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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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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Cut Cloud Costs with Smarter PostgreSQL CPU Core Allocation

Cut Cloud Costs with Smarter PostgreSQL CPU Core Allocation

Cloud costs can quickly spiral out of control if resources are not optimized. One of the most significant contributors to these costs is CPU core allocation, which forms the basis of the instance size with every major cloud provider. Many organizations over-provision cores for their PostgreSQL databases, paying for unused capacity, or under-provision them, leading to poor performance and missed SLAs.This blog will explore strategies to allocate CPU cores effectively for PostgreSQL databases, ensuring optimal performance while keeping cloud expenses in check.The Cost-Performance Tradeoff in the CloudCloud providers charge based on resource usage, and CPU cores are among the most expensive components. Allocating too many cores leads to wasted costs, while too few can cause performance bottlenecks.PostgreSQL databases are particularly sensitive to CPU allocation, as different workloads—OLTP (Online Transaction Processing) vs. OLAP (Online Analytical Processing)—place varying demands on processing power. Finding the right balance is essential to achieving both cost-efficiency and performance reliability.How CPU Core Allocation Impacts PostgreSQLPostgreSQL can leverage multi-core systems effectively, but how you allocate cores depends on your workload:- OLTP Workloads: High concurrency workloads benefit from multiple cores, allowing PostgreSQL to process many small transactions simultaneously. - OLAP Workloads: Analytical queries often rely on parallel execution, utilizing a few powerful cores to handle complex operations like aggregations and joins.Additionally, PostgreSQL supports parallel query execution, which can distribute certain operations across multiple cores. However, parallelism primarily benefits large analytical queries and can sometimes degrade performance for small or simple queries due to overhead. It is critical to assess your workload before over-allocating resources.
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Idle Transactions Cause Table Bloat? Wait, What?

Yup, you read it right. Idle transactions can cause massive table bloat that the vacuum process may not be able to address. Bloat causes degradation in performance and can keep encroaching disk space with dead tuples. This blog delves into how idle transactions cause table bloat, why this is problematic, and practical strategies to avoid it.What Is Table Bloat? Table bloat in PostgreSQL occurs when unused or outdated data, known as dead tuples, accumulates in tables and indexes. PostgreSQL uses a Multi-Version Concurrency Control (MVCC) mechanism to maintain data consistency. Each update or delete creates a new version of a row, leaving the old version behind until it is cleaned up by the autovacuum process or manual vacuuming. Bloat becomes problematic when these dead tuples pile up and are not removed, increasing the size of tables and indexes. The larger the table, the slower the queries, leading to degraded database performance and higher storage costs.How Idle Transactions Cause Table Bloat Idle transactions in PostgreSQL are sessions that are connected to the database but not actively issuing queries. There are two primary states of idle transactions:Idle: The connection is open, but no transaction is running. Idle in Transaction: A transaction has been opened (e.g., via BEGIN) but has neither been committed nor rolled back.
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