Skip to main content

Autonomous Transaction

In Oracle's database products, an autonomous transaction is an independent transaction that is initiated by another transaction.

Autonomous Transactions are independant transactions that can be called from within another transaction.

You can call Autonomous Transaction from within PL/SQL Block using pragma Autonomous_transaction

When an autonomous transaction is called, the original transaction (calling transaction) is temporarily suspended. The autonomous transaction must commit or roll back before it returns control to the calling transaction.

The easiest way to understand autonomous transactions is to see them in action. To do this, we create a test table and populate it with two rows. Notice that the data is not commited.

CREATE TABLE at_test (
id NUMBER NOT NULL,
description VARCHAR2(50) NOT NULL
);

INSERT INTO at_test (id, description) VALUES (1, 'Description for 1');
INSERT INTO at_test (id, description) VALUES (2, 'Description for 2');

SELECT * FROM at_test;

ID DESCRIPTION
---------- --------------------------------------------------
1 Description for 1
2 Description for 2

2 rows selected.

SQL>
Next, we insert another 8 rows using an anonymous block declared as an autonomous transaction, which contains a commit statement.

DECLARE
PRAGMA AUTONOMOUS_TRANSACTION;
BEGIN
FOR i IN 3 .. 10 LOOP
INSERT INTO at_test (id, description)
VALUES (i, 'Description for ' || i);
END LOOP;
COMMIT;
END;
/

PL/SQL procedure successfully completed.

SELECT * FROM at_test;

ID DESCRIPTION
---------- --------------------------------------------------
1 Description for 1
2 Description for 2
3 Description for 3
4 Description for 4
5 Description for 5
6 Description for 6
7 Description for 7
8 Description for 8
9 Description for 9
10 Description for 10

10 rows selected.

SQL>
As expected, we now have 10 rows in the table. If we now issue a rollback statement we get the following result.

ROLLBACK;
SELECT * FROM at_test;

ID DESCRIPTION
---------- --------------------------------------------------
3 Description for 3
4 Description for 4
5 Description for 5
6 Description for 6
7 Description for 7
8 Description for 8
9 Description for 9
10 Description for 10

8 rows selected.

SQL>
The 2 rows inserted by our current session (transaction) have been rolled back, while the rows inserted by the autonomous transactions remain. The presence of the PRAGMA AUTONOMOUS_TRANSACTION compiler directive made the anonymous block run in its own transaction, so the internal commit statement did not affect the calling session. As a result rollback was still able to affect the DML issued by the current statement.

Comments

Popular posts from this blog

Error: could not find function "read.xlsx" while reading .xlsx file in R

Got this during the execution of following command in R > dat Error: could not find function "read.xlsx" Tried following command > install.packages("xlsx", dependencies = TRUE) Installing package into ‘C:/Users/amajumde/Documents/R/win-library/3.2’ (as ‘lib’ is unspecified) also installing the dependencies ‘rJava’, ‘xlsxjars’ trying URL 'https://cran.rstudio.com/bin/windows/contrib/3.2/rJava_0.9-8.zip' Content type 'application/zip' length 766972 bytes (748 KB) downloaded 748 KB trying URL 'https://cran.rstudio.com/bin/windows/contrib/3.2/xlsxjars_0.6.1.zip' Content type 'application/zip' length 9485170 bytes (9.0 MB) downloaded 9.0 MB trying URL 'https://cran.rstudio.com/bin/windows/contrib/3.2/xlsx_0.5.7.zip' Content type 'application/zip' length 400968 bytes (391 KB) downloaded 391 KB package ‘rJava’ successfully unpacked and MD5 sums checked package ‘xlsxjars’ successfully unpacked ...

What is Tensor Parallelism and relationship between Buffer and GPU

  Tensor Parallelism in GPU Tensor parallelism is a technique used to distribute the computation of large tensor operations across multiple GPUs or multiple cores within a GPU .   It is an essential method for improving the performance and scalability of deep learning models, particularly when dealing with very large models that cannot fit into the memory of a single GPU. Key Concepts Tensor Operations : Tensors are multidimensional arrays used extensively in deep learning. Common tensor operations include matrix multiplication, convolution, and element-wise operations. Parallelism : Parallelism involves dividing a task into smaller sub-tasks that can be executed simultaneously. This approach leverages the parallel processing capabilities of GPUs to speed up computations. How Tensor Parallelism Works Splitting Tensors : The core idea of tensor parallelism is to split large tensors into smaller chunks that can be processed in parallel. Each chunk is assigned to a different GP...

What's replicated, what's not?

Logged operations are replicated. These include, but are not limited to: DDL DML Create/alter table space Create/alter storage group Create/alter buffer pool XML data. Logged LOBs Not logged operations are not replicated. These include, but are not limited to: Database configuration parameters (this allows primary and standby databases to be configured differently). "Not logged initially" tables Not logged LOBs UDF (User Defined Function) libraries. UDF DDL is replicated. But the libraries used by UDF (such as C or Java libraries)  are not replicated, because they are not stored in the database. Users must manually copy the libraries to the standby. Note: You can use database configuration parameter  BLOCKNONLOGGED  to block not logged operations on the primary.