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Introduction
Architecture Supported table formats Supported engines Iceberg format Paimon format Mixed format User cases Self-managed streaming Lakehouse Stream-and-batch-fused data pipe... -
Mixed-Iceberg
Compared with Iceberg format, Mixed-Iceberg format provides more features: Stronger primary key constraints that also apply to Spark OLAP performance that is production-ready fo... -
Self-Optimizing
Introduction Self-optimizing mechanism Self-optimizing scheduling policy Quota Balanced Introduction Lakehouse is characterized by its openness and loose coupling, with data... -
CDC Ingestion
Ingest into one table Iceberg format Mixed-Iceberg format Ingest Into multiple tables Iceberg format Mixed-Iceberg format CDC stands for Change Data Capture, which is a broa... -
Deployment
System requirements Download the distribution Source code compilation Configuration Configure the service address Configure system database Configure high availability Config... -
Catalogs
Introduce multi-catalog How to use Future work Introduce multi-catalog A catalog is a metadata namespace that stores information about databases, tables, views, indexes, users... -
Configurations
Multi-level configuration management Self-optimizing configurations Data-cleaning configurations Mixed Format configurations Reading configurations Writing configurations LogS... -
Managing Optimizers
Optimizer container Local container Flink container External container Optimizer group Add optimizer group Edit optimizer group Remove optimizer group Optimizer Scale-out a... -
Using Tables
Create table Configure LogStore Configure watermark Modify table Upgrade a Hive table Configure self-optimizing Modify optimizer group Adjust optimizing resources Adjust opt... -
Overview
Table format (aka. format) was first proposed by Iceberg, which can be described as follows: It defines the relationship between tables and files, and any engine can query and r...