Videos Archive - PrestoDB

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 SQL Functions – Facebook

In this talk we will show how to use the recently introduced SQL function feature, how it works, and the ongoing work to support invoking arbitrary functions remotely with remote UDF server.

Dynamic UDF Framework and its Applications – Rongrong Zhong, Alluxio & Yanbing Zhang, Bytedance

Presto supports dynamically registered User Defined Functions (UDFs) since 2020. Over the years, we used this framework to add support for SQL UDFs and remote / external UDFs. One common community request in the UDF domain is to support Hive UDFs. Many companies have legacy Hive pipelines, and engineers who are familiar with HQL and Hive UDFs. With remote UDF, one can implement Hive UDF support as UDFs running on the remote cluster. But since HiveUDFs are written in Java, we can also run them inside the engine. We extended the dynamic UDF framework to support Java UDFs, and used this new extension to add HiveUDF support in Presto. With this feature, users can directly use their familiar HiveUDFs and UDAFs in their Presto query.

Realtime Analytics with Presto and Apache Pinot – Xiang Fu

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.

Panel: The Presto Ecosystem

The Presto Ecosystem – Moderated by Dipti Borkar, Ahana; Maxime Beauchemin, Preset; Vinoth Chandar, Apache Hudi; Kishore Gopalakrishna, Apache Pinot & James Sun, Facebook, Inc.

(Chinese) 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.