Presto, a prominent open-source distributed SQL query engine, has been at the leading edge of high-performance data analytics for over a decade. In analytical data processing, the effectiveness of query optimization is paramount. Over the last half-century, optimizing SQL queries has been a hotbed of research and development, resulting in groundbreaking innovations. This blog post…
Introduction Apache Parquet modular encryption provides encryption at-rest and in-transit at finer-grained. In big data world, data analytic tables are usually very wide with hundreds of columns, while only a small number of columns need to be protected. So the finer-grained access control is a better fit than coarse-grained one like table level access control….
Introduction Today’s data is growing very fast, which creates challenges for query engines like Presto. Presto is a popular interactive query engine, because of its scalability, high performance, and smooth integration with Hadoop. As the volume of data grows, Presto needs to read larger chunks of data and load them into memory, which causes higher…
Raptor is a Presto connector (presto-raptor) that is used to power some critical interactive query workloads in Meta (previously Facebook). Though referred to in the ICDE 2019 paper Presto: SQL on Everything, it remains somewhat mysterious to many Presto users because there is no available documentation for this feature. This article will shed some light…
Overview With the wide deployment of Presto in a growing number of companies, Presto is used not only for queries, but also for data ingestion and ETL jobs. There is a need to improve Presto’s file writer performance, especially for popular columnar file formats, e.g. Parquet, and ORC. In this article, we introduce the brand…
RaptorX is an internal project name aiming to boost query latency significantly beyond what vanilla Presto is capable of. This blog post introduces the hierarchical cache work, which is the key building block for RaptorX. With the support of the cache, we are able to boost query performance by 10X. This new architecture can beat…
In the previous article we saw what gains are possible when filtering early and in the right order. In this article we look at how we do this with nested and structured types. We use the 100G TPC-H dataset, but now we group top level columns into structs or maps. Maps, lists and structs are…
In the previous article we looked at the abstract problem statement and possibilities inherent in scanning tables. In this piece we look at the quantitative upside with Presto. We look at a number of queries and explain the findings. The initial impulse motivating this work is the observation that table scan is by far the…
Table scan, on the face of it, sounds trivial and boring. What’s there in just reading a long bunch of records from first to last? Aren’t indexing and other kinds of physical design more interesting? As data has gotten bigger, the columnar table scan has only gotten more prominent. The columnar scan is a fairly…