll use a hash and hope for the best,” you are thinking like a coder, which is fine. But if you talk about the physics of the ID space, explain why LSM trees outperform B-trees for this write-heavy profile, or discuss the birthday paradox and what it means for truncated hashes, you show a deeper understanding of how systems actually work.

That is what I mean when I say that understanding why a system fails is more important than knowing how to build it.

In the end, this is TinyURL at trillion scale: a simple-looking problem on the surface, but a masterclass in distributed systems underneath.

To understand how this works internally we need to understand the sandboxed renderer processes, serializing internal memory structures, navigating the inter-process communication (IPC) frameworks of the host OS, interfacing with legacy and modern clipboard APIs across platforms, and ultimately reconstructing the data into a secure, scriptable Object within a distinct Document Object Model (DOM).

This article dives deep into this exact feature, detailing the lifecycle of a copied image starting from the browser’s rendering engine, traversing through macOS, Windows, and Linux (both X11 and Wayland) OS clipboards, and securely re-entering a sandboxed web application.

Browser-Side Copy Operation

The operation initiates when a user triggers a context menu over an image element and selects “Copy Image.” This action bypasses standard JavaScript clipboard API interceptions, which are typically gated by ClipboardEvent.clipboardData, and directly invokes the browser’s internal native handlers.

Image Retrieval from the Rendering Engine

When “Copy Image” is invoked, the browser must extract the visual data. Modern layout engines, such as Blink in Chrome, Gecko in Firefox, or WebKit in Safari, do not simply fetch the image from the network cache. While the compressed original bytes might exist in the HTTP disk or memory cache, a rendered image may have been modified by CSS, transformed, or drawn to an HTML5 <canvas>.

Instead, the browser’s rendering subsystem extracts the fully decoded bitmap currently residing in memory. In Chromium’s Blink engine, images are represented via the blink::Image abstraction. Specifically, a BitmapImage (which often wraps an SkBitmap or SkImage from the Skia graphics library) contains the raw pixel data. If the browser employs hardware-accelerated compositing, the SkImage may reside in GPU VRAM as an OpenGL texture or Vulkan buffer. To place this on the CPU-bound OS clipboard, the engine must perform a GPU readback - A computationally expensive operation where pixels are copied from VRAM back into system RAM via glReadPixels or equivalent APIs, converting the hardware texture back into a software SkBitmap.

Generation of Internal MIME Representations

The OS clipboard is entirely format-agnostic; it acts as a generic key-value store where keys are format identifiers and values are binary blobs. To ensure the highest probability of successful pasting into diverse native applications, the browser generates multiple simultaneous representations of the image.

A single “Copy Image” action typically generates several internal representations before they are mapped to OS-specific formats. First, the engine re-encodes the raw SkBitmap pixel data into a standard compressed format, overwhelmingly image/png. This re-encoding step is crucial as it ensures a standardized file header and strips out malformed or proprietary data chunks. Second, the browser generates an HTML fragment representing the image, labeled as text/html. This often embeds the image as a Base64 encoded Data URI or provides an <img> tag pointing to the source URL.

<meta charset='utf-8'>
<img src="data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAAEAAAABCAYAAAAfFcSJAAAADUlEQVR42mP8/5+hHgAHggJ/PchI7wAAAABJRU5ErkJggg=="
     alt="Description"
     width="500"
     height="300">

Finally, the absolute URL of the image is provided as text/plain as a fallback for text-only paste targets.

It’s important to know the difference between the 2 copy operations presented to the user. The “Copy Image” command extracts the decoded bitmap, re-encodes it, and places the binary blob on the clipboard alongside HTML and Text fallbacks. Conversely, “Copy Image Address” simply extracts the src attribute from the DOM node and places it on the clipboard exclusively as text/plain.

Inter-Process Communication and Memory Ownership

Because web pages execute in highly restricted, sandboxed “Renderer” processes, they lack the operating system privileges required to interact with the global system clipboard directly. The Renderer must therefore serialize the extracted image and transmit it to the highly privileged “Browser” process.

In Chromium, this boundary is crossed using Mojo, a lightweight message passing system. The Blink pasteboard implementation, specifically blink::Pasteboard::writeImage, formulates an IPC message historically routed via ClipboardHostMsg_WriteImage and now managed via strongly typed Mojo interfaces.

Image data is inherently large. Passing a multi-megabyte decoded bitmap over a standard UNIX domain socket or named pipe via standard message serialization would introduce massive latency and memory duplication. To circumvent this, Mojo utilizes a structure called mojo_base.mojom.BigBuffer.

When a payload exceeds a specific threshold, BigBuffer transparently shifts from an inline byte array to a BigBufferSharedMemoryRegion. The Renderer process requests the OS to allocate an anonymous shared memory segment, writes the encoded PNG bytes into it, and sends merely the file descriptor (or Windows Handle) and size over the Mojo IPC channel. The Browser process maps this shared memory into its own address space, allowing zero-copy transmission of the image payload across the process boundary. Once the Browser process receives this message, the ClipboardHostImpl verifies the data, manages sequence tokens to prevent race conditions, and interfaces with the OS-specific clipboard APIs.

Architectural Diagram: Browser Process Boundary

OS Specific Clipboard Layer Architecture

Clipboards vary across different OSes. The browser must translate its internal web-standard MIME types into the native data structures expected by macOS, Windows, and Linux to ensure seamless interoperability with native applications.

Windows Win32 Clipboard API

On Windows, the clipboard is a shared system resource accessed via the legacy Win32 API. When Chromium’s ClipboardWin::WriteBitmap executes, it translates the incoming SkBitmap into Device Independent Bitmap (DIB) formats.

Windows historically relies on CF_BITMAP (a GDI handle), CF_DIB, and CF_DIBV5. Because standard CF_DIB does not reliably support alpha channels for transparency, modern browsers write CF_DIBV5, which includes a BITMAPV5HEADER specifying color masks, color space information, and alpha values. However, due to rampant bugs in legacy software, such as Microsoft Office mishandling CF_DIBV5 alpha channels resulting in black backgrounds browsers also explicitly write a standardized PNG format blob.

Thus, the Windows clipboard receives both DIB formats and a raw PNG blob. The order of format registration is vital, browsers prioritize the PNG format so that aware applications select it over the lossy or buggy DIB representations.

macOS NSPasteboard

Apple’s macOS handles clipboard operations via the NSPasteboard class, which acts as a client-side Objective-C wrapper around the pbs (pasteboard server) background daemon. The general pasteboard (NSPasteboard.generalPasteboard) manages data copying across the system.

WebKit and Chromium translate their internal representations into UTIs. An image is registered under public.png (or NSPasteboardType.png / NSPasteboardTypePNG). HTML fallbacks are registered as public.html or the proprietary Apple Web Archive format.

When the browser writes to NSPasteboard, it packages the image into an NSPasteboardItem. Unlike Windows, which requires transferring global memory handles, macOS utilizes Mach ports to transfer data to the pbs daemon’s address space. For extremely large files, macOS supports “promised data” (NSFilePromiseProvider), where the clipboard merely holds a reference and defers materialization until the drop or paste occurs. However, for standard web images, the binary PNG is written directly to the pasteboard using setData:forType:.

Linux X11 Selection Model

The X Window System (X11) does not inherently possess a global “clipboard buffer” that stores binary data like Windows or macOS. Instead, X11 relies on “Selections” specifically the CLIPBOARD selection, managed via the ICCCM standard.

When a user copies an image in Firefox or Chrome on X11, the browser calls XSetSelectionOwner, claiming ownership of the CLIPBOARD atom. No image data is transferred to the X server at this point. The browser merely registers itself as the owner. When a user switches to Website B and triggers a paste, the receiving application calls XConvertSelection. The X server sends a SelectionRequest event to the owner (the browser process that copied the image). The requesting application asks for the TARGETS atom to discover what formats are available. The copying browser responds with a list of atoms corresponding to MIME types, such as image/png and text/html.

Once the receiving app requests image/png, the copying browser writes the PNG data to a property on the receiving application’s X window using XChangeProperty. However, the X11 protocol has a maximum request size. For large images, the transfer must be negotiated using the INCR protocol. The data is chunked, often in 256KB increments, requiring a complex state machine of SelectionNotify and PropertyNotify events to stream the image from the sending process to the receiving process memory.

Linux Wayland Clipboard Protocol

Wayland modernizes Linux display architecture by entirely removing the X Server and substituting a secure compositor protocol. Like X11, Wayland lacks a global memory buffer; it is a pure peer-to-peer IPC mechanism mediated by the compositor.

When an image is copied, Chromium’s Ozone/Wayland backend creates a wl_data_source and calls wl_data_source_offer, indicating to the compositor that it possesses image/png. The browser then calls wl_data_device_set_selection to assert ownership.

When pasting, the receiving application asks for the data by sending a wl_data_offer.receive request to the compositor, specifying the MIME type and passing a file descriptor (fd), which is typically one end of a UNIX pipe. The compositor forwards this pipe to the copying browser via a wl_data_source.send event. The copying browser then writes the raw PNG binary data directly into the file descriptor and closes it.

// Architectural Pseudocode for Wayland Data Offer Reception
void wl_data_offer_receive(struct wl_data_offer *offer,
                           const char *mime_type,
                           int fd) {
    // The browser receives the request, writes PNG bytes into 'fd'
    write(fd, png_binary_data, png_size);
    // Closing the file descriptor signals EOF to the receiving application
    close(fd);
}

This file-descriptor-passing model provides excellent performance and security, as massive binary blobs are streamed directly through kernel pipes without passing through a middleman server, avoiding memory duplication.

Pasting into Website B (Reverse Flow)

When the user navigates to Website B and presses Ctrl+V (or Cmd+V), the flow reverses, but introduces significant security checkpoints, sanitization requirements, and DOM API layers.

Gating and Security Checks

Pasting is an inherently dangerous operation. A malicious website could silently read the user’s clipboard, stealing passwords or personally identifiable information (PII) copied from external applications. Consequently, browsers mandate that paste events are heavily gated by “transient user activation” - a recent interaction like a physical click or keypress. If the site attempts to read the clipboard programmatically via the Async Clipboard API (navigator.clipboard.read()), the browser invokes the Permissions API. If the clipboard-read permission has not been explicitly granted, the browser pauses script execution and displays a native permission prompt to the user.

Receiving the Paste Event and OS IPC

Once authorized, the Browser process requests data from the OS clipboard. On Windows, it calls GetClipboardData for formats like CF_DIBV5 or PNG. On macOS, it requests data from the NSPasteboard. On Wayland, it provides a pipe file descriptor to wl_data_offer_receive and reads the incoming stream.

Before this data is allowed back into the sandboxed Renderer process of Website B, it must be aggressively sanitized. An OS clipboard could contain a malformed image crafted to exploit vulnerabilities in libraries like libpng or libjpeg. Furthermore, an image might contain hidden EXIF metadata, such as GPS coordinates, representing a massive privacy violation if unknowingly pasted into a web form.

To mitigate this, the Browser process passes the raw binary blob to a sandboxed utility process. Here, the image is decoded back into an uncompressed bitmap, strictly discarding any metadata, ICC profiles, or malformed chunks. It is then securely re-encoded back into a clean PNG. This sanitized payload is passed via Mojo BigBuffer to Website B’s Renderer process.

DOM Paste Event Flow and DataTransfer

Inside the Renderer, the JavaScript engine fires a paste event on the active DOM element. The event object (ClipboardEvent) contains a DataTransfer property. The engine parses the multiple MIME types provided by the OS and exposes them via the event.clipboardData.items list. This DataTransfer infrastructure is heavily shared with the HTML5 Drag-and-Drop API, utilizing identical underlying C++ data objects to represent the transferring payload.

Because reading heavy binary blobs synchronously would freeze the browser’s main thread, the DataTransfer object utilizes delayed materialization. When a developer loops through clipboardData.items and calls item.getAsFile(), the browser instantiates a JavaScript File (a subclass of Blob). The backing memory for this Blob is a pointer to the shared memory or cached byte array established during the IPC phase.

Different DOM elements handle the default paste behavior differently:

• contenteditable elements: The browser’s editing commands parse the incoming text/html payload from the clipboard. If an image is present, it generates an <img> tag and attempts to insert it into the DOM. If the image is a raw binary, it may be converted into a Base64 data URI.
• textarea elements: These inputs accept only plain text. The browser aggressively filters the clipboard, stripping all HTML tags and ignoring binary image blobs, pasting only the fallback text/plain URL if available.
• <input type="file"> elements: The browser intercepts the paste event and populates the input’s FileList with the reconstructed File object, mimicking the behavior of a user manually selecting a file from the disk.

Async Clipboard API vs. Legacy Clipboard

The legacy document.execCommand('paste') and synchronous ClipboardEvent flow inherently block the main thread. To support modern, rich web applications, browsers have implemented the Async Clipboard API.

When navigator.clipboard.read() is called, it returns a Promise. The browser engine asynchronously queries the OS clipboard, performs the heavy decoding and sanitization off the main thread, and resolves the Promise with an array of ClipboardItem objects. The developer then calls item.getType('image/png'), which returns a secondary Promise resolving to the binary Blob. This completely asynchronous model allows the transfer of multi-megabyte images without degrading UI responsiveness or causing frame drops.

Full End-to-End Data Flow

The following sequence details the complete low-level trace from the initial render on Website A to the final DOM insertion on Website B.

Cross-Browser Architectural Differences

While the general copy-paste pipeline remains conceptually consistent, the internal mechanisms and data structures diverge significantly based on the browser engine architecture.

Chrome (Blink)

Blink prioritizes multi-process security and performance. Its use of Mojo BigBuffer for memory transfers ensures that IPC bottlenecks are minimized, avoiding redundant memory copying. Chromium explicitly manages format prioritization on Windows, placing PNG ahead of CF_DIBV5 to appease applications like Microsoft Word, which possess buggy CF_DIBV5 decoders. Furthermore, Chrome leads the implementation of the Async Clipboard API and recently introduced the unsanitized option to allow specific trusted payloads to bypass the strict image re-encoding step when absolute fidelity is required.

Firefox (Gecko)

Firefox’s architecture relies on the nsIClipboard interface. Data is bundled into an nsITransferable object, which manages various “flavors” (MIME types). A persistent architectural difference in Firefox is its handling of string encodings over X11, often utilizing UTF-16, which has historically caused translation issues with native Java applications expecting UTF-8. Furthermore, Firefox is highly aggressive in providing CF_HDROP (file drop) formats alongside standard image bitmaps, making pasted images appear as physical files to certain OS targets, which can improve compatibility with legacy file managers. Firefox also heavily utilizes kSelectionClipboard to support middle-click paste natively on Linux environments.

Safari (WebKit)

WebKit’s pasteboard implementation (Pasteboard.h and PlatformPasteboardIOS.mm) is tightly integrated with Cocoa paradigms. It directly translates web types into Apple UTIs, such as mapping image/png to public.png and HTML to Apple Web Archive formats. Because Safari runs predominantly on macOS and iOS, it extensively utilizes NSItemProvider to handle promised data, interacting deeply with the pasteboard server (pbs). WebKit handles user activation differently than Blink, requiring developers to resolve ClipboardItem Promises within a very strict, synchronously triggered scope to prevent security exceptions, addressing specific iOS sandbox constraints.

Edge Cases and Protocol Complexities

The standard copy-paste flow is routinely complicated by edge cases involving web specifications, proprietary media types, and strict privacy boundaries.

Cross-Origin Images and CORS Implications

If Website A embeds an image from a different domain (e.g., cdn.example.com), the Same-Origin Policy prevents JavaScript from reading the pixels of that image. If a script draws a cross-origin image to an HTML5 <canvas>, the canvas becomes “tainted,” and calling getImageData() or toBlob() will throw a security exception unless the server provided an Access-Control-Allow-Origin (CORS) header.

However, the native “Copy Image” context menu is a trusted user action initiated outside of the DOM’s execution environment. The browser’s internal C++ handlers possess absolute access to the render tree’s memory and can successfully extract the SkBitmap and write it to the OS clipboard, bypassing CORS entirely. If Website A wishes to implement a custom “Copy” button using the Async Clipboard API, it must obey CORS and utilize crossOrigin="Anonymous" when fetching the image, or the operation will fail.

Copying Animated GIF and WebP

Animated formats present a severe limitation for OS clipboards. Binary formats like CF_DIB on Windows or public.png on macOS are fundamentally designed for static bitmaps. When a user copies an animated GIF via the context menu, the browser typically extracts the currently visible frame from the render tree, encodes it as a static PNG or Bitmap, and places it on the clipboard. Consequently, pasting the GIF into a chat application often results in a static, frozen image. To preserve animations, browsers attempt to write the HTML representation (<img src="...gif">) or file paths (CF_HDROP), relying on the receiving application to parse the HTML or file reference rather than the raw bitmap.

Copying SVG Images

Scalable Vector Graphics (SVG) are mathematically defined paths rather than rasterized pixels. When “Copy Image” is invoked on an <svg> element, the browser cannot easily map it into a generic CF_DIB. Instead, the browser rasterizes the SVG to a target resolution, generating a standard PNG pixel buffer, and places that on the clipboard. Alternatively, the raw XML text of the SVG is placed into the text/html or text/plain slots, enabling vector editors like Adobe Illustrator to reconstruct the mathematical paths from the markup.

Private / Incognito Mode Restrictions

Browsers operate with extreme caution regarding clipboard data in private browsing modes. While data can be copied to the global OS clipboard (as it is the user’s explicit intent), caching the intermediate chunks on disk is strictly prohibited. For massive clipboard transfers (like macOS file promises or Linux Wayland pipe spools) that might ordinarily spill to the filesystem to save memory, the browser must force everything to remain in anonymous volatile memory to ensure no forensic traces survive process termination.

Final Thoughts

Most of the time we never notice any of this, and that’s kind of the point. Modern browsers are designed so that these complexities disappear behind simple user interactions.

Not bad for something we do dozens of times a day.

You could argue that each user could have an individual database user created and be handed the password to that, which would solve the issue of audit logging (who did what on the database), but it was just a management nightmare for us as the infra and security team. Hence we were looking for JIT tools like strongDM or Teleport which would solve these issues, but the cost of acquiring such a tool at our scale was going to be at least 100k USD per annum, which was not something I was comfortable asking my CTO to spend.

That’s when the idea of building an RDS proxy integrated with SSO came into the picture. This would allow us to give access to the databases via corporate email addresses only, with detailed audit logging of the queries run on the database.

This blog goes into detail on implementing this proxy in Go from scratch and maybe helps you understand the fundamentals of PostgreSQL and how it works.

The basic features I was aiming to build for my proxy were:

1. Connection pooling
2. SSL/TLS support
3. SSO auth with Azure AD via Auth0
4. Auditing and observability

The first step in building a proxy is to expose it as a server on a particular port (7777) actively listening for client connections. Once a connection is made, it is passed on to a goroutine to be processed.

Once the connection is made, the proxy then has to establish a connection to the actual PostgreSQL database. To understand how this happens exactly, we need to understand the communication protocol used by PostgreSQL.

PostgreSQL wire protocol (Frontend/Backend Protocol)

PostgreSQL uses a message-based protocol for communication between frontends and backends (clients and servers). The protocol is supported universally on TCP/IP port 5432.

In order to serve multiple clients efficiently, the server launches a new “backend” process for each client. In the current implementation, a new child process is created immediately after an incoming connection is detected. This is transparent to the protocol, however. For purposes of the protocol, the terms “backend” and “server” are interchangeable; likewise “frontend” and “client” are interchangeable.

Every PostgreSQL message has this format:

[Message Type (1 byte)] [Length (4 bytes)] [Message Body (Length-4 bytes)]

• Message type: single character identifying the message
• Length: 32-bit int
• Message body: the actual data

At the proxy level, the messages are referred to like this:

• Frontend messages — sent by the client; the proxy intercepts these and sends them to the DB
• Backend messages — sent by the server; the proxy intercepts these from the DB and sends them to the user

In our implementation, we use pgproto3, which is the encoder and decoder of the PostgreSQL wire protocol version 3.

Startup message

The Startup message in the PostgreSQL wire protocol is the very first message sent when a PostgreSQL connection is established, and it is special enough that it has to be handled separately because:

1. It has no message type.
2. It’s always the first message in any PostgreSQL connection.
3. It contains connection parameters such as username and database name.

Flow:

Client -> StartupMessage -> Proxy -> StartupMessage -> Database

Now that we have an understanding of the protocol, we can move ahead with the message flow. Once the connection is made to the proxy, a new pgproto3.Backend wrapping the raw TCP connection is created and the first call is pgconn.ReceiveStartupMessage().

The PostgreSQL startup message, as mentioned above, has no message type byte. Its format is [length:4 bytes][protocol_version:4 bytes][parameters]. pgproto3 handles this by reading the first 4 bytes, checking if they match the SSL request magic number (80877103), the cancel request magic (80877102), or a protocol version, and returning the appropriate concrete type.

There are three possible message types at this point, each handled by its own branch.

Case A: SSLRequest (*pgproto3.SSLRequest)

The client sends this request before the real startup message when it wants to establish TLS. The proxy must respond with a single byte before the client proceeds.

1. If TLS is not configured: the proxy sends the single byte N (ASCII 78), indicating to the client that TLS is unavailable, and the client immediately sends the real StartupMessage on the same plaintext connection.

2. If TLS is configured: the proxy sends S (ASCII 83), indicating TLS is accepted, wraps the raw net.Conn in a tls.Conn using the server’s certificate, and performs the TLS handshake.

Once done, pc.conn is replaced with the TLS connection and a new pgproto3.Backend connection is built over the TLS connection, and everything subsequent (password and queries) is encrypted.

Once one of the above is completed successfully, the StartupMessage is sent by the client to the proxy.

Case B: StartupMessage (*pgproto3.StartupMessage)

The StartupMessage contains user, database, application_name, client_encoding, and any other parameters the client sends.

Once the proxy receives this, it doesn’t send it to the PostgreSQL server. It instead sends back an AuthenticationCleartextPassword, just like how the PostgreSQL server would.

Case C: CancelRequest (*pgproto3.CancelRequest)

Cancel requests are entirely separate TCP connections, if enabled. A client receiving the SIGKILL or Ctrl+C opens a new connection to the proxy’s port and immediately sends a 16-byte cancel message without any SSL negotiation.

Structure:

Byte offset   Size     Value              Meaning
----------------------------------------------------------
0 - 3         4 bytes  0x00000010 (16)    Total message length
4 - 7         4 bytes  0x04D2162E         Cancel magic number (80877102)
8 - 11        4 bytes  <ProcessID>        The backend PID to cancel
12 - 15       4 bytes  <SecretKey>        The secret key for that PID

Inside the proxy, this message must be assembled manually each time, like this:

buf := make([]byte, 16)

// Message length - 16 bytes
binary.BigEndian.PutUint32(buf[0:4], 16)

// Cancel request code - 80877102
binary.BigEndian.PutUint32(buf[4:8], 80877102)

// Process ID
binary.BigEndian.PutUint32(buf[8:12], cancel.ProcessID)

// Secret key
binary.BigEndian.PutUint32(buf[12:16], cancel.SecretKey)

ProcessID is the identification assigned to the process forked for handling the new connection made.

SecretKey is generated by PostgreSQL within the backend process when a new client connection is established. When PostgreSQL forks a dedicated backend process to handle the connection, it creates a random 32-bit integer and associates it with that process’s PID.

The SecretKey exists only for the purpose of query cancellation.

PostgreSQL then sends both the PID and the SecretKey to the connected client in a BackendKeyData message:

┌──────────────┬──────────────┬──────────────┬──────────────┐
│ 'K' (1 byte) │   Length     │  ProcessID   │  SecretKey   │
│  type byte   │  (4 bytes)   │  (4 bytes)   │  (4 bytes)   │
└──────────────┴──────────────┴──────────────┴──────────────┘

Based on the ProcessID and SecretKey, the proxy must now identify the active connection and cancel it.

In my proxy, we store the activeConnections in a map keyed by uint64(ProcessID << 32 | SecretKey) — a bitfield combining both values into a single efficient map key.

If the connection is found to be active, the cancel request flow is called, which initiates a new TCP connection to the database, not from the connection pool. The raw 16-byte binary cancel message is sent and the connection is closed immediately.

PostgreSQL receives this, validates the PID/SecretKey against its own backend process table, and sends SIGINT to the matching backend process which aborts the in-flight query and returns an ErrorResponse with code 57014 to the client via the existing pooled connection.

Authentication inside the proxy

Now that the StartupMessage has been sent successfully, it’s time for the user authentication part of the proxy. I split it into 3 sequential phases:

1. Open a temporary backend connection - A raw TCP connection to PostgreSQL, not from the pool, is made with the sole purpose of authentication.

2. Request a password from the client - The proxy sends AuthenticationCleartextPassword to the client. From the client’s perspective, the proxy is behaving like a PostgreSQL server requesting a password. The client sends back a PasswordMessage containing whatever was in PGPASSWORD or whatever was entered interactively.

   PostgreSQL generally uses SCRAM-SHA-256 or MD5, but the proxy here always asks the client for cleartext.

3. Determine the authentication flow - The reason for getting the password as cleartext is for the proxy to inspect it and decide whether the password sent is a JWT token or a normal password.

JWT detection: check for the eyJ prefix (base64url encoding of {") and exactly 2 dots (the three-part JWT structure header.payload.signature). Simple heuristic, but correct for all JWTs.

Traditional password flow

The username the client sent is used along with the password. It’s basically a fallback for when someone configures a real PostgreSQL user in the proxy and connects with a traditional password.

Inside JWT validation

1. jwt.Parse is called with a key function. The key function:
   • Checks t.Method.Alg() == "RS256" — rejects anything else
   • Extracts kid (Key ID) from the token header
   • Calls v.getPublicKey(kid)
2. getPublicKey(kid) — this is where JWKS caching happens:
   • Acquires RLock, checks if kid exists in v.publicKeys and time.Since(v.lastKeysFetch) < 1 hour
   • Cache hit: releases RLock, returns the key — no network call
   • Cache miss: releases RLock, acquires full Lock (write), double-checks again (another goroutine may have fetched while waiting), then calls fetchJWKS()
   • fetchJWKS() makes a GET to https://<AUTH0_TENANT>/.well-known/jwks.json with a 10-second timeout, filters for kty=RSA, use=sig, decodes base64url modulus N and exponent E, constructs *rsa.PublicKey objects, stores them all in v.publicKeys keyed by kid, updates v.lastKeysFetch
   • Fetch failure with stale keys: if the JWKS endpoint is down but old keys exist, logs a warning and returns the stale key — this is the graceful degradation path
   • Fetch failure, no keys: returns error

3. jwt.Parse verifies the RS256 signature using the public key returned from the key function. If the signature is invalid, it returns an error.

4. Manual claim validation (after signature passes):
   • iss claim == configured issuer — exact string match
   • aud claim == configured audience — handles both string and []interface{} types (Auth0 can send either)
   • email claim — must be present and non-empty
   • sub claim — must be present and non-empty
   • exp claim — time.Now().After(oauthContext.ExpiresAt) — double-check (jwt.Parse also checks this but the manual check is explicit)
5. Role extraction from extractRoles(): tries claims["role"] first, then claims["roles"] — handles both singular and plural claim names. Each can be a string or []interface{}.

This validation process returns the email, role, and expiry time for the token, which is then used to map the role to a service account configured in the proxy. If no role matches, it ends up using the default role, which has read-only access.

Service accounts are basically users configured in the PostgreSQL database that the proxy uses to connect to the database, since the SSO-returned user does not actually exist inside PostgreSQL.

This also ensures we don’t have to create a PostgreSQL user for every user logging into the database, and the same goes for deletion as well. If a user is removed from Active Directory, they automatically do not have access to the database anymore.

Authentication with PostgreSQL

Now that the proxy has authenticated and authorised the incoming SSO user, it now needs to connect this user/client to the actual PostgreSQL database (backend).

This is done in the same way by sending a StartupMessage to PostgreSQL via a temporary connection, with one small change: the user field is replaced with the service account username before being sent to PostgreSQL. PostgreSQL never sees the original user@email.com that the client sent.

The proxy now enters a loop reading messages from PostgreSQL. This part is referred to as SASL authentication in the PostgreSQL protocol.

1. To begin a SASL authentication exchange, the PostgreSQL server sends an AuthenticationSASL message. It includes a list of SASL authentication mechanisms that the server can accept, in the server’s preferred order.

   The default for this is usually either SCRAM-SHA-256 or MD5, rarely cleartext.

2. The proxy selects the first one in the priority of the supported mechanisms from the list, and sends a SASLInitialResponse message to the server.

   If AuthenticationSASL sends SCRAM-SHA-256, the proxy instantiates an xdg-go/scram SHA-256 client acting on behalf of the service account. The library is used to perform the full cryptographic exchange using the service account’s password. PostgreSQL never sees the JWT — it only sees the service account performing standard SCRAM.

   SCRAM-SHA-256 is a 3-round challenge-response protocol. The proxy first sends the SASLInitialResponse and, with the help of the scram library, starts the conversation by calling Step("") with an empty string — this means “generate the client-first message” (the opening move of SCRAM).

The above in code looks something like this:

client, err := scram.SHA256.NewClient(username, password, "")
if err != nil {
	return logger.Errorf("failed to create SCRAM client: %w", err)
}

scramConversation = client.NewConversation()
initialResponse, err := scramConversation.Step("")
if err != nil {
	return logger.Errorf("SCRAM initial step failed: %w", err)
}

logger.Debug("sending SCRAM initial response to backend")
err = frontend.Send(&pgproto3.SASLInitialResponse{
	AuthMechanism: "SCRAM-SHA-256",
	Data:          []byte(initialResponse),
})
if err != nil {
	return logger.Errorf("failed to send SASL initial response: %w", err)
}

The final payload is a structured ASCII string. It looks like:

n,,n=gprxy_admin,r=fyko+d2lbbFgONRv9qkxdawL

Breaking this down character by character:

authzid (Authorization Identity) is the SASL mechanism component defining the user identity that a client wants to act as.

Client nonce — a cryptographically random base64 string generated fresh for this authentication.

1. PostgreSQL responds with the AuthenticationSASLContinue server-first message, a challenge. This is the message that makes SCRAM secure.

The payload contains r=<combined_nonce>,s=<salt>,i=<iterations>

Combined nonce — client nonce + server nonce appended together. PostgreSQL echoes back the client nonce and appends its own random suffix. The client must verify the prefix matches what it sent.

Salt — a random base64-encoded value stored in pg_authid alongside the user’s password hash. Different for every user.

Iteration count — how many times to apply PBKDF2 to derive the key. Higher = more expensive to brute-force. PostgreSQL defaults to 4096.

1. The proxy responds with the client-final message, containing the client proof as SASLResponse.

To send the response, the proxy first needs to do the cryptographic modifications to the request, for which it calls scramConversation.Step(serverFirstMessage).

The following cryptographic computations are done:

1. SaltedPassword = PBKDF2(SHA-256, password, salt, iterations, 32)
2. ClientKey = HMAC-SHA-256(SaltedPassword, "Client Key")
3. StoredKey = SHA-256(ClientKey)
4. AuthMessage = client-first-message-bare + "," + server-first-message + "," + client-final-message-without-proof
5. ClientSignature = HMAC-SHA-256(StoredKey, AuthMessage)
6. ClientProof = ClientKey XOR ClientSignature

The password never travels on the wire. Only ClientProof does — a value that proves you know the password without revealing it.

The final payload that is sent to the server looks like this:

c=biws,r=fyko+d2lbbFgONRv9qkxdawL3rfcNHYJY1ZVvWVs7j,p=dHzbZapWIk4jUhN+Ute9ytag9zjfMHgsqmmiz9AndVQ=

1. PostgreSQL receives the above payload and does the below computations before sending the final server message AuthenticationSASLFinal.

2. Verifies r= still starts with the client nonce it saw earlier.
3. Computes the same AuthMessage on its side using the stored password hash.
4. Computes StoredKey from pg_authid.
5. Verifies the proof: SHA-256(ClientKey) must equal StoredKey, which it can check without knowing ClientKey directly.

This is the mutual authentication step: PostgreSQL proves to the proxy that it also knows the password. This prevents man-in-the-middle attacks.

The final payload that is sent to the client looks like this:

v=<ServerSignature>

1. The proxy calls scramConversation.Step(serverFinalMessage):

This internally computes the expected ServerSignature using its copy of SaltedPassword and the AuthMessage, then compares it to v= from the server. If they don’t match, it means it is connected to a rogue server.

1. Along with AuthenticationSASLFinal, the server also sends an AuthenticationOk message. The same is passed along to the client, which makes it believe that the authentication is successful.

2. PostgreSQL also sends several of these immediately after AuthenticationOk:

server_version    = 16.1
client_encoding   = UTF8
server_encoding   = UTF8
DateStyle         = ISO, MDY
TimeZone          = UTC
integer_datetimes = on
...

Each is forwarded to the client unchanged. The client caches these for the session.

1. BackendKeyData and ReadyForQuery are sent to the proxy by PostgreSQL, but these are never relayed to the client and the temporary connection is then terminated.

The reason behind this is that this whole authentication process was performed by a temporary connection that was terminated. Relaying that connection’s BackendKeyData, which is mainly used in cancelling requests by extracting the (PID, SecretKey), would result in either of these 3 scenarios:

1. Temp connection PID is already dead - The temp connection is closed immediately after auth. Its backend PostgreSQL process (PID=12345) is gone. The client presses Ctrl+C. The proxy receives (PID=12345, SK=98765). It looks in activeConnections — nothing is registered there with that pair. The cancel is silently dropped. The query keeps running forever.

2. Temp connection PID is registered but wrong - Even if the proxy tried to register the temp connection’s key, what would it point to? The temp connection has no pool connection attached to it. There is no backend to cancel on. The proxy would forward the cancel to PostgreSQL targeting a process that is either dead or belongs to a completely different connection.

3. OS PID reuse - PIDs are finite. The OS can recycle PID=12345 to a completely different PostgreSQL backend process after the temp connection closes. The client’s cancel request, carrying that stale PID, could accidentally cancel a totally unrelated query running on a different client’s connection.

The only correct PID and SecretKey to give the client is the one belonging to the pool connection — the live backend process that is actually executing queries for this client.

Similarly, the ReadyForQuery message is also suppressed and not relayed to the client, as there is no pooled backend connection ready yet for it to start relaying queries.

Post-Authentication Sequence

Now that the user is authenticated successfully with PostgreSQL, the proxy needs a connection from the pool to start running queries.

Let’s now talk about how connection pooling is implemented:

Layer 1: The Top-Level Registry poolManager

var (
    poolManager = make(map[poolKey]*pgxpool.Pool)
    poolMutex   sync.RWMutex
)

This is a global process-wide map — one instance for the entire gprxy process, shared across all goroutines and all client connections. It lives for the lifetime of the process and is never torn down.

The Key: poolKey

type poolKey struct {
	user     string
	database string
}

This is a Go struct used as a map key. Go allows any comparable type as a map key, and structs with only comparable fields are comparable. The two fields together form the composite key.

user here is the original client username — e.g. alice@example.com from the JWT, or bob from traditional auth. It is NOT the service account (gprxy_admin). This is set from msg.Parameters["user"] from the original StartupMessage.

database is the database name from the same StartupMessage — e.g. gprxy_test.

So the map looks like this after several clients connect:

poolManager = {
    {user: "alice@example.com", database: "gprxy_test"}  →  *pgxpool.Pool (up to 5 conns)
    {user: "alice@example.com", database: "analytics"}   →  *pgxpool.Pool (up to 5 conns)
    {user: "bob@example.com",   database: "gprxy_test"}  →  *pgxpool.Pool (up to 5 conns)
    {user: "carol",             database: "gprxy_test"}  →  *pgxpool.Pool (up to 5 conns)
}

Each *pgxpool.Pool value manages its own set of up to 5 real TCP connections to PostgreSQL.

The Lock: sync.RWMutex

poolMutex protects poolManager from concurrent reads and writes across goroutines. Since every client connection runs in its own goroutine, many goroutines can call GetOrCreatePool simultaneously.

A sync.RWMutex allows:

• Many goroutines to read simultaneously — RLock() → RUnlock()
• Only one goroutine to write, blocking all reads — Lock() → Unlock()

The read path (pool already exists) is the fast path — it only holds a read lock for a microsecond. The write path (first connection for this key) is rare and takes a write lock briefly to insert the new pool.

Layer 2: The Double-Checked Locking Pattern

poolMutex.RLock()
pool, exists := poolManager[key]
poolMutex.RUnlock()

if exists {
	return pool, nil
}

poolMutex.Lock()
defer poolMutex.Unlock()

if pool, exists := poolManager[key]; exists {
	return pool, nil
}

This is a classic double-checked locking pattern. Here is why it needs two checks:

Scenario without the second check:

1. Goroutine A: RLock() → pool not found → RUnlock()
2. Goroutine B: RLock() → pool not found → RUnlock()
3. Goroutine A: Lock() → creates pool → Unlock()
4. Goroutine B: Lock() → also creates a second pool → two pools for same key, one is lost

The second check inside the write lock prevents this. When goroutine B gets the write lock after A finishes, it checks again and finds the pool already there, so it returns it instead of creating a duplicate.

Layer 3: What pgxpool.Pool Actually Is

Each value in poolManager is a *pgxpool.Pool. This is not a simple slice of connections. It is a sophisticated object with its own goroutines and internal data structures.

Internal structure (inside pgxpool):

*pgxpool.Pool
├── config         *pgxpool.Config       (MaxConns, timeouts, etc.)
├── p              *puddle.Pool[*pgxpool.connResource]   ← the actual pool
│   ├── resources  []poolResource        (ring buffer of connections)
│   ├── cond       *sync.Cond            (for blocking Acquire calls)
│   └── ...
├── closeChan      chan struct{}          (signal pool close)
└── ...

pgxpool uses the puddle library internally for the actual pooling logic. puddle maintains a list of resources (connections) and a sync.Cond for goroutines waiting for an available connection.

The pool configuration gprxy sets:

config.MaxConns = defaultMaxConns            // 5
config.MinConns = defaultMinConns            // 0
config.MaxConnLifetime = defaultMaxConnLifetime      // 1 hour
config.MaxConnIdleTime = defaultMaxConnIdleTime      // 30 minutes
config.HealthCheckPeriod = defaultHealthCheckPeriod // 1 minute
config.ConnConfig.ConnectTimeout = defaultConnectTimeout // 5 seconds

What each setting actually controls:

MaxConns = 5 The hard ceiling. At most 5 TCP connections to PostgreSQL will ever exist for this (user, database) key. When all 5 are acquired (in use), the 6th pool.Acquire() call blocks — the calling goroutine is parked and put on a wait queue inside puddle. It will be woken up when one of the 5 connections is released.

MinConns = 0 No pre-warming. When the pool is created, zero connections to PostgreSQL are opened. The first pool.Acquire() on a fresh pool will always open a new TCP connection. This is lazy initialization — no connections consumed for idle users.

MaxConnLifetime = 1 hour A background goroutine inside pgxpool periodically checks the age of every connection. Any connection older than 1 hour is closed and removed from the pool, even if it is idle and healthy. This forces periodic reconnection, which is important for:

• Picking up PostgreSQL configuration changes
• Rotating credentials if needed
• Preventing connections from being silently dropped by firewalls or load balancers that kill long-lived idle connections

MaxConnIdleTime = 30 minutes Any connection that has been idle (not acquired by anyone) for 30 minutes is closed. This prevents the pool from holding open connections during quiet periods.

HealthCheckPeriod = 1 minute Every minute, a background goroutine runs through all idle connections and pings each one. Any connection that fails the ping (PostgreSQL restarted, network blip) is removed from the pool. This keeps the pool clean so that Acquire() always returns a working connection.

ConnectTimeout = 5 seconds When a new TCP connection to PostgreSQL needs to be opened, it must complete the entire startup handshake (TCP connect + SCRAM auth + ReadyForQuery) within 5 seconds. If it takes longer, the connection attempt is aborted and an error is returned.

Layer 4: *pgxpool.Conn as a Single Exclusive Handle

When pool.Acquire(ctx) returns, it gives back a *pgxpool.Conn. This is not a connection itself — it is a handle that:

1. Wraps the underlying *pgx.Conn
2. Marks that connection as acquired (in use) in the pool’s internal state
3. Provides a Release() method to return it

*pgxpool.Conn
├── p      *pgxpool.Pool        (pointer back to parent pool)
└── res    *puddle.Resource     (the resource being held)
    └── value *pgxpool.connResource
              └── conn *pgx.Conn   (the actual connection)

The connection is exclusively held — the pool will not give the same underlying *pgx.Conn to any other goroutine while one *pgxpool.Conn holds it. This is what makes it safe for pc.bf.Send() and pc.bf.Receive() to call directly into the TCP socket without any additional locking. The pool’s ownership model guarantees single-writer, single-reader.

How gprxy digs through the layers to get the raw socket

underlyingConn := pc.poolConn.Conn().PgConn().Conn()
bf := pgproto3.NewFrontend(pgproto3.NewChunkReader(underlyingConn), underlyingConn)

The chain of unwrapping:

pc.poolConn              *pgxpool.Conn
  .Conn()                *pgx.Conn         (higher-level pgx connection)
    .PgConn()            *pgconn.PgConn    (low-level wire protocol connection)
      .Conn()            net.Conn          (raw TCP socket)

gprxy bypasses all of pgx’s query execution machinery and talks directly to the raw TCP socket. It wraps it with a pgproto3.Frontend to get PostgreSQL wire protocol serialization/deserialization. This is why gprxy can forward arbitrary protocol messages — because it is working at the wire level, not through pgx’s Query()/Exec() API.

However, this creates a subtle tension: pgx still thinks it “owns” this connection and its internal state machine. gprxy is now sending bytes on the socket that pgx does not know about. This is why the fullResetBeforeRelease step is critical — pgx’s internal state may be out of sync with the actual PostgreSQL session state after gprxy forwards arbitrary queries, and ROLLBACK + DISCARD ALL restores the PostgreSQL session to a clean state before pgx takes back ownership.

Layer 5: Connection Lifecycle State Machine

For a single physical PostgreSQL connection managed by pgxpool, the happy-path lifecycle looks like this:

Important nuance: after pgxpool.NewWithConfig(), the pool object exists immediately, but with MinConns=0 there may be zero physical PostgreSQL connections inside it until the first Acquire() needs one. Also, Release() does not always transition to [IDLE] — if the connection is broken, expired, or otherwise not reusable, pgxpool closes it instead of returning it to the free list.

Layer 6: The Acquire Flow

connection, err := pool.Acquire(context.Background())
if err != nil {
	return nil, logger.Errorf("error while acquiring connection from the database pool: %w", err)
}

err = connection.Ping(context.Background())
if err != nil {
	connection.Release()
	return nil, logger.Errorf("could not ping database: %w", err)
}

return connection, nil

pool.Acquire(ctx) internally does:

1. Lock puddle’s internal mutex
2. Check the free list (idle connections):
   • If found: remove from free list, mark as acquired, return it
3. If free list empty, check total count vs MaxConns:
   • If below MaxConns: unlock, open a new connection (dial + SCRAM auth), lock again, add to acquired set, return
   • If at MaxConns: add this goroutine to a wait queue (sync.Cond.Wait()), unlock, sleep
   • When another goroutine calls Release(): it calls Cond.Signal(), waking one waiter, which retries the acquire

After Acquire() returns, gprxy calls Ping(). This is an extra safety net on top of the health check background goroutine. Between the health check goroutine’s last check (up to 1 minute ago) and right now, the connection could have gone stale. Ping() sends a minimal no-op to PostgreSQL and waits for a response.

If Ping() fails, Release() is called immediately — but gprxy does not put a broken connection back. pgxpool’s Release() is smart: if the underlying connection returns an error, it destroys the connection instead of returning it to the free list. So after connection.Release() on a failed ping, the pool size decreases by 1, and the next Acquire() will open a fresh connection.

Layer 7: The LogPoolStats Observation Window

After every successful AcquireConnection, gprxy calls:

pool.LogPoolStats(user, database)

func LogPoolStats(user, database string) {
	stats := pool.Stat()
	logger.Debug("pool stats for [%s,%s] - total: %d, acquired: %d, idle: %d", user, database, stats.TotalConns(), stats.AcquiredConns(), stats.IdleConns())
}

pool.Stat() returns a snapshot of:

• TotalConns() — total live connections (acquired + idle), max is 5
• AcquiredConns() — currently held by goroutines (including this one just acquired)
• IdleConns() — back in free list, available immediately

TotalConns = AcquiredConns + IdleConns always. For example:

pool stats for [alice@example.com,gprxy_test] - total: 3, acquired: 2, idle: 1

This means 3 real TCP connections exist to PostgreSQL, 2 are in use by active client connections, and 1 is sitting idle waiting to be acquired.

Layer 8: Release and Reset

When a client disconnects, the defer block in handleConnection runs:

if pc.poolConn != nil {
	err := fullResetBeforeRelease(pc)
	if err != nil {
		logger.Error("error while releasing connection back to the pool: %v", err)
	}
	pc.poolConn.Release()
}

fullResetBeforeRelease runs two SQL commands through pgx’s normal execution path (not through pc.bf), because at this point gprxy is done forwarding arbitrary messages:

func fullResetBeforeRelease(connection *Connection) error {
	_, err := connection.poolConn.Exec(context.Background(), "ROLLBACK")
	// ...
	_, err = connection.poolConn.Exec(context.Background(), "DISCARD ALL")
	// ...
	return nil
}

ROLLBACK rolls back any open transaction. Without this, if a client died mid-BEGIN, the pool connection would return to the free list still inside a transaction. Any rows it had locked would remain locked. The next client to get this connection would start their first query inside someone else’s transaction.

DISCARD ALL is a PostgreSQL supercommand that resets everything about the session in one round trip:

• All SET variables back to defaults
• All named prepared statements deallocated
• All open cursors closed
• All LISTEN subscriptions removed
• All advisory locks released
• All cached query plans discarded

After these two commands, the pool connection’s PostgreSQL session is byte-for-byte identical to a fresh connection. pgx’s internal state may still be slightly stale (it didn’t observe gprxy’s arbitrary wire-level queries), but the actual PostgreSQL session is clean.

Then poolConn.Release() is called. Inside pgxpool, this:

1. Locks puddle’s mutex
2. Moves the resource from the acquired set back to the idle free list
3. Calls Cond.Signal() to wake any goroutine blocking on Acquire()
4. Unlocks

The connection is now available for the next client that calls AcquireConnection.

Data Structure Map

PROCESS GLOBAL STATE
─────────────────────────────────────────────────────────────────────

poolManager: map[poolKey]*pgxpool.Pool
│
├── key: {user:"alice@example.com", database:"gprxy_test"}
│   └── *pgxpool.Pool
│       ├── MaxConns: 5
│       ├── free list (idle):
│       │   └── *pgx.Conn [TCP socket to pg:5432, PID=1001, SK=11111]
│       ├── acquired set (in use):
│       │   ├── *pgx.Conn [TCP socket to pg:5432, PID=1002, SK=22222]  ← held by alice session 1
│       │   └── *pgx.Conn [TCP socket to pg:5432, PID=1003, SK=33333]  ← held by alice session 2
│       └── background goroutines:
│           ├── health checker (every 1 minute)
│           └── idle reaper (checks MaxConnIdleTime/MaxConnLifetime)
│
├── key: {user:"bob@example.com", database:"gprxy_test"}
│   └── *pgxpool.Pool
│       ├── MaxConns: 5
│       ├── free list (idle): [empty]
│       └── acquired set (in use):
│           └── *pgx.Conn [TCP socket to pg:5432, PID=1004, SK=44444]  ← held by bob session 1
│
└── poolMutex: sync.RWMutex  (guards the map above)


PER-CLIENT-CONNECTION STATE (one per active client goroutine)
─────────────────────────────────────────────────────────────────────

*Connection (alice session 1)
├── conn:     net.Conn          → TCP socket to alice's psql process
├── poolConn: *pgxpool.Conn     → exclusively holds PID=1002 conn above
├── bf:       *pgproto3.Frontend → wired to PID=1002's raw TCP socket
├── user:     "alice@example.com"
├── db:       "gprxy_test"
└── key:      BackendKeyData{ProcessID:1002, SecretKey:22222}
               ↑ also registered in server.activeConnections

Key Design Properties and Tradeoffs

1. Per-(client-user, database) pools, not a single global pool

Each unique (user, database) pair gets its own pool. This means Alice and Bob each have their own separate bucket of connections. This has pros and cons:

• Pro: isolation — Alice exhausting her 5 connections doesn’t block Bob
• Con: the worst case total connections to PostgreSQL = (number of distinct users) × (number of distinct databases) × 5. With 100 users each connecting to 2 databases that’s 1000 PostgreSQL backend processes — potentially catastrophic at scale.

A shared global pool per database would be more scalable, but then all users compete for the same connections.

2. The pool key uses client identity, not service account

All connections in a pool connect to PostgreSQL as gprxy_admin (from BuildConnectionString), but the pool is keyed on the client user (alice@example.com). This means two different clients who both map to gprxy_admin still have completely separate pools. This provides isolation but wastes connections — both pools independently open connections as the same PostgreSQL user.

3. MinConns=0 means cold start latency

The first connection for any (user, database) pair always pays the full TCP dial + SCRAM handshake cost on the hot path (while the client is waiting). Subsequent connections are fast (free list lookup). If MinConns were 1, the pool would pre-open a connection at creation time, eliminating this latency at the cost of always holding an open connection even for inactive users.

4. The pool is never closed

There is no code path in gprxy that calls pool.Close(). Once a pool exists in poolManager, it lives forever until the process exits. The idle reaper and MaxConnIdleTime handle draining unused connections from within each pool, but the pool object and its entry in poolManager persist. This is a minor memory leak for transient users — if 10,000 different users each connect once, poolManager will have 10,000 entries (each being a small pool object with zero connections) forever.

Now that we have a detailed understanding of connection pooling, let’s get back to the authenticated user who now makes a request for a connection from the pool:

Case A — Pool for this (user, database) key already exists: Read lock acquired, pool found, read lock released. No write lock, no allocation, extremely fast. The existing *pgxpool.Pool object is returned.

Case B — Pool does not exist yet (first connection for this user+database combination): Write lock acquired. The pool configuration struct is built and pgxpool.NewWithConfig is called. With MinConns=0, the pool does not open any connections to PostgreSQL at creation time. It creates the management infrastructure (the pool object, its background goroutines for health checking, etc.) but no actual TCP connections to PostgreSQL yet. The pool is stored in the global poolManager map.

Then comes the part of making an actual connection to the PostgreSQL database. The pool checks its internal free list:

• If an idle connection exists: it returns it immediately and marks it as in-use.
• If no idle connection exists but the pool is below MaxConns (5): it opens a new TCP connection to PostgreSQL, performs the full PostgreSQL startup handshake (this includes SCRAM-SHA-256 authentication using the DSN credentials), and returns the resulting connection.
• If the pool is at MaxConns with none idle: it blocks until one is released by another goroutine.

When a new connection is opened, the full PostgreSQL wire protocol handshake happens inside pgx transparently — StartupMessage, SCRAM exchange, AuthenticationOk, ParameterStatus, BackendKeyData, ReadyForQuery. pgx handles all of this internally and stores the resulting PID and SecretKey on the connection object. This is where the pool connection’s BackendKeyData originates.

After acquire, a ping is sent — a cheap SELECT 1 equivalent at the protocol level. If the connection was idle and the PostgreSQL server closed it server-side (e.g. idle_in_transaction_session_timeout), the ping will fail, the connection is discarded, and AcquireConnection returns an error. This prevents handing the client a dead connection.

After this call returns, pc.poolConn is a live, valid, exclusively-held *pgxpool.Conn.

What the client is doing during all of this: still blocked. It sent StartupMessage, received AuthenticationOk + ParameterStatus messages, and is waiting for BackendKeyData + ReadyForQuery. It has no idea any of this infrastructure work is happening.

pgproto3.NewChunkReader(underlyingConn) wraps the TCP socket in a buffered reader that knows how to read PostgreSQL wire protocol message boundaries. pgproto3.NewFrontend(reader, underlyingConn) creates a Frontend — an object that speaks PostgreSQL from the client side (i.e. it sends queries and reads responses, opposite of Backend which speaks from the server side).

This *pgproto3.Frontend is what pc.bf is. Going forward, every call to pc.bf.Send(msg) writes PostgreSQL wire bytes directly onto the pool connection’s TCP socket to PostgreSQL. Every call to pc.bf.Receive() reads PostgreSQL response bytes off that same socket.

pc.user and pc.db are stored for logging purposes throughout the query loop.

pgconn.PID() and pgconn.SecretKey() return values that pgx stored internally when it completed the pool connection’s startup handshake. These are the PID and SecretKey of the live PostgreSQL backend process that is holding this pool connection on the other end.

pc.key is now overwritten — the dead temp connection’s key is replaced with the live pool connection’s key. This is the correct key that must be given to the client and registered in the cancel registry.

Send BackendKeyData to the client

pgconn here is the *pgproto3.Backend connected to the client — the server-side view of the client connection. pgconn.Send(pc.key) serializes the BackendKeyData struct into the PostgreSQL wire format and writes it to the client’s TCP socket:

K  (1 byte  - message type 'K')
00 00 00 0C (4 bytes - length = 12)
XX XX XX XX (4 bytes - ProcessID)
YY YY YY YY (4 bytes - SecretKey)

The client receives this, parses it, and stores (ProcessID, SecretKey) internally. Its driver will use this if the user or a timeout triggers a query cancellation.

Send ReadyForQuery to the client

TxStatus: 'I' means “idle, not in a transaction”. This is always correct here because the pool connection was just acquired and either just opened or just had DISCARD ALL run on it — it is guaranteed to be in idle state.

Wire format:

Z  (1 byte  - message type 'Z')
00 00 00 05 (4 bytes - length = 5)
49          (1 byte  - 'I' = idle, or 'T' = in transaction, 'E' = error state)

This message unblocks the client. The client’s driver, which has been sitting in ReadyForQuery wait since it sent the StartupMessage, now receives this and considers the connection fully established. It returns the connection object to the application. The application can now call conn.Query(...) or conn.Exec(...).

This ReadyForQuery is synthesized by gprxy itself — it is not forwarded from PostgreSQL. gprxy is lying to the client in the best possible way: the client believes it just completed startup with a PostgreSQL server, but the ReadyForQuery came from the proxy, which only sent it after confirming the pool connection is live and pc.bf is wired up and ready.

Register in the cancel registry

registerConnection stores the *Connection pointer in server.activeConnections keyed by the bit-packed uint64 of (ProcessID << 32 | SecretKey). This happens after ReadyForQuery is sent because the cancel registry only needs to be populated before a query actually runs — and no query can run until the client receives ReadyForQuery and sends the next message. There is no race condition here because both happen in the same goroutine sequentially.

What happens between authentication and ReadyForQuery

Every piece must happen in this exact order. If the pool connection fails (PostgreSQL down, max connections reached, ping fails), gprxy sends ErrorResponse with SQLSTATE 08006 to the client and the connection is torn down cleanly. The client never enters a half-connected state.

Now the proxy is at the query loop entry point, where the user runs queries and the proxy bridges the gap between the user and the running PostgreSQL instance.

Here is the complete deep dive into the entire query loop and cleanup.

The Query Loop Entry Point

After handleStartupMessage returns, control lands here:

logger.Debug("entering query handling loop")
for {
	err := pc.handleMessage(pgc)
	if err != nil {
		logger.Debug("query handling terminated: %v", err)
		return
	}
}

This is an unconditional for {} — it runs forever until handleMessage returns a non-nil error. There is no break condition, no timeout, no idle check. The goroutine lives as long as the client is connected.

pgc is the *pgproto3.Backend connected to the client socket. It is the same one used during startup. It is passed into every handleMessage call.

Anatomy of One Cycle

Part A: Reading the client message

func (pc *Connection) handleMessage(client *pgproto3.Backend) error {
	msg, err := client.Receive()
	if err != nil {
		return logger.Errorf("client receive error: %w", err)
	}

client.Receive() calls into the TCP socket read buffer. The goroutine is parked by the OS here — it is not spinning, not consuming CPU. It wakes only when bytes arrive on the socket.

pgproto3.Backend.Receive() reads the first byte (the message type identifier), then reads the 4-byte length field, then reads exactly length - 4 more bytes to get the full payload, then deserializes everything into a typed Go struct and returns it.

If the client closes the TCP socket (Ctrl+C, process killed, network drop), the OS delivers an EOF to the read call. client.Receive() returns an error wrapping io.EOF, handleMessage returns that error, the outer loop sees non-nil and calls return, which exits handleConnection and triggers the defer.

Part B: Classify and log

key := pc.poolConn.Conn().PgConn().SecretKey()
pid := pc.poolConn.Conn().PgConn().PID()
switch query := msg.(type) {
case *pgproto3.Query:
	logger.Info("[%s] query: %s", pc.user, query.String)
	logger.Debug("query connection PID=%d, secret_key=%d", pid, key)

case *pgproto3.Parse:
	logger.Debug("[%s] parse: statement='%s' query='%s'", pc.user, query.Name, query.Query)

case *pgproto3.Describe:
	objectType := "statement"
	if query.ObjectType == 'P' {
		objectType = "portal"
	}
	logger.Debug("[%s] describe: %s='%s'", pc.user, objectType, query.Name)

case *pgproto3.Bind:
	paramCount := len(query.Parameters)
	logger.Debug("[%s] bind: portal='%s' statement='%s' params=%d", pc.user, query.DestinationPortal, query.PreparedStatement, paramCount)

case *pgproto3.Execute:
	maxRows := "unlimited"
	if query.MaxRows > 0 {
		maxRows = fmt.Sprintf("%d", query.MaxRows)
	}
	logger.Debug("[%s] execute: portal='%s' max_rows=%s", pc.user, query.Portal, maxRows)

case *pgproto3.Sync:
	logger.Debug("[%s] sync: transaction boundary", pc.user)

case *pgproto3.Terminate:
	logger.Info("[%s] client disconnecting gracefully", pc.user)
	return logger.Errorf("client terminated")

default:
	logger.Debug("[%s] unknown message type: %T", pc.user, query)
}

The switch is logging only — it does not change the message or alter routing. Every message type has its own log line.

The Terminate case is the only one that exits early before forwarding. It returns an error immediately — the loop will exit. Notice it returns before the pc.bf.Send(msg) call below. The Terminate message is never forwarded to PostgreSQL. PostgreSQL doesn’t need to be told — the pool connection is not being closed, it is going back to the pool. PostgreSQL will only learn the session ended when gprxy runs ROLLBACK + DISCARD ALL later.

The two lines at the top of the switch:

key := pc.poolConn.Conn().PgConn().SecretKey()
pid := pc.poolConn.Conn().PgConn().PID()

These are read on every single message, but they are only used in the Query log line. This is slightly wasteful — two pointer dereferences on every message regardless of type.

Part C: Forward the message to PostgreSQL

err = pc.bf.Send(msg)
if err != nil {
	return logger.Errorf("unable to send query to backend: %w", err)
}

pc.bf is the *pgproto3.Frontend wired to the pool connection’s raw TCP socket. Send(msg) takes the Go struct, serializes it back into PostgreSQL wire protocol bytes, and writes them to that socket.

This is a complete passthrough — gprxy does no SQL parsing, no query analysis, no modification of any kind. The bytes that PostgreSQL receives are byte-for-byte identical to what the client sent (re-serialized through pgproto3, but semantically identical).

The two PostgreSQL query protocols work differently here:

Simple Query — one message, one forward:

• Client sends one Query message with raw SQL text
• handleMessage is called once
• One pc.bf.Send(query) forwards it
• relayBackendResponse collects all responses until ReadyForQuery

Extended Query — multiple messages, multiple forwards:

• Client sends Parse, Bind, Describe, Execute, Sync — each as a separate message
• handleMessage is called once per message — 5 separate calls for 5 messages
• Each call forwards its one message and then calls relayBackendResponse
• Only Sync produces a ReadyForQuery from PostgreSQL — the other messages get ParseComplete, BindComplete, etc.

This means for an extended query cycle, relayBackendResponse is called 5 times but returns nil (continues to outer loop) after each intermediate response, and finally returns nil after the ReadyForQuery that follows Sync.

Part D: Check for Terminate again

if _, ok := msg.(*pgproto3.Terminate); ok {
	return logger.Errorf("connection terminated")
}

This is actually dead code in practice — the Terminate case in the switch already returned before reaching here. This is a redundant safety net. If somehow execution reaches here with a Terminate message, it exits before calling relayBackendResponse (which would block forever waiting for a backend response that will never come, since Terminate doesn’t produce one).

Part E: Relay all backend responses

return pc.relayBackendResponse(client)

relayBackendResponse: The Response Pump

func (pc *Connection) relayBackendResponse(client *pgproto3.Backend) error {
	for {
		msg, err := pc.bf.Receive()
		if err != nil {
			return logger.Errorf("backend receive error: %w", err)
		}

		err = client.Send(msg)
		if err != nil {
			return logger.Errorf("client send error: %w", err)
		}

		switch msgType := msg.(type) {
		case *pgproto3.ReadyForQuery:
			logger.Debug("query completed, ready for next query (status: %c)", msgType.TxStatus)
			return nil
		case *pgproto3.ErrorResponse:
			logger.Warn("query error: %s (code: %s)", msgType.Message, msgType.Code)
		case *pgproto3.CommandComplete:
			logger.Debug("command completed: %s", msgType.CommandTag)
		}
	}
}