DuckDB vs Redis
- ✦ In-process execution
- ✦ Parquet/CSV/JSON direct query
- ✦ S3 and GCS file access
- ✦ 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?”
Query 50GB Parquet files on S3 directly from Python without ETL, returning results in seconds
Replace pandas aggregations with SQL-based DuckDB queries for 10-50x faster group-by operations
Run dbt models locally against DuckDB instead of cloud warehouses to cut development cycle time
Use DuckDB as a compute engine over Delta Lake or Iceberg tables without a dedicated cluster
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.
Redis Pub/Sub broadcasts events to thousands of connected subscribers in under 1ms, enabling real-time dashboards or notifications without polling the database.
Pricing Intelligence
DuckDB Plans
Free tier available
- • MIT license
- • Embedded in-process
- • No server needed
Redis Plans
Free tier available
- • Full Redis
- • RSAL license
- • Community support
- • Managed Redis
- • Free 30MB tier
- • 99.999% SLA
Feature Matrix
13 differences found across 18 standardized features
- •In-process execution
- •Parquet/CSV/JSON direct query
- •S3 and GCS file access
- •SQL support
- •Python/R/Node.js integration
- •Vectorized execution
- •Parallel query processing
- •Apache Arrow integration
- •Zero-copy pandas exchange
- •Window functions
- •ACID transactions
- •Column-oriented storage
- •Schema inference
- •HTTPFS extension
- •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
- +Runs in-process with zero infrastructure setup
- +Directly queries Parquet and CSV on S3 without ETL
- +Outperforms many server-based DBs on single-machine workloads
- −Single-node only - no horizontal scaling or clustering
- −Not suitable for multi-user concurrent write workloads
- +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.DuckDB Official Pricing–Vendor pricing page
- 2.Redis Official Pricing–Vendor pricing page
- 3.DuckDB Official Website–Official product website
- 4.Redis Official Website–Official product website