How StarRocks and Apache Paimon Transform Data Lake Analytics and Migration

Data‑Lake Architecture and Apache Paimon

Real‑time analytics and strict ACID requirements have driven the evolution from traditional offline warehouses to lake‑warehouse architectures. The classic open‑source lake formats—Iceberg, Hudi, and Delta Lake—are now joined by Apache Paimon, which offers:

Full ACID transaction support, schema evolution, versioning, and time‑travel.

Compatibility with multiple storage back‑ends and compute engines (Flink for streaming, Spark for batch, and various OLAP engines for interactive analysis).

An LSM‑Tree design that enables minute‑level update latency, high‑throughput compaction, and superior read/write performance.

Paimon Advantages

Real‑time updates: Stream‑ing writes with sub‑minute latency, column‑level and aggregation updates, and change‑log generation for downstream consumption.

Seamless stream read/write: Native integration with Flink (stream‑read/stream‑write) and mature Spark support for batch workloads.

High‑performance OLAP queries: Point‑lookup, bitmap and Bloom‑filter indexes, and efficient vectorized execution.

Massive offline processing: Full Append‑table support for ultra‑large data sets.

StarRocks + Paimon: Performance Boost

When StarRocks queries Paimon tables, it can achieve up to three times the throughput of Presto/Trino. The gains stem from:

Advanced cost‑based optimizer (CBO) and vectorized execution engine.

Fine‑grained I/O merging that reduces the number of network reads.

Optional Data Cache on local disks, delivering up to six‑fold speedup.

Materialized views that pre‑compute results, providing up to ten‑fold acceleration and automatic query rewrite, including nested view hierarchies.

Migration Strategies

Trino/Presto to StarRocks

StarRocks includes a Trino dialect parser. Setting the session variable aligns the parser: set sql_dialect="Trino"; In practice, about 90 % of Trino/Presto queries run unchanged; only rare corner cases (e.g., obscure geographic or mathematical functions) require manual adjustment.

Other engines (Hive, Spark‑SQL, Impala, Doris, ClickHouse)

SQLGlot can translate SQL dialects. The open‑source tool is available at https://github.com/sqlglot/sqlglot. A typical usage pattern:

import sqlglot
sql = "SELECT a, b FROM tbl WHERE c > 10"
# Translate from DuckDB syntax to Hive syntax
translated = sqlglot.transpile(sql, read="duckdb", write="hive")[0]
print(translated)

This command accepts three parameters: the source SQL, the read dialect, and the write dialect.

Cluster‑to‑cluster migration

To upgrade an existing StarRocks cluster, create a new cluster, then use the built‑in sync tool to replicate DDL/DML changes in near‑real‑time. After the target cluster is synchronized, switch traffic. The tool also supports hot‑standby and disaster‑recovery scenarios.

Alibaba Cloud EMR Serverless Deployment Modes

Compute‑storage integrated: Internal tables stored with StarRocks’ native format for high‑concurrency, real‑time analysis.

Compute‑storage separation: Data resides in OSS (object storage); BE/CN nodes scale dynamically and support multiple warehouses, resource isolation, and cache management.

Data‑lake analysis mode: Supports Trino/Presto syntax, enabling ad‑hoc queries over external data in HDFS or OSS without additional configuration.

Future Roadmap for StarRocks + Paimon

Metadata caching: Cache Paimon partition metadata to accelerate partition‑aware materialized‑view refreshes.

Enhanced statistics & index support: Full utilization of Bloom filters and bitmap indexes within the Paimon reader.

Write support for Paimon tables: Enable StarRocks to directly write data in the Paimon table format.

Memory optimizations: Reduce memory consumption during metadata retrieval and data reads for large tables.

Key Takeaways

The combination of StarRocks and Apache Paimon delivers a high‑performance, cost‑effective lake‑warehouse solution that supports real‑time updates, fast OLAP queries, and flexible migration paths. Ongoing roadmap items aim to further improve metadata handling, indexing, write capabilities, and resource efficiency.