I build and analyze data systems for a living, and one of the recurring questions I get from engineering teams and startups is: “Which storage should we pick for our real‑time analytics pipeline — Redis, PostgreSQL, or RocksDB?” I’ve spent time prototyping pipelines with all three, tuning them under load, and pushing them into production. Below I share a pragmatic, experience‑based guide to help you choose the right tool depending on your workload, latency and durability needs, operational constraints, and long‑term goals.
What I mean by “real‑time analytics pipeline”
When I say real‑time analytics pipeline I’m describing systems that ingest streams of events (clicks, metrics, transactions), transform or aggregate them (counts, windows, rollups), and serve results back with low latency for dashboards, anomaly detection, or enrichment in other services. These pipelines often mix writes and reads at high rates, require some temporal semantics (e.g., tumbling/ sliding windows), and need predictable tail latency.
Quick mental model: strengths of each option
Before digging into details, here’s how I think about each system in one line:
Redis: ultra‑low latency in‑memory datastore with optional persistence; great for ephemeral state, leaderboards, session stores, and fast caches.PostgreSQL: general‑purpose relational DB with strong consistency, rich query semantics, and ecosystem (SQL, indexes, materialized views); good for analytical stores when ACID and complex queries matter.RocksDB: embeddable LSM‑tree key‑value engine optimized for high write throughput and low storage amplification; excellent for local state in stream processors or custom storage engines.Latency and throughput
Latency and throughput are usually the primary constraints for real‑time analytics.
Redis: If you need sub‑millisecond reads and low‑single‑millisecond writes, Redis wins. It serves everything from memory and can handle hundreds of thousands of ops/sec on commodity instances. Redis Streams and modules (RedisTimeSeries) add streaming/TS functionality.RocksDB: RocksDB shines when you need sustained high write throughput while keeping read latency relatively low. Embedded in systems like Kafka Streams, Flink (with RocksDB state backend), and Cassandra’s storage engine, RocksDB handles millions of small writes efficiently. Reads can be fast—especially for point reads—though tail latencies can be impacted by compactions.PostgreSQL: Traditional Postgres isn’t usually the first choice for ultra‑high‑rate streaming writes at massive scale. However, with careful schema design, partitioning, and connection pooling, Postgres can support a high‑volume analytical workload. Read latency is generally higher than Redis but offers more expressive queries.Durability and consistency
How much do you care about data durability and strong consistency?
Redis: By default Redis is an in‑memory database with options for RDB snapshots and AOF (Append Only File). AOF with fsync policies can approach durability guarantees, but you’ll still accept tradeoffs vs. disk‑first stores. Redis Cluster adds replication and failover, but you should test failure modes.PostgreSQL: Postgres provides ACID guarantees, point‑in‑time recovery, WAL replication, and robust durability. If your analytics pipeline needs strong transactional semantics or consistent joins across datasets, Postgres reduces a lot of complexity.RocksDB: RocksDB is disk‑backed and durable; it depends on the host process to flush and manage WAL. When embedded, durability is as strong as how you integrate it (syncing WAL to disk, replication layer on top). RocksDB by itself doesn’t provide multi‑node replication—you typically build replication at the system level (e.g., Flink checkpointing or a custom replicator).Operational complexity
Operational burden can be the deciding factor for many teams.
Redis: Easy to operate for single instances or managed services (Redis Enterprise, AWS ElastiCache). Clustering, persistence tuning, and eviction policies require expertise. Managed offerings reduce complexity substantially.PostgreSQL: Mature operational tools, backups, and monitoring ecosystems. Managed Postgres (RDS/Aurora, Cloud SQL) makes life easier. Schema migrations and index tuning are normal operational tasks.RocksDB: More operationally intensive when used standalone: compaction tuning, memory vs. disk configuration, and dealing with compaction stalls. When embedded in a stream processor, much of the ops handling is moved to that runtime, but you still need to manage memory and storage pressure carefully.Query expressiveness and analytics features
If you want rich analytical queries, joins, window functions, or ad‑hoc exploration:
PostgreSQL: Wins hands down. You get SQL, materialized views, complex joins, indexes, and extensions (timescaleDB, Citus for scaling). For long‑tail analytics and ad‑hoc SQL exploration, Postgres is the practical choice.Redis: Supports simple data structures and commands; RedisTimeSeries and modules add useful primitives, but you won’t replace SQL analytics with Redis. Use Redis for serving pre‑computed aggregates, not for exploratory SQL queries.RocksDB: RocksDB is a key‑value store—there’s no built‑in query language. It’s ideal for storing computed state or indexes that your application or stream processor will query by key.Cost considerations
Price is often overlooked during prototyping and bites you later.
Redis: Memory is expensive. If your dataset fits in RAM (or mostly) and you need very low latency, Redis is worth the cost. If your working set grows into tens of gigabytes to terabytes, costs escalate unless you use hybrid approaches (Redis on Flash in some proprietary editions).PostgreSQL: Disk‑backed storage makes Postgres cost‑efficient for larger datasets. You can scale vertically or use sharding/partitioning to scale horizontally. Managed services add cost but save time.RocksDB: RocksDB is disk‑efficient and therefore cheaper for large state sizes. Since it runs embedded, you can colocate it with your processing nodes and avoid cross‑network IO costs, which is economical at scale.Common architectures and patterns I use
Hybrid: Redis + Postgres: Use Redis as a fast serving cache for the latest aggregates and Postgres for durable storage and historical queries. Write to both (or write‑through) and use streaming to backfill Postgres asynchronously.Stream processor + RocksDB: When building event processing pipelines with Apache Flink, Kafka Streams, or Samza, I use RocksDB as the state backend. It gives high write throughput, local reads for windowing, and efficient snapshots via checkpoints.Postgres for ELT and ad‑hoc analytics: For teams that want SQL everywhere, I push for Postgres (optionally timescaleDB) receiving batched writes from Kafka or CDC. This simplifies analytics using SQL clients and BI tools.When I pick each in practice
Choose Redis when: you need extremely low latency, the working set fits mostly in memory, and you’re serving hot aggregates or leaderboards. Also excellent for coordination and short‑lived state.Choose RocksDB when: you run stream processing that requires local state (windowing, joins) and high write throughput with cost‑efficient disk storage. Use it embedded via Flink/Kafka Streams.Choose PostgreSQL when: you need complex queries, strong consistency, and a durable home for historical analytics or when your team prefers SQL and mature operational tooling.| Criteria | Redis | PostgreSQL | RocksDB |
|---|
| Best for | Low‑latency serving, caches, ephemeral aggregates | Durable analytics, SQL queries, joins | Embedded high‑throughput state, stream processing |
| Durability | Configurable (AOF/RDB), weaker than disk‑first by default | Strong ACID durability | Durable, depends on WAL/flush config |
| Query power | Primitive ops, modules extend features | Full SQL | Key‑based access only |
| Operational complexity | Moderate, simpler with managed service | Moderate, mature ecosystem | Higher if standalone; moderate when managed by framework |
Practical checklist before you decide
I ask the teams I consult to answer these quickly — they tend to reveal the right choice:
What is your 95th/99th percentile read and write latency target?How large is your working set, and will it grow beyond RAM feasibility?Do you need SQL and ad‑hoc querying or mainly key/aggregate access?How important is strong consistency versus eventual or best‑effort persistence?What ops expertise and budget do you have for running stateful systems?Answering those will often narrow the options to one or two candidates. If you’re still unsure, I recommend prototyping the critical path: simulate your event stream using a load generator, measure tail latencies and CPU/memory patterns under realistic compaction or replication scenarios, and test failure modes (node restarts, network partitions). Real results beat assumptions every time.
