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
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.
Why more Presto and Trino users are switching to StarRocks
The benefits of StarRocks reach beyond querying external data sources. Here are some additional great reasons to switch:
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.
Compare Trino and Presto to StarRocks
Designed for the analytics needs of modern enterprises, StarRocks delivers the capabilities and performance. Trino and Presto can't say the same.
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Comparison
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Query engine |
 Java-based query engine |
C++ based high-performance query engine |
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Query executions |
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Real-time analytics |
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Concurrency supports |
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Data lake queries and local storage support |
 No support for local storage, data lake queries only |
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Materialized views support |
Rudimentary support for materialized views |
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Point of failure |
Single point of failure at the coordinator node |
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Trino | Presto
StarRocks
Featured success stories
These leading enterprises have made the move to StarRocks. Learn how they've supercharged query performance, eliminated maintenance work, and accelerated insights:
Explore our in-depth guide and learn about the key differences between Trino and StarRocks. See how StarRocks offers unique competitive advantages over Trino, backed by real customer case studies.
Read the Comparison Guide