Transcript Transaction Management Overview

Chapter 16
Transaction Management Overview
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Transactions

Concurrent execution of user programs is essential for
good DBMS performance.

Because disk accesses are frequent, and relatively slow, it is
important to keep the cpu humming by working on several
user programs concurrently.
A user’s program may carry out many operations on
the data retrieved from the database, but the DBMS is
only concerned about what data is read/written
from/to the database.
 A transaction is the DBMS’s abstract view of a user
program: a sequence of reads and writes.
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Concurrency in a DBMS

Users submit transactions, and can think of each
transaction as executing by itself.
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Concurrency is achieved by the DBMS, which interleaves
actions (reads/writes of DB objects) of various transactions.
Each transaction must leave the database in a consistent
state if the DB is consistent when the transaction begins.
• DBMS will enforce some ICs, depending on the ICs
declared in CREATE TABLE statements.
• Beyond this, the DBMS does not really understand the
semantics of the data. (e.g., it does not understand how
the interest on a bank account is computed).
Issues: Effect of interleaving transactions, and crashes.
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Atomicity of Transactions
A transaction might commit after completing all its
actions, or it could abort (or be aborted by the DBMS)
after executing some actions.
 A very important property guaranteed by the DBMS
for all transactions is that they are atomic. That is, a
user can think of a Xact as always executing all its
actions in one step, or not executing any actions at all.


DBMS logs all actions so that it can undo the actions of
aborted transactions.
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Example

Consider two transactions (Xacts):
T1:
T2:
BEGIN A=A+100, B=B-100 END
BEGIN A=1.06*A, B=1.06*B END
Intuitively, the first transaction is transferring $100
from B’s account to A’s account. The second is
crediting both accounts with a 6% interest payment.
 There is no guarantee that T1 will execute before T2 or
vice-versa, if both are submitted together. However,
the net effect must be equivalent to these two
transactions running serially in some order.
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Example (Contd.)
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Consider a possible interleaving (schedule):
T1:
T2:
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A=1.06*A,
B=B-100
B=1.06*B
This is OK. But what about:
T1:
T2:

A=A+100,
A=A+100,
A=1.06*A, B=1.06*B
B=B-100
The DBMS’s view of the second schedule:
T1:
T2:
R(A), W(A),
R(A), W(A), R(B), W(B)
R(B), W(B)
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Transactions: ACID Properties

Atomicity. Transactions can never “partly commit”; their
updates are applied “all or nothing”.
 Logging, undo the actions of incomplete transactions
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Consistency. Each transaction T transitions the dataset from
one semantically consistent state to another.
 Responsibility of user, w.r.t application’s consistency criterion
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Isolation. All updates by T1 are either entirely visible to T2,
or are not visible at all. Ch 17
 no see/access to intermediate data of other transactions
 locking or timestamp-based concurrency control
 May interleave, net effect is identical to a series of transactions
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Durability. Updates made by T persist once T commits.
 Logging, writing updates to stable storage
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Atomicity and Durability: recovery manager, Ch 18
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Scheduling Transactions
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Schedule: a list of actions (read, write, abort, commit) from a
set of transactions
Serial schedule: Schedule that does not interleave the actions of
different transactions.
Equivalent schedules: For any database state, the effect (on the
set of objects in the database) of executing the first schedule is
identical to the effect of executing the second schedule.
Serializable schedule: A schedule that is equivalent to some
serial execution of the transactions.
If each transaction preserves consistency, every serializable
schedule preserves consistency
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Anomalies with Interleaved Execution

Reading Uncommitted Data (WR Conflicts,
“dirty reads”):
T1:
T2:
A=A+100,
T1:
T2:
R(A), W(A),
A=1.06*A, B=1.06*B
B=B-100
R(A), W(A), R(B), W(B)
R(B), W(B)
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Anomalies with Interleaved Execution
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T1:
T2:
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Unrepeatable Reads (RW Conflicts):
R(A),
R(A), W(A), C
R(A), W(A), C
A is number of copies of book, T1 and T2
both want to place an order on the book
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Anomalies (Continued)

T1:
T2:
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Overwriting Uncommitted Data (WW
Conflicts):
W(A),
W(A), W(B), C
W(B), C
Two employees salaries must be equal, T1 set
to 2000, T2 set to 1000
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Note on conflicts
WR, RW, WW
 Presence of conflicts <> unserializable
schedule
 CC uses locking and other mechanisms to
“sort the conflicts” nicely, so that the net
effect is equivalent to same serial schedule
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Note on serializable schedule
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A serializable schedule over a set S of
transactions is a schedule whose effect on any
consistent database instance is guaranteed to be
identical to that of some complete serial schedule
over the set of committed transactions in S.
 The definition of serializability relies on the actions of
aborted transactions being undone completely, which
may be impossible in some situations
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Aborting a Transaction
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If a transaction Ti is aborted, all its actions have to be
undone. Not only that, if Tj reads an object last
written by Ti, Tj must be aborted as well!
T1:
T2:
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R(A), W(A),
R(A), W(A), R(B), W(B), Commit
Abort
In a recoverable schedule, transactions commit only after all
transactions whose changes they read commit
The above example, unrecoverable
T1:
T2:
R(A), W(A),
Commit
R(A), W(A), R(B), W(B),
Commit
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Aborting a Transaction

If transactions read only the changes of committed
transactions, not only is the schedule recoverable, but also
aborting a transaction can be accomplished without
cascading the abort to other transactions. Such a schedule is
said to avoid cascading aborts (ACA)
 by releasing a transaction’s locks only at commit time.
 If Ti writes an object, Tj can read this only after Ti commits.
T1:
T2:
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R(A), W(A) Commit
R(A), W(A), R(B), W(B)
In order to undo the actions of an aborted transaction, the
DBMS maintains a log in which every write is recorded. This
mechanism is also used to recover from system crashes: all
active Xacts at the time of the crash are aborted when the
system comes back up.
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Lock-Based Concurrency Control

Strict Two-phase Locking (Strict 2PL) Protocol:
 Most widely used local protocol
 Each Xact must obtain a S (shared) lock on object before
reading, and an X (exclusive) lock on object before writing.
 All locks held by a transaction are released when the
transaction completes
 If an Xact holds an X lock on an object, no other Xact can
get a lock (S or X) on that object.
 If an Xact holds a lock (S or X) on an object, no other Xact
can get an X lock on that object.
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Strict 2PL allows only serializable schedules.
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Strict 2PL Example
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T1:
T2:
A=A+100,
T1:
T2:
R(A), W(A),
A=1.06*A, B=1.06*B
B=B-100
R(A), W(A), R(B), W(B)
R(B), W(B)
Not allowed, forced to be executed as follows:
 a serial execution of two transactions, in this example
T1: X(A)R(A)W(A)X(B)R(B) W(B)Commit
T2:
X(A)R(A)W(A)X(B)R(B)W(B)
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Issues in locking
Deadlock
 Must prevent or detect (and resolve)
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 More details in ch 17
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Performance
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The Log
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The following actions are recorded in the log:
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Ti writes an object: the old value and the new value.
• Log record must go to disk before the changed page!
Ti commits/aborts: a log record indicating this action.
Log records are chained together by Xact id, so it’s
easy to undo a specific Xact.
 Log is often duplexed and archived on stable storage.
 All log related activities (and in fact, all CC related
activities such as lock/unlock, dealing with deadlocks
etc.) are handled transparently by the DBMS.
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Recovering From a Crash
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There are 3 phases in the Aries recovery algorithm:
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Analysis: Scan the log forward (from the most recent
checkpoint) to identify all Xacts that were active, and all dirty
pages in the buffer pool at the time of the crash.
Redo: Redoes all updates to dirty pages in the buffer pool,
as needed, to ensure that all logged updates are in fact
carried out and written to disk.
Undo: The writes of all Xacts that were active at the crash
are undone (by restoring the before value of the update,
which is in the log record for the update), working
backwards in the log. (Some care must be taken to handle
the case of a crash occurring during the recovery process!)
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Summary
Concurrency control and recovery are among the
most important functions provided by a DBMS.
 Users need not worry about concurrency.


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System automatically inserts lock/unlock requests and
schedules actions of different Xacts in such a way as to
ensure that the resulting execution is equivalent to
executing the Xacts one after the other in some order.
Write-ahead logging (WAL) is used to undo the
actions of aborted transactions and to restore the
system to a consistent state after a crash.

Consistent state: Only the effects of commited Xacts seen.
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