Tez, An Introduction

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Alan F. Gates

Founder & Architect

@alanfgates

© 2014 Hortonworks

In The Beginning of Hadoop…

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...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

But, It Was Too Low Level

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But it was too rigid

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But, It Was Batch

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YARN to the Rescue

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© 2014 Hortonworks

Why Tez? Enable Data Processing In Many Tools

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•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

Why Tez? Span Batch and Interactive

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•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

Why Tez? Preserve MapReduce Experience

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•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