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new ee29bc402 [lake/iceberg] Add iceberg documents for lakehouse support
in fluss (#1640)
ee29bc402 is described below
commit ee29bc402bd0645117c6a7d0017c86dee3e59b13
Author: MehulBatra <66407733+mehulba...@users.noreply.github.com>
AuthorDate: Wed Sep 10 18:34:04 2025 +0530
[lake/iceberg] Add iceberg documents for lakehouse support in fluss (#1640)
---------
Co-authored-by: Mehul Batra <mehulbatra@Mehuls-MacBook-Pro.local>
Co-authored-by: luoyuxia <luoyu...@alumni.sjtu.edu.cn>
---
.../tiered-storage/lakehouse-storage.md | 2 +-
.../integrate-data-lakes/iceberg.md | 335 +++++++++++++++++++++
.../integrate-data-lakes/lance.md | 2 +
3 files changed, 338 insertions(+), 1 deletion(-)
diff --git a/website/docs/maintenance/tiered-storage/lakehouse-storage.md
b/website/docs/maintenance/tiered-storage/lakehouse-storage.md
index d4a24845e..2b0cf0d10 100644
--- a/website/docs/maintenance/tiered-storage/lakehouse-storage.md
+++ b/website/docs/maintenance/tiered-storage/lakehouse-storage.md
@@ -9,7 +9,7 @@ Lakehouse represents a new, open architecture that combines the
best elements of
Lakehouse combines data lake scalability and cost-effectiveness with data
warehouse reliability and performance.
Fluss leverages the well-known Lakehouse storage solutions like Apache Paimon,
Apache Iceberg, Apache Hudi, Delta Lake as
-the tiered storage layer. Currently, only Apache Paimon is supported, but more
kinds of Lakehouse storage support are on the way.
+the tiered storage layer. Currently, only Apache Paimon, Apache Iceberg, Lance
are supported, with more kinds of Lakehouse storage support are on the way.
Fluss's datalake tiering service will tier Fluss's data to the Lakehouse
storage continuously. The data in Lakehouse storage can be read both by Fluss's
client in a streaming manner and accessed directly
by external systems such as Flink, Spark, StarRocks and others. With data
tiered in Lakehouse storage, Fluss
diff --git a/website/docs/streaming-lakehouse/integrate-data-lakes/iceberg.md
b/website/docs/streaming-lakehouse/integrate-data-lakes/iceberg.md
new file mode 100644
index 000000000..3275b3583
--- /dev/null
+++ b/website/docs/streaming-lakehouse/integrate-data-lakes/iceberg.md
@@ -0,0 +1,335 @@
+---
+title: Iceberg
+sidebar_position: 2
+---
+
+# Iceberg
+
+## Introduction
+
+[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).
+
+> **NOTE**: Iceberg requires JDK11 or later. Please ensure that both your
Fluss deployment and the Flink cluster used for tiering services are running on
JDK11+.
+
+
+## Configure Iceberg as LakeHouse Storage
+
+### Configure Iceberg in Cluster Configurations
+
+To configure Iceberg as the lakehouse storage, you must configure the
following configurations in `server.yaml`:
+```yaml
+# Iceberg configuration
+datalake.format: iceberg
+
+# the catalog config about Iceberg, assuming using Hadoop catalog,
+datalake.iceberg.type: hadoop
+datalake.iceberg.warehouse: /tmp/iceberg
+```
+
+Fluss processes Iceberg configurations by stripping the `datalake.iceberg.`
prefix and uses the stripped configurations (without the prefix
`datalake.iceberg.`) to initialize the Iceberg catalog.
+This approach enables passing custom configurations for iceberg catalog
initiation. Checkout the [Iceberg Catalog
Properties](https://iceberg.apache.org/docs/1.9.1/configuration/#catalog-properties)
for more details on the available configurations of catalog.
+
+Fluss supports all Iceberg-compatible catalog types. For catalogs such as
`hive`, `hadoop`, `rest`, `glue`, `nessie`, and `jdbc`, you can specify them
using the configuration `datalake.iceberg.type` with the corresponding value
(e.g., `hive`, `hadoop`, etc.).
+For other types of catalogs, you can use `datalake.iceberg.catalog-impl:
<your_iceberg_catalog_impl_class_name>` to specify the catalog implementation.
+For example, configure with `datalake.iceberg.catalog-impl:
org.apache.iceberg.snowflake.SnowflakeCatalog` to use Snowflake catalog.
+
+> **NOTE**:
+> 1: Some catalog requires Hadoop related classes such as `hadoop`, `hive`
catalog. Make sure hadoop related classes are in your classpath. You can either
download from [pre-bundled Hadoop
jar](https://repo.maven.apache.org/maven2/org/apache/flink/flink-shaded-hadoop-2-uber/2.8.3-10.0/flink-shaded-hadoop-2-uber-2.8.3-10.0.jar)
+or
[hadoop.tar.gz](https://archive.apache.org/dist/hadoop/common/hadoop-2.8.5/hadoop-2.8.5.tar.gz)
which required to be unzipped. Then put hadoop related jars into
`FLUSS_HOME/plugins/iceberg`.
+> 2: Fluss only bundles the catalog implementation included in `iceberg-core`
module. For any other catalog implementations not bundled within `iceberg-core`
module (e.g., Hive Catalog), you must place the corresponding JAR file into the
into `FLUSS_HOME/plugins/iceberg`.
+> 3: The version if Iceberg that Fluss bundles is based on `1.9.2`, please
make sure the jars you put is compatible with `Iceberg-1.9.2`
+
+### Start Tiering Service to Iceberg
+
+Then, you must start the datalake tiering service to tier Fluss's data to
Iceberg. For guidance, you can refer to [Start The Datalake Tiering Service
+](maintenance/tiered-storage/lakehouse-storage.md#start-the-datalake-tiering-service).
Although the example uses Paimon, the process is also applicable to Iceberg.
+
+However, for the [Prepare required
jars](maintenance/tiered-storage/lakehouse-storage.md#prepare-required-jars)
step, adhere to the dependency management guidelines listed below:
+- Put [fluss-flink connector jar](/downloads) into `${FLINK_HOME}/lib`, you
should choose a connector version matching your Flink version. If you're using
Flink 1.20, please use
[fluss-flink-1.20-$FLUSS_VERSION$.jar](https://repo1.maven.org/maven2/org/apache/fluss/fluss-flink-1.20/$FLUSS_VERSION$/fluss-flink-1.20-$FLUSS_VERSION$.jar)
+- If you are using [Amazon S3](http://aws.amazon.com/s3/), [Aliyun
OSS](https://www.aliyun.com/product/oss) or [HDFS(Hadoop Distributed File
System)](https://hadoop.apache.org/docs/stable/) as Fluss's [remote
storage](maintenance/tiered-storage/remote-storage.md),
+ you should download the corresponding [Fluss filesystem
jar](/downloads#filesystem-jars) and also put it into `${FLINK_HOME}/lib`
+- Put [fluss-lake-iceberg
jar](https://repo1.maven.org/maven2/org/apache/fluss/fluss-lake-iceberg/$FLUSS_VERSION$/fluss-lake-iceberg-$FLUSS_VERSION$.jar)
into `${FLINK_HOME}/lib`
+- Put the jars required by Iceberg Catalog into `${FLINK_HOME}/lib`. For
example, if you are using Hive catalog, you should put
[iceberg-hive-metastore](https://repo1.maven.org/maven2/org/apache/iceberg/iceberg-hive-metastore/1.9.2/iceberg-hive-metastore-1.9.2.jar),
hadoop related jars, hive related jars into `${FLINK_HOME}/lib`
+- Put the jars required by Iceberg FileIO into `${FLINK_HOME}/lib`. For
example, if your Iceberg is backed by S3, you should put
[iceberg-aws-bundle](https://mvnrepository.com/artifact/org.apache.iceberg/iceberg-aws-bundle/1.9.2)
into `${FLINK_HOME}/lib`
+
+Additionally, when following the [Start Datalake Tiering
Service](maintenance/tiered-storage/lakehouse-storage.md#start-datalake-tiering-service)
guide, make sure to use Iceberg-specific configurations as parameters when
starting the Flink tiering job:
+```shell
+<FLINK_HOME>/bin/flink run /path/to/fluss-flink-tiering-$FLUSS_VERSION$.jar \
+ --fluss.bootstrap.servers localhost:9123 \
+ --datalake.format iceberg \
+ --datalake.iceberg.type hadoop \
+ --datalake.iceberg.warehouse /tmp/iceberg
+```
+
+## Table Mapping Between Fluss and Iceberg
+
+When a Fluss 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.
+
+Here is an example using Flink SQL to create a table with data lake enabled:
+```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'
+);
+```
+
+You can also specify Iceberg [table
properties](https://iceberg.apache.org/docs/latest/configuration/#table-properties)
when creating a datalake-enabled Fluss table by using the `iceberg.` prefix
within the Fluss table properties clause.
+Here is an example to change iceberg format to `orc` and set
`commit.retry.num-retries` to `5`:
+```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' = 'orc',
+ 'iceberg.commit.retry.num-retries' = '5'
+);
+```
+
+### 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
+- **Bucket partitioning**: Automatically partitioned by the primary key using
Iceberg's bucket transform with the bucket num of Fluss to align with Fluss
+- **Sorted by system column `__offset`**: Sorted by the system column
`__offset` (which is derived from the Fluss change log) to preserve the data
order and facilitate mapping back to the original Fluss change log
+
+```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
+
+The table mapping for Fluss log table are a little of different depending on
whether the bucket key is specified or not.
+
+#### No Bucket Key
+Log Table without bucket in Fluss are mapped to Iceberg tables with:
+- **Identity partitioning**: Using identity partitioning on the `__bucket`
system column, which enables to seek to the data files in iceberg if a
specified Fluss bucket is given
+- **Sorted by system column `__offset`**: Sorted by the system column
`__offset` (which is derived from the Fluss log data) to preserve the data
order and facilitate mapping back to the original Fluss log data
+
+```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
+Log Table with one bucket key in Fluss are mapped to Iceberg tables with:
+- **Bucket partitioning**: Automatically partitioned by the bucket key using
Iceberg's bucket transform with the bucket num of Fluss to align with Fluss
+- **Sorted by system column `__offset`**: Sorted by the system column
`__offset` (which is derived from the Fluss log data) to preserve the data
order and facilitate mapping back to the original Fluss log data
+
+```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 following the above rules:
+
+```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 other Engines
+
+Since data tiered to Iceberg from Fluss is stored as standard Iceberg tables,
you can use any Iceberg-compatible engine. Below is an example using
[StarRocks](https://docs.starrocks.io/docs/data_source/catalog/iceberg/iceberg_catalog/):
+
+#### StarRocks with Hadoop Catalog
+
+```sql title="StarRocks SQL"
+CREATE EXTERNAL CATALOG iceberg_catalog
+PROPERTIES (
+ "type" = "iceberg",
+ "iceberg.catalog.type" = "hadoop",
+ "iceberg.catalog.warehouse" = "/tmp/iceberg"
+);
+```
+
+```sql title="Query Examples"
+-- Basic query
+SELECT COUNT(*) FROM iceberg_catalog.fluss.orders;
+
+-- Time travel query
+SELECT * FROM iceberg_catalog.fluss.orders
+FOR SYSTEM_VERSION AS OF 123456789;
+
+-- Query with bucket filtering for efficiency
+SELECT * FROM iceberg_catalog.fluss.orders
+WHERE __bucket = 1 AND __offset >= 100;
+```
+
+> **NOTE**: The configuration values must match those used when configuring
Iceberg as the lakehouse storage for Fluss in `server.yaml`.
+
+## Data Type Mapping
+
+When integrating with Iceberg, Fluss automatically converts between Fluss data
types and Iceberg data types:
+
+| Fluss Data Type | Iceberg Data Type | Notes
|
+|-------------------------------|-------------------------------|---------------------|
+| BOOLEAN | BOOLEAN |
|
+| TINYINT | INTEGER | Promoted to
INT |
+| SMALLINT | INTEGER | Promoted to
INT |
+| INT | INTEGER |
|
+| BIGINT | LONG |
|
+| FLOAT | FLOAT |
|
+| DOUBLE | DOUBLE |
|
+| DECIMAL | DECIMAL |
|
+| STRING | STRING |
|
+| CHAR | STRING | Converted to
STRING |
+| DATE | DATE |
|
+| TIME | TIME |
|
+| TIMESTAMP | TIMESTAMP (without timezone) |
|
+| TIMESTAMP WITH LOCAL TIMEZONE | TIMESTAMP (with timezone) |
|
+| BINARY | BINARY |
|
+| BYTES | BINARY | Converted to
BINARY |
+
+
+## Maintenance and Optimization
+
+### Auto Compaction
+
+The table option `table.datalake.auto-compaction` (disabled by default)
provides per-table control over automatic compaction.
+When enabled for a specific table, compaction is automatically triggered
during write operations to that table by the tiering service.
+
+### Snapshot Metadata
+
+Fluss adds specific metadata to Iceberg snapshots for traceability:
+
+- **commit-user**: Set to `__fluss_lake_tiering` to identify Fluss-generated
snapshots
+- **fluss-bucket-offset**: JSON string containing the Fluss bucket offset
mapping to track the tiering progress:
+ ```json
+ [
+ {"bucket": 0, "offset": 1234},
+ {"bucket": 1, "offset": 5678},
+ {"bucket": 2, "offset": 9012}
+ ]
+ ```
+
+## Limitations
+
+When using Iceberg as the lakehouse storage layer with Fluss, the following
limitations currently exist:
+
+- **Union Read**: Union read of data from both Fluss and Iceberg layers is not
supported
+- **Complex Types**: Array, Map, and Row types are not supported
+- **Multiple bucket keys**: Not supported until Iceberg implements
multi-argument partition transforms
diff --git a/website/docs/streaming-lakehouse/integrate-data-lakes/lance.md
b/website/docs/streaming-lakehouse/integrate-data-lakes/lance.md
index 2361e8a25..082ef04a7 100644
--- a/website/docs/streaming-lakehouse/integrate-data-lakes/lance.md
+++ b/website/docs/streaming-lakehouse/integrate-data-lakes/lance.md
@@ -5,6 +5,8 @@ sidebar_position: 3
# Lance
+## Introduction
+
[Lance](https://lancedb.github.io/lance/) is a modern table format optimized
for machine learning and AI applications.
To integrate Fluss with Lance, you must enable lakehouse storage and configure
Lance as the lakehouse storage. For more details, see [Enable Lakehouse
Storage](maintenance/tiered-storage/lakehouse-storage.md#enable-lakehouse-storage).
