ALLUXIO COMMUNITY OFFICE HOUR
Presto, an open-source distributed SQL engine, is commonly used to query an existing Hive data warehouse. Due to existing applications, tech debt or operational challenges in the past, Presto may not be able to achieve its full potential but bound and limited by the past decisions. Particularly, challenges include overloaded Hive Metastore with slow and unpredictable access, unoptimized data formats and layouts such as too many small files, or lack of influence over the existing Hive system and other Hive applications.
Ideally, Presto would access data independently from how the data was originally stored or managed. Alluxio, as a data orchestration layer provides the physical data independence, for Presto to interact with the data more efficiently. In addition to caching for IO acceleration, Alluxio also provides a catalog service to abstract the metadata in the Hive Metastore, and transformations to expose the data in compute-optimized way. In this talk, we describe some of the challenges of using Presto with Hive, and introduce Alluxio data orchestration for solving those challenges.
In this Office Hour, we will go over:
- Typical challenges of using Presto with Hive
- Overview of the different services of Alluxio Structured Data Management in Alluxio 2.1
- A demo of using Alluxio Structured Data Management with Presto
ALLUXIO COMMUNITY OFFICE HOUR
Presto, an open-source distributed SQL engine, is commonly used to query an existing Hive data warehouse. Due to existing applications, tech debt or operational challenges in the past, Presto may not be able to achieve its full potential but bound and limited by the past decisions. Particularly, challenges include overloaded Hive Metastore with slow and unpredictable access, unoptimized data formats and layouts such as too many small files, or lack of influence over the existing Hive system and other Hive applications.
Ideally, Presto would access data independently from how the data was originally stored or managed. Alluxio, as a data orchestration layer provides the physical data independence, for Presto to interact with the data more efficiently. In addition to caching for IO acceleration, Alluxio also provides a catalog service to abstract the metadata in the Hive Metastore, and transformations to expose the data in compute-optimized way. In this talk, we describe some of the challenges of using Presto with Hive, and introduce Alluxio data orchestration for solving those challenges.
In this Office Hour, we will go over:
- Typical challenges of using Presto with Hive
- Overview of the different services of Alluxio Structured Data Management in Alluxio 2.1
- A demo of using Alluxio Structured Data Management with Presto
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Fireworks AI is a leading inference cloud provider for Generative AI, powering real-time inference and fine-tuning services for customers' applications that require minimal latency, high throughput, and high concurrency. Their GPU infrastructure spans 10+ clouds and 15+ regions, serving enterprises and developers deploying production AI workloads at scale.
With model sizes reaching 70GB+, Fireworks AI faced critical challenges: eliminating cold start delays, managing highly concurrent model downloads across GPU clusters, reducing tens of thousands in annual cloud egress costs, and automating manual pipeline management that consumed 4+ hours weekly. They chose Alluxio as their solution to scale with their hyper-growth without requiring dedicated infrastructure resources.
In this tech talk, Akram Bawayah, Software Engineer at Fireworks AI, and Bin Fan, VP of Technology at Alluxio, share how Fireworks AI uses Alluxio to power their multi-cloud inference infrastructure.
They discuss:
- How Fireworks AI uses Alluxio in its high-performance model distribution system to deliver fast, reliable inference across multiple clouds
- How implementing Alluxio distributed caching achieved 1TB/s+ model deployment throughput, reducing model loading from hours to minutes while significantly cutting cloud egress costs
- How to simplify infrastructure operations and seamlessly scale model distribution across multi-cloud GPU environments
