pvary commented on code in PR #13360: URL: https://github.com/apache/iceberg/pull/13360#discussion_r2159261692
########## docs/docs/flink-table-maintenance.md: ########## @@ -0,0 +1,268 @@ +--- +title: "Flink Table Maintenance " +--- +<!-- + - Licensed to the Apache Software Foundation (ASF) under one or more + - contributor license agreements. See the NOTICE file distributed with + - this work for additional information regarding copyright ownership. + - The ASF licenses this file to You under the Apache License, Version 2.0 + - (the "License"); you may not use this file except in compliance with + - the License. You may obtain a copy of the License at + - + - http://www.apache.org/licenses/LICENSE-2.0 + - + - Unless required by applicable law or agreed to in writing, software + - distributed under the License is distributed on an "AS IS" BASIS, + - WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + - See the License for the specific language governing permissions and + - limitations under the License. + --> + +# Flink Table Maintenance + +## Overview +When using **Apache Iceberg tables** in a **Flink streaming environment**, it's important to automate table maintenance operations such as `snapshot expiration`, `small file compaction`, and `orphan file cleanup`. + +Previously, these maintenance tasks were only available through **Iceberg Spark Actions**, requiring a separate Spark cluster. However, maintaining **Spark infrastructure** just for table optimization adds **complexity** and **operational overhead**. + +The `TableMaintenance` API in **Apache Iceberg** enables **Flink jobs** to execute these maintenance tasks **natively** within the same streaming job or as an independent Flink job, avoiding the need for external systems. This approach simplifies **architecture**, reduces **costs**, and improves **automation**. + +## Quick Start +Here's a simple example that sets up automated maintenance for an Iceberg table. + +```java +StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment(); +TableLoader tableLoader = TableLoader.fromHadoopTable("path/to/table"); +TriggerLockFactory lockFactory = TriggerLockFactory.defaultLockFactory(); + +TableMaintenance.forTable(env, tableLoader, lockFactory) + .uidSuffix("my-maintenance-job") + .rateLimit(Duration.ofMinutes(10)) + .lockCheckDelay(Duration.ofSeconds(10)) + .add(ExpireSnapshots.builder() + .scheduleOnCommitCount(10) + .maxSnapshotAge(Duration.ofMinutes(10)) + .retainLast(5) + .deleteBatchSize(5) + .parallelism(8)) + .add(RewriteDataFiles.builder() + .scheduleOnDataFileCount(10) + .targetFileSizeBytes(128 * 1024 * 1024) + .partialProgressEnabled(true) + .partialProgressMaxCommits(10)) + .append(); + +env.execute("Table Maintenance Job"); +``` + +## Configuration Options + +### TableMaintenance Builder + +| Method | Description | Default | +|--------|-------------|---------| +| `uidSuffix(String)` | Unique identifier suffix for the job | Random UUID | +| `rateLimit(Duration)` | Minimum interval between task executions | 60 seconds | +| `lockCheckDelay(Duration)` | Delay for checking lock availability | 30 seconds | +| `parallelism(int)` | Default parallelism for maintenance tasks | System default | +| `maxReadBack(int)` | Max snapshots to check during initialization | 100 | + +### ExpireSnapshots Configuration + +| Method | Description | Default Value | Type | +|--------|-------------|---------------|------| +| `maxSnapshotAge(Duration)` | Maximum age of snapshots to retain | No limit | Duration | +| `retainLast(int)` | Minimum number of snapshots to retain | 1 | int | +| `deleteBatchSize(int)` | Number of files to delete in each batch | 1000 | int | +| `scheduleOnCommitCount(int)` | Trigger after N commits | No automatic scheduling | int | +| `scheduleOnDataFileCount(int)` | Trigger after N data files | No automatic scheduling | int | +| `scheduleOnDataFileSize(long)` | Trigger after total data file size (bytes) | No automatic scheduling | long | +| `scheduleOnIntervalSecond(long)` | Trigger after time interval (seconds) | No automatic scheduling | long | +| `parallelism(int)` | Parallelism for this specific task | Inherits from TableMaintenance | int | + + + +### RewriteDataFiles Configuration + +| Method | Description | Default Value | Type | +|--------|-------------|---------------|------| +| `targetFileSizeBytes(long)` | Target size for rewritten files | Table property or 512MB | long | +| `partialProgressEnabled(boolean)` | Enable partial progress commits | false | boolean | +| `partialProgressMaxCommits(int)` | Maximum commits for partial progress | 10 | int | +| `scheduleOnCommitCount(int)` | Trigger after N commits | 10 | int | +| `scheduleOnDataFileCount(int)` | Trigger after N data files | 1000 | int | +| `scheduleOnDataFileSize(long)` | Trigger after total data file size (bytes) | 100GB | long | +| `scheduleOnIntervalSecond(long)` | Trigger after time interval (seconds) | 600 (10 minutes) | long | +| `maxRewriteBytes(long)` | Maximum bytes to rewrite per execution | Long.MAX_VALUE | long | Review Comment: Describe what it is used for - namely to reduce the work done in a single run if there are too many data to rewrite -- This is an automated message from the Apache Git Service. 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