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Zeppelin

Zeppelin, a web-based notebook that enables interactive data analytics. You can make beautiful data-driven, interactive and collaborative documents with SQL, Scala and more.

Building

  1. Obtaining the source code
$ git clone https://github.com/apache/incubator-zeppelin
  1. Building
$ cd incubator-zeppelin
$ mvn clean package -Pspark-1.3 -Dhadoop.version=2.2.0 -Phadoop-2.2 -DskipTests

Requires Apache Maven 3 Requires nodejs and npm

Starting Zeppeling

$ cd $ZEPPELIN_HOME
$ bin/zeppelin-deamon.sh start

Using a web browser access http://localhost:8080 OR if you're running the pre-packaged VM http://192.168.56.10:8080

Enabling Geode OQL Interpreter

  • Go to the Interpreter page and click +Create
  • Fill the form using the following information:
    • Name: Geode
    • Interpreter: geode
  • Leave default values and click on Save

If needed adjust the hostname and port for the locator by clicking on edit on the upper right corner of the Interpreter.

This feature is experimental

Executing Geode OQL queries

  • Click on Notebook and create a new Note
  • Type the following text and click on Play icon on the right.
%geode
select * from /Stocks
  • Select different visualizations
  • Use common SQL instructions such as order or group by and run different queries

Note: Make sure you have a Geode cluster up and running and data loaded in the /Stocks region.

Enabling Geode Spark Connector

  • Access the Zeppelin interface at http://192.168.56.10:8080
  • Click on Interpreter and select Edit under the spark interpreter. Add parameters at the end of the list:
Param Value
spark.gemfire.locators localhost[10334]
spark.kryo.registrator io.pivotal.gemfire.spark.connector.GemFireKryoRegistrator
  • Click on the notebook and load the libraries for the connector:
%dep
z.reset()
z.load("/home/vagrant/incubator-geode/gemfire-spark-connector/gemfire-spark-connector/target/scala-2.10/gemfire-spark-connector_2.10-0.5.0.jar")
z.load("/home/vagrant/incubator-geode/gemfire-assembly/build/install/apache-geode/lib/gemfire-core-dependencies.jar")
z.load("/home/vagrant/incubator-geode/gemfire-assembly/build/install/apache-geode/lib/gemfire-core-1.0.0-incubating-SNAPSHOT.jar")

Click on the play icon (upper right of the note) to execute. PS: The directories here assume you've created the VM as described in the VM instructions. Update the paths properly if needed

Executing SparkSQL with Geode data

  • Insert a new note in the notebook and copy the following content:

    %spark
import io.pivotal.gemfire.spark.connector._
import sqlContext.implicits._

val stocks = sqlContext.gemfireOQL("SELECT Symbol, LastTradeDate, LastTradeTime, tradeTimestamp, Open, LastTradePriceOnly, DaysHigh, DaysLow, entryTimestamp FROM /Stocks s")
stocks.registerTempTable("stocks")

val predictions = sqlContext.gemfireOQL("SELECT entryTimestamp, close, ema, rsi, ema_diff, low_diff, high_diff, predicted FROM /Predictions s")

predictions.registerTempTable("predictions")
stocks.registerTempTable("stocks")

val test = sqlContext.sql("select * from stocks s order by s.entryTimestamp desc")

test.printSchema()

Click on the play icon (upper right of the note) to execute.

  • Execute queries and play with different visualizations

Add another note in the notebook and use the %sql interpreter to query the tables we just registered.

%sql
select * from stocks

References