Guosmilesmile commented on code in PR #13853: URL: https://github.com/apache/iceberg/pull/13853#discussion_r2285564190
########## docs/docs/flink-table-maintenance.md: ########## @@ -0,0 +1,361 @@ +--- +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 + +In **Apache Iceberg** deployments within **Flink streaming environments**, implementing automated table maintenance operations—including `snapshot expiration`, `small file compaction`, and `orphan file cleanup`—is critical for optimal query performance and storage efficiency. + +Traditionally, these maintenance operations were exclusively accessible through **Iceberg Spark Actions**, necessitating the deployment and management of dedicated Spark clusters. This dependency on **Spark infrastructure** solely for table optimization introduces significant **architectural complexity** and **operational overhead**. + +The `TableMaintenance` API in **Apache Iceberg** empowers **Flink jobs** to execute maintenance tasks **natively**, either embedded within existing streaming pipelines or deployed as standalone Flink jobs. This eliminates dependencies on external systems, thereby **streamlining architecture**, **reducing operational costs**, and **enhancing automation capabilities**. + +## Supported Features (Flink) + +### ExpireSnapshots +Removes old snapshots and their files. Internally uses `cleanExpiredFiles(true)` when committing, so expired metadata/files are cleaned up automatically. + +```java +.add(ExpireSnapshots.builder() + .maxSnapshotAge(Duration.ofDays(7)) + .retainLast(10) + .deleteBatchSize(1000)) +``` + +### RewriteDataFiles +Compacts small files to optimize file sizes. Supports partial progress commits and limiting maximum rewritten bytes per run. + +```java +.add(RewriteDataFiles.builder() + .targetFileSizeBytes(256 * 1024 * 1024) + .minFileSizeBytes(32 * 1024 * 1024) + .partialProgressEnabled(true) + .partialProgressMaxCommits(5)) +``` + +## Lock Management + +The `TriggerLockFactory` is essential for coordinating maintenance tasks. It prevents concurrent maintenance operations on the same table, which could lead to conflicts or data corruption. This locking mechanism is necessary even for a single job, as multiple instances of the same task could otherwise conflict. + +### Why Locks Are Needed +- **Concurrent Access**: Multiple Flink jobs may attempt maintenance simultaneously +- **Data Consistency**: Ensures only one maintenance operation runs per table at a time +- **Resource Management**: Prevents resource conflicts and scheduling issues +- **Avoid Duplicate Work**: Even when only a single compaction job is scheduled, multiple instances could attempt the same operation, leading to redundant work and wasted resources. + +### Supported Lock Types + +#### JDBC Lock Factory +Uses a database table to manage distributed locks: + +```java +Map<String, String> jdbcProps = new HashMap<>(); +jdbcProps.put("jdbc.user", "flink"); +jdbcProps.put("jdbc.password", "flinkpw"); + +TriggerLockFactory lockFactory = new JdbcLockFactory( + "jdbc:postgresql://localhost:5432/iceberg", // JDBC URL + "catalog.db.table", // Lock ID (unique identifier) + jdbcProps // JDBC connection properties +); +``` + +#### ZooKeeper Lock Factory +Uses Apache ZooKeeper for distributed coordination: + +```java +TriggerLockFactory lockFactory = new ZkLockFactory( + "localhost:2181", // ZooKeeper connection string + "catalog.db.table", // Lock ID (unique identifier) + 60000, // sessionTimeoutMs + 15000, // connectionTimeoutMs + 3000, // baseSleepTimeMs + 3 // maxRetries +); +``` + +## Quick Start + +The following example demonstrates the implementation of automated maintenance for an Iceberg table within a Flink environment. + +```java +StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment(); +TableLoader tableLoader = TableLoader.fromHadoopTable("path/to/table"); + +Map<String, String> jdbcProps = new HashMap<>(); +jdbcProps.put("jdbc.user", "flink"); +jdbcProps.put("jdbc.password", "flinkpw"); + +// JdbcLockFactory Example +TriggerLockFactory lockFactory = new JdbcLockFactory( + "jdbc:postgresql://localhost:5432/iceberg", // JDBC URL + "catalog.db.table", // Lock ID (unique identifier) + jdbcProps // JDBC connection properties +); + +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 | + +### Maintenance Task Common Options + +| Method | Description | Default Value | Type | +|--------|-------------|---------------|------| +| `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 | +| `scheduleOnPosDeleteFileCount(int)` | Trigger after N positional delete files | No automatic scheduling | int | +| `scheduleOnPosDeleteRecordCount(long)` | Trigger after N positional delete records | No automatic scheduling | long | +| `scheduleOnEqDeleteFileCount(int)` | Trigger after N equality delete files | No automatic scheduling | int | +| `scheduleOnEqDeleteRecordCount(long)` | Trigger after N equality delete records | No automatic scheduling | long | +| `scheduleOnInterval(Duration)` | Trigger after time interval | No automatic scheduling | Duration | + +### ExpireSnapshots Configuration + +| Method | Description | Default Value | Type | +|--------|-------------|---------------|------| +| `maxSnapshotAge(Duration)` | Maximum age of snapshots to retain | 5 days | Duration | +| `retainLast(int)` | Minimum number of snapshots to retain | 1 | int | +| `deleteBatchSize(int)` | Number of files to delete in each batch | 1000 | int | +| `planningWorkerPoolSize(int)` | Number of worker threads for planning snapshot expiration | Shared worker pool | int | +| `cleanExpiredMetadata(boolean)` | Remove expired metadata files when expiring snapshots | false | boolean | + +### RewriteDataFiles Configuration + +| Method | Description | Default Value | Type | +|--------|-------------|---------------|------| +| `targetFileSizeBytes(long)` | Target size for rewritten files | Table property or 512MB | long | +| `minFileSizeBytes(long)` | Minimum size of files eligible for compaction | 0 | long | +| `maxFileSizeBytes(long)` | Maximum size of files eligible for compaction | Long.MAX_VALUE | long | +| `minInputFiles(int)` | Minimum number of files to trigger rewrite | 1 | int | +| `deleteFileThreshold(int)` | Minimum delete-file count per data file to force rewrite | Integer.MAX_VALUE | int | +| `rewriteAll(boolean)` | Rewrite all data files regardless of thresholds | false | boolean | +| `maxFileGroupSizeBytes(long)` | Maximum total size of a file group | 107374182400 (100GB) | long | +| `maxFilesToRewrite(int)` | Maximum number of files to rewrite per task | Integer.MAX_VALUE | int | +| `partialProgressEnabled(boolean)` | Enable partial progress commits | false | boolean | +| `partialProgressMaxCommits(int)` | Maximum commits allowed for partial progress when partialProgressEnabled is true | 10 | int | +| `maxRewriteBytes(long)` | Maximum bytes to rewrite per execution | Long.MAX_VALUE | long | + +## Complete Example + +```java +public class TableMaintenanceJob { + public static void main(String[] args) throws Exception { + StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment(); + env.enableCheckpointing(60000); // Enable checkpointing + + // Configure table loader + TableLoader tableLoader = TableLoader.fromCatalog( + CatalogLoader.hive("my_catalog", configuration), + TableIdentifier.of("database", "table") + ); + + // Set up maintenance with comprehensive configuration + TableMaintenance.forTable(env, tableLoader, TriggerLockFactory.defaultLockFactory()) Review Comment: `TriggerLockFactory.defaultLockFactory()` We don't have this method. 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