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The intelligent tuning feature of Azure Database for PostgreSQL - Flexible Server is designed to enhance overall
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performance automatically and help prevent possible issues. It continuously monitors the database instance's overall
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status and performance and automatically optimizes the workload's performance.
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The Azure Database for PostgreSQL - Flexible Server is equipped with an inherent intelligence mechanism that can
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dynamically adapt the database to your workload, thereby automatically enhancing performance. This feature comprises two
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automatic tuning functionalities:
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***Autovacuum tuning**: This function diligently tracks the bloat ratio and adjusts autovacuum settings accordingly. It
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factors in both current and predicted resource usage to ensure your workload isn't disrupted.
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***Writes tuning**: This feature persistently monitors the volume and patterns of write operations, modifying
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parameters that affect write performance. These parameters
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include `bgwriter_delay`, `checkpoint_completion_target`, `max_wal_size`, and `min_wal_size`. The primary aim of these
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adjustments is to enhance both system performance and reliability, thereby proactively averting potential
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Azure Database for PostgreSQL - Flexible Server has an intelligent tuning feature that's designed to enhance
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performance automatically and help prevent problems. Intelligent tuning continuously monitors the PostgreSQL database's
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status and dynamically adapts the database to your workload.
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This feature comprises two
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automatic tuning functions:
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***Autovacuum tuning**: This function tracks the bloat ratio and adjusts autovacuum settings accordingly. It
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factors in both current and predicted resource usage to prevent workload disruptions.
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***Writes tuning**: This function monitors the volume and patterns of write operations, and it modifies
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parameters that affect write performance. These adjustments enhance both system performance and reliability, to proactively avert potential
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complications.
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Learn how to enable intelligent tuning using [Azure portal](how-to-enable-intelligent-performance-portal.md) or [CLI](how-to-enable-intelligent-performance-cli.md).
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You can enable intelligent tuning by using the [Azure portal](how-to-enable-intelligent-performance-portal.md) or the [Azure CLI](how-to-enable-intelligent-performance-cli.md).
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## Why intelligent tuning?
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The autovacuum process is a critical part of maintaining the health and performance of a PostgreSQL database. It helps
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to reclaim storage occupied by "dead" rows, freeing up space and ensuring the database continues to run smoothly.
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Equally important is the tuning of write operations within the database, a task that typically falls to database
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administrators (DBAs).
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reclaim storage occupied by "dead" rows, freeing up space and keeping the database running smoothly.
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However, constantly monitoring a database and fine-tuning write operations can be challenging and time-consuming. This
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becomes increasingly complex when dealing with multiple databases, and might even become an impossible task when
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managing a large number of them.
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Equally important is the tuning of write operations within the database. This task typically falls to database
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administrators. Constantly monitoring a database and fine-tuning write operations can be challenging and time-consuming. This
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task becomes increasingly complex when you're dealing with multiple databases.
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This is where intelligent tuning steps in. Rather than manually overseeing and tuning your database, the intelligent
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tuning feature can effectively shoulder some of the load. It helps in the automatic monitoring and tuning of the
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database, allowing you to focus on other important tasks.
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This is where intelligent tuning steps in. Rather than manually overseeing and tuning your database, you can use intelligent
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tuning to automatically monitor and tune the
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database. You can then focus on other important tasks.
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Intelligent tuning provides an autovacuum tuning feature that vigilantly monitors the bloat ratio, adjusting settings as needed to ensure optimal resource utilization. It proactively manages the "cleaning" process of the database, mitigating performance issues caused by outdated data.
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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.
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In addition, the Writes Tuning aspect of intelligent tuning 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 effectively enhances system performance and reliability, ensuring smooth and efficient operation even under high write loads.
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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.
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In summary, intelligent tuning provides an efficient solution for database monitoring and tuning, taking the hard and
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tedious tasks off your plate. By using this automatic tuning feature, you can rely on the Azure Database for
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PostgreSQL - Flexible Server to maintain the optimal performance of your databases, saving you valuable time and
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resources.
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When you use intelligent tuning, you can save valuable time and resources by relying on Azure Database for
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PostgreSQL - Flexible Server to maintain the optimal performance of your databases.
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###How does intelligent tuning work?
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## How does intelligent tuning work?
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Intelligent Tuning is an ongoing monitoring and analysis process that not only learns about the characteristics of your
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workload but also tracks your current load and resource usage such as CPU or IOPS. By doing so, it makes sure not to
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Intelligent tuning is an ongoing monitoring and analysis process that not only learns about the characteristics of your
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workload but also tracks your current load and resource usage, such as CPU or IOPS. It doesn't
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disturb the normal operations of your application workload.
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The process allows the database to dynamically adjust to your workload by discerning the current bloat ratio, write
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performance, and checkpoint efficiency on your instance. Armed with these insights, intelligent tuning deploys tuning
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actions designed to not only enhance your workload's performance but also to circumvent potential pitfalls.
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performance, and checkpoint efficiency on your instance. With these insights, intelligent tuning deploys tuning
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actions that enhance your workload's performance and avoid potential pitfalls.
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## Autovacuum tuning
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###Autovacuum tuning
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Intelligent tuning adjusts five significant parameters related to
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Intelligent tuning adjusts five parameters related to
and `autovacuum_vacuum_cost_delay`. These parameters regulate components such as the fraction of the table that sets off
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a VACUUM process, the cost-based vacuum delay limit, the pause interval between autovacuum runs, the minimum count of
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updated or dead tuples needed to start a VACUUM, and the pause duration between cleanup rounds.
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and `autovacuum_vacuum_cost_delay`. These parameters regulate components such as:
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> [!IMPORTANT]
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> Autovacuum tuning is currently supported for the General Purpose and Memory Optimized server compute tiers that have
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> four or more vCores, Burstable server compute tier is not supported.
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- The fraction of the table that sets off
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a `VACUUM` process.
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- The cost-based vacuum delay limit.
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- The pause interval between autovacuum runs.
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- The minimum count of
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updated or dead tuples needed to start a `VACUUM` process.
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- The pause duration between cleanup rounds.
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> [!IMPORTANT]
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> It's important to keep in mind that intelligent tuning modifies autovacuum-related parameters at the server level, not
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> at individual table levels. Also, if autovacuum is turned off, intelligent tuning cannot operate correctly. For
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> intelligent tuning to optimize the process, the autovacuum feature must be enabled.
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> 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.
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While the autovacuum daemon triggers two operations - VACUUM and ANALYZE, intelligent tuning only fine-tunes the VACUUM
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process. The ANALYZE process, which gathers statistics on table contents to help the PostgreSQL query planner choose the
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most suitable query execution plan, is currently not adjusted by this feature.
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Although the autovacuum daemon triggers two operations (`VACUUM` and `ANALYZE`), intelligent tuning fine-tunes only the `VACUUM`
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process. This feature currently doesn't adjust the `ANALYZE` process, which gathers statistics on table contents to help the PostgreSQL query planner choose the
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most suitable query execution plan.
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One key feature of intelligent tuning is that it includes safeguards to measure resource utilization like CPU and IOPS.
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This means that it will not ramp up autovacuum activity when your instance is under heavy load. This way, intelligent
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Intelligent tuning includes safeguards to measure resource utilization like CPU and IOPS.
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It won't increase autovacuum activity when your instance is under heavy load. This way, intelligent
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tuning ensures a balance between effective cleanup operations and the overall performance of your system.
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When optimizing autovacuum, intelligent tuning considers the server's average bloat, using statistics about live and
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dead tuples. To lessen bloat, intelligent tuning might reduce parameters like the scale factor or naptime, triggering
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the VACUUM process sooner and, if necessary, decreasing the delay between rounds.
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When intelligent tuning is optimizing autovacuum, it considers the server's average bloat by using statistics about live and
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dead tuples. To lessen bloat, intelligent tuning might reduce parameters like the scale factor or naptime. It might trigger
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the `VACUUM` process sooner and, if necessary, decrease the delay between rounds.
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On the other hand, if the bloat is minimal and the autovacuum process is too aggressive, then parameters such as delay,
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scale factor, and naptime may be increased. This balance ensures minimal bloat and the efficient use of the resources by
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the autovacuum process.
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On the other hand, if the bloat is minimal and the autovacuum process is too aggressive, intelligent tuning might increase parameters such as delay,
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scale factor, and naptime. This balance minimizes bloat and helps ensure that the autovacuum process is using resources efficiently.
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## Writes tuning
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### Writes tuning
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Intelligent tuning adjusts four parameters related to writes
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tuning:`bgwriter_delay`, `checkpoint_completion_target`, `max_wal_size`, and `min_wal_size`. The behavior and benefits of adjusting some of these are described below.
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tuning: `bgwriter_delay`, `checkpoint_completion_target`, `max_wal_size`, and `min_wal_size`.
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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 PostgreSQL
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that handle write operations. The other are the checkpointer process and back-end writes (standard client processes, such
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as application connections).
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The `bgwriter_delay` parameter determines the frequency at which the background writer process is awakened to clean "dirty" buffers (those buffers that are new or modified). The background writer process is one of three processes in PostgreSQL
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that handle write operations, the other two being the checkpointer process and backends (standard client processes, such
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as application connections). The background writer process's primary role is to alleviate the load from the main
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checkpointer process and decrease the strain of backend writes. By adjusting the `bgwriter_delay` parameter, which governs the frequency of background writer rounds, we can also optimize the performance of DML queries.
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The background writer process's primary role is to alleviate the load from the main
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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.
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The `checkpoint_completion_target` parameter is part of the second write mechanism supported by PostgreSQL, specifically
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the checkpointer process. Checkpoints occur at constant intervals defined by `checkpoint_timeout` (unless forced by
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The `checkpoint_completion_target` parameter is part of the second write mechanism that PostgreSQL supports, specifically
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the checkpointer process. Checkpoints occur at constant intervals that `checkpoint_timeout` defines (unless forced by
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exceeding the configured space). To avoid overloading the I/O system with a surge of page writes, writing dirty buffers
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during a checkpoint is spread out over a period of time. This duration is controlled by
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the `checkpoint_completion_target`, specified as a fraction of the checkpoint interval, which is set
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using `checkpoint_timeout`.
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While the default value of `checkpoint_completion_target` is 0.9 (since PostgreSQL 14), which generally works best as it
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spreads the I/O load over the maximum time period, there might be rare instances where, due to unexpected fluctuations
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in the number of WAL segments needed, checkpoints may not complete in time. Hence, due to its potential impact on
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performance, `checkpoint_completion_target` has been chosen as a target metric for intelligent tuning.
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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.
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The default value of `checkpoint_completion_target` is 0.9 (since PostgreSQL 14). This value generally works best, because it
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spreads the I/O load over the maximum time period. In rare instances, checkpoints might not finish in time because of unexpected fluctuations
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in the number of needed Write-Ahead Logging (WAL) segments. Potential impact on
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performance is the reason why `checkpoint_completion_target` is a target metric for intelligent tuning.
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## Limitations and known issues
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* Intelligent tuning makes optimizations only in specific ranges. It's possible that the feature won't make any changes.
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* ANALYZE settings are not adjusted by intelligent tuning.
* 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.
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## Next steps
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*[Configure intelligent performance for Azure Database for PostgreSQL - Flexible Server using Azure portal](how-to-enable-intelligent-performance-portal.md)
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*[Configure intelligent performance for Azure Database for PostgreSQL - Flexible Server using Azure CLI](how-to-enable-intelligent-performance-cli.md)
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*[Configure intelligent tuning for Azure Database for PostgreSQL - Flexible Server by using the Azure portal](how-to-enable-intelligent-performance-portal.md)
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*[Configure intelligent tuning for Azure Database for PostgreSQL - Flexible Server by using the Azure CLI](how-to-enable-intelligent-performance-cli.md)
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*[Troubleshooting guides for Azure Database for PostgreSQL - Flexible Server](concepts-troubleshooting-guides.md)
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*[Autovacuum Tuning in Azure Database for PostgreSQL - Flexible Server](how-to-autovacuum-tuning.md)
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*[Autovacuum tuning in Azure Database for PostgreSQL - Flexible Server](how-to-autovacuum-tuning.md)
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*[Troubleshoot high IOPS utilization for Azure Database for PostgreSQL - Flexible Server](how-to-high-io-utilization.md)
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*[Best practices for uploading data in bulk in Azure Database for PostgreSQL - Flexible Server](how-to-bulk-load-data.md)
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*[Troubleshoot high CPU utilization in Azure Database for PostgreSQL - Flexible Server](how-to-high-cpu-utilization.md)
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