0, a single binary build of Spark SQL can be used to query different versions of Hive metastores, using the configuration described below. OTA4H allows direct, fast, parallel, secure and consistent access to master data in Oracle database using Hive SQL, Spark SQL, as well as Hadoop and Spark APIs that support SerDes, HCatalog, InputFormat and StorageHandler. If Hive dependencies can be found on the classpath, Spark will load them automatically. Looker was tested with Hive on Tez (hive. Informatica BDM can be used to perform data ingestion into a Hadoop cluster, data processing on the cluster and extraction of data from the Hadoop cluster. RStudio IDE. I am trying to configure SAP HANA SMART DATA Access to connect SAP HANA with HADOOP HIVE using simba ODBC driver. CREATE, DROP, TRUNCATE, ALTER, SHOW, DESCRIBE, USE, LOAD, INSERT, JOIN and many more Hive Commands. Browsing Hive tables requires that the active user be authorized to access the database and table and the Waterline service user have read access to the backing file. 0 and earlier releases support reading these Hive primitive data types with HCatLoader: boolean. You can follow below command to start Hive Thrift server Once the Hive Thrift server service is started. Lastly, we can verify the data of hive table. In this video I have explained about how to read hive table data using the HiveContext which is a SQL execution engine. I have copied the hive-site. For Hive access, perform the following tasks: Perform the pre-deployment tasks. sql("show databases;") spark. In HDInsight 4. This is very helpful to accommodate all the existing users into Spark SQL. broadcastTimeout. Since the data is in JSON format on HDFS, there are a few options for what Hive SerDe to use. The 1-minute data is stored in MongoDB and is then processed in Hive or Spark via the MongoDB Hadoop Connector, which allows MongoDB to be an input or output to/from Hadoop and Spark. This blog post is about accessing the Hive Metastore from Hue, the open source Hadoop UI and clearing up some confusion about HCatalog usage. The Apache Hive Warehouse Connector (HWC) is a library that allows you to work more easily with Apache Spark and Apache Hive by supporting tasks such as moving data between Spark DataFrames and Hive tables, and also directing Spark streaming data into Hive tables. If you drop an unmanaged table, Spark will delete the metadata entry for that table, and because of that drop table statement, you won't be able to access that table using Spark SQL. * Created at AMPLabs in UC Berkeley as part of Berkeley Data Analytics Stack (BDAS). Using the Apache Ranger administration console, users can easily manage policies around accessing a resource (file, folder, database, table, column etc) for a particular set of users and/or. spark-shell has to be launched by specifying the Copy to Hadoop jars in the --jars option. but let’s keep the transactional table for any other posts. This information is for Spark 2. Hive Tables. The subsequent pipeline needs to apply a single Hive query to one of the external tables and create a new table (it's just a test case so I'm starting with trivial tasks). Hive Compatibility. Some more configurations need to be done after the successful. Not able to create table in Hive with SparkSession when. Hive was primarily used for the sql parsing in 1. The notebook runs authenticated as a Watson Studio Local user bob. Once you have access to HIVE , the first thing you would like to do is Create a Database and Create few tables in it. Hive meta store is a place, usually a relational database like MySQL or Derby which stored meta data about the files. mode (SaveMode. Could you please let. The Apache Hive Warehouse Connector (HWC) is a library that allows you to work more easily with Apache Spark and Apache Hive by supporting tasks such as moving data between Spark DataFrames and Hive tables, and also directing Spark streaming data into Hive tables. You can find out the table type by the SparkSession API spark. When you create a Hive table, the table definition (column names, data types, comments, etc. Integrate Spark-SQL (Spark 2. A better way is to use ALTER TABLE statements to change the existing table schema instead. Step3: Create an HDInsight Spark cluster named "chepraspark" by configuring Metastore settings with same Azure SQL Database. Users who do not have an existing Hive deployment can still create a HiveContext. Hive Warehouse Connector works like a bridge between Spark and Hive. Voila, you are executing HiveQL query with the previously seen WHERE statement. HBase and Hive are two hadoop based big data technologies that serve different purposes. Default: 5 * 60 When negative, it is assumed infinite (i. Spark can work on data present in multiple sources like a local filesystem, HDFS, Cassandra, Hbase, MongoDB etc. Spark SQL can automatically capture the schema of a JSON dataset and load it as a DataFrame. Default: 5 * 60 When negative, it is assumed infinite (i. Quick Start. Hive Metastore log does not register any access when the workbench is trying to access Hive. I'm trying to connect to the Hive warehouse directory located in HDInsight by using Spark on IntelliJ. This allows you to more easily store metadata for your external tables on Amazon S3 outside of your cluster. On all of the worker nodes, the following must be installed on the classpath:. To serialize/deserialize data from the tables defined in the Glue Data Catalog, Spark SQL needs the Hive SerDe class for the format defined in the Glue Data Catalog in the classpath of the spark job. Today, Hive has over 3,200 alumni and members in 130 countries and chapters in 17 cities globally. sql("show databases;") spark. Connect to your favorite Spark shell (pyspark in our case) and test the connection to the Hive table using the Spark Hive context. 0 create table spark-sql create external table rds cluster management mysql spark s3 spark dataframe python hivecontext. I have explained using pyspark shell and a python program. 2 Solution: Per Spark SQL programming guide, HiveContext is a super set of the SQLContext. I am using Spark 1. Spark SQL: There are no access rights for users. Using a Snappy session, you can read an existing hive tables that are defined in an external hive catalog, use hive tables as external tables from SnappySession for queries, including joins with tables defined in SnappyData catalog, and also define new Hive table or view to be stored in external hive catalog. getcwd()) ['Leveraging Hive with Spark using Python. Each time we refresh our Data Table in Spotfire, the Spark SQL Connector launches a Spark job. sql as below. Additional features include the ability to write queries using the more complete HiveQL parser, access to Hive UDFs, and the. First I created an EMR cluster (EMR 5. Using HWC, we can write out any DataFrame into a Hive table. Using PySpark to READ and WRITE tables With Spark’s DataFrame support, you can use pyspark to READ and WRITE from Phoenix tables. This scenario based certification exam demands basic programming using Python or Scala along with Spark and other Big Data technologies. Get to know the latest recipes in development in Hive including CRUD operations; Understand Hive internals and integration of Hive with different frameworks used in today's world. Supports different data formats (Avro, csv, elastic search, and Cassandra) and storage systems (HDFS, HIVE tables, mysql, etc). Previous versions only supported table partition manipulation. Using Hive with Spark. You integrate Spark-SQL with Hive when you want to run Spark-SQL queries on Hive tables. The Hive integration allows querying the data in DynamoDB directly using HiveQL, a SQL-like language that can express analytical queries. Spark SQL includes a server mode with industry standard JDBC and ODBC connectivity. For more information on Hive authorization and privileges, see Understanding Qubole Hive Authorization. In this blog, we will see how to access and query HBase tables using Apache Spark. So this feature helps a user to issue SQL queries, whether the table present in Hadoop or in the NOSQL based database such as HBase, MongoDB, Cassandra, Amazon DynamoDB. HiveContext is a superset of SqlContext, so it can do what SQLContext can do and much more. aws-secret-key settings, and also allows EC2 to automatically rotate credentials on a regular basis without any additional work on your part. Hive Integration in Spark. Also, can portion and bucket, tables in Apache Hive. This is part 1 of a 2 part series for how to update Hive Tables the easy way Historically, keeping data up-to-date in Apache Hive required custom application development that is complex, non-performant […]. 0 provides builtin support for Hive features including the ability to write queries using HiveQL, access to Hive UDFs, and the ability to read data from Hive tables. aws-access-key and hive. SQL-only table access control. I am new to Spark I am trying to access Hive table to Spark. Hive scripts use an SQL-like language called Hive QL (query language) that abstracts programming models and supports typical data warehouse interactions. Spark can work on data present in multiple sources like a local filesystem, HDFS, Cassandra, Hbase, MongoDB etc. table ("src") df. In the last post (Data Query between BDA and Exadata (Part 1): Query Hive Table from Oracle on Exadata), I show the way to use Oracle Big Data SQL from Oracle table to access hive table on BDA. The unmanaged files are external tables. 1, the DataFrame data written to the Hive table. xml to Spark conf/. Oracle Table Access for Hadoop and Spark (OTA4H) is an Oracle Big Data Appliance feature that converts Oracle tables to Hadoop and Spark datasources. If you get table not found errors when running the query, you are probably trying to access a dataset that you did not declare as input. read spark-xml maven build metastore. What is Hibernate? Hibernate is a pure Java object-relational mapping (ORM) and persistence framework that allows you to map plain old Java objects to relational database tables using (XML) configuration files. That means they reside somewhere outside the database directory. An alternative to running 'show tables' or 'show extended tables' from the CLI is to use the web-based schema browser. You can follow below command to start Hive Thrift server Once the Hive Thrift server service is started. Each time we refresh our Data Table in Spotfire, the Spark SQL Connector launches a Spark job. It is natural to store access logs in folders named by the date logs that are generated. The data is stored in the form of tables (just like RDBMS). With a HiveContext, you can access Hive or Impala tables represented in the metastore database. Spark primitives are applied to RDDs. Since Spark SQL manages the tables, doing a DROP TABLE example_data deletes both the metadata and data. Importing Data into Cloudera Data Science Workbench Cloudera Data Science Workbench allows you to run analytics workloads on data imported from local files, Apache HBase, Apache Kudu, Apache Impala, Apache Hive or other external data stores such as Amazon S3. Yes, you have it. In order to move the data from staging to base, I am trying the "Exchange partition" on the hive table from spark. However, since Hive has a large number of dependencies, these dependencies are not included in the default Spark distribution. Here is an example of executing hive query through HiveContext. ipynb', 'derby. Hive -Spark2 JDBC driver use thrift server, you should start thrift server before attempting to connect to remove HiveServer2. Methods to Access Hive Tables from Apache Spark; Hive JDBC driver is one of the most widely used driver to connect to HiveServer2. getcwd()) ['Leveraging Hive with Spark using Python. This Running Queries Using Apache Spark SQL tutorial provides in-depth knowledge about spark sql, spark query, dataframe, json data, parquet files, hive queries Running SQL Queries Using Spark SQL lesson provides you with in-depth tutorial online as a part of Apache Spark & Scala course. For all other Hive versions, Azure Databricks recommends that you download the metastore JARs and set the configuration spark. HiveContext, as it can perform SQL query over Hive tables. Columns in HBase are comprised of a column family prefix, cf in this example, followed by a colon and then a column qualifier suffix, a in this case. 1, but fails on 2. Prerequisites. Core Spark - Transformations and Actions to process the data. getOrCreate(). Hive was also introduced as a query engine by Apache. Hive Tables. (Looker supports Hive 1. log'] Initially, we do not have metastore_db. We recommend this configuration when you require a persistent metastore or a metastore shared by different clusters, services, applications, or AWS accounts. Input tables are stored in Spark cache. Run below script in hive CLI. It can have partitions and buckets, dealing with heterogeneous input formats and schema. Hive metastore consists of two fundamental units: A service that provides metastore access to other Apache Hive services. It may be temporary metadata like temp table, registered udfs on SQL context or permanent metadata like Hive meta store or HCatalog. version to match the version of your. Hope this tutorial illustrated some of the ways you can integrate Hive and Spark. // Create the output Hive table spark. Apache Hive: There are access rights for users, groups as well as roles. functions import * from pyspark. Whats people lookup in this blog: Create Hive Table Using Spark Sql Context; Complete The Code To Create A Hive Table Using Spark Sqlcontext. Spark now persists table partition metadata in the system catalog (a. Like Hive, when dropping an EXTERNAL table, Spark only drops the metadata but keeps the data files intact. Spark SQL: As same as Hive, Spark SQL also support for making data persistent. And after that add /usr/lib/hive/lib/*. getcwd()) ['Leveraging Hive with Spark using Python. Reason: org. Informatica BDM can be used to perform data ingestion into a Hadoop cluster, data processing on the cluster and extraction of data from the Hadoop cluster. Welcome to Apache HBase™ Apache HBase™ is the Hadoop database, a distributed, scalable, big data store. HBase scales linearly to handle huge data sets with billions of rows and millions of columns, and it easily combines data sources that use a wide variety of different structures and schemas. Hi, I am trying to access the already existing table in hive by using pyspark e. when i again start the spark-shell , then earlier table i created, was no longer existing, so exactly where this table and metadata is stored and all. Hive features a metastore that maintains metadata about Hive tables (location and schema) and partitions that it makes programmatically available to developers via a metastore service API. You can load your data using SQL or DataFrame API. I have spark 1. "spark_vora". Note that schema inference can be a very time-consuming operation for tables with thousands of partitions. In Auzre Databricks, Global tables are registered to the Hive metastore. Accessing ORC Data in Hive Tables Apache Spark on HDP supports the Optimized Row Columnar (ORC) file format, a self-describing, type-aware, column-based file format that is one of the primary file formats supported in Apache Hive. Prerequisites. DataFrame data will be written to hive, the default is hive default database, insertInto does not specify the parameters of the database, this article uses the following way to write data to the hive table or hive table partition, for reference only. Apache Hive: Schema flexibility and evolution. We learnt that Presto uses the metastore service of Hive to access the hive table details. I spent the whole yesterday learning Apache Hive. It provides information about metastore deployment modes, recommended network setup, and cluster configuration requirements, followed by instructions for configuring clusters to connect to an external metastore. The Vora source will show a database/schema of ‘spark_vora’ and the various tables. Read Optimized View - Provides excellent query performance on pure columnar storage, much like plain Parquet tables. And from my personal experience, it is the most common workflow. As a result. Apache HCatalog is a project enabling non-Hive scripts to access Hive tables. Apache HBase is an open source NoSQL database that provides real-time read/write access to those large datasets. MS SQL is the external metastore. Data Access using Spark. Supports different data formats (Avro, csv, elastic search, and Cassandra) and storage systems (HDFS, HIVE tables, mysql, etc). Since Hive has a large number of dependencies, these dependencies are not included in the default Spark distribution. The user cannot access the permanent table with same name as temporary tables during that session without dropping or renaming the temporary table. Another method is to use Spark provided hive-jdbc driver. This blog post was published on Hortonworks. If you already have a Hive metastore, such as the one used by Azure HDInsight, you can use Spark SQL to query the tables the same way you do it in Hive with the advantage to have a centralized metastore to manage your table schemas from both Databricks and HDInsight. Hive: Hive on HDP 2. To use these features, you do not need to have an existing Hive setup. San Diego Supercomputer Center is an Organized Research Unit of UC San Diego. Table Access Control. Hive is a pure data warehousing database that stores data in the form of tables. Apache Spark * An open source, Hadoop-compatible, fast and expressive cluster-computing platform. It may be temporary metadata like temp table, registered udfs on SQL context or permanent metadata like Hive meta store or HCatalog. Spark SQL runs unmodified Hive queries on current data. extraClassPath and spark. Table access control (table ACLs) lets you programmatically grant and revoke access to your data from Python and SQL. As a result. Create a data pipeline based on messaging using Spark and Hive In this spark project, we will simulate a simple real-world batch data pipeline based on messaging using Spark and Hive. Data in Apache Hive can be categorized into Table, Partition, and Bucket. On spark SQL , I am able to list all tables , but queries on hive bucketed tables are not returning records. Integrate Spark-SQL (Spark 2. A Hive partition table is created which is partition by a column say yearofexperience. Creating Hive tables requires that the active user can create tables in at least one Hive database and have write access to the folder where the source files reside. from spark or pyspark shell use the below commands to access hive database objects. Hive is an open-source, data warehouse, and analytic package that runs on top of a Hadoop cluster. You can also replace Hive with Spark SQL to get better performance. Otherwise, keep reading! Spark-HBase Connector. Hue makes it easy to create Hive tables. Use SQLConf. When you do not specify the “external” keyword in the create statement, the table created is a managed table. sql import * from pyspark. HDFS, Cassandra, Hive, etc) SnappyData comes bundled with the libraries to access HDFS (Apache compatible). %%sql tells Jupyter Notebook to use the preset sqlContext to run the Hive query. Not able to create table in Hive with SparkSession when. (6 replies) Hi, I have observed that Spark SQL is not returning records for hive bucketed ORC tables on HDP. ) are stored in the Hive Metastore. Apache Hive: Schema flexibility and evolution. Connect quickly to Amazon EMR Hive distributions from the leading analytic and reporting tools. You can follow below command to start Hive Thrift server Once the Hive Thrift server service is started. The process is fast and highly efficient compared to Hive. This feature is available for beta access. For users who need these security mechanisms, we have built the Hive Warehouse Connector (HWC), which allows Spark users to access the transactional tables via LLAP's daemons. The Spark SQL connector evolved out of the Hive connector, thus the need for the Hive Thrift Server. Hive is a append only database and so update and delete is not supported on hive external and managed table. Both PySpark ( Python ) and SparkR ( R ) APIs have these features. This behavior is controlled by the spark. These queries often needed raw string manipulation and. Data movement happens between Spark and CAS through SAS generated Scala code. This is part 1 of a 2 part series for how to update Hive Tables the easy way Historically, keeping data up-to-date in Apache Hive required custom application development that is complex, non-performant […]. Congratulations! You just did a round trip of using Spark shell, reading data from HDFS, creating an Hive table in ORC format, querying the Hive Table, and persisting data using Spark SQL. PS: That means, the same scaling issues that you might have in Hive metastore will be present in DataBricks metastore access. Scalability − Use the same engine for both. Hive is a pure data warehousing database that stores data in the form of tables. x cluster as HDInsight cluster. broadcastTimeout method to access the current value. Hive Authorization policies are stored in the Qubole Metastore which acts as a shared central component and stores metadata related to Hive Resources like Hive Tables. Run the Hive Metastore in Docker. sql("show databases;") spark. Table of Contents Uses for an external metastoreMetastore password managementWalkthroughSetting up the metastoreDeploying Azure Databricks in a VNETSetting up the Key Vault Uses for an external metastore Every Azure Databricks deployment has a central Hive metastore accessible by all clusters to persist table metadata, including table and column names as well as storage location. mode (SaveMode. Thus, in this blog we are using CDH vm to integrate hive tables with the tableau. Apache Spark * An open source, Hadoop-compatible, fast and expressive cluster-computing platform. We will also look at Hive Database objects data types and how to load data into Hive Tables. 0) and hive metastore (0. Lens takes care of initializing and invoking the Spark code (Lens acts as a Spark shell). Some common ways of creating a managed table are: SQL. So my question is : Does Phoenix support spark-sql query the hive external table mapped from Phoenix ? I am working on hdp3. As described, Spark doesn't natively support writing to Hive's managed ACID tables. Hi Zhan Zhang, With the pre-bulit version 1. If you drop an unmanaged table, Spark will delete the metadata entry for that table, and because of that drop table statement, you won't be able to access that table using Spark SQL. Data operations can be performed using a SQL interface called HiveQL. The following tables describes the options for LKM Spark to Hive. "spark_vora". When dropping a MANAGED table, Spark removes both metadata and data files. Both PySpark ( Python ) and SparkR ( R ) APIs have these features. Partition is a very useful feature of Hive. We can use Hive tables in any Spark-based application. That means they reside somewhere outside the database directory. To enable SQL-only table access control on a cluster and restrict that cluster to use only SQL commands, set the following flag in the cluster's Spark conf:. Set TBLPROPERTIES to enable ACID transactions on Hive Tables. 4 LKM Spark to Hive. functions import * from pyspark. Hive comes bundled with the Spark library as HiveContext, which inherits from SQLContext. "" at "SparkVora". This blog post was published on Hortonworks. In HDInsight 4. Hive, Spark SQL in. 0, I come with the following errors: [[email protected] spark]$. To create a Hive table using Spark SQL, we can use the following code: When the jar submission is done and we execute the above query, there shall be a creation of a table by name “spark_employee” in Hive. Scalability − Use the same engine for both. These queries often needed raw string manipulation and. You can create and populate one Hive tables with the help of the others. Stay tuned for the next part, coming soon! Historically, keeping data up-to-date in Apache Hive required custom. Once you have access to HIVE , the first thing you would like to do is Create a Database and Create few tables in it. The Apache Hive Warehouse Connector (HWC) is a library that allows you to work more easily with Apache Spark and Apache Hive by supporting tasks such as moving data between Spark DataFrames and Hive tables, and also directing Spark streaming data into Hive tables. Table Operations such as Creation, Altering, and Dropping tables in Hive can be observed in this tutorial. Profiling Hive tables requires that the Waterline Data service user have read access to the Hive database and table. Spark SQL: As same as Hive, Spark SQL also support for making data persistent. I have created a Hadoop cluster and loaded some tables to hive. tablename") You can give any query inside spark. Apache Ranger offers a centralized security framework to manage fine grained access control over Hadoop and related components (Apache Hive, HBase etc. We want the Hive Metastore to use PostgreSQL to be able to access it from Hive and Spark simultaneously. Currently today, the Hive Metastore ("Hive context") is the only supported service. And after that add /usr/lib/hive/lib/*. 1) Created Spark Context. Now I have created a spark cluster and wish to see if we can query the tables from there. xml to Spark conf/. The Spark-HBase connector comes out of the box with HBase, giving this method the advantage of having no external dependencies. Hive Compatibility. More specifically, it’s helpful to know what types of questions are commonly asked during a Hive interview, along with the answers that your interviewer is likely looking. Oracle SQL Connector for HDFS can query or load data in text files or Hive tables over text files. spark streaming upgrade hivecontext version spark sql azure databricks udf hive spark 2. The first insert is at row1, column cf:a, with a value of value1. Accessing ORC Data in Hive Tables Apache Spark on HDP supports the Optimized Row Columnar (ORC) file format, a self-describing, type-aware, column-based file format that is one of the primary file formats supported in Apache Hive. sql Get unlimited access to the best stories on. Hive Warehouse Connector works like a bridge between Spark and Hive. One of the ways to get data from HBase is to scan. Another method is to use Spark provided hive-jdbc driver. It provides information about metastore deployment modes, recommended network setup, and cluster configuration requirements, followed by instructions for configuring clusters to connect to an external metastore. It uses the Spark SQL execution engine to work with data stored in Hive. engine is set to mr — then Hive will return query results too slowly to be practical. The table in Hive is logically made up of the data being stored. You integrate Spark-SQL with Hive when you want to run Spark-SQL queries on Hive tables. Stay tuned for the next part, coming soon! Historically, keeping data up-to-date in Apache Hive required custom. Using HiveContext, you can create and find tables in the HiveMetaStore and write queries on it using HiveQL. To access Hive Server using JDBC client, you have to install JDBC driver. Hive was built for querying and analyzing big data. 6 with default options. With this feature, users can build machine learning models backed by Hive tables, and later call these models via Hive UDFs. we will read a csv file as a dataframe and write the contents of dataframe to a partitioned hive table. Hope this tutorial illustrated some of the ways you can integrate Hive and Spark. The difference will be that queries running on Spark will be executed as per the Spark execution plan, and underlying data will be processed as per Spark. Hadoop/ BigData Admin San Diego Supercomputer Center March 2017 – Present 2 years 9 months. This includes data stored in Hive 3 tables and thus we need a way to provide efficient, high-performance, ACID compliant access to Hive 3 table data from Spark. A table created by Hive resides in the Hive catalog. 0 Answers Use AWS RDS - MySQL as hive metastore 1 Answer How to fetch hive table metadata from outside of Databricks 0 Answers. This project's goal is the hosting of very large tables -- billions of rows X millions of columns -- atop clusters of commodity hardware. getTable (added in Spark 2. In this video I have explained about how to read hive table data using the HiveContext which is a SQL execution engine. Now I have created a spark cluster and wish to see if we can query the tables from there. Creating the Hive table over Ranger Audit Logs on HDFS. Note that Spark should have been built with Hive support and more details can be found in the SQL programming guide. Import org. Tables on cloud storage must be mounted to Databricks File System. Understanding Hive Tables Understanding Partition and Skew Analyzing Big Data with Apache Hive Demonstration: Computing NGrams Joining Datasets in Apache Hive Computing NGrams of Emails in Avro Format Using HCatalog withApachePig DAY 4 - WORKING WITH SPARK CORE, SPARK SQL AND OOZIE Advanced Apache Hive Programming (Continued) Hadoop 2 and YARN. We have described how to load data from Hive Table using Apache Pig, in this post, I will use an example to show how to save data to Hive table using Pig. Stay tuned for the next part, coming soon! Historically, keeping data up-to-date in Apache Hive required custom. To access Hive Server using JDBC client, you have to install JDBC driver. The following; from pyspark. Which allows to have ACID properties for a particular hive table and allows to delete and update. Hive, Spark SQL in. The Hive server user needs read access to this folder. Table of Hive is organized in partitions by grouping same types of data together based on any column or partition key. Output tables are on disk (Impala has no notion of a cached table). Integrate with BI, Reporting, Analytics, ETL Tools, and Custom Solutions. Let's check that you have access to content of access log table. Hive is a append only database and so update and delete is not supported on hive external and managed table. When you do not specify the “external” keyword in the create statement, the table created is a managed table. >sqlContext = HiveContext(sc) >results = sqlContext. It uses the Spark SQL execution engine to work with data stored in Hive. >sqlContext = HiveContext(sc) >results = sqlContext. Using this connector, you can run a certain type of queries in Phoenix more efficiently than using Hive or other applications, however, this is not a universal tool that can run all types of queries. In this blog post, I’ll demonstrate how we can access a HBASE table through Hive from a PySpark script/job on an AWS EMR cluster. 0 of spark against the yarn cluster installed by ambari 1. The following tables describes the options for LKM Spark to Hive. It allows you to create Spark programs interactively and submit work to the framework. NOTE : I have tried to provide some of the reference links to articles. sql Get unlimited access to the best stories on. You are creating a fresh table not dependent on an existing table; You can access table schema and create the table along with pointing its data location. Oracle Table Access for Hadoop and Spark (OTA4H) is an Oracle Big Data Appliance feature that converts Oracle tables to Hadoop and Spark datasources. This information is for Spark 2. I want to access tables in this database in spark application using hivecontext. Put hive-site. Can be easily integrated with all Big Data tools and frameworks via Spark-Core. You use the Hive Warehouse Connector API to access any managed Hive table from Spark. Spark SQL supports Apache Hive using HiveContext. From Apache Spark, you access ACID v2 tables and external tables in Apache Hive 3 using the Hive Warehouse Connector. There are two ways to use Impala to query tables in Hive.