Why users are migrating from Trino and Presto to StarRocks
Trino and Apache Presto are arguably the most popular open source engines for data lakehouse queries. Compared to their predecessors like Hive, Trino and Presto can reduce query latencies from tens of minutes to tens of seconds. While these performance improvements were good years ago, it's not enough for modern analytics work. Today, Trino and Presto users struggle with interactive query scenarios where query latency needs to be in the sub-second range to support their ad-hoc queries, operational analytics, and user-facing analytics.
This is just the tip of the iceberg when it comes to Trino and Presto's limitations. Other major challenges include:
HIGH QUERY LATENCY
NO REAL-TIME ANALYTICS
LIMITED HIGH-CONCURRENCY SUPPORT
StarRocks vs. Trino and Presto
GREATER PERFORMANCE WITH STARROCKS' NATIVE TABLE
GREATER PERFORMANCE WITH STARROCKS ON DATA LAKES
HIGH PERFORMANCE EVEN WITH 1,000S OF CONCURRENT USERS
Query external data sources.
No ingestion needed.
In addition to more efficient analysis of local data, StarRocks can work as the query engine to analyze data stored in data lakes such as Apache Hive, Apache Iceberg, Apache Hudi, and Delta Lake.
With StarRocks' external catalog, users are able to query external data sources seamlessly with zero-migration, analyzing data from different systems such as HDFS and Amazon S3, in various file formats such as Parquet, ORC, and CSV.
Why more Presto and Trino users are switching to StarRocks
Deliver unparalleled performance in any scenario
Work with the freshest data, even on your data lake
Accelerate your analytics with intelligent materialized views
Compare Trino and Presto to StarRocks
Trino | Presto
|Java-based query engine
|C++ based high-performance query engine
No vectorized query executions
Fully vectorized query executions
No real-time analytics
Supports batch and real-time analytics
|Supports a limited number of concurrent users
High concurrency with 10,000+ QPS
Data lake queries and local storage support
|No support for local storage, data lake queries only
Supports both data lake queries and local storage
Materialized views support
|Rudimentary support for materialized views
Intelligent materialized views with real-time updates
Point of failure
|Single point of failure at the coordinator node
MPP architecture with no single point of failure
Featured success stories
Airbnb Embraces a New Generation of Fast Analytics with StarRocks
YooZoo Games Wins Big with StarRocks As Their Next-Gen OLAP Layer