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There is another partitioned table(INTERVAL PARTITONED ON DAY) which is even bigger than TABLE A, as its loaded with around 30 million of data every day.This table too has around 1.5 Years of data, same duration as table A. Please note that the above structure is just a subset of the actual table structure but these are all the columns that are required in the process. I need to join TABLE A with TABLE B, based on three columns(DATE, ID, SECURITY). If it is not possible to update backed up records from another session, then you probably don't have any locking problems..topic_pill.topic_pill a.topic_pill:hover a.action_button.action_button:active.action_button:hover.action_button:focus.action_button:hover.action_button:focus .count.action_button:hover .count.action_button:focus .count:before.action_button:hover .count:before.submit_button.submit_button:active.submit_button:hover.submit_button:not(.fake_disabled):hover.submit_button:not(.fake_disabled):focus._type_serif_title_large.js-wf-loaded ._type_serif_title_large.amp-page [email protected] only screen and (min-device-width:320px) and (max-device-width:360px).u-margin-top--lg.u-margin-left--sm.u-flex.u-flex-auto.u-flex-none.bullet. You can determine the number of rows that will be deleted by running the following Oracle SELECT statement before performing the delete. You may wish to delete records in one table based on values in another table.Since you can't list more than one table in the Oracle FROM clause when you are performing a delete, you can use the Oracle EXISTS clause.Quickly implement a high-performance, globally available, and lower-cost secure cloud data warehouse.Azure SQL Data Warehouse lets you independently scale compute and storage, while pausing and resuming your data warehouse within minutes through a massively parallel processing architecture designed for the cloud.
Asuuming that your procedure inserts rows to hist table and then deletes them from production one, imagine the following scenario: a. - Create a dummy table with the data to be backed up - Exchange the partition of the history table with the dummy table - create the indexes FYI, for history tables you will definitely benefit from partitioning. Also, to make it bit easier, we can do the entire table update in chunks, meaning, in one shot we can go for updating few weeks of data and then go for the other set an so on.This way we can achieve updating over 900 million rows of data.Actual column count in both the tables would be around 40 columns. After joining, take column (VOTING_RIGHTS) from TABLE B multiply it with another column in TABLE A(OS_ACTUAL) and the result should be updated in the newly added (Outstanding_VR) column in TABLE A.All OUTSTANDING_* and VOTING_RIGHT columns are FLOAT(126). One important point to note here is that TABLE B, can have multiple records for the above said combination of three columns(DATE, ID, SECURITY) but will have same value of VOTING_RIGHTS across the records, so if we can take any one record for getting the value of VOTING RIGHTS, that should suffice. Read somewhere that BULK COLLECT with ROWID is fast but also read that its not a good technique to use ROWID.