On-Demand Recordings from PrestoCon’s, Webinars, Meetups, and more
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.
In this talk, we provide a glimpse at the results of our latest benchmark test which compares the speed and cost of processing data inside Snowflake (ELT) vs. processing and serving prepared live tables from a data lake using Upsolver (ETL).
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.
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.
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.
In this talk, Technical Steering Committee member and Committer, and Software Engineer at Facebook shares Presto reliability and scalability at Facebook.
An overview of how we are evolving Presto to be the next generation query engine at Facebook and beyond.
In this talk, Intel shares its Presto contribution plans and collaboration with Presto Community.
In this PrestoCon talk, team of community experts share Presto evolution, Today & Beyond
Cartona is one of the fastest growing B2B e-commerce marketplaces in Egypt that connects retailers with suppliers, wholesalers, and production companies. We needed to federate across multiple data sources, including transactional databases like Postgres and AWS S3 data lake. In this session, we’ll talk about how Presto allows us to join across all of these data sources without having to copy or ingest data – it’s all done in place. In addition, we’ll talk about how we were up and running in less than an hour with the Ahana Cloud managed service. It gives us the power of Presto and the ease of use without the need to manage it or have deep skills to deploy and operate it.
In the existing Presto architecture, single coordinator has become a bottleneck in a number of ways for cluster scalability. – With an increasing number of workers, the coordinator has the potential of slow down due to a high number of tasks. – In high QPS use cases, we have found workers can become starved of splits by excessive CPU being spend on task updates in coordinator. – Also with single coordinator, we have an upper limit on the worker pool because of above-mentioned reasons. To overcome with this challenges, we are coming up with a new architecture which supports multiple coordinators in a single cluster.
RaptorX is an internal project name aiming to boost query latency significantly beyond what vanilla Presto is capable of. For this session, we introduce the hierarchical cache work including Alluxio data cache, fragment result cache, etc. Cache 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 performance oriented connectors like Raptor with the added benefit of continuing to work with disaggregated storage.
Prism is a gateway service for all Presto queries at Uber. It addresses Uber specific needs in four main areas – resource management, query gating, monitoring, and security. It is responsible for proxying over three million weekly queries from 6000+ weekly active users across all of Uber. Presto has variable execution times due to high multi-tenancy at Uber. Prism helps in overcoming those challenges using features like query routing, load balancing, query gating, session parameter checks, failover clusters which helps in maintaining a 99.9% availability and reliability SLA for Presto at Uber. Functionality – Query Execution: 1. Async execution API returns data stream 2. Async execution API returns File Descriptor – Routing – Prism can route queries to different clusters based on client sources. Other functionalities: Load Balancing, Query Gating, Failover, Session Properties, Security
Presto is complicated with many intricacies. Ahana Cloud is the only managed service for Presto on AWS that simplifies Presto, bringing its power to platform teams of any size or skill set. In this session we’ll give you a quick overview of Ahana Cloud, including managing multiple Presto clusters seamlessly, querying a range of data sources, as well as just-released capabilities.
In this world, most analytics products either focus on ad-hoc analytics, which requires query flexibility without guaranteed latency, or low latency analytics with limited query capability. In this talk, we will explore how to get the best of both worlds using Apache Pinot and Presto: 1. How people do analytics today to trade-off Latency and Flexibility: Comparison over analytics on raw data vs pre-join/pre-cube dataset. 2. Introduce Apache Pinot as a column store for fast real-time data analytics and Presto Pinot Connector to cover the entire landscape. 3. Deep dive into Presto Pinot Connector to see how the connector does predicate and aggregation push down. 4. Benchmark results for Presto Pinot connector.
Yunlei will share the infrastructure for petabytes scale log data collection, storage and analysis at Alibaba . Presto plays a key role in the infrastructure. Presto processes over hundreds of billions of query, about over 1 quadrillion rows every day .
The Presto Ecosystem – Moderated by Dipti Borkar, Ahana; Maxime Beauchemin, Preset; Vinoth Chandar, Apache Hudi; Kishore Gopalakrishna, Apache Pinot & James Sun, Facebook, Inc.
Apache Iceberg is an open table format for huge analytic datasets. At Twitter, engineers are working on the Presto-Iceberg connector, aiming to bring high-performance data analytics on Iceberg to the Presto ecosystem. Here, Chunxu would like to share what they have learned during the development, hoping to shed light on the future work of interactive queries.
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