Presto on Elastic Capacity – Neerad Somanchi & Abhisek Saikia, Meta

    Presto on Elastic Capacity – Neerad Somanchi & Abhisek Saikia, Meta

    Presto on elastic capacity – Elasticity of a shared fleet is one of the fundamental pillars of the IaaS (Infrastructure-as-a-Service) world. The ability of services to efficiently use both guaranteed and non-guaranteed (opportunistic) capacity is important in such a setting. Presto is great when it runs on guaranteed capacity (i.e, capacity that is fixed and stable). But what if we want Presto to leverage elastic (opportunistic) capacity, i.e, capacity that is shifting, but in a predictable manner (think Amazon EC2 Spot Blocks)? In this lightning presentation, Neerad Somanchi and Abhisek Saikia will talk about how a recent feature developed for Presto can help it efficiently utilize such elastic compute.

    Presto Connector for DataCTRL – Mario Ceste, Jr., SAP NS2

    Presto Connector for DataCTRL – Mario Ceste, Jr., SAP NS2

    DataCTRL is a data management platform for ingesting large quantities of disparate data sets. We’ve written a connector for Presto which allows our users to leverage the data they’ve ingested using SQL. Integrating Presto with our platform has given our customers a quick and effective way to query their data while also building additional data products.

    Presto Authorization with Apache Ranger – Reetika Agrawal, Ahana & William Brooks, Privacera

    Presto Authorization with Apache Ranger – Reetika Agrawal, Ahana & William Brooks, Privacera

    Apache Ranger has been the user’s choice to support authorization in various data platforms from small-scale to enterprise-grade production environments. At Ahana, engineers are working on the Presto-Ranger integration, aiming to support global fine-grained data access control across all catalogs for Presto, while also providing auditing and monitoring of user access. We would like to collaborate with the Privacera and share our learnings, what we developed so far, and also hope to shed light on the future work of the Ranger Presto Plugin with Apache Ranger committer.

    Presto at Tencent at Scale: Usability Extension, Stability Improvement and Performance Optimization – Junyi Huang & Pan Liu

    Presto at Tencent at Scale: Usability Extension, Stability Improvement and Performance Optimization – Junyi Huang & Pan Liu

    Presto has been adopted at Tencent as scale to serve scenarios of ad-hoc queries and interactive queries for different business units. In this talk, we’d like to share our practice of Presto in production. In details, we’ll talk about our works to further improve the stability, extend the usability, and optimize the performance of Presto. The works all together make Presto better fit in our production environment, which we think will also benefit the community.

    Presto at Bytedance – Pengfei Chang, Bytedance

    Presto at Bytedance – Pengfei Chang, Bytedance


    Presto has been widely used in Bytedance, e.g. DataWarehouse, BI Tools, Ads and so on. Meanwhile the presto team of Bytedance also delivered many important features and optimizations like Hive UDF Wrapper, multiple coordinator, runtime filter and so on which extend Presto usages and enhance Presto stababilities.

    Prestissimo – Presto-on-Velox for Faster More Efficient Queries – Orri Erling, Meta

    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.

    Open Source Data Lake Analytics: Trends and Opportunities – Biswapesh Chattopadhyay, Meta

    Open Source Data Lake Analytics: Trends and Opportunities – Biswapesh Chattopadhyay, Meta

    Open source data analytics is undergoing an interesting transformation as the industry rapidly evolves around it. Accelerating migration to the cloud, the rise of immensely well funded proprietary vendors, fast evolving needs of the users all contribute to this. This talk goes into detail about the trends and opportunities in the OSS data analytics space, and a call to action on how this space can stay relevant.

    Introducing Materialized View in Presto – Rohit Jain, Meta

    Introducing Materialized View in Presto – Rohit Jain, Meta

    The materialized view is a well-known technique in the data world, it is used to increase the performance and efficiency of queries by precomputing and persisting results. We are announcing materialized view support in the PrestoDB in this talk. Please join us to learn more about it.

    Handling Billions of Messages with PrestoDB in the Country of Pyramids – Ravishankar Nair

    Handling Billions of Messages with PrestoDB in the Country of Pyramids – Ravishankar Nair

    Millions of messages are legacy, and in the new modern world of data, we like “billions”. This is exactly the terminology in the use case we faced from a very prominent client in Egypt. The scenario demanded more attention as this valuable client did multiple proof of the concepts with many other open sources and could not meet exact SLA and needs. The client wanted to have more than a hundred billion( yes, “b”) messages in eight hours to be ingested and further queried without much latency. The presentation will be a live demonstration of how we can architect such a solution with PrestoDB under the hood and some simple but advanced ingestion capabilities and data formats. 

    Delta Lake Connector for Presto – Denny Lee, Databricks

    Delta Lake Connector for Presto – Denny Lee, Databricks

    Delta lake is an open-source project that enables building a lakehouse architecture on top of existing storage systems such as S3, ADLS, GCS, and HDFS. We – the Presto and Delta Lake communities – have come together to make it easier for Presto to leverage the reliability of data lakes by integrating with Delta Lake. In this session, we would like to share the design decisions and internals of the Presto/Delta connector.

    Authorizing Presto with AWS Lake Formation – Jalpreet Singh Nanda, Ahana & Roy Hasson, Amazon

    Authorizing Presto with AWS Lake Formation – Jalpreet Singh Nanda, Ahana & Roy Hasson, Amazon

    AWS Lake Formation is a service that allows data platform users to set up a secure data lake in days. Creating a data lake with Presto and Lake Formation is as simple as defining data sources and what data access and security policies you want to apply. At Ahana and Amazon, engineers are working on Presto and Lake Formation integration to support Authorization on Presto. This means that Presto clusters will be enforce data permissions on user queries against Lake Formation backed data lakes, which is a tightly integrated Lake Formation, AWS Glue, and Amazon S3 data lake stack. In this session we will present high level design, our leanings, future plans and demo how data platform users can use Lake Formation integration to support fine-grained data access controls on Presto.

    After RaptorX: Improve Performance Understanding and Workload Analysis in Presto – Ke Wang & Bin Fan

    After RaptorX: Improve Performance Understanding and Workload Analysis in Presto – Ke Wang & Bin Fan

    RaptorX, an umbrella project presented in PrestoCon Day in March, enabled the Presto interactive fleet in Facebook to reduce latency by 10x, based on a set of architectural improvements and optimizations with hierarchical caching. This presentation provides an update on the follow-up enhancement. Bin Fan from Alluxio will talk about the exploration of a probabilistic algorithm in Alluxio caching to estimate cache working set and the implementation of shadow cache Ke Wang from Facebook will talk about how shadow cache is used to understand the system bottleneck for better resource allocation and query routing decisions. She will also cover a recent improvement in collecting and aggregating per-query runtime statistics on the Presto engine to better understand the time breakdown, resource usage breakdown and cache hit rate on a per-query basis, which can help identify areas of improvement.

    A Tour of Presto Iceberg Connector – Beinan Wang, Alluxio & Chunxu Tang, Twitter

    A Tour of Presto Iceberg Connector – Beinan Wang, Alluxio & Chunxu Tang, Twitter

    Apache Iceberg is an open table format for huge analytic datasets. The Presto Iceberg connector consolidates the SQL engine and the table format, to empower high-performant data analytics. Here, Beinan and Chunxu would like to discuss and share the architectural design of the Presto Iceberg connector, advanced Iceberg feature support (such as native iceberg connector, row-level deletion, and iceberg v2 support), and the future roadmap.