Join operations are often used in a typical data analytics flow in order to correlate two data sets. Apache Spark, being a unified analytics engine, has also provided a solid foundation to execute a wide variety of Join scenarios.

At a very high level, Join operates on two input data sets and the operation works by matching each of the data records belonging to one of the input data sets with every other data record belonging to another input data set. On finding a match or a non-match (as per a given condition), the Join operation could either output an individual record, being matched, from either of the two data sets or a Joined record. The joined record basically represents the combination of individual records, being matched, from both the data sets.

Important Aspects of Join Operation:

Let us now understand the three important aspects that affect the execution of Join operation in Apache Spark. These are:

1) Size of the Input Data sets: The size of the input data sets directly affects the execution efficiency and reliability of the Join operation. Also, the comparative sizing of the input data sets affects the selection of the Join mechanism which could further affect the efficiency and reliability of the Join mechanism.

2) The Join Condition: Condition or the clause on the basis of which the input data sets are being joined is termed as Join Condition. The condition typically involves logical comparison(s) between attributes belonging to the input data sets. Based on the Join condition, Joins are classified into two broad categories, Equi Join and Non-Equi Joins.

Equi Joins involves either one equality condition or multiple equality conditions that need to be satisfied simultaneously. Each equality condition being applied between the attributes from the two input data sets. For example, (A.x == B.x) or ((A.x == B.x) and (A.y == B.y)) are the two examples of Equi Join conditions on the x, y attributes of the two input data sets, A and B, participating in a Join operation.

Non-Equi Joins do not involve equality conditions. However, they may allow for multiple equality conditions that must not be satisfied simultaneously. For example, (A.x < B.x) or ((A.x == B.x) or (A.y == B.y)) are the two examples of Non-Equi Join conditions on the x, y attributes of the two input data sets, A and B, participating in a Join operation.

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Demystifying Joins in Apache Spark
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