Incorta spark integration
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Incorta allows users to create materialized views (MVs) using Spark. It provides functions to read data from Incorta tables and save Spark DataFrames as MVs. The document discusses Spark integration with Incorta, including installing and configuring Spark, and creating the first MV using Spark Python APIs. It demonstrates reading data from Incorta and saving a DataFrame as a new MV.
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Incorta spark integration
- 2. Incorta Spark Integration Dylan Wan Solution Architect at Incorta
- 3. Agenda • Spark Overview • Incorta and Spark • Installation and Configuration • Create your first MV in Incorta • Demo
- 5. Why Spark? • General purpose framework for parallel processing in cluster • Functional programming available in • Scala • Python • Java • Spark SQL and Dataframe • Using the same framework for • Data Streaming • Machine Learning • Graph processing Spark Core Spark SQL Spark Streaming Machine Learning Graph Processing Standalone Scheduler YARN Meso
- 6. Incorta Server Spark Server Spark Execution Flow Driver Program Spark Master (Cluster Manager)Spark Context Worker Node Executor Cache Task Task Worker Node Executor Cache Task Task http://spark.apache.org/docs/latest/cluster- overview.html
- 7. Spark Concepts • Spark Context – like a connection in JDBC to hold the DB session to a database. It is the connection to Spark cluster • Master and Workers have its own JVM process and Listener Port • Master and Workers have its Web UI for display the progress • Application codes are sent and assigned to executors • Executors read, write and process the data • Memory can be controlled at the worker level and are allocated to individually executors
- 8. Spark Dataframe • Organized Data into named columns like database table • A dataframe can be created from a parquet file • A dataframe can be written into and stored as a parquet file • A dataframe can be processed via DataFrame API https://spark.apache.org/docs/1.6.0/api/python/pyspark.sql.html#pyspark. sql.DataFrame • A dataframe can be registered as a table and processed by SQL
- 10. Incorta Data Load web service *.csv *.xlsx SQL DB Extract Load Backup Restart Formula Column, Reference, etc
- 11. Materialized View in Incorta • An object created within Incorta, not loaded from data sources • Created based on other tables loaded from Incorta • Once a MV is loaded, it works like other tables. • Join from and to another table or MV • Formula columns can be created in a MV • Aliases can be created against a MV • MV can be defined via Spark Python and Spark SQL • Spark Python or Spark SQL are executed as part of the regular Loader jobs
- 12. Incorta Data Load web service *.csv *.xlsx SQL DB Extract Load Backup Restart Formula Column, Reference, etc Read Save
- 14. Spark Installation • Download Spark • http://spark.apache.org/downloads.html • Select Package Type of “Prebuilt for Hadoop XX” • Select Spark 1.6.2 for Incorta Release 2.8 or before • Download the Tarball file or get the link from browser • Run wget <download URL> from the server machine • Unzip and uncompress the tar file • tar –xzvf spark-1.6.2-bin-hadoop2.6.tgz • The spark is ready to use! Try this • bin/pyspark • exit()
- 15. Spark Configurations • Edit the spark-env.sh in the <Spark Home>/conf • Change the WebUI ports if there is any conflict (optional) • If not all ports are open to use or available from browser, you can specify • SPARK_MASTER_WEBUI_PORT • SPARK_WORKER_WEBUI_PORT • Specify the external IP for monitoring Spark jobs (optional) • If the server machine runs under a firewall and the external IP and internal IP are different • Set SPARK_PUBLIC_DNS to the external IP • Limit the memory used by Spark jobs (optional) • SPARK_WORKER_MEMORY • Control total available, not the individual assignment to the executors
- 16. Spark Configurations • Enable Logging – Useful for investigating issues (recommended) • Create a directory for holding he log files • cd <spark home> • mkdir eventlogs • Edit the spark-defaults.conf in <spark home>/conf • spark.eventLog.enabled true • spark.eventLog.dir <spark home>/eventlogs • Enable History Server (recommended) • Edit <spark home>/conf/spark-env.sh • SPARK_HISTORY_OPTS="-Dspark.history.fs.logDirectory=<spark home>/eventlogs” • Start the history server • ./sbin/start-history-server.sh
- 17. Spark Configuration – Hive metastore DB • Hive metadata is stored in Hive metastore • Hive metastore requires a database • Create the hive-site.xml in <spark home>/conf • Edit <Spark Home>/conf/spark- env.sh • SPARK_HIVE=true • SPARK_SUBMIT_CLASSPATH • SPARK_CLASSPATH • Make sure JDBC driver is available to Spark
- 18. hive-site.xml for mySQL [incorta@clorox2-poc spark-1.6.2-bin-hadoop2.6]$ cat ~/spark-1.6.2-bin-hadoop2.6/conf/hive-site.xml <?xml version="1.0" encoding="UTF-8"?> <configuration> <property> <name>javax.jdo.option.ConnectionURL</name> <value>jdbc:mysql://localhost:3307/mydb?createDatabaseIfNotExist=true</value> <description>metadata is stored in a MySQL server</description> </property> <property> <name>javax.jdo.option.ConnectionDriverName</name> <value>com.mysql.jdbc.Driver</value> <description>MySQL JDBC driver class</description> </property> <property> <name>javax.jdo.option.ConnectionUserName</name> <value>root</value> <description>user name for connecting to mysql server</description> </property> <property> <name>javax.jdo.option.ConnectionPassword</name> <value>mysql_root</value> <description>password for connecting to mysql server</description> </property> </configuration>
- 19. Starting Spark Master and Worker • Go to <Spark Home> and start the spark master process • sbin/start-master.sh • Check the log file to get the master WebUI URL • Open the webUI from a browser • Start the spark slave processes • sbin/start-slave.sh spark://<spark master host>:7077 • Check the log file to ensure that the worker started properly • Refresh the browser page to check worker processes • Start history server (optional, but recommended) • sbin/start-history-server.sh
- 20. Spark Processes
- 21. Incorta Configuration • Edit <Incorta Home>/incorta/server.properties • spark.home=/home/incorta/spark-1.6.2-bin-hadoop2.6 • spark.master.url=spark://clorox2-poc:7077 • Please ensure that the spark.master.url set to the URL generated in the log file when you launch the Spark master. • You can also see it in the Spark Master Web UI
- 22. Monitoring • Spark Master WebUI • Check if the job is submitted to Spark master • Check if the worker has allocated the resources to execute the job • Check DAG for optimizing the performance • Incorta Log • Use tail –f <incorta home>/server/logs/incorta/<tenant>/incorta-…out • See runtime errors
- 23. Create your first MV in Incorta
- 24. Understand read() and save() • Read(“schema.table”) – get the data from incorta • Save(dataframe) – create the data from the dataframe as an MV • These are incorta functions, internally they call • sqlContext.read.parquet • df.write.mode("overwrite").parq uet
- 25. Demo