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Where to leave the data ? – Parallel systems – Scalable Distributed Data Structures – Dynamic Hash Table (P2P)

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Presentation on theme: "Where to leave the data ? – Parallel systems – Scalable Distributed Data Structures – Dynamic Hash Table (P2P)"— Presentation transcript:

1 Where to leave the data ? – Parallel systems – Scalable Distributed Data Structures – Dynamic Hash Table (P2P)

2 Introduction Parallel machines are quite common and affordable Databases are growing increasingly large – large volumes of transaction data are collected and stored for later analysis. – multimedia objects like images are increasingly stored in databases Large-scale parallel database systems increasingly used for: – storing large volumes of data – processing time-consuming decision-support queries – providing high throughput for transaction processing

3 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.

4 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 i th 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 tothe partitioning attribute value of a tuple. Send tuple to disk i.

5 I/O Parallelism (Cont.) Partitioning techniques (cont.): Range partitioning: – Choose an attribute as the partitioning attribute. – A partitioning vector [v o, v 1,..., v n-2 ] is chosen. – Let v be the partitioning attribute value of a tuple. Tuples such that v i v i+1 go to disk I + 1. Tuples with v < v 0 go to disk 0 and tuples with v v n-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.

6 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 = Locating all tuples such that the value of a given attribute lies within a specified range – range queries. – E.g., 10 r.A < 25.

7 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

8 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

9 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.

10 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.

11 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.

12 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/n th 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

13 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

14 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 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! /ufs/mk/monet5/Linux/mTests/

15 Scalable Distributed Data Structures The leading researcher is Withold Litwin

16 Why SDDSs Multicomputers need data structures and file systems Trivial extensions of traditional structures are not best hot-spots scalability parallel queries distributed and autonomous clients distributed RAM & distance to data

17 What is an SDDS ? +Data are structured +records with keys objects with an OID + more semantics than in Unix flat-file model + abstraction popular with applications + allows for parallel scans +function shipping +Data are on servers – always available for access +Overflowing servers split into new servers – appended to the file without informing the clients +Queries come from multiple autonomous clients – available for access only on their initiative no synchronous updates on the clients +There is no centralized directory for access computations

18 +Clients can make addressing errors Clients have less or more adequate image of the actual file structure, Servers are able to forward the queries to the correct address – perhaps in several messages + Servers may send Image Adjustment Messages Clients do not make same error twice See the SDDS talk for more on it – – Or the LH* ACM-TODS paper (Dec. 96) What is an SDDS ?

19 An SDDS Clients growth through splits under inserts Servers

20 An SDDS Clients growth through splits under inserts Servers

21 An SDDS Clients growth through splits under inserts Servers

22 An SDDS Clients growth through splits under inserts Servers

23 An SDDS Clients growth through splits under inserts Servers

24 An SDDS Clients

25 An SDDS

26 Clients IAM An SDDS

27 Clients An SDDS

28 Clients An SDDS

29 Known SDDSs Hash SDDS (1993) 1-d tree LH* DDH Breitbart & al RP* Kroll & Widmayer Breitbart & Vingralek m-d trees DS Classics H-Avail. LH*m, LH*g Security LH*s k-RP* dPi-tree Nardelli-tree s-availability LH* SA LH* RS

30 LH* ( A classic) Allows for the primary key (OID) based hash files – generalizes the LH addressing schema variants used in Netscape products, LH-Server, Unify, Frontpage, IIS, MsExchange... Typical load factor % In practice, at most 2 forwarding messages – regardless of the size of the file In general, 1 m/insert and 2 m/search on the average 4 messages in the worst case Search time of 1 ms (10 Mb/s net), of 150 s (100 Mb/s net) and of 30 s (Gb/s net)

31 High-availability LH* schemes In a large multicomputer, it is unlikely that all servers are up Consider the probability that a bucket is up is 99 % – bucket is unavailable 3 days per year If one stores every key in only 1 bucket – case of typical SDDSs, LH* included Then file reliability : probability that n-bucket file is entirely up is: 37 % for n = % for n = 1000 Acceptable for yourself ?

32 High-availability LH* schemes Using 2 buckets to store a key, one may expect the reliability of: – 99 % for n = 100 – 91 % for n = 1000 High-availability files – make data available despite unavailability of some servers RAIDx, LSA, EvenOdd, DATUM... High-availability SDDS – make sense – are the only way to reliable large SDDS files

33 P2P datastructures

34 Chord lookup algorithm properties Interface: lookup(key) IP address Efficient: O(log N) messages per lookup – N is the total number of servers Scalable: O(log N) state per node Robust: survives massive failures Simple to analyze

35 Chord Hashes a Key to its Successor N32 N10 N100 N80 N60 Circular ID Space Successor: node with next highest ID K33, K40, K52 K11, K30 K5, K10 K65, K70 K100 Key ID Node ID

36 Basic Lookup N32 N10 N5 N20 N110 N99 N80 N60 N40 Where is key 50? Key 50 is At N60 Lookups find the IDs predecessor Correct if successors are correct

37 Successor Lists Ensure Robust Lookup N32 N10 N5 N20 N110 N99 N80 N60 Each node remembers r successors Lookup can skip over dead nodes to find blocks N40 10, 20, 32 20, 32, 40 32, 40, 60 40, 60, 80 60, 80, 99 80, 99, , 110, 5 110, 5, 10 5, 10, 20

38 Chord Finger Table Accelerates Lookups N80 ½ ¼ 1/8 1/16 1/32 1/64 1/128

39 Chord lookups take O(log N) hops N32 N10 N5 N20 N110 N99 N80 N60 Lookup(K19) K19


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