hive-default.xml.template

<?xml version="1.0"?>
<?xml-stylesheet type="text/xsl" href="configuration.xsl"?>
<!--
   Licensed to the Apache Software Foundation (ASF) under one or more
   contributor license agreements.  See the NOTICE file distributed with
   this work for additional information regarding copyright ownership.
   The ASF licenses this file to You under the Apache License, Version 2.0
   (the "License"); you may not use this file except in compliance with
   the License.  You may obtain a copy of the License at

       http://www.apache.org/licenses/LICENSE-2.0

   Unless required by applicable law or agreed to in writing, software
   distributed under the License is distributed on an "AS IS" BASIS,
   WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   See the License for the specific language governing permissions and
   limitations under the License.
-->

<configuration>

<!-- WARNING!!! This file is provided for documentation purposes ONLY!     -->
<!-- WARNING!!! Any changes you make to this file will be ignored by Hive. -->
<!-- WARNING!!! You must make your changes in hive-site.xml instead.       -->


<!-- Hive Execution Parameters -->
<property>
  <name>mapred.reduce.tasks</name>
  <value>-1</value>
    <description>The default number of reduce tasks per job.  Typically set
  to a prime close to the number of available hosts.  Ignored when
  mapred.job.tracker is "local". Hadoop set this to 1 by default, whereas hive uses -1 as its default value.
  By setting this property to -1, Hive will automatically figure out what should be the number of reducers.
  </description>
</property>

<property>
  <name>hive.exec.reducers.bytes.per.reducer</name>
  <value>1000000000</value>
  <description>size per reducer.The default is 1G, i.e if the input size is 10G, it will use 10 reducers.</description>
</property>

<property>
  <name>hive.exec.reducers.max</name>
  <value>999</value>
  <description>max number of reducers will be used. If the one
	specified in the configuration parameter mapred.reduce.tasks is
	negative, hive will use this one as the max number of reducers when
	automatically determine number of reducers.</description>
</property>

<property>
  <name>hive.cli.print.header</name>
  <value>false</value>
  <description>Whether to print the names of the columns in query output.</description>
</property>

<property>
  <name>hive.cli.print.current.db</name>
  <value>false</value>
  <description>Whether to include the current database in the hive prompt.</description>
</property>

<property>
  <name>hive.cli.prompt</name>
  <value>hive</value>
  <description>Command line prompt configuration value. Other hiveconf can be used in
        this configuration value. Variable substitution will only be invoked at the hive
        cli startup.</description>
</property>

<property>
  <name>hive.cli.pretty.output.num.cols</name>
  <value>-1</value>
  <description>The number of columns to use when formatting output generated
        by the DESCRIBE PRETTY table_name command.  If the value of this property
        is -1, then hive will use the auto-detected terminal width.</description>
</property>

<property>
  <name>hive.exec.scratchdir</name>
  <value>/tmp/hive-${user.name}</value>
  <description>Scratch space for Hive jobs</description>
</property>

<property>
  <name>hive.exec.local.scratchdir</name>
  <value>/tmp/${user.name}</value>
  <description>Local scratch space for Hive jobs</description>
</property>

<property>
  <name>hive.test.mode</name>
  <value>false</value>
  <description>whether hive is running in test mode. If yes, it turns on sampling and prefixes the output tablename</description>
</property>

<property>
  <name>hive.test.mode.prefix</name>
  <value>test_</value>
  <description>if hive is running in test mode, prefixes the output table by this string</description>
</property>

<!-- If the input table is not bucketed, the denominator of the tablesample is determinied by the parameter below   -->
<!-- For example, the following query:                                                                              -->
<!--   INSERT OVERWRITE TABLE dest                                                                                  -->
<!--   SELECT col1 from src                                                                                         -->
<!-- would be converted to                                                                                          -->
<!--   INSERT OVERWRITE TABLE test_dest                                                                             -->
<!--   SELECT col1 from src TABLESAMPLE (BUCKET 1 out of 32 on rand(1))                                             -->
<property>
  <name>hive.test.mode.samplefreq</name>
  <value>32</value>
  <description>if hive is running in test mode and table is not bucketed, sampling frequency</description>
</property>

<property>
  <name>hive.test.mode.nosamplelist</name>
  <value></value>
  <description>if hive is running in test mode, dont sample the above comma seperated list of tables</description>
</property>

<property>
  <name>hive.metastore.uris</name>
  <value></value>
  <description>Thrift uri for the remote metastore. Used by metastore client to connect to remote metastore.</description>
</property>

<property>
  <name>javax.jdo.option.ConnectionURL</name>
  <value>jdbc:derby:;databaseName=metastore_db;create=true</value>
  <description>JDBC connect string for a JDBC metastore</description>
</property>

<property>
  <name>javax.jdo.option.ConnectionDriverName</name>
  <value>org.apache.derby.jdbc.EmbeddedDriver</value>
  <description>Driver class name for a JDBC metastore</description>
</property>

<property>
  <name>javax.jdo.PersistenceManagerFactoryClass</name>
  <value>org.datanucleus.api.jdo.JDOPersistenceManagerFactory</value>
  <description>class implementing the jdo persistence</description>
</property>

<property>
  <name>javax.jdo.option.DetachAllOnCommit</name>
  <value>true</value>
  <description>detaches all objects from session so that they can be used after transaction is committed</description>
</property>

<property>
  <name>javax.jdo.option.NonTransactionalRead</name>
  <value>true</value>
  <description>reads outside of transactions</description>
</property>

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

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

<property>
  <name>javax.jdo.option.Multithreaded</name>
  <value>true</value>
  <description>Set this to true if multiple threads access metastore through JDO concurrently.</description>
</property>

<property>
  <name>datanucleus.connectionPoolingType</name>
  <value>DBCP</value>
  <description>Uses a DBCP connection pool for JDBC metastore</description>
</property>

<property>
  <name>datanucleus.validateTables</name>
  <value>false</value>
  <description>validates existing schema against code. turn this on if you want to verify existing schema </description>
</property>

<property>
  <name>datanucleus.validateColumns</name>
  <value>false</value>
  <description>validates existing schema against code. turn this on if you want to verify existing schema </description>
</property>

<property>
  <name>datanucleus.validateConstraints</name>
  <value>false</value>
  <description>validates existing schema against code. turn this on if you want to verify existing schema </description>
</property>

<property>
  <name>datanucleus.storeManagerType</name>
  <value>rdbms</value>
  <description>metadata store type</description>
</property>

<property>
  <name>datanucleus.autoCreateSchema</name>
  <value>true</value>
  <description>creates necessary schema on a startup if one doesn't exist. set this to false, after creating it once</description>
</property>

<property>
  <name>datanucleus.autoStartMechanismMode</name>
  <value>checked</value>
  <description>throw exception if metadata tables are incorrect</description>
</property>

<property>
  <name>datanucleus.transactionIsolation</name>
  <value>read-committed</value>
  <description>Default transaction isolation level for identity generation. </description>
</property>

<property>
  <name>datanucleus.cache.level2</name>
  <value>false</value>
  <description>Use a level 2 cache. Turn this off if metadata is changed independently of hive metastore server</description>
</property>

<property>
  <name>datanucleus.cache.level2.type</name>
  <value>SOFT</value>
  <description>SOFT=soft reference based cache, WEAK=weak reference based cache.</description>
</property>

<property>
  <name>datanucleus.identifierFactory</name>
  <value>datanucleus1</value>
  <description>Name of the identifier factory to use when generating table/column names etc. 'datanucleus1' is used for backward compatibility with DataNucleus v1</description>
</property>

<property>
  <name>datanucleus.plugin.pluginRegistryBundleCheck</name>
  <value>LOG</value>
  <description>Defines what happens when plugin bundles are found and are duplicated [EXCEPTION|LOG|NONE]</description>
</property>

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

<property>
  <name>hive.metastore.execute.setugi</name>
  <value>false</value>
  <description>In unsecure mode, setting this property to true will cause the metastore to execute DFS operations using the client's reported user and group permissions. Note that this property must be set on both the client and server sides. Further note that its best effort. If client sets its to true and server sets it to false, client setting will be ignored.</description>
</property>

<property>
  <name>hive.metastore.event.listeners</name>
  <value></value>
  <description>list of comma seperated listeners for metastore events.</description>
</property>

<property>
  <name>hive.metastore.partition.inherit.table.properties</name>
  <value></value>
  <description>list of comma seperated keys occurring in table properties which will get inherited to newly created partitions. * implies all the keys will get inherited.</description>
</property>

<property>
  <name>hive.metadata.export.location</name>
  <value></value>
  <description>When used in conjunction with the org.apache.hadoop.hive.ql.parse.MetaDataExportListener pre event listener, it is the location to which the metadata will be exported. The default is an empty string, which results in the metadata being exported to the current user's home directory on HDFS.</description>
</property>

<property>
  <name>hive.metadata.move.exported.metadata.to.trash</name>
  <value></value>
  <description>When used in conjunction with the org.apache.hadoop.hive.ql.parse.MetaDataExportListener pre event listener, this setting determines if the metadata that is exported will subsequently be moved to the user's trash directory alongside the dropped table data. This ensures that the metadata will be cleaned up along with the dropped table data.</description>
</property>

<property>
  <name>hive.metastore.partition.name.whitelist.pattern</name>
  <value></value>
  <description>Partition names will be checked against this regex pattern and rejected if not matched.</description>
</property>

<property>
  <name>hive.metastore.disallow.incompatible.col.type.change</name>
  <value></value>
  <description>If true (default is false), ALTER TABLE operations which change the type of 
    a column (say STRING) to an incompatible type (say MAP&lt;STRING, STRING&gt;) are disallowed.  
    RCFile default serde (ColumnarSerde) serializes the values in such a way that the
    datatypes can be converted from string to any type. The map is also serialized as
    a string, which can be read as a string as well. However, with any binary 
    serialization, this is not true. Blocking the ALTER TABLE prevents ClassCastExceptions
    when subsequently trying to access old partitions. 
      
    Primitive types like INT, STRING, BIGINT, etc are compatible with each other and are 
    not blocked.  

    See HIVE-4409 for more details.
</description>
</property>

<property>
  <name>hive.metastore.end.function.listeners</name>
  <value></value>
  <description>list of comma separated listeners for the end of metastore functions.</description>
</property>

<property>
  <name>hive.metastore.event.expiry.duration</name>
  <value>0</value>
  <description>Duration after which events expire from events table (in seconds)</description>
</property>

<property>
  <name>hive.metastore.event.clean.freq</name>
  <value>0</value>
  <description>Frequency at which timer task runs to purge expired events in metastore(in seconds).</description>
</property>

<property>
  <name>hive.metastore.connect.retries</name>
  <value>5</value>
  <description>Number of retries while opening a connection to metastore</description>
</property>

<property>
  <name>hive.metastore.failure.retries</name>
  <value>3</value>
  <description>Number of retries upon failure of Thrift metastore calls</description>
</property>

<property>
  <name>hive.metastore.client.connect.retry.delay</name>
  <value>1</value>
  <description>Number of seconds for the client to wait between consecutive connection attempts</description>
</property>

<property>
  <name>hive.metastore.client.socket.timeout</name>
  <value>20</value>
  <description>MetaStore Client socket timeout in seconds</description>
</property>

<property>
  <name>hive.metastore.rawstore.impl</name>
  <value>org.apache.hadoop.hive.metastore.ObjectStore</value>
  <description>Name of the class that implements org.apache.hadoop.hive.metastore.rawstore interface. This class is used to store and retrieval of raw metadata objects such as table, database</description>
</property>

<property>
  <name>hive.metastore.batch.retrieve.max</name>
  <value>300</value>
  <description>Maximum number of objects (tables/partitions) can be retrieved from metastore in one batch. The higher the number, the less the number of round trips is needed to the Hive metastore server, but it may also cause higher memory requirement at the client side.</description>
</property>

<property>
  <name>hive.metastore.batch.retrieve.table.partition.max</name>
  <value>1000</value>
  <description>Maximum number of table partitions that metastore internally retrieves in one batch.</description>
</property>

<property>
  <name>hive.default.fileformat</name>
  <value>TextFile</value>
  <description>Default file format for CREATE TABLE statement. Options are TextFile and SequenceFile. Users can explicitly say CREATE TABLE ... STORED AS &lt;TEXTFILE|SEQUENCEFILE&gt; to override</description>
</property>

<property>
  <name>hive.default.rcfile.serde</name>
  <value>org.apache.hadoop.hive.serde2.columnar.LazyBinaryColumnarSerDe</value>
  <description>The default SerDe hive will use for the rcfile format</description>
</property>

<property>
  <name>hive.fileformat.check</name>
  <value>true</value>
  <description>Whether to check file format or not when loading data files</description>
</property>

<property>
  <name>hive.map.aggr</name>
  <value>true</value>
  <description>Whether to use map-side aggregation in Hive Group By queries</description>
</property>

<property>
  <name>hive.groupby.skewindata</name>
  <value>false</value>
  <description>Whether there is skew in data to optimize group by queries</description>
</property>

<property>
  <name>hive.optimize.multigroupby.common.distincts</name>
  <value>true</value>
  <description>Whether to optimize a multi-groupby query with the same distinct.
    Consider a query like:

      from src
        insert overwrite table dest1 select col1, count(distinct colx) group by col1
        insert overwrite table dest2 select col2, count(distinct colx) group by col2;

    With this parameter set to true, first we spray by the distinct value (colx), and then
    perform the 2 groups bys. This makes sense if map-side aggregation is turned off. However,
    with maps-side aggregation, it might be useful in some cases to treat the 2 inserts independently, 
    thereby performing the query above in 2MR jobs instead of 3 (due to spraying by distinct key first).
    If this parameter is turned off, we dont consider the fact that the distinct key is the same across
    different MR jobs.
  </description>
</property>

<property>
  <name>hive.groupby.mapaggr.checkinterval</name>
  <value>100000</value>
  <description>Number of rows after which size of the grouping keys/aggregation classes is performed</description>
</property>

<property>
  <name>hive.mapred.local.mem</name>
  <value>0</value>
  <description>For local mode, memory of the mappers/reducers</description>
</property>

<property>
  <name>hive.mapjoin.followby.map.aggr.hash.percentmemory</name>
  <value>0.3</value>
  <description>Portion of total memory to be used by map-side group aggregation hash table, when this group by is followed by map join</description>
</property>

<property>
  <name>hive.map.aggr.hash.force.flush.memory.threshold</name>
  <value>0.9</value>
  <description>The max memory to be used by map-side group aggregation hash table, if the memory usage is higher than this number, force to flush data</description>
</property>

<property>
  <name>hive.map.aggr.hash.percentmemory</name>
  <value>0.5</value>
  <description>Portion of total memory to be used by map-side group aggregation hash table</description>
</property>

<property>
  <name>hive.session.history.enabled</name>
  <value>false</value>
  <description>Whether to log hive query, query plan, runtime statistics etc </description>
</property>

<property>
  <name>hive.map.aggr.hash.min.reduction</name>
  <value>0.5</value>
  <description>Hash aggregation will be turned off if the ratio between hash
  table size and input rows is bigger than this number. Set to 1 to make sure
  hash aggregation is never turned off.</description>
</property>

<property>
  <name>hive.optimize.cp</name>
  <value>true</value>
  <description>Whether to enable column pruner</description>
</property>

<property>
  <name>hive.optimize.index.filter</name>
  <value>false</value>
  <description>Whether to enable automatic use of indexes</description>
</property>

<property>
  <name>hive.optimize.index.groupby</name>
  <value>false</value>
  <description>Whether to enable optimization of group-by queries using Aggregate indexes.</description>
</property>

<property>
  <name>hive.optimize.ppd</name>
  <value>true</value>
  <description>Whether to enable predicate pushdown</description>
</property>

<property>
  <name>hive.optimize.ppd.storage</name>
  <value>true</value>
  <description>Whether to push predicates down into storage handlers.  Ignored when hive.optimize.ppd is false.</description>
</property>

<property>
  <name>hive.ppd.recognizetransivity</name>
  <value>true</value>
  <description>Whether to transitively replicate predicate filters over equijoin conditions.</description>
</property>

<property>
  <name>hive.optimize.groupby</name>
  <value>true</value>
  <description>Whether to enable the bucketed group by from bucketed partitions/tables.</description>
</property>

<property>
  <name>hive.optimize.skewjoin.compiletime</name>
  <value>false</value>
  <description>Whether to create a separate plan for skewed keys for the tables in the join.
    This is based on the skewed keys stored in the metadata. At compile time, the plan is broken
    into different joins: one for the skewed keys, and the other for the remaining keys. And then,
    a union is performed for the 2 joins generated above. So unless the same skewed key is present
    in both the joined tables, the join for the skewed key will be performed as a map-side join.

    The main difference between this paramater and hive.optimize.skewjoin is that this parameter
    uses the skew information stored in the metastore to optimize the plan at compile time itself.
    If there is no skew information in the metadata, this parameter will not have any affect.
    Both hive.optimize.skewjoin.compiletime and hive.optimize.skewjoin should be set to true.
    Ideally, hive.optimize.skewjoin should be renamed as hive.optimize.skewjoin.runtime, but not doing
    so for backward compatibility.

    If the skew information is correctly stored in the metadata, hive.optimize.skewjoin.compiletime
    would change the query plan to take care of it, and hive.optimize.skewjoin will be a no-op.
  </description>
</property>

<property>
  <name>hive.optimize.union.remove</name>
  <value>false</value>
  <description>
    Whether to remove the union and push the operators between union and the filesink above
    union. This avoids an extra scan of the output by union. This is independently useful for union
    queries, and specially useful when hive.optimize.skewjoin.compiletime is set to true, since an
    extra union is inserted.

    The merge is triggered if either of hive.merge.mapfiles or hive.merge.mapredfiles is set to true.
    If the user has set hive.merge.mapfiles to true and hive.merge.mapredfiles to false, the idea was the
    number of reducers are few, so the number of files anyway are small. However, with this optimization,
    we are increasing the number of files possibly by a big margin. So, we merge aggresively.</description>
</property>

<property>
  <name>hive.mapred.supports.subdirectories</name>
  <value>false</value>
  <description>Whether the version of hadoop which is running supports sub-directories for tables/partitions.
    Many hive optimizations can be applied if the hadoop version supports sub-directories for
    tables/partitions. It was added by MAPREDUCE-1501</description>
</property>

<property>
  <name>hive.multigroupby.singlemr</name>
  <value>false</value>
  <description>Whether to optimize multi group by query to generate single M/R
  job plan. If the multi group by query has common group by keys, it will be
  optimized to generate single M/R job.</description>
</property>

<property>
  <name>hive.map.groupby.sorted</name>
  <value>false</value>
  <description>If the bucketing/sorting properties of the table exactly match the grouping key, whether to
    perform the group by in the mapper by using BucketizedHiveInputFormat. The only downside to this
    is that it limits the number of mappers to the number of files.
  </description>
</property>

<property>
  <name>hive.map.groupby.sorted.testmode</name>
  <value>false</value>
  <description>If the bucketing/sorting properties of the table exactly match the grouping key, whether to
    perform the group by in the mapper by using BucketizedHiveInputFormat. If the test mode is set, the plan
    is not converted, but a query property is set to denote the same.
  </description>
</property>

<property>
  <name>hive.new.job.grouping.set.cardinality</name>
  <value>30</value>
  <description>
    Whether a new map-reduce job should be launched for grouping sets/rollups/cubes.
    For a query like: select a, b, c, count(1) from T group by a, b, c with rollup;
    4 rows are created per row: (a, b, c), (a, b, null), (a, null, null), (null, null, null).
    This can lead to explosion across map-reduce boundary if the cardinality of T is very high,
    and map-side aggregation does not do a very good job. 

    This parameter decides if hive should add an additional map-reduce job. If the grouping set
    cardinality (4 in the example above), is more than this value, a new MR job is added under the
    assumption that the orginal group by will reduce the data size.
  </description>
</property>

<property>
  <name>hive.join.emit.interval</name>
  <value>1000</value>
  <description>How many rows in the right-most join operand Hive should buffer before emitting the join result. </description>
</property>

<property>
  <name>hive.join.cache.size</name>
  <value>25000</value>
  <description>How many rows in the joining tables (except the streaming table) should be cached in memory. </description>
</property>

<property>
  <name>hive.smbjoin.cache.rows</name>
  <value>10000</value>
  <description>How many rows with the same key value should be cached in memory per smb joined table. </description>
</property>

<property>
  <name>hive.optimize.skewjoin</name>
  <value>false</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 a 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.mapred.mode</name>
  <value>nonstrict</value>
  <description>The mode in which the hive operations are being performed.
     In strict mode, some risky queries are not allowed to run. They include:
       Cartesian Product.
       No partition being picked up for a query.
       Comparing bigints and strings.
       Comparing bigints and doubles.
       Orderby without limit.
  </description>
</property>

<property>
  <name>hive.enforce.bucketmapjoin</name>
  <value>false</value>
  <description>If the user asked for bucketed map-side join, and it cannot be performed,
    should the query fail or not ? For eg, if the buckets in the tables being joined are
    not a multiple of each other, bucketed map-side join cannot be performed, and the
    query will fail if hive.enforce.bucketmapjoin is set to true.
  </description>
</property>

<property>
  <name>hive.exec.script.maxerrsize</name>
  <value>100000</value>
  <description>Maximum number of bytes a script is allowed to emit to standard error (per map-reduce task). This prevents runaway scripts from filling logs partitions to capacity </description>
</property>

<property>
  <name>hive.exec.script.allow.partial.consumption</name>
  <value>false</value>
  <description> When enabled, this option allows a user script to exit successfully without consuming all the data from the standard input.
  </description>
</property>

<property>
  <name>hive.script.operator.id.env.var</name>
  <value>HIVE_SCRIPT_OPERATOR_ID</value>
  <description> Name of the environment variable that holds the unique script operator ID in the user's transform function (the custom mapper/reducer that the user has specified in the query)
  </description>
</property>

<property>
  <name>hive.script.operator.truncate.env</name>
  <value>false</value>
  <description>Truncate each environment variable for external script in scripts operator to 20KB (to fit system limits)</description>
</property>

<property>
  <name>hive.exec.compress.output</name>
  <value>false</value>
  <description> This controls whether the final outputs of a query (to a local/hdfs file or a hive table) is compressed. The compression codec and other options are determined from hadoop config variables mapred.output.compress* </description>
</property>

<property>
  <name>hive.exec.compress.intermediate</name>
  <value>false</value>
  <description> This controls whether intermediate files produced by hive between multiple map-reduce jobs are compressed. The compression codec and other options are determined from hadoop config variables mapred.output.compress* </description>
</property>

<property>
  <name>hive.exec.parallel</name>
  <value>false</value>
  <description>Whether to execute jobs in parallel</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.exec.rowoffset</name>
  <value>false</value>
  <description>Whether to provide the row offset virtual column</description>
</property>

<property>
  <name>hive.task.progress</name>
  <value>false</value>
  <description>Whether Hive should periodically update task progress counters during execution.  Enabling this allows task progress to be monitored more closely in the job tracker, but may impose a performance penalty.  This flag is automatically set to true for jobs with hive.exec.dynamic.partition set to true.</description>
</property>

<property>
  <name>hive.hwi.war.file</name>
  <value>lib/hive-hwi-@VERSION@.war</value>
  <description>This sets the path to the HWI war file, relative to ${HIVE_HOME}. </description>
</property>

<property>
  <name>hive.hwi.listen.host</name>
  <value>0.0.0.0</value>
  <description>This is the host address the Hive Web Interface will listen on</description>
</property>

<property>
  <name>hive.hwi.listen.port</name>
  <value>9999</value>
  <description>This is the port the Hive Web Interface will listen on</description>
</property>

<property>
  <name>hive.exec.pre.hooks</name>
  <value></value>
  <description>Comma-separated list of pre-execution hooks to be invoked for each statement.  A pre-execution hook is specified as the name of a Java class which implements the org.apache.hadoop.hive.ql.hooks.ExecuteWithHookContext interface.</description>
</property>

<property>
  <name>hive.exec.post.hooks</name>
  <value></value>
  <description>Comma-separated list of post-execution hooks to be invoked for each statement.  A post-execution hook is specified as the name of a Java class which implements the org.apache.hadoop.hive.ql.hooks.ExecuteWithHookContext interface.</description>
</property>

<property>
  <name>hive.exec.failure.hooks</name>
  <value></value>
  <description>Comma-separated list of on-failure hooks to be invoked for each statement.  An on-failure hook is specified as the name of Java class which implements the org.apache.hadoop.hive.ql.hooks.ExecuteWithHookContext interface.</description>
</property>

<property>
  <name>hive.metastore.init.hooks</name>
  <value></value>
  <description>A comma separated list of hooks to be invoked at the beginning of HMSHandler initialization. Aninit hook is specified as the name of Java class which extends org.apache.hadoop.hive.metastore.MetaStoreInitListener.</description>
</property>

<property>
  <name>hive.client.stats.publishers</name>
  <value></value>
  <description>Comma-separated list of statistics publishers to be invoked on counters on each job.  A client stats publisher is specified as the name of a Java class which implements the org.apache.hadoop.hive.ql.stats.ClientStatsPublisher interface.</description>
</property>

<property>
  <name>hive.client.stats.counters</name>
  <value></value>
  <description>Subset of counters that should be of interest for hive.client.stats.publishers (when one wants to limit their publishing). Non-display names should be used</description>
</property>

<property>
  <name>hive.merge.mapfiles</name>
  <value>true</value>
  <description>Merge small files at the end of a map-only job</description>
</property>

<property>
  <name>hive.merge.mapredfiles</name>
  <value>false</value>
  <description>Merge small files at the end of a map-reduce job</description>
</property>

<property>
  <name>hive.heartbeat.interval</name>
  <value>1000</value>
  <description>Send a heartbeat after this interval - used by mapjoin and filter operators</description>
</property>

<property>
  <name>hive.merge.size.per.task</name>
  <value>256000000</value>
  <description>Size of merged files at the end of the job</description>
</property>

<property>
  <name>hive.merge.smallfiles.avgsize</name>
  <value>16000000</value>
  <description>When the average output file size of a job is less than this number, Hive will start an additional map-reduce job to merge the output files into bigger files.  This is only done for map-only jobs if hive.merge.mapfiles is true, and for map-reduce jobs if hive.merge.mapredfiles is true.</description>
</property>

<property>
  <name>hive.mapjoin.smalltable.filesize</name>
  <value>25000000</value>
  <description>The threshold for the input file size of the small tables; if the file size is smaller than this threshold, it will try to convert the common join into map join</description>
</property>

<property>
  <name>hive.ignore.mapjoin.hint</name>
  <value>true</value>
  <description>Ignore the mapjoin hint</description>
</property>

<property>
  <name>hive.mapjoin.localtask.max.memory.usage</name>
  <value>0.90</value>
  <description>This number means how much memory the local task can take to hold the key/value into in-memory hash table; If the local task's memory usage is more than this number, the local task will be abort by themself. It means the data of small table is too large to be hold in the memory.</description>
</property>

<property>
  <name>hive.mapjoin.followby.gby.localtask.max.memory.usage</name>
  <value>0.55</value>
  <description>This number means how much memory the local task can take to hold the key/value into in-memory hash table when this map join followed by a group by; If the local task's memory usage is more than this number, the local task will be abort by themself. It means the data of small table is too large to be hold in the memory.</description>
</property>

<property>
  <name>hive.mapjoin.check.memory.rows</name>
  <value>100000</value>
  <description>The number means after how many rows processed it needs to check the memory usage</description>
</property>

<property>
  <name>hive.auto.convert.join</name>
  <value>false</value>
  <description>Whether Hive enable 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 enable the optimization about converting common join into mapjoin based on the input file 
    size. If this paramater 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.script.auto.progress</name>
  <value>false</value>
  <description>Whether Hive Tranform/Map/Reduce Clause should automatically send progress information to TaskTracker to avoid the task getting killed because of inactivity.  Hive sends progress information when the script is outputting to stderr.  This option removes the need of periodically producing stderr messages, but users should be cautious because this may prevent infinite loops in the scripts to be killed by TaskTracker.  </description>
</property>

<property>
  <name>hive.script.serde</name>
  <value>org.apache.hadoop.hive.serde2.lazy.LazySimpleSerDe</value>
  <description>The default serde for trasmitting input data to and reading output data from the user scripts. </description>
</property>

<property>
  <name>hive.binary.record.max.length</name>
  <value>1000</value>
  <description>Read from a binary stream and treat each hive.binary.record.max.length bytes as a record.
  The last record before the end of stream can have less than hive.binary.record.max.length bytes</description>
</property>
    
<property>
  <name>hive.server2.transport.mode</name>
  <value>binary</value>
  <description>Server transport mode. "binary" or "http".</description>
</property>    

<property>
  <name>hive.server2.thrift.http.port</name>
  <value>10001</value>
  <description>Port number when in HTTP mode.</description>
</property> 

<property>
  <name>hive.server2.thrift.http.path</name>
  <value>cliservice</value>
  <description>Path component of URL endpoint when in HTTP mode.</description>
</property> 

<property>
  <name>hive.server2.thrift.http.min.worker.threads</name>
  <value>5</value>
  <description>Minimum number of worker threads when in HTTP mode.</description>
</property> 

<property>
  <name>hive.server2.thrift.http.max.worker.threads</name>
  <value>500</value>
  <description>Maximum number of worker threads when in HTTP mode.</description>
</property> 

<property>
  <name>hive.script.recordreader</name>
  <value>org.apache.hadoop.hive.ql.exec.TextRecordReader</value>
  <description>The default record reader for reading data from the user scripts. </description>
</property>

<property>
  <name>stream.stderr.reporter.prefix</name>
  <value>reporter:</value>
  <description>Streaming jobs that log to stardard error with this prefix can log counter or status information.</description>
</property>

<property>
  <name>stream.stderr.reporter.enabled</name>
  <value>true</value>
  <description>Enable consumption of status and counter messages for streaming jobs.</description>
</property>

<property>
  <name>hive.script.recordwriter</name>
  <value>org.apache.hadoop.hive.ql.exec.TextRecordWriter</value>
  <description>The default record writer for writing data to the user scripts. </description>
</property>

<property>
  <name>hive.input.format</name>
  <value>org.apache.hadoop.hive.ql.io.CombineHiveInputFormat</value>
  <description>The default input format. Set this to HiveInputFormat if you encounter problems with CombineHiveInputFormat.</description>
</property>

<property>
  <name>hive.udtf.auto.progress</name>
  <value>false</value>
  <description>Whether Hive should automatically send progress information to TaskTracker when using UDTF's to prevent the task getting killed because of inactivity.  Users should be cautious because this may prevent TaskTracker from killing tasks with infinte loops.  </description>
</property>

<property>
  <name>hive.mapred.reduce.tasks.speculative.execution</name>
  <value>true</value>
  <description>Whether speculative execution for reducers should be turned on. </description>
</property>

<property>
  <name>hive.exec.counters.pull.interval</name>
  <value>1000</value>
  <description>The interval with which to poll the JobTracker for the counters the running job. The smaller it is the more load there will be on the jobtracker, the higher it is the less granular the caught will be.</description>
</property>

<property>
  <name>hive.querylog.location</name>
  <value>/tmp/${user.name}</value>
  <description>
    Location of Hive run time structured log file
  </description>
</property>

<property>
  <name>hive.querylog.enable.plan.progress</name>
  <value>true</value>
  <description>
    Whether to log the plan's progress every time a job's progress is checked.
    These logs are written to the location specified by hive.querylog.location
  </description>
</property>

<property>
  <name>hive.querylog.plan.progress.interval</name>
  <value>60000</value>
  <description>
    The interval to wait between logging the plan's progress in milliseconds.
    If there is a whole number percentage change in the progress of the mappers or the reducers,
    the progress is logged regardless of this value.
    The actual interval will be the ceiling of (this value divided by the value of
    hive.exec.counters.pull.interval) multiplied by the value of hive.exec.counters.pull.interval
    I.e. if it is not divide evenly by the value of hive.exec.counters.pull.interval it will be
    logged less frequently than specified.
    This only has an effect if hive.querylog.enable.plan.progress is set to true.
  </description>
</property>

<property>
  <name>hive.enforce.bucketing</name>
  <value>false</value>
  <description>Whether bucketing is enforced. If true, while inserting into the table, bucketing is enforced. </description>
</property>

<property>
  <name>hive.enforce.sorting</name>
  <value>false</value>
  <description>Whether sorting is enforced. If true, while inserting into the table, sorting is enforced. </description>
</property>

<property>
  <name>hive.optimize.bucketingsorting</name>
  <value>true</value>
  <description>If hive.enforce.bucketing or hive.enforce.sorting is true, dont create a reducer for enforcing
    bucketing/sorting for queries of the form: 
    insert overwrite table T2 select * from T1;
    where T1 and T2 are bucketed/sorted by the same keys into the same number of buckets.
  </description>
</property>

<property>
  <name>hive.enforce.sortmergebucketmapjoin</name>
  <value>false</value>
  <description>If the user asked for sort-merge bucketed map-side join, and it cannot be performed,
    should the query fail or not ?
  </description>
</property>

<property>
  <name>hive.auto.convert.sortmerge.join</name>
  <value>false</value>
  <description>Will the join be automatically converted to a sort-merge join, if the joined tables pass
    the criteria for sort-merge join.
  </description>
</property>

<property>
  <name>hive.auto.convert.sortmerge.join.bigtable.selection.policy</name>
  <value>org.apache.hadoop.hive.ql.optimizer.AvgPartitionSizeBasedBigTableSelectorForAutoSMJ</value>
  <description>The policy to choose the big table for automatic conversion to sort-merge join.
    By default, the table with the largest partitions is assigned the big table. All policies are:
    . based on position of the table - the leftmost table is selected
    org.apache.hadoop.hive.ql.optimizer.LeftmostBigTableSMJ.
    . based on total size (all the partitions selected in the query) of the table 
    org.apache.hadoop.hive.ql.optimizer.TableSizeBasedBigTableSelectorForAutoSMJ.
    . based on average size (all the partitions selected in the query) of the table 
    org.apache.hadoop.hive.ql.optimizer.AvgPartitionSizeBasedBigTableSelectorForAutoSMJ.
    New policies can be added in future.
  </description>
</property>

<property>
  <name>hive.auto.convert.sortmerge.join.to.mapjoin</name>
  <value>false</value>
  <description>If hive.auto.convert.sortmerge.join is set to true, and a join was converted to a sort-merge join,
    this parameter decides whether each table should be tried as a big table, and effectviely a map-join should be
    tried. That would create a conditional task with n+1 children for a n-way join (1 child for each table as the
    big table), and the backup task will be the sort-merge join. In some casess, a map-join would be faster than a
    sort-merge join, if there is no advantage of having the output bucketed and sorted. For eg. if a very big sorted
    and bucketed table with few files (say 10 files) are being joined with a very small sorter and bucketed table
    with few files (10 files), the sort-merge join will only use 10 mappers, and a simple map-only join might be faster
    if the complete small table can fit in memory, and a map-join can be performed.
  </description>
</property>

<property>
  <name>hive.metastore.ds.connection.url.hook</name>
  <value></value>
  <description>Name of the hook to use for retriving the JDO connection URL. If empty, the value in javax.jdo.option.ConnectionURL is used </description>
</property>

<property>
  <name>hive.metastore.ds.retry.attempts</name>
  <value>1</value>
  <description>The number of times to retry a metastore call if there were a connection error</description>
</property>

<property>
   <name>hive.metastore.ds.retry.interval</name>
   <value>1000</value>
   <description>The number of miliseconds between metastore retry attempts</description>
</property>

<property>
  <name>hive.metastore.server.min.threads</name>
  <value>200</value>
  <description>Minimum number of worker threads in the Thrift server's pool.</description>
</property>

<property>
  <name>hive.metastore.server.max.threads</name>
  <value>100000</value>
  <description>Maximum number of worker threads in the Thrift server's pool.</description>
</property>

<property>
  <name>hive.metastore.server.tcp.keepalive</name>
  <value>true</value>
  <description>Whether to enable TCP keepalive for the metastore server. Keepalive will prevent accumulation of half-open connections.</description>
</property>

<property>
  <name>hive.metastore.sasl.enabled</name>
  <value>false</value>
  <description>If true, the metastore thrift interface will be secured with SASL. Clients must authenticate with Kerberos.</description>
</property>

<property>
  <name>hive.metastore.thrift.framed.transport.enabled</name>
  <value>false</value>
  <description>If true, the metastore thrift interface will use TFramedTransport. When false (default) a standard TTransport is used.</description>
</property>

<property>
  <name>hive.metastore.kerberos.keytab.file</name>
  <value></value>
  <description>The path to the Kerberos Keytab file containing the metastore thrift server's service principal.</description>
</property>

<property>
  <name>hive.metastore.kerberos.principal</name>
  <value>hive-metastore/_HOST@EXAMPLE.COM</value>
  <description>The service principal for the metastore thrift server. The special string _HOST will be replaced automatically with the correct host name.</description>
</property>

<property>
  <name>hive.cluster.delegation.token.store.class</name>
  <value>org.apache.hadoop.hive.thrift.MemoryTokenStore</value>
  <description>The delegation token store implementation. Set to org.apache.hadoop.hive.thrift.ZooKeeperTokenStore for load-balanced cluster.</description>
</property>

<property>
  <name>hive.cluster.delegation.token.store.zookeeper.connectString</name>
  <value>localhost:2181</value>
  <description>The ZooKeeper token store connect string.</description>
</property>

<property>
  <name>hive.cluster.delegation.token.store.zookeeper.znode</name>
  <value>/hive/cluster/delegation</value>
  <description>The root path for token store data.</description>
</property>

<property>
  <name>hive.cluster.delegation.token.store.zookeeper.acl</name>
  <value>sasl:hive/host1@EXAMPLE.COM:cdrwa,sasl:hive/host2@EXAMPLE.COM:cdrwa</value>
  <description>ACL for token store entries. List comma separated all server principals for the cluster.</description>
</property>

<property>
  <name>hive.metastore.cache.pinobjtypes</name>
  <value>Table,StorageDescriptor,SerDeInfo,Partition,Database,Type,FieldSchema,Order</value>
  <description>List of comma separated metastore object types that should be pinned in the cache</description>
</property>

<property>
  <name>hive.optimize.reducededuplication</name>
  <value>true</value>
  <description>Remove extra map-reduce jobs if the data is already clustered by the same key which needs to be used again. This should always be set to true. Since it is a new feature, it has been made configurable.</description>
</property>

<property>
  <name>hive.optimize.correlation</name>
  <value>false</value>
  <description>exploit intra-query correlations.</description>
</property>

<property>
  <name>hive.optimize.reducededuplication.min.reducer</name>
  <value>4</value>
  <description>Reduce deduplication merges two RSs by moving key/parts/reducer-num of the child RS to parent RS.
  That means if reducer-num of the child RS is fixed (order by or forced bucketing) and small, it can make very slow, single MR.
  The optimization will be disabled if number of reducers is less than specified value.</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>strict</value>
  <description>In strict mode, the user must specify at least one static partition in case the user accidentally overwrites all partitions.</description>
</property>

<property>
  <name>hive.exec.max.dynamic.partitions</name>
  <value>1000</value>
  <description>Maximum number of dynamic partitions allowed to be created in total.</description>
</property>

<property>
  <name>hive.exec.max.dynamic.partitions.pernode</name>
  <value>100</value>
  <description>Maximum number of dynamic partitions allowed to be created in each mapper/reducer node.</description>
</property>

<property>
  <name>hive.exec.max.created.files</name>
  <value>100000</value>
  <description>Maximum number of HDFS files created by all mappers/reducers in a MapReduce job.</description>
</property>

<property>
  <name>hive.exec.default.partition.name</name>
  <value>__HIVE_DEFAULT_PARTITION__</value>
  <description>The default partition name in case the dynamic partition column value is null/empty string or anyother values that cannot be escaped. This value must not contain any special character used in HDFS URI (e.g., ':', '%', '/' etc). The user has to be aware that the dynamic partition value should not contain this value to avoid confusions.</description>
</property>

<property>
  <name>hive.stats.dbclass</name>
  <value>jdbc:derby</value>
  <description>The default database that stores temporary hive statistics.</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.jdbcdriver</name>
  <value>org.apache.derby.jdbc.EmbeddedDriver</value>
  <description>The JDBC driver for the database that stores temporary hive statistics.</description>
</property>

<property>
  <name>hive.stats.dbconnectionstring</name>
  <value>jdbc:derby:;databaseName=TempStatsStore;create=true</value>
  <description>The default connection string for the database that stores temporary hive statistics.</description>
</property>

<property>
  <name>hive.stats.default.publisher</name>
  <value></value>
  <description>The Java class (implementing the StatsPublisher interface) that is used by default if hive.stats.dbclass is not JDBC or HBase.</description>
</property>

<property>
  <name>hive.stats.default.aggregator</name>
  <value></value>
  <description>The Java class (implementing the StatsAggregator interface) that is used by default if hive.stats.dbclass is not JDBC or HBase.</description>
</property>

<property>
  <name>hive.stats.jdbc.timeout</name>
  <value>30</value>
  <description>Timeout value (number of seconds) used by JDBC connection and statements.</description>
</property>

<property>
  <name>hive.stats.retries.max</name>
  <value>0</value>
  <description>Maximum number of retries when stats publisher/aggregator got an exception updating intermediate database. Default is no tries on failures.</description>
</property>

<property>
  <name>hive.stats.retries.wait</name>
  <value>3000</value>
  <description>The base waiting window (in milliseconds) before the next retry. The actual wait time is calculated by baseWindow * failues  baseWindow * (failure  1) * (random number between [0.0,1.0]).</description>
</property>

<property>
  <name>hive.stats.reliable</name>
  <value>false</value>
  <description>Whether queries will fail because stats cannot be collected completely accurately.
    If this is set to true, reading/writing from/into a partition may fail becuase the stats
    could not be computed accurately.
  </description>
</property>

<property>
  <name>hive.stats.collect.tablekeys</name>
  <value>false</value>
  <description>Whether join and group by keys on tables are derived and maintained in the QueryPlan.
    This is useful to identify how tables are accessed and to determine if they should be bucketed.
  </description>
</property>

<property>
  <name>hive.stats.collect.scancols</name>
  <value>false</value>
  <description>Whether column accesses are tracked in the QueryPlan.
    This is useful to identify how tables are accessed and to determine if there are wasted columns that can be trimmed.
  </description>
</property>

<property>
  <name>hive.stats.ndv.error</name>
  <value>20.0</value>
  <description>Standard error expressed in percentage. Provides a tradeoff between accuracy and compute cost.A lower value for error indicates higher accuracy and a higher compute cost.
  </description>
</property>

<property>
  <name>hive.stats.key.prefix.max.length</name>
  <value>200</value>
  <description>
    Determines if when the prefix of the key used for intermediate stats collection
    exceeds a certain length, a hash of the key is used instead.  If the value &lt; 0 then hashing
    is never used, if the value >= 0 then hashing is used only when the key prefixes length
    exceeds that value.  The key prefix is defined as everything preceding the task ID in the key.
  </description>
</property>

<property>
  <name>hive.support.concurrency</name>
  <value>false</value>
  <description>Whether hive supports concurrency or not. A zookeeper instance must be up and running for the default hive lock manager to support read-write locks.</description>
</property>

<property>
  <name>hive.lock.numretries</name>
  <value>100</value>
  <description>The number of times you want to try to get all the locks</description>
</property>

<property>
  <name>hive.unlock.numretries</name>
  <value>10</value>
  <description>The number of times you want to retry to do one unlock</description>
</property>

<property>
  <name>hive.lock.sleep.between.retries</name>
  <value>60</value>
  <description>The sleep time (in seconds) between various retries</description>
</property>

<property>
  <name>hive.zookeeper.quorum</name>
  <value></value>
  <description>The list of zookeeper servers to talk to. This is only needed for read/write locks.</description>
</property>

<property>
  <name>hive.zookeeper.client.port</name>
  <value>2181</value>
  <description>The port of zookeeper servers to talk to. This is only needed for read/write locks.</description>
</property>

<property>
  <name>hive.zookeeper.session.timeout</name>
  <value>600000</value>
  <description>Zookeeper client's session timeout. The client is disconnected, and as a result, all locks released, if a heartbeat is not sent in the timeout.</description>
</property>

<property>
  <name>hive.zookeeper.namespace</name>
  <value>hive_zookeeper_namespace</value>
  <description>The parent node under which all zookeeper nodes are created.</description>
</property>

<property>
  <name>hive.zookeeper.clean.extra.nodes</name>
  <value>false</value>
  <description>Clean extra nodes at the end of the session.</description>
</property>

<property>
  <name>fs.har.impl</name>
  <value>org.apache.hadoop.hive.shims.HiveHarFileSystem</value>
  <description>The implementation for accessing Hadoop Archives. Note that this won't be applicable to Hadoop vers less than 0.20</description>
</property>

<property>
  <name>hive.archive.enabled</name>
  <value>false</value>
  <description>Whether archiving operations are permitted</description>
</property>

<property>
  <name>hive.fetch.output.serde</name>
  <value>org.apache.hadoop.hive.serde2.DelimitedJSONSerDe</value>
  <description>The serde used by FetchTask to serialize the fetch output.</description>
</property>

<property>
  <name>hive.exec.mode.local.auto</name>
  <value>false</value>
  <description> Let hive determine whether to run in local mode automatically </description>
</property>

<property>
  <name>hive.exec.drop.ignorenonexistent</name>
  <value>true</value>
  <description>
    Do not report an error if DROP TABLE/VIEW specifies a non-existent table/view
  </description>
</property>

<property>
  <name>hive.exec.show.job.failure.debug.info</name>
  <value>true</value>
  <description>
  	If a job fails, whether to provide a link in the CLI to the task with the
  	most failures, along with debugging hints if applicable.
  </description>
</property>

<property>
  <name>hive.auto.progress.timeout</name>
  <value>0</value>
  <description>
    How long to run autoprogressor for the script/UDTF operators (in seconds).
    Set to 0 for forever.
  </description>
</property>

<!-- HBase Storage Handler Parameters -->

<property>
  <name>hive.hbase.wal.enabled</name>
  <value>true</value>
  <description>Whether writes to HBase should be forced to the write-ahead log.  Disabling this improves HBase write performance at the risk of lost writes in case of a crash.</description>
</property>

<property>
  <name>hive.table.parameters.default</name>
  <value></value>
  <description>Default property values for newly created tables</description>
</property>

<property>
  <name>hive.entity.separator</name>
  <value>@</value>
  <description>Separator used to construct names of tables and partitions. For example, dbname@tablename@partitionname</description>
</property>

<property>
  <name>hive.ddl.createtablelike.properties.whitelist</name>
  <value></value>
  <description>Table Properties to copy over when executing a Create Table Like.</description>
</property>

<property>
  <name>hive.variable.substitute</name>
  <value>true</value>
  <description>This enables substitution using syntax like ${var} ${system:var} and ${env:var}.</description>
</property>

<property>
  <name>hive.variable.substitute.depth</name>
  <value>40</value>
  <description>The maximum replacements the substitution engine will do.</description>
</property>

<property>
  <name>hive.conf.validation</name>
  <value>true</value>
  <description>Eables type checking for registered hive configurations</description>
</property>

<property>
  <name>hive.security.authorization.enabled</name>
  <value>false</value>
  <description>enable or disable the hive client authorization</description>
</property>

<property>
  <name>hive.security.authorization.manager</name>
  <value>org.apache.hadoop.hive.ql.security.authorization.DefaultHiveAuthorizationProvider</value>
  <description>the hive client authorization manager class name.
  The user defined authorization class should implement interface org.apache.hadoop.hive.ql.security.authorization.HiveAuthorizationProvider.
  </description>
</property>

<property>
  <name>hive.security.metastore.authorization.manager</name>
  <value>org.apache.hadoop.hive.ql.security.authorization.DefaultHiveMetastoreAuthorizationProvider</value>
  <description>authorization manager class name to be used in the metastore for authorization.
  The user defined authorization class should implement interface org.apache.hadoop.hive.ql.security.authorization.HiveMetastoreAuthorizationProvider. 
  </description>
</property>

<property>
  <name>hive.security.authenticator.manager</name>
  <value>org.apache.hadoop.hive.ql.security.HadoopDefaultAuthenticator</value>
  <description>hive client authenticator manager class name.
  The user defined authenticator should implement interface org.apache.hadoop.hive.ql.security.HiveAuthenticationProvider.</description>
</property>

<property>
  <name>hive.security.metastore.authenticator.manager</name>
  <value>org.apache.hadoop.hive.ql.security.HadoopDefaultMetastoreAuthenticator</value>
  <description>authenticator manager class name to be used in the metastore for authentication. 
  The user defined authenticator should implement interface org.apache.hadoop.hive.ql.security.HiveAuthenticationProvider.</description>
</property>

<property>
  <name>hive.security.authorization.createtable.user.grants</name>
  <value></value>
  <description>the privileges automatically granted to some users whenever a table gets created.
   An example like "userX,userY:select;userZ:create" will grant select privilege to userX and userY,
   and grant create privilege to userZ whenever a new table created.</description>
</property>

<property>
  <name>hive.security.authorization.createtable.group.grants</name>
  <value></value>
  <description>the privileges automatically granted to some groups whenever a table gets created.
   An example like "groupX,groupY:select;groupZ:create" will grant select privilege to groupX and groupY,
   and grant create privilege to groupZ whenever a new table created.</description>
</property>

<property>
  <name>hive.security.authorization.createtable.role.grants</name>
  <value></value>
  <description>the privileges automatically granted to some roles whenever a table gets created.
   An example like "roleX,roleY:select;roleZ:create" will grant select privilege to roleX and roleY,
   and grant create privilege to roleZ whenever a new table created.</description>
</property>

<property>
  <name>hive.security.authorization.createtable.owner.grants</name>
  <value></value>
  <description>the privileges automatically granted to the owner whenever a table gets created.
   An example like "select,drop" will grant select and drop privilege to the owner of the table</description>
</property>

<property>
  <name>hive.metastore.authorization.storage.checks</name>
  <value>false</value>
  <description>Should the metastore do authorization checks against the underlying storage
  for operations like drop-partition (disallow the drop-partition if the user in
  question doesn't have permissions to delete the corresponding directory
  on the storage).</description>
</property>

<property>
  <name>hive.error.on.empty.partition</name>
  <value>false</value>
  <description>Whether to throw an excpetion if dynamic partition insert generates empty results.</description>
</property>

<property>
  <name>hive.index.compact.file.ignore.hdfs</name>
  <value>false</value>
  <description>True the hdfs location stored in the index file will be igbored at runtime.
  If the data got moved or the name of the cluster got changed, the index data should still be usable.</description>
</property>

<property>
  <name>hive.optimize.index.filter.compact.minsize</name>
  <value>5368709120</value>
  <description>Minimum size (in bytes) of the inputs on which a compact index is automatically used.</description>
</property>

<property>
  <name>hive.optimize.index.filter.compact.maxsize</name>
  <value>-1</value>
  <description>Maximum size (in bytes) of the inputs on which a compact index is automatically used.
  A negative number is equivalent to infinity.</description>
</property>

<property>
  <name>hive.index.compact.query.max.size</name>
  <value>10737418240</value>
  <description>The maximum number of bytes that a query using the compact index can read. Negative value is equivalent to infinity.</description>
</property>

<property>
  <name>hive.index.compact.query.max.entries</name>
  <value>10000000</value>
  <description>The maximum number of index entries to read during a query that uses the compact index. Negative value is equivalent to infinity.</description>
</property>

<property>
  <name>hive.index.compact.binary.search</name>
  <value>true</value>
  <description>Whether or not to use a binary search to find the entries in an index table that match the filter, where possible</description>
</property>

<property>
  <name>hive.exim.uri.scheme.whitelist</name>
  <value>hdfs,pfile</value>
  <description>A comma separated list of acceptable URI schemes for import and export.</description>
</property>

<property>
  <name>hive.lock.mapred.only.operation</name>
  <value>false</value>
  <description>This param is to control whether or not only do lock on queries
  that need to execute at least one mapred job.</description>
</property>

<property>
  <name>hive.limit.row.max.size</name>
  <value>100000</value>
  <description>When trying a smaller subset of data for simple LIMIT, how much size we need to guarantee
   each row to have at least.</description>
</property>

<property>
  <name>hive.limit.optimize.limit.file</name>
  <value>10</value>
  <description>When trying a smaller subset of data for simple LIMIT, maximum number of files we can
   sample.</description>
</property>

<property>
  <name>hive.limit.optimize.enable</name>
  <value>false</value>
  <description>Whether to enable to optimization to trying a smaller subset of data for simple LIMIT first.</description>
</property>

<property>
  <name>hive.limit.optimize.fetch.max</name>
  <value>50000</value>
  <description>Maximum number of rows allowed for a smaller subset of data for simple LIMIT, if it is a fetch query.
   Insert queries are not restricted by this limit.</description>
</property>

<property>
  <name>hive.limit.pushdown.memory.usage</name>
  <value>0.3f</value>
  <description>The max memory to be used for hash in RS operator for top K selection.</description>
</property>

<property>
  <name>hive.rework.mapredwork</name>
  <value>false</value>
  <description>should rework the mapred work or not.
  This is first introduced by SymlinkTextInputFormat to replace symlink files with real paths at compile time.</description>
</property>

<property>
  <name>hive.exec.concatenate.check.index</name>
  <value>true</value>
  <description>If this sets to true, hive will throw error when doing
   'alter table tbl_name [partSpec] concatenate' on a table/partition
    that has indexes on it. The reason the user want to set this to true
    is because it can help user to avoid handling all index drop, recreation,
    rebuild work. This is very helpful for tables with thousands of partitions.</description>
</property>

<property>
  <name>hive.sample.seednumber</name>
  <value>0</value>
  <description>A number used to percentage sampling. By changing this number, user will change the subsets
   of data sampled.</description>
</property>

<property>
	<name>hive.io.exception.handlers</name>
	<value></value>
	<description>A list of io exception handler class names. This is used
		to construct a list exception handlers to handle exceptions thrown
		by record readers</description>
</property>

<property>
  <name>hive.autogen.columnalias.prefix.label</name>
  <value>_c</value>
  <description>String used as a prefix when auto generating column alias.
  By default the prefix label will be appended with a column position number to form the column alias. Auto generation would happen if an aggregate function is used in a select clause without an explicit alias.</description>
</property>

<property>
  <name>hive.autogen.columnalias.prefix.includefuncname</name>
  <value>false</value>
  <description>Whether to include function name in the column alias auto generated by hive.</description>
</property>

<property>
  <name>hive.exec.perf.logger</name>
  <value>org.apache.hadoop.hive.ql.log.PerfLogger</value>
  <description>The class responsible logging client side performance metrics.  Must be a subclass of org.apache.hadoop.hive.ql.log.PerfLogger</description>
</property>

<property>
  <name>hive.start.cleanup.scratchdir</name>
  <value>false</value>
  <description>To cleanup the hive scratchdir while starting the hive server</description>
</property>

<property>
  <name>hive.output.file.extension</name>
  <value></value>
  <description>String used as a file extension for output files. If not set, defaults to the codec extension for text files (e.g. ".gz"), or no extension otherwise.</description>
</property>

<property>
  <name>hive.insert.into.multilevel.dirs</name>
  <value>false</value>
  <description>Where to insert into multilevel directories like
  "insert directory '/HIVEFT25686/chinna/' from table"</description>
</property>

<property>
  <name>hive.warehouse.subdir.inherit.perms</name>
  <value>false</value>
  <description>Set this to true if the the table directories should inherit the
    permission of the warehouse or database directory instead of being created
    with the permissions derived from dfs umask</description>
</property>

<property>
  <name>hive.exec.job.debug.capture.stacktraces</name>
  <value>true</value>
  <description>Whether or not stack traces parsed from the task logs of a sampled failed task for
  			   each failed job should be stored in the SessionState
  </description>
</property>

<property>
  <name>hive.exec.driver.run.hooks</name>
  <value></value>
  <description>A comma separated list of hooks which implement HiveDriverRunHook and will be run at the
  			   beginning and end of Driver.run, these will be run in the order specified
  </description>
</property>

<property>
  <name>hive.ddl.output.format</name>
  <value>text</value>
  <description>
    The data format to use for DDL output.  One of "text" (for human
    readable text) or "json" (for a json object).
  </description>
</property>

<property>
  <name>hive.transform.escape.input</name>
  <value>false</value>
  <description>
    This adds an option to escape special chars (newlines, carriage returns and
    tabs) when they are passed to the user script. This is useful if the hive tables
    can contain data that contains special characters.
  </description>
</property>

<property>
  <name>hive.exec.rcfile.use.explicit.header</name>
  <value>true</value>
  <description>
    If this is set the header for RC Files will simply be RCF.  If this is not
    set the header will be that borrowed from sequence files, e.g. SEQ- followed
    by the input and output RC File formats.
  </description>
</property>

<property>
  <name>hive.exec.orc.dictionary.key.size.threshold</name>
  <value>0.8</value>
  <description>
    If the number of keys in a dictionary is greater than this fraction of the total number of
    non-null rows, turn off dictionary encoding.  Use 1 to always use dictionary encoding.
  </description>
</property>

<property>
  <name>hive.multi.insert.move.tasks.share.dependencies</name>
  <value>false</value>
  <description>
    If this is set all move tasks for tables/partitions (not directories) at the end of a
    multi-insert query will only begin once the dependencies for all these move tasks have been
    met.
    Advantages: If concurrency is enabled, the locks will only be released once the query has
                finished, so with this config enabled, the time when the table/partition is
                generated will be much closer to when the lock on it is released.
    Disadvantages: If concurrency is not enabled, with this disabled, the tables/partitions which
                   are produced by this query and finish earlier will be available for querying
                   much earlier.  Since the locks are only released once the query finishes, this
                   does not apply if concurrency is enabled.
  </description>
</property>

<property>
  <name>hive.fetch.task.conversion</name>
  <value>minimal</value>
  <description>
    Some select queries can be converted to single FETCH task minimizing latency.
    Currently the query should be single sourced not having any subquery and should not have
    any aggregations or distincts (which incurrs RS), lateral views and joins.
    1. minimal : SELECT STAR, FILTER on partition columns, LIMIT only
    2. more    : SELECT, FILTER, LIMIT only (TABLESAMPLE, virtual columns)
  </description>
</property>

<property>
  <name>hive.cache.expr.evaluation</name>
  <value>true</value>
  <description>
    If true, evaluation result of deterministic expression referenced twice or more will be cached.
    For example, in filter condition like ".. where key + 10 > 10 or key + 10 = 0"
    "key + 10" will be evaluated/cached once and reused for following expression ("key + 10 = 0").
    Currently, this is applied only to expressions in select or filter operator.
  </description>
</property>



<property>
  <name>hive.hmshandler.retry.attempts</name>
  <value>1</value>
  <description>The number of times to retry a HMSHandler call if there were a connection error</description>
</property>

<property>
   <name>hive.hmshandler.retry.interval</name>
   <value>1000</value>
   <description>The number of miliseconds between HMSHandler retry attempts</description>
</property>

<property>
   <name>hive.server.read.socket.timeout</name>
   <value>10</value>
   <description>Timeout for the HiveServer to close the connection if no response from the client in N seconds, defaults to 10 seconds.</description>
</property>

<property>
   <name>hive.server.tcp.keepalive</name>
   <value>true</value>
   <description>Whether to enable TCP keepalive for the Hive server. Keepalive will prevent accumulation of half-open connections.</description>
</property>

<property>
   <name>hive.decode.partition.name</name>
   <value>false</value>
   <description>Whether to show the unquoted partition names in query results.</description>
</property>

<property>
  <name>hive.log4j.file</name>
  <value></value>
  <description>Hive log4j configuration file.
  If the property is not set, then logging will be initialized using hive-log4j.properties found on the classpath.
  If the property is set, the value must be a valid URI (java.net.URI, e.g. "file:///tmp/my-logging.properties"), which you can then extract a URL from and pass to PropertyConfigurator.configure(URL).</description>
</property>

<property>
  <name>hive.exec.log4j.file</name>
  <value></value>
  <description>Hive log4j configuration file for execution mode(sub command).
  If the property is not set, then logging will be initialized using hive-exec-log4j.properties found on the classpath.
  If the property is set, the value must be a valid URI (java.net.URI, e.g. "file:///tmp/my-logging.properties"), which you can then extract a URL from and pass to PropertyConfigurator.configure(URL).</description>
</property>

<property>
  <name>hive.exec.infer.bucket.sort</name>
  <value>false</value>
  <description>
    If this is set, when writing partitions, the metadata will include the bucketing/sorting
    properties with which the data was written if any (this will not overwrite the metadata
    inherited from the table if the table is bucketed/sorted)
  </description>
</property>

<property>
  <name>hive.exec.infer.bucket.sort.num.buckets.power.two</name>
  <value>false</value>
  <description>
    If this is set, when setting the number of reducers for the map reduce task which writes the
    final output files, it will choose a number which is a power of two, unless the user specifies
    the number of reducers to use using mapred.reduce.tasks.  The number of reducers
    may be set to a power of two, only to be followed by a merge task meaning preventing
    anything from being inferred.
    With hive.exec.infer.bucket.sort set to true:
    Advantages:  If this is not set, the number of buckets for partitions will seem arbitrary,
                 which means that the number of mappers used for optimized joins, for example, will
                 be very low.  With this set, since the number of buckets used for any partition is
                 a power of two, the number of mappers used for optimized joins will be the least
                 number of buckets used by any partition being joined.
    Disadvantages: This may mean a much larger or much smaller number of reducers being used in the
                   final map reduce job, e.g. if a job was originally going to take 257 reducers,
                   it will now take 512 reducers, similarly if the max number of reducers is 511,
                   and a job was going to use this many, it will now use 256 reducers.
                 
  </description>
</property>

<property>
  <name>hive.groupby.orderby.position.alias</name>
  <value>false</value>
  <description>Whether to enable using Column Position Alias in Group By or Order By</description>
</property>

 <property>
  <name>hive.server2.thrift.min.worker.threads</name>
  <value>5</value>
  <description>Minimum number of Thrift worker threads</description>
</property>

<property>
  <name>hive.server2.thrift.max.worker.threads</name>
  <value>500</value>
  <description>Maximum number of Thrift worker threads</description>
</property>

<property>
  <name>hive.server2.async.exec.threads</name>
  <value>50</value>
  <description>Number of threads in the async thread pool for HiveServer2</description>
</property>

<property>
  <name>hive.server2.async.exec.shutdown.timeout</name>
  <value>10</value>
  <description>Time (in seconds) for which HiveServer2 shutdown will wait for async 
  threads to terminate</description>
</property>

<property>
  <name>hive.server2.thrift.port</name>
  <value>10000</value>
  <description>Port number of HiveServer2 Thrift interface.
  Can be overridden by setting $HIVE_SERVER2_THRIFT_PORT</description>
</property>

<property>
  <name>hive.server2.thrift.bind.host</name>
  <value>localhost</value>
  <description>Bind host on which to run the HiveServer2 Thrift interface.
  Can be overridden by setting $HIVE_SERVER2_THRIFT_BIND_HOST</description>
</property>

<property>
  <name>hive.server2.authentication</name>
  <value>NONE</value>
  <description>
    Client authentication types.
       NONE: no authentication check
       LDAP: LDAP/AD based authentication
       KERBEROS: Kerberos/GSSAPI authentication
       CUSTOM: Custom authentication provider
               (Use with property hive.server2.custom.authentication.class)
  </description>
</property>

<property>
  <name>hive.server2.custom.authentication.class</name>
  <value></value>
  <description>
    Custom authentication class. Used when property
    'hive.server2.authentication' is set to 'CUSTOM'. Provided class
    must be a proper implementation of the interface
    org.apache.hive.service.auth.PasswdAuthenticationProvider. HiveServer2
    will call its Authenticate(user, passed) method to authenticate requests.
    The implementation may optionally extend the Hadoop's
    org.apache.hadoop.conf.Configured class to grab Hive's Configuration object.
  </description>
</property>

<property>
  <name>>hive.server2.authentication.kerberos.principal</name>
  <value></value>
  <description>
    Kerberos server principal
  </description>
</property>

<property>
  <name>>hive.server2.authentication.kerberos.keytab</name>
  <value></value>
  <description>
    Kerberos keytab file for server principal
  </description>
</property>
 
<property>
  <name>hive.server2.authentication.ldap.url</name>
  <value></value>
  <description>
    LDAP connection URL
  </description>
</property>

<property>
  <name>hive.server2.authentication.ldap.baseDN</name>
  <value></value>
  <description>
    LDAP base DN
  </description>
</property>

<property>
  <name>hive.server2.enable.doAs</name>
  <value>true</value>
  <description>
   Setting this property to true will have hive server2 execute
    hive operations as the user making the calls to it.
  </description>
</property>

<property>
  <name>hive.server2.table.type.mapping</name>
  <value>CLASSIC</value>
  <description>
   This setting reflects how HiveServer will report the table types for JDBC and other
   client implementations that retrieves the available tables and supported table types
     HIVE : Exposes the hive's native table tyes like MANAGED_TABLE, EXTERNAL_TABLE, VIRTUAL_VIEW
     CLASSIC : More generic types like TABLE and VIEW
  </description>
</property>

<property>
  <name>hive.server2.thrift.sasl.qop</name>
  <value>auth</auth>
  <description>Sasl QOP value; Set it to one of following values to enable higher levels of
     protection for hive server2 communication with clients.
      "auth" - authentication only (default)
      "auth-int" - authentication plus integrity protection
      "auth-conf" - authentication plus integrity and confidentiality protection
     This is applicable only hive server2 is configured to use kerberos authentication.
 </description>
</property>

<property>
  <name>hive.metastore.schema.verification</name>
  <value>true</value>
   <description>
   Enforce metastore schema version consistency.
   True: Verify that version information stored in metastore matches with one from Hive jars.  Also disable automatic
         schema migration attempt. Users are required to manully migrate schema after Hive upgrade which ensures
         proper metastore schema migration. (Default)
   False: Warn if the version information stored in metastore doesn't match with one from in Hive jars.
   </description>
</property>


</configuration>