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

• Decide necessity

• Try getProgramAccounts + caches first; move to indexer if slow or large.

• Event design

• Add program logs/events with discriminators and key fields; avoid verbose logs.

• Choose stack

• Options: custom listener + DB, Helius/webhooks to queue, GraphQL subgraph equivalents, or hosted indexers.

• Backfill

• Use getSignaturesForAddress/getTransaction or snapshot; store cursor; verify counts.

• Live ingestion

• Subscribe to logs or webhooks; ensure dedupe and ordering by slot + tx index.

• Query API

• Expose REST/GraphQL tailored to frontend/bot needs; add caching.

• 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.