benchmarks

Organizations face challenges in maintaining the performance of mission-critical databases under demanding workloads. Amazon Relational Database Service (Amazon RDS) offers a managed, cost-effective, and scalable solution. In PostgreSQL, transactions are logged in a Write-Ahead Log (WAL) buffer before being stored in a WAL segment, which is crucial for data recovery and replication. The performance of these transactions is linked to the storage system for WAL segments. By default, PostgreSQL stores all files, including WAL, in one location, but separating WAL storage can enhance performance. Amazon introduced the Dedicated Log Volume (DLV) feature, allowing users to store WAL segments on a separate volume of 1,024 GiB with 3,000 Provisioned IOPS. DLV features write coalescing, reducing IOPS demand by consolidating log writes into larger operations, improving throughput and efficiency while potentially lowering costs. A benchmarking solution involves executing a script on an Amazon EC2 instance to evaluate performance across two RDS for PostgreSQL instances: one with DLV enabled and one without. To benchmark, prerequisites include launching an Amazon EC2 Linux instance, installing pgbench, and creating two RDS instances—one with DLV and one without. The benchmarking script resets WAL statistics and simulates a workload with 64 concurrent clients processing half a million transactions each. The infrastructure for benchmarking includes an EC2 instance with 16 vCPUs and 128 GiB memory, and two RDS instances with 64 vCPUs and 512 GiB memory each, using IO2 storage with 10,000 PIOPS. Key database configuration parameters impact performance, particularly those related to commit, checkpoint, and WAL settings. The benchmarking script uses pgbench to create a TPC-B-like workload and measures performance metrics such as transactions per second (TPS) and latency. Results indicate that enabling DLV significantly reduces runtime from 77 minutes to 40 minutes (a 92.5% improvement), increases TPS by 91.83%, and reduces latency by 95.56%. Key WAL statistics show improvements in write and sync times, confirming DLV's effectiveness in enhancing database performance. Monitoring techniques for DLV include using Amazon CloudWatch for metrics like IOPS and latency, and PostgreSQL's pg_stat_wal for detailed WAL activity statistics. The DLV feature is compatible with PIOPS storage types and supported in specific PostgreSQL versions across all regions.