StarRocks is a Native Vectorized Engine implemented in C++, while Trino is implemented in Java and uses limited vectorization technology. Vectorization technology helps StarRocks utilize CPU processing power more efficiently. This type of query engine has the following characteristics:
- It can fully utilize the efficiency of columnar data management. This type of query engine reads data from columnar storage, and the way they manage data in memory, as well as the way operators process data, is columnar. Such engines can use the CPU cache more effectively, improving CPU execution efficiency.
- It can fully utilize the SIMD instructions supported by the CPU. This allows the CPU to complete more data calculations in fewer clock cycles. According to data provided by StarRocks, using vectorized instructions can improve overall performance by 3-10 times.
- It can compress data more efficiently to greatly reduce memory usage. This makes this type of query engine more capable of handling large data volume query requests.
In fact, Trino is also exploring vectorization technology. Trino has some SIMD code, but it’s behind compared to StarRocks in terms of depth and coverage. Trino is still working on improving their vectorization efforts (read https://github.com/trinodb/trino/issues/14237). Meta’s Velox project aims to use vectorization technology to accelerate Trino queries. However, so far, very few companies have formally used Velox in production environments.
Comparison of the Open Source Query Engines: Trino and StarRocks
Comparison of the Open Source Query Engines: Trino and StarRocks