Configuration of Hive is done by placing your hive-site.xml, core-site.xml (for security configuration), You can also manually update or drop a Hive partition directly on HDFS using Hadoop commands, if you do so you need to run the MSCK command to synch up HDFS files with Hive Metastore. It is required to process this dataset in spark. at org.apache.spark.sql.DataFrame.head(DataFrame.scala:1422) ‎04-21-2017 # warehouse_location points to the default location for managed databases and tables, "Python Spark SQL Hive integration example". and its dependencies, including the correct version of Hadoop. And now you check its first rows. # +---+-------+ Analyzing the data in Hive The Spark streaming consumer app has parsed the flume events and put the data on hdfs. by the hive-site.xml, the context automatically creates metastore_db in the current directory and present on the driver, but if you are running in yarn cluster mode then you must ensure 01:59 PM. # |311|val_311| Once we have data of hive table in the Spark data frame, we can further transform it as per the business needs. Data Pipeline 22#UnifiedAnalytics #SparkAISummit Read datafile Parquet table Dataframe Apply schema on Dataframe from Hive table corresponds to text file Perform transformation- timestamp conversion etc Add partitioned column to How to read the data from hive table using Spark How to store the data into Spark Data frame using scala and then after doing some transformation, How to store the Spark data frame again back to another new table which has been partitioned by Date column. data_source must be one of TEXT, CSV, JSON, JDBC, PARQUET, ORC, HIVE, DELTA, or LIBSVM, or a fully-qualified class name of a custom implementation of org.apache.spark.sql.sources.DataSourceRegister. creating table, you can create a table using storage handler at Hive side, and use Spark SQL to read it. # +--------+ Here is an example using the SparkSession(spark object below) to access a Hive table as a DataFrame, then converted to an RDD so it can be passed to a SnappySession to store it in a SnappyData Table. the “input format” and “output format”. Run hive queries from spark → DataSimplfy → Kick start your BigData journey here → So far we have seen running Spark SQL queries on RDDs. This instructional blog post explores how to run hive queries from spark application. Users who do not have an existing Hive deployment can still enable Hive support. Version of the Hive metastore. at sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43) data_source must be one of TEXT, CSV, JSON, JDBC, PARQUET, ORC, HIVE, DELTA, or LIBSVM, or a fully-qualified class name of a custom implementation of org.apache.spark… Use the ssh command to connect to your HBase cluster. // You can also use DataFrames to create temporary views within a SparkSession. In some cases, you may want to copy or clone or duplicate the data ,structure of Hive table to a new table. Spark stores a managed table inside the database directory location. # +---+------+---+------+ 10:02 PM. connectivity to a persistent Hive metastore, support for Hive serdes, and Hive user-defined functions. Edit the command below by replacing HBASECLUSTER with the name of … // The results of SQL queries are themselves DataFrames and support all normal functions. // Turn on flag for Hive Dynamic Partitioning, // Create a Hive partitioned table using DataFrame API. Create Test Data Set Let us create sample Apache Spark dataFrame that you want to store to Hive table. Spark SQL also supports reading and writing data stored in Apache Hive. In this post we have used Spark1.x version. By default, we will read the table files as plain text. Start the Spark Shell First, we have to start the Spark Shell. Towards a folder with JSON object, you can use that with JSON method. One of the most important pieces of Spark SQL’s Hive support is interaction with Hive metastore, "SELECT * FROM records r JOIN src s ON r.key = s.key", // Create a Hive managed Parquet table, with HQL syntax instead of the Spark SQL native syntax, "CREATE TABLE hive_records(key int, value string) STORED AS PARQUET", // Save DataFrame to the Hive managed table, // After insertion, the Hive managed table has data now, "CREATE EXTERNAL TABLE hive_bigints(id bigint) STORED AS PARQUET LOCATION '$dataDir'", // The Hive external table should already have data. adds support for finding tables in the MetaStore and writing queries using HiveQL. Step 1: Create a table in Cassandra and insert records into it. @Greg Polanchyck if you have an existing ORC table in the Hive metastore, and you want to load the whole table into a Spark DataFrame, you can use the sql method on the hiveContext to run: Created // Queries can then join DataFrame data with data stored in Hive. # |238|val_238| A fileFormat is kind of a package of storage format specifications, including "serde", "input format" and at sun.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:62) org.apache.spark.*). format(“serde”, “input format”, “output format”), e.g. In order to do this, set spark.sql.hive.convertMetastoreParquet=false, forcing Spark to fallback to using the Hive Serde to read the data (planning/executions is A comma separated list of class prefixes that should explicitly be reloaded for each version Specifying storage format for Hive tables When you create a Hive table, you need to define how this table should read/write data from/to file system, i.e. automatically. at scala.collection.IterableLike$class.head(IterableLike.scala:91) ‎07-27-2017 at org.apache.spark.deploy.SparkSubmit$.doRunMain$1(SparkSubmit.scala:181) Starting from Spark 1.4.0, a single binary ‎07-10-2016 When you create a Hive table, you need to define how this table should read/write data from/to file system, Code example # Write into Hive // Queries can then join DataFrames data with data stored in Hive. Once defined explicitly (using format method) or implicitly ( spark.sql.sources.default configuration property), source is resolved using DataSource utility. Use the Apache Spark Catalog API to list the tables in the databases contained in the metastore. at java.lang.reflect.Method.invoke(Method.java:498) In this article, I will show how to save a Spark DataFrame as a dynamically partitioned Hive table. Working with HiveTables means we are working on Hive MetaStore. From Spark 2.0, you can easily read data from Hive data warehouse and also write/append new data to Hive tables. Requirement Assume you have the hive table named as reports. Note that independent of the version of Hive that is being used to talk to the metastore, internally Spark SQL df = spark.read.jdbc(url=url,table='testdb.employee',properties=db_properties) In the above code, it takes url to connect the database , and it takes table name , … *Note: In this tutorial we have configured the Hive … Spark supports JDBC Data Sources for many popular RDBMS like oracle, mysql, postgresql, db2, mssql, teradata, etc, but not for Hive. Let us load Data into table from HDFS by following step by step instructions. Welcome to ClearUrDoubt.com. // Partitioned column `key` will be moved to the end of the schema. An example of classes that should The following options can be used to specify the storage # +---+-------+ Note that these Hive dependencies must also be present on all of the worker nodes, as property can be one of three options: A classpath in the standard format for the JVM. "output format". access data stored in Hive. But, what I would really like to do is to read established Hive ORC tables into Spark without having to know the HDFS path and filenames. table_name USING data_source The file format to use for the table. Other classes that need at org.apache.spark.deploy.SparkSubmit$.main(SparkSubmit.scala:121) to be shared are those that interact with classes that are already shared. at org.apache.spark.sql.DataFrame.first(DataFrame.scala:1429) The spark session read table will create a data frame from the whole table that was stored in a disk. They define how to read delimited files into rows. at scala.collection.IndexedSeqLike$Elements.next(IndexedSeqLike.scala:64) # Key: 0, Value: val_0 df = sparkSession.createDataFrame(data) Creating Spark Session sparkSession = SparkSession.builder.appName("example-pyspark-read-and-write").getOrCreate() How to write a table into Hive? But you can run Spark SQL queries on Hive tables too. Below I will query a hive table from a specified hive schema and load it as a spark data frame using Spark SQL. Create a folder on HDFS under /user/cloudera HDFS Path javachain~hadoop]$ hadoop fs -mkdir javachain Move the text file from local file system into Location of the jars that should be used to instantiate the HiveMetastoreClient. options are. These options can only be used with "textfile" fileFormat. In case if you have requirement to save Spark DataFrame as Hive table, then you can follow below steps to create a Hive table out of Spark dataFrame. When working with Hive one must instantiate SparkSession with Hive support. # Queries can then join DataFrame data with data stored in Hive. at scala.collection.mutable.ArrayOps$ofRef.head(ArrayOps.scala:108) CREATE EXTERNAL TABLE 'bigdata _etl. // Aggregation queries are also supported. For example, Hi All, I have table 1 in hive say emp1, which has columns empid int, name string, dept string, salary double. // Queries can then join DataFrames data with data stored in Hive. # | 86| val_86| All other properties defined with OPTIONS will be regarded as Hive serde properties. You can read my other post about using Spark2 JDBC driver to connect to remote Hive server2: I created an ORC table in Hive, then did the following commands from the tutorial in scala, but from the exception, it appears that the read/load is expecting the HDFS filename. at sun.reflect.NativeMethodAccessorImpl.invoke0(Native Method) Note that, Hive storage handler is not supported yet when # |key| value|key| value| These 2 options specify the name of a corresponding, This option specifies the name of a serde class. // You can also use DataFrames to create temporary views within a SparkSession. Instead, use spark.sql.warehouse.dir to specify the default location of database in warehouse. # | 2| val_2| 2| val_2| However, since Hive has a large number of dependencies, these dependencies are not included in the at com.apollobit.jobs.TestData.main(TestData.scala) they are packaged with your application. With Spark’s DataFrame support, you can use pyspark to READ and WRITE from Phoenix tables. You also need to define how this table should deserialize the data of Hive that Spark SQL is communicating with. at com.apollobit.jobs.TestData$.main(TestData.scala:32) I successfully worked through Tutorial -400 (Using Hive with ORC from Apache Spark). # +--------+. # The items in DataFrames are of type Row, which allows you to access each column by ordinal. // The items in DataFrames are of type Row, which allows you to access each column by ordinal. That worked well ! # ... # You can also use DataFrames to create temporary views within a SparkSession. default Spark distribution. i.e. Skip to main content We're migrating our documentation. Note that -Greg, Created CREATE TABLE src(id int) USING hive OPTIONS(fileFormat 'parquet'). which enables Spark SQL to access metadata of Hive tables. # +--------+ // The items in DataFrames are of type Row, which lets you to access each column by ordinal. Create a data file (for our example, I am creating a file with comma-separated columns) Now use the Hive LOAD command to load the file into the table. Test_dataset ' ( Id ' int, Name ' String, 'surname ' String, 'age ' int ) ROW FORMAT Delimited Fields terminated BY ', ' Stored AS textfile Location '/path/to/csv_data '; at scala.collection.mutable.ArrayOps$ofRef.scala$collection$IndexedSeqOptimized$super$head(ArrayOps.scala:108) Please mention an example code. Created custom appenders that are used by log4j. The underlying files will be stored in S3. creates a directory configured by spark.sql.warehouse.dir, which defaults to the directory When working with Hive, one must instantiate SparkSession with Hive support, including spark-warehouse in the current directory that the Spark application is started. One of the most important pieces of Spark SQL’s Hive support is interaction with Hive metastore, The underlying files will be stored in S3. 03:03 AM, @slachterman Thank you very much ! Hive ALTER TABLE command is used to update or drop a partition from a Hive Metastore and HDFS location (managed table). Hence, the system will automatically create a warehouse for storing table data. Spark Issue with Hive when reading Parquet data generated by Spark The purpose of this article is to primarily address the exception below: Failed with exception java.io.IOException:parquet.io.ParquetDecodingException: Can not read value at 1 in block 0 in file # | 4| val_4| 4| val_4| to rows, or serialize rows to data, i.e. For example, Hive UDFs that are declared in a This at scala.collection.IndexedSeqOptimized$class.head(IndexedSeqOptimized.scala:120) Re: How to read table into Spark using the Hive tablename, not HDFS filename? Hive data source can only be used with tables, you can not read files of Hive data source directly. prefix that typically would be shared (i.e. Hey there! You have a working Spark application; You know what RDDs and DataFrames are (and the … # |count(1)| And … # The results of SQL queries are themselves DataFrames and support all normal functions. # |key| value| at org.apache.spark.deploy.SparkSubmit$.org$apache$spark$deploy$SparkSubmit$runMain(SparkSubmit.scala:731) Currently we support 6 fileFormats: 'sequencefile', 'rcfile', 'orc', 'parquet', 'textfile' and 'avro'. When the. Example: Load a DataFrame Given a table TABLE1 and a Zookeeper url of localhost:2181, you can load the table as a DataFrame using … But, what I would really like to do is to read established Hive ORC tables into Spark without having to know the HDFS path and filenames. If Hive dependencies can be found on the classpath, Spark will load them # | 500 | Available # | 5| val_5| 5| val_5| # +---+------+---+------+ You can now read the data using a hive external table for further processing. val ds = spark.table("hiveTable") val rdd = ds.rdd val session = new SnappySession(sparkContext) val df = session.createDataFrame(rdd, ds.schema) … be shared is JDBC drivers that are needed to talk to the metastore. To achieve this, Hive provides the options to create the table with or without data from the another table. How to read table into Spark using the Hive tablename, not HDFS filename? How can I use Spark to read from hive and write the output to HDFS back? at scala.collection.Iterator$anon$2.next(Iterator.scala:37) If you have requirement to connect to Apache Hive tables from Apache Spark program, then Spark provided jdbc driver can save your day. // ... Order may vary, as spark processes the partitions in parallel. Because the schema is defined in the table in Hive, Spark will not attempt to infer the schema (infer schema) from the files stored in that location. The following options can be used to configure the version of Hive that is used to retrieve metadata: A comma-separated list of class prefixes that should be loaded using the classloader that is Copy I successfully worked through Tutorial -400 (Using Hive with ORC from Apache Spark). How do I read directly from the Hive table, not HDFS? "SELECT key, value FROM src WHERE key < 10 ORDER BY key". Syntax: [database_name.] # Key: 0, Value: val_0 I searched, but could not find an existing answer. org.apache.spark.api.java.function.MapFunction. the hive.metastore.warehouse.dir property in hive-site.xml is deprecated since Spark 2.0.0. How to read table into Spark using the Hive tablen... 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