Videos Archive - PrestoDB

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.

Presto on Spark – Facebook – Virtual Meetup

At Facebook, we have spent the past several years in independently building and scaling both Presto and Spark to Facebook scale batch workloads. It is now increasingly evident that there is significant value in coupling Presto’s state-of-art low-latency evaluation with Spark’s robust and fault tolerant execution engine. In this talk, we’ll take a deep dive in Presto and Spark architecture with a focus on key differentiators (e.g., disaggregated shuffle) that are required to further scale Presto.

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!

Presto On Spark: Scaling not Failing with Spark – Ariel Weisberg, Meta & Shradha Ambekar, Intuit

Presto on Spark is an integration between Presto and Spark that leverages Presto’s compiler/evaluation as a library and Spark’s large scale processing capabilities. It enables a unified SQL experience between interactive and batch use cases. A unified option for batch data processing and ad hoc is very important for creating the experience of queries that scale instead of fail without requiring rewrites between different SQL dialects. In this session, we’ll talk about Presto On Spark architecture, why it matters and its implementation/usage at Intuit.