Maintaining Data Integrity: An Introduction to ACID-Compliant Databases

Discover how ACID databases ensure data consistency and integrity, and their modern features for scalability, performance, and security.

Maintaining Data Integrity: An Introduction to ACID-Compliant Databases

What is an ACID-compliant database?

An ACID-compliant database is a type of database system that adheres to the ACID properties. ACID is an acronym that stands for Atomicity, Consistency, Isolation, and Durability, and these properties ensure that database transactions are processed reliably and accurately.

  • Atomicity refers to the property that ensures that a database transaction is treated as a single, indivisible operation, and either all or none of the transaction is executed. This ensures that the database remains in a consistent state even if a transaction fails.
  • Consistency refers to the property that ensures that a transaction brings the database from one consistent state to another consistent state. This ensures that the database remains accurate and reliable, and the data is valid.
  • Isolation refers to the property that ensures that concurrent transactions do not interfere with each other. This means that each transaction is processed in isolation, and the results are consistent with the order in which the transactions were executed.
  • Durability refers to the property that ensures that the results of a transaction are permanent and will survive any system failure. This ensures that the database remains reliable and can recover from any system failure or error.

Overall, an ACID-compliant database provides a high degree of reliability and accuracy, making it a suitable choice for applications that require a high level of data integrity, such as financial applications or e-commerce systems.

3 transactional errors

ACID-compliant databases are necessary because they provide a high degree of reliability and accuracy in database transactions. By adhering to the ACID properties, an ACID-compliant database can prevent the "big three transactional errors" of Dirty Read, Non-Repeatable Read, and Phantom Read.

Dirty Read

A dirty read occurs when one database transaction reads data that has been modified by another uncommitted transaction, meaning that the data being read may be inconsistent or incorrect. This can happen in database systems that do not provide isolation between transactions, which can lead to inconsistent and inaccurate data.

Here's an example of a dirty read in code:

Assume that there are two transactions, Transaction A and Transaction B, that are updating the same data record in a database.

Transaction A:

COPYCOPY

sqlCopy code
BEGIN TRANSACTION;
UPDATE users SET balance = balance - 100 WHERE id = 1;

Transaction B:

COPYCOPY

sqlCopy code
BEGIN TRANSACTION;
SELECT balance FROM users WHERE id = 1;

If Transaction A has not yet committed, and Transaction B reads the data before the update has been committed, it will read the old value of the balance. This could result in Transaction B displaying inaccurate information to the user or making incorrect decisions based on the old data.

In other words, the read operation in Transaction B is dirty because it is reading data that has been modified but not yet committed by Transaction A. This can lead to inconsistencies in the data, and it's essential to ensure that database transactions are properly isolated to avoid dirty reads.

Non-repeatable read

A non-repeatable read occurs when a transaction reads a row multiple times, and the data changes between the reads. This can happen in database systems that provide some level of transaction isolation but not full isolation.

Here are a few examples of non-repeatable reads in code:

Example 1:

Assume that Transaction A and Transaction B are updating the same data record in a database.

Transaction A:

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sqlCopy code
BEGIN TRANSACTION;
UPDATE users SET status = 'inactive' WHERE id = 1;
COMMIT;

Transaction B:

COPYCOPY

sqlCopy code
BEGIN TRANSACTION;
SELECT status FROM users WHERE id = 1;
-- Transaction B sees 'active'COMMIT;

BEGIN TRANSACTION;
SELECT status FROM users WHERE id = 1;
-- Transaction B sees 'inactive'COMMIT;

In this example, Transaction B reads the status field twice, and the value changes between the reads due to the update made by Transaction A. This is a non-repeatable read.

Phantom read

A phantom read occurs when a transaction reads a set of rows that satisfy a search condition, and then a second transaction inserts or deletes a row that satisfies the same condition, causing the first transaction to see an additional row that was not there during the first read. This can occur in database systems that provide some level of transaction isolation but not full isolation.

Here are a few examples of phantom reads in code:

Example 1:

Assume that Transaction A is reading data while Transaction B is inserting new data.

Transaction A:

COPYCOPY

sqlCopy code
BEGIN TRANSACTION;
SELECT COUNT(*) FROM orders WHERE status = 'pending';
-- Transaction A sees 10 ordersCOMMIT;

BEGIN TRANSACTION;
SELECT COUNT(*) FROM orders WHERE status = 'pending';
-- Transaction A sees 11 ordersCOMMIT;

Transaction B:

COPYCOPY

sqlCopy code
BEGIN TRANSACTION;
INSERT INTO orders (customer_id, status) VALUES (123, 'pending');
COMMIT;

In this example, Transaction A reads the count of pending orders twice, and the count changes between the reads due to the insertion made by Transaction B. This is a phantom read.

Other Database properties

BASE

This stands for Basically Available, Soft-state, Eventually consistent. BASE properties are used in NoSQL databases, which prioritize high availability and scalability over consistency. Unlike ACID, BASE does not ensure immediate consistency after a write operation, but it guarantees eventual consistency over time.

CAP

This stands for Consistency, Availability, and Partition tolerance. CAP is a property used in distributed database systems that cannot satisfy all three properties simultaneously. These systems can only guarantee two out of the three properties, which are chosen based on the specific needs of the system.

PACELC

This stands for Partition-tolerance, Availability, Consistency, Else. PACELC is used to describe distributed systems and provides a trade-off between consistency, availability, and partition tolerance, with a fourth option (Else) for systems that do not fit into the other categories.

ACID2

This is an extension of the ACID properties, which includes additional features such as multi-version concurrency control, snapshot isolation, and repeatable read.

Conclusion

In conclusion, ACID databases play a critical role in ensuring data consistency, accuracy, and reliability in mission-critical applications. While alternative database properties such as BASE and PACELC prioritize availability and scalability over consistency, ACID remains a standard for data integrity and consistency. Modern ACID-compliant databases have evolved to offer high levels of scalability, performance, and availability, and advanced security features to ensure data privacy and protection. As data privacy and security become increasingly important, ACID databases are likely to remain a standard for ensuring data integrity and consistency in the future. However, as new technologies and use cases emerge, there may be a need for alternative database properties that prioritize availability and scalability over consistency.

ILLA Cloud

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