system-design-data-architecture
Choose and scale the data layer: SQL versus NoSQL per access pattern, single data ownership, replication and read scaling, partition key choice, hot partition and celebrity key mitigation. Use when selecting a store, planning sharding, or fixing a data-tier bottleneck.
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SKILL.md
Data Architecture
Priority: P1 (HIGH)
Access patterns choose the store. Ownership precedes schema. Shard last, not first.
Store Selection
- List every read and write access pattern with its QPS, latency target, and consistency need.
- Default to a relational store. It wins until a specific pattern proves it cannot serve.
- Move a pattern to a specialized store only when the relational store fails that named pattern: key-value for hot lookups, wide-column for massive ordered writes, document for schema-variant aggregates, graph for multi-hop traversal, search index for text and facets, object store for blobs.
- Every additional store adds sync lag, dual-write risk, and one more operational surface. Justify it.
- Blobs never live in the primary database; store bytes in object storage and keep the reference.
Ownership and Consistency
- One writer owns each entity. Cross-service reads use an API or an event stream, never a shared table.
- Classify each flow before choosing replication: money, stock, and identity need strong consistency; feeds, counters, and analytics tolerate eventual.
- Read replicas serve reads only, and replica lag is visible to users. Route read-after-write to the primary or pin the session.
- Cross-entity atomicity across services needs a saga with compensations, not a distributed transaction.
Scaling Order
Apply in order and stop as soon as headroom is sufficient: index and query fixes, then read replicas, then caching, then vertical scale, then partition or archive cold data, then shard. Sharding is last because it costs cross-shard queries, rebalancing, and a permanent partition key commitment.
Partitioning
- Choose a partition key that is high-cardinality, present in the hottest query, and evenly distributed.
- Hash partitioning spreads load but kills range scans; range partitioning keeps scans but creates a moving hot partition on time-ordered keys.
- Mitigate a celebrity key by salting the key, replicating the record, or serving it from a dedicated cache.
- Plan resharding before launch: virtual buckets mapped to physical shards let you move buckets without rewriting keys.
Anti-Patterns
- No premature sharding: an unindexed query at 300 QPS is not a sharding problem.
- No shared database between services: it silently couples deploy and schema lifecycles.
- No dual writes without reconciliation: use an event log or CDC, then reconcile.
- No unbounded table growth: define retention, archival tier, and deletion at design time.
- No denormalization without an update path: every copy needs an owner and a refresh trigger.
Verify
- Every access pattern mapped to exactly one store with a stated reason
- Single writer named per entity
- Consistency class stated per flow
- Partition key chosen with a hot-partition mitigation
- Retention and archival defined
References
- Database Scaling - store comparison, replication topologies, sharding mechanics, migration patterns
Files
3- SKILL.md
fd5551c2303.6 KB - evals/evals.json
b5e2bd17fa4.9 KB - references/database-scaling.md
91e84a6bd94.2 KB
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