Data Engineering Podcast
Shining Some Light In The Black Box Of PostgreSQL Performance
Episode guests
Podcast summary created with Snipd AI
Quick takeaways
- Understanding query optimization and addressing performance problems are crucial for optimizing database performance.
- Developers can gain confidence in exploring database performance by identifying bottlenecks, monitoring system utilization, and understanding the I/O stack.
- Advancements in database performance management tools aim to simplify performance management, increase productivity, and reduce the cognitive load associated with database optimization.
Deep dives
The Importance of Understanding Database Performance
Optimizing database performance is crucial for a smooth customer experience and overall business success. Slow queries or performance issues can negatively impact a website or application, leading to frustrated users and potential loss of revenue. Understanding how databases work, including query planning and execution, is essential in identifying and addressing performance problems. Issues like missing indexes, N+1 queries, and inefficient join methods can often be resolved through proper query optimization. While tools like ORMs can provide convenience, gaining SQL proficiency is important for effectively optimizing database performance.
Common Challenges in Performance Engineering for Databases
Addressing performance problems in a database can be intimidating, especially for those who are not familiar with database internals. There is often a hesitancy to delve into database optimization due to perceived complexity and lack of understanding. However, gaining confidence in exploring database performance is crucial. Identifying bottlenecks, monitoring system utilization, and understanding the I/O stack are essential steps in diagnosing performance issues. By using tools like explain plans, auto-vacuum logs, and metrics provided by cloud providers, developers can start to pinpoint problem areas and make informed optimizations. Additionally, understanding index usage, improving join methods, and avoiding N+1 queries can greatly enhance performance in database-driven applications.
The Future of Database Performance Management
The industry is seeing advancements in database performance management tools aimed at reducing the burden of manual tuning and optimization. Projects like Ottertun provide automated analysis of runtime logs to recommend configuration parameter changes and optimizations. However, there is a balance to strike between automation and user control. Database systems are critical components of applications, and unexpected changes can have significant consequences. Tools that offer recommendations while allowing for user approval are becoming more prevalent. Additionally, specialized extensions and indexing recommendations are helping improve performance without users needing to deeply understand and tweak database configuration parameters. The goal is to simplify performance management, increase productivity, and reduce the cognitive load associated with database optimization.
Importance of Tuning Synchronous Commit and Postgres
Tuning the synchronous commit parameter in Postgres can have significant performance implications. It controls whether Postgres waits for a write operation to be written to disk before returning the result. Disabling synchronous commit can improve performance in most cases, but also increases the risk of losing a transaction in case of a crash. Financial services systems should not disable it to ensure data consistency, but for other systems, it can be beneficial. It is essential to understand the parameters that influence costs, such as random page cost and work_mem, as they can affect query performance. Testing and validating these parameter changes on a per-connection basis can help identify the desired optimization without disrupting the whole database.
Improving Performance with PG Analyze and Setting Database Parameters
PG Analyze, a performance monitoring tool for Postgres, offers valuable insights for optimizing queries. It provides execution plans, index recommendations, and query statistics, making it easier to identify and fix performance issues. Enabling extensions like PG Stat Statements and Auto Explain in Postgres can gather query data without incurring significant performance overhead. Collaborating with application engineers and sharing data in APM systems can provide a holistic view of performance bottlenecks and enable efficient optimizations. A crucial goal for the future of databases is community-driven development with less dependency on proprietary database technology. Building better user experience and design-focused tooling can bridge the gap between users and databases, enhancing usability and making performance tuning more accessible.
Summary
Databases are the core of most applications, but they are often treated as inscrutable black boxes. When an application is slow, there is a good probability that the database needs some attention. In this episode Lukas Fittl shares some hard-won wisdom about the causes and solution of many performance bottlenecks and the work that he is doing to shine some light on PostgreSQL to make it easier to understand how to keep it running smoothly.
Announcements
- Hello and welcome to the Data Engineering Podcast, the show about modern data management
- Introducing RudderStack Profiles. RudderStack Profiles takes the SaaS guesswork and SQL grunt work out of building complete customer profiles so you can quickly ship actionable, enriched data to every downstream team. You specify the customer traits, then Profiles runs the joins and computations for you to create complete customer profiles. Get all of the details and try the new product today at dataengineeringpodcast.com/rudderstack
- You shouldn't have to throw away the database to build with fast-changing data. You should be able to keep the familiarity of SQL and the proven architecture of cloud warehouses, but swap the decades-old batch computation model for an efficient incremental engine to get complex queries that are always up-to-date. With Materialize, you can! It’s the only true SQL streaming database built from the ground up to meet the needs of modern data products. Whether it’s real-time dashboarding and analytics, personalization and segmentation or automation and alerting, Materialize gives you the ability to work with fresh, correct, and scalable results — all in a familiar SQL interface. Go to dataengineeringpodcast.com/materialize today to get 2 weeks free!
- Data lakes are notoriously complex. For data engineers who battle to build and scale high quality data workflows on the data lake, Starburst powers petabyte-scale SQL analytics fast, at a fraction of the cost of traditional methods, so that you can meet all your data needs ranging from AI to data applications to complete analytics. Trusted by teams of all sizes, including Comcast and Doordash, Starburst is a data lake analytics platform that delivers the adaptability and flexibility a lakehouse ecosystem promises. And Starburst does all of this on an open architecture with first-class support for Apache Iceberg, Delta Lake and Hudi, so you always maintain ownership of your data. Want to see Starburst in action? Go to dataengineeringpodcast.com/starburst and get $500 in credits to try Starburst Galaxy today, the easiest and fastest way to get started using Trino.
- This episode is brought to you by Datafold – a testing automation platform for data engineers that finds data quality issues before the code and data are deployed to production. Datafold leverages data-diffing to compare production and development environments and column-level lineage to show you the exact impact of every code change on data, metrics, and BI tools, keeping your team productive and stakeholders happy. Datafold integrates with dbt, the modern data stack, and seamlessly plugs in your data CI for team-wide and automated testing. If you are migrating to a modern data stack, Datafold can also help you automate data and code validation to speed up the migration. Learn more about Datafold by visiting dataengineeringpodcast.com/datafold
- Your host is Tobias Macey and today I'm interviewing Lukas Fittl about optimizing your database performance and tips for tuning Postgres
Interview
- Introduction
- How did you get involved in the area of data management?
- What are the different ways that database performance problems impact the business?
- What are the most common contributors to performance issues?
- What are the useful signals that indicate performance challenges in the database?
- For a given symptom, what are the steps that you recommend for determining the proximate cause?
- What are the potential negative impacts to be aware of when tuning the configuration of your database?
- How does the database engine influence the methods used to identify and resolve performance challenges?
- Most of the database engines that are in common use today have been around for decades. How have the lessons learned from running these systems over the years influenced the ways to think about designing new engines or evolving the ones we have today?
- What are the most interesting, innovative, or unexpected ways that you have seen to address database performance?
- What are the most interesting, unexpected, or challenging lessons that you have learned while working on databases?
- What are your goals for the future of database engines?
Contact Info
- @LukasFittl on Twitter
Parting Question
- From your perspective, what is the biggest gap in the tooling or technology for data management today?
Closing Announcements
- Thank you for listening! Don't forget to check out our other shows. Podcast.__init__ covers the Python language, its community, and the innovative ways it is being used. The Machine Learning Podcast helps you go from idea to production with machine learning.
- Visit the site to subscribe to the show, sign up for the mailing list, and read the show notes.
- If you've learned something or tried out a project from the show then tell us about it! Email hosts@dataengineeringpodcast.com) with your story.
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Links
- PGAnalyze
- Citus Data
- ORM == Object Relational Mapper
- N+1 Query
- Autovacuum
- Write-ahead Log
- pg_stat_io
- random_page_cost
- pgvector
- Vector Database
- Ottertune
- Citus Extension
- Hydra
- Clickhouse
- MyISAM
- MyRocks
- InnoDB
- Great Expectations
- OpenTelemetry
The intro and outro music is from The Hug by The Freak Fandango Orchestra / CC BY-SA
Sponsored By:
- Starburst:  This episode is brought to you by Starburst - a data lake analytics platform for data engineers who are battling to build and scale high quality data pipelines on the data lake. Powered by Trino, Starburst runs petabyte-scale SQL analytics fast at a fraction of the cost of traditional methods, helping you meet all your data needs ranging from AI/ML workloads to data applications to complete analytics. Trusted by the teams at Comcast and Doordash, Starburst delivers the adaptability and flexibility a lakehouse ecosystem promises, while providing a single point of access for your data and all your data governance allowing you to discover, transform, govern, and secure all in one place. Starburst does all of this on an open architecture with first-class support for Apache Iceberg, Delta Lake and Hudi, so you always maintain ownership of your data. Want to see Starburst in action? Try Starburst Galaxy today, the easiest and fastest way to get started using Trino, and get $500 of credits free. [dataengineeringpodcast.com/starburst](https://www.dataengineeringpodcast.com/starburst)
- Rudderstack:  Introducing RudderStack Profiles. RudderStack Profiles takes the SaaS guesswork and SQL grunt work out of building complete customer profiles so you can quickly ship actionable, enriched data to every downstream team. You specify the customer traits, then Profiles runs the joins and computations for you to create complete customer profiles. Get all of the details and try the new product today at [dataengineeringpodcast.com/rudderstack](https://www.dataengineeringpodcast.com/rudderstack)
- Materialize:  You shouldn't have to throw away the database to build with fast-changing data. Keep the familiar SQL, keep the proven architecture of cloud warehouses, but swap the decades-old batch computation model for an efficient incremental engine to get complex queries that are always up-to-date. That is Materialize, the only true SQL streaming database built from the ground up to meet the needs of modern data products: Fresh, Correct, Scalable — all in a familiar SQL UI. Built on Timely Dataflow and Differential Dataflow, open source frameworks created by cofounder Frank McSherry at Microsoft Research, Materialize is trusted by data and engineering teams at Ramp, Pluralsight, Onward and more to build real-time data products without the cost, complexity, and development time of stream processing. Go to [materialize.com](https://materialize.com/register/?utm_source=depodcast&utm_medium=paid&utm_campaign=early-access) today and get 2 weeks free!
- Datafold:  This episode is brought to you by Datafold – a testing automation platform for data engineers that finds data quality issues before the code and data are deployed to production. Datafold leverages data-diffing to compare production and development environments and column-level lineage to show you the exact impact of every code change on data, metrics, and BI tools, keeping your team productive and stakeholders happy. Datafold integrates with dbt, the modern data stack, and seamlessly plugs in your data CI for team-wide and automated testing. If you are migrating to a modern data stack, Datafold can also help you automate data and code validation to speed up the migration. Learn more about Datafold by visiting [dataengineeringpodcast.com/datafold](https://www.dataengineeringpodcast.com/datafold) today!
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