spark sql warehouse dir

packages in Spark_classpathSpark_classpath="/opt/sequoiadb/java/sequoiadb.jar:/opt/sequoiadb/spark/spark-sequoiadb_ 2.10-1.12.jar:/opt/sequoiadb/hadoop/hadoop-connector-2.2.jar:/opt/spark-1.3.1-bin-hadoop2.6/lib/ Postgresql-9.3-1103.jdbc41.jar"4 setting classpath, adding PostgreSQL JDBC driver pathExport classpath=/opt/postgresql-9.3-1103. Jdbc4.jar:${classpath}

metastore_ in the current app directory DB and create a directory configured by Spark.sql.warehouse.dir, if not configured, the default is the Spark-warehouse directory under the current app directory. Note: Starting with the Spark 2.0.0 release, The Hive.metastore.warehouse.dir property inside the Hive-site.xml has been replaced by Spark.sql.warehouse.dir, whic

I believe that the students who develop applications through Java and SQL Server have experienced the following similar problems.The JDBC official driver provided by Microsoft is not placed in the MAVEN repository, so if your Java application needs to access SQL Server, you have to download Sqljdbc4.jar to local, and then install the driver each time through the following MAVEN command:Install:install-file

Label:Hive (http://en.wikipedia.org/wiki/Apache_Hive) (non-strict source order translation) Apache Hive is a data Warehouse framework built on Hadoop that provides the data's profile, query, and analysis capabilities. It was originally developed by Facebook and is now being used by companies like Netflix. Amazon maintains a branch that is customized for you. Hive provides a class-SQL voice--HIVEQL that tran

matching record in the right table, returns null if the lookup succeeds, or returns only the record on the leftmost side, as shown in the basic implementation process.SummarizeJoin is a very important grammatical feature in database query, in the database field can be said to be "the world of the join", Sparksql as a distributed Data Warehouse system, provides us with a comprehensive join support, and in the internal implementation of the silent to d

information1,2015,12,0,2,4,02,2015,8,5,0,5,33,2015,3,16,4,1,54,2015,3,0,0,0,05,2015,3,0,3,0,06,2015,3,32,0,0,07,2015,3,0,16,3,328,2015,19,36,0,0,0,39,2015,5,6,30,0,2,210,2015,10,6,56,40,0,321,2014,12,0,2,4,02,2014,38,5,40,5,33,2014,23,16,24,1,54,2014,23,0,20,0,05,2014,3,0,3,20,06,2014,23,32,0,0,07,2014,43,0,16,3,328,2014,49,36,0,20,0,39,2014,45,6,30,0,22,210,2014,40,6,56,40,0,22Employee Payroll ListEmployee ID, Salary1,50002,100003,60004,70005,50006,110007,120008,55009,650010,4500The constructi

hive, Spark SQL supports any storage format supported by hive (SerDe), including files, Rcfiles, ORC, parquet, Avro, and Protocol Buffer (of course Spark SQL can also read these files directly). To connect to a deployed hive, you need to copy Hive-site.xml, Core-site.xml, Hdfs-site.xml to Spark's./conf/Directory If yo

Hive Tables Copy the hive_home/conf/ hive-site.xml file to the spark_home/conf/ When isn't configured by the Hive-site.xml, the context automatically creates metastore_db and warehouse In the current directory. SC is an existing sparkcontext.val SqlContext = new Org.apache.spark.sql.hive.HiveContext (SC) sqlcontext.sql ("CREATE TABLE IF not EXISTS src (key INT, value STRING) ") Sqlcontext.sql (" LOAD DATA LOCAL inpath ' examples/src/main/resources/

Hive TablesCopy the hive_home/conf/ hive-site.xml file to the spark_home/conf/When isn't configured by the Hive-site.xml, the context automatically creates metastore_db and warehouse In the current directory.//SC is an existing sparkcontext.Val SqlContext = New org.Apache.Spark.SQL.Hive.Hivecontext(SC)SqlContext.SQL("CREATE TABLE IF not EXISTS src (key INT, value STRING)")SqlContext.SQL("LOAD DATA LOCAL inpath ' examples/src/main/resources/kv1.txt '

How can we better use and monitor the Sparksql? Maybe we'll have a deeper understanding of what it's all about. The difference between the traditional database and Spark's SQL parsing has been written in the previous summary. Then let's get down to the straight-cut theme ~Today's spark supports a wide variety of data source queries and loads, is compatible with hive, and can be used in JDBC or ODBC to conne

Label:1. Setting the degree of parallelism in the shuffle process: Spark.sql.shuffle.partitions (sqlcontext.setconf ()) 2, in the Hive Data Warehouse construction process, the reasonable set of data types, such as can be set to int, do not set to bigint. Reduce the unnecessary memory overhead caused by the data type. 3, when writing SQL, try to give a clear column name, such as select name from students. Do

Import Java.sql.DriverManagerimport Java.util.Propertiesimport com.zhaopin.tools. {dateutils, Textutils}import org.apache.log4j. {level, Logger}import org.apache.spark.sql.sparksession/** * Created by XiaoYan on 2018/5/21. */object IHRDOWNLOADPG {def main (args:array[string]) {//Set Spark log Level Logger.getlogger ("Org.apache.spark"). SetLevel (level.error) System.setproperty ("Hadoop_user_name", "Hive") val sp