Finding Bottlenecks and Avoiding Over-Optimization via Explain Plans

Performance optimization in a production database is important, but trying to over-optimize can make things more complicated without real improvements.In this post, I’ll share two very basic EXPLAIN ANALYZE outputs from a production system. A user asked us to help optimize these queries. I've changed the table and column names for privacy. We will look at how to spot slow parts of a query, improve performance the right way, and avoid unnecessary tuning.Plan A: Identifying and Resolving a BottleneckExecution Plan A (Before Optimization) Nested Loop (cost=1000.42..25607.38 rows=1 width=129) (actual time=78.521..90.445 rows=0 loops=1) -> Gather (cost=1000.00..25598.95 rows=1 width=65) (actual time=78.520..90.443 rows=0 loops=1) Workers Planned: 2 Workers Launched: 2 -> Parallel Seq Scan on <table_1> e (cost=0.00..24598.85 rows=1 width=65) (actual time=75.351..75.351 rows=0 loops=3) Filter: ((<column_1>) = '<date_value>'::date) AND ((<column_2>)::text = '<event_type>'::text) Rows Removed by Filter: <number_removed_rows> -> Index Scan using <index_name> on <table_2> a (cost=0.42..8.43 rows=1 width=41) (never executed) Index Cond: ((<column_3>)::text = (<column_4>)::text) Filter: ((<column_5>)::text = '<default_value>'::text) Planning Time: 0.466 ms Execution Time: 90.580 ms
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SELECT FOR UPDATE – Reduce Contention and Avoid Deadlocks to Improve Performance in PostgreSQL

Relational databases are at the heart of countless applications around the world, from high-traffic e-commerce websites to enterprise resource planning (ERP) systems and financial services. Concurrency management—where multiple database transactions operate on the same data simultaneously—is critical to getting good performance and avoiding problems like deadlocks or data inconsistencies.When multiple transactions need to modify the same rows, ensuring data consistency can become tricky. A single wrong approach to locking can lead to suboptimal performance or even bring your application to a standstill as numerous transactions block one another. One tool in PostgreSQL’s arsenal to handle concurrency is SELECT FOR UPDATE. It allows you to lock specific rows before updating them, preventing other transactions from modifying those rows until your transaction completes.In this blog, we will dive deep into SELECT FOR UPDATE in PostgreSQL. We will explore how it helps in reducing contention, avoiding deadlocks, and ultimately boosting performance when dealing with highly concurrent applications.
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When HASH partitioning works better than RANGE in PostgreSQL

When HASH partitioning works better than RANGE

I have always been a fan of RANGE partitioning using a date/time value in PostgreSQL. This isn't always possible, however, and I recently came across a scenario where a table had grown large enough that it had to be partitioned, and the only reasonable key to use was a UUID styled identifier.The goal of this post is to highlight when and why hashing your data across partitions in PostgreSQL might be a better approach.Range vs. Hash Partitioning in PostgreSQLRange Partitioning (A Quick Recap)Range partitioning in PostgreSQL uses boundary values that define slices of the data, often by date or numeric ranges. If you have a transactions table, you might create monthly partitions based on a transaction_date column. This is intuitive for time-series data because each partition holds rows from a specific date range.Advantages of Range Partitioning:Easy pruning for date-based queries. Straightforward approach to archiving old data: drop an entire partition for a past month, rather than issuing a massive DELETE. Pairs nicely with time-based ingestion pipelines, where every day or month gets its own partition. But as convenient as that is, there are cases where range partitioning runs into problems.Why Range Partitioning Can Fall ShortData Skew: If a huge portion of data lands in a single time interval—say, because of a traffic spike in the current month—that monthly partition might end up significantly larger than the others. Complex Backfills: Not everyone ingests data in an orderly, daily manner. Sometimes you need to re-ingest or correct data that spans multiple periods. Merging or splitting range partitions can get cumbersome. Non-Date Dimensions: Some tables aren’t naturally tied to a sequential numeric or date dimension. If your queries center on user IDs or device IDs, dividing by date might not solve your performance issues.
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Important PostgreSQL Parameters: Understanding Their Importance and Recommended Values

Important PostgreSQL Parameters: Understanding Their Importance and Recommended Values

Have you ever experienced your database slowing down as the amount of data increases? If so, one important factor to consider is tuning PostgreSQL parameters to match your specific workload and requirements. PostgreSQL has many parameters because it is designed to be highly flexible and customizable to meet a wide range of use cases and workloads. Each parameter allows you to fine-tune different aspects of the database, such as memory management, query optimization, connection handling, and more. This flexibility helps database administrators to optimize performance based on hardware resources, workload requirements, and specific business needs.In this blog, I will cover some of the important PostgreSQL parameters, explain their role, and provide recommended values to help you fine-tune your database for better performance and scalability. Memory-Related ParametersMemory-related parameters in PostgreSQL control how the database allocates and manages memory. Tuning these settings is important for improving query performance and preventing resource bottlenecks.Name: work_mem Description: Sets the maximum amount of memory used by internal operations like sorts and hashes before writing to disk. Increasing it can improve performance for complex queries Default: 4MB Recommended: Typically, setting work_mem to 1-2% of the total system's available memory is recommended, i.e., if the total system memory is 256 GB, assign 3 to 5 GB for work_mem. Note: This may lead to higher memory usage for operations that involve sorting.Name: shared_buffers Description: Determines the amount of memory allocated for caching database data. Default: 128MB Recommendation: Typically, setting shared_buffers to 25-40% of the total system memory is recommended, i.e., if the total system memory is 256 GB, assign 64-102 GB for shared_buffers.Name: maintenance_work_mem Description: Specifies the amount of memory used for maintenance operations like VACUUM, CREATE INDEX, and ALTER TABLE. Increasing it can speed up these operations. Default: 64MB Recommendation: it's recommended to set 5-10% of the total system memory, i.e., if the total system memory is 256 GB, assign 13 to 26 GB for maintenance_work_mem.
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A Guide to Restoring a PostgreSQL Database from Disaster Using Azure Flexible Server

Backups are crucial for any mission-critical application as they protect against unforeseen disasters. Regular backups help minimize the Recovery Point Objective (RPO), allowing systems to recover quickly with minimal data loss. However, it's equally important to store backups safely. If backups are kept in the same location as the primary site and something goes wrong, you may have no way to recover, leading to complete data loss.To reduce these risks, many organizations choose fully managed servers to host their databases. One popular option is Azure Flexible Server for PostgreSQL, which offers a reliable, scalable, and managed solution. Azure provides 3 levels of redundancy in three different ways, and not only that, you can recover backups using these same three methods. These areLocally Redundant Storage Zone Redundant Storage Geo RedundantEach level of redundancy offers unique advantages when it comes to restoring backups. In today's blog, we will explore all three types of backups and recovery methods. We will dive into the differences between each type and learn how to restore your backup if your primary site goes down.
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