Explain how hash join algorithm can execute query
The hash join algorithm is a popular method used in database systems to execute queries efficiently. It is particularly useful when joining large tables on their common attributes.
Here is a step-by-step explanation of how the hash join algorithm executes a query:
1. Initial setup: The algorithm requires two input tables, commonly referred to as the build table and the probe table. Each table consists of one or more attributes.
2. Building hash table: The algorithm starts by processing the build table. It creates a hash table by hashing the values of the common attribute(s) and storing the corresponding rows in buckets based on the hash value. This step is done only once and takes O(n) time complexity, where n is the size of the build table.
3. Probing: Next, the algorithm processes the probe table. It hashes the values of the common attribute(s) for each row and checks if the hash value exists in the hash table. If a match is found, the algorithm performs the join operation (e.g., combining rows) and produces the result. This step is repeated for all rows in the probe table.
4. Joining duplicates: In some cases, there may be multiple rows in the build table that have the same hash value. To handle this, the hash join algorithm uses an additional data structure called a hash bucket. Each bucket contains all the rows with the same hash value. When a match is found in the probe table, the algorithm processes all the rows in the corresponding hash bucket to check for further matches.
5. Result generation: As the probe table is processed, the hash join algorithm produces the joined result by combining the matching rows from both tables. The final result is typically stored in a temporary table or returned to the user directly.
The hash join algorithm has several advantages, including efficient performance for large tables, a small memory footprint (compared to other join algorithms), and the ability to handle duplicates effectively. However, it also has some limitations, such as high memory requirements for larger hash tables and a higher computational cost for hash function calculations. Nonetheless, hash join remains a popular choice due to its overall efficiency and effectiveness in executing join operations in database queries.