[PIG-4771] Implement FR Join for spark engine

• Type: Sub-task
• Status: Closed
• Priority: Major
• Resolution: Fixed
• Fix Version/s: spark-branch
• Component/s: spark
• Assignee: liyunzhang
• Reporter: liyunzhang
• Created: 05/Jan/16 04:27
• Updated: 21/Jun/17 09:18
• Resolved: 17/May/16 04:36

Description

We use regular join to replace FR join in current code base(fd31fda). We need to implement FR join.

Some info collected from https://pig.apache.org/docs/r0.11.0/perf.html#replicated-joins:
Replicated Joins
Fragment replicate join is a special type of join that works well if one or more relations are small enough to fit into main memory. In such cases, Pig can perform a very efficient join because all of the hadoop work is done on the map side. In this type of join the large relation is followed by one or more small relations. The small relations must be small enough to fit into main memory; if they don't, the process fails and an error is generated.

Usage
Perform a replicated join with the USING clause (see JOIN (inner) and JOIN (outer)). In this example, a large relation is joined with two smaller relations. Note that the large relation comes first followed by the smaller relations; and, all small relations together must fit into main memory, otherwise an error is generated.

big = LOAD 'big_data' AS (b1,b2,b3);

tiny = LOAD 'tiny_data' AS (t1,t2,t3);

mini = LOAD 'mini_data' AS (m1,m2,m3);

C = JOIN big BY b1, tiny BY t1, mini BY m1 USING 'replicated';

Conditions
Fragment replicate joins are experimental; we don't have a strong sense of how small the small relation must be to fit into memory. In our tests with a simple query that involves just a JOIN, a relation of up to 100 M can be used if the process overall gets 1 GB of memory.

Issue Links

• is related to: PIG-4891 Implement FR join by broadcasting small rdd not making more copys of data
• links to: Review Board