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PostgreSQL Performance & Query Optimization · レッスン

取り込みに向けたWALとチェックポイントのチューニング

WAL設定と非ログテーブルを調整し、I/Oの停滞なしに高い書き込みレートを維持します。

「取り込みに向けたWALとチェックポイントのチューニング」はCoddyKit上の無料PostgreSQL Performance & Query Optimizationレッスンです。 これはレッスン3/4です。 下記で完全なレッスンを無料で読むことができます。その後、ブラウザ内の組み込みコードエディタと24時間対応のAIチューターでハンズオン演習できます。 これはPostgreSQL Performance & Query Optimization学習パスの一部であり、ウェブとCoddyKitアプリ全体で進捗が同期されます。 PostgreSQL Performance & Query Optimizationコースには全4レッスンが含まれています。

このレッスンの一部はまだ翻訳されておらず、英語で表示されています。

Why WAL Matters for Ingestion

Every change you commit in PostgreSQL is first written to the Write-Ahead Log (WAL) before the data files are updated. This guarantees durability and crash recovery, but during heavy bulk loads the WAL becomes a major source of I/O.

  • Each INSERT or COPY generates WAL records.
  • Periodically a checkpoint flushes dirty pages from shared buffers to disk.
  • If checkpoints fire too often, you pay double I/O and get stalls.

Tuning WAL and checkpoint behaviour is the key to sustaining high write rates without I/O spikes.

Inspecting Current WAL Settings

Before changing anything, look at what your server is running with. The relevant knobs live in pg_settings and can be queried with SHOW.

The most important ingestion-related parameters are max_wal_size, checkpoint_timeout, checkpoint_completion_target, and wal_compression.

SELECT name, setting, unit
FROM pg_settings
WHERE name IN (
  'max_wal_size',
  'min_wal_size',
  'checkpoint_timeout',
  'checkpoint_completion_target',
  'wal_compression'
);

Raising max_wal_size

A checkpoint is triggered either by checkpoint_timeout elapsing or by WAL volume reaching max_wal_size. During a big load the default (often 1 GB) is hit constantly, forcing checkpoint after checkpoint.

  • Raising max_wal_size lets WAL accumulate longer between checkpoints.
  • Fewer checkpoints means dirty pages get coalesced and written once instead of repeatedly.

For an ingestion window, values like 8–32 GB are common.

ALTER SYSTEM SET max_wal_size = '16GB';
SELECT pg_reload_conf();

Spreading Checkpoint I/O

checkpoint_completion_target controls how much of the interval PostgreSQL uses to spread out the checkpoint writes. A value of 0.9 means the writes are smeared across 90% of the time until the next checkpoint, avoiding a sharp I/O burst.

Combined with a longer checkpoint_timeout, this turns spiky checkpoint storms into a smooth, sustained write stream.

ALTER SYSTEM SET checkpoint_timeout = '30min';
ALTER SYSTEM SET checkpoint_completion_target = 0.9;
SELECT pg_reload_conf();

Compressing WAL Records

When full-page images are written after a checkpoint (the first modification of a page), they bloat the WAL. wal_compression compresses those full-page images, trading a little CPU for substantially less WAL volume and disk I/O.

  • On modern PostgreSQL you can choose the algorithm, e.g. lz4 or zstd.
  • Less WAL written also means faster replication and fewer checkpoints from the size trigger.
ALTER SYSTEM SET wal_compression = 'lz4';
SELECT pg_reload_conf();

Unlogged Tables: Skip the WAL Entirely

An unlogged table writes no WAL at all. For staging tables in an ETL pipeline this can dramatically increase throughput, because you bypass the single biggest write cost.

  • Data is still written to disk, but not durably logged.
  • Trade-off: the table is truncated automatically after a crash and is not replicated to standbys.

Perfect for re-buildable staging data; never for the system of record.

CREATE UNLOGGED TABLE staging_events (
  id        bigint,
  payload   jsonb,
  loaded_at timestamptz DEFAULT now()
);

The Staging-to-Final Pattern

A robust ETL design loads raw rows into a fast unlogged staging table, transforms them, then moves the cleaned result into the durable final table.

  • The bulk COPY hits the unlogged table at full speed.
  • The final INSERT ... SELECT writes WAL only once, for validated data.

You get speed where durability does not matter and safety where it does.

INSERT INTO events (id, payload, loaded_at)
SELECT id, payload, loaded_at
FROM staging_events
WHERE payload IS NOT NULL;

TRUNCATE staging_events;

Promoting an Unlogged Table

If a staging table needs to become durable after the load completes, you can convert it in place instead of copying rows. Setting it to LOGGED rewrites the table and begins WAL-logging it.

  • The conversion itself generates WAL for the whole table, so do it once at the end.
  • Going back to UNLOGGED before the next load avoids per-row WAL again.
ALTER TABLE staging_events SET LOGGED;

COPY Beats Row-by-Row INSERT

Even with WAL tuned, how you load matters. COPY batches rows into far fewer, larger WAL records than thousands of individual INSERT statements, and avoids per-statement parse and plan overhead.

Combine COPY with an unlogged staging table and you reach the highest sustainable ingest rate.

COPY staging_events (id, payload)
FROM '/data/events.csv'
WITH (FORMAT csv, HEADER true);

Monitoring Checkpoint Pressure

To know whether your tuning worked, watch the checkpoint statistics. The key signal is the ratio of requested (size-triggered) checkpoints to timed ones.

  • Many requested checkpoints means max_wal_size is still too small for your load.
  • Mostly timed checkpoints means WAL volume is comfortably within budget.

In newer versions these counters live in pg_stat_checkpointer; older versions use pg_stat_bgwriter.

SELECT num_timed, num_requested,
       buffers_written, write_time, sync_time
FROM pg_stat_checkpointer;

Resetting After the Load

Aggressive ingestion settings are great during a load window but waste recovery time and disk afterwards. Once the batch finishes, restore conservative values and force a clean checkpoint so the next crash recovery is fast.

  • Lower max_wal_size and checkpoint_timeout back to steady-state values.
  • Run a manual CHECKPOINT to flush everything immediately.
ALTER SYSTEM SET max_wal_size = '2GB';
ALTER SYSTEM SET checkpoint_timeout = '5min';
SELECT pg_reload_conf();
CHECKPOINT;

Quick Check

You are bulk-loading 200 million rows into a re-buildable staging table that will be validated and copied into the durable table afterward. Which choice most directly reduces WAL write volume during the load?

Recap

To sustain high write rates without I/O stalls:

  • Raise max_wal_size and checkpoint_timeout so checkpoints fire less often, and set checkpoint_completion_target near 0.9 to spread the writes.
  • Enable wal_compression to shrink full-page images.
  • Use UNLOGGED staging tables to skip WAL for re-buildable data, then move validated rows into the durable table.
  • Prefer COPY over row-by-row inserts.
  • Monitor pg_stat_checkpointer for requested-vs-timed checkpoints, and reset conservative values after the load.

よくある質問

「取り込みに向けたWALとチェックポイントのチューニング」レッスンは無料ですか？

はい。「取り込みに向けたWALとチェックポイントのチューニング」の完全なテキストはこのウェブで無料で読めます。インタラクティブに演習し（組み込みコードエディタと24時間対応のAIチューター）、PostgreSQL Performance & Query Optimizationコースの残りをアンロックするには、CoddyKit PROにアップグレードしてください。 PostgreSQL Performance & Query Optimizationコースには全4レッスンが含まれています。

「取り込みに向けたWALとチェックポイントのチューニング」で何を学びますか？

WAL設定と非ログテーブルを調整し、I/Oの停滞なしに高い書き込みレートを維持します。 ブラウザで直接実行するハンズオンコードでPostgreSQL Performance & Query Optimizationを演習し、24時間対応のAIチューターがレッスンを進める中での質問に答えます。

PostgreSQL Performance & Query Optimizationを始めるのに経験は必要ですか？

事前経験は必要ありません。CoddyKitのPostgreSQL Performance & Query Optimizationは初級者から上級者向けに構成されているため、ここから始めるか最初から始めて、自分のペースで進むことができます。 これはレッスン3/4です。

「取り込みに向けたWALとチェックポイントのチューニング」レッスンにはどのくらい時間がかかりますか？

ほとんどのCoddyKitレッスンは約5～10分かかります。各レッスンはコンパクトでインタラクティブなので、着実に進歩し、ウェブとアプリ全体で正確に前回の場所から再開できます。

このPostgreSQL Performance & Query Optimizationレッスンでコードを書いて実行できますか？

はい。すべてのPostgreSQL Performance & Query Optimizationレッスンに組み込みコードエディタが含まれているため、ブラウザでリアルコードを書いて実行し、即座のAIフィードバックを取得できます。ローカル設定は不要です。

このコースのすべてのレッスン

  1. COPYと複数行INSERTのスループット
  2. ロード中のインデックスと制約の延期
  3. 取り込みに向けたWALとチェックポイントのチューニング
  4. ON CONFLICTによる大規模Upsert
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