Why is my SQL query slow? Watch EXPLAIN ANALYZE turn a million-row scan into ten block reads with one index, and count an N+1 loop in the act.

YouTube video by Punyapal Shah

On Target’s Data Platform team, we tune our job-orchestration platform to stay fast and reliable through seasonal sale peak load.

When working with MongoDB to handle significant amounts of data, the choice between bulk write and simple write operations becomes a critical factor in optimizing performance and resource utilization....

Hey there, fellow developers! If you've ever dabbled in SQL, you've probably heard the golden rule:

Indexes are a fundamental aspect of database optimization, enabling efficient querying and improving the overall performance of MongoDB applications. This article delves into what MongoDB indexes are,...

That viral SQL performance tip about filtering before joining? It is complete nonsense. Here is why query optimizers make it irrelevant.

Learn how to write SQL that the query optimizer understands—reduce costs, avoid slow queries, and improve performance in Snowflake and Databricks.

Quick Brief The Launch: Supabase released Agent Skills for Postgres Best Practices on January 21, 2026 an open-source framework with 8 performance-focused rule categories The Impact: Targets the 4 million+ developers and 100,000+ customers building on Supabase, addressing optimization gaps in AI-generated PostgreSQL queries The Context: Follows Supabase's $100M Series E at $5B valuation (October 2025), positioning the company to capture the AI-native database infrastructure market…

The Database Performance Snapshot Performance Impact: 50–70% Query Time ReductionBest For: SME Owners, WordPress Developers, High-Traffic SitesCritical Metric: Keep Autoload Size Under 900 KBAdwaitX Verdict: Database cleanup is the #1 overlooked factor killing backend performance and TTFB. The Elephant in the Room: Your Database is Probably Bloated WordPress databases don't stay clean. Every draft, plugin setting, and page revision accumulates silently, choking your server's ability to respond quickly.

The article discusses the problem of cache stampedes, where multiple simultaneous requests for a cached resource trigger a surge in database queries, overwhelming the connection pool. This occurs when the cache expires under heavy load, causing redundant database calls. The solution presented is 'single-flight caching,' which ensures only one computation occurs for a given key, with subsequent requests waiting for the same result. This is implemented using an in-flight promise system that tracks ongoing computations. The approach maintains fast cache hits while preventing redundant queries. The text emphasizes that cache stampedes are not mitigated by larger pools or timeouts but by sharing computation across concurrent requests. The example highlights how this technique prevents database overload for a high-traffic key, reducing from 200 queries to a single database call. The article concludes that the key to avoiding stampedes lies in coordinating computations rather than relying solely on caching.

Dive into a systematic approach to diagnosing performance issues and making optimizations for PostgreSQL. Rahila Syed (PostgreSQL developer) shares insights in her talk “Journey of developing a performance optimization feature in PostgreSQL” at POSETTE: An Event for Postgres 2026. Abstract: In this talk, I will share the journey of identifying and optimizing a performance bottleneck in PostgreSQL. The session will walk through a systematic approach to diagnosing performance issues — distinguishing whether the bottleneck lies in the CPU, I/O, or network — and how iterative profiling and analysis can guide effective optimizations. Using perf and other diagnostic tools, we’ll examine how bottlenecks can shift during optimization, sometimes masking real gains. I will demonstrate how to effectively measure improvement in performance through careful tuning of the database, along with use of pgbench and custom benchmarking scripts tailored to the optimization under test. As a practical example, we will explore an optimization in PostgreSQL’s physical replication that enables the WAL sender to transmit WAL records to standbys before they are flushed to disk on the primary. This enhancement aims to reduce replication latency by leveraging WAL buffers to send data more proactively, minimizing disk reads and improving network utilization. For large transactions, this approach allows most WAL data to be sent in parallel with ongoing writes on master, aligning the flush operations on primary and standby more closely and significantly reducing replication lag. Rahila Syed has approximately 13 years of experience working as a PostgreSQL developer. ► Video chapters: ⏩ 00:00 – Music & introduction ⏩ 00:48 – Physical replication basics ⏩ 03:54 – WAL flow from primary to standby ⏩ 05:26 – WAL buffers, fsync, and tuning ⏩ 08:41 – WAL sender loop and behavior ⏩ 15:51 – Perf profiling and flamegraph insights ⏩ 22:01 – Sending unflushed WAL ⏩ 26:03 – Benchmark results & performance gains ⏩ 28:26 – WAL receiver changes & alternative implementations ⏩ 32:36 – Conclusion 📕 Everything you need to know about POSETTE: An Event for Postgres can be found at: https://posetteconf.com ✅ Learn more: watch more POSETTE talks: https://aka.ms/posette-playlist 📌 Let’s connect: LinkedIn: https://www.linkedin.com/company/posetteconf/ X – @PosetteConf, https://x.com/PosetteConf Mastodon – @posetteconf, https://mastodon.social/@posetteconf Bluesky – @posetteconf.com, https://aka.ms/posette-on-bluesky #PosetteConf #PostgreSQL #OpenSource

Partition pruning in PostgreSQL is typically effective only with partition key columns. However, by leveraging certain data patterns, it's possible to achieve pruning even on non-partition key columns