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 <a href=https://hive.apache.org><img src=https://hive.apache.org/images/hive.svg width=550 height=300 alt="Apache Software Foundation"></a>
 <h1 class="text-outline black-text">Apache Hive </h1>
 <h4 class=thin> The Apache Hive ™ is a distributed, fault-tolerant data warehouse system that enables analytics at a massive scale and
 facilitates reading, writing, and managing petabytes of data residing in distributed storage using SQL.</h4>
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 <h2 class=section-header-text style=text-align:center>What is Hive?</h2>
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 <p>
 Apache Hive is a distributed, fault-tolerant data warehouse system that enables analytics at a massive scale.
 Hive Metastore(HMS) provides a central repository of metadata that can easily be analyzed to make informed,
 data driven decisions, and therefore it is a critical component of many data lake architectures.
 Hive is built on top of Apache Hadoop and supports storage on S3, adls, gs etc though hdfs.
 Hive allows users to read, write, and manage petabytes of data using SQL.
 </p>
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 <h2 class=section-header-text style=text-align:center>Key Features</h2>
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 <div class="row divs">
 <hs2terminal class=col-md>
 <div id=hs2 data-termynal data-termynal data-ty-typedelay=40 data-ty-linedelay=700>
 <span data-ty=input>beeline -u "jdbc:hive2://host:10001/default"</span>
 <span data-ty>Connected to: Apache Hive </span>
 <span data-ty></span>
 <span data-ty=input>jdbc:hive2://host:10001/>select count(*) from test_t1;</span>
 <span data-ty></span>
 </div>
 </hs2terminal>
 <div class="col-md feature-border">
 <h2 class=topic-text-style>Hive-Server 2 (HS2)</h2>
 <p>
 HS2 supports multi-client concurrency and authentication.
 It is designed to provide better support for open API clients like JDBC and ODBC.
 </p>
 <a href=https://cwiki.apache.org/confluence/display/Hive/HiveServer2+Overview class=icon-block>
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 <h2 class=topic-text-style>Hive Metastore Server (HMS)</h2>
 <p>
 The Hive Metastore (HMS) is a central repository of metadata for Hive tables and partitions in a relational database,
 and provides clients (including Hive, Impala and Spark) access to this information using the metastore service API.
 It has become a building block for data lakes that utilize the diverse world of open-source software, such as Apache Spark and Presto.
 In fact, a whole ecosystem of tools, open-source and otherwise, are built around the Hive Metastore, some of which this diagram illustrates.
 </p>
 <a href=https://cwiki.apache.org/confluence/display/hive/design class=icon-block>
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 <a href=https://cwiki.apache.org/confluence/display/hive/design>
 <img src=https://hive.apache.org/images/HMS.jpg width=600 height=400 alt="Apache Software Foundation"></a>
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 <a href=https://cwiki.apache.org/confluence/display/hive/hive+transactions>
 <img src=https://hive.apache.org/images/hiveAcid.svg width=300 height=300 alt="Apache Software Foundation"></a>
 </a>
 </div>
 <div class="col-md feature-border">
 <h2 class=topic-text-style>Hive ACID</h2>
 <p>
 Hive provides full ACID support for <a href=https://orc.apache.org/>ORC</a> tables and insert only support to all other formats.
 </p>
 <a href=https://cwiki.apache.org/confluence/display/hive/hive+transactions class=icon-block>
 <button class="custom-button feature-button-style" role=button>Learn More</button>
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 <h2 class=topic-text-style>Hive Data Compaction</h2>
 <p>
 Query-based and MR-based data compactions are supported out-of-the-box.
 </p>
 <a href=https://cwiki.apache.org/confluence/display/hive/languagemanual+ddl#LanguageManualDDL-AlterTable/PartitionCompact class=icon-block>
 <button class="custom-button feature-button-style" role=button>Learn More</button>
 </a>
 </div>
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 <compaction>
 <div id=comp data-termynal data-ty-typedelay=40 data-ty-linedelay=70>
 <span data-ty=input>jdbc:hive2://> alter table test_t1 compact "MAJOR";</span>
 <span data-ty>Done!</span>
 <span data-ty=input>jdbc:hive2://> alter table test_t1 compact "MINOR";</span>
 <span data-ty>Done!</span>
 <span data-ty=input>jdbc:hive2://> show compactions;</span>
 </div>
 </compaction>
 </div>
 </div>
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 <a href=https://iceberg.apache.org/docs/latest/hive/>
 <img src=https://hive.apache.org/images/hiveIceberg.png width=500 height=250 alt="Apache Software Foundation"></a>
 </a>
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 <div class="col-md feature-border">
 <h2 class=topic-text-style>Hive Iceberg</h2>
 <p>
 Hive provides out of the box support for Apache Iceberg Tables, a cloud-native,
 high-performance open table format, via Hive StorageHandler.
 </p>
 <a href=https://iceberg.apache.org/docs/latest/hive/ class=icon-block>
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 <h2 class=topic-text-style>Security and Observability</h2>
 <p>
 Apache Hive supports kerberos auth and integrates with Apache Ranger and Apache Atlas for security and
 observability.
 </p>
 <a href=https://atlas.apache.org/1.2.0/Hook-Hive.html class=icon-block>
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 <img src=https://hive.apache.org/images/ker.jpeg width=200 height=150 alt="Apache Software Foundation"></a>
 <img src=https://hive.apache.org/images/atlas.png width=400 height=100 alt="Apache Software Foundation"></a>
 <img src=https://hive.apache.org/images/ranger.png width=400 height=100 alt="Apache Software Foundation"></a>
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 <img src=https://hive.apache.org/images/llap.png width=600 height=350 alt="Apache Software Foundation"></a>
 </div>
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 <h2 class=topic-text-style>Hive LLAP</h2>
 <p>
 Apache Hive enables interactive and subsecond SQL through Low Latency Analytical Processing (LLAP),
 introduced in Hive 2.0 that makes Hive faster by using persistent query infrastructure and optimized data caching
 </p>
 <a href=https://cwiki.apache.org/confluence/display/Hive/LLAP class=icon-block>
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 <h2 class=topic-text-style>Query planner and Cost based Optimizer</h2>
 <p>
 Hive uses Apache Calcite's cost based query optimizer (CBO) and query execution framework to optimize sql queries.
 </p>
 <a href=https://cwiki.apache.org/confluence/display/Hive/Cost-based+optimization+in+Hive class=icon-block>
 <button class="custom-button feature-button-style" role=button>Learn More</button>
 </a>
 </div>
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 <explain>
 <div id=explain data-termynal data-ty-typedelay=40 data-ty-linedelay=70 style=text-align:left>
 <span data-ty=input>jdbc:hive2://> explain cbo select ss.ss_net_profit, sr.sr_net_loss from store_sales ss join store_returns sr on (ss.ss_item_sk=sr.sr_item_sk) limit 5 ;</span>
 <span data-ty>+---------------------------------------------+</span>
 <span data-ty> Explain </span></span>
 <span data-ty>+---------------------------------------------+</span>
 <span data-ty> CBO PLAN: </span>
 <span data-ty> HiveSortLimit(fetch=[5]) </span>
 <span data-ty> HiveProject(ss_net_profit=[$1], sr_net_loss=[$3]) </span>
 <span data-ty> &ensp;&ensp;HiveJoin(condition=[=($0, $2)], joinType=[inner]) </span>
 <span data-ty> &ensp;&ensp;&ensp;&ensp;HiveProject(ss_item_sk=[$2], ss_net_profit=[$22]) </span>
 <span data-ty> &ensp;&ensp;&ensp;&ensp;HiveFilter(condition=[IS NOT NULL($2)]) </span>
 <span data-ty> &ensp;&ensp;&ensp;&ensp;&ensp;&ensp;HiveTableScan(table=[[tpcds_text_10, store_sales]], table:alias=[ss]) </span>
 <span data-ty> &ensp;&ensp;&ensp;&ensp;HiveProject(sr_item_sk=[$2], sr_net_loss=[$19]) </span>
 <span data-ty> &ensp;&ensp;&ensp;&ensp;HiveFilter(condition=[IS NOT NULL($2)]) </span>
 <span data-ty> &ensp;&ensp;&ensp;&ensp;&ensp;&ensp;HiveTableScan(table=[[tpcds_text_10, store_returns]], table:alias=[sr]) </span>
 <span data-ty>+---------------------------------------------+</span>
 </div>
 <script src=https://hive.apache.org/js/termynal.js data-termynal-container=#explain></script>
 </explain>
 </div>
 </div>
 <div class="row divs">
 <div class=col-md>
 <replication class=col-md>
 <div id=repl data-termynal data-ty-typedelay=40 data-ty-linedelay=70 style=width:830px>
 <span data-ty=input>jdbc:hive2://> repl dump src with (</span>
 <span data-ty=input>. . .> 'hive.repl.dump.version'= '2',</span>
 <span data-ty=input>. . .> 'hive.repl.rootdir'= 'hdfs://&lt;host>:&lt;port>/user/replDir/d1'</span>
 <span data-ty=input>. . .> );</span>
 <span data-ty>Done!</span>
 <span data-ty=input>jdbc:hive2://> repl load src into tgt with (</span>
 <span data-ty=input>. . .> 'hive.repl.rootdir'= 'hdfs://&lt;host>:&lt;port>/user/replDir/d1'</span>
 <span data-ty=input>. . .> );</span>
 <span data-ty>Done!</span>
 </div>
 </replication>
 </div>
 <div class="col-md feature-border">
 <h2 class=topic-text-style>Hive Replication</h2>
 <p>
 Hive supports bootstap and incremental replication for backup and recovery.
 </p>
 <a href=https://cwiki.apache.org/confluence/display/Hive/Replication class=icon-block>
 <button class="custom-button feature-button-style" role=button>Learn More</button>
 </a>
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	<a href=https://hive.apache.org><img src=https://hive.apache.org/images/hive.svg width=550 height=300 alt="Apache Software Foundation"></a>
	<h1 class="text-outline black-text">Apache Hive </h1>
	<h4 class=thin> The Apache Hive ™ is a distributed, fault-tolerant data warehouse system that enables analytics at a massive scale and
	facilitates reading, writing, and managing petabytes of data residing in distributed storage using SQL.</h4>
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	<h2 class=section-header-text style=text-align:center>What is Hive?</h2>
	</div>
	<p>
	Apache Hive is a distributed, fault-tolerant data warehouse system that enables analytics at a massive scale.
	Hive Metastore(HMS) provides a central repository of metadata that can easily be analyzed to make informed,
	data driven decisions, and therefore it is a critical component of many data lake architectures.
	Hive is built on top of Apache Hadoop and supports storage on S3, adls, gs etc though hdfs.
	Hive allows users to read, write, and manage petabytes of data using SQL.
	</p>
	</div>
	<div class="row section-header-style">
	<h2 class=section-header-text style=text-align:center>Key Features</h2>
	</div>
	<div class="row divs">
	<hs2terminal class=col-md>
	<div id=hs2 data-termynal data-termynal data-ty-typedelay=40 data-ty-linedelay=700>
	<span data-ty=input>beeline -u "jdbc:hive2://host:10001/default"</span>
	<span data-ty>Connected to: Apache Hive </span>
	<span data-ty></span>
	<span data-ty=input>jdbc:hive2://host:10001/>select count(*) from test_t1;</span>
	<span data-ty></span>
	</div>
	</hs2terminal>
	<div class="col-md feature-border">
	<h2 class=topic-text-style>Hive-Server 2 (HS2)</h2>
	<p>
	HS2 supports multi-client concurrency and authentication.
	It is designed to provide better support for open API clients like JDBC and ODBC.
	</p>
	<a href=https://cwiki.apache.org/confluence/display/Hive/HiveServer2+Overview class=icon-block>
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	<h2 class=topic-text-style>Hive Metastore Server (HMS)</h2>
	<p>
	The Hive Metastore (HMS) is a central repository of metadata for Hive tables and partitions in a relational database,
	and provides clients (including Hive, Impala and Spark) access to this information using the metastore service API.
	It has become a building block for data lakes that utilize the diverse world of open-source software, such as Apache Spark and Presto.
	In fact, a whole ecosystem of tools, open-source and otherwise, are built around the Hive Metastore, some of which this diagram illustrates.
	</p>
	<a href=https://cwiki.apache.org/confluence/display/hive/design class=icon-block>
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	<a href=https://cwiki.apache.org/confluence/display/hive/design>
	<img src=https://hive.apache.org/images/HMS.jpg width=600 height=400 alt="Apache Software Foundation"></a>
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	<a href=https://cwiki.apache.org/confluence/display/hive/hive+transactions>
	<img src=https://hive.apache.org/images/hiveAcid.svg width=300 height=300 alt="Apache Software Foundation"></a>
	</a>
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	<h2 class=topic-text-style>Hive ACID</h2>
	<p>
	Hive provides full ACID support for <a href=https://orc.apache.org/>ORC</a> tables and insert only support to all other formats.
	</p>
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	<h2 class=topic-text-style>Hive Data Compaction</h2>
	<p>
	Query-based and MR-based data compactions are supported out-of-the-box.
	</p>
	<a href=https://cwiki.apache.org/confluence/display/hive/languagemanual+ddl#LanguageManualDDL-AlterTable/PartitionCompact class=icon-block>
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	<compaction>
	<div id=comp data-termynal data-ty-typedelay=40 data-ty-linedelay=70>
	<span data-ty=input>jdbc:hive2://> alter table test_t1 compact "MAJOR";</span>
	<span data-ty>Done!</span>
	<span data-ty=input>jdbc:hive2://> alter table test_t1 compact "MINOR";</span>
	<span data-ty>Done!</span>
	<span data-ty=input>jdbc:hive2://> show compactions;</span>
	</div>
	</compaction>
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	<a href=https://iceberg.apache.org/docs/latest/hive/>
	<img src=https://hive.apache.org/images/hiveIceberg.png width=500 height=250 alt="Apache Software Foundation"></a>
	</a>
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	<h2 class=topic-text-style>Hive Iceberg</h2>
	<p>
	Hive provides out of the box support for Apache Iceberg Tables, a cloud-native,
	high-performance open table format, via Hive StorageHandler.
	</p>
	<a href=https://iceberg.apache.org/docs/latest/hive/ class=icon-block>
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	<h2 class=topic-text-style>Security and Observability</h2>
	<p>
	Apache Hive supports kerberos auth and integrates with Apache Ranger and Apache Atlas for security and
	observability.
	</p>
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	<img src=https://hive.apache.org/images/ker.jpeg width=200 height=150 alt="Apache Software Foundation"></a>
	<img src=https://hive.apache.org/images/atlas.png width=400 height=100 alt="Apache Software Foundation"></a>
	<img src=https://hive.apache.org/images/ranger.png width=400 height=100 alt="Apache Software Foundation"></a>
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	<h2 class=topic-text-style>Hive LLAP</h2>
	<p>
	Apache Hive enables interactive and subsecond SQL through Low Latency Analytical Processing (LLAP),
	introduced in Hive 2.0 that makes Hive faster by using persistent query infrastructure and optimized data caching
	</p>
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	<h2 class=topic-text-style>Query planner and Cost based Optimizer</h2>
	<p>
	Hive uses Apache Calcite's cost based query optimizer (CBO) and query execution framework to optimize sql queries.
	</p>
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	<explain>
	<div id=explain data-termynal data-ty-typedelay=40 data-ty-linedelay=70 style=text-align:left>
	<span data-ty=input>jdbc:hive2://> explain cbo select ss.ss_net_profit, sr.sr_net_loss from store_sales ss join store_returns sr on (ss.ss_item_sk=sr.sr_item_sk) limit 5 ;</span>
	<span data-ty>+---------------------------------------------+</span>
	<span data-ty> Explain </span></span>
	<span data-ty>+---------------------------------------------+</span>
	<span data-ty> CBO PLAN: </span>
	<span data-ty> HiveSortLimit(fetch=[5]) </span>
	<span data-ty> HiveProject(ss_net_profit=[$1], sr_net_loss=[$3]) </span>
	<span data-ty> &ensp;&ensp;HiveJoin(condition=[=($0, $2)], joinType=[inner]) </span>
	<span data-ty> &ensp;&ensp;&ensp;&ensp;HiveProject(ss_item_sk=[$2], ss_net_profit=[$22]) </span>
	<span data-ty> &ensp;&ensp;&ensp;&ensp;HiveFilter(condition=[IS NOT NULL($2)]) </span>
	<span data-ty> &ensp;&ensp;&ensp;&ensp;&ensp;&ensp;HiveTableScan(table=[[tpcds_text_10, store_sales]], table:alias=[ss]) </span>
	<span data-ty> &ensp;&ensp;&ensp;&ensp;HiveProject(sr_item_sk=[$2], sr_net_loss=[$19]) </span>
	<span data-ty> &ensp;&ensp;&ensp;&ensp;HiveFilter(condition=[IS NOT NULL($2)]) </span>
	<span data-ty> &ensp;&ensp;&ensp;&ensp;&ensp;&ensp;HiveTableScan(table=[[tpcds_text_10, store_returns]], table:alias=[sr]) </span>
	<span data-ty>+---------------------------------------------+</span>
	</div>
	<script src=https://hive.apache.org/js/termynal.js data-termynal-container=#explain></script>
	</explain>
	</div>
	</div>
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	<div class=col-md>
	<replication class=col-md>
	<div id=repl data-termynal data-ty-typedelay=40 data-ty-linedelay=70 style=width:830px>
	<span data-ty=input>jdbc:hive2://> repl dump src with (</span>
	<span data-ty=input>. . .> 'hive.repl.dump.version'= '2',</span>
	<span data-ty=input>. . .> 'hive.repl.rootdir'= 'hdfs://<host>:<port>/user/replDir/d1'</span>
	<span data-ty=input>. . .> );</span>
	<span data-ty>Done!</span>
	<span data-ty=input>jdbc:hive2://> repl load src into tgt with (</span>
	<span data-ty=input>. . .> 'hive.repl.rootdir'= 'hdfs://<host>:<port>/user/replDir/d1'</span>
	<span data-ty=input>. . .> );</span>
	<span data-ty>Done!</span>
	</div>
	</replication>
	</div>
	<div class="col-md feature-border">
	<h2 class=topic-text-style>Hive Replication</h2>
	<p>
	Hive supports bootstap and incremental replication for backup and recovery.
	</p>
	<a href=https://cwiki.apache.org/confluence/display/Hive/Replication class=icon-block>
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