How to Optimize Postgresql Database Performance

Don’t index just filters. Index what you need. If you index only your WHERE columns, you leave performance on the table. One of the most effective yet overlooked techniques is Covering Indexes.  Unlike standard indexes that only help filter rows, covering indexes include all columns required for a query. It will reduce query execution time by eliminating the need to access the main table. 𝗪𝗵𝘆 𝗖𝗼𝘃𝗲𝗿𝗶𝗻𝗴 𝗜𝗻𝗱𝗲𝘅𝗲𝘀? • By including all required columns, the query can be resolved entirely from the index, avoiding table lookups. • Can speed up join queries by reducing access to the base table. 𝗖𝗼𝗹𝘂𝗺𝗻𝘀 𝘁𝗼 𝗜𝗻𝗰𝗹𝘂𝗱𝗲: • WHERE: Filters rows. • SELECT: Data to retrieve. • ORDER BY: Sorting columns. 𝗦𝘁𝗲𝗽𝘀 𝘁𝗼 𝗖𝗿𝗲𝗮𝘁𝗲 𝗖𝗼𝘃𝗲𝗿𝗶𝗻𝗴 𝗜𝗻𝗱𝗲𝘅𝗲𝘀 1- Use execution plans to identify queries that perform frequent table lookups. 2- Focus on columns in WHERE, SELECT, and ORDER BY. 3- Don’t create multiple indexes with overlapping columns unnecessarily. 𝗖𝗼𝘃𝗲𝗿𝗶𝗻𝗴 𝗜𝗻𝗱𝗲𝘅𝗲𝘀 𝗮𝗿𝗲 𝗻𝗼𝘁 𝗳𝗼𝗿 𝗳𝗿𝗲𝗲. • Each insert, update, or delete operation must update the index, which can slow down write-heavy workloads. • Covering indexes consumes more disk space. Covering indexes are a powerful tool for database performance, especially for read-heavy applications.  While they can increase write costs, the trade-off is often worth it for the dramatic speedups in query performance.  Every table lookup wastes precious time. Fix it!

We optimized patient search speed by 90%—here’s how! On our FHIR-powered platform, patient search is used all day. Slow searches = frustrated users. Our initial queries on patient resources were slow, taking over 3 seconds in some cases. Instead of adding a search engine like Elasticsearch or Typesense, we optimized PostgreSQL’s full-text search using `tsvector` and `tsquery`. The result? ✅ Patient name search down from 1284ms to 2.7ms ✅ Identifier search down from 3121ms to 1.39ms ✅ CPU usage reduced by 73% All without adding new dependencies. PostgreSQL’s native capabilities are more powerful than you think! Read the whole blog here: https://lnkd.in/eKYbDN2A

Did you know about a Postgres feature that can lead to up to 1000x speedups in the performance of a particular query? If you read my blog post from 5 years ago (which landed on HN's first page), then you’re already familiar with Create Statistics. For everyone else, here’s what makes it so powerful. Create Statistics can be used to improve the performance of your queries by helping Postgres understand the correlation between columns. Let’s say you want to see all sales for Q1 in January. It makes sense for you to store data for both the month and the quarter because you’re running all kinds of financial reports, so you’ll have a month column and a quarter column. But here’s the catch: you understand intuitively how they’re correlated - that the month is within the quarter, which renders the Q1 column redundant - but Postgres does not. That’s because Postgres doesn’t automatically collect correlation statistics between these 2 columns. So if Postgres plans your query without incorporating these correlation statistics, you’ll wind up with inefficient query plans. Instead, imagine giving Postgres the information you already have, so that it can derive a statistical relationship between the two columns. Using Create Statistics, the database will quickly figure out that the second column is redundant. It’s such a simple shift, and it can improve specific queries by an order of magnitude (or several). Now you may wonder where the 1000x came from, - a Postgres user gave me this exact number when he described the difference that Create Statistics made to his query performance. Create Statistics is another one of those Postgres gems that more people should know about.