per user session is right thing to do, but it seems that Spark assumes one. With the iterator in control, Hive can initialize the operator chain before processing the first row, and de-initialize it after all input is consumed. The user will be able to get statistics and diagnostic information as before (counters, logs, and debug info on the console). Differences between Apache Hive and Apache Spark. . c. CM -> Hive -> configuration -> set hive.execution.engine to spark, this is a permanent setup and it will control all the session including Oozie . Block level bitmap indexes and virtual columns (used to build indexes). , we will need to inject one of the transformations. Spark application developers can easily express their data processing logic in SQL, as well as the other Spark operators, in their code. As discussed above, SparkTask will use SparkWork, which describes the task plan that the Spark job is going to execute upon. The determination of the number of reducers will be the same as it’s for MapReduce and Tez. This configures Spark to log Spark events that encode the information displayed in the UI to persisted storage. Internally, the SparkTask.execute() method will make RDDs and functions out of a SparkWork instance, and submit the execution to the Spark cluster via a Spark client. Fortunately, Spark provides a few transformations that are suitable to substitute MapReduce’s shuffle capability, such as. Neither semantic analyzer nor any logical optimizations will change. Spark application developers can easily express their data processing logic in SQL, as well as the other Spark operators, in their code. How to generate SparkWork from Hive’s operator plan is left to the implementation. Thus, this part of design is subject to change. Reusing the operator trees and putting them in a shared JVM with each other will more than likely cause concurrency and thread safety issues. RDDs can be created from Hadoop, s (such as HDFS files) or by transforming other RDDs. File Management System: – Hive has HDFS as its default File Management System whereas Spark does not come … The number of partitions can be optionally given for those transformations, which basically dictates the number of reducers. Upload all the jars available in $SPARK_HOME/jars to hdfs folder(for example:hdfs:///xxxx:8020/spark-jars). While this comes for “free” for MapReduce and Tez, we will need to provide an equivalent for Spark. application_1587017830527_6706 . We know that a new execution backend is a major undertaking. Note that this is just a matter of refactoring rather than redesigning. Hive queries, especially those involving multiple reducer stages, will run faster, thus improving user experience as Tez does. For other existing components that aren’t named out, such as UDFs and custom Serdes, we expect that special considerations are either not needed or insignificant. , to be shared by both MapReduce and Spark. There is an existing. However, this work should not have any impact on other execution engines. Add the following new properties in hive-site.xml. Finally, allowing Hive to run on Spark also has performance benefits. ”. ” as the master URL. Secondly, providing such an alternative further increases Hive's adoption as it exposes Spark users to a viable, feature-rich de facto standard SQL tools on Hadoop. SQL queries can be easily translated into Spark transformation and actions, as demonstrated in Shark and Spark SQL. There are two related projects in the Spark ecosystem that provide Hive QL support on Spark: Shark and Spark SQL. 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. Job execution is triggered by applying a foreach() transformation on the RDDs with a dummy function. Some of these (such as indexes) are less important due to Spark SQL’s in-memory computational model. For example,  Hive's operators, however, need to be initialized before being called to process rows and be closed when done processing. However, this can be further investigated and evaluated down the road. instance can be executed by Hive's task execution framework in the same way as for other tasks. We expect that Spark community will be able to address this issue timely. Hive variables will continue to work as it is today. This could be tricky as how to package the functions impacts the serialization of the functions, and Spark is implicit on this. Usage: – Hive is a distributed data warehouse platform which can store the data in form of tables like relational databases whereas Spark is an analytical platform which is used to perform complex data analytics on big data. It inevitably adds complexity and maintenance cost, even though the design avoids touching the existing code paths. If two ExecMapper instances exist in a single JVM, then one mapper that finishes earlier will prematurely terminate the other also. This class provides similar functions as. instance, some further translation is necessary, as. Hive’s current way of trying to fetch additional information about failed jobs may not be available immediately, but this is another area that needs more research. On Mon, Mar 2, 2015 at 5:15 PM, scwf wrote: yes, have placed spark-assembly jar in hive lib folder. For instance, Hive's, doesn't require the key to be sorted, but MapReduce does it nevertheless. Finally, it seems that Spark community is in the process of improving/changing the shuffle related APIs. There is an existing UnionWork where a union operator is translated to a work unit. Note that Spark's built-in map and reduce transformation operators are functional with respect to each record. 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 “. If an application has logged events over the course of its lifetime, then the Standalone master’s web UI will automatically re-render the application’s UI after the application has finished. 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. The Shark project translates query plans generated by Hive into its own representation and executes them over Spark. instances exist in a single JVM, then one mapper that finishes earlier will prematurely terminate the other also. Therefore, we will likely extract the common code into a separate class, MapperDriver, to be shared by both MapReduce and Spark. This is what worked for us. The HWC library loads data from LLAP daemons to Spark executors in parallel. Performance: Hive queries, especially those involving multiple reducer stages, will run faster, thus improving user experience as Tez does. It should be “spark”. 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. Following instructions have been tested on EMR but I assume it should work on the on-prem cluster or on other cloud provider environments, though I have not tested it there. Many of these organizations, however, are also eager to migrate to Spark. Fortunately, Spark provides a few transformations that are suitable to substitute MapReduce’s shuffle capability, such as partitionBy, groupByKey, and sortByKey. Defining SparkWork in terms of MapWork and ReduceWork makes the new concept easier to be understood. While sortByKey provides no grouping, it’s easy to group the keys as rows with the same key will come consecutively. class implements MapReduce Mapper interface, but the implementation in Hive contains some code that can be reused for Spark. We will further determine if this is a good way to run Hive’s Spark-related tests. In the same time, Spark offers a way to run jobs in a local cluster, a cluster made of a given number of processes in the local machine. On the contrary, we will implement it using MapReduce primitives. Note that Spark's built-in map and reduce transformation operators are functional with respect to each record. This blog totally aims at differences between Spark SQL vs Hive in Apach… Basic “job succeeded/failed” as well as progress will be as discussed in “Job monitoring”. While it's mentioned above that we will use MapReduce primitives to implement SQL semantics in the Spark execution engine, union is one exception. Spark can be run on Kubernetes, and Spark Thrift Server compatible with Hive Server2 is a great candidate. Thus, it’s very likely to find gaps and hiccups during the integration. 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. Spark SQL, composant du framework Apache Spark, est utilisé pour effectuer des traitements sur des données structurées en exécutant des requêtes de type SQL sur les données Spark… Now when we have our metastore running, let’s define some trivial spark job example so we can use to test our Hive Metastore. MapReduceCompiler compiles a graph of MapReduceTasks and other helper tasks (such as MoveTask) from the logical, operator plan. Required fields are marked *, You may use these HTML tags and attributes: 

 , org.apache.spark.serializer.KryoSerializer, 2. that are provided by Spark, RDDs can be processed and analyzed to fulfill what MapReduce jobs can do without having intermediate stages. The main work to implement the Spark execution engine for Hive lies in two folds: query planning, where Hive operator plan from semantic analyzer is further translated a task plan that Spark can execute, and query execution, where the generated Spark plan gets actually executed in the Spark cluster. Semantic Analysis and Logical Optimizations, while it’s running. While this comes for “free” for MapReduce and Tez, we will need to provide an equivalent for Spark. Its main responsibility is to compile from Hive logical operator plan a plan that can be execute on Spark. Hive is the best option for performing data analytics on large volumes of data using SQLs. Moving to Hive on Spark enabled Seagate to continue processing petabytes of data at scale with significantly lower total cost of ownership. 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. Presently, a fetch operator is used on the client side to fetch rows from the temporary file (produced by FileSink in the query plan). per application because of some thread-safety issues. Therefore, for each. Hive on Spark. Run any query and check if it is being submitted as a spark application. Rather we will depend on them being installed separately. Spark natively supports accumulators of numeric value types and standard mutable collections, and programmers can add support for new types. In Hive, SHOW PARTITIONS command is used to show or list all partitions of a table from Hive Metastore, In this article, I will explain how to list all partitions, filter partitions, and finally will see the actual HDFS location of a partition. Hive is a popular open source data warehouse system built on Apache Hadoop. Run the 'set' command in Oozie itself 'along with your query' as follows . Spark provides WebUI for each SparkContext while it’s running. The number of partitions can be optionally given for those transformations, which basically dictates the number of reducers. The Hive metastore holds metadata about Hive tables, such as their schema and location. During the task plan generation, SparkCompiler may perform physical optimizations that's suitable for Spark. Accessing Hive from Spark. Spark SQL is a feature in Spark. So we will discuss Apache Hive vs Spark SQL on the basis of their feature. ExecMapper class implements MapReduce Mapper interface, but the implementation in Hive contains some code that can be reused for Spark. A table can have one or more partitions that correspond to … Hive continues to work on MapReduce and Tez as is on clusters that don't have spark. Execution engine property is controlled by “hive.execution.engine” in hive-site.xml. , specifically, the operator chain starting from. Spark SQL is a feature in Spark. However, Tez has chosen to create a separate class, , but the function's implementation will be different, made of the operator chain starting from. Internally, the, method will make RDDs and functions out of a. instance, and submit the execution to the Spark cluster via a Spark client. It can have partitions and buckets, dealing with heterogeneous input formats and schema evolution. ” command will show a pattern that Hive users are familiar with. Standardizing on one execution backend is convenient for operational management, and makes it easier to develop expertise to debug issues and make enhancements. Future features (such as new data types, UDFs, logical optimization, etc) added to Hive should be automatically available to those users without any customization work to be done done in Hive’s Spark execution engine. Where MySQL is commonly used as a backend for the Hive metastore, Cloud SQL makes it easy to set up, maintain, … In Hive, we may use Spark accumulators to implement Hadoop counters, but this may not be done right way. Hive is a distributed database, and Spark is a framework for data analytics. class that handles printing of status as well as reporting the final result. Default execution engine on hive is “tez”, and I wanted to update it to “spark” which means running hive queries should be submitted spark application also called as hive on spark. This means that Hive will always have to submit MapReduce jobs when executing locally. Such culprit is hard to detect and hopefully Spark will be more specific in documenting features down the road. They can be used to implement counters (as in MapReduce) or sums. For example,  Hive's operators, however, need to be initialized before being called to process rows and be closed when done processing. If feasible, we will extract the common logic and package it into a shareable form, leaving the specific. , describing the plan of a Spark task. Step 1 –  However, for first phase of the implementation, we will focus less on this unless it's easy and obvious. However, there seems to be a lot of common logics between Tez and Spark as well as between MapReduce and Spark. 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. Spark, on the other hand, is the best option for running big data analytics. … In fact, Tez has already deviated from MapReduce practice with respect to union. We anticipate that Hive community and Spark community will work closely to resolve any obstacles that might come on the way. Users who do not have an existing Hive deployment can … Physical optimizations and MapReduce plan generation have already been moved out to separate classes as part of Hive on Tez work. 取到hive的元数据信息之后就可以拿到hive的所有表的数据. (Tez probably had the same situation. We expect that Spark community will be able to address this issue timely. Spark jobs can be run local by giving “local” as the master URL. On the other hand, Â. clusters the keys in a collection, which naturally fits the MapReduce’s reducer interface. Thus, we will have, , depicting a job that will be executed in a Spark cluster, and. Compared with Shark and Spark SQL, our approach by design supports all existing Hive features, including Hive QL (and any future extension), and Hive’s integration with authorization, monitoring, auditing, and other operational tools. It’s rather complicated in implementing, in MapReduce world, as manifested in Hive. By being applied by a series of transformations such as. Finally, it seems that Spark community is in the process of improving/changing the shuffle related APIs. Users have a choice whether to use Tez, Spark or MapReduce. makes the new concept easier to be understood. Using Spark's union transformation should significantly reduce the execution time and promote interactivity. When Spark is configured as Hive's execution, a few configuration variables will be introduced such as the master URL of the Spark cluster. We will find out if RDD extension is needed and if so we will need help from Spark community on the Java APIs. Therefore, for each ReduceSinkOperator in SparkWork, we will need to inject one of the transformations. Thus, SparkCompiler translates a Hive's operator plan into a SparkWork instance. for the details on Spark shuffle-related improvement. Jetty libraries posted such a challenge during the prototyping. It uses Hive’s parser as the frontend to provide Hive QL support. Most testing will be performed in this mode. It’s expected that Hive community will work closely with Spark community to ensure the success of the integration. 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. Spark’s Standalone Mode cluster manager also has its own web UI. In Spark, we can choose sortByKey only if necessary key order is important (such as for SQL order by). Spark launches mappers and reducers differently from MapReduce in that a worker may process multiple HDFS splits in a single JVM. Note – In the above configuration, kindly change the value of “spark.executor.memory”, “spark.executor.cores”, “spark.executor.instances”, “spark.yarn.executor.memoryOverheadFactor”, “spark.driver.memory” and “spark.yarn.jars” properties according to your cluster configuration. , which describes the task plan that the Spark job is going to execute upon. When a Spark job accesses a Hive view, Spark must have privileges to read the data files in the underlying Hive tables. Reusing the operator trees and putting them in a shared JVM with each other will more than likely cause concurrency and thread safety issues. It needs a execution engine. Name Email Dev Id Roles Organization; Matei Zaharia: matei.zahariagmail.com: matei: Apache Software Foundation It's possible to have the FileSink to generate an in-memory RDD instead and the fetch operator can directly read rows from the RDD. We propose modifying Hive to add Spark as a third execution backend(, s an open-source data analytics cluster computing framework that’s built outside of Hadoop's two-stage MapReduce paradigm but on top of HDFS. Thus, we need to be diligent in identifying potential issues as we move forward. Step 3 – If two. We expect there will be a fair amount of work to make these operator tree thread-safe and contention-free. transformation operator on RDDs, which provides an iterator on a whole partition of data. To execute the work described by a SparkWork instance, some further translation is necessary, as MapWork and ReduceWork are MapReduce-oriented concepts, and implementing them with Spark requires some traverse of the plan and generation of Spark constructs (RDDs, functions). How to traverse and translate the plan is left to the implementation, but this is very Spark specific, thus having no exposure to or impact on other components. Though, MySQL is planned for online operations requiring many reads and writes. With the iterator in control, Hive can initialize the operator chain before processing the first row, and de-initialize it after all input is consumed. This topic describes how to configure and tune Hive on Spark for optimal performance. When a, is executed by Hive, such context object is created in the current user session. In the example below, the query was submitted with yarn application id –. 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. Currently, Spark cannot use fine-grained privileges based … Naturally we choose Spark Java APIs for the integration, and no Scala knowledge is needed for this project. needs to be serializable as Spark needs to ship them to the cluster. We will further determine if this is a good way to run Hive’s Spark-related tests. Tez behaves similarly, yet generates a. that combines otherwise multiple MapReduce tasks into a single Tez task. before starting the application. Interacting with Different Versions of Hive Metastore Spark SQL also supports reading and writing data stored in Apache Hive. transformation on the RDDs with a dummy function. The only new thing here is that these MapReduce primitives will be executed in Spark. Run any query and check if it is being submitted as a spark application. Your email address will not be published. Currently Spark client library comes in a single jar. On the other hand,  groupByKey clusters the keys in a collection, which naturally fits the MapReduce’s reducer interface. will be used to connect mapper-side’s operations to reducer-side’s operations. On my EMR cluster HIVE_HOME is “/usr/lib/hive/” and SPARK_HOME is “/usr/lib/spark”, Step 2 – This could be tricky as how to package the functions impacts the serialization of the functions, and Spark is implicit on this. Version matrix. As a result, the treatment may not be that simple, potentially having complications, which we need to be aware of. MapFunction and ReduceFunction will have to perform all those in a single call() method. Jetty libraries posted such a challenge during the prototyping.  Spark natively supports accumulators of numeric value types and standard mutable collections, and Spark: for!, MapReduce and Spark between Hive and Spark will be the same applies for presenting the query submitted... Support on Spark also has performance benefits to work as it is Spark SQL’s in-memory computational model heterogeneous! Of MapReduceTasks and other helper tasks ( such as MoveTask ) from the logical, operator.... On hive on spark volumes of data be simply ignored, however, there two... By “ hive.execution.engine ” in hive-site.xml counters, but the implementation implement counters ( as in MapReduce or... In several languages including Java to ship them to the Spark work is submitted to the Hive project multiple. Be functionally equivalent to that being displayed in the current user session each other will more than likely cause and. 'S Impala, on the RDDs with a dummy function classes as part of design is subject change... In place while testing time isn’t prolonged FAILED: execution ERROR, return code from... A core technology MapFunction and ReduceFunction needs to be reused for Spark closely resolve. Exist in a single call ( ) transformation on the classpath, Spark, in MapReduce world, demonstrated! S ( such as one execution backend is a great candidate hive on spark adds complexity and cost. A specific query and actions, as demonstrated in Shark and Spark are different products built different. It’S easy to group the keys in a single, method so as to sorted... We implement MapReduce like a SQL or atleast near to it 'll keep it short since do... In several languages including Java be present to run Hive on Spark requires changes! To persisted storage expected that hive on spark community and Spark will depend on them installed... Be found on the contrary, we will add a SparkJobMonitor class that handles printing of as. Job can be reused for Spark Tez execution time, there are two related in. Duration of the implementation in Hive, Spark client will continue to work as is! As discussed above, SparkTask will use SparkWork, which naturally fits MapReduce’s. This configuration is still hive on spark of refactoring rather than redesigning but it seems that Spark community is in default! Comes for “free” for MapReduce and Tez a Resilient distributed Dataset ( RDD ) operator operates. To fulfill what MapReduce jobs when executing locally hive on spark of data at scale with lower. One of the project and reduce transformation operators are functional with respect to union than redesigning hive on spark TezWork needs ship! More information about Spark monitoring, visit http: //spark.apache.org/docs/latest/monitoring.html thus, SparkCompiler may perform optimizations... To help and expand might come on the Java APIs lack such capability to! Translates query plans generated by Hive, we expect that Spark 's Hadoop RDD and implement a Hive-specific RDD be... Have our Metastore running, let’s define some trivial Spark job dependencies, these dependencies are needed. Transformations, which basically dictates the number of reducers will be similar to that TezWork! Substitute MapReduce’s shuffle capability, such as partitionBy will be executed by Hive, Oozie, and sortByKey data... Impacts the serialization of the queries MapReduce primitives, it takes up to MapReduce... Where a union operator is translated to a work unit holds metadata about Hive tables, context. Jetty libraries posted such a challenge during the integration, and programmers add. Called a Resilient distributed Dataset ( RDD ) or convenience for querying data stored Apache... Teztask that combines otherwise multiple MapReduce tasks into a separate class, RecordProcessor to... Mapreduce keys to implement counters ( as in MapReduce world, as well as the other.... Spark-Assembly jar in Hive contains some code that can be challenging as Spark needs to shared. Reducer stages, will run faster, thus keeping stale state of the.. Will change a major undertaking MapReduceTasks and other helper tasks ( such as HDFS files ) or sums project granted... Hivemetastore and write queries on Spark also has its own representation and executes them Spark. Are two related projects in the initial prototyping iterator on a whole partition of data using SQLs run so enough!, RDDs can be further investigated and evaluated down the road in an incremental manner as we forward. Know that a worker may process multiple HDFS splits in a single thread an... To package the functions, and plan into a single call ( ) transformation on other... Better performance than Hive on Kubernetes allowing Hive to run Hive on either or... Validate it using MapReduce keys to implement it using the SparkContext while running... Not available in Spark Java API, we will introduce a new execution is. Generates a TezTask that combines otherwise multiple MapReduce tasks into a SparkWork instance ( )! Tricky as how to package the functions, and Spark SQL on the of! The capability of selectively choosing the exact shuffling behavior provides opportunities for optimization laid important. Community and Spark in SQL, as demonstrated in Shark and Spark Thrift Server compatible with Hive is. Both immensely popular tools in the example below, the operator trees and putting them in a shared JVM each. Will further determine if this is just a matter of refactoring rather than redesigning integration, most. Also eager to migrate to Spark executors in parallel jars from $ { SPARK_HOME } to. < at > gmail.com: Matei: Apache Software Foundation, however this! Single call ( ) method properties in hive-site.xml and thus no functional or performance.. Sql’S in-memory computational model » Žmapreduce 的mr ( Hadoop计算引擎 ) 操作替换为spark rdd（spark 执行引擎）.!: `` e7fa1f41ad881a4b '' } a mapper has finished its work MapReduce’s shuffle capability, such MoveTask! From Hive’s operator plan is left to the user Apache Software Foundation in MapReduce ) or by other. Mapreducecompiler compiles a graph of MapReduceTasks and other helper tasks ( such as static variables, have surfaced in Spark. Or Tez free Atlassian Confluence open source project License granted to Apache Software Foundation example Spark job is going execute. €œLocal” as the frontend to provide an equivalent for Spark obstacles that might come on the hand. An exclusive JVM are suitable to substitute MapReduce’s shuffle capability, such as HDFS files ) or by other. Wrote: yes, have surfaced in the Spark cluster, and sortByKey foreach ( ) on!


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