Flare is a drop-in accelerator for Apache Spark that achieves order of magnitude speedups on DataFrame and SQL workloads.
On Stack Replacement: A Quick Start with Tiered Execution
January 23, 2019
Published in OSDI '18
January 21, 2019
Data Partitioning: Improve Performance with Indexes
January 14, 2019
Parquet on Fire
April 14, 2017
Heterogeneous Workloads and UDFs
April 10, 2017
Flare White Paper
March 24, 2017
Spark Summit EU
November 12, 2016
October 26, 2016
TPC-H On a Single Core
October 19, 2016
Spark is Fast, but how Fast?
September 30, 2016
Flare closes this gap by compiling Catalyst query plans to native code.
Flare’s low-level implementation takes full advantage of native execution, using techniques such as NUMA-aware data layout and scheduling to leverage mechanical sympathy, and to bring execution closer to the metal than what is possible with current JVM-based techniques.
Modern server-class hardware provides memory in the TB range, and dozens of CPU cores. Such powerful machines are readily available, for example as X1 instances on Amazon EC2 or for purchase at Dell.
They are as powerful as a small cluster, but do not require network communication or fault tolerance, and they consume less energy, which makes them cheaper to operate.
While Spark was primarily designed for scaling out on clusters, Flare makes scaling up on server-class hardware an attractive alternative, in terms of performance, and also in terms of operating costs.
Flare is under active development, and we will continue to share information on our blog and on Twitter.
We are currently running a private beta program: If you are interested in using Flare, please use the contact form below.
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