Learning & Operating Presto | Caching in Presto (Sept 28, 2023)
Dive into the new O’Reilly ebook, Learning & Operating Presto. Hear from Alluxio on different ways to get caching in Presto.
Speeding Up Presto in ByteDance – Shengxuan Liu, Bytedance & Beinan Wang, Alluxio
Shengxuan Liu from ByteDance and Beinan Wang from Alluxio will present the practical problems and interesting findings during the launch of Presto Router and Alluxio Local Cache. Their talk covers how ByteDance’s Presto team implements the cache invalidation and dashboard for Alluxio’s Local Cache. Shengxuan will also share his experience using a customized cache strategy to improve the cache efficiency and system reliability.
Scaling Cache for Presto Iceberg Connector – Beinan Wang, Alluxio & Chunxu Tang
While using the Presto Iceberg connector, the in-heap cache in Presto is likely overloaded. In this talk, Beinan and Chunxu will share the design, implementation, and optimization of the off-heap cache to address the scalability challenges. You will learn how to cache Iceberg data and metadata for the Presto Iceberg connector, followed by future work on improving table scans using Apache Arrow.
How Jampp architected a real time bidding system on AWS with PrestoDB
In this presentation, Dante and Fede will show Jampp’s data architecture, how they’re training their machine learning algorithms directly from Presto to identify potential purchasers, and why Presto has become 24/7 critical to ensure ads are relevant to users.
Presto for Real Time Analytics at Uber – Ankit Sultana, Uber
The Real Time Analytics Platform at Uber serves 100M+ queries daily and is used for several critical features: from end-user app features to radius selection for Uber Eats. All these queries are proxied via a custom internal fork of Presto (named Neutrino) that is optimized for low-latency/high-throughput (50ms latency at 1000s of RPS). With this talk we plan to share our learnings over the last 6 months and how we run Presto reliably at this scale for real-time analytics.
Architecting Your Data Platform with Presto and Alluxio in Heterogeneous Environment – Adit Madan
As the cloud is evolving and the adoption of a hybrid-cloud or multi-cloud approach grows, the data architecture must adapt to heterogeneous environments. In this talk, Adit Madan shares insights on how to architect a data platform with Presto and Alluxio that provides agility and simplicity to your data team.
Speed Up Presto at Uber with Alluxio Caching – Chen Liang, Uber & Beinan Wang, Alluxio
At Uber, Presto is heavily used as one of the primary data analytics tools, and Presto’s query performance has profound production impact at Uber. As part of the Presto optimization effort, we turned to explore Alluxio as a caching solution. Alluxio is an open source data orchestration platform often used by many compute frameworks as the caching layer. Alluxio caching is currently enabled on ~2000 nodes across 6 clusters at Uber. In this presentation, we will talk about our journey at Uber of integrating Alluxio cache into Presto. We will discuss the Uber specific challenges we encountered and how we addressed them. We will also present the performance improvements we have seen. Besides, we will also discuss our plan and next steps, and potential future collaboration opportunities with the community.
Presto Query Analysis for Data Layout Formatting and Query Result Caching – Gurmeet Singh, Uber
In this talk, I will be talking about a microservice that we have built at Uber to be able to analyze Presto queries. The Presto Query Engine does not provide endpoints for query analysis purposes. One has to either execute the query or gather insights from the query explain plan. In this talk, I will talk about 1. The work that we had to do to do the query analysis in a microservice using Presto as a library. 2. Doing predicate analysis on the queries to come up with data formatting recommendations in order to improve query performance. 3. Using the analysis service for query result cache invalidation. The analysis figures out whether the results from a previous run of the query are still valid and can be reused.
Self-serve Data Architecture Evolution with Presto and Alluxio Across Clouds – Adit Madan, Alluxio
In this presentation, Adit Madan shares insights to help architect a data platform ready to minimize the impact of change and evolution. He will co-relate industry trends for a multi-tenant environment with how the Presto & Alluxio stack drives agility for hundreds of users in the cloud, across multiple datacenters and a hybrid cloud.
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.
RaptorX: Building a 10X Faster Presto – James Sun, Facebook, Inc
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.
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.