1. Database System Architectures • Client-server Database System • Parallel Database System • Distributed Database System Wei Jiang

2. Centralized Systems CPU Disk Controller Printer Controller Tape-drive Controller System bus Memory Controller Memory

3. Single-user system A desktop unit used by a single person Only one CPU, one or two disk Multi-user System It serves a large number of users who are connected to system via terminals More disks, more memory, multiple CPUs Databases are designed in two ways due to Computer System Database • Designed by Single user • Not support concurrency control • Provision for crash-recovery are either absent or primitive • Do not support SQL,but provide a simpler language, such as QBE • Designed by multi-user • Support full transactional features Currently development Databases designed for Single-processor machines provide multitasking

4. Client-Server Systems General structure of a client-server System …. Client Client Client Client Server Front-end and back-end functionality Front-end SQL uer- interface Forms interface Report writer Graphical interface Interface (SQL +API) SQL engine Back-end

5. Transaction-server Systems Provide an interface to which client can send requests to perform an action, in response to which they execute the action and send back results to the client. Requests may be specified by using SQL, or through a specialized application program interface Data-server Systems Allow clients to interact with the servers by making requests to read or update data, in units such as files or pages. For example, file servers provide a file-system interface where clients can create, update,read and delete files. Server System Architectures

6. Transaction Server Process Structure • Server Processes: receive user queries, execute them and sent result back • Lock manager process: implements lock manager functionality, which includes lock grant, lock release, and deadlock detection • Database writer process: for output modified buffer blocks back to disk • Log writer process: process output log records from the log record buffer to stable storage • Checkpoint process: performs periodic checkpoints • Process monitor process: monitors other processes, and take recovery actions for the failed process The shared memory: Buffer pool, Lock table, Log buffer,Cached query plans Mutual exclusion rule

7. Data Servers • Prefetching page shipping:when an item (both tables and tuples) is requested, it also sends back other items that are likely to be used in near future • De-escalation Locking:if the client does not need a prefetched item, it can transfer locks on the items back to the server, and the lock can be allocated to other clients • Data Caching Coherency:even if a transaction finds cached data, it must make sure that those data are up to date. A message must still be exchanged with the server to check validity of the data, and acquire a lock on data. • Call back lock caching:If a client requests a lock from the server, the server must call back all conflicting locks on the data item from any other client that have cached the locks.

8. Parallel Systems • Purpose: improve processing and I/O speeds • Two kinds: 1)a coarse-grain parallel machine 2)a massively parallel machine • Measures of performance: 1)throughput: the number of tasks that can be completed in a time interval 2)response time: the amount of time it takes to complete a single task from the time it is submitted • Two important issues: speedup and scaleup

9. Def: Running a given task in less time by increasing the degree of parallelism Goal: to process the task in time inversely proportional to the number of processors and disks allocated. Calculation: speedup is Ts/Tn when the larger system has N times the resources of smaller system Type: 1)linear speedup: N 2)sublinear speedup: <N Speedup Linear speedup speed Sublinear speedup resources

10. Def: the ability to process larger tasks in the same amount of time by providing more resources. Calculation: Ts / Ta (Ts-execution time of tasks Q on machine with size Ms; Ta-execution time of tasks N*Q on machine with size N*Ms) Type: 1)linear scaleup: Ts = Ta 2)sublinear scaleup: Ta > Ts Scaleup Linear scaleup Ts/Ta Sublinear scaleup Problem size

11. Scaleup Continue Two kinds of scaleup • Batch scaleup: The large jobs whose runtime depends on the size of database. • Transaction scaleup: The rate at which transactions are submitted to the database depends on the size of the database . The factors work against efficient parallel operation • Startup costs:the startup time overshadow the actual processing time, affecting speedup adversely • Interference:it competes with existing processes for commonly held resources • Skew:it is often difficult to divide a task into exactly equal-sized parts

12. Interconnection Networks Bus: send and receive data from a a single communication bus.It can handle communication from only one component at a time. Work well for small numbers of processors, but not scale well with increasing parallelism Mesh: The number of communication links grows as the number of components grows, and work well with increasing parallelism Hypercube: The components are numbered in binary, each of the n components is connected to log(n) other components. The communication delays are lower than mesh bus mesh hypercube

13. Parallel Database Architectures • Shared Memory: • --Def: The processors and disks have access to a common memory via a bus or through an interconnection network. • --strength: extremely efficient communications between processes • --weakness: Maintaining cache increasing overhead • Shared Disk: • --Def: all processors can access all disks directly, but the processors have private memory. • --strength: 1) each processor has its own memory, the memory bus is not a bottleneck. • 2)if a processor fails, the other processors can take over its tasks. • --weakness: The interconnection to the disk subsystem is now a bottleneck.

14. Parallel Database continued Shared Nothing: --Def: each node of machine consists of a processor, memory, and one or more disks. --strength: more scalable and easily support a large number of processors. --weakness: cost high Hierarchical: --Def: combines the above three

15. Distributed System Def: The database is stored on several computers which communicate with each other through communication media. The main difference between Distributed database and share-nothing parallel database: 1)the databases are separated geographically, so a slow interconnection 2)there exist a local transaction and global transaction The reasons for building Distributed System: 1)sharing data 2)Autonomy 3)Availability

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