This configuration is required for a Kerberized cluster.
Note that 2.enabled should be set to false for YARN client deploy mode, and true for YARN cluster deploy mode (by default).For example, thrift://localhost:9083 to indicate Metastore URI. Also, ensure .metastoreUri is configured properly.Make sure. is pointed into a suitable HDFS-compatible staging directory, e.g.Use the HiveServer2 Interactive JDBC URL, rather than the traditional HiveServer2's JDBC URL. For example, jdbc:hive2://localhost:10000. For example, HiveServer2's JDBC URL should be specified in 2.jdbc.url as well as configured in the cluster. . should be set for the application name of the LLAP service since this library utilizes LLAP.‘Interactive Query' (LLAP) should be enabled (see 7.Note: This does not need Hive LLAP daemons to be running. Internally this fully utilizes Apache Hive’s Streaming API to write Apache’s DataFrame out to Apache Hive’s table. format(HiveWarehouseSession.STREAM_TO_STREAM)
#Hwc buckets islide code
The code below illustrate writing data from Apache Spark to Apache Hive table in Structured Streaming. Native Apache ORC writer is used instead, which has many important fixes in terms of performance and stability.Since it is implemented by Data Source V2, it supports a commit protocol and supports atomic write operation.Hive Streaming API is used in both batch and streaming write, which Apache Hive introduced to continuously digest data.SQL / DataFrame & Structured Streaming Write Support It leverages Apache Hive LLAP and retrieves data from Hive table into Spark DataFrame. Val df = hive.executeQuery("SELECT * FROM tableA") It is implemented by Data Source V2 which has a columnar format support and various functionalitiesįor instance, it is able to read an Apache Hive table in Apache Spark as below: import.Apache Spark's Apache Arrow integration is fully utilized for vectorized operations, faster and compact data interactions.Live Long And Process (LLAP) is fully utilized which Apache Hive introduced for faster performance.SQL / DataFrame and Structured Streaming write support.SQL / DataFrame APIs interacting with both transactional and non-transactional tables in Apache Hive.This library provides both Scala (Java compatible) and Python APIs for:
Those tables in Hive should not directly be accessible within Apache Spark APIs themselves. In other words, some features such as ACID tables or Apache Ranger with Apache Hive table are only available via this library in Apache Spark. Note: From HDP 3.0, catalogs for Apache Hive and Apache Spark are separated, and they use their own catalog namely, they are mutually exclusive - Apache Hive catalog can only be accessed by Apache Hive or this library, and Apache Spark catalog can only be accessed by existing APIs in Apache Spark. Therefore, this library, Hive Warehouse Connector, was implemented as a data source to overcome the limitations and provide those modern functionalities in Apache Hive to Apache Spark users. as of Spark 2.Īpache Spark supports a pluggable approach for various data sources and Apache Hive itself can be also considered as one data source. However, not all the modern features from Apache Hive are supported, for instance, ACID table in Apache Hive, Ranger integration, Live Long And Process (LLAP), etc. It allows an access to tables in Apache Hive and some basic use cases can be achieved by this. In case of Apache Spark, it provides a basic Hive compatibility. As both systems evolve, it is critical to find a solution that provides the best of both worlds for data processing needs. Both provide compatibilities for each other. Both provide their own efficient ways to process data by the use of SQL, and is used for data stored in distributed file systems. MotivationĪpache Spark and Apache Hive integration has always been an important use case and continues to be so. Short Description: This article targets to describe and demonstrate Apache Hive Warehouse Connector which is a newer generation to read and write data between Apache Spark and Apache Hive.
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