![]() In general, the rules described in the previous sections also apply to stored procedures. Output for example, one thread might delete data before another thread tries to update it.Įxecute the threads synchronously rather than asynchronously, to control the order in which steps are performed. Note that even with separate connections, your code can still hit race conditions that generate unpredictable Use a separate connection for each thread. Snowflake recommends that multi-threaded client programs do at least one of the following: Similarly, changing the AUTOCOMMIT setting in one thread affects the AUTOCOMMIT setting in all other threads If the threads are running asynchronously, the results can be unpredictable. A BEGIN TRANSACTION, COMMIT, or ROLLBACK by one thread affects all threads using that shared connection. Or more threads share the same connection, then those threads also share the current transaction in thatĬonnection. Snowflake JDBC Driver) or connector (such as the Snowflake Connector for Python) is multi-threaded. This situation can occur when a client application using a Snowflake driver (such as the This can lead to unexpected results, suchĪs one thread rolling back work done in another thread. Share the same session, and thus share the same transaction. If the statements are executed by SnowSQL using the -f (filename) option, then execution does not halt at the firstĮrror, and the statements after the error are executed.Īlthough multiple sessions cannot share the same transaction, multiple threads using a single connection If the statements are executed through Snowsight, execution halts at the first error. If these statements are not inside a stored procedure, then the behavior depends on how the statements are executed. If the stored procedure handles the exception and commits the statements prior to the failed INSERT statement, but does notĮxecute the statements after the failed INSERT statement, then only the row with value 1 is stored in the table. In that case, the table does not have either 1 or 2. If the exception is not handled, then the stored procedure never completes, and the COMMIT is neverĮxecuted, so the open transaction is implicitly rolled back. If these statements are inside a stored procedure written in Snowflake Scripting language, the failed INSERT statement The statements after the failed INSERT statement might or might not be executed, depending upon how the statements are run and Snowflake rolls back the active transaction and issues an error message. Regardless of whether the stored procedure’s active transaction was started explicitly or implicitly, TRUE or FALSE, and regardless of whether or not the new value is different from the previous value.įor example, even if you set AUTOCOMMIT to FALSE when it is already FALSE, an implicit COMMIT is executed.Īn implicit ROLLBACK is executed at the following (if a transaction is already active): The execution of an ALTER SESSION SET AUTOCOMMIT statement, regardless of whether the new value is The first DML statement or query statement after disabling AUTOCOMMIT.Īn implicit COMMIT is executed at the following (if a transaction is already active): a DDL statement, or an explicit commit or rollback). This is true regardless of what ended the The first DML statement or query statement after a transaction ends. ROLLBACK are rolled back even if AUTOCOMMIT is TRUE.Īn implicit BEGIN TRANSACTION is executed at: Statements inside an explicit BEGIN TRANSACTION. Statements inside an explicit transaction are not affected by AUTOCOMMIT. In other words, that statement is automatically committed if it succeeds, and The default setting for AUTOCOMMIT is on.Įach statement outside an explicit transaction is treated as though it is inside its own implicit If not specified, the ATR is stored in the default collection in the default scope in the bucket containing the first mutated document within the transaction.Snowflake supports an AUTOCOMMIT parameter. Specifies the collection where the active transaction records (ATRs) and client records are stored. Specifies the maximum time to wait for a KV operation before timing out. Specifies that a DML statement is a singleton transaction. Within this timeframe, if there are concurrency or node issues, a combination of wait and retry operations are used until the transaction reaches this time.įor more information, see Transactions Error Handling. The transaction expiry timer (which is configurable) will begin ticking once the transaction starts. See SQL++ Transactions Settings for details.Ĭonfigures how long a transaction should last before it is rolled back. ![]() Request-level parameters when using queries within transactions. Transactions can be configured using a number of settings and request-level parameters.
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