StarRocks and Paimon Data Lake Capabilities, Migration Solutions, and Future Roadmap

The presentation introduces the data‑lake capabilities of StarRocks and Apache Paimon, explaining why a lake architecture is needed for real‑time analytics, ACID support, and cost‑performance balance. It highlights three main advantages of lake warehouses: unified management with ACID transactions, open‑source storage flexibility, and a balance between cost and performance.

Paimon is described as a rapidly emerging open‑source lake format that integrates well with Flink and Spark, offering real‑time updates, high‑performance OLAP queries, rich indexing (bitmap, Bloom filters), and large‑scale batch processing.

Combining StarRocks with Paimon yields a high‑speed analytical solution. By replacing engines such as Presto, Trino, or Impala with StarRocks, users can achieve 3‑10× performance gains thanks to StarRocks' advanced CBO optimizer, vectorized execution, fine‑grained IO merging, and data‑cache mechanisms. Materialized views and data‑cache further accelerate queries.

Migration strategies are covered in detail:

For Trino/Presto workloads, simply set set sql_dialect="Trino" in StarRocks to achieve near‑transparent migration.

For other engines (Hive, Spark‑SQL, Impala, Doris, ClickHouse), the open‑source SQLGlot tool can convert SQL dialects; usage involves specifying the source engine (e.g., "duckdb") and target engine (e.g., "hive").

Cluster‑to‑cluster migration recommends creating a new StarRocks cluster, copying data via a built‑in sync tool, and switching traffic once replication completes, enabling zero‑downtime upgrades and disaster‑recovery setups.

The article also outlines the Alibaba Cloud EMR Serverless StarRocks offerings: an integrated storage‑compute version, a storage‑compute separation version, and a lake‑analysis version that directly queries external data in OSS/HDFS without extra configuration.

Key operational steps for lake analysis include creating an external catalog, e.g., CREATE EXTERNAL CATALOG paimon_fs_catalog , and defining materialized views with CREATE MATERIALIZED VIEW ... REFRESH EVERY 1 MINUTE to achieve sub‑second query latency.

Future roadmap focuses on four areas: metadata caching for Paimon, enhanced statistics and index support (Bloom filter, bitmap), write‑back capability to Paimon tables, and memory‑optimised large‑table read performance.