Understanding I O Statistics for Postgres Performance

Postgres performance can be significantly impacted by I/O statistics, which track disk reads and writes.

High disk read rates can indicate inefficient query plans or inadequate indexing.

Monitoring I/O statistics helps identify performance bottlenecks.

A high disk write rate can be a sign of frequent table updates or large transactions.

Postgres tracks system-wide I/O statistics, which can be queried using the pg_stat_io view.

These statistics are tracked for a given backend type, I/O object type, and I/O context.

The main statistics include counting I/O operations: reads, writes, and extends.

Each I/O operation's size in bytes is noted to help interpret the statistics.

Additionally, the number of shared buffer evictions, ring buffer re-uses, and fsync calls are tracked.

On Postgres 16, this system-wide information is always available.

You can find the complete details of each field in the Postgres documentation.

Temporary relations are tracked in pg_stat_io, marked as io_object = "temp relation".

Temporary files, such as those used for sorts or extensions like pg_stat_statements, are not tracked.

Here's a breakdown of what's not captured by pg_stat_io:

Tracking performance changes is crucial to understand the effectiveness of your tuning tactics.

Logical reads are a great metric to track, as they refer to pages pulled from the cache (memory) versus physical reads, which indicates the number of pages from disk.

If you add an index, you can easily tell if the total number of pages read goes up or down by checking your logical read counts.

The same concept applies to worktable and logical LOB read properties, where any time SQL Server is having to write data out to disk (tempdb in this case), performance will be slower.

If SQL Server is needing to move around large objects that comprise of multiple 8kB pages each, things will be slow, and keeping track of how many LOB logical reads SQL Server is performing can help you focus on removing that overhead from your query.

Here are some key statistics to track:

These statistics can help you understand the I/O operations issued by Postgres, including reads, writes, and extends, as well as the size in bytes of each operation.

In Postgres 16, this system-wide information is always available, and you can find the complete details of each field in the Postgres documentation.

Autovacuum and VACUUMs are crucial for Postgres servers, helping to clean up dead rows and make space reusable. Every Postgres server needs the occasional VACUUM, whether scheduled manually or handled by autovacuum.

If not tuned correctly, VACUUM and autovacuum can have a dramatic effect on I/O activity. Historically, the best bet was to look at the output of log_autovacuum_min_duration.

With pg_stat_io, you can now see a system-wide measurement of the impact of VACUUM, by looking at everything marked as io_context = 'vacuum', or associated with the autovacuum worker backend type. In one example, the autovacuum worker read 44.4 GB of data and wrote 23.1 GB of data.

Tracking these statistics over time will help you have a clear picture of whether autovacuum is to blame for an I/O spike during business hours. It will also help you make changes to tune autovacuum with more confidence, such as making autovacuum more aggressive to prevent bloat.

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You can finally see both bulk reads and bulk writes in Postgres, and the I/O activity they cause.

With pg_stat_io, you can filter for the new bulkwrite and bulkread values in io_context to gain visibility into this activity.

This allows you to track 495 GB of bulk read activity and 21 GB of bulk write activity, which was previously hard to identify.

Monitoring shared buffer evictions can help you understand how your database is handling workload stability and sizing. By default, shared_buffers is set to 128MB, which is roughly 16,000 pages, each 8KB in size.

This fixed pool of pages is crucial for caching data, especially when your working set exceeds shared_buffers. If the database has to remove some of the old data from cache, it's called evicting a buffer page.

Eviction has two main effects: data that was in the Postgres buffer cache before is no longer in the cache, and if the page that was evicted was marked as "dirty", the process evicting it also has to write the old page to disk.

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A sudden spike in evictions can indicate that the cached data was needed again shortly afterwards. If you see this pattern, it may be a sign that your shared_buffers setting is too small.

To determine if your shared_buffers setting is sufficient, you can use the pg_stat_io metric to track evictions for each backend type across the system. This can help you identify if your workload is consistently exceeding shared_buffers.

If you're unsure about your shared_buffers setting, you can use the pg_buffercache extension to look at the current shared buffers contents in detail.

Here's a summary of the effects of evictions:

By monitoring shared buffer evictions and understanding their effects, you can make informed decisions about your shared_buffers setting and improve workload stability and sizing.

Bulk read and write activities can be challenging to track in Postgres.

Postgres uses a special dedicated ring buffer for bulk read and write operations, which keeps shared buffers undisturbed.

This buffer is responsible for large sequential scans and COPY operations.

You can monitor this activity by filtering for bulkread and bulkwrite values in io_context.

495 GB of bulk read activity and 21 GB of bulk write activity can be easily identified with this approach.

These evictions are from the special bulk read / bulk write ring buffer, not from regular shared buffers.

This visibility into bulk read and write strategies is a game-changer for Postgres users.

To get a better understanding of how your queries are executing, you can use the SET STATISTICS IO and STATISTICS TIME options in SQL Server. These options provide additional insight into server resources being used.

You can turn on these options by right clicking anywhere in a Query window, selecting Query Options-> Advanced, and checking either or both "SET STATISTICS TIME" or "SET STATISTICS IO". This data can be especially helpful when testing or tuning queries.

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Here are some key statistics to keep in mind:

In Postgres, you can query pg_stat_io to get a sense for which information is tracked, including I/O operations, I/O context, and buffer cache hits. This information can be used to calculate an accurate buffer cache hit ratio and track cumulative system-wide I/O times.

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To get a better understanding of how your queries are executing, you can use the SET STATISTICS IO and SET STATISTICS TIME options. This will give you valuable information on server resources being used.

These options can be turned on and off using the SET STATISTICS IO command. For example, you can use SET STATISTICS IO to see how many pages are accessed for each process that SQL Server executes.

To turn off or on STATISTICS IO and STATISTICS TIME options inside a Query window, right-click anywhere and select Query Options-> Advanced. From there, you can check either or both “SET STATISTICS TIME” or “SET STATISTICS IO” and then click OK to save.

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Using the SET STATISTICS IO and TIME options can provide additional insight into how your queries are performing. This is especially helpful when testing or tuning queries.

Here are some key points to keep in mind:

Postgres 15 introduced a new cumulative statistics system using shared memory, which has greatly improved the ability to collect advanced statistics.

This change has already led to the creation of pg_stat_io, a view that provides system-wide I/O statistics.

The new cumulative statistics system has also enabled the tracking of system-wide buffer cache hits, allowing for an accurate buffer cache hit ratio calculation.

The buffer cache hit ratio is a crucial metric for optimizing Postgres performance.

Before Postgres 15, statistics tracking was slow and error-prone, limited by the statistics collector process.

The addition of pg_stat_io has made it easier to track additional information about how Postgres operates.

Here are some key features of pg_stat_io:

To get the most out of io statistics, you should look at them under realistic load, either from production logs or from simulated load in a test environment.

Realistic load is important because io statistics depend not just on the API and database but also on the current state of the caches and indexes.

Interpreting io statistics requires considering the current state of your system, including the caches and indexes, which can impact the accuracy of your results.

Looking at io statistics under realistic load gives you a more accurate picture of your system's performance and helps you identify areas for improvement.

In the realm of IoT statistics, transactions and queries play a crucial role in data collection and analysis.

A transaction in IoT refers to the exchange of data between devices, which can be as simple as a sensor sending a temperature reading to a central server.

This data is then used to inform queries, which are essentially questions asked of the data to extract meaningful insights.

For example, a query might ask for the average temperature reading over a specific period of time.

Datomic logs an io-stats map for every transaction. This helps you understand the performance and efficiency of your database.

In Datomic Pro, io-stats are logged under the :tx/process :event. You can use this information to optimize your database for better performance.

In Cloud, io-stats are logged under the “TxSucceeded” message, with all keys converted to CloudWatch JSON Pascal case conventions. This makes it easier to search for io-stats in the Cloudwatch console.

You can search for io-stats in the Cloudwatch console with a filter like {$.Msg=”TxSucceeded”}. This allows you to quickly find the io-stats related to successful transactions.

Given a transaction t, you can use the log API to query the datoms produced by the transaction. This helps you understand the data changes made by each transaction.

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The Query API is a powerful tool for retrieving data from a database. It's essential to understand how to use it effectively to get the most out of your database.

You can pass the :io-context qualified keyword to distinguish different domain operations, which will return a map with :ret and :io-stats. This allows you to track I/O operations and get a better understanding of how your database is performing.

Datomic's Query API includes arities that allow you to pass :io-context, which will return an io-stats map. This map reports information on the number of I/O operations, such as reads and writes.

To measure transaction time, you need to consider the time spent on processing the transaction, which is reported by the io-stats tx-with time. This time is only one component of the total transaction time experienced by a caller.

The total transaction time includes interprocess communication time, time queued on the transacting process, tx-with time, and storage write time. To measure total transaction time, you need to time calls to transact from Datomic peers or clients.

Here's a summary of the different measures of transaction time:

This table helps you understand the different components of transaction time and how to measure them.

Running the same query twice on an unloaded system can produce very different io-stats, showing reads down into other tiers, or no reads-through to dev storage at all.

The object cache is special, as segments in it are already in memory and don't require I/O. This is why the first tier in the :reads map is always greater than the count of the next tier.

Logical reads are a key indicator of query performance, with more pages read resulting in slower queries. You can check this by running a query with the STATISTICS IO setting turned on.

The :reads map reports counts of segments accessed through each cache/storage tier, and the total time spent waiting on I/O in each storage tier.

Here's a breakdown of the :reads map tier keys:

The counts for higher tiers include the misses that read through to the next tier, which is why the :ocache count will always be greater than the count of the next tier.

Worktables/Workfiles are temporary objects created in tempdb to process query results, and can indicate that SQL is doing more work than it needs to.

Datomic's io-stats depend on the current state of the caches.

Running the same query twice on an unloaded system can produce very different io-stats, which highlights the dynamic nature of the caching system.

The most trivial example of this is that the first call to find the average artist name length in Mbrainz begins with no data in the object cache, resulting in reads down into other tiers.

On the other hand, the second call to the same query shows no reads-through to dev storage, and is satisfied entirely from the object cache.

This demonstrates that the segments needed by the query fit entirely in the object cache, which is a key factor in determining io-stats.

Real-world queries are likely to land in between these two extremes, finding some but not all the data they need in each cache tier.

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Let's dive into some examples and use cases for i/o statistics.

SQL Server uses logical and physical reads to process statements, and these reads can be seen in the message output.

The message output can help you understand how many reads are being used by SQL Server as it processes statements.

You can use the message output to identify areas where your database is being inefficient and make optimizations accordingly.

This can help improve the overall performance of your database and reduce the number of reads being used.

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