Videos Archive - PrestoDB

Getting started with the new Redis HBO for Presto (Aug 30, 2023)

Learn more about the new open-source Redis-based Historical Statistics Provider for Presto from Jay Narale, software engineer at Uber who built it. Redis is an open-source in-memory database that integrates with Presto through a dedicated connector. Now with a Redis history-based optimizer, you can enhance the efficiency and speed of query execution for Presto by using historical stats to generate optimized plans for your queries. Jay will cover how the Redis HBO utilizes the in-memory capabilities of Redis to store & analyze historical query execution data, which helps the optimizer make informed decisions about query planning and resource allocation based on the historical patterns of queries, leading to improved execution times and resource utilization.

Apache Hudi for Presto and how it’s used at Bytedance

This month we’ll have Yanbing Zhang, software engineer at Bytedance, share more about their work on the Apache Hudi Connector for Presto and how they’re using it at Bytedance.

Fireside Chat: Journey to Innovation: Unleashing the Power of Open Source Through Open Governance

The Presto Foundation is the organization that oversees the development of the Presto open source project. Hosted at the Linux Foundation, the Presto Foundation operates under a community governance model with representation from all its members. In this fireside chat, we’ll hear more from Girish Baliga, Chair of the Presto Foundation, on what it actually means to be a Presto Foundation member and why this governance model is so important for open source projects. We’ll also talk with Vikram Murali of IBM, the newest member of the Presto Foundation. He’ll share more about IBM’s journey to Presto, how they’re using it in IBM’s new watsonx.data lakehouse, and why the Presto Foundation played an important role in IBM’s decision to choose Presto.

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.

Utilizing Presto in Projects Involving Billions of Data Points – Biddut Sarker Bijoy, Goava

Biddut Sarker Bijoy will discuss his experience utilizing Presto in projects involving billions of data points. Many of the big data projects Biddut Sarker Bijoy worked on have had computational issues, such as the need to shrink datasets or the challenge of making sense of terabytes of data. Nonetheless, Biddut Sarker Bijoy recently worked on a project that required him to clean up a large database. Sometimes we all need to clean up a database. It was not a straightforward one for many reasons. Biddut Sarker Bijoy have found using Presto is the best-fitted one for this problem because of its architectural behavior and how it works with billions of data points within a very short amount of time as well as with a less amount of cost.

Keynote Panel: Presto at Scale – Shradha Ambekar, Gurmeet Singh, Neerad Somanchi & Rupa Gangatirkar

Over the last decade Presto has become one of the most widely adopted open source SQL query engines. In use at companies large and small, Presto’s performance, reliability, and efficiency at scale have become critical to many companies’ data infrastructures. In this panel we’ll hear from three of the largest companies running Presto at scale – Meta, Uber, and Intuit. They’ll share more about their learnings, some of their impressive performance metrics with Presto, and what they envision going forward for Presto at their respective companies.

Implementing Lakehouse Architecture with Presto at Bolt – Kostiantyn Tsykulenko, Bolt.eu

Bolt.eu is the first European mobility super-app. We have over 100M users across Europe and Africa and have to deal with data at a large scale on a daily basis (over 100k queries daily). Previously we were using a traditional data warehouse solution based on Redshift but we’ve faced scalability issues that were hard to overcome and after doing our research we chose Presto as the solution. In just a single year we’ve managed to migrate to the Lakehouse architecture using AWS, Presto, Spark and Delta lake. We would like to talk about our journey, some of the challenges we’ve encountered and how we solved them.

Query Performance Optimization at Alibaba Cloud Log Analytics Service – Bin Wang, Alibaba Cloud

Row-limited operators are fast short-circuiting CSE rule by combining FilterNode and ProjectNode and complex function splitting Avoid unnecessary intermediate data structures Optimization Merge similar TableScan Merge arbitrary() and inner complex scalar function

Presto at Adobe: How Adobe Advertising uses Presto for Adhoc Query, Custom Reporting, and Internal Pipelines

Rajmani Arya, Varun Senthilnathan & Manoj Kumar Dhakad, Adobe Advertising: We are from the Product Engineering team in Adobe Advertising (https://business.adobe.com/in/product…. Adobe Advertising is a digital advertisement platform. We take care of accumulating all data, providing platform intelligence, building and maintaining machine leaning capabilities, building and maintaining internal pipelines that form derived data to be used by other teams. The volume of total incoming raw data ranges between 8 to 10 tb/ day spread across 7 regions. The total data in the system currently is about 7pb. This data is largely stored in Hive tables with a central metastore. We use Presto in three ways: 1. Data studio – an internal tool to enable data analysts, sales, marketing and other teams to do adhoc querying. This is also used by data engineers to do adhoc querying for engineering tasks. 2. Custom Reports – We create reports for customers to get performance insights on their campaigns. We have 100s of reports that are run on a daily basis. 3. Internal Pipelines – Presto is used to retrieve data to power 100s of pipelines run daily to generate derived data.

Presto on AWS Journey at Twilio – Lesson Learned and Optimization – Aakash Pradeep & Badri Tripathy

Twilio as a leader in cloud communication platforms is very heavy on data and data-based decision making. Most data related use cases are currently powered by the Presto engine. Two years back we started the Journey with Presto in Twilio and today the system has scaled to a multi-PB data lakehouse and supports more than 75k queries per day. In this journey, we learned a lot about how to effectively operationalize Presto on AWS and some of the tricks to have better query reliability, query performance, guard-railing the clusters and save cost. With this talk, we want to share this experience with the community.

Customer-Facing Presto at Rippling – Andy Li, Rippling

Presto is used for a variety of cases, but tends to be used for larger scale analytical queries. We have been transitioning to using Presto to power our data platform and customer-facing scripting language, RQL (Rippling Query Language) to run arbitrary customer queries to power core products. Presto helps enable diverse, federated querying at scale. In this talk, Andy will cover where Presto sits in Rippling’s ecosystem as a core query layer, our collaboration and contributions for closer integration with Apache Pinot, and learnings on using Presto to handle a large variety of query patterns.

Real Time Analytics at Uber with Presto-Pinot

In this talk, seasoned engineers at Uber will walk through the real time analytics use cases at Uber and the work they have done on the Presto architecture and the Presto-Pinot connector to address them.

Presto at Bytedance- Hive UDF Wrapper for Presto

Presto has been widely used at Bytedance in several ways such as in the data warehouse, BI tools, ads etc. And, the Presto team at Bytedance has also delivered many key features and optimizations such as the Hive UDF wrapper, coordinator, runtime filter and so on which extend Presto usages and enhance Presto stabilities. Nowadays, most companies will use both Hive (or Spark) and Presto together. But Presto UDFs have very different syntax and internal mechanisms compared with Hive UDFs. This restricts Presto usage while users need to maintain 2 kinds of functions. In this talk, we will present a way to execute Hive UDF/UDAF inside Presto.

Discovering Data with Presto and Amundsen at Lyft

Amundsen is an open-source data discovery and metadata platform which is part of LF AI & Data foundation. In this talk, we will deep dive into Amundsen’s architecture and how we integrate Amundsen with Presto to power the data preview and data exploration.

Presto at Walmart and enhancements for cross cloud query federation

In this talk we are going to introduce Presto cross environment query federation which will enable query execution across different clouds and on-prem Presto clusters. This helps in reducing the network data transfer which results in lower Egress and Ingress costs when we are querying across clouds.

Predicting Resource Usages of Future Queries Based on 10M Presto Queries at Twitter

Here, Chunxu and Beinan would like to share what they have learned in developing a highly-scalable query predictor service through applying machine learning algorithms to ~10 million historical Presto queries to classify queries based on their CPU times and peak memory bytes. At Twitter, this service is helping to improve the performance of Presto clusters and provide expected execution statistics on Business Intelligence dashboards.

Optimizing Presto for Uber scale

In this talk, we present some of the work streams we have underway at Uber to optimize Presto performance. In particular, we will cover enabling aggregation pushdown in queries in order to use statistics in the file headers/footers, our investigations into and attempts to efficiently executing approximate queries, and our experience with humongous object allocation in Presto.

Building the Presto Open Source Community – Ahana Round Table

In this round table moderated by Eric Kavanagh of The Bloor Group, panelists from Uber, Facebook, Ahana, and Alibaba will discuss all aspects of building a thriving open source community around PrestoDB including why Presto is so popular & the problems it solves, the open source model the foundation follows, why governance and transparency are so important to an open source community, and what the community looks for in open source projects.