Matchups: PostgreSQL Caching vs MySQL Query Cache

Overview

Picture your database as a cosmic library, where queries must retrieve knowledge instantly. PostgreSQL Caching, part of PostgreSQL since 1986, is the versatile scholar—leveraging shared buffers, query plans, and MVCC to cache data and plans efficiently. It powers 35% of relational DB workloads (2024).

MySQL Query Cache, a feature of MySQL since 2001 (deprecated in 8.0), is the focused archivist—storing query results in memory for identical queries. It was used in 20% of legacy MySQL apps before its removal, replaced by external caching like Redis.

Both optimize query performance, reducing latency to milliseconds, but their approaches differ: PostgreSQL’s caching is robust and built-in, MySQL’s query cache is outdated but relevant for legacy systems. They drive apps from SaaS to analytics, ensuring speed.

Fun Fact: PostgreSQL’s shared buffers cache 80% of hot data—speed without extras!

Section 1 - Syntax and Core Offerings

PostgreSQL caches via shared buffers and plans—example: tune buffers and check cache hits:

SET shared_buffers = '4GB'; SELECT * FROM pg_stat_activity WHERE state = 'active'; EXPLAIN ANALYZE SELECT * FROM users WHERE id = 1001;

MySQL Query Cache (pre-8.0) uses configs—example: enable and check cache:

SET GLOBAL query_cache_size = 134217728; -- 128MB SET GLOBAL query_cache_type = ON; SHOW STATUS LIKE 'Qcache%'; SELECT * FROM users WHERE id = 1001;

PostgreSQL’s shared buffers cache 10M rows in RAM—example: ~5ms reads for 1M queries with 90% buffer hits. It caches plans for prepared statements, reducing parse time by 50%. MySQL Query Cache stores exact query results—example: ~3ms reads for 500k identical queries with 85% hits. Deprecated in 8.0, it’s limited to text-based queries and invalidated on table changes.

PostgreSQL suits modern apps—example: cache analytics queries; MySQL Query Cache fits legacy—example: cache static reports. PostgreSQL is robust, MySQL outdated—both speed queries.

Section 2 - Scalability and Performance

PostgreSQL scales with RAM—example: a SaaS app caches 1TB of data in 32GB buffers, serving 50,000 qps at ~10ms with 90% buffer hits. MVCC and WAL ensure consistency, but large datasets need tuning (e.g., work_mem, effective_cache_size). Plan caching saves ~100µs/query.

MySQL Query Cache scales poorly—example: a retail app caches 100M rows in 1GB cache, hitting 30,000 qps at ~8ms with 85% hits. Frequent invalidations (e.g., table writes) drop CHR to 70%, and single-threaded locking limits concurrency. External caches like Redis outperform it.

Scenario: PostgreSQL caches a dashboard’s aggregates; MySQL Query Cache speeds a legacy CMS. PostgreSQL wins for modern scale, MySQL for old setups—both perform with tuning (e.g., PostgreSQL’s bgwriter, MySQL’s cache limits).

Key Insight: PostgreSQL’s buffer cache is like a cosmic vault—data stays hot!

Section 3 - Use Cases and Ecosystem

PostgreSQL excels in complex apps—example: Airbnb caches 10M listings, cutting query time by 80%. It’s ideal for analytics, GIS, or transactional apps. MySQL Query Cache suits legacy apps—think WordPress caching 1M page queries before 8.0.

Ecosystem-wise, PostgreSQL integrates with pgBouncer, Patroni, and clouds (AWS RDS)—example: cache with connection pooling. MySQL Query Cache (pre-8.0) pairs with MySQL’s ecosystem or Memcached—example: offload caching post-deprecation. PostgreSQL is modern, MySQL Query Cache archaic.

Practical case: PostgreSQL caches a BI tool’s queries; MySQL Query Cache speeds an old forum. PostgreSQL is future-proof, MySQL legacy—pick by app age.

Section 4 - Learning Curve and Community

PostgreSQL’s curve is moderate—tune buffers in days, master plans in weeks due to depth. MySQL Query Cache’s gentle—enable in hours, optimize in days, but deprecated status limits relevance.

Communities shine: PostgreSQL’s forums and PGConf detail caching; MySQL’s docs (pre-8.0) and Stack Overflow cover Query Cache. Example: PostgreSQL’s guides teach buffer tuning; MySQL’s dive into cache stats. Adoption’s quick—PostgreSQL for modern, MySQL for legacy.

Newbies start with PostgreSQL’s configs; intermediates explore plan caching. PostgreSQL’s docs are rich, MySQL’s dated—both fuel learning.

Quick Tip: Tune PostgreSQL’s shared_buffers—it’s a caching playground!

Section 5 - Comparison Table

Aspect	PostgreSQL	MySQL Query Cache
Mechanism	Buffers, plans	Query results
Performance	~10ms, 90% hits	~8ms, 85% hits
Scalability	RAM-based	Limited, deprecated
Features	MVCC, plan caching	Text-based cache
Best For	Modern apps	Legacy apps

PostgreSQL’s robustness fits modern apps; MySQL Query Cache suits legacy. Pick by app era.

Conclusion

PostgreSQL and MySQL Query Cache are database caching veterans with divergent paths. PostgreSQL excels in modern, complex apps, leveraging shared buffers and plan caching for analytics, transactions, or GIS—ideal for SaaS or BI tools. MySQL Query Cache, though deprecated, remains relevant for legacy apps pre-8.0, caching static queries simply—suitable for old CMS or forums. Weigh scalability (RAM vs. limited), performance (~10ms vs. ~8ms), and relevance (modern vs. legacy).

For a new app, PostgreSQL shines; for an old MySQL setup, Query Cache works. Blend with external caches—PostgreSQL with pgBouncer, MySQL with Redis—for cosmic speed. Test both; PostgreSQL’s open-source and MySQL’s legacy docs make it accessible.

Pro Tip: Benchmark PostgreSQL’s buffers vs. Redis—see caching soar!

Tech Matchups: PostgreSQL Caching vs MySQL Query Cache