How to Efficiently Perform Incremental Data Updates in Hive?

Efficient Incremental Data Updates in Hive

Hive provides various approaches to update data incrementally within a main table. Let's explore the most effective methods.

Full Outer Join

If the merge operation with ACID mode is unavailable, you can utilize a full outer join to find entries to be updated:

insert overwrite target_data [partition() if applicable]
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 --restrict partitions if applicable
    FULL JOIN increment_data i on (t.PK=i.PK);

Union All with Row Number

Alternatively, you can use UNION ALL with row_number() to avoid the full join:

INSERT INTO target_data (pk, col1, col2, ...)
SELECT pk, col1, col2, ...
FROM increment_data
WHERE pk NOT IN (SELECT pk FROM target_data)
UNION ALL
SELECT pk, col1, col2, ...
FROM target_data;

This solution is particularly efficient if you wish to update all columns with the new data.

Partition Optimization

To enhance performance, you can restrict partitions in the target_data table that will be overwritten by using the WHERE partition_col IN (select distinct partition_col from increment_data) clause. Additionally, passing the partition list as a parameter in the WHERE clause can further accelerate the operation.

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