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When managing a vast Hive table that requires regular updates, finding an efficient approach is crucial. The recent enhancements to Hive include update/insert/delete capabilities, but choosing the optimal solution remains a challenge.
One effective method involves using a FULL OUTER JOIN to merge the incremental update data with the existing main table. By joining on the primary key, it identifies both updated and new entries. The query below demonstrates this approach:
INSERT OVERWRITE target_data [partition()] SELECT -- Select new if exists, old if not exists CASE WHEN i.PK IS NOT NULL THEN i.PK ELSE t.PK END AS PK, CASE WHEN i.PK IS NOT NULL THEN i.COL1 ELSE t.COL1 END AS COL1, ... CASE WHEN i.PK IS NOT NULL THEN i.COL_n ELSE t.COL_n END AS COL_n FROM target_data t -- Restrict partitions if applicable FULL JOIN increment_data i ON (t.PK = i.PK);
Optimizations can be applied to improve performance, such as restricting partitions in the target table that will be overwritten. Passing the partition list as a parameter can significantly speed up the process.
If the incremental updates require updating all columns with new data, a UNION ALL operation with row_number() can be employed as an alternative to FULL OUTER JOIN. This approach often offers improved performance:
SELECT PK, COL1, ... COL_N FROM target_data UNION ALL SELECT PK, COL1, ... COL_N FROM increment_data;
The row_number() window function assigns a unique number to each row, allowing the query to identify and prioritize the update records.
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