Debugging Playbook

Debugging Playbook

High-level mental model

Searchlite is a single-index, single-writer system with:

• buffered writes (CLI or HTTP)
• durability via WAL + atomic manifest updates
• immutable segments + periodic compaction
• “refresh” semantics for making new commits visible to readers (especially in HTTP mode)

Most “weirdness” is one of:

1. you didn’t commit (writes are buffered)
2. you didn’t refresh (reader is stale)
3. your schema isn’t configured for what you’re asking (e.g., missing fast fields for filters/aggs)
4. you’re seeing crash-window replay artifacts (extra segments until compaction)
5. you’re hitting HTTP limits (body size, concurrency, timeouts)

10-minute triage checklist

1. Identify surface area

   • CLI? embedded Rust API? HTTP server?

2. Capture exact versions + features

   • git SHA/tag (if known)
   • build flags / enabled features (vectors changes behavior materially)

3. Get a minimal reproduction

   • schema JSON
   • the smallest NDJSON/JSON doc set that reproduces
   • the exact search request JSON or CLI flags

4. Check index state

   • CLI: searchlite inspect <index> and searchlite compact <index>
   • HTTP: GET /inspect, GET /stats, optionally POST /compact

5. Confirm lifecycle steps

   • Writes are queued → must call commit
   • Depending on config, may need refresh for immediate visibility

CLI debugging moves

A) “I added docs but search returns nothing”

Common causes:

• Documents missing the required ID field (doc_id_field, default _id)
• You didn’t run commit
• Query is hitting an analyzer mismatch or field mismatch

Checklist:

• Verify you ran:
  • searchlite add …
  • searchlite commit …
• Run:
  • searchlite inspect <index>
  • searchlite search <index> --q "..." --return-stored (or request JSON with return_stored: true)
• If segments ballooned after a crash, run:
  • searchlite compact <index>

B) “Filters/aggs don’t work / always empty”

Filters and aggregations depend on fast fields.

• Ensure the target keyword/numeric fields are "fast": true in schema.
• For nested filters, ensure nested subfields are fast too and use Nested filters properly.

HTTP debugging moves

A) Safety + environment sanity

HTTP server has no auth / no rate limiting; don’t expose it to untrusted networks. When debugging production-ish behavior, always capture:

• bind addr (--bind / SEARCHLITE_BIND_ADDR)
• index path (--index / SEARCHLITE_INDEX_PATH)
• resource limits:
  • --max-body-bytes
  • --max-concurrency
  • --request-timeout-secs
  • shutdown grace
• refresh behavior:
  • --refresh-on-commit

B) Confirm lifecycle and visibility

Writes issued via:

• POST /add (NDJSON streaming)
• POST /bulk (JSON bulk)
• POST /delete

…are queued and only become durable/searchable after:

• POST /commit

Then visibility depends on staleness needs:

• if --refresh-on-commit is enabled, new data becomes visible immediately
• otherwise call:
  • POST /refresh

C) Use maintenance endpoints for ground truth

• POST /refresh — reload readers
• POST /compact — merge segments + clean up fragmentation
• GET /inspect — manifest + segments info
• GET /stats — doc/segment counts

D) Interpret HTTP errors

All errors return:

• {"error":{"type":"...","reason":"..."}}

If you see a 413/timeout-ish behavior:

• check --max-body-bytes and --request-timeout-secs
• for NDJSON streaming, reduce batch sizes / request size

Ranking correctness + “why did this score happen?”

Search supports explicit debugging aids:

explain: true

• Returns per-hit score breakdowns.
• Use this for correctness (“why did doc X win?”), not for performance runs.

profile: true

• Adds execution stats like:
  • candidates examined
  • scored docs
  • postings advances
  • timing buckets (search vs rescore)
• Use this for performance triage (“why is this query slow?”).

Both are off by default for overhead reasons.

“This query is slow” playbook

1. Enable profile: true on the request and capture the profile blob.
2. Compare execution modes:
   • execution=bm25 (baseline full scoring)
   • execution=wand (default exact pruning)
   • execution=bmw (block-max pruning)
3. Confirm you’re not forcing expensive features:
   • rescore windows
   • heavy highlighting
   • large aggregations on high-cardinality fields
4. If using aggregations:
   • confirm required fields are fast
   • consider sampling knobs where supported
5. If using vector search (feature vectors):
   • remember ANN is approximate; raising candidate_size/ef_search increases recall but costs CPU

Vector search “it errors / results look wrong”

Common causes:

• wrong vector dimension (rejected)
• using cosine without normalization assumptions (Searchlite normalizes cosine vectors automatically)
• expecting exact results from ANN (HNSW is approximate)

Knobs to tune:

• candidate_size (oversampling)
• ef_search (beam width)
• vector_filter (reduce candidate set)

Nested filter “it matches too much / too little”

Key rule: nested filters are evaluated per object, not across the entire document.

Correct pattern:

• wrap clauses under Nested with path
• for deeper nesting, use a nested Nested inside the parent nested filter

Common gotcha:

• forgetting to mark nested filter fields as fast: true

Storage/durability anomalies

Symptoms:

• “extra segments” after crash
• “duplicate generation” feeling after restart

Remember the documented crash window:

• if the process dies after manifest persisted but before WAL truncation, WAL replay reapplies last batch
• data isn’t lost, but you may see extra segment generations until compaction

Fix:

• run compact (CLI or HTTP) to merge and clean up

What to attach to a bug report

• schema JSON
• small docs ndjson/json
• exact request JSON (include execution, profile, explain if relevant)
• output of inspect + stats
• server flags/env (HTTP)
• if durability related: note whether crash/restart occurred

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