The short answer is that Spark is not entirely compatible with recent versions of Hive found in CDH, but may still work for a lot of use cases. The Spark bits are still there. You have to add Hive to the classpath yourself.

Hive and Spark Integration Tutorial | Hadoop Tutorial for Beginners 2018 | Hadoop Training Videos #1https://acadgild.com/big-data/big-data-development-traini

Since Hive 2.2.0, Hive on Spark runs with Spark 2.0.0 and above, which doesn't have an assembly jar. To run with YARN mode (either yarn-client or yarn-cluster), link the following jars to HIVE_HOME/lib. One of the most important pieces of Spark SQL’s Hive support is interaction with Hive metastore, which enables Spark SQL to access metadata of Hive tables. Starting from Spark 1.4.0, a single binary build of Spark SQL can be used to query different versions of Hive metastores, using the … Integration Partners Spark Hire partners and integrates with the world’s leading applicant tracking systems to empower more efficient customer workflows. LIVE.

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Ask Question Asked 4 years, 7 months ago. Active 4 years, 4 months ago. Viewed 3k times 2. 1. I am looking for a way to configure Hive for Spark SQL integration testing such that tables are written either in a temporary directory or … Classpath issues when using Spark's Hive integration. written by Lars Francke on 2018-03-22 We were investigating a weird Spark exception recently.

The Hive Warehouse Connector makes it easier to use Spark and Hive together. The HWC library loads data from LLAP daemons to Spark executors in parallel. This process makes it more efficient and adaptable than a standard JDBC connection from Spark to Hive.

Jun 23, 2017 Hive Integration in Spark. From very beginning for spark sql, spark had good integration with hive. Hive was primarily used for the sql parsing in 

Spark hive integration. 0 votes . 1 view.

Now in HDP 3.0 both spark and hive ha their own meta store. Hive uses the "hive" catalog, and Spark uses the "spark" catalog. With HDP 3.0 in Ambari you can find below configuration for spark. As we know before we could access hive table in spark using HiveContext/SparkSession but now in HDP 3.0 we can access hive using Hive Warehouse Connector.

Version Compatibility.

Right now Spark SQL is very coupled to a specific version of Hive for two primary reasons. Metadata: we use the Hive Metastore client to retrieve information about tables in a metastore. Execution: UDFs, UDAFs, SerDes, HiveConf and various helper functions for configuration. A Hive metastore warehouse (aka spark-warehouse) is the directory where Spark SQL persists tables whereas a Hive metastore (aka metastore_db) is a relational database to manage the metadata of the persistent relational entities, e.g. databases, tables, columns, partitions. Conceptually, Hudi stores data physically once on DFS, while providing 3 different ways of querying, as explained before. Once the table is synced to the Hive metastore, it provides external Hive tables backed by Hudi’s custom inputformats.
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Databricks provides a managed Apache Spark platform to simplify running production applications, real-time data exploration, and infrastructure complexity. A key piece of the infrastructure is the Apache Hive Metastore, which acts as a data catalog that abstracts away the schema and table properties to allow users to quickly access the data. SparkSession is now the new entry point of Spark that replaces the old SQLContext and HiveContext. Note that the old SQLContext and HiveContext are kept for backward compatibility. A new catalog interface is accessible from SparkSession - existing API on databases and tables access such as listTables, createExternalTable, dropTempView, cacheTable are moved here.

Hive was primarily used for the sql parsing in 1.3 and for metastore and catalog API’s in later versions. In spark 1.x, we needed to use HiveContext for accessing HiveQL and the hive metastore. From spark 2.0, there is no more extra context to create.
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2014-07-01 · Spark is a fast and general purpose computing system which supports a rich set of tools like Shark (Hive on Spark), Spark SQL, MLlib for machine learning, Spark Streaming and GraphX for graph processing. SAP HANA is expanding its Big Data solution by providing integration to Apache Spark using the HANA smart data access technology.

Contribute to krishnakalyan3/mastering-apache-spark-book development by creating an account on GitHub. If backward compatibility is guaranteed by Hive versioning, we can always use a lower version Hive metastore client to communicate with the higher version Hive metastore server. For example, Spark 3.0 was released with a builtin Hive client (2.3.7), so, ideally, the version of server should >= 2.3.x. 2014-07-01 · Spark is a fast and general purpose computing system which supports a rich set of tools like Shark (Hive on Spark), Spark SQL, MLlib for machine learning, Spark Streaming and GraphX for graph processing. SAP HANA is expanding its Big Data solution by providing integration to Apache Spark using the HANA smart data access technology. Once the Hudi tables have been registered to the Hive metastore, it can be queried using the Spark-Hive integration.