Because it can become rather tricky to collect pending UPDATE and INSERT statements somehow, this will usually leave you with a tradeoff between performance and exclusive locking. When concurrent access is an issue you will not want to lock your database for that long. Unfortunately, sometimes, computing the changes may take some time. So it makes sense to bundle many changes to the database into a single transaction by executing them and the jointly committing the whole bunch of them. Exclusively locking the databaseĪs already mentioned above, an open (uncommitted) transaction will block changes from concurrent connections. Note: While both resources refer to INSERT, the situation will be very much the same for UPDATE for the same arguments. It's written in C, but the results would be similar would one do the same in Python. It is absolutely helpful to understand the details here, so do not hesitate to follow the link and dive in. But it will only do a few dozen transactions per second. It is already noted as a FAQ:Īctually, SQLite will easily do 50,000 or more INSERT statements per second on an average desktop computer. The performance of database changes dramatically depends on how you do them. This means that you'll have to start a transaction explicitly if you want DDL statements to be part of a transaction. When a large number of changes is to be done, two other aspects enter the scene: Performance Changed in version 3.6: sqlite3 used to implicitly commit an open transaction before DDL statements. There are a couple of caveats that you should be aware of when using SQLite in a multithreaded environment. It seems that you're talking about multithreading here. So it depends whether you can live with the situation that a cuncurrent reader, be it in the same script or in another program, will be off by two at times. First of all, there's a difference between multiprocessing (multiple processes) and multithreading (multiple threads within one process). Sql = 'insert into "mytable"(data) values(17)' Rows = conn2.cursor().execute(sql).fetchall()Ĭur.execute('update "mytable" set data = data + 1 where "id" = ?', (rowid,)) # simulate another script accessing tha database concurrently # functions are syntactic sugar only and use global conn, cur, rowid "id" INTEGER PRIMARY KEY AUTOINCREMENT, - rowid You can investigate what happens by running the following script and investigating its output: import os Because of the limited concurrency features of sqlite, the database can only be read while a transaction is open. Unless it is committed, it remains visible only locally for the connection to which the change was done. This is what everybody thinks of at first sight: When a change to the database is committed, it becomes visible for other connections. Whether you call mit() once at the end of the procedure of after every single database change depends on several factors.
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