Thanks helpful
0: jdbc:hive2://rhes564:10010/default> explain dependency select * from sales
where year = 2001 and month = 12;
+-------------------------------------------------------------------------------------------------------------------------------------------------------------------+--+
|
Explain
|
+-------------------------------------------------------------------------------------------------------------------------------------------------------------------+--+
|
{"input_partitions":[{"partitionName":"oraclehadoop@sales@year=2001/month=12"}],"input_tables":[{"tablename":"oraclehadoop@sales","tabletype":"MANAGED_TABLE"}]}
|
In above sales is partitioned on year and month.
My interpretation is that only that partition is searched for?
Dr Mich Talebzadeh
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From: Jörn Franke [mailto:jornfra...@gmail.com]
Sent: 08 January 2016 09:54
To: user@hive.apache.org
Subject: Re: Impact of partitioning on certain queries
Try explain dependency
On 08 Jan 2016, at 10:47, Mich Talebzadeh <m...@peridale.co.uk
<mailto:m...@peridale.co.uk> > wrote:
Thanks Gopal.
Basically the following is true:
1. The storage layer is HDFS
2. The execution engine is MR, Tez, Spark etc
3. The access layer is Hive
When we say the access layer is Hive, is the assumption correct that we are
referring to optimiser (loosly related to the optimiser in RDBMS). For example
is Hive optimiser aware of the number of underlying partitions. The reason I am
asking this question is that with EXPLAIN I only see Table scan and it does
refer to any partition or partition elimination?
Cheers
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-----Original Message-----
From: Gopal Vijayaraghavan [mailto:go...@hortonworks.com] On Behalf Of Gopal
Vijayaraghavan
Sent: 08 January 2016 09:34
To: user@hive.apache.org <mailto:user@hive.apache.org>
Subject: Re: Impact of partitioning on certain queries
> Ok we hope that partitioning improves performance where the predicate
>is on partitioned columns
Nope.
Partitioning *only* improves performance if your queries run with
set hive.mapred.mode=strict;
That's the "use strict" easy way to make sure you're writing good queries.
Even then, schema design in hive is something you need to learn with the
assumption that neither the storage layer, nor the compute layer is part of
"hive".
It floats itself in an "access" layer above both. Not sure there's any legacy
tech to draw parallels with that.
If you haven't seen this before, here's an example of the problem
<http://www.slideshare.net/Hadoop_Summit/hive-at-yahoo-letters-from-the-tren>
http://www.slideshare.net/Hadoop_Summit/hive-at-yahoo-letters-from-the-tren
ches/24
Cheers,
Gopal
