How to Efficiently Update Large Hive Tables with Incremental Data?

Incremental Updates in Hive: Efficient Methods

Maintaining a large main table in Hive and periodically loading incremental updates presents a challenge due to performance concerns. While Hive supports update operations, direct deletions can be inefficient. Here are some effective strategies to approach this scenario:

Full Outer Join (FOJ) with OVERWRITE

FOJ allows you to combine data from two tables, including rows that are present in only one of them. By joining the incremental update table with the main table, you can overwrite existing rows and insert new ones:

INSERT OVERWRITE target_data [partition()]
SELECT
  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
FULL JOIN
  increment_data i ON (t.PK = i.PK);

UNION ALL with row_number()

Alternatively, you can use a UNION ALL operation followed by row_number() to achieve incremental updates more efficiently than FOJ:

INSERT INTO target_data
SELECT
  *,
  ROW_NUMBER() OVER (PARTITION BY PK ORDER BY ID) AS row_num
FROM
(SELECT
  *
FROM
  target_data
UNION ALL
SELECT
  *
FROM
  increment_data) AS t;

This approach assigns a unique row number to each record. Rows with the same PK but different row numbers represent incremental updates.

Optimization Tips

• Optimize both methods by restricting partitions to only those affected by the updates.
• Using UNION ALL with row_number() can provide significant performance gains when updating all columns.
• For cases where merge operations are unavailable in ACID mode, these strategies offer practical solutions for incremental updates.

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