Solutions
Workloads we run every day.
Six common database patterns we see across customers — two per engine. Pick the one closest to your problem to see what production looks like on Luceris.
How to pick
Most workloads fall into a handful of shapes. Transactional with strong consistency? Postgres. Hot path that needs to stay under a millisecond? Redis. Append-mostly with aggregate queries over billions of rows? ClickHouse. The cards below are the patterns we see most often — each links to a dedicated page with code and capacity planning.
Multi-tenant SaaS backend
Row-level security or schema-per-tenant. Per-tenant credentials at the proxy. Easy fan-out for noisy tenants.
PostgreSQLVector search & RAG
pgvector with IVFFlat or HNSW indexes. Mix relational filtering with vector similarity in a single query.
RedisSession & cache layer
Sessions in the hot path, HTTP cache in front of slower stores. TTL eviction, per-tenant isolation.
RedisRealtime leaderboards & rate-limit
Sorted sets for leaderboards, INCR + EXPIRE for rate-limits, Lua for atomic token-bucket logic.
ClickHouseProduct analytics
Event ingestion at scale, materialized rollups, customer-facing dashboards with sub-second response.
ClickHouseObservability & log analytics
Logs and metrics in ClickHouse, partitioned by day, tiered to S3 on TTL. Replace expensive vendors.
Frequently asked questions
Can I run more than one of these patterns at once?
Do I have to pick a use case up front?
Can I get a reference architecture for my use case?
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