PostgreSQL Performance & Query Optimization · 课时

使用 FILTER 和条件聚合优化查询

学习 FILTER 子句和基于 CASE 的条件聚合如何在一次表扫描中计算多个指标，而无需运行多个独立查询

使用 FILTER 和条件聚合优化查询 是 CoddyKit 上的免费 PostgreSQL Performance & Query Optimization 课时。这是第 4 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。

The Problem: Many Counts, One Table

Dashboards often need several metrics from the same table — total orders, paid orders, refunded orders. Running three separate queries scans the table three times. We can do it in one pass.

Conditional Aggregation with CASE

The classic trick wraps a CASE inside an aggregate. Rows that do not match contribute NULL, which COUNT and SUM ignore.

SELECT
  COUNT(*) AS total,
  COUNT(CASE WHEN status = 'paid' THEN 1 END) AS paid
FROM orders;

The Cleaner FILTER Clause

PostgreSQL offers a more readable form: the FILTER clause attached to any aggregate. It expresses intent directly.

SELECT
  COUNT(*) AS total,
  COUNT(*) FILTER (WHERE status = 'paid')     AS paid,
  COUNT(*) FILTER (WHERE status = 'refunded') AS refunded
FROM orders;

Why This Is Faster

All metrics are computed in a single scan of the table. The planner reads each row once and updates every aggregate, instead of scanning the table separately for each metric.

FILTER with SUM and AVG

FILTER works with any aggregate, not just COUNT. Compute conditional sums and averages in the same query.

SELECT
  SUM(total) FILTER (WHERE status = 'paid') AS revenue,
  AVG(total) FILTER (WHERE status = 'paid') AS avg_paid
FROM orders;

Combining with GROUP BY

FILTER shines inside grouped queries, producing a pivot-like result with one row per group and several conditional columns.

SELECT
  region,
  COUNT(*) FILTER (WHERE status = 'paid')     AS paid,
  COUNT(*) FILTER (WHERE status = 'refunded') AS refunded
FROM orders
GROUP BY region;

Pivoting Months into Columns

A common report turns rows into columns. FILTER makes a clean monthly pivot without extension functions.

SELECT
  product_id,
  SUM(total) FILTER (WHERE month = 1) AS jan,
  SUM(total) FILTER (WHERE month = 2) AS feb
FROM sales
GROUP BY product_id;

Reading the Plan

EXPLAIN ANALYZE confirms a single Aggregate node over one scan. Compare it against three separate queries to see the saved scans.

EXPLAIN ANALYZE
SELECT
  COUNT(*) FILTER (WHERE status = 'paid') AS paid,
  COUNT(*) FILTER (WHERE status = 'refunded') AS refunded
FROM orders;

FILTER vs WHERE

Remember the difference:

WHERE removes rows before any aggregate sees them
FILTER keeps all rows but restricts which ones a specific aggregate counts

Use FILTER when different aggregates need different conditions.

Combining with Indexes

If most metrics target a subset (e.g. only recent rows), add a WHERE for the shared condition so an index narrows the scan, then use FILTER for the per-metric splits.

SELECT
  COUNT(*) FILTER (WHERE status = 'paid') AS paid
FROM orders
WHERE created_at >= now() - interval '30 days';

Counting Distinct Conditionally

FILTER also pairs with COUNT(DISTINCT ...), letting you count unique customers per status in one scan instead of several grouped queries.

SELECT
  COUNT(DISTINCT customer_id) FILTER (WHERE status = 'paid') AS paying_customers
FROM orders;

Quick Check

Test your conditional aggregation knowledge.

Recap

You learned conditional aggregation:

Compute many metrics in one scan with FILTER or CASE
FILTER is more readable and works with any aggregate
Combine with GROUP BY for pivot-style reports
WHERE removes rows; FILTER restricts a single aggregate
Add a shared WHERE so indexes narrow the scan

常见问题解答

「使用 FILTER 和条件聚合优化查询」课时是免费的吗？

是的 — 「使用 FILTER 和条件聚合优化查询」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 FILTER 和条件聚合优化查询」这节课中我会学到什么？

学习 FILTER 子句和基于 CASE 的条件聚合如何在一次表扫描中计算多个指标，而无需运行多个独立查询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 4 节课，共 4 节。

「使用 FILTER 和条件聚合优化查询」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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