![]() ![]() Use a ledger-like mechanism for cumulative values It also allows you to see the evolution of the record over time, and roll-back easily. This means that you can run queries against your "dirty" data and insert it into this table without worrying about deleting items that were accidentally missed off the import. In this model, deleting a product requires you to update products set valid_until = getdate() where product_id = xxx and valid_until is null, and then insert a new row with the "deleted_flag = true". With the versioned model, you have: PRODUCTS " modifying price would be "update products set price = 1 where product_id =. In this model, if you want to delete a product, you execute "delete from product where product_id =. This is a way to deal with change over time without having to resort to history tables it does complicate your queries, so you should use it sparingly.įor instance, instead of having a product table as follows PRODUCTS However, here are two techniques I've used in the past:Įmbed the concept of version and validity into your data model I'm not aware of a single solution to all this - probably because each project is so different. Now by redefining the view to SELECT * FROM main_table_$newid we can atomically swicth the table. DELETE FROM history_table WHERE superseded_id>=įor databases, where performance is a problem, we do this in a secondary database on a different server, then copy the found-to-be-good main table to the production database into a new table main_table_$id with $id being the highest import id and have main_table be a trivial view to SELECT * FROM main_table_$someid.REPLACE INTO main_table SELECT FROM history table WHERE superseded_id=.Find out the import_id of the bad import.Every DELETE moves the row to the history table.Every UPDATE copies the row to the history table before changing it. ![]() Every table has a history table that is identical to the original, but has an additional column "superseded_id" which gets the import-id of the import, that changed the row (deletion is a change) and the primary key is (row_id,superseded_id).Every import/sync is assigned a unique id (auto_increment int64).We have the same problem importing data from web analytics providers - they suffer the same problems as your catalog. If not you are not pointing to THE solution, I would be grateful of any keywords suggestion that would me narrow down which field of expertise to explore. What I am looking for is best practice and pattern/tool to handle such problem. Here are the safety criteria and the solution I an suggesting:Īvoid deleting records when they temporarily disappear from datasource => use "deleted" boulean/date column or an archive/history table.Ĭheck for inconsistent changes => configure rules per columns such as : should never change, should only increment,Ĭheck for integrity issue => (standard problem, no point discussing approach)Ībility to rollback last sync=> restore from history table ? use a version inc/date column ? I need to implement safety mecanism when updating the mysql table to "synchronize" with original data source. I have a live copy in a Mysql table in use by a live system. ( issue might come from manual data entry error, ETL failure.) Data can occasionally be unavailable or incomplete or inconsistent. I have an unreliable datasource of product catalog. ![]() Let's take an example with only 1 table to make it easier. I am looking for pattern, framework or best practice to handle a generic problem of application level data synchronisation. ![]()
0 Comments
Leave a Reply. |