Documentdb Sharding

Sharding — Azure DocumentDB

Azure DocumentDB shards collections horizontally by hashing a shard key from each document and bucketing documents into logical shards, which the service then maps onto physical shards (the actual nodes that store data and serve traffic). The service hides the placement: you pick a shard key, the service handles the hash range and rebalancing.

The decisions that you own:

1. Whether to shard at all. Sharding is not the default and is not always the right answer — single-shard clusters scale up vertically and avoid the cross-shard tax.
2. What to shard on. The shard key is the single biggest determinant of long-term performance. It can be changed later (sh.reshardCollection), but only at significant cost once the collection is large.
3. How big each physical shard should be. The cluster tier and storage SKU set the CPU / memory / IOPS budget per physical shard, and that's what your shard key needs to fit inside.

Rules

• [sharding-when-to-shard](sharding-when-to-shard.md) — Default to single-shard. Shard only when a collection's storage or transaction volume can exceed one physical shard's budget (e.g., > 32 TB on the largest storage SKU). Sharded and unsharded collections can coexist.
• [sharding-shard-key-selection](sharding-shard-key-selection.md) — Read-heavy → pick the most frequent query filter to localize to one physical shard. Write-heavy → pick the highest-cardinality, evenly-distributed field. Avoid hot keys (monotonic IDs, timestamps, tenant IDs with skew).
• [sharding-logical-vs-physical](sharding-logical-vs-physical.md) — Mental model: logical shards are unbounded in count and size; physical shards are bounded by the cluster's compute/storage budget. Multiple logical shards map to one physical shard, never the reverse. Cross-shard transactions are supported but not free.
• [sharding-scaling-out-vs-up](sharding-scaling-out-vs-up.md) — Scale up (bigger tier / storage SKU) grows per-shard capacity without rebalancing; scale out (more physical shards) rebalances logical shards across the new layout. Read-heavy benefits from a bigger tier; write-heavy benefits from more shards or a bigger storage SKU.
• [sharding-hot-partition-diagnosis](sharding-hot-partition-diagnosis.md) — Symptoms (uneven CPU / IOPS / storage across shards) and remediation: reshard, change the key, or add a secondary high-cardinality field.
• [sharding-how-to-commands](sharding-how-to-commands.md) — sh.shardCollection / db.adminCommand({ shardCollection: "db.collection", key: {...} }), sh.reshardCollection, and the requirement to create an explicit index on the shard key (with enableLargeIndexKeys: true).
• [sharding-logical-shard-size-budget](sharding-logical-shard-size-budget.md) — Keep individual logical shards below 4 TB for best performance, even though the service imposes no hard cap.

Quick decision flow

collection's expected size or throughput ≤ one physical shard's budget?
  ├─ yes → leave unsharded. Scale up if needed.
  └─ no  → shard.
            ├─ read-heavy?  → key = most frequent query filter
            └─ write-heavy? → key = highest-cardinality, evenly distributed field

References

• Sharding for horizontal scalability in Azure DocumentDB
• Compute and storage in Azure DocumentDB
• Related: indexing/ (index on the shard key), query-optimization/ (single-shard vs scatter-gather queries), high-availability/ (replica sets within each physical shard)

Source & license

This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code.

• Author: Azure
• Source: Azure/documentdb-agent-kit
• License: MIT

Install and usage instructions live in the source repository linked above.