Tim Spurway - Hustle: a column oriented, distributed event database
654 views · Published 8 June 2014 · 1:27:04 · Indexed 25 October 2026
Channel: PyData · 2014 · People & Blogs
PyData SV 2014 Hustle was born out of the need to efficiently process and query billions of event records per day. It was built on the proven petabyte scale Disco MapReduce framework, and the surreally fast LMDB persistent data layer. Hustle sports the following feature set: column oriented - super fast queries append-only event semantics distributed insert - massive write loads compressed - save up to 50% memory/disk space Python DSL query language 00:00 Welcome! 00:10 Help us add time stamps or captions to this video! See the description for details. Want to help add timestamps to our YouTube videos to help with discoverability? Find out more here: https://github.com/numfocus/YouTubeVideoTimestamps
More from this channel
-
31:23
Kostas Perifanos - Authorship Attribution Using Python
-
31:08
Prash Majmudar - Measuring the Digital Economy Using Big Data
-
37:48
Mike Muller - Shared Memory Parallelism with Python
-
35:01
Martin Goodson - Most Winning A/B Test Results are Illusory
-
1:13:42
David Cournapeau & Simon Jagoe - Narrowing the Gap Between Array Computing and Databases
-
41:59
Dirk Gorissen - Measuring and Predicting Departures from Routine in Human Mobility
-
44:17
Jonny Edwards - Recommenders in Python
-
54:39
Gael Varoquaux - Building a cutting-edge data processing environment on a budget