#spark #hadoop #analytics #apache #zeppelin #scala
I was looking for a cool dashboard based query interface for analytics. I stumbled upon a cool open source project called Apache Zeppelin,
Zeppelin is a modern web-based tool for the data scientists to collaborate over large-scale data exploration and visualization projects. It is a notebook style interpreter that enable collaborative analysis sessions sharing between users. Zeppelin is independent of the execution framework itself. Current version runs on top of Apache Spark but it has pluggable interpreter APIs to support other data processing systems. More execution frameworks could be added at a later date i.e Apache Flink, Crunch as well as SQL-like backends such as Hive, Tajo, MRQL.
As their apache proposal mentioned, it does have good support for pluggable interpreters (a lot), ie. you can query files, databases, hadoop etc using this interface seamlessly. This application is easily executable in you workstation, if you want to try out. Download from the project site and follow the installation guide.
Run the zeppelin server daemon, and access the UI at http://localhost:8088
We can use different interpreters in notebooks and display the results in dashboard. I was interested in plain simple SQL db, like postgre.
create a tables sales and insert some sample data.
create table sales(category varchar, units integer);
insert into sales values('Men-Shirts', 134344);
insert into sales values('Men-Shoes', 56289);
insert into sales values('Men-Wallets', 19377);
insert into sales values('Men-Watches', 345673);
insert into sales values('Women-Shirts', 87477);
insert into sales values('Women-Skirts', 140533);
insert into sales values('Women-Shoes', 77301);
insert into sales values('Electronics-Mobile', 67457);
insert into sales values('Electronics-Tablets', 21983);
insert into sales values('Electronics-Accessories', 865390);
Create a notebook,
setup the connection properties in psql interpreter configuration.
and run with %psql interpreter. In the notebook, type in,
%psql select * from sales
Then I decided to use the spark code. As it supports jdbc source, use that in the spark context. In Spark, JdbcRDD can be used to connect with a relational data source. RDDs are a unit of compute and storage in Spark but lack any information about the structure of the data i.e. schema. Dataframes combine RDDs with Schema. To support postgre as source, you need the driver loaded to execute the queries or building schema. Copy the driver to $ZEPLLIN_HOME/interpreter/spark and restart the daemon. If you don't do this, you will not be able to source postgre and may get jdbc connection errors like "No suitable driver found" etc.
Use the notebook to provide the spark code,
In the %sql (to be noted, its not %psql) interpreter provide,
%sql select * from sales
You have to schedule the %sql notebook only and the dashboard is updated based on the data inserts when the cron job is triggered.