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Version: Next

Upgrade Notes from v0.9 to v1.0

Cluster Configuration Changes​

New datalake.enabled Cluster Configuration​

Starting in v1.0, Fluss introduces the cluster-level configuration datalake.enabled to control whether the cluster is ready to create and manage lakehouse tables.

Behavior Changes​

The behavior of Fluss regarding lakehouse table configuration is determined by the combination of datalake.enabled and datalake.format. The specific rules are as follows:

  • If datalake.enabled is unset, Fluss defaults to legacy behavior: In this state, configuring datalake.format alone automatically enables lakehouse tables.
  • If datalake.enabled is set to false, lakehouse functionality remains disabled. The datalake.format parameter is optional in this scenario. When datalake.format is explicitly configured, it pre-binds the specified lake format to newly created tables, preparing them for future integration without immediately activating lakehouse tables.
  • If datalake.enabled is set to true, lakehouse functionality is fully enabled. In this state, datalake.format is strictly required and must be provided for the configuration to take effect.

To enable lakehouse tables for the cluster, configure both options together:

datalake.enabled: true
datalake.format: paimon

To pre-bind the lake format without enabling lakehouse tables yet, configure:

datalake.enabled: false
datalake.format: paimon

This mode is useful when you want newly created tables to carry the lake format in advance, while postponing lakehouse enablement at the cluster level. After datalake.enabled is later set to true, tables created under this configuration can still turn on table.datalake.enabled without being recreated.

Notes for Existing Deployments​

If your existing deployment or internal scripts only set datalake.format, they will continue to work with the legacy behavior as long as datalake.enabled remains unset.

For new configuration examples and operational guidance, we recommend explicitly configuring datalake.enabled together with datalake.format.