Paimon

Apahce Paimon innovatively combines lake format and LSM structure, bringing efficient updates into the lake architecture. To integrate Fluss with Paimon, you must enable lakehouse storage and configure Paimon as lakehouse storage. See more detail about Enable Lakehouse Storage.

Introduction

When a table with option 'table.datalake.enabled' = 'true' is created or altered in Fluss, Fluss will create a corresponding Paimon table with same table path as well. The schema of the Paimon table is as same as the schema of the Fluss table, except for there are two extra columns __offset and __timestamp appended to the last. These two columns are used to help Fluss client to consume the data in Paimon in streaming way like seek by offset/timestamp, etc.

Then datalake tiering service compacts the data from Fluss to Paimon continuously. For primary key table, it will also generate change log in Paimon format which enables you streaming consume it in Paimon way.

Read tables

Read by Flink

For the table with option 'table.datalake.enabled' = 'true', there are two part of data: the data remains in Fluss and the data already in Paimon. Now, you have two view of the table: one view is the Paimon data which has minute-level latency, one view is the full data union Fluss and Paimon data which is the latest within second-level latency.

Flink empowers you to decide to choose which view:

• Only Paimon means a better analytics performance but with worse data freshness
• Combing Fluss and Paimon means a better data freshness but with analytics performance degrading

Read data only in Paimon

To point to read data in Paimon, you must specify the table with $lake suffix, the following SQL shows how to do that:

Flink SQL

-- assume we have a table named `orders`

-- read from paimon
SELECT COUNT(*) FROM orders$lake;

-- we can also query the system tables 
SELECT * FROM orders$lake$snapshots;

When specify the table with $lake suffix in query, it just acts like a normal Paimon table, so it inherits all ability of Paimon table. You can enjoy all the features that Flink's query supports/optimization on Paimon, like query system tables, time travel, etc. See more about Paimon's sql-query.

Union read data in Fluss and Paimon

To point to read the full data that union Fluss and Paimon, you just query it as a normal table without any suffix or others, the following SQL shows how to do that:

Flink SQL

-- query will union data of Fluss and Paimon
SELECT SUM(order_count) as total_orders FROM ads_nation_purchase_power;

The query may look slower than only querying data in Paimon, but it queries the full data which means better data freshness. You can run the query multi-times, you should get different results in every one run as the data is written to the table continuously.

Read by other engines

As the tired data in Paimon compacted from Fluss is also a standard Paimon table, you can use any engines that support Paimon to read the data. Here, we take StarRocks as the engine to read the data:

First, create a Paimon catalog for StarRocks:

StarRocks SQL

CREATE EXTERNAL CATALOG paimon_catalog
PROPERTIES
(
  "type" = "paimon",
  "paimon.catalog.type" = "filesystem",
  "paimon.catalog.warehouse" = "/tmp/paimon_data_warehouse"
);

NOTE: The configuration value paimon.catalog.type and paimon.catalog.warehouse should be same as how you configure the Paimon as lakehouse storage for Fluss in server.yaml.

Then, you can query the orders table by StarRocks:

StarRocks SQL

-- the table is in database `fluss`
SELECT COUNT(*) FROM paimon_catalog.fluss.orders;

-- query the system tables, to know the snapshots of the table
SELECT * FROM paimon_catalog.fluss.enriched_orders$snapshots;

Data Type Mapping

When integrate with Paimon, Fluss automatically converts between Fluss data type and Paimon data type. The following content shows the mapping between Fluss data type and Paimon data type:

Fluss Data Type	Paimon Data Type
BOOLEAN	BOOLEAN
TINYINT	TINYINT
SMALLINT	SMALLINT
INT	INT
BIGINT	BIGINT
FLOAT	FLOAT
DOUBLE	DOUBLE
DECIMAL	DECIMAL
STRING	STRING
CHAR	CHAR
DATE	DATE
TIME	TIME
TIMESTAMP	TIMESTAMP
TIMESTAMP WITH LOCAL TIMEZONE	TIMESTAMP WITH LOCAL TIMEZONE