This approach avoids or reduces the necessity of any customization work in Hive’s Spark execution engine. Spark SQL supports a different use case than Hive. The Shark project translates query plans generated by Hive into its own representation and executes them over Spark. We will keep Hive’s join implementations. From an infrastructure point of view, we can get sponsorship for more hardware to do continuous integration. Name Email Dev Id Roles Organization; Matei Zaharia: matei.zahariagmail.com: matei: Apache Software Foundation Note that this is just a matter of refactoring rather than redesigning. It's possible we need to extend Spark's Hadoop RDD and implement a Hive-specific RDD. As a result, the treatment may not be that simple, potentially having complications, which we need to be aware of. 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 variables will be passed through to the execution engine as before. Required fields are marked *, You may use these HTML tags and attributes:
 , org.apache.spark.serializer.KryoSerializer, 2. Hadoop 2.9.2 Tez 0.9.2 Hive 2.3.4 Spark 2.4.2 Hadoop is installed in cluster mode. Spark jobs can be run local by giving “. Spark launches mappers and reducers differently from MapReduce in that a worker may process multiple HDFS splits in a single JVM. from Hive’s operator plan is left to the implementation. By being applied by a series of transformations such as. Reusing the operator trees and putting them in a shared JVM with each other will more than likely cause concurrency and thread safety issues. Hive Partition is a way to organize large tables into smaller logical tables based on values of columns; one logical table (partition) for each distinct value. Evaluate Confluence today. As specified above, Spark transformations such as partitionBy 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 – It should be “spark”. Spark’s Standalone Mode cluster manager also has its own web UI. While sortByKey provides no grouping, it’s easy to group the keys as rows with the same key will come consecutively. 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. For instance, variable ExecMapper.done is used to determine if a mapper has finished its work. 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. During the task plan generation, SparkCompiler may perform physical optimizations that's suitable for Spark. Hive on Spark. This process makes it more efficient and adaptable than a standard JDBC connection from Spark to Hive. Further optimization can be done down the road in an incremental manner as we gain more and more knowledge and experience with Spark. The approach of executing Hive’s MapReduce primitives on Spark that is different from what Shark or Spark SQL does has the following direct advantages: Spark users will automatically get the whole set of Hive’s rich features, including any new features that Hive might introduce in the future. Spark application developers can easily express their data processing logic in SQL, as well as the other Spark operators, in their code. 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. There is an existing UnionWork where a union operator is translated to a work unit. SparkWork will be very similar to TezWork, which is basically composed of MapWork at the leaves and ReduceWork (occassionally, UnionWork) in all other nodes. To view the web UI after the fact, set. This could be tricky as how to package the functions impacts the serialization of the functions, and Spark is implicit on this. Using HiveContext, you can create and find tables in the HiveMetaStore and write queries on it using HiveQL. Tez behaves similarly, yet generates a TezTask that combines otherwise multiple MapReduce tasks into a single Tez task. On the other hand, Â. clusters the keys in a collection, which naturally fits the MapReduce’s reducer interface. This means that Hive will always have to submit MapReduce jobs when executing locally. It inevitably adds complexity and maintenance cost, even though the design avoids touching the existing code paths. Hive and Spark are different products built for different purposes in the big data space. Spark provides WebUI for each SparkContext while it’s running. Add the following new properties in hive-site.xml. Its main responsibility is to compile from Hive logical operator plan a plan that can be execute on Spark. (3)接下来就可以通过spark sql来操作hive表中的数据. 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. While RDD extension seems easy in Scala, this can be challenging as Spark's Java APIs lack such capability. Please refer to https://issues.apache.org/jira/browse/SPARK-2044 for the details on Spark shuffle-related improvement. 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). A SparkTask instance can be executed by Hive's task execution framework in the same way as for other tasks. Note that Spark's built-in map and reduce transformation operators are functional with respect to each record. The number of partitions can be optionally given for those transformations, which basically dictates the number of reducers. However, Hive’s map-side operator tree or reduce-side operator tree operates in a single thread in an exclusive JVM. If two ExecMapper instances exist in a single JVM, then one mapper that finishes earlier will prematurely terminate the other also. 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. While this comes for “free” for MapReduce and Tez, we will need to provide an equivalent for Spark. Finally, it seems that Spark community is in the process of improving/changing the shuffle related APIs. This could be tricky as how to package the functions impacts the serialization of the functions, and Spark is implicit on this. Run any query and check if it is being submitted as a spark application. Hive on Spark provides us right away all the tremendous benefits of Hive and Spark both. Hive is the best option for performing data analytics on large volumes of data using SQLs. Jetty libraries posted such a challenge during the prototyping. See: Hive on Spark: Join Design Master for detailed design. It is not easy to run Hive on Kubernetes. Once the Spark work is submitted to the Spark cluster, Spark client will continue to monitor the job execution and report progress. Spark launches mappers and reducers differently from MapReduce in that a worker may process multiple HDFS splits in a single JVM. On the contrary, we will implement it using MapReduce primitives. Physical optimizations and MapReduce plan generation have already been moved out to separate classes as part of Hive on Tez work. Allow Yarn to cache necessary spark dependency jars on nodes so that it does not need to be distributed each time when an application runs. Again this can be investigated and implemented as a future work. Note that Spark's built-in map and reduce transformation operators are functional with respect to each record. And Mapreduce, YARN, Spark served the purpose. Hive continues to work on MapReduce and Tez as is on clusters that don't have spark. 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. On Mon, Mar 2, 2015 at 5:15 PM, scwf wrote: yes, have placed spark-assembly jar in hive lib folder. Accessing Hive from Spark. 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. Each has different strengths depending on the use case. Thus, naturally Hive tables will be treated as RDDs in the Spark execution engine. Therefore, we will likely extract the common code into a separate class, MapperDriver, to be shared by both MapReduce and Spark. Spark’s primary abstraction is a distributed collection of items called a Resilient Distributed Dataset (RDD). Finally, it seems that Spark community is in the process of improving/changing the shuffle related APIs. As discussed above, SparkTask will use SparkWork, which describes the task plan that the Spark job is going to execute upon. While Apache Hive and Spark SQL perform the same action, retrieving data, each does the task in a different way. So we will discuss Apache Hive vs Spark SQL on the basis of their feature. Lastly, Hive on Tez has laid some important groundwork that will be very helpful to support a new execution engine such as Spark. The main design principle is to have no or limited impact on Hive’s existing code path and thus no functional or performance impact. set hive.execution.engine=spark; Hive on Spark was added in HIVE-7292. 1. 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. In Hive, tables are created as a directory on HDFS. However, Tez has chosen to create a separate class, RecordProcessor, to do something similar.). 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”. (Tez probably had the same situation. , we will need to inject one of the transformations. Secondly, we expect the integration between Hive and Spark will not be always smooth. It's worth noting that during the prototyping Spark caches function globally in certain cases, thus keeping stale state of the function. How to generate SparkWork from Hive’s operator plan is left to the implementation. Some important design details are thus also outlined below. As Hive is more sophisticated in using MapReduce keys to implement operations that’s not directly available such as join, above mentioned transformations may not behave exactly as Hive needs. As Hive is more sophisticated in using MapReduce keys to implement operations that’s not directly available such as. If Spark is run on Mesos or YARN, it is still possible to reconstruct the UI of a finished application through Spark’s history server, provided that the application’s event logs exist. For example,  Hive's operators, however, need to be initialized before being called to process rows and be closed when done processing. 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. Jetty libraries posted such a challenge during the prototyping. 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. So, after multiple configuration trials, I was able to configure hive on spark, and below are the steps that I had followed.  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