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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Leveraging autovacuum in PostgreSQL to optimize performance and reduce costs

Autovacuum is one of PostgreSQL's most powerful features, designed to maintain database health and optimize performance by automating routine maintenance tasks. However, improper configuration can lead to performance bottlenecks, excessive costs due to resource inefficiency, or uncontrolled table bloat. This blog explores what autovacuum is, its role in performance optimization and cost reduction, and best practices for configuring its parameters.What is Autovacuum? Autovacuum is a background process in PostgreSQL responsible for maintaining table health by performing two critical tasks:1. Vacuuming - Removes dead tuples (rows that have been updated or deleted but are no longer visible). - Frees up space for reuse to prevent table bloat and reduce storage costs.2. Analyzing - Updates table statistics used by the query planner to optimize execution plans, improving query performance.Without autovacuum, dead tuples can accumulate, leading to: - Table Bloat: Increased disk usage drives up storage costs and slows query performance. - Transaction ID Wraparound: A situation that forces the system to go into ‘safe mode’, blocking non-superuser transactions to protect data integrity. This can render the database unusable if not addressed, causing downtime and increased operational costs.By automating these tasks, autovacuum ensures consistent database performance and minimizes unnecessary costs.
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