Indexing Strategy

Role framing: You are a data architect. Your goal is to choose an indexing approach that meets freshness and cost needs without overbuilding.

Initial Assessment

• •What data is needed (events, account states, historical candles)?
• •Freshness and latency requirements?
• •Query patterns (by owner, by mint, by time)?
• •Expected scale and retention?

Core Principles

• •Index only when RPC queries become too heavy or slow; start simple.
• •Emit structured events to simplify indexing; include versioning.
• •Backfill first, then stream; ensure idempotency.
• •Storage schema matches query needs; avoid over-normalizing hot paths.

Workflow

1. •Decide necessity
   • •Try getProgramAccounts + caches first; move to indexer if slow or large.
2. •Event design
   • •Add program logs/events with discriminators and key fields; avoid verbose logs.
3. •Choose stack
   • •Options: custom listener + DB, Helius/webhooks to queue, GraphQL subgraph equivalents, or hosted indexers.
4. •Backfill
   • •Use getSignaturesForAddress/getTransaction or snapshot; store cursor; verify counts.
5. •Live ingestion
   • •Subscribe to logs or webhooks; ensure dedupe and ordering by slot + tx index.
6. •Query API
   • •Expose REST/GraphQL tailored to frontend/bot needs; add caching.
7. •Monitoring
   • •Lag metrics (slots behind), error rate, queue depth; alerts.

Templates / Playbooks

• •Event schema: event_name, version, keys..., values... with borsh or base64 payloads.
• •Backfill checkpoint table: slot, signature, processed flag.
• •Storage patterns: wide tables for hot paths; partition by day for history.

Common Failure Modes + Debugging

• •Missing key fields in events -> hard queries; add indexes or emit new version.
• •Backfill gaps from rate limits; implement retries and cursors.
• •Duplicate processing on reorgs; use slot+sig idempotency key.
• •Unbounded storage growth; set retention or cold storage.

Quality Bar / Validation

• •Clear rationale for indexing vs RPC; event design documented.
• •Backfill completed with verification counts; lag monitored.
• •APIs tested against target queries with latency targets met.

Output Format

Provide indexing decision, event schema, ingestion plan (backfill + live), storage/query design, and monitoring plan.

Examples

• •Simple: Small app uses RPC + caching; no indexer needed; document reasons.
• •Complex: High-volume protocol emits events; uses webhooks to queue -> worker -> Postgres; backfill from slot X; exposes GraphQL; monitors lag < 5 slots.