Transcript Timestamp Ordering

Set 9, Part 2
timestamp ordering,
distributed CC and OO CC
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Set 9, Part 2, Concurrency Control
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Outline of notes
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Set 1: Introduction ✔
Set 2: Architecture ✔
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Centralized Relational
Distributed DBMS
Object-Oriented DBMS
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Set 3: Database Design ✔
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Centralized Relational
Distributed DBMS
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Set 4: Data Modeling ✔
Set 5: Querying ✔
Set 6: XML Model and Querying ✔
Set 7: Algebraic Query
Optimization
✔
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Centralized Relational
Distributed DBMS
Object-Oriented DBMS
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Set 8: Storage, Indexing, and
Execution Strategies
✔
Set 8, Part 2: Costs
and OO Implementation ✔
Set 8, Part 3: XML Implementation
Issues
✔
Set 9: Transactions and
Concurrency Control
✔
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Set 9, Part 2
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CC with timestamps
Distributed DBMS
Object-Oriented DBMS
Set 10: Recovery
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Centralized Relational
Centralized Relational
Distributed DBMS
Set 11: Database Security
Set 9, Part 2, Concurrency Control
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Concurrency Control with Timestamps
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The transaction manager assigns a unique timestamp to each
transaction when it arrives.
How?
 with a centralized database, i.e. a single transaction
manager (TM), use a counter or a clock time
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with many TMs, i.e. a distributed database, use the site or
TM ID and a counter or a clock
if a clock is used, the TM cannot issue the next timestamp
until the clock ticks
with a counter, the database would have to be restarted
periodically to prevent the values from growing in length.
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Timestamp Ordering
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Rule: if a read or write from transaction i conflicts
with a read or write from transaction j, then
process the operations in timestamp order.
Theorem: A log representing a concurrent
execution of a set of transactions such that the
above rule is followed, is serializable.
Basic Timestamp Ordering: abort any transaction
for which the rule cannot be followed because a
read or write arrives at the scheduler “too late”.
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Implementation
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for each item x in the database, maintain, in a table:
the maximum TS of any transaction which has read
x, and
the maximum TS of any transaction which has
written x.
slightly more complicated than that. The RM does
not process operations instantaneously. So the
scheduler keeps
max-r-scheduled(x)
max-w-scheduled(x)
to make sure the RM does the operations in the right
order, the scheduler has to queue them up, in
timestamp order, with one queue per data item.
when a transaction is aborted, it is restarted with a
new, larger (later) timestamp, to avoid being
rejected again.
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Algorithm (simple version)
Transaction T, with timestamp TS(T), tries to do
operation p(x), where p is either read or write, and
x is a data item.
1.
compare TS(T) with max-r-scheduled(x) and/or
max-w-scheduled(x), depending on which
operations p(x) conflicts with (read-write and writewrite are the conflicting combinations).
2.
if TS(T) is < any of these things it conflicts with,
then T is aborted and restarted with a larger
timestamp.
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3. if TS(T) is > the timestamps on x of all the
operations it conflicts with, then update max-rscheduled(x) or max-w-scheduled(x) and put the
operation in the queue of operations scheduled for
x. If the system doesn't crash, these operations
will eventually be carried out.
Note: with locking, we don't have to worry
about this queuing, because the scheduler
issues the locks, and therefore only allows
one write or multiple reads on a data item
x to be passed on to the RM at any one
time.
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Revised Timestamp Ordering Algorithm
Assume in this discussion that
tR(x) = max-r-scheduled(x)
tW(x) = max-w-scheduled(x)
t is the timestamp of transaction T
1.
W-R synchronization
Transaction T with timestamp t wants to read x:
if t ≥ tW(x)
then { put the read in the queue for x;
if t > tR(x)
then set tR(x) to t }
else abort T (and restart it with a new TS)
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Revised Algorithm, cont’d
2. R-W synchronization
Transaction T wants to write x:
if t ≥ tR(x) and t ≥ tW(x)
then { put the write in the queue for x;
if t > tW(x)
then set tW(x) to t }
else if t < tR(x)
then abort T (and restart it with a new TS)
3. W-W synchronization
(still have transaction T wanting to write x:)
if tR(x) ≤ t < tW(x)
then do nothing, i.e. ignore the write (no harm done so no need to
abort T.)
This is slightly different from the previous algorithm. This last step is called
the Thomas write rule.
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Another View

Reading
TS of T reading x (Value of tR(x) not relevant)
< tW(x) Abort
≥ tW(x) Do the read; Update tR(x) if t > tR(x)

Writing
TS of T writing x <tR(x)
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≥ tR(x)
< tW(x) abort
Ignore (Thomas write rule)
≥ tW(x) abort
Do the write,
Update tW(x) if t > tW(x)
Set 9, Part 2, Concurrency Control
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Maintaining timestamps on data items
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keep a table of triples:
(x, max-r-scheduled(x), max-w-scheduled(x))
if the data items are small, this table could be very large.
periodically purge the table of all entries whose timestamps are
“too old” for any older transaction to be likely to be still in the
system.
pick a time interval δ, and purge everything older than the current
time t - δ. Tag the table with this timestamp, t - δ.
modify the scheduler so that if a data item x is not in the table, its
timestamp is assumed to be t - δ, and act accordingly. This may
occasionally reject a transaction unnecessarily.
there exists a tradeoff between δ and the size of the table.
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Example using timestamps:

Let TS(T1) =1, TS(T2) = 2, TS(T3) = 3
Schedule
tR(x) tW(x) tR(y) tW(y) tR(z) tW(z)
Initially
readT2(x)
writeT3 (x)
writeT1(y)
readT2 (y)
writeT2 (z)
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Example using timestamps:

Let TS(T1) =1, TS(T2) = 2, TS(T3) = 3
Schedule
tR(x) tW(x) tR(y) tW(y) tR(z) tW(z)
Initially
0
0
0
0
0
0
readT2(x)
2
0
0
0
0
0
writeT3 (x)
2
3
0
0
0
0
writeT1(y)
2
3
0
1
0
0
readT2 (y)
2
3
2
1
0
0
writeT2 (z)
2
3
2
1
0
2
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Comments on this Schedule
If locks were used instead, then, to have the same
schedule or transaction log, T2 cannot possibly be
two-phase, because it would have had to release the
lock on x, so T3 can write it, before getting the lock
on y, which T1 needs in the meantime. This is,
however, a serializable schedule. It is just not one
that can be achieved by two-phase locking.
Therefore, two-phase locking is not the same as
timestamp ordering.
They both guarantee serializability, but generate
different schedules.
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All Serializable Schedules
2PL
Schedules
?
TO
Schedules
*
The previous schedule falls into the set of schedules generated by
Timestamp ordering and not by 2PL.
? Is there a schedule in 2PL which is not possible with TO?
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Here’s a 2PL schedule
for the ? on the previous
slide:
Step
T0
1
Slock(A)
2
Read(A)
T1
3
Xlock(B)
4
write(B)
5
Unlock(B)
6
Slock(B)
7
Read(B)
8
Unlock all
Assuming by the schedule that T0 starts first, so TS of T0 < TS of T1.
Suppose TS of T0 is 0 and TS of T1 is 1, then this schedule is not possible
because Read(B) at step 7 would not be allowed.
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Multiversion Timestamp Ordering

basic idea is to keep more than one version of a
data item, a sequence of them with increasing
timestamps.
(Write)
Timestamps
Read at time t
xk
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xv
xw
if a transaction wants to read x, with timestamp
t, it will read the version with the largest TS < t
in other words, it will behave as if it had
executed “on time” (here it will read xv)
so, reads are never rejected
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Multiversion TO cont’d

to write, for a transation with TS t, if there
are no reads from t to xw, then write is OK
(Write)
Timestamps
Write at time t
xk
xv
Write at time t
xk

xw
there was a
read here
xv
(Write)
Timestamps
xw
in the second case, the write is not allowed
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Why is two-phase locking used?
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It interferes very little with the design and
programming of transactions
It has much less overhead (lock table in
main memory vs. timestamps maintained on
potentially all data items)
I believe that in performance analyses, it
gives much better throughput
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Serialization Graph Testing
(pessimistic version)
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idea is to keep a graph with active transactions and some recently
committed transactions as the nodes.
if transaction Ti wishes to do an operation on a data item x, put an edge
from Tj to Ti for every transaction Tj for which a conflicting operation
has been previously scheduled, showing that Tj precedes Ti.
if the resulting graph contains a cycle involving Ti, abort Ti and delete it
from the graph.
if the graph is acyclic, schedule the operation by queuing it up for the
RM as for Basic Timestamp ordering.
nodes can be deleted from the graph when the corresponding transaction
has committed and it has no incoming edges left in the graph.
the scheduler never schedules any operations that are not serializable.
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Optimistic Techniques
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Two-Phase Locking and Timestamp Ordering are classified as pessimistic
techniques. They assume something will conflict and prevent against it.
Optimistic techniques assume that nothing will conflict in most cases,
and therefore just let the transactions run. When a transaction is ready
to commit, it checks what has happened to see if it is serializable.
If not, the transaction is aborted, rolled back and restarted.
If there are very few conflicts, it works well.
If there are a lot of conflicts, it has poor performance because a lot of
work gets repeated.
There is an optimistic version of 2PL, timestamp ordering and
serialization graph testing.
The Optimistic Serialization Graph Testing algorithm works as follows:
hold all writes until the commit point at which time the graph is built to
see if there were any conflicts.
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Newest SQL Server versions
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to serve a variety of workloads, with emphasis on regular
transaction processing, including data warehousing, they’ve
added column stores
to be optimized for larger (cheaper) main memory and multiple
cores
use multiversion timestamp ordering, with an optimistic
approach
Pros:
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no overhead for locking
highly parallelizable
Cons:
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there is overhead for validation
more frequent aborts
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Distributed Concurrency Control
the correctness criterion becomes:
One copy serializability
This means:
the resulting schedule with read and write steps
that refer to individual copies must be
equivalent to a serial schedule with only one
copy per data item.
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System Architecture
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Taxonomy of Distributed Concurrency
Control Mechanisms
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Pessimistic
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Locking
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Timestamp Ordering
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Centralized
Primary Copy
Distributed
Basic TO
Multi-version TO
Conservative TO
Hybrid
Optimistic
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Locking
Timestamp Ordering
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Locking-Based Protocols
All use two-phase locking in order to guarantee
local serializability
Centralized 2PL or Primary site 2PL:
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the lock manager for the database is at one site (i.e.
one scheduler looks after the entire database).
in this case the coordinating TM sends messages
directly to the various RM's once locks are granted
obviously a tremendous bottleneck, and reliability
problems if this site fails.
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Primary Copy 2PL
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Various schedulers (lock managers) are responsible
for certain data items.
Each data item is handled by 1 scheduler. That
scheduler grants locks on it. So there is a primary
copy for each data item.
Fewer bottlenecks than the previous method.
This was used in the Distributed Ingres prototype.
In this case, the directories need to know the
primary site location for each data item.
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Distributed 2PL
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expects schedulers to be present at each site which handle the
locks for the local data items.
without replicated data, similar to primary copy.
if data is replicated, then usually use a ROWA (Read One Write
All) replica control protocol:
 for reading, need a lock on one copy
for writing, need a lock on all copies.
was used in the R* prototype, and in Tandem's NonStop SQL.
in order to ensure two-phase locking is going on at all sites, i.e.,
not true that locks are released at one site before some locks are
granted at another for a given transaction, can use Strict twophase locking (i.e. hold all locks until the commit point).
there are issues to resolve if the network becomes partitioned
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DB2 and Oracle
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looking at the web pages for these two
systems, it looks like they both use a master
(primary) copy idea when there are replicas.
updates are made on the master copy and
later propagated out to the replicas
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Distributed Deadlocks,
Centralized Detection
Locking implies Deadlocks are possible.
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each scheduler keeps a waits-for-graph in which nodes correspond to
transactions. There is an edge from Ti to Tj if Ti is waiting for a lock which
conflicts with a lock on the same data item held by Tj.
must take the union of these various local graphs
Centralized Detection means that all the waits-for-graphs are periodically
shipped to a single site and the deadlock detection algorithm is run there.
The cost of detecting deadlocks is much greater than in the centralized
case because of transmission costs and delays.
the global deadlock detector must also get extra information to select a
victim.
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Phantom Deadlocks
some of the edges in the waits-for-graph in a
cycle are no longer true because the lock
being waited for has been granted. This
happens because of delays in sending the
information to the global deadlock detector.
Note: If there really is a deadlock, it will
remain until something intervenes.
 The problem with phantom deadlocks is they
may cause unnecessary aborts.
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Distributed Deadlock Detection
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all sites are running deadlock detection as required
more than 90% of cycles in a waits-for-graph are of
length 2
centralized deadlock detection is very slow at
detecting these short cycles.
one problem with distributed deadlock detection is
that if more than one site finds the deadlock, they
may select different victims.
A very cheap method is just to abort a transaction if
it times out waiting for a lock, i.e. assume it is
involved in a deadlock.
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Path Pushing
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Send paths in the waits-for-graph to other sites for deadlock detection.
to reduce traffic, assign a total order to the transactions, and only
send a path from the waits-for-graph, Ti → ... → Tj if Ti < Tj in the
ordering.
if furthermore, we assume that each transaction is only active at one
site at a time, (i.e. it does all its operations at one site, then goes to
the next for some more -- very restrictive on transaction design), then
when a transaction moves from Site A to Site B, send all the paths
ending at Tj along with it. So if at Site B, Tj needs a lock held by some
transaction on one of these paths, the deadlock can be detected
there.
None of these techniques is very good.
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Timestamp-Based Deadlock Avoidance
basic idea is that if Ti is going to wait for a lock held by Tj, it
should only do so if we can guarantee that a deadlock will
not result
 transactions will still be forced to abort.
First approach:
 assign priorities to transactions.
 Ti is allowed to wait for a lock held by Tj only if Ti has a
higher priority. Otherwise, Ti aborts and restarts with a
higher priority
 cycles cannot occur in the waits-for-graph, so we don't need
it.
 this method can lead to cyclic restart, or livelock, as in the
following example:

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1.
2.
3.
4.
5.
6.
7.
8.
9.
10.
Ti starts with priority 1
Ti sets a write lock on x
Tj starts with priority 2
Tj sets a write lock on y
Ti tries to lock y, and is forced to abort
Ti restarts with priority 3
Ti write locks x
Tj tries to lock x, and is forced to abort
Tj restarts with priority 4
Tj write locks y
go back to step5
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A Better Approach
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assign a unique (over the distributed database)
timestamp to each transaction
any transaction that stays alive long enough
will eventually have the smallest (oldest)
timestamp
Therefore, assign the priorities to be the
inverse of their timestamps.
if Ti is trying to obtain a lock for which Tj holds
a conflicting lock, then there are two main
strategies (use one of them):
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Wait-Die
if Timestamp(Ti) < Timestamp(Tj)
then Ti waits
else abort Ti (Ti dies)
Wound-Wait
if Timestamp(Ti) < Timestamp(Tj)
then abort Tj (try to kill it)
else Ti waits
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Furthermore
if a transaction is aborted, it is restarted with its
original timestamp. Thus it eventually has the highest
priority and runs to completion.
Thus there are no livelocks and no deadlocks
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Wounding is actually an attempt to kill Tj. It may not
work if Tj has committed in the meantime. In either
case, the locks Ti wants are released
older, active transaction is never aborted
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Behaviour of Wait-Die
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older transactions may wait for different locks from younger and younger
transactions.
thus this method favours younger transactions.
once a transaction has all its locks, it never aborts.
this method is also called non-preemptive
Behaviour of Wound-Wait
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older transactions force their way through to completion.
younger ones abort, and get restarted with their original timestamp.
Meanwhile the older transaction got its lock, so the restarted young
transaction now waits.
this method is also called preemptive: older transactions preempt younger
ones.
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Distributed Timestamp-Based
Concurrency Control
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distributed timestamp-based schedulers can behave
exactly like the centralized ones,
as long as distributed timestamps are issued correctly,
each site has enough information to proceed
to ensure unique timestamps over the network, have to
append some site ID bits to the timestamp which is
issued in one of the ways discussed for centralized
databases.
if one site's clock is slow or fast, it could mean that its
transactions have trouble.
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For Cloud-based Systems: CAP Theorem
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Consistency: all nodes see the same data at the
same time – note this is different from the
definition of consistency for ACID
Availability: a guarantee that every request
receives a response about whether it was
successful or failed
Partition tolerance: the system continues to
operate despite arbitrary message loss or failure of
part of the system
The CAP theorem says that a distributed system
can only satisfy two of these properties at a time
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Base
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Basically Available Soft-state services with
Eventual-consistency
provides a lot of Availability and can be
achieved by optimistic techniques.
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Concurrency Control for
Object-Oriented Databases
Characteristics of Work Done in OODBs:
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design environments, say for program development or computeraided design
still want sharing and concurrent access
may be short transactions mixed with long transactions; design
activities which can last for the working day of the designer.
the commit point is under user control; they may have a commit
button on an interactive interface
may want to support co-operative work, where there is simultaneous
access by more than one user.
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Do not want
(want to minimize):
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work blocked because another transaction
holds a lock
work rolled back at the commit point
because something is not serializable
Do want:
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concurrent work with some notion of
correctness.
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Multiple Granularity Locking
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Same idea as with relational databases
complications arise from schema updates,
inheritance and shared subobjects
an object is highly likely to be the target of an
update, and therefore the smallest granule. It is
more likely to be the object of an individual lock
than an individual tuple in a relational database.
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Consider the following tree of
kinds of granules
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Some Details
on the objects themselves, only S and X locks are possible.
 the following lock modes are available on classes and sets of instances (or
other collections, e.g. lists)
S lock on a set means the set is locked in S mode, and all the instances are
implicitly locked in S mode.
X lock on a set means the set is locked in X mode, and all instances in the set
can be read or written.
IS lock (intention shared) on a set means the instances will be explicitly
locked in Shared mode as necessary.
IX lock on the set (Intention eXclusive) means instances will be locked in S or
X mode as necessary.
SIX lock on a set (Shared, Intent eXclusive) means the set is locked in S
mode, and all instances too. Instances to be written will be locked in X
mode as necessary.
these are almost the same as we had before, except here we explicitly talk
about what granules they apply to.

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Rules
The following rules are also familiar, but slightly different.
1.
To set an explicit S lock on a lockable granule, first set
an IS lock on all direct ancestors, along any one
ancestor chain, of the lockable granule in the DAG
2.
To set an X lock on a lockable granule, first set an IX or
SIX lock on all direct ancestors, along every ancestor
chain, of the lockable granule in the DAG
3.
Set all locks in root to leaf order
4.
Release all locks in leaf to root order, or all at once at
the end of the transaction.
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Lock Compatibility Matrix
IS
IS Y
IX
S
SIX
X
Y
Y
Y
-
IX S
SIX X
Y
Y
Y
-
Y
-
Y
-
-
-
Which is what we had before without the Update
locks. This version is symmetric.
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OODB example classes
Class Employee public type tuple
(name : string,
ID : string,
jobtitle : string;
worksFor : Department,
startDate : Date) end;
Class Department public type
tuple
(deptName : string,
deptLoc : string
empsOf : set(Employee) )
end;
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Class WorksOn public type tuple
(who : Employee,
what : Project,
howMuch : real) end;
Class Project public type tuple
(projName : string,
targetDate : Date,
projLocation : string)
end;
Set 5, OODB Query Languages
51
Possible Granules
MySchema
MyDatabase
ClassDefinitions
Employee
EmpCollection
Project
ProjectCollection
SmithObject
ProjectP1
CS4411/9538
Set 9, Part 2, Concurrency Control
52
Suppose we have the following commands in the
interactive tool:
set schema MySchema
method x in class Employee
... defining a new method for class Employee
It has to lock the schema and the class definition for
class Employee so that the class definition can be
changed. At the very least, IX mode for schema
MySchema, the ClassDefinitions node, and X mode to
write (update) the class Employee class definition.
CS4411/9538
Set 9, Part 2, Concurrency Control
53
If we begin a session with
set schema MySchema
set (data)base MyDatabase
query
...
By the time it gets to the query statement, it should know it needs to lock the
whole schema for reading at lower levels. Before that, it may have
assumed it was the previous session and locked things in IX mode.
Note that the schema definitions need to be read to compile the queries.
So, if a query is written which accesses SmithObject, the locking might be:
IS on schema MySchema
S on Class Employee (to read the class definition to compile the query)
IS on Database MyDatabase
IS on EmpCollection
S on SmithObject
Unlock everything
CS4411/9538
Set 9, Part 2, Concurrency Control
54
For a transaction to write a new Employee object:
IX on schema MySchema
S on class Employee (need to verify the structure of the
new object)
IX on (data)base MyDatabase
X on EmpCollection (adding a new object reference to the
set)
X on the page that the new student object will be put on
What is wrong with getting this X lock on EmpCollection?
We have not got the required locks on all its ancestors (we
were supposed to get IX on the class definition).
CS4411/9538
Set 9, Part 2, Concurrency Control
55
More OO lock modes



Schemes have been devised based on
multiple granularity locking to deal with
OODBs, with shared subobjects, and how
they can be updated.
All based on introducing more lock modes
and coming up with the lock compatibility
table.
here’s the worst one
CS4411/9538
Set 9, Part 2, Concurrency Control
56
ISOS: Intent Shared, these are subObjects which are Shared
IXOS: Intent eXclusive, these are subObjects which are Shared
SIXOS: Shared, Intent eXclusive access, these are subObjects which are Shared.
IS
IX
S
SIX
X
IS
Y
Y
Y
Y
-
Y
-
-
Y
-
-
IX
Y
Y
-
-
-
-
-
-
-
-
-
S
Y
-
Y
-
-
Y
-
-
Y
-
-
SIX
Y
-
-
-
-
-
-
-
-
-
-
X
-
-
-
-
-
-
-
-
-
-
-
ISO
Y
-
Y
-
-
Y
Y
Y
Y
Y
Y
IXO
-
-
-
-
-
Y
Y
-
Y
Y
-
SIXO
-
-
-
-
-
Y
-
-
Y
-
-
ISOS
Y
-
Y
-
-
Y
Y
Y
Y
-
-
IXOS
-
-
-
-
-
Y
Y
-
-
-
-
SIXOS
-
-
-
-
-
Y
-
-
-
-
-
CS4411/9538
ISO IXO SIXO ISOS IXOS SIXOS
Set 9, Part 2, Concurrency Control
57
Concurrency Control for LongRunning Transactions



Long running transactions are typical in Object-Oriented
Database applications, which include things like software
development and computer-aided design.
Solutions can be “arranged” by users so that work can be
carried out in tandem, but the operations might not
technically be serializable.
We will look briefly at one idea which allows more
concurrent work while preserving theoretical properties.
CS4411/9538
Set 9, Part 2, Concurrency Control
58
Sagas






this idea is used for workflow systems
workflows are usually represented by a directed
graph
a workflow is made up of (small) actions and special
actions commit and abort.
there are no edges leaving a commit or abort node.
there is usually one start node
for each action A, there must be a compensating
action A-1 which reverses the effect of A
CS4411/9538
Set 9, Part 2, Concurrency Control
59
CS4411/9538
Set 9, Part 2, Concurrency Control
60



Each action is executed like a short transaction, with
standard concurrency control.
If the execution leads to the abort termination, all
the actions which have been performed, say A1 ... An
are compensated in reverse order by their
compensating actions in reverse order
An-1 An-1-1 ... A1-1.
the individual actions need to be of a type that are
reversible.
CS4411/9538
Set 9, Part 2, Concurrency Control
61
What scenario do XML databases fall into?
CS4411/9538
Set 9, Part 2, Concurrency Control
62