In this month's newsletter, we wrap up 2024 with new pganalyze features to safely tune queries in production as well as new resources to make it easy to invest in your Postgres performance this coming year.
Happy New Year and welcome to the final 2024 edition of the pganalyze newsletter!
In 2024, we saw many exciting developments in the Postgres landscape. In February last year, we launched pganalyze Index Advisor 3.0, introducing a powerful constraint programming model that helps you discover more effective indexing strategies. In September, the Postgres community celebrated the release of PostgreSQL 17—a performance-focused update featuring streaming I/O, smarter query planning, and other enhancements.
Rounding out the year, we introduced two additional features. pganalyze Plan Statistics provides deeper visibility into query execution, initially available for Amazon Aurora users, and the newly added buffer cache statistics unlock a valuable new lens for understanding how queries interact with shared memory.
To cap it all off, we’re excited to announce Query Tuning Workbooks is now available in beta, offering a structured path to identifying and resolving performance bottlenecks. If improving Postgres performance is part of your strategic initiatives for 2025, we've made it easy to evaluate Postgres software vendors with this customizable template.
I'm excited, looking ahead to another year of making Postgres the best open-source database, and pganalyze the best product for monitoring and tuning your Postgres servers. And, as always, you can find the detailed list of product updates at the end.
All the best,
Lukas
Introducing Query Tuning Workbooks: Safely Tune Postgres Queries on Production
Optimizing Postgres queries can be a complex challenge. A query that performs well for one customer's data might degrade performance for another. Testing changes in production is risky, and existing methods like running EXPLAIN ANALYZE in a sandbox often don't provide a complete picture.
That's why we're excited to introduce Query Tuning Workbooks in pganalyze (currently in beta). This new feature provides a dedicated environment for safely tuning Postgres queries on production.
With Query Tuning Workbooks, you can:
Benchmark slow queries and experiment with rewrites, planner settings, and hints
Compare resulting EXPLAIN plans side-by-side
Test different query variants with varying parameters.
Share results with your team for collaborative optimization.
Key benefits include a structured workflow for query tuning and the ability to easily measure how different parameters affect query plans. This means you can test changes and validate outcomes before deploying them to your live system.
As 2025 budgets are finalized, now is the perfect time to evaluate your options for Postgres performance and monitoring solutions. Our new Vendor Evaluation Template streamlines the entire vendor selection process—helping you quickly short-list Postgres-centric vendors, prioritize essential features, and automate side-by-side comparisons. With built-in scoring and a reusable dashboard, you can easily evaluate demos, identify the best-fit solution, and stay organized for ongoing evaluations. Take charge of your 2025 budget decisions with confidence and download our template to ensure you’re getting the best return on your Postgres investment.
If Postgres is the operational backbone of your applications, it’s crucial to invest in the right tools to maximize its performance and ROI. That’s why we’re excited to introduce our new pganalyze Product Tour—a guided walkthrough that shows how pganalyze empowers engineers and database teams to make measurable optimizations. Navigate to the feature that matters most to you, whether its Query Performance or Index Advisor, or explore the Product Tour from the start to see pganalyze in action.
The collector now optionally executes on-demand EXPLAIN ANALYZE queries for the new Query Tuning feature via the new pganalyze.explain_analyze() helper
This helper is owned by a separate user which gets assigned table read permissions, and avoids granting the collector user unnecessary permissions directly
By creating the helper function you opt into automated query runs through the collector.
For high security environments, Query Tuning Workbooks can still be used without this feature by running queries manually
For easily creating the helper in all databases on a server the new "--generate-explain-analyze-helper-sql" command is added
The enable_query_runner setting introduced in 0.63.0 is removed, since the helper function is now mandatory to use this feature
Update pg_query to v6 / Postgres 17 parser
Install script: Add AlmaLinux and Rocky Linux support
PII filtering bug fixes
Correctly handle secondary lines that were not analyzed
Detect bind parameters in CONTEXT as statement_parameter
Other bug fixes
Azure: Improve system metrics error handling, and correctly return most recent value
