Goals

Checklist

•  User stories documented (Albert/Vinisha)
•  User stories reviewed (Nitin)
•  Design documented (Albert/Vinisha)
•  Design reviewed (Terence/Andreas)
•  Feature merged ()
•  Examples and guides ()
•  Integration tests () 
•  Documentation for feature ()
•  Blog post

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1. A data scientist wants to join data from multiple datasets to run a machine learning model. The first dataset, Customers, contains cust_id, cust_name and location. Second dataset, Purchases, contains purchase_id, cust_id and store. Third dataset, Transactions contains cust_id, tranasaction_data and purchase_amount. The data scientist wants to inner join these datasets on cust_id. 

   Customers:

   cust_id	cust_name	location
   1	Alex	Canada
   2	Bob	California
   3	Cathie	Texas

   Purchases:

   purchase_id	cust_id	store
   23	1	Walmart
   24	2	Dstore
   25	2	Walmart

   Transactions:

   cust_id	transaction_date	purchase_amount
   1	2015-01-23	20
   2	2015-01-12	400
   3	2014-03-28	600

    

   Joining above three datasets would give us following output:
   1 -> <Customers: (1, Alex, Canada), Purchases: (23, 1, Walmart), Transactions: (1, 2015-01-23, 20)>

   2 -> <Customers: (2, Bob, California), Purchases: (24, 2, Dstore), Transactions: (2, 2015-01-12, 400)>

   2 -> <Customers: (2, Bob, California), Purchases: (25, 2, Walmart), Transactions: (2, 2015-01-12, 400)>

2. A data scientist wants to join data from multiple datasets. The first dataset, Customers, contains cust_id, cust_name and location. Second dataset, Purchases, contains purchase_id, cust_id and store. Third dataset, Transactions contains purchase_id, tranasaction_data and purchase_amount. The data scientist wants to inner join Customers and Purchases on cust_id and the resultant dataset can be joined to Transactions on purchase_id

   Customers:

   cust_id	cust_name	location
   1	Alex	Canada
   2	Bob	California
   3	Cathie	Texas

   Purchases:

   purchase_id	cust_id	store
   23	1	Walmart
   24	2	Dstore
   25	2	Walmart

   CustomersPurchases:

   cust_id	cust_name	location	purchase_id	store
   1	Alex	Canada	23	Walmart
   2	Bob	California	24	Dstore
   3	Bob	California	25	Walmart

   Transactions:  

   purchase_id	transaction_date	purchase_amount
   23	2015-01-23	20
   24	2015-01-12	400
   25	2014-03-28	600

   CustomersPurchasesTransactions:

   purchase_id	cust_id	cust_name	location	store	transaction_date	purchase_amount
   23	1	Alex	Canada	Walmart	2015-01-23	20
   24	2	Bob	California	Dstore	2015-01-12	400
   25	3	Bob	California	Walmart	2014-03-28	600

3. A data scientist wants to join two or more datasets and wants to selects columns present in the output of join. For example, Customers has cust_id, cust_name and location. Second dataset, Purchases, has purchase_id, cust_id and store. After inner join of these 2 datasets on cust_id, output dataset should have cust_id, cust_name and purchase_id.

   Customers:

   cust_id	cust_name	location
   1	Alex	Canada
   2	Bob	California
   3	Cathie	Texas

   Purchases:

   purchase_id	cust_id	store
   23	1	Walmart
   24	2	Dstore
   25	2	Walmart

   CustomersPurchases:

   cust_id	cust_name	purchase_id
   1	Alex	23
   2	Bob	24
   2	Bob	25

...

1. Hydrator will provide, one by one, input records from customers and purchases with stage name to Joiner plugin.
2. joinOn() method in the plugin will interpret schema for that stage and return join key for each stage. 
   For example: For customers:  joinOn(Customers, (1, Alex, Canada)) will return 1, for Purchases: joinOn(Purchases, (23, 1, Walmart)) will return 1
3. Hydrator will be responsible for joining records based on the type of join using following 2 engines. 
   1. For Map Reduce:
      In map() phase, joinOn() will get called for each stage record and intermediate output with tagged record will be emitted. For example:-
      For Customers record, (1, Alex, Canada) => Intermediate output: <1, Customers: (1, Alex, Canada)>
      For Purchases record, (23, 1, Walmart) => Intermediate output: <1, Purchases: (23, 1, Walmart)>
      In reduce() phase, depending on plugin type, hydrator will join intermediate output records. For example,
      For cust_id = 1, output of inner join will be: 1 -> <Customers: (1, Alex, Canada), Purchases: (23, 1, Walmart)>
   2. For Spark:
      Spark has join api for pairedRdd. For each record from each source, joinOn can be called from hydrator for each record and spark join api can be used on paired rdd: rdd1.join(rdd2). 
      To join more than 2 sources on same key, spark join() api can be called multiple times.
      For example, For inputs Customers: (1, Alex, Canada), Purchases: (23, 1, Walmart), after inner join, output will be: 1 -> <Customers: (1, Alex, Canada), Purchases: (23, 1, Walmart)>

Implementation:

Config.json for the plugin will look like. As we are using special characters for providing join keys, we will need to validate stage names.

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• Join can be applied only on equality
• Join is applied only on AND for composite keys and not for OR
• Join plugin will be available for batch and not realtime as it requires mapreduce and spark programs to be executed.
• Join plugin will not use 'where' clause like sql join.
• Join plugin requires to have join key from each source for each join condition. For example, if we have 3 inputs from Customers, Transactions and Purchases, then join keys should be Customers.id=Transactions.id=Purchases.id&Customers.name=Transactions.name=Purchases.name. We can not have Customers.id=Transactions.id=Purchases.id&Customers.name=Purchases.name. To achieve the same result as Customers.id=Transactions.id=Purchases.id&Customers.name=Purchases.name condition, we can use Joiner plugin twice in datapipeline by joining Customers.id=Purchases.id&Customers.name=Purchases.name first and then join on Transactions.id, which will give the same result.