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Concept
Hash join
Summary
The hash join is an example of a join algorithm and is used in the implementation of a relational database management system. All variants of hash join algorithms involve building hash tables from the tuples of one or both of the joined relations, and subsequently probing those tables so that only tuples with the same hash code need to be compared for equality in equijoins.
Hash joins are typically more efficient than nested loops joins, except when the probe side of the join is very small. They require an equijoin predicate (a predicate comparing records from one table with those from the other table using a conjunction of equality operators '=' on one or more columns).
The classic hash join algorithm for an inner join of two relations proceeds as follows:
First, prepare a hash table using the contents of one relation, ideally whichever one is smaller after applying local predicates. This relation is called the build side of the join. The hash table entries are mappings from the value of the (composite) join attribute to the remaining attributes of that row (whichever ones are needed).
Once the hash table is built, scan the other relation (the probe side). For each row of the probe relation, find the relevant rows from the build relation by looking in the hash table.
The first phase is usually called the "build" phase, while the second is called the "probe" phase. Similarly, the join relation on which the hash table is built is called the "build" input, whereas the other input is called the "probe" input.
This algorithm is simple, but it requires that the smaller join relation fits into memory, which is sometimes not the case. A simple approach to handling this situation proceeds as follows:
For each tuple in the build input
Add to the in-memory hash table
If the size of the hash table equals the maximum in-memory size:
Scan the probe input , and add matching join tuples to the output relation
Reset the hash table, and continue scanning the build input
Do a final scan of the probe input and add the resulting join tuples to the output relation
This is essentially the same as the block nested loop join algorithm.
Official source
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