ch23.pptx distrubuted database management

Database System Concepts, 7th
Ed.
©Silberschatz, Korth and Sudarshan
See www.db-book.com for conditions on re-use
Chapter 23: Parallel and Distributed Transaction
Processing

23.2
Database System Concepts - 7th
Edition
Distributed Transactions
 Local transactions
• Access/update data at only one database
 Global transactions
• Access/update data at more than one database
 Key issue: how to ensure ACID properties for transactions in a system
with global transactions spanning multiple database

23.3
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 Transaction may access data at several sites.
• Each site has a local transaction manager
• Each site has a transaction coordinator
 Global transactions submitted to any transaction coordinator

23.4
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 Each transaction coordinator is responsible for:
• Starting the execution of transactions that originate at the site.
• Distributing sub-transactions at appropriate sites for execution.
• Coordinating the termination of each transaction that originates at
the site
 transaction must be committed at all sites or aborted at all sites.
 Each local transaction manager responsible for:
• Maintaining a log for recovery purposes
• Coordinating the execution
and commit/abort of the
transactions executing
at that site.

23.5
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System Failure Modes
 Failures unique to distributed systems:
• Failure of a site.
• Loss of massages
 Handled by network transmission control protocols such as TCP-IP
• Failure of a communication link
 Handled by network protocols, by routing messages via alternative
links
• Network partition
 A network is said to be partitioned when it has been split into two
or more subsystems that lack any connection between them
• Note: a subsystem may consist of a single node
 Network partitioning and site failures are generally indistinguishable.

23.6
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Commit Protocols
 Commit protocols are used to ensure atomicity across sites
• a transaction which executes at multiple sites must either be
committed at all the sites or aborted at all the sites.
 Cannot have transaction committed at one site and aborted at
another
 The two-phase commit (2PC) protocol is widely used
 Three-phase commit (3PC) protocol avoids some drawbacks of 2PC,
but is more complex
 Consensus protocols solve a more general problem, but can be used
for atomic commit
• More on these later in the chapter
 The protocols we study all assume fail-stop model – failed sites simply
stop working, and do not cause any other harm, such as sending
incorrect messages to other sites.
• Protocols that can tolerate some number of malicious sites
discussed in bibliographic notes online

23.7
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Two Phase Commit Protocol (2PC)
 Execution of the protocol is initiated by the coordinator after the last step of
the transaction has been reached.
 The protocol involves all the local sites at which the transaction executed
 Protocol has two phases
 Let T be a transaction initiated at site Si, and let the transaction coordinator
at Si be Ci

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Phase 1: Obtaining a Decision
 Coordinator asks all participants to prepare to commit transaction Ti.
• Ci adds the records <prepare T> to the log and forces log to stable
storage
• sends prepare T messages to all sites at which T executed
 Upon receiving message, transaction manager at site determines if it can
commit the transaction
• if not, add a record <no T> to the log and send abort T message to Ci
• if the transaction can be committed, then:
 add the record <ready T> to the log
 force all records for T to stable storage
 send ready T message to Ci
Transaction is now in ready state at the site

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Phase 2: Recording the Decision
 T can be committed of Ci received a ready T message from all the
participating sites: otherwise T must be aborted.
 Coordinator adds a decision record, <commit T> or <abort T>, to the log
and forces record onto stable storage. Once the record stable storage it is
irrevocable (even if failures occur)
 Coordinator sends a message to each participant informing it of the
decision (commit or abort)
 Participants take appropriate action locally.

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Two-Phase Commit Protocol

23.11
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Handling of Failures - Site Failure
When site Sk recovers, it examines its log to determine the fate of
transactions active at the time of the failure.
 Log contain <commit T> record: site executes redo (T)
 Log contains <abort T> record: site executes undo (T)
 Log contains <ready T> record: site must consult Ci to determine the fate
of T.
• If T committed, redo (T)
• If T aborted, undo (T)
 The log contains no control records concerning T implies that Sk failed
before responding to the prepare T message from Ci
• since the failure of Sk precludes the sending of such a
response Ci must abort T
• Sk must execute undo (T)

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Handling of Failures- Coordinator Failure
 If coordinator fails while the commit protocol for T is executing then
participating sites must decide on T’s fate:
1. If an active site contains a <commit T> record in its log, then T must
be committed.
2. If an active site contains an <abort T> record in its log, then T must be
aborted.
3. If some active participating site does not contain a <ready T> record in
its log, then the failed coordinator Ci cannot have decided to commit T.
Can therefore abort T.
4. If none of the above cases holds, then all active sites must have a
<ready T> record in their logs, but no additional control records (such
as <abort T> of <commit T>). In this case active sites must wait for Ci
to recover, to find decision.
 Blocking problem: active sites may have to wait for failed coordinator to
recover.

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Handling of Failures - Network Partition
 If the coordinator and all its participants remain in one partition, the failure
has no effect on the commit protocol.
 If the coordinator and its participants belong to several partitions:
• Sites that are not in the partition containing the coordinator think the
coordinator has failed, and execute the protocol to deal with failure of
the coordinator.
 No harm results, but sites may still have to wait for decision from
coordinator.
 The coordinator and the sites are in the same partition as the coordinator
think that the sites in the other partition have failed, and follow the usual
commit protocol.
 Again, no harm results

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Recovery and Concurrency Control
 In-doubt transactions have a <ready T>, but neither a
<commit T>, nor an <abort T> log record.
 The recovering site must determine the commit-abort status of such
transactions by contacting other sites; this can slow and potentially block
recovery.
 Recovery algorithms can note lock information in the log.
• Instead of <ready T>, write out <ready T, L> L = list of locks held by T
when the log is written (read locks can be omitted).
• For every in-doubt transaction T, all the locks noted in the
<ready T, L> log record are reacquired.
 After lock reacquisition, transaction processing can resume; the commit or
rollback of in-doubt transactions is performed concurrently with the
execution of new transactions.

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Avoiding Blocking During Consensus
 Blocking problem of 2PC is a serious concern
 Idea: involve multiple nodes in decision process, so failure of a few nodes
does not cause blocking as long as majority don’t fail
 More general form: distributed consensus problem
• A set of n nodes need to agree on a decision
• Inputs to make the decision are provided to all the nodes, and then
each node votes on the decision
• The decision should be made in such a way that all nodes will “learn”
the same value for the even if some nodes fail during the execution of
the
protocol, or there are network partitions.
• Further, the distributed consensus protocol should not block, as long as
a majority of the nodes participating remain alive and can communicate
with each other
 Several consensus protocols, Paxos and Raft are popular
• More later in this chapter

23.16
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Using Consensus to Avoid Blocking
 After getting response from 2PC participants, coordinator can initiate
distributed consensus protocol by sending its decision to a set of
participants who then use consensus protocol to commit the decision
• If coordinator fails before informing all consensus participants
 Choose a new coordinator, which follows 2PC protocol for failed
coordinator
 If a commit/abort decision was made as long as a majority of
consensus participants are accessible, decision can be found
without blocking
• If consensus process fails (e.g., split vote), restart the consensus
 Split vote can happen if a coordinator send decision to some
participants and then fails, and new coordinator send a different
decision
 The three phase commit protocol is an extension of 3PC which avoids
blocking under certain assumptions
• Ideas are similar to distributed consensus.
 Consensus is also used to ensure consistency of replicas of a data item
• Details later in the chapter

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Distributed Transactions via Persistent Messaging
 Notion of a single transaction spanning multiple sites is inappropriate for
many applications
• E.g., transaction crossing an organizational boundary
• Latency of waiting for commit from remote site
 Alternative models carry out transactions by sending messages
• Code to handle messages must be carefully designed to ensure
atomicity and durability properties for updates
 Isolation cannot be guaranteed, in that intermediate stages are
visible, but code must ensure no inconsistent states result due to
concurrency
• Persistent messaging systems are systems that provide transactional
properties to messages
 Persistent messages are guaranteed to be delivered exactly once

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Persistent Messaging
 Example: funds transfer between two banks
• Two phase commit would have the potential to block updates on the
accounts involved in funds transfer
• Alternative solution:
 Debit money from source account and send a message to other
site
 Site receives message and credits destination account
• Messaging has long been used for distributed transactions (even
before computers were invented!)
 Atomicity issue
• once transaction sending a message is committed, message must
guaranteed to be delivered
 Guarantee as long as destination site is up and reachable, code to
handle undeliverable messages must also be available
• e.g., credit money back to source account.
• If sending transaction aborts, message must not be sent

23.19
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Error Conditions with Persistent Messaging
 Code to handle messages has to take care of variety of failure situations
(even assuming guaranteed message delivery)
• E.g., if destination account does not exist, failure message must be
sent back to source site
• When failure message is received from destination site, or destination
site itself does not exist, money must be deposited back in source
account
 Problem if source account has been closed
• get humans to take care of problem
 User code executing transaction processing using 2PC does not have to
deal with such failures
 There are many situations where extra effort of error handling is worth the
benefit of absence of blocking
• E.g., pretty much all transactions across organizations

23.20
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Persistent Messaging Implementation

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Persistent Messaging (Cont.)
 Receiving site may get duplicate messages after a very long delay
• To avoid keeping processed messages indefinitely
 Messages are given a timestamp
 Received messages older than some cutoff are ignored
 Stored messages older than the cutoff can be deleted at receiving
site
 Workflows provide a general model of transactional processing involving
multiple sites and possibly human processing of certain steps
• E.g., when a bank receives a loan application, it may need to
 Contact external credit-checking agencies
 Get approvals of one or more managers
and then respond to the loan application
• Persistent messaging forms the underlying infrastructure for workflows
in a distributed environment

Concurrency Control in Distributed
Databases

23.23
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Concurrency Control
 Modify concurrency control schemes for use in distributed environment.
 We assume that each site participates in the execution of a commit
protocol to ensure global transaction atomicity.
 We assume all replicas of any item are updated
• Will see how to relax this in case of site failures later

23.24
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Single-Lock-Manager Approach
 In the single lock-manager approach, lock manager runs on a single
chosen site, say Si
• All lock requests sent to central lock manager
 The transaction can read the data item from any one of the sites at which a
replica of the data item resides.
 Writes must be performed on all replicas of a data item
 Advantages of scheme:
• Simple implementation
• Simple deadlock handling
 Disadvantages of scheme are:
• Bottleneck: lock manager site becomes a bottleneck
• Vulnerability: system is vulnerable to lock manager site failure.

23.25
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Distributed Lock Manager
 In the distributed lock-manager approach, functionality of locking is
implemented by lock managers at each site
• Lock managers control access to local data items
• Locking is performed separately on each site accessed by transaction
 Every replica must be locked and updated
 But special protocols may be used for replicas (more on this later)
 Advantage: work is distributed and can be made robust to failures
 Disadvantage:
• Possibility of a global deadlock without local deadlock at any single
site
• Lock managers must cooperate for deadlock detection

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Deadlock Handling
Consider the following two transactions and history, with item X and
transaction T1 at site 1, and item Y and transaction T2 at site 2:
Result: deadlock which cannot be detected locally at either site

23.27
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Deadlock Detection
 In the centralized deadlock-detection approach, a global wait-for graph is
constructed and maintained in a single site; the deadlock-detection
coordinator
• Real graph: Real, but unknown, state of the system.
• Constructed graph: Approximation generated by the controller during the
execution of its algorithm .
 the global wait-for graph can be constructed when:
• a new edge is inserted in or removed from one of the local wait-for
graphs.
• a number of changes have occurred in a local wait-for graph.
• the coordinator needs to invoke cycle-detection.
 If the coordinator finds a cycle, it selects a victim and notifies all sites. The
sites roll back the victim transaction.

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Local and Global Wait-For Graphs
Local
Global

23.29
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Example Wait-For Graph for False Cycles
Initial state:

23.30
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False Cycles (Cont.)
 Suppose that starting from the state shown in figure,
1. T2 releases resources at S1
 resulting in a message remove T1  T2 message from the
Transaction Manager at site S1 to the coordinator)
2. And then T2 requests a resource held by T3 at site S2
 resulting in a message insert T2  T3 from S2 to the coordinator
 Suppose further that the insert message reaches before the delete
message
• this can happen due to network delays
 The coordinator would then find a false cycle
T1  T2  T3  T1
 The false cycle above never existed in reality.
 False cycles cannot occur if two-phase locking is used.

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Distributed Deadlocks
 Unnecessary rollbacks may result
• When deadlock has indeed occurred and a victim has been picked,
and meanwhile one of the transactions was aborted for reasons
unrelated to the deadlock.
• Due to false cycles in the global wait-for graph; however, likelihood of
false cycles is low.
 In the distributed deadlock-detection approach, sites exchange wait-for
information and check for deadlocks
• Expensive and not used in practice

23.32
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Leases
 A lease is a lock that is granted for a specific period of time
 If a process needs a lock even after expiry of lease, process can renew
the lease
 But if renewal is not done before end time of lease, the lease expires,
and lock is released
 Leases can be used to that there is only one coordinator for a protocol at
any given time
• Coordinator gets a lease and renews it periodically before expire
• If coordinator dies, lease will not be renewed and can be acquired by
backup coordinator

23.33
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Leases (Cont.)
 Coordinator must check that it still has lease when performing action
• Due to delay between check and action, must check that expiry is at
least some time t’ into the future
 t’ includes delay in processing and maximum network delay
 Old messages must be ignored
 Leases depend on clock synchronization

23.34
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Distributed Timestamp-Based Protocols
 Timestamp based concurrency-control protocols can be used in distributed
systems
 Each transaction must be given a unique timestamp
 Main problem: how to generate a timestamp in a distributed fashion
• Each site generates a unique local timestamp using either a logical
counter or the local clock.
• Global unique timestamp is obtained by concatenating the unique local
timestamp with the unique identifier.

23.35
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Distributed Timestamps
 A node with a slow clock will assign smaller timestamps
• Still logically correct: serializability not affected
• But: “disadvantages” transactions
 To fix this problem
• Keep clocks synchronized using network time protocol
• Or, define within each node Ni a logical clock (LCi), which generates
the unique local timestamp
 Require that Ni advance its logical clock whenever a request is
received from a transaction Ti with timestamp < x,y> and x is
greater that the current value of LCi.
 In this case, site Ni advances its logical clock to the value x + 1

23.36
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Distributed Timestamp Ordering
 Centralized TSO and multiversion TSO easily extended to distributed
setting
• Transactions use a globally unique timestamp
• Each site that performs a read or write performs same checks as in
centralized case
 Clocks at sites should be synchronized
• Otherwise a transaction initiated at a site with a slower clock may get
restarted repeatedly.

23.37
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Distributed Validation
 The validation protocol used in centralized systems can be extended to
distributed systems
 Start/validation/finish timestamp for a transaction Ti may be issued by any
of the participating nodes
• Must ensure StartTS(Ti) < TS(Ti) < FinishTS(Ti)
 Validation for Ti is done at each node that performed read/write
• Validation checks for transaction Ti are same as in centralized case
 Ensure that no transaction that committed after Ti started has
updated any data item read by Ti.
• A key difference from centralized case is that may Ti start validation
after a transaction with a higher validation timestamp has already
finished validation
 In that case Ti is rolled back

23.38
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Distributed Validation (Cont.)
 Two-phase commit (2PC) needed to ensure atomic commit across sites
• Transaction is validated, then enters prepared state
• Writes can be performed (and transaction finishes) only after 2PC
makes a commit decision
• If transaction Ti is in prepared state, and another transaction Tk reads
old value of data item written by Ti, Tk will fail if Ti commits
 Can make the read by Tk wait, or create a commit dependency for
Tk on Ti.

23.39
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Distributed Validation (Cont.)
 Distributed validation is not widely used, but optimistic concurrency
control without read-validation is widely used in distributed settings
• Version numbers are stored with data items
• Writes performed at commit time ensure that the version number of a
data item is same as when data item was read
• Hbase supports atomic checkAndPut() as well as checkAndMutate()
operations; see book for details

23.41
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Replication
 High availability is a key goal in a distributed database
• Robustness: the ability to continue function despite failures
 Replication is key to robustness
 Replication decisions can be made at level of data items, or at the level of
partitions

23.42
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Consistency of Replicas
 Consistency of replicas
• Ideally: all replicas should have the same value  updates performed
at all replicas
 But what if a replica is not available (disconnected, or failed)?
• Suffices if reads get correct value, even if some replica is out of date
• Above idea formalized by linearizability: given a set of read and write
operations on a (replicated) data item
 There must be a linear ordering of operations such that each read
sees the value written by the most recent preceding write
 If o1 finishes before o2 begins (based on external time), then o1
must precede o2 in the linear order
 Note that linearizability only addresses a single (replicated) data item;
serializability is orthogonal

23.43
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Consistency of Replicas
 Cannot differentiate node failure from network partition in general
• Backup coordinator should takeover if primary has failed
• Use multiple independent links, so single link failure does not result in
partition, but it is possible all links have failed
 Protocols that require all copies to be updated are not robust to failure
 Will see techniques that can allow continued processing during failures,
whether node failure or network partition
• Key idea: decisions made based on successfully writing/reading
majority
 Alternative: asynchronous replication: commit after performing update
on a primary copy of the data item, and update replicas asynchronously
• Lower overheads, but risk of reading stale data, or lost updates on
primary failure

23.44
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Concurrency Control With Replicas
 Focus here on concurrency control with locking
• Failures addressed later
• Ideas described here can be extended to other protocols
 Primary copy
• one replica is chosen as primary copy for each data item
 Node containing primary replica is called primary node
• concurrency control decisions made at the primary copy only
 Benefit: Low overhead
 Drawback: primary copy failure results in loss of lock information and non-
availability of data item, even if other replicas are available
• Extensions to allow backup server to take over possible, but vulnerable
to problems on network partition

23.45
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Concurrency Control With Replicas (Cont.)
 Majority protocol:
• Transaction requests locks at multiple/all replicas
• Lock is successfully acquired on the data item only if lock obtained
at a majority of replicas
 Benefit: resilient to node failures and node failures
• Processing can continue as long as at least a majority of replicas are
accessible
 Overheads
• Higher cost due to multiple messages
• Possibility of deadlock even when locking single item
 How can you avoid such deadlocks?

23.46
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Concurrency Control With Replicas (Cont.)
 Biased protocol
• Shared lock can be obtained on any replica
 Reduces overhead on reads
• Exclusive lock must be obtained on all replicas
 Blocking if any replica is unavailable

23.47
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Quorum Consensus Protocol
Quorum consensus protocol for locking
 Each site is assigned a weight; let S be the total of all site weights
 Choose two values read quorum QR and write quorum QW
• Such that Qr + Qw > S and 2 * Qw > S
 Each read must lock enough replicas that the sum of the site weights
is ≥ Qr
 Each write must lock enough replicas that the sum of the site weights
is ≥ Qw
 Can choose Qr and Qw to tune relative overheads on reads and writes
• Suitable choices result in majority and biased protocols.
 What are they?

23.48
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Dealing with Failures
 Read one write all copies protocol assumes all copies are available
• Will block if any site is not available
 Read one write all available (ignoring failed sites) is attractive, but
incorrect
• Failed link may come back up, without a disconnected site ever being
aware that it was disconnected
• The site then has old values, and a read from that site would return
an incorrect value
• With network partitioning, sites in each partition may update same
item concurrently
 believing sites in other partitions have all failed

23.49
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Handling Failures with Majority Protocol
 The majority protocol with version numbers
• Each replica of each item has a version number
• Locking is done using majority protocol, as before, and version
numbers are returned along with lock allocation
• Read operations read the value from the replica with largest version
number
• Write operations
 Find highest version number like reads, and set new version
number to old highest version + 1
 Writes are then performed on all locked replicas and version
number on these replicas is set to new version number
 Read operations that find out-of-date replicas may optionally write the
latest value and version number to replicas with lower version numbers
• no need to obtain locks on all replicas for this task

23.50
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 Atomic commit of updated replicas must be ensured using either
• 2 phase commit on all locked replicas, or
• distributed consensus protocol such as Paxos (more on this later)
 Failure of nodes during 2PC can be ignored as long as majority of sites
enter prepared state
 Failure of coordinator can cause blocking
• Consensus protocols can avoid blocking

23.51
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 Benefits of majority protocol
• Failures (network and site) do not affect consistency
 Reads are guaranteed to see latest successfully written version of
a data item
• Protocol can proceed as long as
 Sites available at commit time contain a majority of replicas of any
updated data items
 During reads a majority of replicas are available to find version
numbers
• No need for any special reintegration protocol: nothing needs to be
done if nodes fail and subsequently recover
 Drawback of majority protocol
• Higher overhead, especially for reads

23.52
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Reducing Read Cost
 Quorum consensus can be used to reduce read cost
• But at increased risk of blocking of writes due to failures
 Use primary copy scheme:
• perform all updates at primary copy
• reads only need to be done at primary copy
• But what if primary copy fails
 Need to ensure new primary copy is chosen
• Leases can ensure there is only 1 primary copy at a time
 New primary copy needs to have latest committed version of data
item
• Can use consensus protocol to avoid blocking

23.53
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Reducing Read Cost
 Chain replication:
• Variant of primary copy scheme
• Replicas are organized into a chain
• Writes are done at head of chain, and passed on to subsequent replicas
• Reads performed at tail
 Ensures that read will get only fully replicated copy
• Any node failure requires reconfiguration of chain

23.54
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Reconfiguration and Reintegration
 To be robust, a distributed system must either
• Follow protocols like the majority protocol that work in spite of failures
or
• Use other protocols like primary copy protocol, but
 Detect failures (failed/non-reachable nodes)
 Reconfigure the system to remove failed nodes, and assign their
tasks to other sites, so computation may continue
 Recover/reintegrate nodes a node or link is repaired
 Failure detection: distinguishing link failure from site failure is hard
• (partial) solution: have multiple links, multiple link failure is likely a site
failure

23.55
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Reconfiguration
 Reconfiguration:
• Abort all transactions that were active at a failed site
• If replicated data items were at failed site, update system catalog to
remove them from the list of replicas.
 This should be reversed when failed site recovers, but additional
care needs to be taken to bring values up to date
• If a failed site was a central server for some subsystem, an election
must be held to determine the new server
 E.g., name server, concurrency coordinator, global deadlock
detector

23.56
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Reconfiguration (Cont.)
 Since network partition may not be distinguishable from site failure, the
following situations must be avoided
• Two or more central servers elected in distinct partitions
• More than one partition updates a replicated data item
 Updates must be able to continue even if some sites are down
 Solution: majority based approach

23.57
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Site Reintegration
 When failed site recovers, it must catch up with all updates that it
missed while it was down
• Problem: updates may be happening to items whose replica is
stored at the site while the site is recovering
• Solution 1: halt all updates on system while reintegrating a site
 Unacceptable disruption
• Solution 2: lock all replicas of all data items at the site, update to
latest version, then release locks
 Can do this for one partition at a time

23.58
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Comparison with Remote Backup
 Remote backup systems (Section 19.7) are also designed to provide high
availability
 Remote backup systems are simpler and have lower overhead
• All actions performed at a single site, and only log records shipped
• No need for distributed concurrency control, or 2 phase commit
 Using distributed databases with replicas of data items can provide higher
availability by having multiple (> 2) replicas and using the majority
protocol
• Also avoid failure detection and switchover time associated with
remote backup systems

Extended Concurrency Control Protocols

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Multiversion 2PL and Globally Consistent Timestamps
 Recall multiversion 2PL protocol:
• Read only transactions get timestamp at start
 Ti reads latest committed version of data items with TS <
startTS(Ti)
• Update transactions perform 2PL, and also get timestamp at commit
• Serialization order defined by timestamp
 Question: can we use MV2PL in a distributed system
 Answer: yes, but a lot of conditions apply
• If commits are serialized at central coordinator, timestamps can be
given based on counter
• But if commits are distributed, how to give timestamps in a consistent
manner?
 Clocks may not be in sync, later commit may get lower timestamp
 Out of order timestamp issual may result in serialization order not
matching timestamp order

23.61
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 Centralized coordinator to assign consistent timestamps
• Can be done, but becomes bottleneck
 Google Spanner ideas:
• In an ideal world, clocks are synchronized, and can be used to assign
commit timestamps to transactions
• In reality, clocks are out of sync
• Key ideas
 Use atomic clocks, GPS etc to periodically get precise time
 Derive bound on how out-of-sync a node’s clock t’ can be w.r.t. to
actual time t
• t’ – ε ≤ t ≤ t’ + ε
 Introduce commit wait: hold locks for some period and assign
timestamp ts such that locks were definitely held at actual time ts

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 Google Spanner ideas (cont):
• If version of x has timestamp ts, then x definitely had that value at time
ts
• System can generate transactionally consistent snapshop as of actual
time ts (external consistency)
• Commit processing can still take time
 With 2PC status of transaction may not be known for a while
• Reads may have to wait till status of transaction is known
 But read-only transactions can use a snapshot timestamp ts such
that all transactions before that timestamp have been committed or
aborted
• Read can proceed without waiting
• But perhaps with older versions of data

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Other Concurrency Control Techniques
 Distributed snapshot isolation
• Running Snapshot Isolation separately on each node may result in
different serialization orders at different nodes
• Extensions to SI to ensure consistent ordering have been proposed
 Concurrency control in federated databases
• Local transactions
• Global transactions
• Local serializability may not guarantee global serializability unless all
nodes use 2PL
• Use idea of tickets to create conflicts that will ensure serializability

Replication With Weak Degrees of Consistency

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Consistency
 Recall: Consistency in Databases (ACID):
• Database has a set of integrity constraints
• A consistent database state is one where all integrity constraints are
satisfied
• Each transaction run individually on a consistent database state must
leave the database in a consistent state
 Recall: Consistency in distributed systems with replication
• Strong consistency: a schedule with read and write operations on an
object should give results and final state equivalent to some schedule
on a single copy of the object, with order of operations from a single
site preserved
• Weak consistency (several forms)

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Availability
 Traditionally, availability of centralized server
 For distributed systems, availability of system to process requests
• For large system, at almost any point in time there’s a good chance
that
 a node is down or even
 Network partitioning
 Availability: ability to continue operations despite node and network
failures.

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CAP “Theorem”
 Three properties of a system
• Consistency
 an execution of a set of operations (reads and writes) on replicated
data is said to be consistent if its result is the same as if the
operations were executed on a single node, in a sequential order
that is consistent with the ordering of operations issued by each
process (transaction)
• Availability (system can run even if parts have failed)
 Via replication
• Partitions (network can break into two or more parts, each with active
systems that can’t talk to other parts)
 Brewer’s CAP “Theorem”: You can have at most two of these three
properties for any system

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CAP “Theorem” (Cont.)
 Very large systems will partition at some point
 Choose one of consistency or availability
• Traditional database choose consistency
• Many web applications choose availability
 Except for specific parts such as order processing
 Latency is another factor
• Many applications choose to serve potentially stale data to reduce
latency

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Replication with Weak Consistency
 Many systems support replication of data with weak degrees of
consistency (I.e., without a guarantee of serializabiliy)
• In quorum consistency notation: allow QR and QW to be set such that
QR + QW ≤ S or 2*QW ≤ S
 E.g., can be set in MongoDB and Cassandra
• Usually only when not enough sites are available to ensure quorum
 But sometimes to allow fast local reads
• Tradeoff of consistency versus availability or latency
 Key issues:
• Reads may get old versions
• Some replicas may not get updated
• Different updates may be applied to different replicas
 Question: how to detect, and how to resolve
 Will see in detail later

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Eventual Consistency
 When no updates occur for a long period of time, eventually all updates
will propagate through the system and all the nodes will be consistent
 For a given accepted update and a given node, eventually either the
update reaches the node or the node is removed from service
 Known as BASE (Basically Available, Soft state, Eventual consistency),
as opposed to ACID
• Soft state: copies of a data item may be inconsistent
• Eventually Consistent : Copies may be allowed to become
inconsistent, but (once partitioning is resolved) eventually all copies
become consistent with each other
 at some later time, if there are no more updates to that data item

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Asynchronous Replication
 With asynchronous replication, updates are done at the primary node
(also known as master node), and then propagated to replicas
• Transaction can commit once update is done at primary node
• Propagation after commit is also referred to as lazy propagation
• Allows updates to occur even if some sites are disconnected from the
network, but at the cost of consistency
 Replicas may not be up-to-date
• Transactions that can live with old data can read from replicas
• Snapshot reads at a point in time can also be served from replicas
that are sufficiently up-to-date
 E.g., in Google Spanner
• each replica maintains a timestamp tsafe such that all updates
with timestamp t < tsafe have already been received
• Reads of a transaction can be satisfied by a replicate if
transaction timestamp t < tsafe

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Asynchronous Replication
 Master-slave replication: updates performed only at master, and
asynchronously propagated to replicas
• replicas can only satisfy reads
 Multimaster replication (or update-anywhere replication): updates can
be performed at any replica, and propagated synchronously or
asynchronously to other replicas
 Updates must be propagated to replicas even if there are failures, and
processed in the correct order at the replicas
• Persistent messaging systems can be used for this, with minor
extensions to ensure in-order delivery
• Publish-subscribe systems such as Kafka can also be used for this
task
 More flexible, support parallelism by having multiple topics and
partitions of topics
• Fault-tolerance is important
 Can use log-replication with two-safe protocol (Section 19.7)

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Asynchronous View Maintenance
 Materialized views can be useful in distributed systems
• Secondary indices can be considered as a simple form of materialized
view in a parallel database
 E.g., given relation r(A,B,C) where A is the primary key on which r
is partitioned, a secondary index on B is simply a projection of r on
(B,A), partitioned on B.
• Materialized aggregate views are also very useful in many contexts
 Performing view maintenance as part of the original transaction may not be
possible (if the underlying database does not support distributed
transactions), or may be expensive
 Asynchronous maintenance of materialized views, after the original
transaction commits, is a good option in such a case
• Applications using the view/index must then be aware that it may be a
little out-of-date

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Requirements for Asynchronous View Maintenance
Requirements:
1. Updates must be delivered and processed exactly once despite failures
2. Derived data (such as materialized views/indices) must be updated in
such a way that it will be consistent with the underlying data
• Formalized as eventual consistency: if there are no updates for a
while, eventually the derived data will be consistent with the underlying
data
3. Queries should get a transactionally consistent view of derived data
• Potentially a problem with long queries that span multiple nodes
• E.g., without transactional consistency, a scan of relation may miss
some older updates and see some later updates
• Not supported by many systems, supported via snapshots in some
systems

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Detecting Inconsistency
 Data items are versioned
 Each update creates a new immutable version
 In absence of failure, there is a single latest version
 But with failures and weak consistency, versions can diverge
• Different nodes may perform different updates on same data
• Need to detect, and fix such situations
 Key idea: vector-vector identifies each data version
• Set of (node, counter) pairs
 E.g., with two nodes N1 and N2, ([N1,2],[N2,1])
 Represented as a vector [2, 1]
• An update to a data item at a node increments the counter for that node
• Define a partial order across versions

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Vector Vectors
 Examples of vector vectors
• ([Sx,1]): data item created by site Sx
• ([Sx,2]): data item created by site Sx, and later updated
• ([Sx,2],[Sy,1]): data item updated twice by site Sx and once by Sy
 Update by a site Sx increments the counter for Sx, but leaves
counters from other sites unchanged
• ([Sx,4],[Sy,1]) newer than ([Sx,3],[Sy,1])
• But ([Sx,2],[Sy,1]) incomparable with ([Sx,1],[Sy,2])
• Read operation may find incomparable versions
 Such versions indicate inconsistent concurrent updates
 All such versions returned by read operation
 Up to application to reconcile multiple versions

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 Item D1 created by Node
N1
 D1 updated by Node N1
 D1 concurrently updated by
node N2 and N3 (usually
due to network partitioning)
 Subsequent read from N2
and N3 returns two
incomparable versions
 Application merges versions
and writes new version
Example of Vector Clock in action

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Extensions for Detecting Inconsistency
 Two replicas may diverge, and divergence is not detected until the replicas
is read
• To detect divergence early, one approach is to scan all replicas of all
items periodically
 But requires a lot of network, CPU and I/O load
 Alternative approach based on Merkle trees covered shortly

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How to Reconcile Inconsistent Versions?
 Reconciliation is application specific
• E.g., two sites concurrent insert items to cart
 Merge adds both items to the final cart state
• E.g., S1 adds item A, S2 deletes item B
 Merge adds item A, but deleted item B resurfaces
 Cannot distinguish S2 deletes B from S1 add B
 Problem: operations are inferred from states of divergent versions
• Better alternative:
 Keep track of history of operations
 Merge operation histories

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Order Independent Operations
 Basic idea: updates are performed as logical operations
• Data store is aware of the set of operations that can be carried out
• Operation performed at place where data (replica) is stored
 If result of a sequence of operations is independent of the operation
ordering
• Independent update operations can be merged in different orders at
different replicas, but will lead to same result
• Eventual consistency can be ensured relatively easily

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Detecting Differences Using Merkle Trees
 Merkle Tree: A data structure that can
• Efficiently sign contents of a tree
• Efficiently find differences (if any) between two replicas
 Example

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Detecting Differences Using Merkle Trees (Cont.)
 Overall cost of finding differences with Merkle tree
• O(m log2 N) with N data items and m differences using binary tree
• Each operation requires communication between the two trees
(nodes)
• Use wider trees to reduce height/cost
 Cost is O(m logK N) if each node has K children instead of 2
children
 Particularly important due to high network latency
 Merkle trees originally used for verification of contents of a collection
• Include digital signature at root in this case.

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Weak Consistency Models for Applications
 Read-your-writes
• if a process has performed a write, a subsequent read will reflect the
earlier write operation
 Session consistency
• Read-your-writes in the context of a session, where application
connects to storage system
 Monotonic consistency
• For reads: later reads never return older version than earlier reads
• For writes: serializes writes by a single process
 Minimum requirement
 Sticky sessions: all operations from a session on a data item go to the
same node
 Can be implemented by specifying a version vector in get() operations
• Result of get guaranteed to be at least as new as specified version
vector

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Coordinator Selection
 Backup coordinators
• Backup coordinator maintains enough information locally to assume
the role of coordinator if the actual coordinator fails
 executes the same algorithms and maintains the same internal
state information as the actual coordinator
• allows fast recovery from coordinator failure but involves overhead
during normal processing.
 Backup coordinator approach vulnerable to two-site failure
• Failure of coordinator and backup leads to non-availability
• Key question: how to choose a new coordinator from a set of
candidates
 Choice done by a master: vulnerable to master failure
 Election algorithms are key

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Coordinator Selection
 Coordinator selection using a fault-tolerant lock manager
• Coordinator gets a lease on a coordinator lock, and renews the lease
as long as it is alive
• If coordinator dies or gets disconnected, lease is losk
• Other nodes can detect coordinator failure using heart-beat messages
• Nodes request coordinator lock lease from lock manager; only 1 node
gets the lease, and becomes new coordinator
 Fault-tolerant coordination services such as ZooKeeper, Chubby
• Provide fault-tolerant lock management services
• And are widely used for coordinator section
• Store (small amounts) of data in files
• Create and delete files
 Which can be used as locks/leases
• Coordinator releases lease if it is not renewed in time
• Can watch for changes on a file
• But these services themselves need a coordinator……..

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Election of Coordinator
 Election algorithms
• Used to elect a new coordinator in case of failures
 Heartbeat messages used to detect failure of coordinator
• One-time election protocol
 Proposers: Nodes that propose themselves as coordinator and send
vote requests to other nodes
 Acceptors: Nodes that can vote for candidate proposers
 Learners: Nodes that ask acceptors who they voted for, to find
winner
• A node can perform all above roles
 Problems with this protocol
• What if no one won the election due to split vote?
• If election is rerun, need to identify which election a request is for
• General approach
 Candidates make a proposal with a term number
• Term number is 1 more than term number of previous election
known to candidate

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Election of Coordinator
 Election algorithms (Cont.)
• Stale messages corresponding to old terms can be ignored
 If a candidate wins majority vote it becomes coordinator
 Otherwise election is rerun with term number incremented
 Minimizing chances of split elections:
• Use node IDs to decide who to vote for
 e.g., max node ID (Bully algorithm)
 Candidates withdraw if they find another candidate with
higher ID
• Randomized retry: candidates wait for random time intervals
before retrying
 High probability that only one node is asking to be elected at
a time
• Special case of distributed consensus

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Issues with Multiple Coordinators
 Coordinator may get disconnected, and new coordinator elected, without
old coordinator ever knowing about the election
• Multiple nodes may thus believe they are coordinators
 Called a split-brain situation
 Solutions:
• Term numbers can be used to identify coordinator
 Majority of node will know of latest coordinator term since they
voted for it
 Messages with old term number (stale messages) can be ignored
• Leases can be used to ensure only one coordinator at a time
 Delayed messages may still be received from old coordinator
• Term numbers can be used to ignore such delayed messages

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Distributed Consensus
 Motivating example: commit decision in two-phase commit (2PC)
• Decision made by coordinator alone: vulnerable to blocking problem
 If coordinator fails/gets disconnected at certain key points, rest of
system does not know if the decision was to commit/abort, and
must block till coordinator recovers
• Multiple nodes must participate in decision process to ensure fault
tolerance
 Although initial proposal for decision may be made by a single
node
• Goal: A decision making protocol that is non-blocking as long as a
majority of participating nodes are up and reachable
 2PC is a special case of a more general class of decision problems that
must be made by a collection of nodes in a fault-tolerant, non-blocking
manner

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Distributed Consensus
 Distributed consensus problem: A set of n nodes (called participants)
need to agree on a decision by executing a protocol such that
• All participants “learn” the same value for the decision
 even if some nodes fail during the execution of the protocol,
messages are lost, or there are network partitions
• The protocol should not block, and must terminate, as long as some
majority of nodes are alive and can communicate with each other

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Distributed Consensus: Overview
 An real system needs to make a series of decisions: multiple consensus
protocol
 Problem can be abstracted as adding a record to a log
• Each node has a copy of the log, and log records are appended at
each node
• Potential for conflicts between the nodes on what record is appended
at what point in the log
• The multiple consensus protocol must ensure that the log is uniquely
defined
 Copies of the log may temporarily differ, but must be made
consistent subsequently
• May require deleting parts of the log on a node
 Actions can be taken on a log record only after consensus has
been reached for that position in the log

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Distributed Consensus: Overview (Cont.)
 Several protocols proposed
• We outline key ideas behind Paxos and Raft
• The Zab protocol used in ZooKeeper is another widely used
consensus protocol
 Key idea: voting to make a decision
• A particular decision succeeds only if a majority of the participating
nodes have voted for it
 Prevents more than one decision being chosen in a round
 If majority of nodes are up and agree on a decision voting will not
block
• But devil in the details!

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Paxos Consensus Protocol
 Assume a collection of processes that can propose values
• Different processes may propose different values
• Proposals are sent to acceptors which collectively choose from among
the proposals
 A single execution of a distributed consensus protocol must ensure that:
• At most a single value from amongst those proposed is chosen
collectively by the acceptors
• If a value has been chosen, then learner processes should be able to
learn the chosen value
 In case no value is chosen (split-voting), protocol reexecutes
• Protocol should not block, and must terminate, as long as some
majority of the nodes participating remain alive and can communicate
with each other

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Paxos Consensus Protocol: Overview
 Key idea: Consensus is reached when a majority of acceptors have accepted a
particular proposal
• Learner finds what value (if any) was accepted by a majority of acceptors
 If a majority vote for a particular value, all is fine, BUT
• Vote may get split, requiring further rounds to reach a majority
• Worse, even if a majority accept a value (and even if a learner learns of the
majority), some of the acceptors (and the learner) may die or get
disconnected
 Remaining nodes may not be a majority
 If this is treated as failure and another round is run, a different proposal
may get accepted, with different learners learning different values!
• Once acceptor has voted for a particular proposal in a round, it cannot
change its mind for that round
 Decision must be logged to ensure no change in decision if acceptor
dies and comes back up

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Paxos: Overview
 To deal with split vote Paxos uses a coordinator
• Proposals serialized through coordinator, so only one value is typically
proposed in a round
• Paxos works correctly (but less efficiently) even if there are multiple
coordinators
• Coordinator can be elected
 Different values getting majorities in different nodes is a more serious
problem. To solve it further rounds should give same result.
 Key idea:
• Each proposal in Paxos has a unique number
• Acceptors accept highest numbered proposal received in a round
• Proposers will not create new proposals with a different number
• Two phase protocol

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Paxos Made Simple
 Phase 1
• Phase 1a: A proposer selects a proposal number n and sends a
prepare request with number n to a majority of acceptors
 Number has to be chosen in some unique way
• Phase 1b: If an acceptor receives a prepare request with number n
 If n is less than that of any prepare request to which it has already
responded then it ignores the request
 Else it remembers n and responds to the request
• If it has already accepted a proposal with number m and value
v, it sends (m, v) with the response
• Otherwise it indicates to the proposer that it has not accepted
any value earlier
• NOTE: responding is NOT the same as accepting

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Paxos Made Simple
 Phase 2
• Phase 2a: Proposer Algorithm: If the proposer receives a response to
its prepare requests (numbered n) from a majority of acceptors
 then it sends an accept request to each of those acceptors for a
proposal numbered n with a value v, where v is
• the value selected by the proposer if none of the acceptors
indicated it had already accepted a value.
• Otherwise v is the value of the highest-numbered proposal
among the responses
 i.e., proposer backsoff from its own proposal and votes for
highest numbered proposal already accepted by at least
one acceptor
 If proposer does not hear from a majority it takes no further action
in this round

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Paxos Made Simple
 Phase 2
• Phase 2b: Acceptor Algorithm: If an acceptor receives an accept
request for a proposal numbered n,
 If it has earlier responded to a prepare message with number n1
> n it ignores the message
 Otherwise it accepts the proposed value v with number n.
• Note: acceptor may accept different values with increasing n

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Paxos Details
 Key idea: if a majority of acceptors accept a value v (with number n), then
even if there are further proposals with number n1 > n, the value proposed
will be value v
• Why?:
 A value can be accepted with number n only if a majority of nodes
(say P) respond to a prepare message with number n
 Any subsequent majority (say A) will have nodes in common with
the first majority P, and at least one of those nodes would have
responded with value v and number n
• If a higher numbered proposal p was accepted earlier by even
one node majority would have responded to p, and will ignore n
 Further rounds will use this value v (since highest accepted value
is used in Phase 2a)

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Paxos Details (Cont.)
 At end of phase 2, it is possible that there is no majority have agreed on a
value
• Learners that believe majority was not reached can initiate a fresh
proposal
• If majority had actually been reached, same value will be chosen
again
 Many more details under cover
 Above is for a single decision. Multi-Paxos: extension which deals with a
series of decisions
 Many variants of Paxos optimized for different scenarios

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The Log-Based Consensus Protocols
 Fault-tolerant log, to which records are appended
 Each participating node maintains a replica of a log
 Key goal: keep the log replicas in sync
• Logical view of atomically appending records to all copies of the log
• Can’t actually be done atomically; logs may diverge
 Consensus protocols must ensure
• Even if a log replica is temporarily inconsistent with another, it will be
brought back to sync
 May require log deletion and replacement
• A log entry will not be treated as committed until the algorithm
guarantees that it will never be deleted

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The Raft Consensus Algorithm
 Raft is based on having a coordinator, called a leader
• Essential in Raft, unlike Paxos, where coordinator is an optimization
 Other nodes are called followers
 Leaders may die and get replaced
• Time divided into terms, each term has a unique leader
• Terms have increasing numbers

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The Raft Leader Election
 Leaders are elected using randomized retry algorithm outlined in Section
23.7.2
• Recall that algorithm already uses notion of term
• Voting is done for a specific term
 Can change in another term
• Nodes track currentTerm based on messages received
 Leader N1 may get disconnected and get reconnected after new leader
N2 is elected
• N1 may not even know it was disconnected and may continue leader
actions

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Example of Raft Logs
 Number in each entry indicates term
 Example log entries are assignments to variables

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Raft Log Replication
 Appending a log entry done by sending log append request to leader
 Leader sends AppendEntries request to all followers, with these
parameters
• term
• previousLogEntryPosition
• previousLogEntryTerm
• logEntries: array allowing multiple log records to be appended
• leaderCommitIndex: an index such that all log records before the
index are committed
 Followers carry out checks and respond (next slide)
 If majority of nodes respond with true, leader can report successful log
append to initiating node
• Otherwise more work is needed, explained later

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Raft AppendEntries Procedure
 Follower that receives AppendEntries message does the following
1. If term in message is less than followers currentTerm, Return false
2. If log does not have an entry at previousLogEntryPosition with term
matching previousLogEntryTerm, Return false
3. If entry at previousLogEntryPosition is different from first log record
in AppendEntries message, delete existing entry and all subsequent
entries in log
4. Any log records in logEntries that are not already in log are
appended to log
5. Follower maintains local commitIndex
 if leaderCommitIndex > commitIndex, set
commitIndex=min(leaderCommitIndex, last log entry index)
6. Return true

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Raft AppendEntries Procedure (Cont.)
 If leader N1 receives a false message from follower with a higher
currentTerm, N1 realizes it is no longer a leader and becomes a follower
 Different followers may have different log states
 If leader receives false from a node, log in that node is out of date and
needs updating
• Leader retries AppendEntries for that node, starting from an earlier
point in its own log
• May get false several times, until it goes far enough back in log to find
a matching log entry
 Key remaining issue: if a leader dies, and another one takes over, the log
must be brought to consistent state
• New leader may have an older log

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Raft Leader Replacement
 Raft protocol ensures any node elected as leader has all committed log
entries
• Candidate must send information about its own log state when
seeking votes
• Node votes for candidate only if candidates log state is at least as up-
to-date as its own (we omit details)
• Since majority have voted for new leader, any committed log entry will
be in new leaders log
 Raft forces all other nodes to replicate leaders log
• Log records at new leader may get committed when log gets
replicated
• Leader cannot count number of replicas with a record from an earlier
term and declare it committed if it is at majority
 Details are subtle, and omitted
 Instead, leader must replicate a new log record in its current term

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Raft Protocol
 There are many more subtle details that need to be taken care of
• Consistency even in face of multiple failures and restarts
• Maintaining cluster membership, cluster membership changes
 Raft has been proven formally correct
 See bibliographic notes for more details of above

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Fault-Tolerant Services using
Replicated State Machines
 Key requirement: make a service fault tolerant
• E.g., lock manager, key-value storage system, ….
 State machines are a powerful approach to creating such services
 A state machine
• Has a stored state, and receives inputs
• Makes state transitions on each input, and may output some results
 Transitions and output must be deterministic
 A replicated state machine is a state machine that is replicated on multiple
nodes
• All replicas must get exactly the same inputs
 Replicated log! State machine processes only committed inputs!
• Even if some of the nodes fail, state and output can be obtained from other
nodes

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Replicated State Machine
 Replicated state machine based on replicated log
 Example commands assign values to variables

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Uses of Replicated State Machines
 Replicated state machines can be used to implement wide variety of
services
• Inputs can specify operations with parameters
• But operations must be deterministic
• Result of operation can be sent from any replica
 Gets executed only when log record is committed in replicated log
 Usually sent from leader, which knows which part of log is
committed
 Example: Fault-tolerant lock manager
• State: lock table
• Operations: lock requests and lock releases
• Output: grant, or rollback requests on deadlock
• Centralized implementation is made fault tolerant by simply running it
on a replicated state machine

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Uses of Replicated State Machines
 Fault tolerant key-value store
• State: key-value storage state
• Operations: get() and put() are first logged
 Operations executed when the log record is in committed state
 Note: even get() operations need to be processed via log
 Google Spanner uses replicated state machine to implement key-value
store
• Data is partitioned, and each partition is replicated across multiple
nodes
• Replicas of a partition form a Paxos group with one node as leader
• Operations initiated at leader, and replicated to other nodes

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Two-Phase Commit Using Consensus
 Basic two-phase commit can result in blocking
 Non-blocking two-phase commit can be implemented using consensus
• Key idea: Record commit decisions using consensus protocol instead
of logging it at coordinator
• As long as majority of sites are up and reachable, decision will be
known
 Blocking is then avoided
 Used e.g. in Google spanner, for transactions that span partitions
• 2PC is coordinated by Paxos group leader at any 1 partition
• Lock table is implemented using replicated state machine
 Even if leader fails, new leader can see up-to-date lock state

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Extra Slides – Material Not in Text
 Weak Consistency
 Miscellaneous

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Dynamo: Basics
 Provides a key-value store with basic get/put interface
• Data values entirely uninterpreted by system
 Unlike Bigtable, PNUTS, Megastore, etc.
 Underlying storage based on DHTs using consistent hashing with virtual
processors
 Replication (N-ary)
• Data stored in node to which key is mapped, as well as N-1
consecutive successors in ring
• Replication at level of key range (virtual node)
• Put call may return before data has been stored on all replicas
 Reduces latency, at risk of consistency
 Programmer can control degree of consistency (QR, QW and S) per
instance (relation)

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Performing Put/Get Operations
 Get/put requests handled by a coordinator (one of the nodes containing a
replica of the item)
 Upon receiving a put() request for a key
• the coordinator generates the vector clock for the new version and
writes the new version locally
• The coordinator then sends the new version (along with the new vector
clock) to the N highest-ranked reachable nodes.
• If at least QW-1 nodes respond then the write is considered successful.
 For a get() request
• the coordinator requests all existing versions of data for that key from
the N highest-ranked reachable nodes in the preference list for that key,
• Waits for QR responses before returning the result to the client.
• Returns all causally unrelated (incomparable) versions
• Application should reconcile divergent versions and write back a
reconciled version superseding the current versions

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How to Reconcile Inconsistent Versions?
 Reconciliation is application specific
• E.g., two sites concurrent insert items to cart
 Merge adds both items to the final cart state
• E.g., S1 adds item A, S2 deletes item B
 Merge adds item A, but deleted item B resurfaces
 Cannot distinguish S2 deletes B from S1 add B
 Problem: operations are inferred from states of divergent versions
 Better solution (not supported in Dynamo) keep track of history of
operations

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Availability vs Latency
 Abadi’s classification system: PACELC
• CAP theorem only matters when there is a partition
• Even if partitions are rare, applications may trade off consistency for
latency
 E.g. PNUTS allows inconsistent reads to reduce latency
• Critical for many applications
 But update protocol (via master) ensures consistency over
availability
• Thus Abadi asks two questions:
 If there is Partitioning, how does system tradeoff Availability for
Consistency
 Else how does system trade off Latency for Consistency
• E.g., Megastore: PC/EC
PNUTS: PC/EL
Dynamo (by default): PA/EL

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Amazon Dynamo
 Distributed data storage system supporting very high availability
• Even at cost of consistency
• E.g., motivation from Amazon: Web users should always be able to
add items to their cart
 Even if they are connected to an app server which is now in a
minority partition
 Data should be synchronized with majority partition eventually
 Inconsistency may be visible (briefly) to users
• preferable to losing a customer!
 DynamoDB: part of Amazon Web Service, can subscribe and use over the
Web

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Bully Algorithm Details
 If site Si sends a request that is not answered by the coordinator within a
time interval T, assume that the coordinator has failed Si tries to elect itself
as the new coordinator.
 Si sends an election message to every site with a higher identification
number, Si then waits for any of these processes to answer within T.
 If no response within T, assume that all sites with number greater than i
have failed, Si elects itself the new coordinator.
 If answer is received Si begins time interval T’, waiting to receive a
message that a site with a higher identification number has been elected.

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Bully Algorithm (Cont.)
 If no message is sent within T’, assume the site with a higher number has
failed; Si restarts the algorithm.
 After a failed site recovers, it immediately begins execution of the same
algorithm.
 If there are no active sites with higher numbers, the recovered site forces all
processes with lower numbers to let it become the coordinator site, even if
there is a currently active coordinator with a lower number.

Database System Concepts, 7th
Ed.
See www.db-book.com for conditions on re-use
End of Chapter 23

ch23.pptx distrubuted database management

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