![]() In this case you have multiple database writes instances, put them inside a transaction. ![]() We use the commit method of the connection object to commit this transaction to the database. ![]() There is also another efficient way that you can use to improve the level of speed during SQLite transactions. We use the cursor method execute() to run the SQL query. I really recommend to use this method for a bulk transaction for at least 20K data and what can I say is very fast and efficient. It really makes sense to consider a bulk transaction right? Sure why not. Generally the SQLite is in auto-commit mode that means SQLite automatically starts a transaction for each command, process and commit the transaction. Let’s say you have 20,000 candidate records to be inserted in your database. Instead of writing to (and locking) the file each and every time a write query is executed, the write will only happen once when. When we execute a SQL query, we might cause an error on the database. In this case you have multiple database writes instances, put them inside a transaction. Using context managers for automatic commit and rollback. You can test this by opening the database in both the python interpreter and the sqlite3 command line tool. statement, because SQLite does not support executing the CREATE TABLE. ![]() Despite that fact that they can do it in a bulk transaction. There is also another efficient way that you can use to improve the level of speed during SQLite transactions. The Python SQLite library implicitly issues a COMMIT whenever you execute a CREATE TABLE. Advantages are stricter transactions, increased speed. Well, Transactions are a feature for which relational databases are known for. This is very expensive, since it requires reopening, writing to, and closing the journal file for each statement. Makes it possible to easily switch between the standard Python sqlite3 module (Pysqlite) and APSW. When it comes of handling huge amount of data, the most common things that developer always does is to store data in a single manner each SQL statement has a new transaction started for it. ![]()
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