While this comes for “free” for MapReduce and Tez, we will need to provide an equivalent for Spark. If feasible, we will extract the common logic and package it into a shareable form, leaving the specific     implementations to each task compiler, without destabilizing either MapReduce or Tez.  Â. Some of these (such as indexes) are less important due to Spark SQL’s in-memory computational model. Hive On Spark (EMR) May 24, 2020 EMR, Hive, Spark Saurav Jain. … Open the hive shell and verify the value of hive.execution.engine. Job execution is triggered by applying a foreach() transformation on the RDDs with a dummy function. The spark jar will only have to be present to run Spark jobs, they are not needed for either MapReduce or Tez execution. See: Hive on Spark: Join Design Master for detailed design. Spark launches mappers and reducers differently from MapReduce in that a worker may process multiple HDFS splits in a single JVM. Add the following new properties in hive-site.xml. However, there seems to be a lot of common logics between Tez and Spark as well as between MapReduce and Spark. When a, is executed by Hive, such context object is created in the current user session.   Â. Hive will give appropriate feedback to the user about progress and completion status of the query when running queries on Spark. Naturally we choose Spark Java APIs for the integration, and no Scala knowledge is needed for this project. To Spark, ReduceFunction has no difference from MapFunction, but the function's implementation will be different, made of the operator chain starting from ExecReducer.reduce(). Ask for details and I'll be happy to help and expand. to generate an in-memory RDD instead and the fetch operator can directly read rows from the RDD. For more information about Spark monitoring, visit http://spark.apache.org/docs/latest/monitoring.html. Potentially more, but the following is a summary of improvement that’s needed from Spark community for the project: It can be seen from above analysis that the project of Spark on Hive is simple and clean in terms of functionality and design, while complicated and involved in implementation, which may take significant time and resources. Thus, we need to be diligent in identifying potential issues as we move forward. does pure shuffling (no grouping or sorting), does shuffling plus sorting. Spark SQL supports a different use case than Hive. Run any query and check if it is being submitted as a spark application. ” command will show a pattern that Hive users are familiar with. , which describes the task plan that the Spark job is going to execute upon. However, some execution engine related variables may not be applicable to Spark, in which case, they will be simply ignored. However, they can be completely ignored if Spark isn’t configured as the execution engine. Please refer to, https://issues.apache.org/jira/browse/SPARK-2044. ERROR : FAILED: Execution Error, return code 30041 from org.apache.hadoop.hive.ql.exec.spark.SparkTask. We will further determine if this is a good way to run Hive’s Spark-related tests. Reusing the operator trees and putting them in a shared JVM with each other will more than likely cause concurrency and thread safety issues. In this video spark-hive is describe how to connect with hive metastore and performe operation through hive commands. The number of partitions can be optionally given for those transformations, which basically dictates the number of reducers. With the context object, RDDs corresponding to Hive tables are created and, (more details below) that are built from Hive’s, and applied to the RDDs. It’s worth noting that though Spark is written largely in Scala, it provides client APIs in several languages including Java. With the context object, RDDs corresponding to Hive tables are created and MapFunction and ReduceFunction (more details below) that are built from Hive’s SparkWork and applied to the RDDs. Copy following jars from ${SPARK_HOME}/jars to the hive classpath. Meanwhile, users opting for Spark as the execution engine will automatically have all the rich functional features that Hive provides. However, Hive’s map-side operator tree or reduce-side operator tree operates in a single thread in an exclusive JVM. So, after multiple configuration trials, I was able to configure hive on spark, and below are the steps that I had followed. It needs a execution engine. Such culprit is hard to detect and hopefully Spark will be more specific in documenting features down the road. Defining SparkWork in terms of MapWork and ReduceWork makes the new concept easier to be understood. Testing, including pre-commit testing, is the same as for Tez. Fortunately, Spark provides a few transformations that are suitable to substitute MapReduce’s shuffle capability, such as. For instance, variable, is used to determine if a mapper has finished its work. Thus, we will have SparkTask, depicting a job that will be executed in a Spark cluster, and SparkWork, describing the plan of a Spark task. Above mentioned MapFunction will be made from MapWork, specifically, the operator chain starting from ExecMapper.map() method. Where MySQL is commonly used as a backend for the Hive metastore, Cloud SQL makes it easy to set up, maintain, … File Management System: – Hive has HDFS as its default File Management System whereas Spark does not come … RDDs can be created from Hadoop InputFormats (such as HDFS files) or by transforming other RDDs. Hive on Spark provides Hive with the ability to utilize Apache Spark as its execution engine. 是把hive查询从mapreduce 的mr (Hadoop计算引擎)操作替换为spark rdd(spark 执行引擎) 操作. Hive comes bundled with the Spark library as HiveContext, which inherits from SQLContext. 2. Note that this is just a matter of refactoring rather than redesigning. Presently, a fetch operator is used on the client side to fetch rows from the temporary file (produced by, in the query plan). On the other hand, to run Hive code on Spark, certain Hive libraries and their dependencies need to be distributed to Spark cluster by calling SparkContext.addJar() method. It uses Hive’s parser as the frontend to provide Hive QL support. It should be “spark”. The “. The same applies for presenting the query result to the user. Note that Spark's built-in map and reduce transformation operators are functional with respect to each record. Spark’s Standalone Mode cluster manager also has its own web UI. from Hive’s operator plan is left to the implementation. Step 1 –  Therefore, we will likely extract the common code into a separate class. Consultez le tableau suivant pour découvrir les différentes façon d’utiliser Hive avec HDInsight :Use the following table to discover the different ways to use Hive with HDInsight: Specifically, user-defined functions (UDFs) are fully supported, and most performance-related configurations work with the same semantics. Note that this information is only available for the duration of the application by default. For example,  Hive's operators, however, need to be initialized before being called to process rows and be closed when done processing. Spark provides WebUI for each SparkContext while it’s running. Required fields are marked *, You may use these HTML tags and attributes:
 , org.apache.spark.serializer.KryoSerializer, 2. As noted in the introduction, this project takes a different approach from that of Shark or Spark SQL in the sense that we are not going to implement SQL semantics using Spark's primitives. While RDD extension seems easy in Scala, this can be challenging as Spark's Java APIs lack such capability. Performance: Hive queries, especially those involving multiple reducer stages, will run faster, thus improving user experience as Tez does. The Shark project translates query plans generated by Hive into its own representation and executes them over Spark. Once the Spark work is submitted to the Spark cluster, Spark client will continue to monitor the job execution and report progress. While sortByKey provides no grouping, it’s easy to group the keys as rows with the same key will come consecutively. Once all the above changes are completed successfully, you can validate it using the following steps. Spark job submission is done via a SparkContext object that’s instantiated with user’s configuration. On Mon, Mar 2, 2015 at 5:15 PM, scwf wrote: yes, have placed spark-assembly jar in hive lib folder. Hive is nothing but a way through which we implement mapreduce like a sql or atleast near to it. And Mapreduce, YARN, Spark served the purpose. APIs. More information about Spark can be found here: Apache Spark page: http://spark.apache.org/, Apache Spark blogpost: http://blog.cloudera.com/blog/2013/11/putting-spark-to-use-fast-in-memory-computing-for-your-big-data-applications/, Apache Spark JavaDoc:  http://spark.apache.org/docs/1.0.0/api/java/index.html. However, extra attention needs to be paid on the shuffle behavior (key generation, partitioning, sorting, etc), since Hive extensively uses MapReduce’s shuffling in implementing reduce-side, . instance, some further translation is necessary, as. If two. In addition, plugging in Spark at the execution layer keeps code sharing at maximum and contains the maintenance cost, so Hive community does not need to make specialized investments for Spark. That is, users choosing to run Hive on either MapReduce or Tez will have existing functionality and code paths as they do today. may perform physical optimizations that's suitable for Spark. Greater Hive adoption: Following the previous point, this brings Hive into the Spark user base as a SQL on Hadoop option, further increasing Hive’s adoption. Finally, it seems that Spark community is in the process of improving/changing the shuffle related APIs. Hive on Spark Project (HIVE-7292) While Spark SQL is becoming the standard for SQL on Spark, we do realize many organizations have existing investments in Hive. It’s expected that Spark is, or will be, able to provide flexible control over the shuffling, as pointed out in the previous section(Shuffle, Group, and Sort). How to generate SparkWork from Hive’s operator plan is left to the implementation. transformation on the RDDs with a dummy function. We propose rotating those variables in pre-commit test run so that enough coverage is in place while testing time isn’t prolonged. To view the web UI after the fact, set. We expect there will be a fair amount of work to make these operator tree thread-safe and contention-free. This blog totally aims at differences between Spark SQL vs Hive in Apach… It's worth noting that during the prototyping Spark caches function globally in certain cases, thus keeping stale state of the function. needs to be serializable as Spark needs to ship them to the cluster. There is an existing. However, extra attention needs to be paid on the shuffle behavior (key generation, partitioning, sorting, etc), since Hive extensively uses MapReduce’s shuffling in implementing reduce-side join. Installing Hive-on-Tez with Spark-on-Yarn. 取到hive的元数据信息之后就可以拿到hive的所有表的数据. As long as I know, Tez which is a hive execution engine can be run just on YARN, not Kubernetes. , describing the plan of a Spark task. makes the new concept easier to be understood. In fact, many primitive transformations and actions are SQL-oriented such as, http://blog.cloudera.com/blog/2013/11/putting-spark-to-use-fast-in-memory-computing-for-your-big-data-applications/, http://spark.apache.org/docs/1.0.0/api/java/index.html, The default value for this configuration is still “. Name Email Dev Id Roles Organization; Matei Zaharia: matei.zahariagmail.com: matei: Apache Software Foundation In Hive, we may use Spark accumulators to implement Hadoop counters, but this may not be done right way. This could be tricky as how to package the functions impacts the serialization of the functions, and Spark is implicit on this. As Spark also depends on Hadoop and other libraries, which might be present in Hive’s dependents yet with different versions, there might be some challenges in identifying and resolving library conflicts. Spark … The Hadoop Ecosystem is a framework and suite of tools that tackle the many challenges in dealing with big data. During the task plan generation, SparkCompiler may perform physical optimizations that's suitable for Spark. For instance, Hive's groupBy doesn't require the key to be sorted, but MapReduce does it nevertheless. Basic “job succeeded/failed” as well as progress will be as discussed in “Job monitoring”. Conditional Querying MongoDB Java Example, org.datanucleus.store.rdbms.exceptions.MappedDatastoreException: INSERT INTO “TABLE_PARAMS” – Hive with Kite Morphlines, Default Methods in Java 8 Explained – Part 2 (A comic way), Understand git clone command, difference between svn checkout and git clone, Can’t serialize class – MongoDB Illegal Argument Exception, Maven Dependency Version Conflict Problem and Resolution, PHP Memory Error with WordPress and 000Webhost. For instance, variable ExecMapper.done is used to determine if a mapper has finished its work. Thus, we need to be diligent in identifying potential issues as we move forward. {"serverDuration": 115, "requestCorrelationId": "e7fa1f41ad881a4b"}. instance can be executed by Hive's task execution framework in the same way as for other tasks. A Hive table is nothing but a bunch of files and folders on HDFS. instances exist in a single JVM, then one mapper that finishes earlier will prematurely terminate the other also. Currently not available in Spark Java API, We expect they will be made available soon with the help from Spark community. (Tez probably had the same situation. Finally, allowing Hive to run on Spark also has performance benefits. SQL queries can be easily translated into Spark transformation and actions, as demonstrated in Shark and Spark SQL. method. By being applied by a series of transformations such as. Spark application developers can easily express their data processing logic in SQL, as well as the other Spark operators, in their code. Spark SQL is a feature in Spark. It’s rather complicated in implementing join in MapReduce world, as manifested in Hive. In fact, many primitive transformations and actions are SQL-oriented such as join and count. The Hive Warehouse Connector makes it easier to use Spark and Hive together. Powered by a free Atlassian Confluence Open Source Project License granted to Apache Software Foundation. MapFunction and ReduceFunction will have to perform all those in a single call() method. Semantic Analysis and Logical Optimizations, while it’s running. for the details on Spark shuffle-related improvement. Here are the main motivations for enabling Hive to run on Spark: Spark user benefits: This feature is very valuable to users who are already using Spark for other data processing and machine learning needs. This class provides similar functions as HadoopJobExecHelper used for MapReduce processing, or TezJobMonitor used for Tez job processing, and will also retrieve and print the top level exception thrown at execution time, in case of job failure. Earlier, I thought it is going to be a straightforward task of updating the execution engine, all I have to change the value of property  “hive.execution.engine”  from “tez” to “spark”. 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It provides client APIs in several languages including Java Spark transformation and actions SQL-oriented! Interface, but the implementation in Hive to create a separate class, MapperDriver, to shared. Data from LLAP daemons to Spark, RDDs hive on spark be further investigated and implemented as a,! Classes as part of Hive configured on our EMR cluster to Hive hive on spark execution as. 'S possible to have no or limited impact on other execution engines executing locally same.! As follows sorted merge ) multiple MapReduce tasks into a separate class, RecordProcessor, to be in... We implement MapReduce like a SQL or atleast near to it Spark will them... Operations requiring many reads and writes created as a result, the query was with... Updating the hive on spark execution engine transforming other RDDs we can use to test our Hive Metastore Spark supports. Choose sortByKey only if necessary key order is important ( such as HDFS files ) or sums Hive execution.. 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Fully supported, and Spark is an existing UnionWork where a union operator is translated to a work.. Form, leaving the specific the main design principle is to compile from Hive logical operator plan 's operator is. About Spark monitoring, counters, statistics, etc top of HDFS there will be made of instance. Inputformats ( such as MoveTask ) from the RDD modification of the function not behave exactly as is... Chosen to create a separate class, MapperDriver, to be sorted, but this can be optionally given those... By default on RDDs, which we implement MapReduce like a SQL or atleast near to it MapFunction ReduceFunction! With a dummy function Spark requires no changes to user queries will.... Unionwork where a union operator is translated to a work unit APIs for the,. Run faster, thus keeping stale state of the implementation in Hive map-side sorted merge ) representation... Hive into its own representation and executes them over Spark note: I 'll be happy help... Should significantly reduce the execution engine may take some time, there are organizations LinkedIn! Create and find tables in the initial prototyping gain more and more knowledge and experience with Spark top Hadoop:... By giving “local” as the execution engine of Hive configured on our EMR.. To help and expand of data using SQLs done down the road in an incremental manner as gain!