Performance Improvement in Hive (Properties in HIVE -SITE.XML)

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There are list of Properties Available to increase the Performance in hive. Use the Properties Appropriate to your Need.


<?xml version="1.0" encoding="UTF-8" standalone="no"?>
<?xml-stylesheet type="text/xsl" href="configuration.xsl"?>
<configuration>

<property>
<name>javax.jdo.option.ConnectionURL</name>
<value>jdbc:mysql://localhost:3306/metastore_db_3?createDatabaseIfNotExist=true&amp;useSSL=false</value>
<description>JDBC connect string for a JDBC metastore</description>
</property>

<property>
<name>javax.jdo.option.ConnectionDriverName</name>
<value>com.mysql.jdbc.Driver</value>
<description>Driver class name for a JDBC metastore</description>
</property>

<property>
<name>javax.jdo.option.ConnectionUserName</name>
<value>hiveuser</value>
<description>username to use against metastore database</description>
</property>

<property>
<name>javax.jdo.option.ConnectionPassword</name>
<value>Hive@123</value>
<description>password to use against metastore database</description>
</property>

<property>
<name>hive.metastore.schema.verification</name>
<value>false</value>
<description>Auto creates necessary schema on a startup if one doesn't exist. Set this to false, after creating it once.To enable auto create also set hive.metastore.schema.verification=false. Auto creation is not recommended for production use cases, run schematool command instead.</description>
</property>

<property>
<name>hive.metastore.schema.verification.record.version</name>
<value>true</value>
<description>
When true the current MS version is recorded in the VERSION table. If this is disabled and verification is
enabled the MS will be unusable.
</description>
</property>

<property>
<name>hive.metastore.warehouse.dir</name>
<value>/user/hive-metastore-dir/warehouse</value>
<description>location of default database for the warehouse</description>
</property>

<property>
<name>spark.sql.warehouse.dir</name>
<value>/user/hive-metastore-dir/warehouse</value>
<description>location of default database for the warehouse</description>
</property>

<property>
<name>datanucleus.autoCreateSchema</name>
<value>true</value>
</property>

<property>
<name>datanucleus.fixedDatastore</name>
<value>true</value>
</property>

<property>
<name>datanucleus.autoCreateTables</name>
<value>True</value>
</property>

<property>
<name>hive.support.concurrency</name>
<value>true</value>
<description>
Whether Hive supports concurrency control or not.
A ZooKeeper instance must be up and running when using zookeeper Hive lock manager
</description>
</property>

<property>
<name>hive.enforce.bucketing</name>
<value>true</value>
<description></description>
</property>

<property>
<name>hive.exec.dynamic.partition</name>
<value>true</value>
<description>Whether or not to allow dynamic partitions in DML/DDL.</description>
</property>

<property>
<name>hive.exec.dynamic.partition.mode</name>
<value>nonstrict</value>
<description>
In strict mode, the user must specify at least one static partition
in case the user accidentally overwrites all partitions.
In nonstrict mode all partitions are allowed to be dynamic.
</description>
</property>

<property>
<name>hive.txn.manager</name>
<value>org.apache.hadoop.hive.ql.lockmgr.DbTxnManager</value>
<description></description>
</property>

<property>
<name>hive.compactor.initiator.on</name>
<value>true</value>
<description></description>
</property>

<property>
<name>hive.compactor.worker.threads</name>
<value>1</value>
<description></description>
</property>

<property>
<name>hive.exec.scratchdir</name>
<value>/tmp/hive</value>
<description>Scratch space for Hive jobs</description>
</property>

<property>
<name>hive.metastore.uris</name>
<!-- <value>thrift://localost:9083</value> -->
<value/>
<description>Thrift URI for the remote metastore. Used by metastore client to connect to remote metastore.</description>
</property>

<property>
<name>hive.optimize.bucketmapjoin</name>
<value>true</value>
</property>

<property>
<name>hive.vectorized.execution.enabled</name>
<value>true</value>
</property>

<property>
<name>hive.exec.parallel</name>
<value>true</value>
<description>Whether to execute jobs in parallel</description>
</property>

<property>
<name>mapred.compress.map.output</name>
<value>true</value>
</property>

<property>
<name>mapred.output.compress</name>
<value>true</value>
</property>

<property>
<name>hive.execution.engine</name>
<value>mr</value>
<description>
Expects one of [mr, tez, spark].
Chooses execution engine. Options are: mr (Map reduce, default), tez, spark. While MR
remains the default engine for historical reasons, it is itself a historical engine
and is deprecated in Hive 2 line. It may be removed without further warning.
</description>
</property>

<property>
<name>hive.auto.convert.join</name>
<value>true</value>
<description>Whether Hive enables the optimization about converting common join into mapjoin based on the input file size</description>
</property>

<property>
<name>hive.auto.convert.join.noconditionaltask</name>
<value>true</value>
<description>
Whether Hive enables the optimization about converting common join into mapjoin based on the input file size.
If this parameter is on, and the sum of size for n-1 of the tables/partitions for a n-way join is smaller than the
specified size, the join is directly converted to a mapjoin (there is no conditional task).
</description>
</property>

<property>
<name>hive.auto.convert.join.noconditionaltask.size</name>
<value>10000000</value>
<description>
If hive.auto.convert.join.noconditionaltask is off, this parameter does not take affect.
However, if it is on, and the sum of size for n-1 of the tables/partitions for a n-way join is smaller than this size,
the join is directly converted to a mapjoin(there is no conditional task). The default is 10MB
</description>
</property>

<property>
<name>hive.auto.convert.join.use.nonstaged</name>
<value>false</value>
<description>
For conditional joins, if input stream from a small alias can be directly applied to join operator without
filtering or projection, the alias need not to be pre-staged in distributed cache via mapred local task.
Currently, this is not working with vectorization or tez execution engine.
</description>
</property>

<property>
<name>hive.optimize.skewjoin</name>
<value>true</value>
<description>
Whether to enable skew join optimization.
The algorithm is as follows: At runtime, detect the keys with a large skew. Instead of
processing those keys, store them temporarily in an HDFS directory. In a follow-up map-reduce
job, process those skewed keys. The same key need not be skewed for all the tables, and so,
the follow-up map-reduce job (for the skewed keys) would be much faster, since it would be a
map-join.
</description>
</property>

<property>
<name>hive.skewjoin.key</name>
<value>100000</value>
<description>
Determine if we get a skew key in join. If we see more than the specified number of rows with the same key in join operator,
we think the key as a skew join key.
</description>
</property>

<property>
<name>hive.skewjoin.mapjoin.map.tasks</name>
<value>10000</value>
<description>
Determine the number of map task used in the follow up map join job for a skew join.
It should be used together with hive.skewjoin.mapjoin.min.split to perform a fine grained control.
</description>
</property>

<property>
<name>hive.skewjoin.mapjoin.min.split</name>
<value>33554432</value>
<description>
Determine the number of map task at most used in the follow up map join job for a skew join by specifying
the minimum split size. It should be used together with hive.skewjoin.mapjoin.map.tasks to perform a fine grained control.
</description>
</property>

<property>
<name>hive.optimize.skewjoin</name>
<value>true</value>
<description>
Whether to enable skew join optimization.
The algorithm is as follows: At runtime, detect the keys with a large skew. Instead of
processing those keys, store them temporarily in an HDFS directory. In a follow-up map-reduce
job, process those skewed keys. The same key need not be skewed for all the tables, and so,
the follow-up map-reduce job (for the skewed keys) would be much faster, since it would be a
map-join.
</description>
</property>

<property>
<name>hive.exec.parallel.thread.number</name>
<value>8</value>
<description>How many jobs at most can be executed in parallel</description>
</property>

<property>
<name>hive.cbo.enable</name>
<value>true</value>
<description>Flag to control enabling Cost Based Optimizations using Calcite framework.</description>
</property>

<property>
<name>hive.compute.query.using.stats</name>
<value>true</value>
<description>
When set to true Hive will answer a few queries like count(1) purely using stats
stored in metastore. For basic stats collection turn on the config hive.stats.autogather to true.
For more advanced stats collection need to run analyze table queries.
</description>
</property>

<property>
<name>hive.stats.fetch.partition.stats</name>
<value>true</value>
<description>
Annotation of operator tree with statistics information requires partition level basic
statistics like number of rows, data size and file size. Partition statistics are fetched from
metastore. Fetching partition statistics for each needed partition can be expensive when the
number of partitions is high. This flag can be used to disable fetching of partition statistics
from metastore. When this flag is disabled, Hive will make calls to filesystem to get file sizes
and will estimate the number of rows from row schema.
</description>
</property>

<property>
<name>hive.stats.fetch.column.stats</name>
<value>true</value>
<description>
Annotation of operator tree with statistics information requires column statistics.
Column statistics are fetched from metastore. Fetching column statistics for each needed column
can be expensive when the number of columns is high. This flag can be used to disable fetching
of column statistics from metastore.
</description>
</property>

<property>
<name>hive.stats.autogather</name>
<value>true</value>
<description>A flag to gather statistics automatically during the INSERT OVERWRITE command.</description>
</property>

<property>
<name>hive.stats.dbclass</name>
<value>fs</value>
<description>
Expects one of the pattern in [jdbc(:.*), hbase, counter, custom, fs].
The storage that stores temporary Hive statistics. In filesystem based statistics collection ('fs'),
each task writes statistics it has collected in a file on the filesystem, which will be aggregated
after the job has finished. Supported values are fs (filesystem), jdbc:database (where database
can be derby, mysql, etc.), hbase, counter, and custom as defined in StatsSetupConst.java.
</description>
</property>

<property>
<name>hive.cbo.enable</name>
<value>true</value>
<description>Flag to control enabling Cost Based Optimizations using Calcite framework.</description>
</property>

<property>
<name>hive.compute.query.using.stats</name>
<value>true</value>
<description>
When set to true Hive will answer a few queries like count(1) purely using stats
stored in metastore. For basic stats collection turn on the config hive.stats.autogather to true.
For more advanced stats collection need to run analyze table queries.
</description>
</property>

</configuration>



Thanks,
Have Fun!!!!

Please Leave your Comment Below If you Have Any Clarifications Regarding Installations or Any Suggestions.

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