Database Recovery Failure Classification

Database Recovery Failure Classification
Database Recovery
• Face it — $#!† happens
• While all systems should have some sort of recovery
scheme in the event of failure, this is particularly
important for databases
• Specifically, we want a database recovery scheme to
restore a database to the most recent possible
consistent state that existed before the failure
• We also want high availability: minimize the time during
which the database is not usable
Failure Classification
• Transaction failure: Individual transactions fail
Logical error: Internal problem within the transaction
System error: External problem during transaction
execution (e.g., deadlock)
• System crash: Problem with overall database server
execution; terminates the current process
Fail-stop assumption: Data in non-volatile storage is
unharmed in the event of a system crash
• Disk failure: Problem with storage media
Data Storage
• Volatile storage: Main or cache memory; very fast, but
does not survive a system crash
• Nonvolatile storage: Disks, tapes; significantly slower due
to eletromechanical element, survives system crashes
Flash memory is nonvolatile but fast — traditionally
too small for databases, but that is changing
• Stable storage: Survives everything
Of course, this is just a theoretical ideal — the best
we can do is make data loss sufficiently unlikely
• We can approximate stable storage through redundant
copies, copies at different physical locations, and
controlled updates of these copies
Write to one copy first; upon successful initial write, then write to second copy
Recovery involves comparing the copies, and replacing the copy with a detectable error
(i.e., checksum) or replacing the copy that was written first if no detectable error
• Data access involves different layers of storage space:
physical blocks on disk, buffer blocks in a designated main
memory disk buffer, and private work areas for each
transaction, also in main memory
input(B) and output(B) transfer between physical and buffer blocks
read(X) and write(X) transfer between buffer blocks and the work area, possibly triggering
input(BX) automatically
A buffer manager decides when buffer blocks should be written back to physical blocks
When needed, the database system can perform a force-output with a direct output(B) call
Recovery and Atomicity
• We can’t perform output(B) in the middle of a
transaction — if it fails midway, then the database will
be in an inconsistent state, thus violating a transaction’s
atomicity property
• Rule of thumb: modify the nonvolatile database storage
only after we are sure that a transaction will commit
• General approach: first record a transaction’s
modifications in a separate space, then transfer from
that space to database blocks only after the transaction
commits successfully
Log-Based Recovery
• Logs are the most widely-used approach for recording
database modifications
• A log consists of a sequence of log records
• A database write is recorded in an update log record,
which consists of:
Transaction identifier indicates the writing transaction
Data-item identifier indicates what was written
(typically on-disk location of item)
Old, new values of the item before and after the write
Log Data Model
Start Log Record
Update Log
- data item identifier
- old value
- new value
Log Record
- transaction identifier
Abort Log Record
Checkpoint Log
- active transactions
Commit Log
• Write the log record before modifying the database
• Undo is possible through the old value attribute
• Log should be written to stable storage
Deferred Database
• Perform write operations only after the transaction
partially commits (i.e., performs last action)
• Ignore log records for failed transactions
• redo(T ) operation rewrites all of the data items
modified by Ti (as recorded in the log) — must be
idempotent, meaning that executing redo(Ti) once must
have the exact effect as executing it multiple times
• To recover after a failure: perform redo(T ) for T that
have both start and commit log records
Immediate Database
• Write database modifications immediately; while a
transaction is active, its modifications are called
uncommitted modifications
• Upon failure, perform undo(T ) to write old values back
to the database for failed transactions — again, must
be idempotent
• To recover, perform undo(T ) for transactions that have
a start record but not commit, and perform redo(Ti) for
transactions that have both start and commit records
• Not practical to scan the entire log upon failure —
after all, a lot of writes don’t need to be redone
• So we perform a periodic checkpoint operation: output
(1) pending log records to stable storage, (2) modified
buffer blocks to disk, then (3) checkpoint log record to
stable storage
• On failure, (1) find the most recent checkpoint, (2) find
the last transaction started before that checkpoint,
then (3) perform recovery for that transaction and all
those after
Recovery with Concurrent
Some things to consider when multiple concurrent
transactions are active (note that we still have one disk
buffer and one log):
• During an undo, we should scan the log backward, to
ensure the proper restoration of an item that has been
written multiple times
• Any transaction that writes Q must either commit or
be rolled back (with accompanying undos from the log)
before another transaction writes Q again — strict
two-phase locking will ensure this
• Checkpoint log records must include a list of active
transactions at the time of the checkpoint —
otherwise, we wouldn’t know how when to stop
scanning the log
• Recovery algorithm then requires two passes: one to
determine the transactions to undo and redo, and
another to perform the actual undo and redo
Scan the log backward, up to the most recent checkpoint log record
Every committed transaction goes to the redo-list
Every started transaction not already in the redo-list goes to the undo-list
Transactions in the checkpoint log record not already in the redo-list go to the undo-list
• The second pass then performs the data modifications:
Scanning backward, undo all updates by transactions in the undo-list
Going forward from the most recent checkpoint log record, redo all updates made by
transactions in the redo-list
Buffer Management
• Recall that the journey from main memory to disk still
goes through a volatile section — the disk buffer
• Unlike main memory, the disk buffer’s minimum
storage matches the disk — typically blocks
• Since the information we read/write (log records, data
items) tend to be much smaller than a disk block, it’s
impractical to write out entire blocks for every single
logical read/write from main memory
• So we need some tweaks for efficiency’s sake, without
detracting from our recovery techniques
Log-Record Buffers
• Since log records are small compared to blocks, it’s
costly to touch stable storage for every log record
• So we buffer the logs too…but then, those would be
lost on a system crash, so we need to make sure that
log-record buffering doesn’t break recoverability:
Transactions get the commit state only after their commit log records reach stable storage
The commit log record should be the last log record to reach stable storage
Before a data block is written to the database, all log records for data items in that block
must reach stable storage
• That last rule is called the write-ahead logging (WAL) rule
— keep that in mind, because you’ll hear it again later
Database Buffers
• Database size is generally much larger than available
main memory, so we sometimes need to swap out
blocks as we read data items from the database
Sounds like virtual memory? That’s because it is!
• With the write-ahead logging rule, we can’t swap out a
block until log records for that block’s data are written
• While waiting on the write-ahead, we acquire a latch
— a small, short-duration lock — on the block whose
log entries are being written, so that no writes on that
block can take place during the write-ahead
But What About Disk
• So far, all of these recovery techniques pertain to
system crash errors — loss of volatile storage
• To protect against disk failures, where are our
nonvolatile storage media goes bad, we perform
operations that are very similar to standard file backup
— we dump or copy the entire database to another
form of nonvolatile or stable storage
• We disallow transactions during a dump, and dumps
should include log records too; we can even create a
new dump log record type so the dump goes in the log
Remote Backup
• To complete our recovery picture, we must realize that
failures may have external causes — fires, floods, black
holes — and so we need to have some form of
physical separation for our storage devices
• We say that our main database server resides (in the
real world) in the primary site, and we establish a
remote backup or secondary site that is sufficiently
separated from the primary site to protect against
external disasters
• Practical synchronization can be done by sending log
records from the primary to the secondary site; full
replication (dumps) may occur, but less frequently
• Upon primary site failure, the secondary site (1)
performs recovery as discussed previously, then (2)
takes over transaction processing
• A few things to consider with remote backups:
Reliable detection of failure (i.e., no false alarms)
Transfer of control — when the primary site comes back, it can either become primary
again, or become the new secondary site
Minimal recovery time — ideally, a secondary site should act upon the log records that it
receives, making it a hot spare of the primary site
Time to commit — a tradeoff exists here between full transaction durability and commit
times for a transaction: to ensure full durability, a transaction at the primary site shouldn’t
commit until its log records have reached the secondary site, thus requiring more time
Advanced Recovery
• Note how recovery may trade off concurrency and
availability to protect against lost or inconsistent data
• While this is worthwhile, more sophisticated recovery
techniques seek to have their cake and eat it too
• The state of the art in recovery is the algorithm for
recovery and isolation exploiting semantics (ARIES) —
details in the text and the literature
• While more complex, ARIES achieves less overhead
and greater efficiency while maintaining recoverability
Recovery in PostgreSQL
• Again, concepts hit reality fairly directly with
PostgreSQL — it uses write-ahead logging (WAL) to
facilitate recovery
• Assorted parameters configure the recovery system:
Buffer-to-disk (“sync” in PostgreSQL terms) settings
Commit delays to allow multiple transaction log
entries to reach the disk in a single call
Maximum “distance” between checkpoints (both in
terms of log size and passage of time)
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