![]() Suppose that a migration introduced a database constraint that does not properly align with the business requirements or the assumptions made in other parts in the code, resulting in users not being able to actually save some records. For instance, the widely popular MySQL does not.Įven if a migration was run successfully, it could still be necessary to quickly revert the changes made. While practically all relational databases are ACID-compliant and support transactions, not all do so for structural changes. Just like most business operations, a migration must be done in an all-or-nothing fashion: either the entire data set must be migrated, or no changes must be applied at all. For this reason, we establish the following hard constraints: Managing changes to the database and executing them safely is essential – a botched migration can result in inconsistencies, loss of data or even bring down the entire system. Today we will be looking at how to tackle the problem of managing structural changes in the context of Node.js and PostgreSQL. The reasons for this are manifold: business requirements change, entities grow too large and need to be broken apart or performance issues require additional indices or denormalization. LEFT JOIN #table1 on #table1.id = #table2.Every project that persists data has to inevitably face the fact that the structure of that data is not static and needs to adapt. Print CAST(DATEDIFF(ms, as varchar) + ' milliseconds for insert except' INSERT INTO ('Insert Except', CAST(DATEDIFF(ms, as varchar)) SELECT id, guidd, TimeAdded, ExtraData from #table1 SELECT id, guidd, TimeAdded, ExtraData from #table2 INSERT INTO #table1 (id, guidd, TimeAdded, ExtraData) Print CAST(DATEDIFF(ms, as varchar) + ' milliseconds for merge' INSERT INTO ('Merge', CAST(DATEDIFF(ms, as varchar)) USING (select Id, guidd, TimeAdded, ExtraData from #table2) as Print CAST(DATEDIFF(ms, as varchar) + ' milliseconds for insert where not exists' INSERT INTO VALUES ('Insert Where Not Exists', CAST(DATEDIFF(ms, as varchar)) WHERE NOT EXISTS (Select Id, guidd from #table1 WHERE #table1.id = #table2.id) Print 'Finished populating the temp tables: ' + CAST(GETDATE() as varchar) INSERT INTO #table1 (Id, guidd, TimeAdded, ExtraData) VALUES table gets every other record so we know there are some to be inserted INSERT INTO #table2 (Id, guidd, TimeAdded, ExtraData) this table gets every record so we know we'll insert some Print 'Populating the temp tables: ' + CAST(GETDATE() as varchar) )WITH (PAD_INDEX = OFF, STATISTICS_NORECOMPUTE= OFF, IGNORE_DUP_KEY = OFF, ALLOW_ROW_LOCKS = ON, ALLOW_PAGE_LOCKS = ON, FILLFACTOR = 93) ON ) WITH (PAD_INDEX = OFF, STATISTICS_NORECOMPUTE = OFF, IGNORE_DUP_KEY = OFF, ALLOW_ROW_LOCKS = ON, ALLOW_PAGE_LOCKS = ON, FILLFACTOR = 93) ON , uniqueidentifier null -just to have extra dataĬONSTRAINT PRIMARY KEY CLUSTERED Print '# Started at: ' +Cast(GETDATE() as varchar) ![]() Leave a comment and share the knowledge if you have any suggestions or other ways of doing this! The SQL code is below, so feel free to use it as a basis for conducting your own performance benchmarks. ![]() Note that results may vary by server because the optimization engine on every SQL Server Instance could do it differently. While it seems to dominate for less than 50,000,000, once that threshold is hit the INSERT EXCEPT option proved to be king. I never thought the LEFT JOIN would slow down as much as it did when hitting 50,000,000 records and more. Who else thought the LEFT JOIN would be the dominant player up through 500,000,000 records and beyond? Time to execute, in milliseconds, over 3 runs: Here are the results, in milli-seconds, as to how long each technique took to execute. To ensure SQL Server didn’t keep any queries (or anything for that matter) cached, the following code was run before each test: Other than the LEFT JOIN, what are other ways to accomplish this? Here are a few common ways that will be benchmarked:įor testing purposes, the SQL code was executed on a Windows 2012 Server with 128GB memory, 16 core CPU, using Microsoft SQL Server 2014. That’s when this Curious Consultant started wondering what’s the fastest way to insert new records where one doesn’t already exist during a single process? Even though this is simple, is it the fastest way? The age-old technique and I suspect most common practice is doing a left join where the values are null from the table being inserted into. SQL Developers come across this scenario quite often – having to insert records into a table where a record doesn’t already exist. Fastest way to insert new records where one doesn’t already exist
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