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Microsoft SQL Server 2008 Internals: Transactions and Concurrency

Transaction Processing

No matter what concurrency model you’re working with, an understanding of transactions is crucial. A transaction is the basic unit of work in SQL Server. Typically, it consists of several SQL commands that read and update the database, but the update is not considered final until a COMMIT command is issued (at least for an explicit transaction). In general, when I talk about a modification operation or a read operation, I am talking about the transaction that performs the data modification or the read, which is not necessarily a single SQL statement. When I say that writers will block readers, I mean that so long as the transaction that performed the write operation is active, no other process can read the modified data.

The concept of a transaction is fundamental to understanding concurrency control. The mechanics of transaction control from a programming perspective are beyond the scope of this book, but I discuss basic transaction properties. I also go into detail about the transaction isolation levels because that has a direct impact on how SQL Server manages the data being accessed in your transactions.

An implicit transaction is any individual INSERT, UPDATE, DELETE, or MERGE statement. (You can also consider SELECT statements to be implicit transactions, although SQL Server does not write to the log when SELECT statements are processed.) No matter how many rows are affected, the statement must exhibit all the ACID properties of a transaction, which I tell you about in the next section. An explicit transaction is one whose beginning is marked with a BEGIN TRAN statement and whose end is marked by a COMMIT TRAN or ROLLBACK TRAN statement. Most of the examples I present use explicit transactions because it is the only way to show the state of SQL Server in the middle of a transaction. For example, many types of locks are held for only the duration of the transaction. I can begin a transaction, perform some operations, look around in the metadata to see what locks are being held, and then end the transaction. When the transaction ends, the locks are released; I can no longer look at them.

ACID Properties

Transaction processing guarantees the consistency and recoverability of SQL Server databases. It ensures that all transactions are performed as a single unit of work—even in the presence of a hardware or general system failure. Such transactions are referred to as having the ACID properties, with ACID standing for atomicity, consistency, isolation, and durability. In addition to guaranteeing that explicit multistatement transactions maintain the ACID properties, SQL Server guarantees that an implicit transaction also maintains the ACID properties.

Here’s an example in pseudocode of an explicit ACID transaction:

BEGIN TRANSACTION DEBIT_CREDIT
Debit savings account $1000
Credit checking account $1000
COMMIT TRANSACTION DEBIT_CREDIT

Now let’s take a closer look at each of the ACID properties.

Atomicity

SQL Server guarantees the atomicity of its transactions. Atomicity means that each transaction is treated as all or nothing—it either commits or aborts. If a transaction commits, all its effects remain. If it aborts, all its effects are undone. In the preceding DEBIT_CREDIT example, if the savings account debit is reflected in the database but the checking account credit isn’t, funds essentially disappear from the database—that is, funds are subtracted from the savings account but never added to the checking account. If the reverse occurs (if the checking account is credited and the savings account is not debited), the customer’s checking account mysteriously increases in value without a corresponding customer cash deposit or account transfer. Because of the atomicity feature of SQL Server, both the debit and credit must be completed or else neither event is completed.

Consistency

The consistency property ensures that a transaction won’t allow the system to arrive at an incorrect logical state—the data must always be logically correct. Constraints and rules are honored even in the event of a system failure. In the DEBIT_CREDIT example, the logical rule is that money can’t be created or destroyed: a corresponding, counterbalancing entry must be made for each entry. (Consistency is implied by, and in most situations redundant with, atomicity, isolation, and durability.)

Isolation

Isolation separates concurrent transactions from the updates of other incomplete transactions. In the DEBIT_CREDIT example, another transaction can’t see the work in progress while the transaction is being carried out. For example, if another transaction reads the balance of the savings account after the debit occurs, and then the DEBIT_CREDIT transaction is aborted, the other transaction is working from a balance that never logically existed.

SQL Server accomplishes isolation among transactions automatically. It locks data or creates row versions to allow multiple concurrent users to work with data while preventing side effects that can distort the results and make them different from what would be expected if users were to serialize their requests (that is, if requests were queued and serviced one at a time). This serializability feature is one of the isolation levels that SQL Server supports. SQL Server supports multiple isolation levels so that you can choose the appropriate tradeoff between how much data to lock, how long to hold locks, and whether to allow users access to prior versions of row data. This tradeoff is known as concurrency vs. consistency.

Durability

After a transaction commits, the durability property of SQL Server ensures that the effects of the transaction persist even if a system failure occurs. If a system failure occurs while a transaction is in progress, the transaction is completely undone, leaving no partial effects on the data. For example, if a power outage occurs in the middle of a transaction before the transaction is committed, the entire transaction is rolled back when the system is restarted. If the power fails immediately after the acknowledgment of the commit is sent to the calling application, the transaction is guaranteed to exist in the database. Write-ahead logging and automatic rollback and roll-forward of transactions during the recovery phase of SQL Server startup ensure durability.

Transaction Dependencies

In addition to supporting all four ACID properties, a transaction might exhibit several other behaviors. Some people call these behaviors “dependency problems” or “consistency problems,” but I don’t necessarily think of them as problems. They are merely possible behaviors, and except for lost updates, which are never considered desirable, you can determine which of these behaviors you want to allow and which you want to avoid. Your choice of isolation level determines which of these behaviors is allowed.

Lost Updates

Lost updates occur when two processes read the same data and both manipulate the data, changing its value, and then both try to update the original data to the new value. The second process might overwrite the first update completely. For example, suppose that two clerks in a receiving room are receiving parts and adding the new shipments to the inventory database. Clerk A and Clerk B both receive shipments of widgets. They both check the current inventory and see that 25 widgets are currently in stock. Clerk A’s shipment has 50 widgets, so he adds 50 to 25 and updates the current value to 75. Clerk B’s shipment has 20 widgets, so she adds 20 to the value of 25 that she originally read and updates the current value to 45, completely overriding the 50 new widgets that Clerk A processed. Clerk A’s update is lost.

Lost updates are only one of the behaviors described here that you probably want to avoid in all cases.

Dirty Reads

Dirty reads occur when a process reads uncommitted data. If one process has changed data but not yet committed the change, another process reading the data will read it in an inconsistent state. For example, say that Clerk A has updated the old value of 25 widgets to 75, but before he commits, a salesperson looks at the current value of 75 and commits to sending 60 widgets to a customer the following day. If Clerk A then realizes that the widgets are defective and sends them back to the manufacturer, the salesperson has done a dirty read and taken action based on uncommitted data.

By default, dirty reads are not allowed. Keep in mind that the process updating the data has no control over whether another process can read its data before the first process is committed. It’s up to the process reading the data to decide whether it wants to read data that is not guaranteed to be committed.

Nonrepeatable Reads

A read is nonrepeatable if a process might get different values when reading the same data in two separate reads within the same transaction. This can happen when another process changes the data in between the reads that the first process is doing. In the receiving room example, suppose that a manager comes in to do a spot check of the current inventory. She walks up to each clerk, asking the total number of widgets received today and adding the numbers on her calculator. When she’s done, she wants to double-check the result, so she goes back to the first clerk. However, if Clerk A received more widgets between the manager’s first and second inquiries, the total is different and the reads are nonrepeatable. Nonrepeatable reads are also called inconsistent analysis.

Phantoms

Phantoms occur when membership in a set changes. It can happen only when a query with a predicate—such as WHERE count_of_widgets < 10—is involved. A phantom occurs if two SELECT operations using the same predicate in the same transaction return a different number of rows. For example, let’s say that our manager is still doing spot checks of inventory. This time, she goes around the receiving room and notes which clerks have fewer than 10 widgets. After she completes the list, she goes back around to offer advice to everyone with a low total. However, if during her first walkthrough, a clerk with fewer than 10 widgets returned from a break but was not spotted by the manager, that clerk is not on the manager’s list even though he meets the criteria in the predicate. This additional clerk (or row) is considered to be a phantom.

The behavior of your transactions depends on the isolation level. As mentioned earlier, you can decide which of the behaviors described previously to allow by setting an appropriate isolation level using the command SET TRANSACTION ISOLATION LEVEL <isolation_level>. Your concurrency model (optimistic or pessimistic) determines how the isolation level is implemented—or, more specifically, how SQL Server guarantees that the behaviors you don’t want will not occur.

Isolation Levels

SQL Server 2008 supports five isolation levels that control the behavior of your read operations. Three of them are available only with pessimistic concurrency, one is available only with optimistic concurrency, and one is available with either. We look at these levels now, but a complete understanding of isolation levels also requires an understanding of locking and row versioning. In my descriptions of the isolation levels, I mention the locks or row versions that support that level, but keep in mind that locking and row versioning are discussed in detail later in the chapter.

Read Uncommitted

In Read Uncommitted isolation, all the behaviors described previously, except lost updates, are possible. Your queries can read uncommitted data, and both nonrepeatable reads and phantoms are possible. Read Uncommitted isolation is implemented by allowing your read operations to not take any locks, and because SQL Server isn’t trying to acquire locks, it won’t be blocked by conflicting locks acquired by other processes. Your process is able to read data that another process has modified but not yet committed.

In addition to reading individual values that are not yet committed, the Read Uncommitted isolation level introduces other undesirable behaviors. When using this isolation level and scanning an entire table, SQL Server can decide to do an allocation order scan (in page-number order), instead of a logical order scan (which would follow the page pointers). If there are concurrent operations by other processes that change data and move rows to a new location in the table, your allocation order scan can end up reading the same row twice. This can happen when you’ve read a row before it is updated, and then the update moves the row to a higher page number than your scan encounters later. In addition, performing an allocation order scan under Read Uncommitted can cause you to miss a row completely. This can happen when a row on a high page number that hasn’t been read yet is updated and moved to a lower page number that has already been read.

Although this scenario isn’t usually the ideal option, with Read Uncommitted, you can’t get stuck waiting for a lock, and your read operations don’t acquire any locks that might affect other processes that are reading or writing data.

When using Read Uncommitted, you give up the assurance of strongly consistent data in favor of high concurrency in the system without users locking each other out. So when should you choose Read Uncommitted? Clearly, you don’t want to use it for financial transactions in which every number must balance. But it might be fine for certain decision-support analyses—for example, when you look at sales trends—for which complete precision isn’t necessary and the tradeoff in higher concurrency makes it worthwhile. Read Uncommitted isolation is a pessimistic solution to the problem of too much blocking activity because it just ignores the locks and does not provide you with transactional consistency.

Read Committed

SQL Server 2008 supports two varieties of Read Committed isolation, which is the default isolation level. This isolation level can be either optimistic or pessimistic, depending on the database setting READ_COMMITTED_SNAPSHOT. Because the default for the database option is off, the default for this isolation level is to use pessimistic concurrency control. Unless indicated otherwise, when I refer to the Read Committed isolation level, I am referring to both variations of this isolation level. I refer to the pessimistic implementation as Read Committed (locking), and I refer to the optimistic implementation as Read Committed (snapshot).

Read Committed isolation ensures that an operation never reads data that another application has changed but not yet committed. (That is, it never reads data that logically never existed.) With Read Committed (locking), if another transaction is updating data and consequently has exclusive locks on data rows, your transaction must wait for those locks to be released before you can use that data (whether you’re reading or modifying). Also, your transaction must put share locks (at a minimum) on the data that are visited, which means that data might be unavailable to others to use. A share lock doesn’t prevent others from reading the data, but it makes them wait to update the data. By default, share locks can be released after the data has been processed—they don’t have to be held for the duration of the transaction, or even for the duration of the statement. (That is, if shared row locks are acquired, each row lock can be released as soon as the row is processed, even though the statement might need to process many more rows.)

Read Committed (snapshot) also ensures that an operation never reads uncommitted data, but not by forcing other processes to wait. In Read Committed (snapshot), every time a row is updated, SQL Server generates a version of the changed row with its previous committed values. The data being changed is still locked, but other processes can see the previous versions of the data as it was before the update operation began.

Repeatable Read

Repeatable Read is a pessimistic isolation level. It adds to the properties of Committed Read by ensuring that if a transaction revisits data or a query is reissued, the data does not change. In other words, issuing the same query twice within a transaction cannot pick up any changes to data values made by another user’s transaction because no changes can be made by other transactions. However, the Repeatable Read isolation level does allow phantom rows to appear.

Preventing nonrepeatable reads is a desirable safeguard in some cases. But there’s no free lunch. The cost of this extra safeguard is that all the shared locks in a transaction must be held until the completion (COMMIT or ROLLBACK) of the transaction. (Exclusive locks must always be held until the end of a transaction, no matter what the isolation level or concurrency model, so that a transaction can be rolled back if necessary. If the locks were released sooner, it might be impossible to undo the work because other concurrent transactions might have used the same data and changed the value.) No other user can modify the data visited by your transaction as long as your transaction is open. Obviously, this can seriously reduce concurrency and degrade performance. If transactions are not kept short or if applications are not written to be aware of such potential lock contention issues, SQL Server can appear to stop responding when a process is waiting for locks to be released.

Snapshot

Snapshot isolation (sometimes referred to as SI) is an optimistic isolation level. Like Read Committed (snapshot), it allows processes to read older versions of committed data if the current version is locked. The difference between Snapshot and Read Committed (snapshot) has to do with how old the older versions have to be. (We see the details in the section entitled “Row Versioning,” later in this chapter.) Although the behaviors prevented by Snapshot isolation are the same as those prevented by Serializable, Snapshot is not truly a Serializable isolation level. With Snapshot isolation, it is possible to have two transactions executing simultaneously that give us a result that is not possible in any serial execution. Table 10-1 shows an example of two simultaneous transactions. If they run in parallel, they end up switching the price of two books in the titles table in the pubs database. However, there is no serial execution that would end up switching the values, whether we run Transaction 1 and then Transaction 2, or run Transaction 2 and then Transaction 1. Either serial order ends up with the two books having the same price.

Table 10-1. Two Simultaneous Transactions in Snapshot Isolation That Cannot Be Run Serially

Time

Transaction 1

Transaction 2

1

USE pubs
SET TRANSACTION ISOLATION LEVEL
SNAPSHOT
DECLARE @price money
BEGIN TRAN
USE pubs
SET TRANSACTION ISOLATION LEVEL
SNAPSHOT
DECLARE @price money
BEGIN TRAN

2

SELECT @price = price
FROM titles
WHERE title_id = 'BU1032'
SELECT @price = price
FROM titles
WHERE title_id = 'PS7777'

3

UPDATE titles
SET price = @price
WHERE title_id = 'PS7777'
UPDATE titles
SET price = @price
WHERE title_id = 'BU1032'

4

COMMIT TRAN
COMMIT TRAN

Serializable

Serializable is also a pessimistic isolation level. The Serializable isolation level adds to the properties of Repeatable Read by ensuring that if a query is reissued, rows were not added in the interim. In other words, phantoms do not appear if the same query is issued twice within a transaction. Serializable is therefore the strongest of the pessimistic isolation levels because it prevents all the possible undesirable behaviors discussed earlier—that is, it does not allow uncommitted reads, nonrepeatable reads, or phantoms, and it also guarantees that your transactions can be run serially.

Preventing phantoms is another desirable safeguard. But once again, there’s no free lunch. The cost of this extra safeguard is similar to that of Repeatable Read—all the shared locks in a transaction must be held until the transaction completes. In addition, enforcing the Serializable isolation level requires that you not only lock data that has been read, but also lock data that does not exist! For example, suppose that within a transaction, we issue a SELECT statement to read all the customers whose ZIP code is between 98000 and 98100, and on first execution, no rows satisfy that condition. To enforce the Serializable isolation level, we must lock that range of potential rows with ZIP codes between 98000 and 98100 so that if the same query is reissued, there are still no rows that satisfy the condition. SQL Server handles this situation by using a special kind of lock called a key-range lock. Key-range locks require that there be an index on the column that defines the range of values. (In this example, that would be the column containing the ZIP codes.) If there is no index on that column, Serializable isolation requires a table lock. I discuss the different types of locks in detail in the section on locking. The Serializable level gets its name from the fact that running multiple serializable transactions at the same time is the equivalent of running them one at a time—that is, serially.

For example, suppose that transactions A, B, and C run simultaneously at the Serializable level and each tries to update the same range of data. If the order in which the transactions acquire locks on the range of data is B, C, and then A, the result obtained by running all three simultaneously is the same as if they were run sequentially in the order B, C, and then A. Serializable does not imply that the order is known in advance. The order is considered a chance event. Even on a single-user system, the order of transactions hitting the queue would be essentially random. If the batch order is important to your application, you should implement it as a pure batch system. Serializable means only that there should be a way to run the transactions serially to get the same result you get when you run them simultaneously. Table 10-1 illustrates a case where two transactions cannot be run serially and get the same result.

Table 10-2 summarizes the behaviors that are possible in each isolation level and notes the concurrency control model that is used to implement each level. You can see that Read Committed and Read Committed (snapshot) are identical in the behaviors they allow, but the behaviors are implemented differently—one is pessimistic (locking), and one is optimistic (row versioning). Serializable and Snapshot also have the same No values for all the behaviors, but one is pessimistic and one is optimistic.

Table 10-2. Behaviors Allowed in Each Isolation Level

Isolation Level

Dirty Read

Nonrepeatable Read

Phantom

Concurrency Control

Read Uncommitted

Yes

Yes

Yes

Pessimistic

Read Committed (locking)

No

Yes

Yes

Pessimistic

Read Committed (snapshot)

No

Yes

Yes

Optimistic

Repeatable Read

No

No

Yes

Pessimistic

Snapshot

No

No

No

Optimistic

Serializable

No

No

No

Pessimistic