#ZEPLIN GLOBAL GROUP HOW TO#
If you want to understand how to arrive at the value of the key entered above, here's how.Ī. Select Save and then OK to restart the Livy interpreter. So, if you want to use the spark-csv package, you must set the value of the key to com.databricks:spark-csv_2.10:1.4.0. Navigate to key, and set its value in the format group:id:version. From the top-right corner, select the logged in user name, then select Interpreter. In this article, you'll see how to use the spark-csv package with the Jupyter Notebook. For example, a complete list of community-contributed packages is available at Spark Packages. You can also get a list of available packages from other sources. Search the Maven repository for the complete list of packages that are available. Zeppelin notebook in Apache Spark cluster on HDInsight can use external, community-contributed packages that aren't included in the cluster. How do I use external packages with the notebook? The following screenshot shows the output. Then select settings and make the following changes: Select the Bar Chart icon to change the display. Then select 65 from the Temp drop-down list. Paste this snippet in a new paragraph and press SHIFT + ENTER. Select buildingID, date, targettemp, (targettemp - actualtemp) as temp_diff from hvac where targettemp > "$" When you first run the query, a drop-down is automatically populated with the values you specified for the variable. The next snippet shows how to define a variable, Temp, in the query with the possible values you want to query with. You can also run Spark SQL statements using variables in the query.
settings, appear after you have selected Bar Chart, allows you to choose Keys, and Values. The %sql statement at the beginning tells the notebook to use the Livy Scala interpreter. Select buildingID, (targettemp - actualtemp) as temp_diff, date from hvac where date = "6/1/13" Also the difference between the target and actual temperatures for each building on a given date. Paste the following query in a new paragraph. You can now run Spark SQL statements on the hvac table. %spark2 interpreter is not supported in Zeppelin notebooks across all HDInsight versions, and %sh interpreter will not be supported from HDInsight 4.0 onwards.
From the right-hand corner of the paragraph, select the Settings icon (sprocket), and then select Show title. You can also provide a title to each paragraph. The screenshot looks like the following image: The output shows up at the bottom of the same paragraph. The status on the right-corner of the paragraph should progress from READY, PENDING, RUNNING to FINISHED. Press SHIFT + ENTER or select the Play button for the paragraph to run the snippet.
Register as a temporary table called "hvac" Val hvacText = sc.textFile("wasbs:///HdiSamples/HdiSamples/SensorSampleData/hvac/HVAC.csv")Ĭase class Hvac(date: String, time: String, targettemp: Integer, actualtemp: Integer, buildingID: String) Create an RDD using the default Spark context, sc The above magic instructs Zeppelin to use the Livy Scala interpreter In the empty paragraph that is created by default in the new notebook, paste the following snippet. When you create a Spark cluster in HDInsight, the sample data file, hvac.csv, is copied to the associated storage account under \HdiSamples\SensorSampleData\hvac. It's denoted by a green dot in the top-right corner. From the header pane, navigate to Notebook > Create new note.Įnter a name for the notebook, then select Create Note.Įnsure the notebook header shows a connected status. Replace CLUSTERNAME with the name of your cluster:Ĭreate a new notebook. You may also reach the Zeppelin Notebook for your cluster by opening the following URL in your browser.
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