DATAGRIP SQL DRIVERS
DATAGRIP SQL CODE

In DataGrip, you can run all the files we’ve mentioned above, without opening them.Ĥ. You know where they are located and you can put them in Version Control. User files are SQL scripts you store on your machine. If the scratch file is an SQL file, then it is also an SQL editor which can be associated with any of your connections.ģ.

DATAGRIP SQL CODE

We refer to scratch files as temporary notes or drafts for code ideas which are outside of the project context. Scratch files are like consoles, but they are not attached to a data source. But you can add more consoles to a data source, each of them will then create a new connection, unless that is, you use Single Connection Mode.Ģ. When you create a data source, a database console is created automatically. Query console is an SQL editor attached to a particular data source. There are four types of files where you can write SQL in DataGrip:ġ. You might already have a working directory with tons of scripts we will tell you how you can use DataGrip to deal with them too. In this post, we’ll go over whether it’s better to just use a temporary SQL editor in DataGrip or if it’s more convenient to work directly with SQL files stored on your computer. LOCATION '/datasets/iris-dataset.People working with databases write SQL. 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.

DATAGRIP SQL DRIVERS

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.

Datagrip sql