Quickly find any file, action, class, symbol, tool window, or setting in DataGrip, in your project, and in the current Git repository.įind a command and execute it, open a tool window, or search for a setting. Navigate to the object in the Database Explorer. For example, if you press this shortcut on a table's name in the SELECT statement, you will see the contents of the table. Open the Modify dialog to edit the selected object. For example, if you press this shortcut on a table's name in the SELECT statement, you will see the DDL of this table (the CREATE TABLE statement). Generate database entities, for example: function, procedure, schema, database, table. Use SQL generator to generate DDL definitions for database objects. In a data editor, open and edit the data that is stored in the selected cell. In a data editor, open full list of columns. The suggestion list always contains an item for running all the statements. If you have several statements, select whether you want to execute all statements or a single statement. LOCATION '/datasets/iris-dataset.Run the selected statement. CREATE TABLE IF NOT EXISTS iris_dataset( `sl` double, `sw` double, `pl` double, `pw` double ) To do so you need to execute Spark's saveAsTable() function.Īnother option is to directly create the tables from external files (such as parquet or CSV) from the external SQL tool.įor example to create a new table execute a CREATE TABLE. In both situations they need to be "registered" in the metastore. Tables are created either through an import process using a Reusable Code Block, or created via a Jupyter notebook. Depending on your use case you might need to add more RAM to support more complex joins. SparkSQL scales horizontally so if the performance is not satisfactory add more workers from SparkSQL's Configuration Tab. Execute some queries on the connection:.If not, check your firewall settings at step 2. In DataGrip, click the "+" sign and add a Data Source by selecting the newly added Hive 1.2.1. In the same dialogue, at the Firewall tab make sure your IP is white listed from your current location. Click on the SparkSQL's Edit button and copy the JDBC URL.On the Options select the Apache Spark option in both Dialect and Icon dropdowns. Click on the "+" sign from "Driver files" and add both jarsĬhange the class to ".HiveDriver".Click on the "+" sign and select "Driver":.For the purpose of this demonstration we're going to use Jetbrains's excelent DataGrip. The JDBC connectors should work with all JDBC compatible clients. Configure your BI tool to use the JDBC drivers mkdir ~/jdbc-drivers #you can put these anywhere cd ~/jdbc-driversģ. It also has a Hadoop-core dependency that does not come with it. SparkSQL is compatible with Apache Hive's JDBC connector version 1.x. Deploy the Spark SQL Applicationįrom the Lentiq's left-hand application panel click on the SparkSQL icon.Ĭlick Create Spark SQL. The query engine is SparkSQL which uses Spark's in-memory mechanisms and query planner to execute SQL queries on data. The data is stored in parquet format in the object storage, the schema is stored a metastore database that is linked to Lentiq's meta data management system. Lentiq is compatible with most JDBC/ODBC compatible tools and uses Apache Spark's query engine. Querying data using SQL is a basic but fundamental use of any data lake.
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