ClickHouse vs Redis
- ✦ Column-oriented storage
- ✦ Vectorized query execution
- ✦ Real-time ingestion
- ✦ In-Memory Storage
- ✦ Persistent Storage (RDB/AOF)
- ✦ Pub/Sub Messaging
Real-World Scenarios: When to Choose Which
The question that matters: “In what situation will I regret choosing A over B after 3 months?”
Ingest clickstream events via Kafka, query 100B rows in under 1 second for live dashboards
Redis Pub/Sub broadcasts events to thousands of connected subscribers in under 1ms, enabling real-time dashboards or notifications without polling the database.
Store and query server logs at petabyte scale with 10x better compression than Elasticsearch
Count unique users and calculate click-through rates across billions of ad impressions in milliseconds
Replace InfluxDB with ClickHouse for metrics storage, gaining SQL query support and better compression
Redis stores user session data with consistent sub-millisecond GET operations, handling 100K+ requests per second with a single instance where a database session store would create a bottleneck.
Redis INCR with EXPIRE implements sliding window rate limiting in 2 lines of code, enforcing per-user API quotas without adding a separate rate limiting service.
Pricing Intelligence
ClickHouse Plans
Free tier available
- • Self-hosted
- • Full features
- • Community support
- • $0.20/hr compute from
- • Free trial credits
- • Managed service
- • Dedicated resources
- • SLA
- • Enterprise support
Redis Plans
Free tier available
- • Full Redis
- • RSAL license
- • Community support
- • Managed Redis
- • Free 30MB tier
- • 99.999% SLA
Feature Matrix
12 differences found across 18 standardized features
- •Column-oriented storage
- •Vectorized query execution
- •Real-time ingestion
- •Kafka integration
- •SQL support
- •Distributed tables
- •Compression (LZ4/ZSTD)
- •Approximate query functions
- •Materialized views
- •Replicated tables
- •JSON support
- •Geospatial functions
- •Time-series optimizations
- •REST and native protocols
- •In-Memory Storage
- •Persistent Storage (RDB/AOF)
- •Pub/Sub Messaging
- •Streams
- •Sorted Sets / Lists / Hashes
- •Lua Scripting
- •Transactions (MULTI/EXEC)
- •Cluster Mode
- •Sentinel (HA)
- •Redis Search
- •Redis JSON
- •Redis Time Series
- •Redis Vector Similarity Search
- •Keyspace Notifications
- •TTL / Expiry
- •Replication
Pros & Cons Face-Off
Evaluative strengths and weaknesses: not feature lists
- +Fastest OLAP query performance for analytical queries at scale
- +Aggressive compression cuts storage costs 5-10x vs row-oriented DBs
- +Open source with full feature parity on self-hosted
- −Not designed for OLTP workloads or frequent row updates
- −Complex cluster configuration for self-hosted HA deployments
- +Sub-millisecond latency: fastest data store for caching
- +Universal: supported by every framework and language
- +Rich data structures for real-time use cases
- +Redis Stack adds search, JSON, and vector capabilities
- −License changed to RSAL: Valkey is the open-source BSD fork
- −In-memory storage makes large datasets expensive
At a Glance
Frequently Asked Questions
Related Comparisons
Sources
- 1.ClickHouse Official Pricing–Vendor pricing page
- 2.Redis Official Pricing–Vendor pricing page
- 3.ClickHouse Official Website–Official product website
- 4.Redis Official Website–Official product website