High Volume Updates in Apache Hive

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Apache Hive provides a convenient SQL-based query language to data stored in HDFS. HDFS provides highly scaleable bandwidth to the data, but does not support arbitrary writes. One of Hortonworks` customers needs to store a high volume of customer data (> 1 TB/day) and that data contains a high percentage (15%) of record updates distributed across years. In many high-update use-cases, HBase would suffice, but the current lack of push down filters from Hive into HBase and HBase`s single level keys make it too expensive. Our solution is to use a custom record reader that stores the edit records as separate HDFS files and synthesizes the current set of records dynamically as the table is read. This provides an economical solution to their need that works within the framework provided by Hive. We believe this use case applies to many Hive users and plan to develop and open source a reusable solution.

Tecnología Empresariales

High Volume Updates in Hive
Owen O’Malley
owen@hortonworks.com
@owen_omalley
June 2012

© Hortonworks Inc. 2012 Page 1

Who Am I?

Page 2
© Hortonworks Inc. 2012

A Data Flood

Page 3
© Hortonworks Inc. 2012

The Dataflow

Page 4
© Hortonworks Inc. 2012

The Approach

Page 5
© Hortonworks Inc. 2012

Why not Hbase?

Page 6
© Hortonworks Inc. 2012

Limitations of a Single Key

Page 7
© Hortonworks Inc. 2012

Hive Table Layout

Page 8
© Hortonworks Inc. 2012

Design

Page 9
© Hortonworks Inc. 2012

Repeatable Reads

Page 10
© Hortonworks Inc. 2012

Stitching Buckets Together

Page 11
© Hortonworks Inc. 2012

Limitations

Page 12
© Hortonworks Inc. 2012

Additional Challenges from Hive

Page 13
© Hortonworks Inc. 2012

Hive’s Output Committer

Page 14
© Hortonworks Inc. 2012

Dynamic Partitions

Page 15
© Hortonworks Inc. 2012

Conclusion

Page 16
© Hortonworks Inc. 2012

Thank You!
Questions & Answers

Page 17
© Hortonworks Inc. 2012

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High Volume Updates in Apache Hive

1. High Volume Updates in Hive Owen O’Malley owen@hortonworks.com @owen_omalley June 2012 © Hortonworks Inc. 2012 Page 1

2. Who Am I? Page 2 © Hortonworks Inc. 2012

3. A Data Flood Page 3 © Hortonworks Inc. 2012

4. The Dataflow Page 4 © Hortonworks Inc. 2012

5. The Approach Page 5 © Hortonworks Inc. 2012

6. Why not Hbase? Page 6 © Hortonworks Inc. 2012

7. Limitations of a Single Key Page 7 © Hortonworks Inc. 2012

8. Hive Table Layout Page 8 © Hortonworks Inc. 2012

9. Design Page 9 © Hortonworks Inc. 2012

10. Repeatable Reads Page 10 © Hortonworks Inc. 2012

11. Stitching Buckets Together Page 11 © Hortonworks Inc. 2012

12. Limitations Page 12 © Hortonworks Inc. 2012

13. Additional Challenges from Hive Page 13 © Hortonworks Inc. 2012

14. Hive’s Output Committer Page 14 © Hortonworks Inc. 2012

15. Dynamic Partitions Page 15 © Hortonworks Inc. 2012

16. Conclusion Page 16 © Hortonworks Inc. 2012

17. Thank You! Questions & Answers Page 17 © Hortonworks Inc. 2012