How to Efficiently Update a Large Hive Table Incrementally?

Hive: Efficient Incremental Updates for a Main Table

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.

Using FULL OUTER JOIN for Incremental Updates

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.

Consider UNION ALL row_number() for Column-Level Updates

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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