In database theory, what is the difference between "conflict serializable" and "conflict equivalent"?
My textbook has a section on conflict serializable but glosses over conflict equivalence. These are probably both concepts I am familiar with, but I am not familiar with the terminology, so I am looking for an explanation.
Conflict in DBMS can be defined as two or more different transactions accessing the same variable and atleast one of them is a write operation.
For example:
T1: Read(X)
T2: Read (X)
In this case there's no conflict because both transactions are performing just read operations.
But in the following case:
T1: Read(X)
T2: Write(X)
there's a conflict.
Lets say we have a schedule S, and we can reorder the instructions in them. and create 2 more schedules S1 and S2.
Conflict equivalent: Refers to the schedules S1 and S2 where they maintain the ordering of the conflicting instructions in both of the schedules. For example, if T1 has to read X before T2 writes X in S1, then it should be the same in S2 also. (Ordering should be maintained only for the conflicting operations).
Conflict Serializability: S is said to be conflict serializable if it is conflict equivalent to a serial schedule (i.e., where the transactions are executed one after the other).
From Wikipedia.
Conflict-equivalence
The schedules S1 and S2 are said to be conflict-equivalent if the following conditions are satisfied:
Both schedules S1 and S2 involve the same set of transactions (including ordering of actions within each transaction).
The order of each pair of conflicting actions in S1 and S2 are the same.
Conflict-serializable
A schedule is said to be conflict-serializable when the schedule is conflict-equivalent to one or more serial schedules.
Another definition for conflict-serializability is that a schedule is conflict-serializable if and only if its precedence graph/serializability graph, when only committed transactions are considered, is acyclic (if the graph is defined to include also uncommitted transactions, then cycles involving uncommitted transactions may occur without conflict serializability violation).
Just two terms to describe one thing in different ways.
Conflict equivalent: you need to say Schedule A is conflict equivalent to Schedule B. it must involve two schedules
Conflict serializable: Still use Schedule A and B. we can say Schedule A is conflict serializable. Schedule B is conflict serializable.
We didn't say Schedule A/B is conflict equivalent
We didn't say Schedule A is conflict serializable to Schedule B
If a schedule S can be transformed into a schedule S´ by a series of swaps of non-conflicting instructions, we say that S and S´ are conflict equivalent.
We say that a schedule S is conflict serializable if it is conflict equivalent to a serial schedule.
Conflict serializable means conflict equuivalent to any serial schedule.
Conflict Equivalent Schedules: if a Schedule S can be transformed into a schedule S' by a series of swaps of non conflicting instructions, we say that schedule S & S' are conflict equivalent.
Conflict Serializable Schedule: Schedule S is conflict serializable if it is conflict equivalent to a serial schedule.
Definitions have already been explained perfectly, but I feel this will be very useful to some.
I've developed a small console program (on github) which can test any schedule for conflict serializability and will also draw a precedence graph.
If there is at least one conflict equivalent schedule for considered transaction schedule, it is conflict serializable.
Related
the difference between atomicity and isolation of DBMS is somewhat vague so i am asking for a clear difference between the two ?
Atomicity and isolation, are ensured in classical database transactions by using a commit protocol. This protocol is used to turn temporary storage into permanent storage - that is, the updates to the transaction's data are not visible until the commit protocol validates the stored data. Note that it is the presence of a commit record in the database log which effectively validates the transaction's data.
Atomicity describes the behavior within an individual transaction, and Isolation describes the behavior among one or more transactions.
Atomicity: Either all of the database operations in a transaction are executed or none are.
Isolation: Each transaction appears to execute in isolation from other transactions.
Atomicity: all operations in a transaction are executed as a single unit.
Isolation: transactions don't see intermediate changes of other transactions.
According to these definitions, Isolation is just a part of Atomicity. Because when other transactions can see intermediate impact of our transaction, operations of our transaction cannot be considered as a single atomic unit.
Atomicity means if ONE transaction contains multiple operations, either ALL of them or NONE are performed.
The counterintuitive point is, this definition still allows for interleaving the operations with other transactions. As a result, atomicity itself does not guarantee isolation.
Example: Transaction A and B both contain two operations:
Transaction A:
A1: Read Alice's account balance
A2: Add 100 and write it back to Alice's balance
Transaction B:
B1: Read Alice's account balance
B2: Add 50 and write it back to Alice's balance
Suppose now Alice's balance is 0, and transactions A and B happen concurrently in the order of A1-B1-B2-A2, what is the final balance? It's 100.
Both A and B are atomically committed, in the sense that all their operations occurred. But without proper isolation, the result is probably not what you want.
In DBMS, is a strict schedule always serializable?
If the answer is no, then please provide an example of a strict schedule which is not serializable.
(For the following, I assume conflict-serializability.)
No, a strict schedule is not always serializable. The following schedule is strict (no reading from or overwriting of data items before a commit), but not serializable (there are two conflict pairs: r_1[x] and w_2[x] & w_2[x] and w_1[x], but they are ordered differently). The operations with subscript 1 belong to transaction T_1, those with subscript 2 to transaction T_2.
r_1[x] w_2[x] c_2 w_1[x] c_1
Contrary to the answer given above, a serializable schedule is not necessarily strict. See the following example:
w_1[x] w_2[x] c_2 r_1[y] c_1
Transaction T_2 overwrites the w_1[x] of T_1 before the commit of T_1. Nevertheless, the schedule is (conflict-)equivalent to the serial schedule T_1 T_2.
Every serializable schedule is strict.
But every strict schedule is not serializable.
A Schedule is a serializable schedule if its outcome is equal to the outcome of its transactions executed serially i.e. without interleaving the transactions.
Whereas, a strict schedule is the one in which some other transaction T2 cannot read or write the data written by T1 unless it is committed.
So for example,
In other words in Strict schedule T2 can read the data which is being read by T1, but once T1 writes the data, from that time to the time it commits, T2 cannot read or write the data.
I am reading in google about the conflict serializability and serializable.
But I am not getting the correct definition and the difference between serializable and conflict serializability.
I am getting only one thing.That is Conflict serializability implies serializable.
In many things they told most over the serializable and conflict serializable are same.
Can anyone please explain what is conflict serializable and difference between serializability and serializable with examples.
Thanks for advance !
I was found a answer for my question.
Serializable means the transaction is done in serial manner. That means if the scheduling is done, but the transactions are not use the same variable for read and write.
Example:-
T1 T2
Read(X)
Read(y)
Write(X)
Write(Y)
In this example, the two transactions are does not use the shared variable.
So, in here there is no conflict.
Conflict serializability means the transactions in done in concurrently. The two transactions are used the same variable, the output of the transaction is conflict.
Example:-
T1 T2
Read(X)
Read(X)
Write(X)
Write(X)
Read(Y)
Write(Y)
Read(Y)
Write(Y)
In this example the two transaction T1, and T2 uses the same variable.
So, the transaction T2 writes the X before T1 write. After the T1 writes the X. In here there is no use of transaction T2 writes. This is conflict serializability.
To answer your question, I'll first explain few terminologies, quoting a line from the Operating Systems book by Galvin.
Serial Schedule : A schedule in which each transaction is executed atomically is called a serial schedule just like it's shown in Premraj's answer.
Likewise, when we allow the transactions to overlap their execution,
i.e they aren't atomic any longer, they're called Non Serial
Schedule.
Conflicting Operations : This helps to see whether a Non Serial schedule is(not) equivalent to the one represented by Serial Schedule. Two consecutive operations are said to be in conflict if they access the same data item and at least one of them is write operation.
We try to find out and swap all the Non Conflicting Operations, and if the given Non Serial Schedule can be transformed to a Serial Schedule, we say the given schedule was Conflict Serializable.
Schedule : is a bundle of transactions.
Serializability is a property of a transaction schedule. It relates to the isolation property of a database transaction. Serializability of a schedule means equivalence to a serial schedule.
Conflict Serializable can occur on Non-Serializable Schedule on following 3 conditions:
They must belong to different transactions.
They must operate on same value
at least one of the should have write operation.
Background
Given:
a set of threads
each thread has its own data source
objects in each data source references objects in other data sources
there is a possibility for duplicate objects across various data sources
the threads are writing to a database with an engine that enforces the foreign key constraint
each type of object gets its own table and references the other objects through a foreign key
each thread has its own connection to the database
Proposed Solution
A register class which tracks the ID of the objects that have been written. The inteface of the register class has public methods, thus (represented in Java):
public interface Register
{
synchronized boolean requestObjectLock(int id);
synchronized boolean saveFinalized(int id);
synchronized boolean checkSaved(int id);
}
The method requestObjectLock checks to see if the object has been locked by another thread yet, and returns false it has. Otherwise, it locks that ID and returns true. It is then the responsibility of the calling thread to call saveFinalized when it has been successfully written to the database, and the responsibility of all other threads to check to see whether it has been written already with checkSaved before writing an object that references it. In other words, there are three states an object can be in: unregistered, locked (registered but unwritten), and saved (registered and written).
Reasoning
As far as I know there is no way to guarentee that one SQL query will finish before another when called by different threads. Thus, if an object was only registered or unregistered, it seems possible that a thread could check to see if an object was written, start writing an object that referenced it, and have its query complete (and fail) before the query that actually wrote the referenced object did.
Questions
Is it possible to guarantee the sequence of execution of queries being executed by different threads? And therefore, is this solution overengineered? Is there a simpler solution? On the other hand, is it safe?
The terms you need to research on the database side are "transaction isolation level" and "concurrency control". DBMS platform support varies. Some platforms implement a subset of the isolation levels defined in the SQL standards. (The SQL standards allow this. They're written in terms of what behavior isn't allowed.) And different platforms approach concurrency control in different ways.
Wikipedia, although not authoritative, has a good introduction to isolation levels, and also a good introduction to concurrency control.
As far as I know there is no way to guarentee that one SQL query will
finish before another when called by different threads.
That's kind of true. It's also kind of not true. In SQL standards, transaction isolation levels aren't concerned with who finishes first. They're concerned with behavior that's not allowed.
dirty read: Transaction A can read data written by concurrent, uncommitted transaction B.
nonrepeatable read: Transaction A reads data twice. A concurrent transaction, B, commits between the two reads. The data transaction A read first is different from the data it read second, because of transaction B. (Some people describe transaction A as seeing "same rows, different column values".)
phantom read: Transaction A reads data twice. A concurrent transaction, B, commits between the two reads. Transaction A's two reads return two different sets of rows, because transaction B has affected the evaluation of transaction A's WHERE clause. (Some people describe transaction A as seeing "same column values, different rows".)
You control transaction behavior in SQL using SET TRANSACTION. So SET TRANSACTION SERIALIZABLE means dirty reads, nonrepeatable reads, and phantom reads are impossible. SET TRANSACTION REPEATABLE READ allows phantom reads, but dirty reads and nonrepeatable reads are impossible.
You'll have to check your platform's documentation to find out what it supports. PostgreSQL, for example, supports all four isolation levels syntactically. But internally it only has two levels: read committed and serializable. That means you can SET TRANSACTION READ UNCOMMITTED, but you'll get "read committed" behavior.
Important for you: The effect of a serializable isolation level is to guarantee that transactions appear to have been issued one at a time by a single client. But that's not quite the same thing as saying that if transaction A starts before transaction B, it will commit before transaction B. If they don't affect each other, the dbms is allowed to commit transaction B first without violating the serializable isolation level semantics.
When I have questions myself about how these work, I test them by opening two command-line clients connected to the same database.
I am reading about ACID properties of a database. Atomicity and Consistency seem to be very closely related. I am wondering if there are any scenarios where we need to just support Atomicity but not Consistency or vice-versa. An example would really help!
They are somewhat related but there's a subtle difference.
Atomicity means that your transaction either happens or doesn't happen.
Consistency means that things like referential integrity are enforced.
Let's say you start a transaction to add two rows (a credit and debit which forms a single bank transaction). The atomicity of this has nothing to do with the consistency of the database. All it means it that either both rows or neither row will be added.
On the consistency front, let's say you have a foreign key constraint from orders to products. If you try to add an order that refers to a non-existent product, that's when consistency kicks in to prevent you from doing it.
Both are about maintaining the database in a workable state, hence their similarity. The former example will ensure the bank doesn't lose money (or steal it from you), the latter will ensure your application doesn't get surprised by orders for products you know nothing about.
Atomicity:
In an atomic transaction, a series of
database operations either all occur,
or nothing occurs. A guarantee of
atomicity prevents updates to the
database occurring only partially,
which can cause greater problems than
rejecting the whole series outright.
Consistency:
In database systems, a consistent
transaction is one that does not
violate any integrity constraints
during its execution. If a transaction
leaves the database in an illegal
state, it is aborted and an error is
reported
A database that supports atomicity but not consistency would allow transactions that leave the database in an inconsistent state (that is, violate referential or other integrity checks), provided the transaction completes successfully. For instance, you could add a string to an int column provided that the transaction performing this completed successfully.
Conversely, a database that supports consistency but not atomicity would allow partial transactions to complete, so long as the effects of that transaction didn't break any integrity checks (e.g. foreign keys must match an existing identity).
For instance, you could try adding a new row that included string and int values, and even if the insertion failed half way through losing half the data, the row would be allowed provided that none of the lost data was for required columns and no data was inserted into an incorrectly typed column.
Having said that, consistency relies on atomicity for the reversal of inconsistent transactions.
There is indeed a strong relation between Atomicity and Consistency, but they are not the same:
A DBMS can (theoretically) support Consistency and not Atomicity: for example, consider a transaction that consists SQL operations O1,O2, and O3. Now, assume that after O1 and O2 the DB is already in a consistent state. Then the DBMS can stop the transaction after O1 and O2 without O3 and still preserves consistency. Clearly, such a DBMS does nto supports atomicity (as O3 was not executed by O1 and O2 was).
A DBMS can (theoretically) support Atomicity and not Consistency: this can occur in a multi-user scenario, where atomicity only ensures that all actions of a transaction will be performed (or none of them) but it does not guaranteee that actions of one transaction done concurrently with another transaction may not end up in an inconsistent state.
However, what I do believe (but have not proven formally) is that if your DMBS guarantees both Atomicity and Isolation, then it must also guarantee Consistency.
I was also getting confused when reading about atomicity & consistency. Let's say there is scenario to do batch insert of 1000 records in the account table.
Atomicity of the batch is if all the 1000 records are inserted or none of the records are inserted if there is an error.
Consistency of the batch will be violated if at the account record level, we have put the logic to make the insert successful even if data type didn't match, related record was inserted in the foreign key table and later deleted after the successful account record update.
Hopefully this example clears the confusion.
I have a different understanding of consistency in the ACID context:
Within a transaction, if a given item of data is retrieved and retrieved again later in the same transaction, no changes are seen. That is, the transaction is given a consistent state of the database throughout the transaction. The only updates that can change data visible to the transaction are updates done by the transaction itself.
In my mind, this is tantamount to serializability.