luoyuxia commented on code in PR #1640: URL: https://github.com/apache/fluss/pull/1640#discussion_r2329493989
########## website/docs/maintenance/tiered-storage/lakehouse-storage.md: ########## @@ -22,7 +22,9 @@ Lakehouse Storage is disabled by default, you must enable it manually. ### Lakehouse Storage Cluster Configurations #### Modify `server.yaml` Review Comment: TBH, I think mix paimon and iceberg in this page make it harder for users to focus on the datalake that they care about? Could we move these steps about iceberg to `integrate-data-lakes/iceberg.md` directly? It will be much clear. Let's use just consider this page as an example for paimon only. ########## website/docs/streaming-lakehouse/integrate-data-lakes/iceberg.md: ########## @@ -0,0 +1,339 @@ +--- +title: Iceberg +sidebar_position: 2 +--- + +# Iceberg + +[Apache Iceberg](https://iceberg.apache.org/) is an open table format for huge analytic datasets. It provides ACID transactions, schema evolution, and efficient data organization for data lakes. +To integrate Fluss with Iceberg, you must enable lakehouse storage and configure Iceberg as the lakehouse storage. For more details, see [Enable Lakehouse Storage](maintenance/tiered-storage/lakehouse-storage.md#enable-lakehouse-storage). + +Supported Iceberg Versions: Fluss supports both Iceberg v1 and v2 table formats. Log tables (append-only) are compatible with v1 tables, while primary key tables leverage v2 features such as delete files and merge-on-read capabilities for efficient updates and deletes. + +## Introduction + +When a table is created or altered with the option `'table.datalake.enabled' = 'true'` and configured with Iceberg as the datalake format, Fluss will automatically create a corresponding Iceberg table with the same table path. +The schema of the Iceberg table matches that of the Fluss table, except for the addition of three system columns at the end: `__bucket`, `__offset`, and `__timestamp`. +These system columns help Fluss clients consume data from Iceberg in a streaming fashion, such as seeking by a specific bucket using an offset or timestamp. + +```sql title="Flink SQL" +USE CATALOG fluss_catalog; + +CREATE TABLE fluss_order_with_lake ( + `order_key` BIGINT, + `cust_key` INT NOT NULL, + `total_price` DECIMAL(15, 2), + `order_date` DATE, + `order_priority` STRING, + `clerk` STRING, + `ptime` AS PROCTIME(), + PRIMARY KEY (`order_key`) NOT ENFORCED + ) WITH ( + 'table.datalake.enabled' = 'true', + 'table.datalake.freshness' = '30s' +); +``` + +Then, the datalake tiering service continuously tiers data from Fluss to Iceberg. The parameter `table.datalake.freshness` controls the frequency that Fluss writes data to Iceberg tables. By default, the data freshness is 3 minutes. +For primary key tables, updates and deletes are handled using Iceberg's delete files (equality deletes and position deletes) with a merge-on-read (MOR) strategy for efficient change management. + +Since Fluss version 0.8, you can also specify Iceberg table properties when creating a datalake-enabled Fluss table by using the `iceberg.` prefix within the Fluss table properties clause. + +```sql title="Flink SQL" +CREATE TABLE fluss_order_with_lake ( + `order_key` BIGINT, + `cust_key` INT NOT NULL, + `total_price` DECIMAL(15, 2), + `order_date` DATE, + `order_priority` STRING, + `clerk` STRING, + `ptime` AS PROCTIME(), + PRIMARY KEY (`order_key`) NOT ENFORCED + ) WITH ( + 'table.datalake.enabled' = 'true', + 'table.datalake.freshness' = '30s', + 'table.datalake.auto-maintenance' = 'true', + 'iceberg.write.format.default' = 'parquet', + 'iceberg.commit.retry.num-retries' = '5' +); +``` + +For example, you can specify the Iceberg property `write.format.default` to change the file format of the Iceberg table, or set `commit.retry.num-retries` to configure retry behavior for commits. The `table.datalake.auto-maintenance` option (true by default) enables automatic maintenance tasks such as file compaction and snapshot expiration. + +## Table Types and Bucketing Strategy + +Fluss uses a special bucketing strategy when integrating with Iceberg to ensure data distribution consistency between Fluss and Iceberg layers. This enables efficient data access and future union read capabilities. + +### Bucket Strategy + +When Iceberg is configured as the datalake format, Fluss uses `IcebergBucketingFunction` to bucket data following Iceberg's bucketing strategy. This ensures: +- **Data distribution consistency**: The same record goes to the same bucket in both Fluss and Iceberg +- **Efficient data access**: You can quickly locate data for a specific Fluss bucket within Iceberg +### Primary Key Tables + +Primary key tables in Fluss are mapped to Iceberg tables with: +- **Primary key constraints**: The Iceberg table maintains the same primary key definition +- **Merge-on-read (MOR) strategy**: Updates and deletes are handled efficiently using Iceberg's MOR capabilities +- **Required bucket keys**: Primary key tables must have exactly one bucket key defined +- **Bucket partitioning**: Automatically partitioned by the bucket key using Iceberg's bucket transform + +```sql title="Primary Key Table Example" +CREATE TABLE user_profiles ( + `user_id` BIGINT, + `username` STRING, + `email` STRING, + `last_login` TIMESTAMP, + `profile_data` STRING, + PRIMARY KEY (`user_id`) NOT ENFORCED +) WITH ( + 'table.datalake.enabled' = 'true', + 'bucket.num' = '4', + 'bucket.key' = 'user_id' +); +``` + +**Corresponding Iceberg table structure:** +```sql +CREATE TABLE user_profiles ( + user_id BIGINT, + username STRING, + email STRING, + last_login TIMESTAMP, + profile_data STRING, + __bucket INT, + __offset BIGINT, + __timestamp TIMESTAMP_LTZ, + PRIMARY KEY (user_id) NOT ENFORCED +) PARTITIONED BY (bucket(user_id, 4)) +SORTED BY (__offset ASC); +``` + +### Log Tables (Append-Only) + +Log tables support different bucketing configurations: + +#### No Bucket Key +For log tables without a configured bucket key, Iceberg uses identity partitioning on the `__bucket` system column: + +```sql title="Log Table without Bucket Key" +CREATE TABLE access_logs ( + `timestamp` TIMESTAMP, + `user_id` BIGINT, + `action` STRING, + `ip_address` STRING +) WITH ( + 'table.datalake.enabled' = 'true', + 'bucket.num' = '3' +); +``` + +**Corresponding Iceberg table:** +```sql +CREATE TABLE access_logs ( + timestamp TIMESTAMP, + user_id BIGINT, + action STRING, + ip_address STRING, + __bucket INT, + __offset BIGINT, + __timestamp TIMESTAMP_LTZ +) PARTITIONED BY (IDENTITY(__bucket)) +SORTED BY (__offset ASC); +``` + +#### Single Bucket Key +For log tables with one bucket key, Iceberg uses bucket partitioning: + +```sql title="Log Table with Bucket Key" +CREATE TABLE order_events ( + `order_id` BIGINT, + `item_id` BIGINT, + `amount` INT, + `event_time` TIMESTAMP +) WITH ( + 'table.datalake.enabled' = 'true', + 'bucket.num' = '5', + 'bucket.key' = 'order_id' +); +``` + +**Corresponding Iceberg table:** +```sql +CREATE TABLE order_events ( + order_id BIGINT, + item_id BIGINT, + amount INT, + event_time TIMESTAMP, + __bucket INT, + __offset BIGINT, + __timestamp TIMESTAMP_LTZ +) PARTITIONED BY (bucket(order_id, 5)) +SORTED BY (__offset ASC); +``` + +### Partitioned Tables + +For Fluss partitioned tables, Iceberg first partitions by Fluss partition keys, then by bucket keys: + +```sql title="Partitioned Table Example" +CREATE TABLE daily_sales ( + `sale_id` BIGINT, + `amount` DECIMAL(10,2), + `customer_id` BIGINT, + `sale_date` STRING, + PRIMARY KEY (`sale_id`) NOT ENFORCED +) PARTITIONED BY (`sale_date`) +WITH ( + 'table.datalake.enabled' = 'true', + 'bucket.num' = '4', + 'bucket.key' = 'sale_id' +); +``` + +**Corresponding Iceberg table:** +```sql +CREATE TABLE daily_sales ( + sale_id BIGINT, + amount DECIMAL(10,2), + customer_id BIGINT, + sale_date STRING, + __bucket INT, + __offset BIGINT, + __timestamp TIMESTAMP_LTZ, + PRIMARY KEY (sale_id) NOT ENFORCED +) PARTITIONED BY (IDENTITY(sale_date), bucket(sale_id, 4)) +SORTED BY (__offset ASC); +``` + +## Read Tables + +### Reading with Apache Flink + +For a table with the option `'table.datalake.enabled' = 'true'` and Iceberg configured as the lakehouse storage, you can read data stored in Iceberg using the `$lake` suffix in the table name. + +#### Read Data Only in Iceberg Review Comment: It's a pity that now only paimon support `$lake` suffix to read. -- This is an automated message from the Apache Git Service. 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