LLM Best Practices

Sharding

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This is LLM Best Practices (llmbestpractices.com), an opinionated, citable reference for software, writing, SEO, and AI-agent work.

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Overview

Sharding (horizontal partitioning across nodes) assigns each row to one of several independent servers using a partition-key. Treat it as a last resort after indexing, vertical scaling, caching, and read replicas run out, because cross-shard joins, transactions, and rebalancing add lasting operational cost. Most applications never need it.

Rules

  • Pick a shard key that matches the dominant query filter and spreads load evenly; a poor key creates hot shards. A query with the key hits one shard, and one without it fans out (scatter-gather).
  • Cross-shard joins and transactions are expensive. Denormalize, or co-locate related tables on the same key such as tenant_id.
  • PostgreSQL does not shard natively. Citus, an open-source extension, distributes tables across nodes (Citus 14 supports PostgreSQL 18).
  • Each shard needs its own replicas and pool, so total connections multiply by shard count (connection-pool).
  • Legitimate triggers: write throughput beyond one node, data volume where vacuum and index maintenance are impractical, or regional data-residency rules. Single-node partitioning solves many “table too big” problems first.

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