Videos Archive - PrestoDB

Presto at Varsity Tutors: Using Federated Queries to Power External Reporting – John Cross

Varsity Tutors is a learning platform that enables online academic, professional, and enrichment learning. A growing part of their offering partners with school districts to provide customized support for teachers and students. Varsity Tutors for Schools provides external reporting capabilities including student assessments, progress reports, and more. To provide these timely reports, Varsity Tutors (an AWS shop) uses Presto scripts to perform federated queries across MySQL, Postgres, and Redshift and writes data back to S3. They use Ahana Cloud as their managed service for Presto. In this session, John will discuss what technologies they evaluated, why they chose Presto, and their current data architecture including how they handle security for cross-account writes and how they perform upserts into the final reporting database.

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.

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!