Guosmilesmile commented on code in PR #13853: URL: https://github.com/apache/iceberg/pull/13853#discussion_r2285573904
########## 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: Review Comment: ```suggestion Uses Apache ZooKeeper for distributed locks: ``` ########## 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. This part should be changed, as it has appeared once before. ########## 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 | Review Comment: Default value is null. About the description we can add `If this option is not specified, all eligible files will be rewritten.` ########## 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 +); +``` + Review Comment: Can we add some specific configuration item explanations, such as `INIT_LOCK_TABLES_PROPERTY`, since this configuration item is mentioned in the SQL part below but does not appear in the Java part? https://github.com/apache/iceberg/blob/0478ff7dc8e4c048e0a1101df11656a00b9f81cd/flink/v2.0/flink/src/main/java/org/apache/iceberg/flink/maintenance/api/JdbcLockFactory.java#L48 ########## 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 | Review Comment: Default value is 5 ########## 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 | Review Comment: Default value is `75% of target file size` ########## 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 | + Review Comment: Miss a method `filter` ########## 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 | Review Comment: Default value is `180% of target file size` ########## 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()) + .uidSuffix("production-maintenance") + .rateLimit(Duration.ofMinutes(15)) + .lockCheckDelay(Duration.ofSeconds(30)) + .parallelism(4) + + // Daily snapshot cleanup + .add(ExpireSnapshots.builder() + .maxSnapshotAge(Duration.ofDays(7)) + .retainLast(10)) + + // Continuous file optimization + .add(RewriteDataFiles.builder() + .targetFileSizeBytes(256 * 1024 * 1024) + .minFileSizeBytes(32 * 1024 * 1024) + .scheduleOnDataFileCount(20) + .partialProgressEnabled(true) + .partialProgressMaxCommits(5) + .maxRewriteBytes(2L * 1024 * 1024 * 1024) + .parallelism(6)) + + .append(); + + env.execute("Iceberg Table Maintenance"); + } +} +``` + +## IcebergSink with Post-Commit Integration + +Apache Iceberg Sink V2 for Flink allows automatic execution of maintenance tasks after data is committed to the table, using the addPostCommitTopology(...) method. + +```java +Map<String, String> flinkConf = new HashMap<>(); + +// Enable compaction and maintenance features +flinkConf.put(FlinkWriteOptions.COMPACTION_ENABLE.key(), "true"); + +// Configure JDBC lock settings +flinkConf.put(LockConfig.LOCK_TYPE_OPTION.key(), LockConfig.JdbcLockConfig.JDBC); +flinkConf.put(LockConfig.JdbcLockConfig.JDBC_URI_OPTION.key(), "jdbc:postgresql://localhost:5432/iceberg"); +flinkConf.put(LockConfig.LOCK_ID_OPTION.key(), "catalog.db.table"); + +// Add any other maintenance-related options here as needed +// ... + +IcebergSink.forRowData(dataStream) + .table(table) + .tableLoader(tableLoader) + .setAll(flinkConf) + .append(); +``` + +### SQL Examples + +You can enable maintenance and configure locks using SQL before executing writes: + +```sql +-- Enable Iceberg V2 Sink and compaction (maintenance) +SET 'table.exec.iceberg.use.v2.sink' = 'true'; +SET 'compaction-enabled' = 'true'; + +-- Configure maintenance lock (JDBC) +SET 'flink-maintenance.lock.type' = 'jdbc'; +SET 'flink-maintenance.lock.lock-id' = 'catalog.db.table'; +SET 'flink-maintenance.lock.jdbc.uri' = 'jdbc:postgresql://localhost:5432/iceberg'; +SET 'flink-maintenance.lock.jdbc.init-lock-table' = 'true'; + +-- Now run writes; maintenance will be scheduled post-commit +INSERT INTO db.tbl SELECT ...; +``` + +Or specify options in table DDL: + +```sql +CREATE TABLE db.tbl ( + ... +) WITH ( + 'connector' = 'iceberg', + 'catalog-name' = 'my_catalog', + 'namespace' = 'db', + 'table' = 'tbl', + 'compaction-enabled' = 'true', + + 'flink-maintenance.lock.type' = 'jdbc', + 'flink-maintenance.lock.lock-id' = 'catalog.db.table', + 'flink-maintenance.lock.jdbc.uri' = 'jdbc:postgresql://localhost:5432/iceberg', + 'flink-maintenance.lock.jdbc.init-lock-table' = 'true' +); +``` + +## Lock Configuration (SQL) + +These keys are used in SQL (SET or table WITH options) and are applicable when writing with compaction enabled. + +- JDBC + +| Key | Description | Default | +|-----|-------------|---------| +| `flink-maintenance.lock.type` | Set to `jdbc` | | +| `flink-maintenance.lock.lock-id` | Unique lock ID per table | | +| `flink-maintenance.lock.jdbc.uri` | JDBC URI | | +| `flink-maintenance.lock.jdbc.init-lock-table` | Auto-create lock table | `false` | + +- ZooKeeper + +| Key | Description | Default | +|-----|-------------|---------| +| `flink-maintenance.lock.type` | Set to `zookeeper` | | +| `flink-maintenance.lock.lock-id` | Unique lock ID per table | | +| `flink-maintenance.lock.zookeeper.uri` | ZK connection URI | | +| `flink-maintenance.lock.zookeeper.session-timeout-ms` | Session timeout (ms) | `60000` | +| `flink-maintenance.lock.zookeeper.connection-timeout-ms` | Connection timeout (ms) | `15000` | +| `flink-maintenance.lock.zookeeper.max-retries` | Max retries | `3` | +| `flink-maintenance.lock.zookeeper.base-sleep-ms` | Base sleep between retries (ms) | `3000` | + +## Best Practices + +### Resource Management +- Use dedicated slot sharing groups for maintenance tasks +- Set appropriate parallelism based on cluster resources +- Enable checkpointing for fault tolerance + +### Scheduling Strategy +- Avoid too frequent executions with `rateLimit` +- Use `scheduleOnCommitCount` for write-heavy tables +- Use `scheduleOnDataFileCount` for fine-grained control + +### Performance Tuning +- Adjust `deleteBatchSize` based on storage performance +- Enable `partialProgressEnabled` for large rewrite operations +- Set reasonable `maxRewriteBytes` limits + Review Comment: May be we can add `Setting an appropriate maxFileGroupSizeBytes can break down large FileGroups into smaller ones, thereby increasing the speed of parallel processing.` ########## 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()) + .uidSuffix("production-maintenance") + .rateLimit(Duration.ofMinutes(15)) + .lockCheckDelay(Duration.ofSeconds(30)) + .parallelism(4) + + // Daily snapshot cleanup + .add(ExpireSnapshots.builder() + .maxSnapshotAge(Duration.ofDays(7)) + .retainLast(10)) + + // Continuous file optimization + .add(RewriteDataFiles.builder() + .targetFileSizeBytes(256 * 1024 * 1024) + .minFileSizeBytes(32 * 1024 * 1024) + .scheduleOnDataFileCount(20) + .partialProgressEnabled(true) + .partialProgressMaxCommits(5) + .maxRewriteBytes(2L * 1024 * 1024 * 1024) + .parallelism(6)) + + .append(); + + env.execute("Iceberg Table Maintenance"); + } +} +``` + +## IcebergSink with Post-Commit Integration + +Apache Iceberg Sink V2 for Flink allows automatic execution of maintenance tasks after data is committed to the table, using the addPostCommitTopology(...) method. + +```java +Map<String, String> flinkConf = new HashMap<>(); + +// Enable compaction and maintenance features +flinkConf.put(FlinkWriteOptions.COMPACTION_ENABLE.key(), "true"); + +// Configure JDBC lock settings +flinkConf.put(LockConfig.LOCK_TYPE_OPTION.key(), LockConfig.JdbcLockConfig.JDBC); +flinkConf.put(LockConfig.JdbcLockConfig.JDBC_URI_OPTION.key(), "jdbc:postgresql://localhost:5432/iceberg"); +flinkConf.put(LockConfig.LOCK_ID_OPTION.key(), "catalog.db.table"); + +// Add any other maintenance-related options here as needed +// ... + +IcebergSink.forRowData(dataStream) + .table(table) + .tableLoader(tableLoader) + .setAll(flinkConf) + .append(); +``` + +### SQL Examples + +You can enable maintenance and configure locks using SQL before executing writes: + +```sql +-- Enable Iceberg V2 Sink and compaction (maintenance) +SET 'table.exec.iceberg.use.v2.sink' = 'true'; +SET 'compaction-enabled' = 'true'; + +-- Configure maintenance lock (JDBC) +SET 'flink-maintenance.lock.type' = 'jdbc'; +SET 'flink-maintenance.lock.lock-id' = 'catalog.db.table'; +SET 'flink-maintenance.lock.jdbc.uri' = 'jdbc:postgresql://localhost:5432/iceberg'; +SET 'flink-maintenance.lock.jdbc.init-lock-table' = 'true'; + +-- Now run writes; maintenance will be scheduled post-commit +INSERT INTO db.tbl SELECT ...; +``` + +Or specify options in table DDL: + +```sql +CREATE TABLE db.tbl ( + ... +) WITH ( + 'connector' = 'iceberg', + 'catalog-name' = 'my_catalog', + 'namespace' = 'db', + 'table' = 'tbl', Review Comment: Key should be `catalog-database` and `catalog-table` -- This is an automated message from the Apache Git Service. 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