Published on February 20, 2014
Tez, An Introduction Alan F. Gates Founder & Architect @alanfgates Page 1
In The Beginning of Hadoop… ...there was MapReduce –It could handle data sizes way beyond those of its competitors –It was resilient in the face of failure –It made it easy for users to bring their code and algorithms to the data (i.e. free to program in Java instead of just SQL) © 2014 Hortonworks Page 2
But, It Was Too Low Level © 2014 Hortonworks Page 3
But it was too rigid © 2014 Hortonworks Page 4
But, It Was Batch © 2014 Hortonworks Page 5
YARN to the Rescue © 2014 Hortonworks Page 6
Why Tez? Enable Data Processing In Many Tools •An execution engine that can be used by Hive, Pig, Cascading, and others •Right now SQL on hadoop is hot, and we want to enable that •But we also want to keep in mind that there’s a lot else to be done in Hadoop (machine learning, ETL, graph processing, etc.) and we want to open up the work we’re doing to those groups as well. © 2014 Hortonworks Page 7
Why Tez? Span Batch and Interactive •It’s hard for customers to use different tools depending on their data size •It’s hard for applications like Hive to use different back end engines depending on the inputs and outputs © 2014 Hortonworks Page 8
Why Tez? Preserve MapReduce Experience •MapReduce represents engineering centuries of work •Much has been learned (mostly the hard way) about scale and resiliency •We are not excited to reinvent those wheels, we would rather rebuild the vehicle on top of them © 2014 Hortonworks Page 9
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Hadoop Meetup (HUG) February 2014: Tez introduction and insights.
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February 2014 HUG: Hive On Tez ... 1. Hive on TezGunther Hagleitner (firstname.lastname@example.org)© Hortonworks Inc. 2013.
1. Apache Tez : Accelerating Hadoop Query Processing Bikas Saha @bikassaha© Hortonworks Inc. 2013Page 1. 2. Tez – Introduction • Distributed execution ...