How to Optimize Postgresql Database Performance

Your database has 50 indexes. And it's SLOWER than having 5. Here's what most engineers get wrong about indexing: We treat indexes like free performance boosts. But every index you add is a hidden contract: - Every INSERT now updates N+1 data structures - Every UPDATE potentially rewrites multiple B-trees - The query planner gets confused with too many choices - Your working set no longer fits in memory I learned this the hard way at scale. We had a table with 34 indexes. Reads were fast. Writes were dying. P99 latency on inserts hit 1.2 seconds. The fix? We dropped 28 indexes. But here's the part nobody talks about: We replaced them with 3 composite indexes that covered 94% of our query patterns. The trick was analyzing pg_stat_user_indexes. Most of our indexes had ZERO scans in 30 days. They were dead weight burning I/O on every write. Here's the framework I now use: 1. Audit index usage monthly (pg_stat_user_indexes) 2. Every index must justify its write amplification cost 3. Composite indexes > single-column indexes (almost always) 4. Covering indexes eliminate heap lookups entirely 5. Partial indexes for queries that filter on a constant The result after cleanup: • Write latency dropped 73% • Storage shrank by 40% • Read performance stayed identical The best performance optimization isn't adding something new. It's removing what shouldn't be there. 💬 What's the worst index bloat you've seen? #SystemDesign #DatabaseEngineering #SoftwareEngineering #PostgreSQL #Performance

What's the worst PostgreSQL configuration mistake you've seen in production? I'll start: shared_buffers = 128 MB on a 64 GB server. Default PostgreSQL configuration. Running in production. For two years. On a database serving 50,000 queries per second. The database was using 128 MB of its own cache and relying entirely on the operating system's page cache for everything else. It worked — PostgreSQL is remarkably resilient — but it was leaving enormous performance on the table. Changed shared_buffers to 16 GB (25% of RAM), adjusted effective_cache_size to 48 GB, and query response times dropped 40% overnight. Other configuration horrors I've seen: • 𝗺𝗮𝘅_𝗰𝗼𝗻𝗻𝗲𝗰𝘁𝗶𝗼𝗻𝘀 = 𝟭𝟬𝟬𝟬 with no connection pooler. Each connection using 10 MB of RAM. Server running out of memory under load. • 𝗿𝗮𝗻𝗱𝗼𝗺_𝗽𝗮𝗴𝗲_𝗰𝗼𝘀𝘁 = 𝟰.𝟬 on an NVMe SSD. The planner thought random reads were 4x more expensive than sequential reads, avoiding index scans that would have been faster. Should have been 1.1. • 𝘄𝗼𝗿𝗸_𝗺𝗲𝗺 = 𝟰 𝗠𝗕 on an analytics workload. Every complex query spilling to disk for sorts and hash joins. Changed to 256 MB and query times dropped from minutes to seconds. • 𝗮𝘂𝘁𝗼𝘃𝗮𝗰𝘂𝘂𝗺 = 𝗼𝗳𝗳. Yes, someone turned it off. The table bloat was spectacular. The PostgreSQL defaults are intentionally conservative. They're designed to run on a Raspberry Pi, not to perform well on your production server. Five settings — shared_buffers, effective_cache_size, work_mem, maintenance_work_mem, random_page_cost — capture 80% of the performance gains. What's yours? I know there are worse stories out there. #PostgreSQL #Database #Configuration #DevOps #DBA

With a background in data engineering and business analysis, I’ve consistently seen the immense impact of optimized SQL code on improving the performance and efficiency of database operations. It indirectly contributes to cost savings by reducing resource consumption. Here are some techniques that have proven invaluable in my experience: 1. Index Large Tables: Indexing tables with large datasets (>1,000,000 rows) greatly speeds up searches and enhances query performance. However, be cautious of over-indexing, as excessive indexes can degrade write operations. 2. Select Specific Fields: Choosing specific fields instead of using SELECT * reduces the amount of data transferred and processed, which improves speed and efficiency. 3. Replace Subqueries with Joins: Using joins instead of subqueries in the WHERE clause can improve performance. 4. Use UNION ALL Instead of UNION: UNION ALL is preferable over UNION because it does not involve the overhead of sorting and removing duplicates. 5. Optimize with WHERE Instead of HAVING: Filtering data with WHERE clauses before aggregation operations reduces the workload and speeds up query processing. 6. Utilize INNER JOIN Instead of WHERE for Joins: INNER JOINs help the query optimizer make better execution decisions than complex WHERE conditions. 7. Minimize Use of OR in Joins: Avoiding the OR operator in joins enhances performance by simplifying the conditions and potentially reducing the dataset earlier in the execution process. 8. Use Views: Creating views instead of results that can be accessed faster than recalculating the views each time they are needed. 9. Minimize the Number of Subqueries: Reducing the number of subqueries in your SQL statements can significantly enhance performance by decreasing the complexity of the query execution plan and reducing overhead. 10. Implement Partitioning: Partitioning large tables can improve query performance and manageability by logically dividing them into discrete segments. This allows SQL queries to process only the relevant portions of data. #SQL #DataOptimization #DatabaseManagement #PerformanceTuning #DataEngineering

𝗛𝗼𝘄 𝘁𝗼 𝗶𝗺𝗽𝗿𝗼𝘃𝗲 𝗱𝗮𝘁𝗮𝗯𝗮𝘀𝗲 𝗽𝗲𝗿𝗳𝗼𝗿𝗺𝗮𝗻𝗰𝗲? Here are the most important ways to improve your database performance: 𝟭. 𝗜𝗻𝗱𝗲𝘅𝗶𝗻𝗴 Add indexes to columns you frequently search, filter, or join. Think of indexes as the book's table of contents - they help the database find information without scanning every record. But remember: too many indexes slow down write operations. 💡 𝗕𝗼𝗻𝘂𝘀 𝘁𝗶𝗽: Regularly drop unused indexes. They waste space and slow down writing without providing any benefit. 𝟮. 𝗠𝗮𝘁𝗲𝗿𝗶𝗮𝗹𝗶𝘇𝗲𝗱 𝗩𝗶𝗲𝘄𝘀 Pre-compute and store complex query results. This saves processing time when users need the data again. Schedule regular refreshes to keep the data current. 𝟯. 𝗩𝗲𝗿𝘁𝗶𝗰𝗮𝗹 𝗦𝗰𝗮𝗹𝗶𝗻𝗴 Add more CPU, RAM, or faster storage to your database server. This is the most straightforward approach, but has physical and cost limitations. 𝟰. 𝗗𝗲𝗻𝗼𝗿𝗺𝗮𝗹𝗶𝘇𝗮𝘁𝗶𝗼𝗻 Duplicate some data to reduce joins. This technique trades storage space for speed and works well when reads outnumber writes significantly. 𝟱. 𝗗𝗮𝘁𝗮𝗯𝗮𝘀𝗲 𝗖𝗮𝗰𝗵𝗶𝗻𝗴 Store frequently accessed data in memory. This reduces disk I/O and dramatically speeds up read operations. Popular options include Redis and Memcached. 𝟲. 𝗥𝗲𝗽𝗹𝗶𝗰𝗮𝘁𝗶𝗼𝗻 Create copies of your database to distribute read operations. This works well for read-heavy workloads but requires managing data consistency. 𝟳. 𝗦𝗵𝗮𝗿𝗱𝗶𝗻𝗴 Split your database horizontally across multiple servers. Each shard contains a subset of your data based on a key like user_id or geography. This distributes both read and write loads. 𝟴. 𝗣𝗮𝗿𝘁𝗶𝘁𝗶𝗼𝗻𝗶𝗻𝗴 Divide large tables into smaller, more manageable pieces within the same database. This improves query and maintenance operations on huge tables. 🎁 𝗕𝗼𝗻𝘂𝘀: 🔹 𝗔𝗻𝗮𝗹𝘆𝘇𝗲 𝗲𝘅𝗲𝗰𝘂𝘁𝗶𝗼𝗻 𝗽𝗹𝗮𝗻𝘀. Use EXPLAIN ANALYZE to see precisely how your database executes queries. This reveals hidden bottlenecks and helps you target optimization efforts where they matter most. 🔹 𝗔𝘃𝗼𝗶𝗱 𝗰𝗼𝗿𝗿𝗲𝗹𝗮𝘁𝗲𝗱 𝘀𝘂𝗯𝗾𝘂𝗲𝗿𝗶𝗲𝘀. These run once for every row the outer query returns, creating a performance nightmare. Rewrite them as JOINs for dramatic speed improvements. 🔹 𝗖𝗵𝗼𝗼𝘀𝗲 𝗮𝗽𝗽𝗿𝗼𝗽𝗿𝗶𝗮𝘁𝗲 𝗱𝗮𝘁𝗮 𝘁𝘆𝗽𝗲𝘀. Using VARCHAR(4000) when VARCHAR(40) would work wastes space and slows performance. Right-size your data types to match what you're storing. #technology #systemdesign #databases #sql #programming

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!

Do you know about Postgres' HOT chains optimization? One more reason to be mindful about index creation. When you UPDATE a row, Postgres creates a new version of the row and marks the old version as dead. This is fundamental to Postgres's MVCC. The dead rows will eventually get cleaned up by VACUUM. Naturally, Postgres also has to update all the indexes pointing to the dead row. Adding overhead to the update operation and also resulting in more dead tuples (in the index) and more work for VACUUM. Already, this is a good reason to be mindful about index creation. HOT chains, or Heap-Only Tuple chains, are an optimization introduced to reduce index bloat during updates. When you update a row AND the new version can fit on the same page as the old version, AND none of the indexed columns changed, Postgres can create a heap-only tuple. This new tuple is linked to the old one via a chain pointer. Crucially, the indexes don't need to be updated. The index still points to the old tuple location, and Postgres follows the chain to find the current version. This dramatically reduces write amplification since updating one row doesn't require updating every index on that table. HOT chain pruning is the process of cleaning up these chains. When Postgres needs space on a page or when VACUUM runs, it can prune HOT chains by removing intermediate dead tuples that are no longer visible to any transaction. This is much cheaper than full VACUUM because it doesn't need to touch indexes at all - it just compacts the heap page and updates the chain pointers. However, there's a catch. For HOT to work, the new tuple must fit on the same page AND no indexed columns can change. Every index you add to a table is another column that, if updated, will not allow the use of HOT chains optimization. If you have an index on column A and you update column B, that's fine for HOT. But if you have indexes on both A and B, updating either breaks HOT optimization. Many developers reflexively create indexes on every column that appears in a WHERE clause, but each index has a hidden cost beyond storage and insert performance. Every additional index reduces the likelihood that updates can use HOT optimization. In workloads with frequent updates, this can lead to severe index bloat as each update creates new index entries. Best practices: - Consider your query and update patterns when choosing what to index. If a table is frequently updated but rarely queried on certain columns, those columns are poor index candidates. - Consider using covering indexes strategically rather than multiple separate indexes. - Monitor HOT efficiency by querying pg\_stat\_user\_tables to see the ratio of hot\_update to n\_tup\_upd - a low ratio indicates HOT isn't working well. - Use pg_stat_user_indexes to find unused indexes. pg_qualstats to find popular WHERE columns. And HypoPG to make sure new indexes will be useful.

Your autovacuum is fine. Your table design is the conversation worth having. We recently worked with a customer who had a 1 TB table handling heavy DELETE operations. Autovacuum was scanning the entire terabyte every cycle just to clean up dead tuples. Performance was suffering, and the natural instinct was to start tweaking autovacuum settings. Here is the thing, though. Autovacuum was doing exactly what it was designed to do. The real question was why it had so much ground to cover in the first place. When you DELETE a row in PostgreSQL, the row stays on disk. It becomes a dead tuple. Autovacuum comes along later to reclaim that space. On a small table, this works beautifully. On a 1 TB table with constant DELETEs, autovacuum has to walk the entire table to find and clean up those dead tuples. Every single cycle. That is a table structure problem, and it is a fairly common pattern we see in production PostgreSQL environments. The recommendation here was straightforward: partition the table. Once partitioned, autovacuum only needs to process the specific partitions where DELETEs are actually happening. Instead of dragging through a full terabyte, cleanup targets just the partitions that need attention. Partitioning turned a whole-table maintenance burden into a focused, efficient operation. I know there is a whole corner of the PostgreSQL world that loves fine-tuning autovacuum parameters. And look, proper autovacuum configuration absolutely matters. But when your table is a terabyte and DELETEs are a core part of the workload, tuning the knobs on autovacuum is treating the symptom. The table structure is where the real leverage lives. A few things worth keeping in mind if you are running large tables with heavy DELETEs: → Dead tuples from DELETEs accumulate until autovacuum reclaims them. On big tables, that accumulation gets expensive fast. → Autovacuum scans the full table to find dead tuples. The bigger the table, the longer the scan, and the heavier the impact on real-time performance. → Partitioning gives autovacuum a smaller, more targeted surface area. Cleanup becomes proportional to the activity, and the rest of the table stays untouched. The best autovacuum tuning sometimes has nothing to do with autovacuum at all. It starts with asking whether the table was designed for the workload it is actually carrying.

Been doing a lot of optimization work on queues to support users scaling to tens of billions of workflows/month. Two particularly interesting changes, both around Postgres indexes. First, converting several infrequently-used indexes into partial indexes so they’re only maintained when they’re actually needed. Second, building a dedicated index for the main workflow dequeue query on (queue name, status, priority, timestamp) so workers can dequeue entirely from an index without having to do non-indexed lookups or sorts. I gave a talk about these optimizations at last week’s user group–check it out: 👇

Here are some proven SQL Optimization strategies every engineer should know ⬇️ 1. Select Only What You Need ❌ SELECT * → Loads unnecessary data, increases I/O. ✅ Select only required columns to minimize processing. 2. Use Proper Indexing ↳ Index frequently filtered columns (WHERE, JOIN, GROUP BY). ↳ Avoid over-indexing (it slows down INSERT/UPDATE). ↳ Leverage covering indexes for heavy queries. 3. Optimize Joins ↳ Ensure JOIN keys are indexed. ↳ Prefer INNER JOINs when possible over OUTER JOINs. ↳ Push filters down before joins to reduce data scanned. 4. Reduce Data Scans ↳ Use PARTITIONING on large tables (date, region, etc.). ↳ Use CBO (Cost-Based Optimizer) hints when available. ↳ Apply filter conditions early. 5. Avoid Complex Subqueries ↳ Replace correlated subqueries with JOINs or CTEs. ↳ Use window functions efficiently instead of multiple nested queries. 6. Monitor & Tune ↳ Always check execution plans. ↳ Look for table scans, sort operations, and large shuffles. ↳ Track query runtime and cost metrics, especially in cloud warehouses like Snowflake, BigQuery, Synapse. ✅ Impact of Optimization: I’ve seen query runtimes drop from 45 minutes to 2 minutes just by applying indexing and partition pruning. That’s not just performance — it’s cost savings, better SLAs, and happier stakeholders.