acid example 

###Atomicity

Transactions are often composed of multiple statements. **Atomicity guarantees that each transaction is treated as a single "unit", which either succeeds completely, or fails completely**: if any of the statements constituting a transaction fails to complete, the entire transaction fails and the database is left unchanged. An atomic system must guarantee atomicity in each and every situation, including power failures, errors and crashes.

###Consistency
Consistency ensures that a transaction can only bring the database from one valid state to another, maintaining database invariants: **any data written to the database must be valid according to all defined rules, including constraints, cascades, triggers, and any combination thereof**. This prevents database corruption by an illegal transaction, but does not guarantee that a transaction is correct.

###Isolation
Transactions are often executed concurrently (e.g., reading and writing to multiple tables at the same time). **Isolation ensures that concurrent execution of transactions leaves the database in the same state that would have been obtained if the transactions were executed sequentially**. Isolation is the main goal of concurrency control; depending on the method used, the effects of an incomplete transaction might not even be visible to other transactions.

###Durability
Durability guarantees that once a transaction has been committed, it will remain committed even in the case of a system failure (e.g., power outage or crash). **This usually means that completed transactions (or their effects) are recorded in non-volatile memory**.

#Examples

The following examples further illustrate the ACID properties. In these examples, the database table has two columns, A and B. An integrity constraint requires that the value in A and the value in B must sum to 100. The following SQL code creates a table as described above:

```sql
CREATE TABLE acidtest (A INTEGER, B INTEGER, CHECK (A + B = 100));
```
###Atomicity failure
In database systems, atomicity (or atomicness; from Greek a-tomos, undividable) is one of the ACID transaction properties. A series of database operations in an atomic transaction will either all occur, or none will occur. The series of operations cannot be separated with only some of them being executed, which makes the series of operations "indivisible". A guarantee of atomicity prevents updates to the database occurring only partially, which can cause greater problems than rejecting the whole series outright. In other words, atomicity means indivisibility and irreducibility.[4] Alternatively, **we may say that a Logical transaction may be made of, or composed of, one or more (several), Physical transactions. Unless and until all component Physical transactions are executed, the Logical transaction will not have occurred – to the effects of the database**. Say our Logical transaction consists of transferring funds from account A to account B. This Logical transaction may be composed of several Physical transactions consisting of first removing the amount from account A as a first Physical transaction and then, as a second transaction, depositing said amount in account B. We would not want to see the amount removed from account A before we are sure it has been transferred into account B. Then, unless and until both transactions have happened and the amount has been transferred to account B, the transfer has not, to the effects of the database, occurred.

###Consistency failure
Consistency is a very general term, which demands that the data must meet all validation rules. In the previous example, the validation is a requirement that A + B = 100. All validation rules must be checked to ensure consistency. 
**Assume that a transaction attempts to subtract 10 from A without altering B. Because consistency is checked after each transaction, it is known that A + B = 100 before the transaction begins. If the transaction removes 10 from A successfully, atomicity will be achieved. However, a validation check will show that A + B = 90, which is inconsistent with the rules of the database. The entire transaction must be cancelled and the affected rows rolled back to their pre-transaction state. If there had been other constraints, triggers, or cascades, every single change operation would have been checked in the same way as above before the transaction was committed.** Similar issues may arise with other constraints. We may have required the data types of both A,B to be integers. If we were then to enter, say, the value 13.5 for A, the transaction will be cancelled, or the system may give rise to an alert in the form of a trigger (if/when the trigger has been written to this effect). Another example would be with integrity constraints, which would not allow us to delete a row in one table whose Primary key is referred to by at least one foreign key in other tables.

###Isolation failure
To demonstrate isolation, we assume two transactions execute at the same time, each attempting to modify the same data. One of the two must wait until the other completes in order to maintain isolation.

Consider two transactions. T1 transfers 10 from A to B. T2 transfers 10 from B to A. Combined, there are four actions:

T1 subtracts 10 from A.
T1 adds 10 to B.
T2 subtracts 10 from B.
T2 adds 10 to A.
If these operations are performed in order, isolation is maintained, although T2 must wait. Consider what happens if T1 fails halfway through. The database eliminates T1's effects, and T2 sees only valid data.

By interleaving the transactions, the actual order of actions might be:

T1 subtracts 10 from A.
T2 subtracts 10 from B.
T2 adds 10 to A.
T1 adds 10 to B.
**Again, consider what happens if T1 fails while modifying B (step 4). By the time T1 fails, T2 has already modified A; it cannot be restored to the value it had before T1 without leaving an invalid database. This is known as a write-write failure,[citation needed] because two transactions attempted to write to the same data field. In a typical system, the problem would be resolved by reverting to the last known good state, canceling the failed transaction T1, and restarting the interrupted transaction T2 from the good state**.

###Durability failure
Consider a transaction that transfers 10 from A to B. First it removes 10 from A, then it adds 10 to B. At this point, the user is told the transaction was a success, however the changes are still queued in the disk buffer waiting to be committed to disk. Power fails and the changes are lost. **The user assumes (understandably) that the changes persist**.

ref: https://en.wikipedia.org/wiki/ACID_(computer_science)#Consistency