Debug Chalk query plan cache misses

Chalk caches a compiled query plan the first time it plans a query, keyed by a normalized BatchQuery. A cache miss forces the engine to replan on the hot path, which is slow. Misses are expected the first time a query shape is seen; they are a problem when a query that "looks identical" to a previous one keeps missing — that means some field in the request is varying between calls.

This skill: (1) finds misses in the logs, and (2) diffs the planned queries to identify exactly which field differs and forces the miss.

What the cache key actually is

The plan cache key wraps the entire normalized BatchQuery. The only normalization applied is that planner_options are resolved to their effective values — so leaving an option unset and setting it explicitly to its default produce the same key and will not cause a spurious miss.

Everything else in the request is part of the key, including:

• given_features — the exact set of input features the caller provides
• target features (optional_target_features / required_target_features)
• tags, required_resolver_tags
• max_staleness_overrides and staleness/recompute flags
• query_name_and_version
• and the remaining BatchQuery fields

So any difference in these forces a new plan. The single most common real-world cause is a varying given_features set — the caller sometimes supplies more or fewer input features (e.g. raw *_original inputs, or derived inputs like email_username) than a previous call. A query that provides a different set of inputs genuinely needs a different plan (different resolvers must run), so it cannot reuse the cached one.

The two log lines that signal a miss

The engine emits these on the planning path:

1. Ad-hoc miss warning (batch_online_query_service.py): Ad-hoc query request with query name/version '(<name>, <version>)' missed the plan cache. The query was: <BatchQuery ...>

2. Plan computation — emitted on every miss (ad-hoc or named), and the most useful line for debugging (local_plan_factory.py): Computed plan for [<target features>] given [<given features>] with planner options <NonNullPlannerOptions(...)>; query.query_name_and_version=(<name>, <version>)

There are also statsd counters if you want to trend rather than read lines: chalk.engine.planner.python_plan_cache_miss vs chalk.engine.planner.python_plan_cache_hit.

Finding misses with chalk logs

chalk logs searches the environment's engine logs. Point it at the right environment/context first (chalk config / the usual project or --environment selection), then query by log fields.

Search for the miss warnings over the last day:

chalk logs --query 'message:"missed the plan cache"' \
  --start-time "24h ago" --end-time "now"

Search for plan computations for one query name (best for diffing):

chalk logs --query 'message:"Computed plan for" message:sv5_idplus_features_parallel' \
  --start-time "24h ago" --end-time "now"

Trend miss volume over time in 10-minute buckets:

chalk logs --aggregate --window-period 10m \
  --query 'message:"missed the plan cache"' --start-time "6h ago"

Tail misses live while reproducing:

chalk logs --follow --query 'message:"missed the plan cache"'

Query syntax

field:value pairs, space-separated (implicit AND). Quote any value that contains a space or a . — e.g. message:"missed the plan cache", query_name:"my.query". Useful fields:

• message — substring match on the log message (use this for the two lines above)
• component — Chalk component, e.g. component:engine
• query_name — filter to a specific named query
• operation_id — internal query id; correlation_id — caller-supplied query id
• trace_id, pod_name, resource_group, deployment, app
• all_filter — match across multiple fields at once

Time flags: --start-time / --end-time accept '1h ago', 'now', or ISO-8601 ('2024-01-01T00:00:00Z'). Add --tui for an interactive viewer.

Debugging workflow

1. Confirm misses are happening and how often:

   chalk logs --query 'message:"missed the plan cache"' --start-time "24h ago"
   

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   If this is empty, plan caching is working — look elsewhere for the latency.

2. Pull the Computed plan for lines for the affected query name:

   chalk logs --query 'message:"Computed plan for" message:<query_name>' \
     --start-time "24h ago"
   

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3. Take the two most recent computations and diff them field by field: compare the given [...] lists first (most common culprit), then the target-feature lists, then the planner options block. Whatever differs is the cause of the miss. Because planner options are normalized before keying, a difference there is a real difference — not a set-vs-default artifact.

4. Attribute the difference to the caller. A varying given set almost always means an upstream service is sending an inconsistent input schema (sometimes including raw/derived inputs, sometimes not). The fix is to make the caller send a stable given_features set on every request so the key is stable and subsequent identical queries hit the cache.

Notes

• Two byte-identical requests should hit the cache on the second call. If they don't, that's a distinct issue from a field mismatch (e.g. cache not being populated/shared) — call it out separately rather than blaming the request.
• Fixing a miss only helps if all callers converge on one request shape. If some callers still send a different given set, those keep missing.
• This skill reads logs and reasons about the request; it does not mutate any environment. Any remediation (changing what a caller sends) happens in the caller's code, not here.