Database Transaction represents a unit of work, that is atomic, consistent, isolated and durable (a.k.a. ACID).

Database Transaction

ACID guarantees are provided by traditional relational database management systems (RDBMS).

  • Atomicity means that operations, that constitute transaction are all either succeed or fail together. So, atomicity implies that there can’t be situation, in which part of operations succeeded and part failed.

  • Consistency means that transaction leaves database in consistent state after execution. So, in practical terms, any data written to database must be valid according to integrity constraints (primary key/foreign key/unique key/etc.), cascades, triggers, and any combination thereof.

  • Isolation determines how operations performed in a transaction are visible to other executing transactions. So, for example, whether data written by transaction is available for read by other concurrent transactions.

  • Durability means that after transaction is committed, database changes are saved permanently. So, if database crushes all committed transactions will be restored. This is most often implemented by using transaction log stored in non-volatile storage. And transaction is committed only after it is entered in the log.

Transaction states

During execution database transaction goes through number of states:

transaction state machine

State transitions example

Assume we have two accounts: one for Alice (account A) and one for Bob (account B). Initially each account has 1000$ balance. The task is to transfer 100$ from account A to account B.

Database transaction for above situation can be represented as follows (in PostgreSQL syntax):


UPDATE accounts SET balance = balance - 100.00
 WHERE name = 'Alice'; (1)

UPDATE accounts SET balance = balance + 100.00
 WHERE name = 'Bob'; (2)


Let’s follow state transitioning for this transaction:

  1. In ACTIVE state read/write operations on database are performed. So, any statements between BEGIN and COMMIT instructions form ACTIVE state.

  2. If transaction reaches COMMIT without failures, then it goes into PARTIALLY COMMITTED state. In PARTIALLY COMMITTED state, balances will have values: A = 900 and B = 1100.

  3. If the transaction executes COMMIT successfully, that is, if it successfully writes the new value of A and B into log file or stable storage, then the transaction is said to be in COMMITTED state.

  4. Transaction may enter FAILED state:

    • In ACTIVE state:

      1. before first UPDATE ended : then A = 1000 and B = 1000

      2. after first UPDATE ended: then A = 900 and B = 1100

      3. before COMMIT and after second UPDATE: then A = 900 and B = 1100

    • In PARTIALLY COMMITTED state: then A = 900 and B = 1100

  5. The transaction enters ABORTED after rollback. In this state DBMS has to undo the changes made so far. So, whatever balances are at the beginning in ABORTED state, after roll back, the state will be reverted to the previous consistent state (A = 1000 and B = 1000)

  6. After entering COMMITTED or ABORTED state, transaction is terminated

Why transaction may fail ?

Database transaction might fail due to one or more of the following reasons:

  • Server failure, e.g. hardware, software or network error, that causes database server to hang or crash

  • Logical transaction failure, e.g. user aborts transaction, division by zero etc.

  • Concurrency failure, e.g. if transaction causes deadlock, or violates serializability

  • Disk failure

DBMS usually can recover from server failure, logical failure or concurrency failure. To deal with disk failures - disk backups needs to be maintained.


It’s possible to control the statements in a transaction in a more granular fashion through the use of savepoints. Savepoints allow you to selectively discard parts of the transaction, while committing the rest. After defining a savepoint with SAVEPOINT, you can if needed roll back to the savepoint with ROLLBACK TO. All the transaction’s database changes between defining the savepoint and rolling back to it are discarded, but changes earlier than the savepoint are kept.

After rolling back to a savepoint, it continues to be defined, so you can roll back to it several times. Conversely, if you are sure you won’t need to roll back to a particular savepoint again, it can be released, so the system can free some resources. Keep in mind that either releasing or rolling back to a savepoint will automatically release all savepoints that were defined after it.

Suppose we debit $100.00 from Alice’s account, and credit Bob’s account, only to find later that we should have credited Wally’s account. We could do it using savepoints like this:


UPDATE accounts SET balance = balance - 100.00
 WHERE name = 'Alice';

SAVEPOINT my_savepoint;

UPDATE accounts SET balance = balance + 100.00
 WHERE name = 'Bob';

-- oops ... forget that and use Wally's account
ROLLBACK TO my_savepoint;

UPDATE accounts SET balance = balance + 100.00
 WHERE name = 'Wally';


This example is, of course, oversimplified, but there’s a lot of control to be had over a transaction block through the use of savepoints.


Even though this post may seem pretty dry, however, it lays good foundation to get started working with transactions in your Data Access Layer.

In the next posts I will comeback to this topic from more practical perspective in the context of our lovely Spring Boot services. So, stay tuned ;)

Oleksii Zghurskyi