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Posted by / 28-May-2017 07:47

Updating millions of rows

So, the starting point is that we do not have all of the fields.Updating only two fields does not fulfill the optimized MERGE query plan conditions.This can be done easily by doing a bulk-insert in a new table and then rename the table to original one.The required indexes and constraint can be created on a new table as required.Another option may be to copy only the rows you need to a new table, drop the old table and then rename the new table to the old table’s name.I’ve used all 3 options above depending on the situation and factors such as indices, load on the server, etc.The fastest way to speed up the update query is to replace it with a bulk-insert operation.It is a minimally logged operation in simple and Bulk-logged recovery model.

In addition to the clustered index update, the index ix_col1 is also updated.

Updating very large tables can be a time taking task and sometimes it might take hours to finish.

In addition to this, it might also cause blocking issues.

The index update and Sort operation together take 64% of the execution cost. Removing index on the column to be updated The same query takes 14-18 seconds when there isn’t any index on col1.

Thus, an update query runs faster if the column to be updated is not an index key column.

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Here are descriptions of the script steps and their completion times: Let’s take a closer look at the breakdown of numbers: Total time (Convert time Optimized INSERT Optimized UPDATE)= 7,648.634ms Non-optimized time (MERGE into target using SOURCE table, 1M UPDATES 1M INSERTS) = 57,134.958 Total savings (Non-optimized MERGE 57 seconds, Optimized MERGE 7.6 seconds)= 49,486.324 To confirm that your MERGE statement is optimized, search for a semi path in the EXPLAIN plan (search for semi).

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