Well that is debatable.
The following table sales is partitioned in Oracle but has local bitmap indexes
that help the query.
select * from sales where prod_id = 10;
no rows selected
Execution Plan
----------------------------------------------------------
Plan hash value: 511273406
---------------------------------------------------------------------------------------------------------------------
| Id | Operation | Name | Rows | Bytes |
Cost (%CPU)| Time | Pstart| Pstop |
---------------------------------------------------------------------------------------------------------------------
| 0 | SELECT STATEMENT | | 347 | 10063 |
93 (0)| 00:00:02 | | |
| 1 | PARTITION RANGE ALL | | 347 | 10063 |
93 (0)| 00:00:02 | 1 | 28 |
| 2 | TABLE ACCESS BY LOCAL INDEX ROWID| SALES | 347 | 10063 |
93 (0)| 00:00:02 | 1 | 28 |
| 3 | BITMAP CONVERSION TO ROWIDS | | | |
| | | |
|* 4 | BITMAP INDEX SINGLE VALUE | SALES_PROD_BIX | | |
| | 1 | 28 |
---------------------------------------------------------------------------------------------------------------------
Obviously at this stage we do not have local indexes in Hive. That could make
it moredefficient for search and IMO will be a great tool.
Cheers,
Dr Mich Talebzadeh
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From: Jörn Franke [mailto:jornfra...@gmail.com]
Sent: 08 January 2016 06:20
To: user@hive.apache.org
Subject: Re: Impact of partitioning on certain queries
This observation is correct and it is the same behavior as you see it in other
databases supporting partitions. Usually you should avoid many small partitions.
On 07 Jan 2016, at 23:53, Mich Talebzadeh <m...@peridale.co.uk
<mailto:m...@peridale.co.uk> > wrote:
Ok we hope that partitioning improves performance where the predicate is on
partitioned columns
I have two tables. One a basic table called smallsales defined as below
CREATE TABLE `smallsales`( |
| `prod_id` bigint, |
| `cust_id` bigint, |
| `time_id` timestamp, |
| `channel_id` bigint, |
| `promo_id` bigint, |
| `quantity_sold` decimal(10,0), |
| `amount_sold` decimal(10,0)) |
| ROW FORMAT SERDE |
| 'org.apache.hadoop.hive.serde2.lazy.LazySimpleSerDe' |
| STORED AS INPUTFORMAT |
| 'org.apache.hadoop.mapred.TextInputFormat' |
| OUTPUTFORMAT |
| 'org.apache.hadoop.hive.ql.io.HiveIgnoreKeyTextOutputFormat' |
| LOCATION |
| 'hdfs://rhes564:9000/user/hive/warehouse/oraclehadoop.db/smallsales' |
| TBLPROPERTIES ( |
| 'COLUMN_STATS_ACCURATE'='true', |
| 'last_modified_by'='hduser', |
| 'last_modified_time'='1451644705', |
| 'numFiles'='1', |
| 'numRows'='5000000', |
| 'rawDataSize'='193437457', |
| 'totalSize'='198437457', |
| 'transient_lastDdlTime'='1451784743') |
+-------------------------------------------------------------------------+--+
So 5 million rows.
I then created a partitioned table called sales as below
| createtab_stmt
|
+-------------------------------------------------------------------------------+--+
| CREATE TABLE `sales`(
|
| `prod_id` bigint,
|
| `cust_id` bigint,
|
| `time_id` timestamp,
|
| `channel_id` bigint,
|
| `promo_id` bigint,
|
| `quantity_sold` decimal(10,0),
|
| `amount_sold` decimal(10,0))
|
| PARTITIONED BY (
|
| `year` int,
|
| `month` int)
|
| CLUSTERED BY (
|
| prod_id,
|
| cust_id,
|
| time_id,
|
| channel_id,
|
| promo_id)
|
| INTO 256 BUCKETS
|
| ROW FORMAT SERDE
|
| 'org.apache.hadoop.hive.ql.io.orc.OrcSerde'
|
| STORED AS INPUTFORMAT
|
| 'org.apache.hadoop.hive.ql.io.orc.OrcInputFormat'
|
| OUTPUTFORMAT
|
| 'org.apache.hadoop.hive.ql.io.orc.OrcOutputFormat'
|
| LOCATION
|
| 'hdfs://rhes564:9000/user/hive/warehouse/oraclehadoop.db/sales'
|
| TBLPROPERTIES (
|
| 'orc.bloom.filter.columns'='PROD_ID,CUST_ID,TIME_ID,CHANNEL_ID,PROMO_ID',
|
| 'orc.bloom.filter.fpp'='0.05',
|
| 'orc.compress'='SNAPPY',
|
| 'orc.create.index'='true',
|
| 'orc.row.index.stride'='10000',
|
| 'orc.stripe.size'='268435456',
|
| 'transient_lastDdlTime'='1451814921')
|
+-------------------------------------------------------------------------------+--+
And loaded data from smallsales to sales table
Stats updated in both
Now when I do the following
0: jdbc:hive2://rhes564:10010/default> select * from smallsales where prod_id =
10;
+---------------------+---------------------+---------------------+------------------------+----------------------+---------------------------+-------------------------+--+
| smallsales.prod_id | smallsales.cust_id | smallsales.time_id |
smallsales.channel_id | smallsales.promo_id | smallsales.quantity_sold |
smallsales.amount_sold |
+---------------------+---------------------+---------------------+------------------------+----------------------+---------------------------+-------------------------+--+
+---------------------+---------------------+---------------------+------------------------+----------------------+---------------------------+-------------------------+--+
No rows selected (2.231 seconds)
Ok if I do the same query from partitioned bucketed table in takes
0: jdbc:hive2://rhes564:10010/default> select * from sales where prod_id = 10;
+----------------+----------------+----------------+-------------------+-----------------+----------------------+--------------------+-------------+--------------+--+
| sales.prod_id | sales.cust_id | sales.time_id | sales.channel_id |
sales.promo_id | sales.quantity_sold | sales.amount_sold | sales.year |
sales.month |
+----------------+----------------+----------------+-------------------+-----------------+----------------------+--------------------+-------------+--------------+--+
+----------------+----------------+----------------+-------------------+-----------------+----------------------+--------------------+-------------+--------------+--+
No rows selected (26.96 seconds)
Note that the second query is order of magnitude slower.
My view is that the query in partitioned table has got to go through every
partitioned file to check the existence of the value, whereas in a
non-partitioned table the operation is much faster. Adding more partition and
buckets also adds more load on NameNode as well.
Are there other reasons?
Thanks
Dr Mich Talebzadeh
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<http://login.sybase.com/files/Product_Overviews/ASE-Winning-Strategy-091908.pdf>
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ISBN 978-0-9563693-0-7.
co-author "Sybase Transact SQL Guidelines Best Practices", ISBN
978-0-9759693-0-4
Publications due shortly:
Complex Event Processing in Heterogeneous Environments, ISBN: 978-0-9563693-3-8
Oracle and Sybase, Concepts and Contrasts, ISBN: 978-0-9563693-1-4, volume one
out shortly
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