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StarRocks vs. Trino and Presto

14.6x Greater Performance with StarRocks' Native Table
3.3x Greater Performance With StarRocks on Data Lakes
10,000 QPS High Performance Even With 1,000s of Concurrent Users

Why users are migrating from Trino and Presto to StarRocks

Trino and Presto are popular open-source SQL query engines for data lakehouse queries, known for reducing query latencies from minutes to seconds. However, their performance, which was once considered good, may not meet the demands of modern analytics work. The limitations most frequently associated with Trino and Presto include:

 

High Query
Latency
The query performance of Trino and Presto is limited by their Java-based query engines. Unlike C++ based engines, these engines can't take full advantage of the vectorized executions of modern CPU's. This makes Trino difficult to handle for interactive analytics scenarios.

 
No real-time
analytics
Trino and Presto were designed as a batch analytics engine. Streaming data must be ingested into the data lake in batches. Customers are forced to bring on an additional platform for real-time analytics.

 
Limited High-Concurrency Support
Even when Trino and Presto can deliver acceptable query performance, they struggle to sustain that performance as concurrency scales. Users often need to maintain another system for highly concurrent use cases. This increases maintenance requirements dramatically.

See Performance Benchmark

See for yourself how StarRocks outperforms Trino and Presto in this head-to-head benchmark comparison. With this guide, you'll understand why more and more enterprises are moving to StarRocks every day.
As one of our happy clients put it: "When accounting for speed, concurrency, scalability, and maintenance, nothing beats StarRocks. Nothing."

Compare Trino and Presto to StarRocks

Trino | Presto

Java-based query engine
⊗    No vectorized query executions
⊗    No real-time analytics
Supports a limited number of concurrent users
⊗    No support for local storage, data lake queries only
Rudimentary support for materialized views
Single point of failure at the coordinator node

StarRocks

C++ based high-performance query engine
Fully vectorized query executions
Supports batch and real-time analytics
High concurrency with 10,000+ QPS
Supports both data lake queries and local storage
Intelligent materialized views with real-time updates
MPP architecture with no single point of failure

StarRocks Advantages

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.

 

Deliver unparalleled performance in any scenario

StarRocks offers 3x greater performance when querying data on the data lake compared to Trino and Presto. StarRocks achieves this thanks to its unique vectorized execution engine, written in C++ to make full use of the SIMD instructions in modern CPUs. And because StarRocks includes an optimized native storage engine, you're able to unify data lake analytics, low latency, and highly concurrent workloads with one database.

 

Work with the freshest data, even on your data lake

Perform analytics on the freshest data possible, even on your data lake, without need for any data migration. Data lake users don't have to set up a separate data pipeline for real-time analytics. Streaming data from sources (such as Apache Kafka) are ingested into StarRocks and made available to analytics in real-time. StarRocks' storage engine also uses the Delete-and-insert pattern, which allows for efficient Partial Update and Upsert operations.

 

Accelerate your analytics with intelligent materialized views

With its breakthrough Intelligent Materialized View (IMV) technology, StarRocks can transparently accelerate queries and simplify data pipelines. IMVs are automatically refreshed to guarantee data consistency, and queries are automatically re-written to leverage IMVs. Expensive ETL jobs can also be replaced with IMVs to simplify data pipelines.

Talk to an engineer

Have questions about CelerData and StarRocks? You can connect with our team of solutions architects and experienced engineers who can answer all of your questions and even offer a personalized demo aligned with your specific needs and analytics scenarios.