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.
Prism: Presto Gateway Service at Uber – Hitarth Trivedi, Uber
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
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.