Building Large-scale Query Operators and Window Functions for Prestissimo using Velox – Aditi Pandit
In this talk, Aditi Pandit, Principal Software Engineer at Ahana and Presto/Velox contributor, will throw the covers back on some of the most interesting portions of working in Prestissimo and Velox. The talk will be based on the experience of implementing the windowing functions in Velox. It will cover the nitty gritty on the vectorized operator, memory management and spilling. This talk is perfect for anyone who is using Presto in production and wants to understand more about the internals, or someone who is new to Presto and is looking for a deep technical understanding of the architecture.
Parquet Column Level Access Control with Presto
Apache Parquet is the major columnar file storage format used by Apache Presto and several other query engines in many big data analytic frameworks today. In a lot of use cases, a portion of the column data is highly sensitive and must be protected. Column encryption at the file format level is supported in the Parquet community. Due to the rewritten code of Parquet in Presto, Parquet column encryption at Presto needs to be ported with modifications to the Presto code page. And the integration with Key Management Service (KMS) and other query engines like Hive and Spark is another challenge. In this talk, we will show the work we have done for enabling Presto for Parquet column decryption including challenges, solutions, integration with Hive/Spark Parquet column encryption and look forward to the next step of encryption work.
Update on Intel’s Participation in the Presto and Velox Communities – David Cohen, Intel
In this talk I will provide a brief update on Intel’s involvement in the Presto and Velox projects.
Speed Up Presto Reading with Paquet Column Indexes – Xinli Shang, & Chen Liang, Uber
Data analytic tables in the big data ecosystem are usually large and some of them can reach petabytes in size. Presto as a fast query engine needs to be intelligent to skip reading unnecessary data based on filters. In addition to the existing filtering to skip partitions, files, and row groups, Apache Parquet Column Index provides further filtering to pages, which is the I/O unit for the Parquet data source. In this presentation, we will show the work that we integrated Parquet Column Index to Presto code base, the performance gains, etc. We will also talk about our effort to open-source this project to PrestoDB and look forward to collaborating with the community to merge!
PrestoDB, the Velox Library, and the Coming Age of Heterogeneous Hardware – Dave Cohen, Intel
This talk will quickly review the market shift to use heterogeneous hardware, the need for new language runtime and library level abstractions to hide the complexity of this hardware, and position the PrestoDB SQL query engine with the Velox and Cachelib libraries embodiments of these abstractions.
Prestissimo – Presto-on-Velox for Faster More Efficient Queries – Orri Erling, Meta
We built a drop-in replacement for the Presto worker using C++ and Velox and saw a dramatic improvements in CPU efficiency and latency for interactive queries. We embraced adaptive execution provided by Velox to efficiently evaluate filters pushed down into scan and automatically enable array-based aggregations and joins. We make extensive use of dictionary encodings to achieve zero-copy execution throughout the engine. We allow for vectorization friendly function implementations, provide ASCII-only fast paths and many other tricks. We’d like to share our learnings, early results and future plans. We are looking forward to invite the community to join our efforts in building the next generation of Presto together.