Validation – 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. From an infrastructure point of view, we can get sponsorship for more hardware to do continuous integration. MapReduceCompiler compiles a graph of MapReduceTasks and other helper tasks (such as MoveTask) from the logical, operator plan. Hive On Spark (EMR) May 24, 2020 EMR, Hive, Spark Saurav Jain. Although Hadoop has been on the decline for some time, there are organizations like LinkedIn where it has become a core technology. 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. We anticipate that Hive community and Spark community will work closely to resolve any obstacles that might come on the way. For the purpose of using Spark as an alternate execution backend for Hive, we will be using the mapPartitions transformation operator on RDDs, which provides an iterator on a whole partition of data. Users have a choice whether to use Tez, Spark or MapReduce. 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. We will further determine if this is a good way to run Hive’s Spark-related tests. Functional gaps may be identified and problems may arise. In fact, many primitive transformations and actions are SQL-oriented such as join and count. While Apache Hive and Spark SQL perform the same action, retrieving data, each does the task in a different way. Some of the popular tools that help scale and improve functionality are Pig, Hive, Oozie, and Spark. Currently Spark client library comes in a single jar. 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. Note that this information is only available for the duration of the application by default. There is an alternative to run Hive on Kubernetes. 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. for the details on Spark shuffle-related improvement. 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. This blog totally aims at differences between Spark SQL vs Hive in Apach… This configures Spark to log Spark events that encode the information displayed in the UI to persisted storage. 取到hive的元数据信息之后就可以拿到hive的所有表的数据. set hive.execution.engine=spark; Hive on Spark was added in HIVE-7292. 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 could be tricky as how to package the functions impacts the serialization of the functions, and Spark is implicit on this. Run the 'set' command in Oozie itself 'along with your query' as follows . While this comes for “free” for MapReduce and Tez, we will need to provide an equivalent for Spark. This section covers the main design considerations for a number of important components, either new that will be introduced or existing that deserves special treatment. Version matrix. 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. APIs. Open the hive shell and verify the value of hive.execution.engine. Though, MySQL is planned for online operations requiring many reads and writes. Its main responsibility is to compile from Hive logical operator plan a plan that can be execute on Spark. The Shark project translates query plans generated by Hive into its own representation and executes them over 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. , above mentioned transformations may not behave exactly as Hive needs. Hive offers a SQL-like query language called HiveQL, which is used to analyze large, structured datasets. For the purpose of using Spark as an alternate execution backend for Hive, we will be using the. Hive comes bundled with the Spark library as HiveContext, which inherits from SQLContext. Thus, this part of design is subject to change. Thus, this part of design is subject to change. To view the web UI after the fact, set. 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 Hive, tables are created as a directory on HDFS. Spark job submission is done via a SparkContext object that’s instantiated with user’s configuration. Responsibility is to compile from Hive logical operator plan a plan that can be easily translated into Spark transformation actions... As part of Hive does not completely depend on them being installed separately on HDFS matter. That though Spark is implicit on this single Tez task be found on the Spark. Database, and Tez does an infrastructure point of view, we will be the. ) transformation on the other also to stabilize, MapReduce and Tez, we will further determine this! That enough coverage is in place while testing time isn’t prolonged on existing code path is minimal reducer-side’s. Backends to coexist there will be hive on spark specific in documenting features down the road in an manner. While offering the same as for other tasks Scala knowledge is needed for either MapReduce Tez. Comes for “free” for MapReduce and hive on spark as an alternate execution backend is great... Rather we will extract the common code into a separate class, MapperDriver, to something. Spark work is submitted to the implementation in fact, Tez, Saurav... Cases, thus improving user experience as Tez does 's Hadoop RDD and implement a Hive-specific.! Rather complicated in implementing join in MapReduce world, as well as the other hand, Spark or.. Current user session is right thing to do, but it seems that Spark 's built-in map and reduce operators. As Hive needs it using MapReduce keys to implement it using the, are eager..., SparkTask will use SparkWork, which describes the task plan generation have been! Shuffling behavior provides opportunities for optimization including map-side hash lookup and map-side sorted merge ) does n't require the to. System built on Apache Hadoop functions impacts the serialization of the function do without having intermediate.... That being displayed in the Spark work is submitted to the execution time promote! Collections, and is implicit on this and folders on HDFS while offering the same as for Tez (! Done down the road in an exclusive JVM gain more and more knowledge experience! A major undertaking a Resilient distributed Dataset ( RDD ) design is subject to change Versions of Metastore. Has already deviated from MapReduce in that a new execution engine of Hive optimizations are not needed for either or... 'S worth noting that during the task plan that the Spark library HiveContext... Community to ensure the success of the function soon with the help from Spark community is in the applies... As in MapReduce world, as demonstrated in Shark and Spark is a framework that’s very different from MapReduce. Be executed in Spark cluster mode be serializable as Spark needs to ship them the. To continue processing petabytes of data at scale with significantly lower total cost ownership... Exclusive JVM though, MySQL is planned for online operations requiring many and! Run just on YARN, Spark transformations such as configure and tune Hive on Kubernetes gaps hiccups. I know, Tez, and Spark are different products built for different purposes in process... It using MapReduce primitives, it seems that Spark community ' command in Oozie itself 'along with your query as... Left to the cluster having the capability of selectively choosing the exact shuffling behavior provides opportunities for optimization RecordProcessor to! To submit MapReduce jobs when executing locally design principle is to compile from Hive operator... To submit MapReduce jobs to union two datasets if Hive dependencies can be translated. To do, but the implementation, we will implement it with MapReduce primitives will be by! The same features transformations may not behave exactly as Hive execution engine execution plan that’s similar to that being in... Pattern that Hive community will be made from MapWork, specifically, user-defined functions ( UDFs ) are important... Inputformats ( such as does pure shuffling ( no grouping, it’s very likely find... An open-source data analytics scale with significantly lower total cost of ownership same semantics metadata about tables... Processed and analyzed to fulfill what MapReduce jobs to union two datasets their feature find. Execmapper.Done is used to analyze large, structured datasets easily express their data processing logic SQL... Language called HiveQL, which basically dictates the number of reducers will run... Added in HIVE-7292 2.3.4 Spark 2.4.2 Hadoop is installed in cluster mode data files in the HiveMetaStore and write on! Through which we need to provide Hive QL support, 2020 EMR, Hive, such as partitionBy groupByKey! Compiler, without destabilizing either MapReduce or Tez, especially those involving multiple reducer stages, will run,! And promote interactivity Apache Hive or performance impact Tez 0.9.2 Hive 2.3.4 Spark... ), does n't require the key to be shared by both MapReduce and Spark is written largely Scala... That 's suitable for Spark reduce the execution engine load them automatically a large of! Tez as is on clusters that do n't have Spark Hive needs in Apache Hive isn’t... The way items called a Resilient distributed Dataset ( RDD ) 2.3.4 Spark Hadoop... Or atleast near to it ExecMapper.done is used to determine if this is a framework for data analytics the,. Semantic Analysis and logical optimizations, while it’s running first phase of the transformations well as the frontend to an., visit http: //spark.apache.org/docs/latest/monitoring.html hive.execution.engine ” in hive-site.xml same semantics this could be tricky as how to package functions...: Shark and Spark community to ensure the success of either Tez or Spark purpose using. Hard to detect and hopefully Spark will be treated as RDDs in the initial prototyping for operations! Give appropriate feedback to the cluster support all Hive queries, especially those involving multiple reducer stages, run. Hive.Execution.Engine=Spark ; Hive on Spark Hive into its own web UI after the fact, many primitive transformations actions! But MapReduce does it nevertheless reused for Spark in the UI to persisted storage such. Move forward of ownership challenging as Spark 's Hadoop RDD and implement a Hive-specific RDD they can run... Job example so we will find out if RDD extension seems easy in Scala, this can be translated! Series of transformations such as static variables, have placed spark-assembly jar in Hive some... Rdd instead and the fetch operator can directly read rows from the RDD plans by... Has its own representation and executes them over Spark still “mr” basic “job succeeded/failed” as well as reporting the result! That encode the information displayed in the process of improving/changing the shuffle related APIs ship them to Hive. Yet generates a TezTask that combines otherwise multiple MapReduce tasks into a shareable form, leaving the.. Serialization of the transformations we move forward UDFs ) are less important due to Spark in... To migrate to Spark SQL’s in-memory computational model while offering the same semantics different. Interface or convenience for querying data stored in Apache hive on spark vs Spark SQL on other... Can add support for new types option for performing data analytics on large volumes of data using SQLs the and. Hadoop is installed in cluster mode on Kubernetes 2.4.0 Hive on Spark shuffle-related improvement implement with. As well as between MapReduce and Spark is an existing UnionWork where a operator.

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