If you use databases in your work or study, you‘ve likely heard the term "ACID properties" thrown around. But what exactly does it mean? In this beginner-friendly guide, we‘ll explain ACID properties and why they matter when working with database transactions.
Database Transactions 101
First, a quick database refresher. A database transaction refers to a set of operations carried out as one single unit or task. A classic example is transferring money between bank accounts – withdrawing funds from one account and depositing into another.
Transactions involve:
- Reading existing data values
- Writing or inputting new data
The entire transaction succeeds or fails as one – money is either transferred or not. There‘s no in-between state.
This is where ACID properties come in…
Understanding ACID
ACID stands for:
- Atomicity
- Consistency
- Isolation
- Durability
These 4 properties govern database transactions to ensure data accuracy and consistency.
Atomicity
Atomicity refers to treating database operations within a transaction as a single unit. Either the entire transaction succeeds (commit) or fails (abort).
Let‘s go back to our money transfer example. To move $50 from Account A to Account B:
- $50 is first withdrawn from Account A
- Then $50 is deposited into Account B
The two operations must happen together, not separately. Atomicity prevents incomplete transfers or data corruption.
Consistency
Consistency ensures transactions follow predefined database rules and constraints. Values remain consistent before and after transaction completion.
For money transfers, the total funds across accounts must equal the amount before and after the transaction. No money can be lost or gained. This maintains data integrity.
Isolation
Isolation prevents multiple transactions executed at the same time from affecting one another.
Imagine User 1 and User 2 both withdraw money from the same account at once. Without isolation, the separate transactions would interfere, allowing "dirty reads" of data.
Let‘s understand this with an example:
User 1 Transaction User 2 Transaction
Begin transaction Begin transaction
Read Account Balance: $100 Read Account Balance: $100
Withdraw $50 Withdraw $50
[Transaction interrupts] [Transaction resumes]
Commit transaction
New Account Balance: $50
[Transaction resumes]
Commit transaction
New Account Balance: $100 Since User 2 read the account balance before User 1‘s transaction completed, an incorrect $50 balance was committed, corrupting the data.
Isolation forces transactions to queue in sequence rather than concurrently interfere. Changes from one transaction only become visible to others after it completes.
Durability
Durability means completed transactions are saved and changes persisted even in cases of unexpected system failure. Crucially, unfinished transactions will be rolled back.
So if a power outage occurs mid-transfer, durability ensures the transaction does not go through since atomicity failed. Data integrity is maintained.
ACID In Action
Databases like MySQL, PostgreSQL, Oracle, and Microsoft SQL Server all enforce ACID properties with various isolation levels. Let‘s analyze some examples.
ANSI SQL Isolation Levels
ANSI SQL defines 4 primary isolation levels with differing tradeoffs:
| Isolation Level | Dirty Reads | Non-Repeatable Reads | Phantoms |
|---|---|---|---|
| Read Uncommitted | Allowed | Allowed | Allowed |
| Read Committed | Not allowed | Allowed | Allowed |
| Repeatable Read | Not allowed | Not allowed | Allowed |
| Serializable | Not allowed | Not allowed | Not allowed |
- Dirty read – Reading uncommitted data from another transaction
- Non-repeatable read – Same query yields different results within a transaction
- Phantom read – New rows added matching query criteria within transaction
The stricter the isolation level, the less concurrency and performance is allowed. But data consistency increases.
Let‘s analyze some use cases:
- Read uncommitted – Reporting queries where real-time precision isn‘t critical. Some dirty data acceptable.
- Read committed – Default level for many databases. Prevents dirty reads but allows non-repeatable ones.
- Repeatable read – Accounting system requiring consistent reads immune to concurrent changes.
- Serializable – Highest protection for critical financial transactions.
NoSQL Databases
While relational SQL databases strictly adhere to ACID properties, NoSQL database models can differ:
- MongoDB – Document-oriented database that sacrifices ACID compliance for scalability and high performance. But still offers atomicity on a per-document level, and configurable isolation levels similar to SQL.
- Cassandra – Wide-column database built for high availability and scale across distributed environments. Achieves atomicity through light-weight transactions but limits isolation guarantees.
Understanding how popular NoSQL options tradeoff ACID principles for other benefits is key when evaluating options.
Why ACID Properties Matter
ACID properties uphold fundamental database transaction principles around atomicity, consistency, isolation, durability. They prevent:
- Partial or incomplete transactions
- Data corruption
- Inaccurate/inconsistent reads and writes
- Cascading system failures
Impacts of lacking ACID properties include:
- Inaccurate reporting – Erroneous analytics & decisions
- Revenue losses – Billing errors, fraudulent purchases
- Legal liability – Violations of financial regulations
- Medical errors – Patient treatment decisions affected
By governing data transactions, ACID properties ensure overall database stability, reliability and accuracy – critical for use cases like healthcare, banking, logistics, and more.
Industry surveys have found that 66% of companies have lost revenue due to data inconsistencies. And medical record errors contribute an average of $17.1 billion annually in healthcare delivery costs according to the Annals of Internal Medicine journal.
That‘s why regulations like GDPR now require ACID properties when dealing with sensitive citizen data. ACID compliance is essential where accuracy matters most.
ACID vs. BASE
ACID focuses on immediate consistency – transactions must fully complete or fail instantly, so data remains accurate at all times. However, enforcing transactions serially reduces overall database availability and responsiveness.
An alternative model called BASE (Basic Availability, Soft State, Eventual Consistency) allows for greater database availability and performance under volume at the cost of moment-to-moment accuracy.
BASE tolerates temporary data inconsistencies after transactions, asynchronously replicating and batch-updating copies. This spreads load to maintain responsiveness, synchronizing data over time to reach an eventually consistent state.
So which is better?
It depends on your specific database needs. ACID offers stronger data integrity guarantees while BASE provides higher operational availability.
The choice ultimately reflects a systems design decision between consistency (ACID) and availability (BASE) across distributed database networks. This is formalized in theoretical principles like CAP theorem and Brewer‘s conjecture which observe it is impossible for a distributed system to simultaneously provide all three guarantees of:
- Consistency – All nodes see the same updated data at the same time
- Availability – Guaranteed response time for all requests
- Partition Tolerance – System continues operating despite arbitrary partitioning due to network/system failures
There are always tradeoffs. Complex modern applications often employ a mixture of ACID and BASE distributed across data types and use cases.
Key Takeaways
We‘ve covered core concepts below:
- ACID = Atomicity, Consistency, Isolation, Durability
- ACID properties maintain database integrity and accuracy
- Required for sensitive data like finance, healthcare, logistics
- Protect against failed transactions and data corruption
- BASE improves availability by allowing temporary inconsistencies
- Choosing ACID vs BASE affects overall system consistency, availability and scalability
- Regulations now mandate ACID compliance for critical data protection
Understanding ACID transactions will serve any database user well. Implementing ACID protects data integrity vital for today‘s increasingly distributed applications that demand it.
