پاورپوینت کامل Chapter 21: Parallel Databases 47 اسلاید در PowerPoint

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پاورپوینت کامل Chapter 21: Parallel Databases 47 اسلاید در PowerPoint

اسلاید ۴: Parallelism in DatabasesData can be partitioned across multiple disks for parallel I/O.Individual relational operations (e.g., sort, join, aggregation) can be executed in paralleldata 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.

اسلاید ۵: I/O ParallelismReduce the time required to retrieve relations from disk by partitioningthe 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 – 1Let i denote result of hash function h applied tothe partitioning attribute value of a tuple. Send tuple to disk i.

اسلاید ۶: 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.

اسلاید ۷: Comparison of Partitioning TechniquesEvaluate 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.

اسلاید ۸: 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 processNo clustering — tuples are scattered across all disks

اسلاید ۹: 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 disksRetrieval work is then well balanced between disks.Good for point queries on partitioning attributeCan lookup single disk, leaving others available for answering other queries. Index on partitioning attribute can be local to disk, making lookup and update more efficientNo clustering, so difficult to answer range queries

اسلاید ۱۰: Comparison of Partitioning Techniques (Cont.)Range partitioning:Provides data clustering by partitioning attribute value.Good for sequential accessGood 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 accessedRemaining 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 wastedExample of execution skew.

اسلاید ۱۱: Partitioning a Relation across DisksIf 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.

اسلاید ۱۲: Handling of SkewThe 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.

اسلاید ۱۳: Handling Skew in Range-PartitioningTo 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

اسلاید ۱۴: Handling Skew using HistogramsBalanced partitioning vector can be constructed from histogram in a relatively straightforward fashionAssume uniform distribution within each range of the histogramHistogram can be constructed by scanning relation, or sampling (blocks containing) tuples of the relation

اسلاید ۱۵: 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 round-robin fashion or based on estimated cost of processing each virtual partitionBasic idea:If any normal partition would have been skewed, it is very likely the skew is spread over a number of virtual partitionsSkewed virtual partitions get spread across a number of processors, so work gets distributed evenly!

اسلاید ۱۶: Interquery ParallelismQueries/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 shared-memory parallel database, because even sequential database systems support concurrent processing.More complicated to implement on shared-disk or shared-nothing architecturesLocking 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.

اسلاید ۱۷: Cache Coherency ProtocolExample 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 diskBefore 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