Database System Architectures

Chapter 20: Database System Architectures

Chapter 20: Database System ArchitecturesCentralized and Client-Server SystemsServer System ArchitecturesParallel SystemsDistributed SystemsNetwork Types

Centralized SystemsRun on a single computer system and do not interact with other computer systems.General-purpose computer system: one to a few CPUs and a number of device controllers that are connected through a common bus that provides access to shared memory.Single-user system (e.g., personal computer or workstation): desk-top unit, single user, usually has only one CPU and one or two hard disks; the OS may support only one user.Multi-user system: more disks, more memory, multiple CPUs, and a multi-user OS. Serve a large number of users who are connected to the system vie terminals. Often called server systems.

Client-Server SystemsServer systems satisfy requests generated at m client systems, whose general structure is shown below:

Client-Server Systems (Cont.)Database functionality can be divided into:Back-end: manages access structures, query evaluation and optimization, concurrency control and recovery.Front-end: consists of tools such as forms, report-writers, and graphical user interface facilities.The interface between the front-end and the back-end is through SQL or through an application program interface.

Client-Server Systems (Cont.)Advantages of replacing mainframes with networks of workstations or personal computers connected to back-end server machines:better functionality for the costflexibility in locating resources and expanding facilitiesbetter user interfaceseasier maintenance

Server System ArchitectureServer systems can be broadly categorized into two kinds:transaction servers which are widely used in relational database systems, anddata servers, used in object-oriented database systems

Transaction ServersAlso called query server systems or SQL server systemsClients send requests to the serverTransactions are executed at the serverResults are shipped back to the client.Requests are specified in SQL, and communicated to the server through a remote procedure call (RPC) mechanism.Transactional RPC allows many RPC calls to form a transaction.Open Database Connectivity (ODBC) is a C language application program interface standard from Microsoft for connecting to a server, sending SQL requests, and receiving results.JDBC standard is similar to ODBC, for Java

Transaction Server Process StructureA typical transaction server consists of multiple processes accessing data in shared memory.Server processesThese receive user queries (transactions), execute them and send results backProcesses may be multithreaded, allowing a single process to execute several user queries concurrentlyTypically multiple multithreaded server processesLock manager processMore on this laterDatabase writer process Output modified buffer blocks to disks continually

Transaction Server Processes (Cont.)Log writer processServer processes simply add log records to log record bufferLog writer process outputs log records to stable storage. Checkpoint processPerforms periodic checkpointsProcess monitor processMonitors other processes, and takes recovery actions if any of the other processes failE.g. aborting any transactions being executed by a server process and restarting it

Transaction System Processes (Cont.)

Transaction System Processes (Cont.)Shared memory contains shared data Buffer poolLock tableLog bufferCached query plans (reused if same query submitted again)All database processes can access shared memoryTo ensure that no two processes are accessing the same data structure at the same time, databases systems implement mutual exclusion using eitherOperating system semaphoresAtomic instructions such as test-and-setTo avoid overhead of interprocess communication for lock request/grant, each database process operates directly on the lock table instead of sending requests to lock manager processLock manager process still used for deadlock detection

Data ServersUsed in high-speed LANs, in cases whereThe clients are comparable in processing power to the serverThe tasks to be executed are compute intensive.Data are shipped to clients where processing is performed, and then shipped results back to the server.This architecture requires full back-end functionality at the clients.Used in many object-oriented database systems Issues:Page-Shipping versus Item-ShippingLockingData CachingLock Caching

Data Servers (Cont.)Page-shipping versus item-shippingSmaller unit of shipping  more messagesWorth prefetching related items along with requested itemPage shipping can be thought of as a form of prefetchingLockingOverhead of requesting and getting locks from server is high due to message delaysCan grant locks on requested and prefetched items; with page shipping, transaction is granted lock on whole page.Locks on a prefetched item can be P{called back} by the server, and returned by client transaction if the prefetched item has not been used. Locks on the page can be deescalatedto locks on items in the page when there are lock conflicts. Locks on unused items can then be returned to server.

Data Servers (Cont.)Data CachingData can be cached at client even in between transactionsBut check that data is up-to-date before it is used (cache coherency)Check can be done when requesting lock on data itemLock CachingLocks can be retained by client system even in between transactionsTransactions can acquire cached locks locally, without contacting serverServer calls back locks from clients when it receives conflicting lock request. Client returns lock once no local transaction is using it.Similar to deescalation, but across transactions.

Parallel SystemsParallel database systems consist of multiple processors and multiple disks connected by a fast interconnection network.A coarse-grainparallel machine consists of a small number of powerful processorsA massively parallel or fine grain parallelmachine utilizes thousands of smaller processors.Two main performance measures:throughput --- the number of tasks that can be completed in a given time intervalresponse time --- the amount of time it takes to complete a single task from the time it is submitted

Speed-Up and Scale-UpSpeedup: a fixed-sized problem executing on a small system is given to a system which is N-times larger.Measured by:speedup = small system elapsed time large system elapsed timeSpeedup is linear if equation equals N.Scaleup: increase the size of both the problem and the systemN-times larger system used to perform N-times larger jobMeasured by:scaleup = small system small problem elapsed time big system big problem elapsed time Scale up is linear if equation equals 1.

Batch and Transaction ScaleupBatch scaleup:A single large job; typical of most decision support queries and scientific simulation.Use an N-times larger computer on N-times larger problem.Transaction scaleup:Numerous small queries submitted by independent users to a shared database; typical transaction processing and timesharing systems.N-times as many users submitting requests (hence, N-times as many requests) to an N-times larger database, on an N-times larger computer.Well-suited to parallel execution.

Factors Limiting Speedup and ScaleupSpeedup and scaleup are often sublinear due to:Startup costs: Cost of starting up multiple processes may dominate computation time, if the degree of parallelism is high.Interference: Processes accessing shared resources (e.g.,system bus, disks, or locks) compete with each other, thus spending time waiting on other processes, rather than performing useful work.Skew: Increasing the degree of parallelism increases the variance in service times of parallely executing tasks. Overall execution time determined by slowest of parallely executing tasks.

Interconnection Network ArchitecturesBus. System components send data on and receive data from a single communication bus;Does not scale well with increasing parallelism.Mesh. Components are arranged as nodes in a grid, and each component is connected to all adjacent componentsCommunication links grow with growing number of components, and so scales better. But may require 2n hops to send message to a node (or n with wraparound connections at edge of grid).Hypercube. Components are numbered in binary; components are connected to one another if their binary representations differ in exactly one bit.n components are connected to log(n) other components and can reach each other via at most log(n) links; reduces communication delays.

Parallel Database ArchitecturesShared memory -- processors share a common memoryShared disk -- processors share a common diskShared nothing -- processors share neither a common memory nor common diskHierarchical -- hybrid of the above architectures

Parallel Database Architectures

Shared MemoryProcessors and disks have access to a common memory, typically via a bus or through an interconnection network.Extremely efficient communication between processors — data in shared memory can be accessed by any processor without having to move it using software.Downside – architecture is not scalable beyond 32 or 64 processors since the bus or the interconnection network becomes a bottleneckWidely used for lower degrees of parallelism (4 to 8).

Shared DiskAll processors can directly access all disks via an interconnection network, but the processors have private memories.The memory bus is not a bottleneckArchitecture provides a degree of fault-tolerance — if a processor fails, the other processors can take over its tasks since the database is resident on disks that are accessible from all processors.Examples: IBM Sysplex and DEC clusters (now part of Compaq) running Rdb (now Oracle Rdb) were early commercial users Downside: bottleneck now occurs at interconnection to the disk subsystem.Shared-disk systems can scale to a somewhat larger number of processors, but communication between processors is slower.

Shared NothingNode consists of a processor, memory, and one or more disks. Processors at one node communicate with another processor at another node using an interconnection network. A node functions as the server for the data on the disk or disks the node owns.Examples: Teradata, Tandem, Oracle-n CUBEData accessed from local disks (and local memory accesses) do not pass through interconnection network, thereby minimizing the interference of resource sharing.Shared-nothing multiprocessors can be scaled up to thousands of processors without interference.Main drawback: cost of communication and non-local disk access; sending data involves software interaction at both ends.

HierarchicalCombines characteristics of shared-memory, shared-disk, and shared-nothing architectures.Top level is a shared-nothing architecture – nodes connected by an interconnection network, and do not share disks or memory with each other.Each node of the system could be a shared-memory system with a few processors.Alternatively, each node could be a shared-disk system, and each of the systems sharing a set of disks could be a shared-memory system.Reduce the complexity of programming such systems by distributed virtual-memory architecturesAlso called non-uniform memory architecture (NUMA)

Distributed SystemsData spread over multiple machines (also referred to as sites or nodes).Network interconnects the machinesData shared by users on multiple machines

Distributed DatabasesHomogeneous distributed databasesSame software/schema on all sites, data may be partitioned among sitesGoal: provide a view of a single database, hiding details of distributionHeterogeneous distributed databasesDifferent software/schema on different sitesGoal: integrate existing databases to provide useful functionalityDifferentiate between local and global transactionsA local transaction accesses data in the single site at which the transaction was initiated.A global transaction either accesses data in a site different from the one at which the transaction was initiated or accesses data in several different sites.

Trade-offs in Distributed SystemsSharing data – users at one site able to access the data residing at some other sites.Autonomy – each site is able to retain a degree of control over data stored locally.Higher system availability through redundancy — data can be replicated at remote sites, and system can function even if a site fails.Disadvantage: added complexity required to ensure proper coordination among sites.Software development cost.Greater potential for bugs.Increased processing overhead.

Implementation Issues for Distributed DatabasesAtomicity needed even for transactions that update data at multiple sitesThe two-phase commit protocol (2PC) is used to ensure atomicityBasic idea: each site executes transaction until just before commit, and the leaves final decision to a coordinatorEach site must follow decision of coordinator, even if there is a failure while waiting for coordinators decision2PC is not always appropriate: other transaction models based on persistent messaging, and workflows, are also used Distributed concurrency control (and deadlock detection) requiredData items may be replicated to improve data availabilityDetails of above in Chapter 22

Network TypesLocal-area networks (LANs) – composed of processors that are distributed over small geographical areas, such as a single building or a few adjacent buildings. Wide-area networks (WANs) – composed of processors distributed over a large geographical area.

Networks Types (Cont.)WANs with continuous connection (e.g. the Internet) are needed for implementing distributed database systemsGroupware applications such as Lotus notes can work on WANs with discontinuous connection:Data is replicated.Updates are propagated to replicas periodically.Copies of data may be updated independently.Non-serializable executions can thus result. Resolution is application dependent.

Database System Architectures

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Database System Architectures