The document outlines practical steps to enhance Hive query performance, including the use of data partitioning, bucketing, and optimized joins. It emphasizes creating structured tables and utilizing various optimization techniques such as compression and vectorization. Additionally, the document suggests leveraging the explain keyword for better query execution insights and mentions advancements like Hive on Tez and LLAP for faster query processing.

1. Practical Steps to Improve Hive Queries
Performance
Sergey Kovalev
Software Engineer at Altoros

2. How Hive works

3. 1. Use partitions whenever possible
/folder1/video_data/file1
id, title, channelId, description, uploadYear
1, title1, channelId1, description1, 2012
2, title2, channelId2, description2, 2012
3, title3, channelId3, description3, 2013
4, title4, channelId4, description4, 2013
/folder1/video_data/2012/file1
1, title1, channelId1, description1, 2012
2, title2, channelId2, description2, 2012
/folder1/video_data/2013/file1
3, title3, channelId3, description3, 2013
4, title4, channelId4, description4, 2013
SELECT * from video WHERE uploadYear=’2013-04-08’

4. 1. Use partitions whenever possible
create table video (
id STRING,
title STRING,
description STRING,
viewCount BIGINT
) PARTITIONED BY (uploadYear date)
STORED AS ORC;
insert into table video PARTITION (uploadYear) select * from video_external;

5. 2. Use bucketing
create table video (
id STRING,
channelId STRING,
title STRING,
description STRING,
) CLUSTERED BY(channelId)
INTO 2 BUCKETS
STORED AS ORC;
create table channel (
id STRING,
title STRING,
description STRING,
viewCount BIGINT
) CLUSTERED BY(id)
INTO 2 BUCKETS
STORED AS ORC;
SELECT v.title FROM video v JOIN channel ch ON v.channelId = ch.id WHERE
ch.viewCount>1000

6. 2. Use bucketing
/folder1/video_data/file1
id, title, channelId, description, uploadYear
1, title1, channelId1, description1, 2012
2, title2, channelId2, description2, 2012
3, title3, channelId3, description3, 2012
4, title4, channelId4, description4, 2012
5, title5, channelId5, description5, 2013
6, title6, channelId6, description6, 2013
7, title7, channelId7, description7, 2013
8, title8, channelId8, description8, 2013
/folder1/video_data/file1
2, title2, channelId2, description2, 2012
4, title4, channelId4, description4, 2012
6, title6, channelId6, description6, 2013
8, title8, channelId8, description8, 2013
/folder1/video_data/file2
1, title1, channelId1, description1, 2012
3, title3, channelId3, description3, 2012
5, title5, channelId5, description5, 2013
7, title7, channelId7, description7, 2013

7. 2. Use bucketing
/folder1/channel_data/file1
id, title, description, viewCount
channelId1, title1, description1, viewCount1
channelId2, title2, description2, viewCount2
channelId3, title3, description3, viewCount3
channelId4, title4, description4, viewCount4
channelId5, title5, description5, viewCount5
channelId6, title6, description6, viewCount6
channelId7, title7, description7, viewCount7
channelId8, title8, description8, viewCount8
/folder1/channel_data/file1
channelId2, title2, description2, viewCount2
channelId4, title4, description4, viewCount4
channelId6, title6, description6, viewCount6
channelId8, title8, description8, viewCount8
/folder1/channel_data/file2
channelId1, title1, description1, viewCount1
channelId3, title3, description3, viewCount3
channelId5, title5, description5, viewCount5
channelId7, title7, description7, viewCount7

8. 3. Partitions + bucketing
create table video (
id STRING,
channelId STRING,
title STRING,
description STRING,
viewCount BIGINT
) PARTITIONED BY (uploadYear date)
CLUSTERED BY(channelId)
INTO 2 BUCKETS
STORED AS ORC;

9. 3. Partitions + bucketing
/folder1/video_data/file1
id, title, channelId, viewCount, uploadYear
1, title1, channelId1, viewCount1, 2012
2, title2, channelId2, viewCount2, 2012
3, title3, channelId3, viewCount3, 2012
4, title4, channelId4, viewCount4, 2012
5, title5, channelId5, viewCount5, 2013
6, title6, channelId6, viewCount6, 2013
7, title7, channelId7, viewCount7, 2013
8, title8, channelId8, viewCount8, 2013
/folder1/video_data/2012/file1
2, title2, description2, viewCount2, 2012
4, title4, description4, viewCount4, 2012
/folder1/video_data/2012/file2
1, title1, description1, viewCount1, 2012
3, title3, description3, viewCount3, 2012
/folder1/video_data/2013/file1
6, title6, description6, viewCount6, 2013
8, title8, description8, viewCount8, 2013
/folder1/video_data/2013/file2
5, title5, description5, viewCount5, 2013
7, title7, description7, viewCount7, 2013

10. 4. Use joins optimization
Shuffle join/Common join:

11. 4. Use joins optimization
Map-side join:

12. 4. Use joins optimization
Sort-merge-bucket (SMB) join:

13. 5. Choose the right input format
Row Data Column Store

14. 6. Other optimization
Avoid highly normalized table structures
Compress map/reduce output
For map output compression, execute set mapred.compress.map.output = true.
For job output compression, execute set mapred.output.compress = true.
Use parallel execution
SET hive.exce.parallel=true;

15. 7. Use the 'explain' keyword to improve the query
execution plan
EXPLAIN query...

16. 7. Use the 'explain' keyword to improve the query
execution plan

17. 8. Stinger Initiative
Use cost-based optimization
Use vectorization
Transactions with ACID semantics

18. 8. Hive on Tez

19. 8. Sub-Second Queries with Hive LLAP
New approach using a hybrid engine that leverages Tez and something new called LLAP (Live
Long and Process)

20. Questiones?