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Perform intelligent tuning in Azure Database for PostgreSQL - Flexible Server

  • Članek

APPLIES TO: Azure Database for PostgreSQL - Flexible Server

Azure Database for PostgreSQL flexible server has an intelligent tuning feature that's designed to enhance performance automatically and help prevent problems. Intelligent tuning continuously monitors the Azure Database for PostgreSQL flexible server database's status and dynamically adapts the database to your workload.

This feature comprises two automatic tuning functions:

  • Autovacuum tuning: This function tracks the bloat ratio and adjusts autovacuum settings accordingly. It factors in both current and predicted resource usage to prevent workload disruptions.
  • Writes tuning: This function monitors the volume and patterns of write operations, and it modifies parameters that affect write performance. These adjustments enhance both system performance and reliability, to proactively avert potential complications.

You can enable intelligent tuning by using the Azure portal or the Azure CLI.

Why intelligent tuning?

The autovacuum process is a critical part of maintaining the health and performance of an Azure Database for PostgreSQL flexible server database. It helps reclaim storage occupied by "dead" rows, freeing up space and keeping the database running smoothly.

Equally important is the tuning of write operations within the database. This task typically falls to database administrators. Constantly monitoring a database and fine-tuning write operations can be challenging and time-consuming. This task becomes increasingly complex when you're dealing with multiple databases.

This is where intelligent tuning steps in. Rather than manually overseeing and tuning your database, you can use intelligent tuning to automatically monitor and tune the database. You can then focus on other important tasks.

The autovacuum tuning function in intelligent tuning monitors the bloat ratio and adjusts settings as needed for optimal resource utilization. It proactively manages the "cleaning" process of the database and mitigates performance problems that outdated data can cause.

The writes tuning function observes the quantity and transactional patterns of write operations. It intelligently adjusts parameters such as bgwriter_delay, checkpoint_completion_target, max_wal_size, and min_wal_size. By doing so, it enhances system performance and reliability, even under high write loads.

When you use intelligent tuning, you can save valuable time and resources by relying on Azure Database for PostgreSQL flexible server to maintain the optimal performance of your databases.

How does intelligent tuning work?

Intelligent tuning is an ongoing monitoring and analysis process that not only learns about the characteristics of your workload but also tracks your current load and resource usage, such as CPU or IOPS. It doesn't disturb the normal operations of your application workload.

The process allows the database to dynamically adjust to your workload by discerning the current bloat ratio, write performance, and checkpoint efficiency on your instance. With these insights, intelligent tuning deploys tuning actions that enhance your workload's performance and avoid potential pitfalls.

Autovacuum tuning

Intelligent tuning adjusts five parameters related to autovacuum: autovacuum_vacuum_scale_factor, autovacuum_cost_limit, autovacuum_naptime, autovacuum_vacuum_threshold, and autovacuum_vacuum_cost_delay. These parameters regulate components such as:

  • The fraction of the table that sets off a VACUUM process.
  • The cost-based vacuum delay limit.
  • The pause interval between autovacuum runs.
  • The minimum count of updated or dead tuples needed to start a VACUUM process.
  • The pause duration between cleanup rounds.

Important

Intelligent tuning modifies autovacuum-related parameters at the server level, not at individual table levels. Also, if autovacuum is turned off, intelligent tuning can't operate correctly. For intelligent tuning to optimize the process, the autovacuum feature must be enabled.

Although the autovacuum daemon triggers two operations (VACUUM and ANALYZE), intelligent tuning fine-tunes only the VACUUM process. This feature currently doesn't adjust the ANALYZE process, which gathers statistics on table contents to help the Azure Database for PostgreSQL flexible server query planner choose the most suitable query execution plan.

Intelligent tuning includes safeguards to measure resource utilization like CPU and IOPS. It won't increase autovacuum activity when your instance is under heavy load. This way, intelligent tuning ensures a balance between effective cleanup operations and the overall performance of your system.

When intelligent tuning is optimizing autovacuum, it considers the server's average bloat by using statistics about live and dead tuples. To lessen bloat, intelligent tuning might reduce parameters like the scale factor or naptime. It might trigger the VACUUM process sooner and, if necessary, decrease the delay between rounds.

On the other hand, if the bloat is minimal and the autovacuum process is too aggressive, intelligent tuning might increase parameters such as delay, scale factor, and naptime. This balance minimizes bloat and helps ensure that the autovacuum process is using resources efficiently.

Writes tuning

Intelligent tuning adjusts four parameters related to writes tuning: bgwriter_delay, checkpoint_completion_target, max_wal_size, and min_wal_size.

The bgwriter_delay parameter determines the frequency at which the background writer process is awakened to clean "dirty" buffers (buffers that are new or modified). The background writer process is one of three processes in Azure Database for PostgreSQL flexible server that handle write operations. The other are the checkpointer process and back-end writes (standard client processes, such as application connections).

The background writer process's primary role is to alleviate the load from the main checkpointer process and decrease the strain of back-end writes. The bgwriter_delay parameter governs the frequency of background writer rounds. By adjusting this parameter, you can also optimize the performance of Data Manipulation Language (DML) queries.

The checkpoint_completion_target parameter is part of the second write mechanism that Azure Database for PostgreSQL flexible server supports, specifically the checkpointer process. Checkpoints occur at constant intervals that checkpoint_timeout defines (unless forced by exceeding the configured space). To avoid overloading the I/O system with a surge of page writes, writing dirty buffers during a checkpoint is spread out over a period of time. The checkpoint_completion_target parameter controls this duration by using checkpoint_timeout to specify the duration as a fraction of the checkpoint interval.

The default value of checkpoint_completion_target is 0.9 (since PostgreSQL 14). This value generally works best, because it spreads the I/O load over the maximum time period. In rare instances, checkpoints might not finish in time because of unexpected fluctuations in the number of needed Write-Ahead Logging (WAL) segments. Potential impact on performance is the reason why checkpoint_completion_target is a target metric for intelligent tuning.

Limitations and known issues

  • Intelligent tuning makes optimizations only in specific ranges. It's possible that the feature won't make any changes.
  • Intelligent tuning doesn't adjust ANALYZE settings.
  • Autovacuum tuning is currently supported for the General Purpose and Memory Optimized server compute tiers that have four or more vCores. The Burstable server compute tier is not supported.

Next steps