PostgreSQL Performance & Query Optimization · 课时

使用 pg_stat_statements 和 pg_buffercache

利用 `pg_stat_statements` 和 `pg_buffercache` 等强大扩展，深入分析性能

使用 pg_stat_statements 和 pg_buffercache 是 CoddyKit 上的免费 PostgreSQL Performance & Query Optimization 课时。这是第 1 节课，共 4 节。你可以在下方免费阅读本课时的完整内容 — 然后在浏览器中使用内置代码编辑器和全天候 AI 导师进行实践。这是 PostgreSQL Performance & Query Optimization 学习路径的一部分，你的进度在网页和 CoddyKit 应用中同步。 PostgreSQL Performance & Query Optimization 课程共包含 4 节课。

本课时的部分内容尚未翻译，以英文显示。

PostgreSQL Extensions: Power-Ups

PostgreSQL is incredibly powerful, and its functionality can be extended even further using extensions.

Extensions are modules that add new functions, data types, operators, and more to your database. They're like plugins that enhance your database's capabilities without modifying its core code.

In this lesson, we'll explore two crucial extensions for performance monitoring: pg_stat_statements and pg_buffercache.

Meet pg_stat_statements: Query Profiler

pg_stat_statements is an invaluable tool for understanding your database's workload. It tracks execution statistics for all SQL statements executed by the server.

This extension helps you identify:

Which queries are run most frequently.
Which queries consume the most total time.
Queries with high average execution times.
Queries that read/write a lot of disk blocks.

It's your go-to for pinpointing performance bottlenecks at the query level.

Activating pg_stat_statements

To use pg_stat_statements, you first need to enable it in your PostgreSQL configuration.

1. Edit your postgresql.conf file and add pg_stat_statements to the shared_preload_libraries parameter. E.g., shared_preload_libraries = 'pg_stat_statements'.

2. Restart your PostgreSQL server for the change to take effect.

3. Finally, connect to your database and create the extension:

CREATE EXTENSION pg_stat_statements;

Deciphering pg_stat_statements Output

Once enabled, pg_stat_statements collects data in a view named pg_stat_statements. Here are some key columns you'll often check:

query: The normalized SQL statement.
calls: How many times the query was executed.
total_time: Total time spent executing this query (in milliseconds).
mean_time: Average execution time per call.
rows: Total rows returned or affected.
shared_blks_hit / shared_blks_read: Cache hits vs. disk reads.

Sorting by total_time helps find the biggest overall resource consumers.

Practical Example: Top Queries

Let's see how to find the top 5 queries that have consumed the most total execution time. This query helps you prioritize your optimization efforts.

The hit_percent column gives you an idea of how effective PostgreSQL's buffer cache is for that query.

SELECT
    query,
    calls,
    total_time,
    mean_time,
    rows,
    100.0 * shared_blks_hit / (shared_blks_hit + shared_blks_read + 1) AS hit_percent
FROM
    pg_stat_statements
ORDER BY
    total_time DESC
LIMIT 5;

Meet pg_buffercache: Memory Inspector

While pg_stat_statements shows you query performance, pg_buffercache gives you insight into PostgreSQL's shared buffer cache. This is the memory area where PostgreSQL stores frequently accessed data blocks.

Understanding what's in the buffer cache helps you determine:

Which tables or indexes are most actively used.
If your shared_buffers setting is adequate.
Whether queries are benefiting from cached data or hitting disk.

Activating pg_buffercache

Enabling pg_buffercache is simpler than pg_stat_statements. It typically does not require modification to shared_preload_libraries or a server restart.

You just need to connect to your database and create the extension:

CREATE EXTENSION pg_buffercache;

Peeking into the Shared Buffer Cache

After creating the extension, you can query the pg_buffercache view to see which relations (tables or indexes) occupy the most buffers. Each buffer typically represents an 8KB data block.

This query shows the top 5 relations by the number of buffers they occupy in the cache:

SELECT
    c.relname AS relation_name,
    count(*) AS buffers_in_cache
FROM
    pg_buffercache b
JOIN
    pg_class c ON b.relfilenode = c.relfilenode
JOIN
    pg_database d ON b.reldatabase = d.oid AND d.datname = current_database()
GROUP BY
    c.relname
ORDER BY
    buffers_in_cache DESC
LIMIT 5;

Interpreting Cache Effectiveness

If a table or index consistently appears at the top of the pg_buffercache output with many buffers, it means that data is frequently accessed and kept in memory.

This is generally a good sign, as memory access is much faster than disk I/O. A high hit_percent in pg_stat_statements often correlates with data being present in the buffer cache.

You can use this information to decide if increasing shared_buffers or optimizing queries to access less data would be beneficial.

Quick Check: Monitoring Tools

Which of the following statements are true regarding pg_stat_statements and pg_buffercache?

Recap: Deep Dives with Extensions

Great job! You've learned how to leverage two powerful PostgreSQL extensions for performance monitoring:

pg_stat_statements: Your essential tool for profiling SQL queries, identifying slow-running or frequently executed statements, and understanding their resource consumption.
pg_buffercache: Gives you visibility into the shared buffer cache, helping you understand what data is actively residing in memory and how effectively your caching is working.

Combined, these extensions provide deep insights into your database's workload and memory utilization, guiding your optimization efforts.

常见问题解答

「使用 pg_stat_statements 和 pg_buffercache」课时是免费的吗？

是的 — 「使用 pg_stat_statements 和 pg_buffercache」的完整文本可在网页上免费阅读。要进行交互式练习（内置代码编辑器和全天候 AI 导师）并解锁 PostgreSQL Performance & Query Optimization 课程的其余内容，请升级到 CoddyKit PRO。 PostgreSQL Performance & Query Optimization 课程共包含 4 节课。

「使用 pg_stat_statements 和 pg_buffercache」这节课中我会学到什么？

利用 `pg_stat_statements` 和 `pg_buffercache` 等强大扩展，深入分析性能你通过在浏览器中直接运行的动手代码来练习 PostgreSQL Performance & Query Optimization，全天候 AI 导师会在你学习这节课的过程中回答你的问题。

学习 PostgreSQL Performance & Query Optimization 需要有经验吗？

无需任何先前经验。CoddyKit 上的 PostgreSQL Performance & Query Optimization 课程适合初学者到高级学习者，你可以从这里开始或从头开始，按照自己的节奏学习。这是第 1 节课，共 4 节。

「使用 pg_stat_statements 和 pg_buffercache」课时需要多长时间？

大多数 CoddyKit 课程大约需要 5–10 分钟。每节课都很精短且互动，所以你能稳步进步，并在网页和应用中从离开的地方继续。

我能在这节 PostgreSQL Performance & Query Optimization 课中编写并运行代码吗？

能。每节 PostgreSQL Performance & Query Optimization 课都包含内置代码编辑器，你可以在浏览器中直接编写并运行真实代码，并获得即时 AI 反馈 — 无需本地设置。

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