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  • UNIT I
  • PARALLEL AND DISTRIBUTED DATABASES     Database System Architectures: Centralized  and ClientServer Architectures– Server System Architectures -Parallel Systems-Distributed Systems Parallel  Databases: I/O Parallelism - Inter and Intra Query Parallelism –Inter and Intra  operation Parallelism –Design of Parallel Systems-Distributed Database  Concepts -Distributed Data Storage –Distributed Transactions –Commit  Protocols –Concurrency Control –Distributed Query Processing –Case Studies
  • Client-Server Architecture for DDBMS  (Data-based) General idea: Divide the functionality into two classes:– --server functions ∗mainly data management, including query processing, optimization, transaction management, etc. –client functions ∗might also include some data management functions (consistency checking, transaction management, etc.) not just user interface •Provides a two-level architecture •More efficient division of work •Different types of client/server architecture –Multiple client/single server –Multiple client/multiple server
  • Client-Server Architecture (Component-based) One server, many clients
  • Components of Client-Server Architecture (Component-based) Many servers, many clients
  • Server Architectures   Introduction   Architecture Models   Problems of Parallel DBMS   Architecture of Parallel DBMS   Data Partitioning   IBM DB2 UDB Enterprise   Oracle Real Application Cluster   Summary: DBMS Architecture Comparisons
  • INTRODUCTION  Searching for performance through the exploitation in parallel of  system resources  An ideal parallel system must have two properties (DeWitt/Gray  [DEW92]): Linear Speedup   N times more resources make it possible to treat a given task in N times less than the reference system (typical case of DSS) Linear Scaleup  N times more resources make it possible to deal with an N times larger problem in the same time as the reference system (typical case of OLTP: updating a N times larger database by a N times larger number of users)
  • Speedup and Scaleup
  • Architecture Models
  • Architecture Models(2)
  • Architecture Models(3)
  • Architecture Models(4)
  • Problems of Parallel DBMS
  • Architecture of Parallel DBMS
  • Architecture of Parallel DBMS(Contd…)
  • Combining data partitioning and pipelined execution
  • Functional Models in a Share Nothing Architecture
  • Data Partitioning
  • Examples of methods of data partitioning
  • IBM DB2 UDB Enterprise Extended  Edition
  • Join Strategies in DB2
  • Design of Parallel Systems (Cont.) • On-line reorganization of data and schema changes  must be supported. – For example, index construction on terabyte databases  can take hours or days even on a parallel system. • Need to allow other processing (insertions/deletions/updates) to  be performed on relation even as index is being constructed. – Basic idea: index construction tracks changes and ``catches  up'‘ on changes at the end. • Also need support for on-line repartitioning and  schema changes (executed concurrently with other  processing).
  • Parallel Databases  • • • • • • • Introduction I/O Parallelism Interquery Parallelism Intraquery Parallelism Intraoperation Parallelism Interoperation Parallelism Design of Parallel Systems
  • Parallelism in Databases • Data can be partitioned across multiple disks for parallel  I/O. • Individual relational operations (e.g., sort, join,  aggregation) can be executed in parallel – data can be partitioned and each processor can work  independently on its own partition. • Queries are expressed in high level language (SQL,  translated to relational algebra) – makes parallelization easier. • Different queries can be run in parallel with each other. Concurrency control takes care of conflicts.  • Thus, databases naturally lend themselves to parallelism.
  • I/O Parallelism • Reduce the time required to retrieve relations from disk by  partitioning • the relations on multiple disks. • Horizontal partitioning – tuples of a relation are divided  among many disks such that each tuple resides on one disk. • Partitioning techniques (number of disks = n): Round-robin:  Send the ith tuple inserted in the relation to disk i mod n.   Hash partitioning:   – Choose one or more attributes as the partitioning attributes.    –  Choose hash function h with range 0…n - 1 – Let i denote result of hash function h applied to the  partitioning attribute value of a tuple. Send tuple to disk i.
  • I/O Parallelism (Cont.) • Partitioning techniques (cont.): • Range partitioning: – Choose an attribute as the partitioning attribute. – A partitioning vector [vo, v1, ..., vn-2] is chosen. – Let v be the partitioning attribute value of a tuple. Tuples such that vi ≤ vi+1 go to disk I + 1. Tuples with v < v0 go to disk 0 and tuples with v ≥ vn-2 go to disk n-1. E.g., with a partitioning vector [5,11], a tuple with partitioning attribute value of 2 will go to disk 0, a tuple with value 8 will go to disk 1, while a tuple with value 20 will go to disk2.
  • Comparison of Partitioning Techniques • Evaluate how well partitioning techniques support the following types of data access: 1.Scanning the entire relation. 2.Locating a tuple associatively – point queries. – E.g., r.A = 25. 3.Locating all tuples such that the value of a given attribute lies within a specified range – range queries. – E.g., 10 ≤ r.A < 25.
  • Comparison of Partitioning Techniques (Cont.) Round robin: • Advantages – Best suited for sequential scan of entire relation on each query. – All disks have almost an equal number of tuples; retrieval work is thus well balanced between disks. • Range queries are difficult to process – No clustering -- tuples are scattered across all disks
  • Comparison of Partitioning Techniques(Cont.) Hash partitioning: • Good for sequential access – Assuming hash function is good, and partitioning attributes form a key, tuples will be equally distributed between disks – Retrieval work is then well balanced between disks. • Good for point queries on partitioning attribute – Can lookup single disk, leaving others available for answering other queries. – Index on partitioning attribute can be local to disk, making lookup and update more efficient • No clustering, so difficult to answer range queries
  • Comparison of Partitioning Techniques (Cont.) • • • • Range partitioning: Provides data clustering by partitioning attribute value. Good for sequential access Good for point queries on partitioning attribute: only one disk needs to be accessed. • For range queries on partitioning attribute, one to a few disks may need to be accessed – Remaining disks are available for other queries. – Good if result tuples are from one to a few blocks. – If many blocks are to be fetched, they are still fetched from one to a few disks, and potential parallelism in disk access is wasted • Example of execution skew.
  • Partitioning a Relation across Disks • If a relation contains only a few tuples which will fit into a single disk block, then assign the relation to a single disk. • Large relations are preferably partitioned across all the available disks. • If a relation consists of m disk blocks and there are n disks available in the system, then the relation should be allocated min(m,n) disks.
  • Handling of Skew • The distribution of tuples to disks may be skewed — that is, some disks have many tuples, while others may have fewer tuples. • Types of skew: – Attribute-value skew. • Some values appear in the partitioning attributes of many tuples; all the tuples with the same value for the partitioning attribute end up in the same partition. • Can occur with range-partitioning and hash-partitioning. – Partition skew. • With range-partitioning, badly chosen partition vector may assign too many tuples to some partitions and too few to others. • Less likely with hash-partitioning if a good hash-function is chosen.
  • Handling Skew in Range-Partitioning • To create a balanced partitioning vector (assuming partitioning attribute forms a key of the relation): – Sort the relation on the partitioning attribute. – Construct the partition vector by scanning the relation in sorted order as follows. • After every 1/nth of the relation has been read, the value of the partitioning attribute of the next tuple is added to the partition vector. – n denotes the number of partitions to be constructed. – Duplicate entries or imbalances can result if duplicates are present in partitioning attributes. • Alternative technique based on histograms used in practice
  • Handling Skew using Histograms Balanced partitioning vector can be constructed from histogram in a relatively straightforward fashion Assume uniform distribution within each range of the histogram Histogram can be constructed by scanning relation, or sampling (blocks containing) tuples of the relation
  • Handling Skew Using Virtual Processor Partitioning • Skew in range partitioning can be handled elegantly using virtual processor partitioning: – create a large number of partitions (say 10 to 20 times the number of processors) – Assign virtual processors to partitions either in roundrobin fashion or based on estimated cost of processing each virtual partition • Basic idea: – If any normal partition would have been skewed, it is very likely the skew is spread over a number of virtual partitions – Skewed virtual partitions get spread across a number of processors, so work gets distributed evenly!
  • Interquery Parallelism • Queries/transactions execute in parallel with one another. • Increases transaction throughput; used primarily to scale up a transaction processing system to support a larger number of transactions per second. • Easiest form of parallelism to support, particularly in a sharedmemory parallel database, because even sequential database systems support concurrent processing. • More complicated to implement on shared-disk or shared-nothing architectures – Locking and logging must be coordinated by passing messages between processors. – Data in a local buffer may have been updated at another processor. – Cache-coherency has to be maintained — reads and writes of data in buffer must find latest version of data.
  • Cache Coherency Protocol • Example of a cache coherency protocol for shared disk systems: – Before reading/writing to a page, the page must be locked in shared/exclusive mode. – On locking a page, the page must be read from disk – Before unlocking a page, the page must be written to disk if it was modified. • More complex protocols with fewer disk reads/writes exist. • Cache coherency protocols for shared-nothing systems are similar. Each database page is assigned a home processor. Requests to fetch the page or write it to disk are sent to the home processor.
  • Intraquery Parallelism • Execution of a single query in parallel on multiple processors/disks; important for speeding up long-running queries. • Two complementary forms of intraquery parallelism : – Intraoperation Parallelism – parallelize the execution of each individual operation in the query. – Interoperation Parallelism – execute the different operations in a query expression in parallel. the first form scales better with increasing parallelism because the number of tuples processed by each operation is typically more than the number of operations in a query
  • Parallel Processing of Relational Operations • Our discussion of parallel algorithms assumes: – read-only queries – shared-nothing architecture – n processors, P0, ..., Pn-1, and n disks D0, ..., Dn-1, where disk Di is associated with processor Pi. • If a processor has multiple disks they can simply simulate a single disk Di. • Shared-nothing architectures can be efficiently simulated on shared-memory and shared-disk systems. – Algorithms for shared-nothing systems can thus be run on shared-memory and shared-disk systems. – However, some optimizations may be possible.
  • Parallel Sort Range-Partitioning Sort • Choose processors P0, ..., Pm, where m ≤ n -1 to do sorting. • Create range-partition vector with m entries, on the sorting attributes • Redistribute the relation using range partitioning – all tuples that lie in the ith range are sent to processor Pi – Pi stores the tuples it received temporarily on disk Di. – This step requires I/O and communication overhead. • • • Each processor Pi sorts its partition of the relation locally. Each processors executes same operation (sort) in parallel with other processors, without any interaction with the others (data parallelism). Final merge operation is trivial: range-partitioning ensures that, for 1 j m, the key values in processor Pi are all less than the key values in Pj.
  • Parallel Sort (Cont.) Parallel External Sort-Merge • Assume the relation has already been partitioned among disks D0, ..., Dn-1 (in whatever manner). • Each processor Pi locally sorts the data on disk Di. • The sorted runs on each processor are then merged to get the final sorted output. • Parallelize the merging of sorted runs as follows: – The sorted partitions at each processor Pi are range-partitioned across the processors P0, ..., Pm-1. – Each processor Pi performs a merge on the streams as they are received, to get a single sorted run. – The sorted runs on processors P0,..., Pm-1 are concatenated to get the final result.
  • Parallel Join • The join operation requires pairs of tuples to be tested to see if they satisfy the join condition, and if they do, the pair is added to the join output. • Parallel join algorithms attempt to split the pairs to be tested over several processors. Each processor then computes part of the join locally. • In a final step, the results from each processor can be collected together to produce the final result.
  • Partitioned Join • For equi-joins and natural joins, it is possible to partition the two input relations across the processors, and compute the join locally at each processor. • Let r and s be the input relations, and we want to compute r r.A=s.B s. • r and s each are partitioned into n partitions, denoted r0, r1, ..., rn-1 and s0, s1, ..., sn-1. • Can use either range partitioning or hash partitioning. • r and s must be partitioned on their join attributes r.A and s.B), using the same range-partitioning vector or hash function. • Partitions ri and si are sent to processor Pi, • Each processor Pi locally computes ri ri.A=si.B si. Any of the standard join methods can be used.
  • Partitioned Join (Cont.)
  • Fragment-and-Replicate Join • Partitioning not possible for some join conditions – e.g., non-equijoin conditions, such as r.A > s.B. • For joins were partitioning is not applicable, parallelization can be accomplished by fragment and replicate technique – Depicted on next slide • Special case – asymmetric fragment-and-replicate: – One of the relations, say r, is partitioned; any partitioning technique can be used. – The other relation, s, is replicated across all the processors. – Processor Pi then locally computes the join of ri with all of s using any join technique.
  • Depiction of Fragment-and-Replicate Joins
  • Fragment-and-Replicate Join (Cont.) • General case: reduces the sizes of the relations at each processor. – r is partitioned into n partitions,r0, r1, ..., r n-1;s is partitioned into m partitions, s0, s1, ..., sm-1. – Any partitioning technique may be used. – There must be at least m * n processors. – Label the processors as – P0,0, P0,1, ..., P0,m-1, P1,0, ..., Pn-1m-1. – Pi,j computes the join of ri with sj. In order to do so, ri is replicated to Pi,0, Pi,1, ..., Pi,m-1, while si is replicated to P0,i, P1,i, ..., Pn-1,i – Any join technique can be used at each processor Pi,j.
  • Fragment-and-Replicate Join (Cont.) • Both versions of fragment-and-replicate work with any join condition, since every tuple in r can be tested with every tuple in s. • Usually has a higher cost than partitioning, since one of the relations (for asymmetric fragment-and-replicate) or both relations (for general fragment-and-replicate) have to be replicated. • Sometimes asymmetric fragment-and-replicate is preferable even though partitioning could be used. – E.g., say s is small and r is large, and already partitioned. It may be cheaper to replicate s across all processors, rather than repartition r and s on the join attributes.
  • Partitioned Parallel Hash-Join Parallelizing partitioned hash join: • Assume s is smaller than r and therefore s is chosen as the build relation. • A hash function h1 takes the join attribute value of each tuple in s and maps this tuple to one of the n processors. • Each processor Pi reads the tuples of s that are on its disk Di, and sends each tuple to the appropriate processor based on hash function h1. Let si denote the tuples of relation s that are sent to processor Pi. • As tuples of relation s are received at the destination processors, they are partitioned further using another hash function, h2, which is used to compute the hash-join locally. (Cont.)
  • Partitioned Parallel Hash-Join (Cont.) • Once the tuples of s have been distributed, the larger relation r is redistributed across the m processors using the hash function h1 – Let ri denote the tuples of relation r that are sent to processor Pi. • As the r tuples are received at the destination processors, they are repartitioned using the function h2 – (just as the probe relation is partitioned in the sequential hash-join algorithm). • Each processor Pi executes the build and probe phases of the hashjoin algorithm on the local partitions ri and s of r and s to produce a partition of the final result of the hash-join. • Note: Hash-join optimizations can be applied to the parallel case – e.g., the hybrid hash-join algorithm can be used to cache some of the incoming tuples in memory and avoid the cost of writing them and reading them back in.
  • Parallel Nested-Loop Join • Assume that – relation s is much smaller than relation r and that r is stored by partitioning. – there is an index on a join attribute of relation r at each of the partitions of relation r. • Use asymmetric fragment-and-replicate, with relation s being replicated, and using the existing partitioning of relation r. • Each processor Pj where a partition of relation s is stored reads the tuples of relation s stored in Dj, and replicates the tuples to every other processor Pi. – At the end of this phase, relation s is replicated at all sites that store tuples of relation r. • Each processor Pi performs an indexed nested-loop join of relation s with the ith partition of relation r.
  • Grouping/Aggregation • Partition the relation on the grouping attributes and then compute the aggregate values locally at each processor. • Can reduce cost of transferring tuples during partitioning by partly computing aggregate values before partitioning. • Consider the sum aggregation operation: – Perform aggregation operation at each processor Pi on those tuples stored on disk Di • results in tuples with partial sums at each processor. – Result of the local aggregation is partitioned on the grouping attributes, and the aggregation performed again at each processor Pi to get the final result. • Fewer tuples need to be sent to other processors during partitioning.
  • Cost of Parallel Evaluation of Operations • If there is no skew in the partitioning, and there is no overhead due to the parallel evaluation, expected speed-up will be 1/n • If skew and overheads are also to be taken into account, the time taken by a parallel operation can be estimated as Tpart + Tasm + max (T0, T1, …, Tn-1) – Tpart is the time for partitioning the relations – Tasm is the time for assembling the results – Ti is the time taken for the operation at processor Pi • this needs to be estimated taking into account the skew, and the time wasted in contentions.
  • Interoperator Parallelism • Pipelined parallelism – Consider a join of four relations • r1 r2 r3 r4 – Set up a pipeline that computes the three joins in parallel • Let P1 be assigned the computation of temp1 = r1 r2 • And P2 be assigned the computation of temp2 = temp1 • And P3 be assigned the computation of temp2 r 4 r3 – Each of these operations can execute in parallel, sending result tuples it computes to the next operation even as it is computing further results • Provided a pipelineable join evaluation algorithm (e.g. indexed nested loops join) is used
  • Factors Limiting Utility of Pipeline Parallelism • Pipeline parallelism is useful since it avoids writing intermediate results to disk • Useful with small number of processors, but does not scale up well with more processors. One reason is that pipeline chains do not attain sufficient length. • Cannot pipeline operators which do not produce output until all inputs have been accessed (e.g. aggregate and sort) • Little speedup is obtained for the frequent cases of skew in which one operator's execution cost is much higher than the others.
  • Independent Parallelism • Independent parallelism – Consider a join of four relations • • • • • r1 r2 r3 r4 Let P1 be assigned the computation of temp1 = r1 r2 And P2 be assigned the computation of temp2 = r3 r4 And P3 be assigned the computation of temp1 temp2 P1 and P2 can work independently in parallel P3 has to wait for input from P1 and P2 – Can pipeline output of P1 and P2 to P3, combining independent parallelism and pipelined parallelism – Does not provide a high degree of parallelism • useful with a lower degree of parallelism. • less useful in a highly parallel system,
  • Distributed Databases • A distributed database system consists of loosely coupled sites that share no physical component • Database systems that run on each site are independent of each other • Transactions may access data at one or more sites
  • Homogeneous Distributed Databases • In a homogeneous distributed database – All sites have identical software – Are aware of each other and agree to cooperate in processing user requests. – Each site surrenders part of its autonomy in terms of right to change schemas or software – Appears to user as a single system • In a heterogeneous distributed database – Different sites may use different schemas and software • Difference in schema is a major problem for query processing • Difference in softwrae is a major problem for transaction processing – Sites may not be aware of each other and may provide only limited facilities for cooperation in transaction processing
  • Distributed Data Storage • Assume relational data model • Replication – System maintains multiple copies of data, stored in different sites, for faster retrieval and fault tolerance. • Fragmentation – Relation is partitioned into several fragments stored in distinct sites • Replication and fragmentation can be combined – Relation is partitioned into several fragments: system maintains several identical replicas of each such fragment.
  • Data Replication • A relation or fragment of a relation is replicated if it is stored redundantly in two or more sites. • Full replication of a relation is the case where the relation is stored at all sites. • Fully redundant databases are those in which every site contains a copy of the entire database.
  • Data Replication (Cont.) • Advantages of Replication – Availability: failure of site containing relation r does not result in unavailability of r is replicas exist. – Parallelism: queries on r may be processed by several nodes in parallel. – Reduced data transfer: relation r is available locally at each site containing a replica of r. • Disadvantages of Replication – Increased cost of updates: each replica of relation r must be updated. – Increased complexity of concurrency control: concurrent updates to distinct replicas may lead to inconsistent data unless special concurrency control mechanisms are implemented. • One solution: choose one copy as primary copy and apply concurrency control operations on primary copy
  • Data Fragmentation • Division of relation r into fragments r1, r2, …, rn which contain sufficient information to reconstruct relation r. • Horizontal fragmentation: each tuple of r is assigned to one or more fragments • Vertical fragmentation: the schema for relation r is split into several smaller schemas – All schemas must contain a common candidate key (or superkey) to ensure lossless join property. – A special attribute, the tuple-id attribute may be added to each schema to serve as a candidate key. • Example : relation account with following schema • Account-schema = (branch-name, account-number, balance)
  • Advantages of Fragmentation • Horizontal: – allows parallel processing on fragments of a relation – allows a relation to be split so that tuples are located where they are most frequently accessed • Vertical: – allows tuples to be split so that each part of the tuple is stored where it is most frequently accessed – tuple-id attribute allows efficient joining of vertical fragments – allows parallel processing on a relation • Vertical and horizontal fragmentation can be mixed. – Fragments may be successively fragmented to an arbitrary depth.
  • Distributed Transactions • Transaction may access data at several sites. • Each site has a local transaction manager responsible for: – Maintaining a log for recovery purposes – Participating in coordinating the concurrent execution of the transactions executing at that site. • Each site has a transaction coordinator, which is responsible for: – Starting the execution of transactions that originate at the site. – Distributing subtransactions at appropriate sites for execution. – Coordinating the termination of each transaction that originates at the site, which may result in the transaction being committed at all sites or aborted at all sites.
  • Transaction System Architecture
  • 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.
  • 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. – not acceptable to have a transaction committed at one site and aborted at another • The two-phase commit (2 PC) protocol is widely used • The three-phase commit (3 PC) protocol is more complicated and more expensive, but avoids some drawbacks of two-phase commit protocol
  • Two Phase Commit Protocol (2PC) • Assumes fail-stop model – failed sites simply stop working, and do not cause any other harm, such as sending incorrect messages to other sites. • 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 • Let T be a transaction initiated at site Si, and let the transaction coordinator at Si be Ci
  • 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
  • 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.
  • Handling of Failures - Site Failure When site Si 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 replies that Sk failed before responding to the prepare T message from Ci – since the failure of Sk precludes the sending of such a response C1 must abort T – Sk must execute undo (T)
  • 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.
  • 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
  • Concurrency Control • Three Phase Commit (3PC): • • • • • • Assumptions: – No network partitioning – At any point, at least one site must be up. – At most K sites (participants as well as coordinator) can fail Phase 1: Obtaining Preliminary Decision: Identical to 2PC Phase 1. – Every site is ready to commit if instructed to do so Phase 2 of 2PC is split into 2 phases, Phase 2 and Phase 3 of 3PC – In phase 2 coordinator makes a decision as in 2PC (called the pre-commit decision) and records it in multiple (at least K) sites – In phase 3, coordinator sends commit/abort message to all participating sites, Under 3PC, knowledge of pre-commit decision can be used to commit despite coordinator failure – Avoids blocking problem as long as < K sites fail Drawbacks: – higher overheads – assumptions may not be satisfied in practice Won’t study it further
  • Alternative Models of Transaction Processing • Notion of a single transaction spanning multiple sites is inappropriate for many applications – E.g. transaction crossing an organizational boundary – No organization would like to permit an externally initiated transaction to block local transactions for an indeterminate period • 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 • Messages are guaranteed to be delivered exactly once • Will discuss implementation techniques later
  • 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 automicity. • We assume all replicas of any item are updated – Will see how to relax this in case of site failures later
  • Single-Lock-Manager Approach • System maintains a single lock manager that resides in a single chosen site, say Si • When a transaction needs to lock a data item, it sends a lock request to Si and lock manager determines whether the lock can be granted immediately – If yes, lock manager sends a message to the site which initiated the request – If no, request is delayed until it can be granted, at which time a message is sent to the initiating site
  • Single-Lock-Manager Approach (Cont.) • 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.
  • Distributed Lock Manager • In this approach, functionality of locking is implemented by lock managers at each site – Lock managers control access to local data items • But special protocols may be used for replicas • Advantage: work is distributed and can be made robust to failures • Disadvantage: deadlock detection is more complicated – Lock managers cooperate for deadlock detection • More on this later • Several variants of this approach – – – – Primary copy Majority protocol Biased protocol Quorum consensus
  • Primary Copy • Choose one replica of data item to be the primary copy. – Site containing the replica is called the primary site for that data item – Different data items can have different primary sites • When a transaction needs to lock a data item Q, it requests a lock at the primary site of Q. – Implicitly gets lock on all replicas of the data item • Benefit – Concurrency control for replicated data handled similarly to unreplicated data - simple implementation. • Drawback – If the primary site of Q fails, Q is inaccessible even though other sites containing a replica may be accessible.
  • Majority Protocol • Local lock manager at each site administers lock and unlock requests for data items stored at that site. • When a transaction wishes to lock an unreplicated data item Q residing at site Si, a message is sent to Si ‘s lock manager. – If Q is locked in an incompatible mode, then the request is delayed until it can be granted. – When the lock request can be granted, the lock manager sends a message back to the initiator indicating that the lock request has been granted.
  • Majority Protocol (Cont.) • In case of replicated data – If Q is replicated at n sites, then a lock request message must be sent to more than half of the n sites in which Q is stored. – The transaction does not operate on Q until it has obtained a lock on a majority of the replicas of Q. – When writing the data item, transaction performs writes on all replicas. • Benefit – Can be used even when some sites are unavailable • details on how handle writes in the presence of site failure later • Drawback – Requires 2(n/2 + 1) messages for handling lock requests, and (n/2 + 1) messages for handling unlock requests. – Potential for deadlock even with single item - e.g., each of 3 transactions may have locks on 1/3rd of the replicas of a data.
  • Biased Protocol • Local lock manager at each site as in majority protocol, however, requests for shared locks are handled differently than requests for exclusive locks. • Shared locks. When a transaction needs to lock data item Q, it simply requests a lock on Q from the lock manager at one site containing a replica of Q. • Exclusive locks. When transaction needs to lock data item Q, it requests a lock on Q from the lock manager at all sites containing a replica of Q. • Advantage - imposes less overhead on read operations. • Disadvantage - additional overhead on writes
  • Quorum Consensus Protocol • A generalization of both majority and biased protocols • 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 – Quorums can be chosen (and S computed) separately for each item • 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 • For now we assume all replicas are written – Extensions to allow some sites to be unavailable described later
  • 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: T1: write (X) write (Y) X-lock on X write (X) T2: write (Y) write (X) X-lock on Y write (Y) wait for X-lock on X Wait for X-lock on Y Result: deadlock which cannot be detected locally at either site
  • Centralized 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.
  • Local and Global Wait-For Graphs Local Global
  • Example Wait-For Graph for False Cycles
  • 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.
  • Unnecessary Rollbacks • 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. • Unnecessary rollbacks can result from false cycles in the global wait-for graph; however, likelihood of false cycles is low.
  • Timestamping • 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.
  • Timestamping (Cont.) • A site with a slow clock will assign smaller timestamps – Still logically correct: serializability not affected – But: “disadvantages” transactions • To fix this problem – Define within each site Si a logical clock (LCi), which generates the unique local timestamp – Require that Si 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 Si advances its logical clock to the value x + 1.
  • Replication with Weak Consistency • Many commercial databases support replication of data with weak degrees of consistency (I.e., without a guarantee of serializabiliy) • E.g.: master-slave replication: updates are performed at a single “master” site, and propagated to “slave” sites. – Propagation is not part of the update transaction: its is decoupled • May be immediately after transaction commits • May be periodic – Data may only be read at slave sites, not updated • No need to obtain locks at any remote site – Particularly useful for distributing information • E.g. from central office to branch-office – Also useful for running read-only queries offline from the main database
  • Replication with Weak Consistency (Cont.) • Replicas should see a transaction-consistent snapshot of the database – That is, a state of the database reflecting all effects of all transactions up to some point in the serialization order, and no effects of any later transactions. • E.g. Oracle provides a create snapshot statement to create a snapshot of a relation or a set of relations at a remote site – snapshot refresh either by recomputation or by incremental update – Automatic refresh (continuous or periodic) or manual refresh
  • Multimaster Replication • With multimaster replication (also called updateanywhere replication) updates are permitted at any replica, and are automatically propagated to all replicas – Basic model in distributed databases, where transactions are unaware of the details of replication, and database system propagates updates as part of the same transaction • Coupled with 2 phase commit – Many systems support lazy propagation where updates are transmitted after transaction commits • Allow updates to occur even if some sites are disconnected from the network, but at the cost of consistency
  • Lazy Propagation (Cont.) • Two approaches to lazy propagation – Updates at any replica translated into update at primary site, and then propagated back to all replicas • Updates to an item are ordered serially • But transactions may read an old value of an item and use it to perform an update, result in non-serializability – Updates are performed at any replica and propagated to all other replicas • Causes even more serialization problems: – Same data item may be updated concurrently at multiple sites! • Conflict detection is a problem – Some conflicts due to lack of distributed concurrency control can be detected when updates are propagated to other sites (will see later, in Section 23.5.4) • Conflict resolution is very messy – Resolution may require committed transactions to be rolled back • Durability violated – Automatic resolution may not be possible, and human intervention may be required
  • Distributed Query Processing • For centralized systems, the primary criterion for measuring the cost of a particular strategy is the number of disk accesses. • In a distributed system, other issues must be taken into account: – The cost of a data transmission over the network. – The potential gain in performance from having several sites process parts of the query in parallel
  • Query Transformation • Translating algebraic queries on fragments. – It must be possible to construct relation r from its fragments – Replace relation r by the expression to construct relation r from its fragments • Consider the horizontal fragmentation of the account relation into account1 = σ branch-name = “Hillside” (account) account2 = σ branch-name = “Valleyview” (account) • The query σ branch-name = “Hillside” (account) becomes σ branch-name = “Hillside” (account1 ∪ account2) which is optimized into σ branch-name = “Hillside” (account1) ∪ σ branch-name = “Hillside” (account2)
  • Example Query (Cont.) • Since account1 has only tuples pertaining to the Hillside branch, we can eliminate the selection operation. • Apply the definition of account2 to obtain σ branch-name = “Hillside” (σ branch-name = “Valleyview” (account) • This expression is the empty set regardless of the contents of the account relation. • Final strategy is for the Hillside site to return account1 as the result of the query.
  • Simple Join Processing • Consider the following relational algebra expression in which the three relations are neither replicated nor fragmented account depositor branch • account is stored at site S1 • depositor at S2 • branch at S3 • For a query issued at site SI, the system needs to produce the result at site SI
  • Possible Query Processing Strategies • Ship copies of all three relations to site SI and choose a strategy for processing the entire locally at site SI. • Ship a copy of the account relation to site S2 and compute temp1 = account depositor at S2. Ship temp1 from S2 to S3, and compute temp2 = temp1 branch at S3. Ship the result temp2 to SI. • Devise similar strategies, exchanging the roles S1, S2, S3 • Must consider following factors: – amount of data being shipped – cost of transmitting a data block between sites – relative processing speed at each site
  • Semijoin Strategy • Let r1 be a relation with schema R1 stores at site S1 Let r2 be a relation with schema R2 stores at site S2 • Evaluate the expression r1 r2 and obtain the result at S1. 1. Compute temp1 ← ∏R1 ∩ R2 (r1) at S1. • 2. Ship temp1 from S1 to S2. • 3. Compute temp2 ← r2 temp1 at S2 • 4. Ship temp2 from S2 to S1. • 5. Compute r1 temp2 at S1. This is the same as r1 r2.
  • Formal Definition • The semijoin of r1 with r2, is denoted by: r1 r2 • it is defined by: • ∏R1 (r1 r2) • Thus, r1 r2 selects those tuples of r1 that contributed to r1 r2. • In step 3 above, temp2=r2 r1. • For joins of several relations, the above strategy can be extended to a series of semijoin steps.