1. Distributed Query Processing Based on “The state of the art in distributed query processing” Donald Kossman (ACM Computing Surveys, 2000)

2. Motivation Cost and scalability: network of off-shelf machines Integration of different software vendors (with own DBMS) Integration of legacy systems Applications inherently distributed, such as workflow or collaborative-design State-of-the-art distributed information technologies (e-businesses)

3. Part 1 : Basics Query Processing Basics –centralized query processing –distributed query processing

4. Problem Statement Input: Query such as „Biological objects in study A referenced in a literature in journal Y“. Output: Answer Objectives: –response time, throughput, first answers, little IO,... Centralized vs. Distributed Query Processing –same basic problem –but, more and different parameters, such(data sites or available machine power) and objectives

5. Steps in Query Processing Input: Declarative Query –SQL, XQuery,... Step 1: Translate Query into Algebra –Tree of operators (query plan generation) Step 2: Optimize Query –Tree of operators (logical) - also select partitions of table –Tree of operators (physical) – also site annotations –(Compilation) Step 3: Execution –Interpretation; Query result generation

6. Algebra –relational algebra for SQL very well understood –algebra for XQuery mostly understood SELECT A.d FROM A, B WHERE A.a = B.b AND A.c = 35 A.d A.a = B.b, A.c = 35 X AB

7. Query Optimization –logical, e.g., push down cheap predicates –enumerate alternative plans, apply cost model –use search heuristics to find cheapest plan A.d A.a = B.b, A.c = 35 X AB A.d hashjoin B.b index A.cB

8. Basic Query Optimization Classical Dynamic Programming algorithm –Performs join order optimization –Input : Join query on n relations –Output : Best join order

9. The Dynamic Prog. Algorithm for i = 1 to n do { optPlan({Ri}) = accessPlans(Ri) prunePlans(optPlan({Ri})) } for i = 2 to n do for all S  { R1, R2 … Rn } such that |S| = i do { optPlan(S) =  for all O  S do { optPlan(S) = optPlan(S)  joinPlans(optPlan(O), optPlan(S – O)) prunePlans(optPlan(S)) } return optPlan({R1, R2, … Rn})

10. Query Execution –library of operators (hash join, merge join,...) –exploit indexes and clustering in database –pipelining (iterator model) A.d hashjoin B.b index A.cB (John, 35, CS) (Mary, 35, EE) (Edinburgh, CS,5.0) (Edinburgh, AS, 6.0) (CS) (AS) (John, 35, CS) John

11. Summary : Centralized Queries Basic SQL (SPJG, nesting) well understood Very good extensibility –spatial joins, time series, UDF, xquery, etc. Current problems –Better statistics : cost model for optimization –Physical database design expensive & complex Some Trends –interactiveness during execution –approximate answers, top-k –self-tuning capabilities (adaptive; robust; etc.)

12. Distributed Query Processing: Basics Idea: Extension of centralized query processing. (System R* et al. in 80s) What is different? –extend physical algebra: send&receive operators –other metrics : optimize for response time –resource vectors, network interconnect matrix –caching and replication –less predictability in cost model (adaptive algos) –heterogeneity in data formats and data models

13. Issues in Distributed Databases Plan enumeration –The time and space complexity of traditional dynamic programming algorithm is very large –Iterative Dynamic Programming (heuristic for large queries) Cost Models –Classic Cost Model –Response Time Model –Economic Models

14. Distributed Query Plan A.d hashjoin B.b index A.cB receive send Forms Of Parallelism?

15. Cost : Resource Utilization 1 8 2 510 16 16 Total Cost = Sum of Cost of Ops Cost = 40

16. Another Metric : Response Time 25, 33 24, 32 0, 12 0, 50, 10 0, 70, 24 0, 60, 18 Total Cost = 40 first tuple = 25 last tuple = 33 first tuple = 0 last tuple = 10 Pipelined parallelism Independent parallelism

17. Query Execution Techniques for Distributed Databases Row Blocking Multi-cast optimization Multi-threaded execution Joins with horizontal partitioning Semi joins Top n queries

18. Query Execution Techniques for DD Row Blocking – –SEND and RECEIVE operators in query plan to model communication –Implemented by TCP/IP, UDP, etc. –Ship tuples in block-wise fashion (batch); smooth burstiness

19. Query Execution Techniques for DD Multi-cast Optimization –Location of sending/receiving may affect communication costs; forwarding versus multi-casting Multi-threaded execution –Several threads for operators at the same site (intra- query parallelism) –May be useful to enable concurrent reads for diverse machines (while continuing query processing) –Must consider if resources warrant concurrent operator execution (say two sorts each needing all memory)

20. Query Execution Techniques for DD Joins with Data (horizontal) partitioning: –Hash-based partitioning to conduct joins on independent partitions Semi Joins : –Reduce communication costs; Send only “join keys” instead of complete tuples to the site to extract relevant join partners Double-pipelined hash joins : –Non-blocking join operators to deliver first results quickly; fully exploit pipelined parallelism, and reduce overall response time Top n queries : –Isloate top n tuples quickly and only perform other expensive operations (like sort, join, etc) on those few (use “stop” operators)

21. Adaptive Algorithms Deal with unpredictable events at run time –delays in arrival of data, burstiness of network –autonomity of nodes, changes in policies Example: double pipelined hash joins –build hash table for both input streams –read inputs in separate threads –good for bursty arrival of data Re-optimization at run time (LEO, etc.) –monitor execution of query –adjust estimates of cost model –re-optimize if delta is too large

22. Special Techniques for Client-Server Architectures Shipping techniques –Query shipping –Data shipping –Hybrid shipping Query Optimization –Site Selection –Where to optimize –Two Phase Optimization

23. Special Techniques for Federated Database Systems Wrapper architecture Query optimization –Query capabilities –Cost estimation Calibration Approach Wrapper Cost Model Parameter Binding

24. Heterogeneity Use Wrappers to “hide“ heterogeneity Wrappers take care of data format, packaging Wrappers map from local to global schema Wrappers carry out caching –connections, cursors, data,... Wrappers map queries into local dialect Wrappers participate in query planning!!! –define the subset of queries that can be handled –give cost information, statistics –“capability-based rewriting“

25. Summary Theory well understood –extend traditional (centralized) query processing –add many more details –heterogenity needs manual work and wrappers Problems in Practice –cost model, statistics –architectures are not fit for adaptivity, heterogeneity –optimizers do not scale for 10,000s of sites –autonomy of sites; systems not built for asynchronous communication

26. Middleware Two kinds of middleware –data warehouses –virtual integration Data Warehouses –good: query response times –good: materializes results of data cleaning –bad: high resource requirements in middleware –bad: staleness of data Virtual Integration –the opposite –caching possible to improve response times

27. Virtual Integration Query Middleware (query decomposition, result composition) DB1DB2 wrapper sub query wrapper sub query

28. IBM Data Joiner SQL Query Data Joiner SQL DB1SQL DB2 wrapper sub query wrapper sub query

29. Adding XML Query Middleware (SQL) DB1DB2 wrapper sub query wrapper sub query XML Publishing

30. XML Data Integration XML Query Middleware (XML) DB1DB2 wrapper XML query wrapper XML query

31. XML Data Integration Example: BEA Liquid Data Advantage –Availability of XML wrappers for all major databases Problems –XML - SQL mapping is very difficult –XML is not always the right language (e.g., decision support style queries)

32. Web Services Idea: Encapsulate Data Source –provide WSDL interface to access data –works very well if query pattern is known Problem: Exploit Capability of Source –WSDL limits capabilities of data source; –good optimization requires „white box“ –example: access by id, access by name, full scan should all combinations be listed in WSDL? Solution: WSDL for Query Planning

33. Summary Middleware looks like a homogenous centralized database –location transparency –data model transparency Middleware provides global schema –data sources map local schemas to global schema Various kinds of middleware (SQL, XML) “Stacks“ of middleware possible Data cleaning requires special attention