Concurrency Control and Recovery

1
Concurrency Control and Recovery

• Zachary G. Ives
• University of Pennsylvania
• CIS 650 Implementing Data Management Systems
• February 7, 2005

Some content on recovery courtesy Hellerstein,
Ramakrishnan, Gehrke
2
Administrivia

• No normal office hour this week (out of town)
• Upcoming talks of interest
• George Candea, Stanford, recovery-oriented
  computing, 2/24
• Mike Swift, U Wash., restartable OS device
  drivers, 3/1
• Andrew Whitaker, U Wash., paravirtualization,
  3/15
• Sihem Amer-Yahia, ATT Research, text queries
  over XML, 3/17
• Krishna Gummadi, U Wash., analysis of P2P
  systems, 3/29
• Muthian Sivathanu, U Wisc., speeding disk access,
  3/31

3
Todays Trivia Question
4
Recall the Fundamental Concepts of Updatable DBMSs

• Transactions as atomic units of operation
• always commit or abort
• can be terminated and restarted by the DBMS an
  essential property
• typically logged, restartable, recoverable
• ACID properties
• Atomicity transactions may abort (rollback)
  due to error or deadlock (Mohan)
• Consistency guarantee of consistency between
  transactions
• Isolation guarantees serializability of
  schedules (Gray, Kung Rob.)
• Durability guarantees recovery if DBMS stops
  running (Mohan)

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Serializability and Concurrent Deposits
Deposit 1 Deposit
2 read(X.bal)
read(X.bal) X.bal X.bal 50 X.bal
X.bal 10 write(X.bal)
write(X.bal)
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Violations of Serializability

• Dirty data data written by an uncommitted
  transaction a dirty read is a read of dirty data
  (WR conflict)
• Unrepeatable read a transaction reads the same
  data item twice and gets different values (RW
  conflict)
• Phantom problem a transaction retrieves a
  collection of tuples twice and sees different
  results

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Two Approaches to Serializability (or other
Consistency Models)

• Locking a pessimistic strategy
• First paper (Gray et al.) hierarchical locking,
  plus ways of compromising serializability for
  performance
• Optimistic concurrency control
• Second paper (Kung Robinson) allow writes by
  each session in parallel, then try to substitute
  them in (or reapply to merge)

8
Locking

• A lock manager grants and releases locks on
  objects
• Two basic types of locks
• Shared locks (read locks) allow other shared
  locks to be granted on the same item
• Exclusive locks (write locks) do not coexist
  with any other locks
• Generally granted in two phase locking (2PL)
  model
• Growing phase locks are granted
• Shrinking phase locks are released (no new
  locks granted)
• Well-formed, two-phase locking guarantees
  serializability
• Strict 2PL shrinking phase is at the end of the
  transaction
• (Note that deadlocks are possible!)

9
Gray et al. Granularity of Locks

• For performance and concurrency, want different
  levels of lock granularity, i.e., a hierarchy of
  locks
• database
• extent
• table
• page
• row
• attribute
• But a problem arises
• What if T1 S-locks a row and T2 wants to X-lock a
  table?
• How do we easily check whether we should give a
  lock to T2?

10
Intention Locks

• Two basic types
• Intention to Share (IS) a descendant item will
  be locked with a share lock
• Intention Exclusive lock a descendant item will
  be locked with an exclusive lock
• Locks are granted top-down, released bottom-up
• T1 grabs IS lock on table, page S lock on row
• T2 cant get X-lock on table until T1 is done
• But T3 can get an IS or S lock on the table

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Lock Compatibility Matrix
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Lock Implementation

• Maintain as a hash table based on items to lock
• Lock/unlock are atomic operations in critical
  sections
• First-come, first-served queue for each locked
  object
• All adjacent, compatible items are a compatible
  group
• The groups mode is the most restrictive of its
  members
• What if a transaction wants to convert (upgrade)
  its lock? Should we send it to the back of the
  queue?
• No will almost assuredly deadlock!
• Handle conversions immediately after the current
  group

13
Degrees of Consistency

• Full locking, guaranteeing serializability, is
  generally very expensive
• So they propose several degrees of consistency as
  a compromise (these are roughly the SQL isolation
  levels)
• Degree 0 T doesnt overwrite dirty data of
  other transactions
• Degree 1 above, plus T does not commit writes
  before EOT
• Degree 2 above, plus T doesnt read dirty data
• Degree 3 above, plus other transactions dont
  dirty any data T read

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Degrees and Locking

• Degree 0 short write locks on updated items
• Degree 1 long write locks on updated items
• Degree 2 long write locks on updated items,
  short read locks on read items
• Degree 3 long write and read locks
• Does Degree 3 prevent phantoms? If not, how do
  we fix this?

15
What If We Dont Want to Lock?

• Conflicts may be very uncommon so why incur the
  overhead of locking?
• Typically hundreds of instructions for every
  lock/unlock
• Examples read-mostly DBs large DB with few
  collisions append-mostly hierarchical data
• Kung Robinson break lock into three phases
• Read and write to private copy of each page
  (i.e., copy-on-write)
• Validation make sure no conflicts between
  transactions
• Write swap the private copies in for the public
  ones

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Validation

• Goal guarantee that only serializable schedules
  result in merging Ti and Tj writes
• Approach find an equivalent serializable
  schedule
• Assign each transaction a number
• Ensure equivalent serializable schedule as
  follows
• If TN(Ti)
• Ti finishes writing before Tj starts reading
  (serial)
• WS(Ti) disjoint from RS(Tj) and Ti finishes
  writing before Tj writes
• WS(Ti) disjoint from RS(Tj) and WS(Ti) disjoint
  from WS(Tj), and Ti finishes read phase before Tj
  completes its read phase

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Why Does This Work?

• Condition 1 obvious since its serial
• Condition 2
• No W-R conflicts since disjoint
• In all R-W conflicts, Ti precedes Tj since Ti
  reads before it writes (and thats before Tj)
• In all W-W conflicts, Ti precedes Tj
• Condition 3
• No W-R conflicts since disjoint
• No W-W conflicts since disjoint
• In all R-W conflicts, Ti precedes Tj since Ti
  reads before it writes (and thats before Tj)

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The Achilles Heel

• How do we assign TNs?
• Not optimistically they get assigned at the end
  of read phase
• Note that we need to maintain all of the read and
  write sets for transactions that are going on
  concurrently long-lived read phases cause
  difficulty here
• Solution bound buffer, abort and restart
  transactions when out of space
• Drawback starvation need to solve by locking
  the whole DB!

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Serial Validation

• Simple writes wont be interleaved, so test
• Ti finishes writing before Tj starts reading
  (serial)
• WS(Ti) disjoint from RS(Tj) and Ti finishes
  writing before Tj writes
• Put in critical section
• Get TN
• Test 1 and 2 for everyone up to TN
• Write
• Long critical section limits parallelism of
  validation, so can optimize
• Outside critical section, get a TN and validate
  up to there
• Before write, in critical section, get new TN,
  validate up to that, write
• Reads no need for TN just validate up to
  highest TN at end of read phase (no critical
  section)

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Parallel Validation

• For allowing interleaved writes
• Save active transactions (finished reading, not
  writing)
• Abort if intersect current read/write set
• Validate
• CRIT Get TN copy active set add self to
  active set
• Check (1), (2) against everything from start to
  finish
• Check (3) against all active set
• If OK, write
• CRIT Increment TN counter, remove self from
  active
• Drawback might conflict in condition (3) with
  someone who gets aborted

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Whos the Top Dog?Optimistic vs. Non-Optimistic

• Drawbacks of the optimistic approach
• Generally requires some sort of global state,
  e.g., TN counter
• If theres a conflict, requires abort and full
  restart
• Study by Agrawal et al. comparing optimistic vs.
  locking
• Need load control with low resources
• Locking is better with moderate resources
• Optimistic is better with infinite or high
  resources
• Both of these provided isolation transactions
  and policies ensure consistency what about
  atomicity, durability?

22
Rollback and Recovery

• The Recovery Manager provides
• Atomicity
• Transactions may abort (rollback) to start or
  to a savepoint.
• Durability
• What if DBMS stops running? (Causes?)

• Desired behavior after system restarts
• T1, T2 T3 should be durable
• T4 T5 should be aborted (effects not seen)

crash!
T1 T2 T3 T4 T5
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Assumptions in Recovery Schemes

• Were using concurrency control via locks
• Strict 2PL at least at the page, possibly record
  level
• Updates are happening in place
• No shadow pages data is overwritten on (deleted
  from) the disk
• ARIES
• Algorithm for Recovery and Isolation Exploiting
  Semantics
• Attempts to provide a simple, systematic simple
  scheme to guarantee atomicity durability with
  good performance
• Lets begin with some of the issues faced by any
  DBMS recovery scheme

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Managing Pages in the Buffer Pool

• Buffer pool is finite, so
• Q How do we guarantee durability of committed
  data?
• A Need policy on what happens when a
  transaction completes, what transactions can do
  to get more pages
• Force write of buffer pages to disk at commit?
• Provides durability
• But poor response time
• Steal buffer-pool frames from uncommited Xacts?
• If not, poor throughput
• If so, how can we ensure atomicity?

No Steal
Steal
Force
Trivial
Desired
No Force
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More on Steal and Force

• STEAL (why enforcing Atomicity is hard)
• To steal frame F Current page in F (say P) is
  written to disk some Xact holds lock on P
• What if the Xact with the lock on P aborts?
• Must remember the old value of P at steal time
  (to support UNDOing the write to page P)
• NO FORCE (why enforcing Durability is hard)
• What if system crashes before a modified page is
  written to disk?
• Write as little as possible, in a convenient
  place, at commit time, to support REDOing
  modifications

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Basic Idea Logging

• Record REDO and UNDO information, for every
  update, in a log
• Sequential writes to log (put it on a separate
  disk)
• Minimal info (diff) written to log, so multiple
  updates fit in a single log page
• Log An ordered list of REDO/UNDO actions
• Log record contains
• 
  
• and additional control info (which well see soon)

27
Write-Ahead Logging (WAL)

• The Write-Ahead Logging Protocol
• Force the log record for an update before the
  corresponding data page gets to disk
• Guarantees Atomicity
• Write all log records for a Xact before commit
• Guarantees Durability (can always rebuild from
  the log)
• Is there a systematic way of doing logging (and
  recovery!)?
• The ARIES family of algorithms

28
WAL the Log
RAM
LSNs
pageLSNs
flushedLSN

• Each log record has a unique Log Sequence Number
  (LSN)
• LSNs always increase
• Each data page contains a pageLSN
• The LSN of the most recent log record
  for an update to
  that page
• System keeps track of flushedLSN
• The max LSN flushed so far
• WAL Before a page is written,
• pageLSN flushedLSN

Log records flushed to disk
Log tail in RAM
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Log Records

• Possible log record types
• Update
• Commit
• Abort
• End (signifies end of commit or abort)
• Compensation Log Records (CLRs)
• To log UNDO actions

LogRecord fields
update records only
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Other Log-Related State

• Transaction Table
• One entry per active Xact
• Contains XID, status (running/commited/aborted),
  and lastLSN
• Dirty Page Table
• One entry per dirty page in buffer pool
• Contains recLSN the LSN of the log record which
  first caused the page to be dirty

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Normal Execution of an Xact

• Series of reads writes, followed by commit or
  abort
• We will assume that write is atomic on disk
• In practice, additional details to deal with
  non-atomic writes
• Strict 2PL
• STEAL, NO-FORCE buffer management, with
  Write-Ahead Logging

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Checkpointing

• Periodically, the DBMS creates a checkpoint
• Minimizes recovery time in the event of a system
  crash
• Write to log
• begin_checkpoint record when checkpoint began
• end_checkpoint record current Xact table and
  dirty page table
• A fuzzy checkpoint
• Other Xacts continue to run so these tables
  accurate only as of the time of the
  begin_checkpoint record
• No attempt to force dirty pages to disk
  effectiveness of checkpoint limited by oldest
  unwritten change to a dirty page. (So its a good
  idea to periodically flush dirty pages to disk!)
• Store LSN of checkpoint record in a safe place
  (master record)

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The Big Picture Whats Stored Where
LOG
RAM
DB
LogRecords
Xact Table lastLSN status Dirty Page
Table recLSN flushedLSN
Data pages each with a pageLSN
master record
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Simple Transaction Abort

• For now, consider an explicit abort of a Xact
• No crash involved
• We want to play back the log in reverse order,
  UNDOing updates
• Get lastLSN of Xact from Xact table
• Can follow chain of log records backward via the
  prevLSN field
• Before starting UNDO, write an Abort log record
• For recovering from crash during UNDO!

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Abort, cont.

• To perform UNDO, must have a lock on data!
• No problem no one else can be locking it
• Before restoring old value of a page, write a
  CLR
• You continue logging while you UNDO!!
• CLR has one extra field undoNextLSN
• Points to the next LSN to undo (i.e. the prevLSN
  of the record were currently undoing).
• CLRs never Undone (but they might be Redone when
  repeating history guarantees Atomicity!)
• At end of UNDO, write an end log record.

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Transaction Commit

• Write commit record to log
• All log records up to Xacts lastLSN are flushed
• Guarantees that flushedLSN ³ lastLSN
• Note that log flushes are sequential, synchronous
  writes to disk
• Many log records per log page
• Commit() returns
• Write end record to log

37
Crash Recovery Big Picture
Oldest log rec. of Xact active at crash

• Start from a checkpoint (found via master record)
• Three phases
• Figure out which Xacts committed since
  checkpoint, which failed (Analysis)
• REDO all actions
• (repeat history)
• UNDO effects of failed Xacts

Smallest recLSN in dirty page table after Analysis
Last chkpt
CRASH
A
R
U
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Recovery The Analysis Phase

• Reconstruct state at checkpoint
• via end_checkpoint record
• Scan log forward from checkpoint
• End record Remove Xact from Xact table
• Other records Add Xact to Xact table, set
  lastLSNLSN, change Xact status on commit
• Update record If P not in Dirty Page Table,
• Add P to D.P.T., set its recLSNLSN

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Recovery The REDO Phase

• We repeat history to reconstruct state at crash
• Reapply all updates (even of aborted Xacts!),
  redo CLRs
• Scan forward from log rec containing smallest
  recLSN in D.P.T. For each CLR or update log rec
  LSN, REDO the action unless
• Affected page is not in the Dirty Page Table, or
• Affected page is in D.P.T., but has recLSN LSN,
  or
• pageLSN (in DB) ³ LSN
• To REDO an action
• Reapply logged action
• Set pageLSN to LSN. No additional logging!

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Recovery The UNDO Phase

• ToUndo l l a lastLSN of a loser Xact
• Repeat
• Choose largest LSN among ToUndo.
• If this LSN is a CLR and undoNextLSNNULL
• Write an End record for this Xact.
• If this LSN is a CLR, and undoNextLSN ! NULL
• Add undoNextLSN to ToUndo
• (Q what happens to other CLRs?)
• Else this LSN is an update. Undo the update,
  write a CLR, add prevLSN to ToUndo.
• Until ToUndo is empty.

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Example of Recovery
LSN LOG
begin_checkpoint end_checkpoint update T1
writes P5 update T2 writes P3 T1 abort CLR Undo
T1 LSN 10 T1 End update T3 writes P1 update T2
writes P5 CRASH, RESTART
00 05 10 20 30 40
45 50 60
prevLSNs
Xact Table lastLSN status Dirty Page
Table recLSN flushedLSN
ToUndo
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Example Crash During Restart
LSN LOG
begin_checkpoint, end_checkpoint update T1
writes P5 update T2 writes P3 T1 abort CLR Undo
T1 LSN 10, T1 End update T3 writes P1 update T2
writes P5 CRASH, RESTART CLR Undo T2 LSN 60 CLR
Undo T3 LSN 50, T3 end CRASH, RESTART CLR Undo
T2 LSN 20, T2 end
00,05 10 20 30 40,45 50
60 70 80,85 90
undonextLSN
Xact Table lastLSN status Dirty Page
Table recLSN flushedLSN
ToUndo
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Additional Crash Issues

• What happens if system crashes during Analysis?
  During REDO?
• How do you limit the amount of work in REDO?
• Flush asynchronously in the background.
• Watch hot spots!
• How do you limit the amount of work in UNDO?
• Avoid long-running Xacts.

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Summary of Logging/Recovery

• Recovery Manager guarantees Atomicity
  Durability
• Use WAL to allow STEAL/NO-FORCE w/o sacrificing
  correctness
• LSNs identify log records linked into backwards
  chains per transaction (via prevLSN)
• pageLSN allows comparison of data page and log
  records

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Summary, Continued

• Checkpointing A quick way to limit the amount
  of log to scan on recovery.
• Recovery works in 3 phases
• Analysis Forward from checkpoint
• Redo Forward from oldest recLSN
• Undo Backward from end to first LSN of oldest
  Xact alive at crash
• Upon Undo, write CLRs
• Redo repeats history Simplifies the logic!