# Chapter 13: Query Processing. Basic Steps in Query Processing

Save this PDF as:

Size: px
Start display at page:

## Transcription

1 Chapter 13: Query Processing! Overview! Measures of Query Cost! Selection Operation! Sorting! Join Operation! Other Operations! Evaluation of Expressions 13.1 Basic Steps in Query Processing 1. Parsing and translation 2. Optimization 3. Evaluation

2 Basic Steps in Query Processing (Cont.)! Parsing and translation! translate the query into its internal form. This is then translated into relational algebra.! Parser checks syntax, verifies relations! Evaluation! The query-execution engine takes a query-evaluation plan, executes that plan, and returns the answers to the query Basic Steps in Query Processing : Optimization! A relational algebra expression may have many equivalent expressions! E.g., σ balance<2500 ( balance (account)) is equivalent to balance (σ balance<2500 (account))! Each relational algebra operation can be evaluated using one of several different algorithms! Correspondingly, a relational-algebra expression can be evaluated in many ways.! Annotated expression specifying detailed evaluation strategy is called an evaluation-plan.! E.g., can use an index on balance to find accounts with balance < 2500,! or can perform complete relation scan and discard accounts with balance

3 Basic Steps: Optimization (Cont.)! Query Optimization: Amongst all equivalent evaluation plans choose the one with lowest cost.! Cost is estimated using statistical information from the database catalog "e.g. number of tuples in each relation, size of tuples, etc.! In this chapter we study! How to measure query costs! Algorithms for evaluating relational algebra operations! How to combine algorithms for individual operations in order to evaluate a complete expression! In Chapter 14! We study how to optimize queries, that is, how to find an evaluation plan with lowest estimated cost 13.5 Measures of Query Cost! Cost is generally measured as total elapsed time for answering query! Many factors contribute to time cost "disk accesses, CPU, or even network communication! Typically disk access is the predominant cost, and is also relatively easy to estimate. Measured by taking into account! Number of seeks * average-seek-cost! Number of blocks read * average-block-read-cost! Number of blocks written * average-block-write-cost "Cost to write a block is greater than cost to read a block data is read back after being written to ensure that the write was successful

4 Measures of Query Cost (Cont.)! For simplicity we just use number of block transfers from disk as the cost measure! We ignore the difference in cost between sequential and random I/O for simplicity! We also ignore CPU costs for simplicity! Costs depends on the size of the buffer in main memory! Having more memory reduces need for disk access! Amount of real memory available to buffer depends on other concurrent OS processes, and hard to determine ahead of actual execution! We often use worst case estimates, assuming only the minimum amount of memory needed for the operation is available! Real systems take CPU cost into account, differentiate between sequential and random I/O, and take buffer size into account! We do not include cost to writing output to disk in our cost formulae 13.7 Selection Operation! File scan search algorithms that locate and retrieve records that fulfill a selection condition.! Algorithm A1 (linear search). Scan each file block and test all records to see whether they satisfy the selection condition.! Cost estimate (number of disk blocks scanned) = b r "b r denotes number of blocks containing records from relation r! If selection is on a key attribute, cost = (b r /2) "stop on finding record! Linear search can be applied regardless of "selection condition or "ordering of records in the file, or "availability of indices

5 Selection Operation (Cont.)! A2 (binary search). Applicable if selection is an equality comparison on the attribute on which file is ordered.! Assume that the blocks of a relation are stored contiguously! Cost estimate (number of disk blocks to be scanned): " log 2 (b r ) cost of locating the first tuple by a binary search on the blocks "Plus number of blocks containing records that satisfy selection condition Will see how to estimate this cost in Chapter Selections Using Indices! Index scan search algorithms that use an index! selection condition must be on search-key of index.! A3 (primary index on candidate key, equality). Retrieve a single record that satisfies the corresponding equality condition! Cost = HT i + 1! A4 (primary index on nonkey, equality) Retrieve multiple records.! Records will be on consecutive blocks! Cost = HT i + number of blocks containing retrieved records! A5 (equality on search-key of secondary index).! Retrieve a single record if the search-key is a candidate key " Cost = HT i + 1! Retrieve multiple records if search-key is not a candidate key " Cost = HT i + number of records retrieved Can be very expensive! " each record may be on a different block one block access for each retrieved record

6 Selections Involving Comparisons! Can implement selections of the form σ A V (r) or σ A V (r) by using! a linear file scan or binary search,! or by using indices in the following ways:! A6 (primary index, comparison). (Relation is sorted on A) "For σ A V (r) use index to find first tuple v and scan relation sequentially from there "For σ A V (r) just scan relation sequentially till first tuple > v; do not use index! A7 (secondary index, comparison). "For σ A V (r) use index to find first index entry v and scan index sequentially from there, to find pointers to records. "For σ A V (r) just scan leaf pages of index finding pointers to records, till first entry > v "In either case, retrieve records that are pointed to requires an I/O for each record Linear file scan may be cheaper if many records are to be fetched! Implementation of Complex Selections! Conjunction: σ θ1 θ2... θn (r)! A8 (conjunctive selection using one index).! Select a combination of θ i and algorithms A1 through A7 that results in the least cost forσ θi (r).! Test other conditions on tuple after fetching it into memory buffer.! A9 (conjunctive selection using multiple-key index).! Use appropriate composite (multiple-key) index if available.! A10 (conjunctive selection by intersection of identifiers).! Requires indices with record pointers.! Use corresponding index for each condition, and take intersection of all the obtained sets of record pointers.! Then fetch records from file! If some conditions do not have appropriate indices, apply test in memory

7 Algorithms for Complex Selections! Disjunction:σ θ1 θ2... θn (r).! A11 (disjunctive selection by union of identifiers).! Applicable if all conditions have available indices. "Otherwise use linear scan.! Use corresponding index for each condition, and take union of all the obtained sets of record pointers.! Then fetch records from file! Negation: σ θ (r)! Use linear scan on file! If very few records satisfy θ, and an index is applicable to θ " Find satisfying records using index and fetch from file Sorting! We may build an index on the relation, and then use the index to read the relation in sorted order. May lead to one disk block access for each tuple.! For relations that fit in memory, techniques like quicksort can be used. For relations that don t fit in memory, external sort-merge is a good choice

8 External Sort-Merge Let M denote memory size (in pages). 1. Create sorted runs. Let i be 0 initially. Repeatedly do the following till the end of the relation: (a) Read M blocks of relation into memory (b) Sort the in-memory blocks (c) Write sorted data to run R i ; increment i. Let the final value of I be N 2. Merge the runs (N-way merge). We assume (for now) that N < M. 1. Use N blocks of memory to buffer input runs, and 1 block to buffer output. Read the first block of each run into its buffer page 2. repeat 1. Select the first record (in sort order) among all buffer pages 2. Write the record to the output buffer. If the output buffer is full write it to disk. 3. Delete the record from its input buffer page. If the buffer page becomes empty then read the next block (if any) of the run into the buffer. 3. until all input buffer pages are empty: External Sort-Merge (Cont.)! If i M, several merge passes are required.! In each pass, contiguous groups of M - 1 runs are merged.! A pass reduces the number of runs by a factor of M -1, and creates runs longer by the same factor. "E.g. If M=11, and there are 90 runs, one pass reduces the number of runs to 9, each 10 times the size of the initial runs! Repeated passes are performed till all runs have been merged into one

9 Example: External Sorting Using Sort-Merge External Merge Sort (Cont.)! Cost analysis:! Total number of merge passes required: log M 1 (b r /M).! Disk accesses for initial run creation as well as in each pass is 2b r "for final pass, we don t count write cost we ignore final write cost for all operations since the output of an operation may be sent to the parent operation without being written to disk Thus total number of disk accesses for external sorting: b r ( 2 log M 1 (b r / M) + 1)

10 Join Operation! Several different algorithms to implement joins! Nested-loop join! Block nested-loop join! Indexed nested-loop join! Merge-join! Hash-join! Choice based on cost estimate! Examples use the following information! Number of records of customer: 10,000 depositor: 5000! Number of blocks of customer: 400 depositor: Nested-Loop Join! To compute the theta join r θ s for each tuple t r in r do begin for each tuple t s in s do begin test pair (t r,t s ) to see if they satisfy the join condition θ if they do, add t r t s to the result. end end! r is called the outer relation and s the inner relation of the join.! Requires no indices and can be used with any kind of join condition.! Expensive since it examines every pair of tuples in the two relations

11 Nested-Loop Join (Cont.)! In the worst case, if there is enough memory only to hold one block of each relation, the estimated cost is n r b s + b r disk accesses.! If the smaller relation fits entirely in memory, use that as the inner relation. Reduces cost to b r + b s disk accesses.! Assuming worst case memory availability cost estimate is! = 2,000,100 disk accesses with depositor as outer relation, and! = 1,000,400 disk accesses with customer as the outer relation.! If smaller relation (depositor) fits entirely in memory, the cost estimate will be 500 disk accesses.! Block nested-loops algorithm (next slide) is preferable Block Nested-Loop Join! Variant of nested-loop join in which every block of inner relation is paired with every block of outer relation. for each block B r of r do begin for each block B s of s do begin for each tuple t r in B r do begin for each tuple t s in B s do begin Check if (t r,t s ) satisfy the join condition if they do, add t r t s to the result. end end end end

12 Block Nested-Loop Join (Cont.)! Worst case estimate: b r b s + b r block accesses.! Each block in the inner relation s is read once for each block in the outer relation (instead of once for each tuple in the outer relation! Best case: b r + b s block accesses.! Improvements to nested loop and block nested loop algorithms:! In block nested-loop, use M 2 disk blocks as blocking unit for outer relations, where M = memory size in blocks; use remaining two blocks to buffer inner relation and output " Cost = b r / (M-2) b s +b r! If equi-join attribute forms a key or inner relation, stop inner loop on first match! Scan inner loop forward and backward alternately, to make use of the blocks remaining in buffer (with LRU replacement)! Use index on inner relation if available (next slide) Indexed Nested-Loop Join! Index lookups can replace file scans if! join is an equi-join or natural join and! an index is available on the inner relation s join attribute "Can construct an index just to compute a join.! For each tuple t r in the outer relation r, use the index to look up tuples in s that satisfy the join condition with tuple t r.! Worst case: buffer has space for only one page of r, and, for each tuple in r, we perform an index lookup on s.! Cost of the join: b r + n r c! Where c is the cost of traversing index and fetching all matching s tuples for one tuple or r! c can be estimated as cost of a single selection on s using the join condition.! If indices are available on join attributes of both r and s, use the relation with fewer tuples as the outer relation

13 Example of Nested-Loop Join Costs! Compute depositor customer, with depositor as the outer relation.! Let customer have a primary B + -tree index on the join attribute customer-name, which contains 20 entries in each index node.! Since customer has 10,000 tuples, the height of the tree is 4, and one more access is needed to find the actual data! depositor has 5000 tuples! Cost of block nested loops join! 400* = 40,100 disk accesses assuming worst case memory (may be significantly less with more memory)! Cost of indexed nested loops join! * 5 = 25,100 disk accesses.! CPU cost likely to be less than that for block nested loops join Merge-Join 1. Sort both relations on their join attribute (if not already sorted on the join attributes). 2. Merge the sorted relations to join them 1. Join step is similar to the merge stage of the sort-merge algorithm. 2. Main difference is handling of duplicate values in join attribute every pair with same value on join attribute must be matched 3. Detailed algorithm in book

14 Merge-Join (Cont.)! Can be used only for equi-joins and natural joins! Each block needs to be read only once (assuming all tuples for any given value of the join attributes fit in memory! Thus number of block accesses for merge-join is b r + b s + the cost of sorting if relations are unsorted.! hybrid merge-join: If one relation is sorted, and the other has a secondary B + -tree index on the join attribute! Merge the sorted relation with the leaf entries of the B + -tree.! Sort the result on the addresses of the unsorted relation s tuples! Scan the unsorted relation in physical address order and merge with previous result, to replace addresses by the actual tuples "Sequential scan more efficient than random lookup Hash-Join! Applicable for equi-joins and natural joins.! A hash function h is used to partition tuples of both relations! h maps JoinAttrs values to {0, 1,..., n}, where JoinAttrs denotes the common attributes of r and s used in the natural join.! r 0, r 1,..., r n denote partitions of r tuples "Each tuple t r r is put in partition r i where i = h(t r [JoinAttrs]).! r 0,, r 1..., r n denotes partitions of s tuples "Each tuple t s s is put in partition s i, where i = h(t s [JoinAttrs]).! Note: In book, r i is denoted as H ri, s i is denoted as H si and n is denoted as n h

15 Hash-Join (Cont.) Hash-Join (Cont.)! r tuples in r i need only to be compared with s tuples in s i Need not be compared with s tuples in any other partition, since:! an r tuple and an s tuple that satisfy the join condition will have the same value for the join attributes.! If that value is hashed to some value i, the r tuple has to be in r i and the s tuple in s i

16 Hash-Join Algorithm The hash-join of r and s is computed as follows. 1. Partition the relation s using hashing function h. When partitioning a relation, one block of memory is reserved as the output buffer for each partition. 2. Partition r similarly. 3. For each i: (a) Load s i into memory and build an in-memory hash index on it using the join attribute. This hash index uses a different hash function than the earlier one h. (b) Read the tuples in r i from the disk one by one. For each tuple t r locate each matching tuple t s in s i using the in-memory hash index. Output the concatenation of their attributes. Relation s is called the build input and r is called the probe input Hash-Join algorithm (Cont.)! The value n and the hash function h is chosen such that each s i should fit in memory.! Typically n is chosen as b s /M * f where f is a fudge factor, typically around 1.2! The probe relation partitions s i need not fit in memory! Recursive partitioning required if number of partitions n is greater than number of pages M of memory.! instead of partitioning n ways, use M 1 partitions for s! Further partition the M 1 partitions using a different hash function! Use same partitioning method on r! Rarely required: e.g., recursive partitioning not needed for relations of 1GB or less with memory size of 2MB, with block size of 4KB

17 Handling of Overflows! Hash-table overflow occurs in partition s i if s i does not fit in memory. Reasons could be! Many tuples in s with same value for join attributes! Bad hash function! Partitioning is said to be skewed if some partitions have significantly more tuples than some others! Overflow resolution can be done in build phase! Partition s i is further partitioned using different hash function.! Partition r i must be similarly partitioned.! Overflow avoidance performs partitioning carefully to avoid overflows during build phase! E.g. partition build relation into many partitions, then combine them! Both approaches fail with large numbers of duplicates! Fallback option: use block nested loops join on overflowed partitions Cost of Hash-Join! If recursive partitioning is not required: cost of hash join is 3(b r + b s ) +2 n h! If recursive partitioning required, number of passes required for partitioning s is log M 1 (b s ) 1. This is because each final partition of s should fit in memory.! The number of partitions of probe relation r is the same as that for build relation s; the number of passes for partitioning of r is also the same as for s.! Therefore it is best to choose the smaller relation as the build relation.! Total cost estimate is: 2(b r + b s log M 1 (b s ) 1 + b r + b s! If the entire build input can be kept in main memory, n can be set to 0 and the algorithm does not partition the relations into temporary files. Cost estimate goes down to b r + b s

18 Example of Cost of Hash-Join customer depositor! Assume that memory size is 20 blocks! b depositor = 100 and b customer = 400.! depositor is to be used as build input. Partition it into five partitions, each of size 20 blocks. This partitioning can be done in one pass.! Similarly, partition customer into five partitions,each of size 80. This is also done in one pass.! Therefore total cost: 3( ) = 1500 block transfers! ignores cost of writing partially filled blocks Hybrid Hash Join! Useful when memory sized are relatively large, and the build input is bigger than memory.! Main feature of hybrid hash join: Keep the first partition of the build relation in memory.! E.g. With memory size of 25 blocks, depositor can be partitioned into five partitions, each of size 20 blocks.! Division of memory:! The first partition occupies 20 blocks of memory! 1 block is used for input, and 1 block each for buffering the other 4 partitions.! customer is similarly partitioned into five partitions each of size 80; the first is used right away for probing, instead of being written out and read back.! Cost of 3( ) = 1300 block transfers for hybrid hash join, instead of 1500 with plain hash-join.! Hybrid hash-join most useful if M >> bs

19 Complex Joins! Join with a conjunctive condition: r s θ1 θ2... θn! Either use nested loops/block nested loops, or! Compute the result of one of the simpler joins r θi s "final result comprises those tuples in the intermediate result that satisfy the remaining conditions θ 1... θ i 1 θ i θ n! Join with a disjunctive condition r θ1 θ2... θn s! Either use nested loops/block nested loops, or! Compute as the union of the records in individual joins r θ i s: (r θ1 s) (r θ2 s)... (r θn s) Other Operations! Duplicate elimination can be implemented via hashing or sorting.! On sorting duplicates will come adjacent to each other, and all but one set of duplicates can be deleted. Optimization: duplicates can be deleted during run generation as well as at intermediate merge steps in external sort-merge.! Hashing is similar duplicates will come into the same bucket.! Projection is implemented by performing projection on each tuple followed by duplicate elimination

20 Other Operations : Aggregation! Aggregation can be implemented in a manner similar to duplicate elimination.! Sorting or hashing can be used to bring tuples in the same group together, and then the aggregate functions can be applied on each group.! Optimization: combine tuples in the same group during run generation and intermediate merges, by computing partial aggregate values "For count, min, max, sum: keep aggregate values on tuples found so far in the group. When combining partial aggregate for count, add up the aggregates "For avg, keep sum and count, and divide sum by count at the end Other Operations : Set Operations! Set operations (, and ): can either use variant of merge-join after sorting, or variant of hash-join.! E.g., Set operations using hashing: 1. Partition both relations using the same hash function, thereby creating, r 1,.., r n r 0, and s 1, s 2.., s n 2. Process each partition i as follows. Using a different hashing function, build an in-memory hash index on r i after it is brought into memory. 3. r s: Add tuples in s i to the hash index if they are not already in it. At end of s i add the tuples in the hash index to the result. r s: output tuples in s i to the result if they are already there in the hash index. r s: for each tuple in s i, if it is there in the hash index, delete it from the index. At end of s i add remaining tuples in the hash index to the result

21 Other Operations : Outer Join! Outer join can be computed either as! A join followed by addition of null-padded non-participating tuples.! by modifying the join algorithms.! Modifying merge join to compute r s! In r s, non participating tuples are those in r Π R (r s)! Modify merge-join to compute r s: During merging, for every tuple t r from r that do not match any tuple in s, output t r padded with nulls.! Right outer-join and full outer-join can be computed similarly.! Modifying hash join to compute r s! If r is probe relation, output non-matching r tuples padded with nulls! If r is build relation, when probing keep track of which r tuples matched s tuples. At end of s i output non-matched r tuples padded with nulls Evaluation of Expressions! So far: we have seen algorithms for individual operations! Alternatives for evaluating an entire expression tree! Materialization: generate results of an expression whose inputs are relations or are already computed, materialize (store) it on disk. Repeat.! Pipelining: pass on tuples to parent operations even as an operation is being executed! We study above alternatives in more detail

22 Materialization! Materialized evaluation: evaluate one operation at a time, starting at the lowest-level. Use intermediate results materialized into temporary relations to evaluate next-level operations.! E.g., in figure below, compute and store σ ( account balance<2500 ) then compute the store its join with customer, and finally compute the projections on customer-name Materialization (Cont.)! Materialized evaluation is always applicable! Cost of writing results to disk and reading them back can be quite high! Our cost formulas for operations ignore cost of writing results to disk, so "Overall cost = Sum of costs of individual operations + cost of writing intermediate results to disk! Double buffering: use two output buffers for each operation, when one is full write it to disk while the other is getting filled! Allows overlap of disk writes with computation and reduces execution time

23 Pipelining! Pipelined evaluation : evaluate several operations simultaneously, passing the results of one operation on to the next.! E.g., in previous expression tree, don t store result of σ balance<2500 ( account )! instead, pass tuples directly to the join.. Similarly, don t store result of join, pass tuples directly to projection.! Much cheaper than materialization: no need to store a temporary relation to disk.! Pipelining may not always be possible e.g., sort, hash-join.! For pipelining to be effective, use evaluation algorithms that generate output tuples even as tuples are received for inputs to the operation.! Pipelines can be executed in two ways: demand driven and producer driven Pipelining (Cont.)! In demand driven or lazy evaluation! system repeatedly requests next tuple from top level operation! Each operation requests next tuple from children operations as required, in order to output its next tuple! In between calls, operation has to maintain state so it knows what to return next! Each operation is implemented as an iterator implementing the following operations " open() E.g. file scan: initialize file scan, store pointer to beginning of file as state E.g.merge join: sort relations and store pointers to beginning of sorted relations as state " next() E.g. for file scan: Output next tuple, and advance and store file pointer E.g. for merge join: continue with merge from earlier state till next output tuple is found. Save pointers as iterator state. " close()

24 Pipelining (Cont.)! In produce-driven or eager pipelining! Operators produce tuples eagerly and pass them up to their parents "Buffer maintained between operators, child puts tuples in buffer, parent removes tuples from buffer "if buffer is full, child waits till there is space in the buffer, and then generates more tuples! System schedules operations that have space in output buffer and can process more input tuples Evaluation Algorithms for Pipelining! Some algorithms are not able to output results even as they get input tuples! E.g. merge join, or hash join! These result in intermediate results being written to disk and then read back always! Algorithm variants are possible to generate (at least some) results on the fly, as input tuples are read in! E.g. hybrid hash join generates output tuples even as probe relation tuples in the in-memory partition (partition 0) are read in! Pipelined join technique: Hybrid hash join, modified to buffer partition 0 tuples of both relations in-memory, reading them as they become available, and output results of any matches between partition 0 tuples "When a new r 0 tuple is found, match it with existing s 0 tuples, output matches, and save it in r 0 "Symmetrically for s 0 tuples

25 Complex Joins! Join involving three relations: loan depositor customer! Strategy 1. Compute depositor customer; use result to compute loan (depositor customer)! Strategy 2. Computer loan depositor first, and then join the result with customer.! Strategy 3. Perform the pair of joins at once. Build and index on loan for loan-number, and on customer for customer-name.! For each tuple t in depositor, look up the corresponding tuples in customer and the corresponding tuples in loan.! Each tuple of deposit is examined exactly once.! Strategy 3 combines two operations into one specialpurpose operation that is more efficient than implementing two joins of two relations

### ! Overview! Measures of Query Cost! Selection Operation! Sorting! Join Operation! Other Operations! Evaluation of Expressions

Chapter 13: Query Processing Chapter 13: Query Processing! Overview! Measures of Query Cost! Selection Operation! Sorting! Join Operation! Other Operations! Evaluation of Expressions Basic Steps in Query

### Query Processing, optimization, and indexing techniques

Query Processing, optimization, and indexing techniques What s s this tutorial about? From here: SELECT C.name AS Course, count(s.students) AS Cnt FROM courses C, subscription S WHERE C.lecturer = Calders

### 8. Query Processing. Query Processing & Optimization

ECS-165A WQ 11 136 8. Query Processing Goals: Understand the basic concepts underlying the steps in query processing and optimization and estimating query processing cost; apply query optimization techniques;

### Query Processing C H A P T E R12. Practice Exercises

C H A P T E R12 Query Processing Practice Exercises 12.1 Assume (for simplicity in this exercise) that only one tuple fits in a block and memory holds at most 3 blocks. Show the runs created on each pass

### SQL Query Evaluation. Winter 2006-2007 Lecture 23

SQL Query Evaluation Winter 2006-2007 Lecture 23 SQL Query Processing Databases go through three steps: Parse SQL into an execution plan Optimize the execution plan Evaluate the optimized plan Execution

### ICS 434 Advanced Database Systems

ICS 434 Advanced Database Systems Dr. Abdallah Al-Sukairi sukairi@kfupm.edu.sa Second Semester 2003-2004 (032) King Fahd University of Petroleum & Minerals Information & Computer Science Department Outline

### Query Processing + Optimization: Outline

Query Processing + Optimization: Outline Operator Evaluation Strategies Query processing in general Selection Join Query Optimization Heuristic query optimization Cost-based query optimization Query Tuning

### Comp 5311 Database Management Systems. 16. Review 2 (Physical Level)

Comp 5311 Database Management Systems 16. Review 2 (Physical Level) 1 Main Topics Indexing Join Algorithms Query Processing and Optimization Transactions and Concurrency Control 2 Indexing Used for faster

### CS 525: Advanced Database Organization. Execution Plan. Query Execution. How to estimate costs. 10: Query Execution. Estimating IOs: Boris Glavic

SQL query CS 55: Advanced Database Organization 0: Query Execution Boris Glavic Slides: adapted from a course taught by Hector Garcia-Molina, Stanford InfoLab CS 55 Notes 0 - Query Execution parse parse

### Query Optimization. Query Optimization. Optimization considerations. Example. Interaction of algorithm choice and tree arrangement.

COS 597: Principles of Database and Information Systems Query Optimization Query Optimization Query as expression over relational algebraic operations Get evaluation (parse) tree Leaves: base relations

### Steps in Query Processing Query Query Parser Parsed query. Query Evaluation and Optimization. An Overview. Plan Generator

Query Evaluation and Optimization An Overview Web Forms Application FEs SQL Interface Plan Executor Operator Evaluator Parser Optimizer Query Evaluation Engine Concurrency Control Transaction Manager Lock

### Database 2 Lecture II. Alessandro Artale

Free University of Bolzano Database 2. Lecture II, 2003/2004 A.Artale (1) Database 2 Lecture II Alessandro Artale Faculty of Computer Science Free University of Bolzano Room: 221 artale@inf.unibz.it http://www.inf.unibz.it/

### Evaluation of Expressions

Query Optimization Evaluation of Expressions Materialization: one operation at a time, materialize intermediate results for subsequent use Good for all situations Sum of costs of individual operations

### Chapter 14 Query Optimization

Chapter 4: Query Optimization Chapter 4 Query Optimization Introduction Catalog Information for Cost Estimation Estimation of Statistics Transformation of Relational Expressions Dynamic Programming for

### Plan for the Query Optimization topic

VICTORIA UNIVERSITY OF WELLINGTON Te Whare Wananga o te Upoko o te Ika a Maui COMP302 Database Systems Plan for the Query Optimization topic COMP302 Database Systems Query Optimisation_04 1 What is Query

### SQL QUERY EVALUATION. CS121: Introduction to Relational Database Systems Fall 2015 Lecture 12

SQL QUERY EVALUATION CS121: Introduction to Relational Database Systems Fall 2015 Lecture 12 Query Evaluation 2 Last time: Began looking at database implementation details How data is stored and accessed

### Overview of Query Evaluation

Overview of Query Evaluation Chapter 12 Comp 521 Files and Databases Fall 2010 1 Overview of Query Evaluation Query: SELECT sname FROM Reserves R, Sailors S WHERE R.sid=S.sid AND R.bid = 100 AND S.rating

### Optimization Overview. Overview of Query Optimization

Optimization Overview Instructor: Sharma Chakravarthy sharma@cse.uta.edu The University of Texas @ Arlington Database Management Systems, S. Chakravarthy 1 Overview of Query Optimization Input: Sql query

### DATABASE DESIGN - 1DL400

DATABASE DESIGN - 1DL400 Spring 2015 A course on modern database systems!! http://www.it.uu.se/research/group/udbl/kurser/dbii_vt15/ Kjell Orsborn! Uppsala Database Laboratory! Department of Information

### External Sorting. Why Sort? 2-Way Sort: Requires 3 Buffers. Chapter 13

External Sorting Chapter 13 Database Management Systems 3ed, R. Ramakrishnan and J. Gehrke 1 Why Sort? A classic problem in computer science! Data requested in sorted order e.g., find students in increasing

### Database System Concepts

Chapter 14: Optimization Departamento de Engenharia Informática Instituto Superior Técnico 1 st Semester 2009/2010 Slides (fortemente) baseados nos slides oficiais do livro c Silberschatz, Korth and Sudarshan.

### OVERVIEW OF QUERY EVALUATION

12 OVERVIEW OF QUERY EVALUATION Exercise 12.1 Briefly answer the following questions: 1. Describe three techniques commonly used when developing algorithms for relational operators. Explain how these techniques

### Chapter 14: Query Optimization

Chapter 14: Query Optimization Database System Concepts 5 th Ed. See www.db-book.com for conditions on re-use Chapter 14: Query Optimization Introduction Transformation of Relational Expressions Catalog

### Storage and Indexing. DBS Database Systems Implementing and Optimising Query Languages. Differences between disk and main memory

DBS Database Systems Implementing and Optimising Query Languages Peter Buneman 9 November 2010 Reading: R&G Chapters 8, 9 & 10.1 Storage and Indexing We typically store data in external (secondary) storage.

### Datenbanksysteme II: Implementation of Database Systems Implementing Joins

Datenbanksysteme II: Implementation of Database Systems Implementing Joins Material von Prof. Johann Christoph Freytag Prof. Kai-Uwe Sattler Prof. Alfons Kemper, Dr. Eickler Prof. Hector Garcia-Molina

### CSE 444 Practice Problems. Query Optimization

CSE 444 Practice Problems Query Optimization 1. Query Optimization Given the following SQL query: Student (sid, name, age, address) Book(bid, title, author) Checkout(sid, bid, date) SELECT S.name FROM

### Chapter 13: Query Optimization

Chapter 13: Query Optimization Database System Concepts, 6 th Ed. See www.db-book.com for conditions on re-use Chapter 13: Query Optimization Introduction Transformation of Relational Expressions Catalog

### External Sorting. Chapter 13. Database Management Systems 3ed, R. Ramakrishnan and J. Gehrke 1

External Sorting Chapter 13 Database Management Systems 3ed, R. Ramakrishnan and J. Gehrke 1 Why Sort? A classic problem in computer science! Data requested in sorted order e.g., find students in increasing

### Overview of Query Evaluation. Overview of Query Evaluation

Overview of Query Evaluation Chapter 12 Database Management Systems 3ed, R. Ramakrishnan and J. Gehrke 1 Overview of Query Evaluation Plan: Tree of R.A. ops, with choice of alg for each op. Each operator

### Chapter 5 More SQL: Complex Queries, Triggers, Views, and Schema Modification

Chapter 5 More SQL: Complex Queries, Triggers, Views, and Schema Modification Copyright 2011 Pearson Education, Inc. Publishing as Pearson Addison-Wesley Chapter 5 Outline More Complex SQL Retrieval Queries

### 7. Indexing. Contents: Single-Level Ordered Indexes Multi-Level Indexes B + Tree based Indexes Index Definition in SQL.

ECS-165A WQ 11 123 Contents: Single-Level Ordered Indexes Multi-Level Indexes B + Tree based Indexes Index Definition in SQL 7. Indexing Basic Concepts Indexing mechanisms are used to optimize certain

### CSE 326, Data Structures. Sample Final Exam. Problem Max Points Score 1 14 (2x7) 2 18 (3x6) 3 4 4 7 5 9 6 16 7 8 8 4 9 8 10 4 Total 92.

Name: Email ID: CSE 326, Data Structures Section: Sample Final Exam Instructions: The exam is closed book, closed notes. Unless otherwise stated, N denotes the number of elements in the data structure

### Unit 4.3 - Storage Structures 1. Storage Structures. Unit 4.3

Storage Structures Unit 4.3 Unit 4.3 - Storage Structures 1 The Physical Store Storage Capacity Medium Transfer Rate Seek Time Main Memory 800 MB/s 500 MB Instant Hard Drive 10 MB/s 120 GB 10 ms CD-ROM

### CIS 631 Database Management Systems Sample Final Exam

CIS 631 Database Management Systems Sample Final Exam 1. (25 points) Match the items from the left column with those in the right and place the letters in the empty slots. k 1. Single-level index files

### Lecture 1: Data Storage & Index

Lecture 1: Data Storage & Index R&G Chapter 8-11 Concurrency control Query Execution and Optimization Relational Operators File & Access Methods Buffer Management Disk Space Management Recovery Manager

### Data Management for Data Science

Data Management for Data Science Database Management Systems: Access file manager and query evaluation Maurizio Lenzerini, Riccardo Rosati Dipartimento di Ingegneria informatica automatica e gestionale

### Rethinking SIMD Vectorization for In-Memory Databases

SIGMOD 215, Melbourne, Victoria, Australia Rethinking SIMD Vectorization for In-Memory Databases Orestis Polychroniou Columbia University Arun Raghavan Oracle Labs Kenneth A. Ross Columbia University Latest

### Handling large amount of data efficiently

Handling large amount of data efficiently 1. Storage media and its constraints (magnetic disks, buffering) 2. Algorithms for large inputs (sorting) 3. Data structures (trees, hashes and bitmaps) Lecture

### Relational Algebra. CSE 544: Relational Operators, Sorting. What about Intersection? 1. Union and 2. Difference. 4. Projection. 3.

Relational Algebra CSE 544: Relational Operators, Sorting Wednesday, 5/12/2004 Operates on relations, i.e. sets Later: we discuss how to extend this to bags Five operators: Union: Difference: - Selection:

### Execution Strategies for SQL Subqueries

Execution Strategies for SQL Subqueries Mostafa Elhemali, César Galindo- Legaria, Torsten Grabs, Milind Joshi Microsoft Corp With additional slides from material in paper, added by S. Sudarshan 1 Motivation

### Q4. What are data model? Explain the different data model with examples. Q8. Differentiate physical and logical data independence data models.

FAQs Introduction to Database Systems and Design Module 1: Introduction Data, Database, DBMS, DBA Q2. What is a catalogue? Explain the use of it in DBMS. Q3. Differentiate File System approach and Database

### Inside the PostgreSQL Query Optimizer

Inside the PostgreSQL Query Optimizer Neil Conway neilc@samurai.com Fujitsu Australia Software Technology PostgreSQL Query Optimizer Internals p. 1 Outline Introduction to query optimization Outline of

### Distributed Databases Query Optimisation. TEMPUS June, 2001

Distributed Databases Query Optimisation TEMPUS June, 2001 1 Topics Query processing in distributed databases Localization Distributed query operators Cost-based optimization 2 Decomposition Query Processing

### Multi-Way Search Trees (B Trees)

Multi-Way Search Trees (B Trees) Multiway Search Trees An m-way search tree is a tree in which, for some integer m called the order of the tree, each node has at most m children. If n

### Overview of Storage and Indexing

Overview of Storage and Indexing Chapter 8 How index-learning turns no student pale Yet holds the eel of science by the tail. -- Alexander Pope (1688-1744) Database Management Systems 3ed, R. Ramakrishnan

### Relational Algebra The Relational Algebra and Relational Calculus. Relational Query Languages

The Relational Algebra and Relational Calculus Relational Algebra Slide 6-2 Relational Query Languages Query languages Allow manipulation and retrieval of data Not like programming languages Not intend

### 2 Exercises and Solutions

2 Exercises and Solutions Most of the exercises below have solutions but you should try first to solve them. Each subsection with solutions is after the corresponding subsection with exercises. 2.1 Sorting

### Symbol Tables. Introduction

Symbol Tables Introduction A compiler needs to collect and use information about the names appearing in the source program. This information is entered into a data structure called a symbol table. The

### Relational Databases

Relational Databases Jan Chomicki University at Buffalo Jan Chomicki () Relational databases 1 / 18 Relational data model Domain domain: predefined set of atomic values: integers, strings,... every attribute

### Relational Query Optimization. Chapter 15

Relational Query Optimization Chapter 15 Highlights of System R Optimizer Impact: Most widely used currently; works well for < 10 joins. Cost estimation: Approximate art at best. Statistics, maintained

Chapter 7. Indexes Table of Contents Objectives... 1 Introduction... 2 Context... 2 Review Questions... 3 Single-level Ordered Indexes... 4 Primary Indexes... 4 Clustering Indexes... 8 Secondary Indexes...

### Chapter 4 Index Structures

Chapter 4 Index Structures Having seen the options available for representing records, we must now consider how whole relations, or the extents of classes, are represented. It is not sufficient 4.1. INDEXES

### Architecture Sensitive Database Design: Examples from the Columbia Group

Architecture Sensitive Database Design: Examples from the Columbia Group Kenneth A. Ross Columbia University John Cieslewicz Columbia University Jun Rao IBM Research Jingren Zhou Microsoft Research In

### Storage and File Structure

Storage and File Structure Chapter 10: Storage and File Structure Overview of Physical Storage Media Magnetic Disks RAID Tertiary Storage Storage Access File Organization Organization of Records in Files

### Efficient Processing of Joins on Set-valued Attributes

Efficient Processing of Joins on Set-valued Attributes Nikos Mamoulis Department of Computer Science and Information Systems University of Hong Kong Pokfulam Road Hong Kong nikos@csis.hku.hk Abstract Object-oriented

### We study algorithms for computing the equijoin of two relations in B system with a standard

Join Processing in Database with Large Main Memories Systems LEONARD D. SHAPIRO North Dakota State University We study algorithms for computing the equijoin of two relations in B system with a standard

### Storing Data: Disks and Files

Storing Data: Disks and Files [R&G] Chapter 9 CS 4320 1 Data on External Storage Disks: Can retrieve random page at fixed cost But reading several consecutive pages is much cheaper than reading them in

### Data Warehousing und Data Mining

Data Warehousing und Data Mining Multidimensionale Indexstrukturen Ulf Leser Wissensmanagement in der Bioinformatik Content of this Lecture Multidimensional Indexing Grid-Files Kd-trees Ulf Leser: Data

### Storage in Database Systems. CMPSCI 445 Fall 2010

Storage in Database Systems CMPSCI 445 Fall 2010 1 Storage Topics Architecture and Overview Disks Buffer management Files of records 2 DBMS Architecture Query Parser Query Rewriter Query Optimizer Query

### Reading Assignment 5 An Overview of Query Optimization in Relational Systems

Reading Assignment 5 An Overview of Query Optimization in Relational Systems José Filipe Barbosa de Carvalho (jose.carvalho@fe.up.pt) 5th December 2007 Advanced Database Systems Technische Universität

### EFFICIENT EXTERNAL SORTING ON FLASH MEMORY EMBEDDED DEVICES

ABSTRACT EFFICIENT EXTERNAL SORTING ON FLASH MEMORY EMBEDDED DEVICES Tyler Cossentine and Ramon Lawrence Department of Computer Science, University of British Columbia Okanagan Kelowna, BC, Canada tcossentine@gmail.com

### L12: Query Optimization

Note: Slides whose titles are put in () are for your reference only. Details will be covered in COMP9315. L12: heuristicbased q.o. parse convert SQL query parse tree logical query plan apply laws improved

### CS 220 Relational Algebra 2/3/2015

CS 220 Relational Algebra 2/3/2015 Relational Query Languages Query = retrieval program Language examples: Theoretical: 1. Relational Algebra 2. Relational Calculus a. tuple relational calculus (TRC) b.

### In-Memory Database: Query Optimisation. S S Kausik (110050003) Aamod Kore (110050004) Mehul Goyal (110050017) Nisheeth Lahoti (110050027)

In-Memory Database: Query Optimisation S S Kausik (110050003) Aamod Kore (110050004) Mehul Goyal (110050017) Nisheeth Lahoti (110050027) Introduction Basic Idea Database Design Data Types Indexing Query

### Extended Operators in SQL and Relational Algebra

Extended Operators in SQL and Relational Algebra T. M. Murali September 15, 2010 Bags or Sets? So far, we have said that relational algebra and SQL operate on relations that are sets of tuples. Real RDBMSs

### In order to minimize the execution cost is possible: . minimize the size of intermediates results.

Query Optimization The query optimizer is the most important block in DBMS: it generates the strategy called execution plan. This module also defines how reads are performed:. directly;. with indices.

### Problem. Indexing with B-trees. Indexing. Primary Key Indexing. B-trees. B-trees: Example. primary key indexing

Problem Given a large collection of records, Indexing with B-trees find similar/interesting things, i.e., allow fast, approximate queries Anastassia Ailamaki http://www.cs.cmu.edu/~natassa 2 Indexing Primary

### CSE 326: Data Structures B-Trees and B+ Trees

Announcements (4//08) CSE 26: Data Structures B-Trees and B+ Trees Brian Curless Spring 2008 Midterm on Friday Special office hour: 4:-5: Thursday in Jaech Gallery (6 th floor of CSE building) This is

### Lecture 6: Query optimization, query tuning. Rasmus Pagh

Lecture 6: Query optimization, query tuning Rasmus Pagh 1 Today s lecture Only one session (10-13) Query optimization: Overview of query evaluation Estimating sizes of intermediate results A typical query

### Query Optimization for Distributed Database Systems Robert Taylor Candidate Number : 933597 Hertford College Supervisor: Dr.

Query Optimization for Distributed Database Systems Robert Taylor Candidate Number : 933597 Hertford College Supervisor: Dr. Dan Olteanu Submitted as part of Master of Computer Science Computing Laboratory

### Physical Data Organization

Physical Data Organization Database design using logical model of the database - appropriate level for users to focus on - user independence from implementation details Performance - other major factor

### Query Processing and Optimization

Query Processing and Optimization Query optimization: finding a good way to evaluate a query Queries are declarative, and can be translated into procedural languages in more than one way Hence one has

### Big Data and Scripting. Part 4: Memory Hierarchies

1, Big Data and Scripting Part 4: Memory Hierarchies 2, Model and Definitions memory size: M machine words total storage (on disk) of N elements (N is very large) disk size unlimited (for our considerations)

### COMP 5138 Relational Database Management Systems. Week 5 : Basic SQL. Today s Agenda. Overview. Basic SQL Queries. Joins Queries

COMP 5138 Relational Database Management Systems Week 5 : Basic COMP5138 "Relational Database Managment Systems" J. Davis 2006 5-1 Today s Agenda Overview Basic Queries Joins Queries Aggregate Functions

### File Systems: Fundamentals

Files What is a file? A named collection of related information recorded on secondary storage (e.g., disks) File Systems: Fundamentals File attributes Name, type, location, size, protection, creator, creation

### Multiway Search Tree (MST)

Multiway Search Tree (MST) Generalization of BSTs Suitable for disk MST of order n: Each node has n or fewer sub-trees S1 S2. Sm, m n Each node has n-1 or fewer keys K1 Κ2 Κm-1 : m-1 keys in ascending

### Previous Lectures. B-Trees. External storage. Two types of memory. B-trees. Main principles

B-Trees Algorithms and data structures for external memory as opposed to the main memory B-Trees Previous Lectures Height balanced binary search trees: AVL trees, red-black trees. Multiway search trees:

### Overview of Storage and Indexing. Data on External Storage. Alternative File Organizations. Chapter 8

Overview of Storage and Indexing Chapter 8 How index-learning turns no student pale Yet holds the eel of science by the tail. -- Alexander Pope (1688-1744) Database Management Systems 3ed, R. Ramakrishnan

### Performance Tuning for the Teradata Database

Performance Tuning for the Teradata Database Matthew W Froemsdorf Teradata Partner Engineering and Technical Consulting - i - Document Changes Rev. Date Section Comment 1.0 2010-10-26 All Initial document

### Databases and Information Systems 1 Part 3: Storage Structures and Indices

bases and Information Systems 1 Part 3: Storage Structures and Indices Prof. Dr. Stefan Böttcher Fakultät EIM, Institut für Informatik Universität Paderborn WS 2009 / 2010 Contents: - database buffer -

### Relational Query Optimization 2

Relational Query Optimization 2 R&G - Chapter 14 For ease and speed in doing a thing do not give the work lasting solidity or exactness of beauty. Plutarch, Life of Pericles Query Optimization Query can

### COS 318: Operating Systems

COS 318: Operating Systems File Performance and Reliability Andy Bavier Computer Science Department Princeton University http://www.cs.princeton.edu/courses/archive/fall10/cos318/ Topics File buffer cache

### Indexing and Hashing C H A P T E R. Practice Exercises Answer: Reasons for not keeping indices on every attribute include:

C H A P T E R Indexing and Hashing Practice Exercises.1 Answer: Reasons for not keeping indices on every attribute include: Every index requires additional CPU time and disk I/O overhead during inserts

### Understanding SQL Server Execution Plans. Klaus Aschenbrenner Independent SQL Server Consultant SQLpassion.at Twitter: @Aschenbrenner

Understanding SQL Server Execution Plans Klaus Aschenbrenner Independent SQL Server Consultant SQLpassion.at Twitter: @Aschenbrenner About me Independent SQL Server Consultant International Speaker, Author

### Chapter 6: Physical Database Design and Performance. Database Development Process. Physical Design Process. Physical Database Design

Chapter 6: Physical Database Design and Performance Modern Database Management 6 th Edition Jeffrey A. Hoffer, Mary B. Prescott, Fred R. McFadden Robert C. Nickerson ISYS 464 Spring 2003 Topic 23 Database

### Chapter 13. Disk Storage, Basic File Structures, and Hashing

Chapter 13 Disk Storage, Basic File Structures, and Hashing Chapter Outline Disk Storage Devices Files of Records Operations on Files Unordered Files Ordered Files Hashed Files Dynamic and Extendible Hashing

### Kathleen Durant PhD Lecture 19 CS 3200 Northeastern University. Slide Content Courtesy of R. Ramakrishnan, J. Gehrke, and J.

Relational Query Optimization Kathleen Durant PhD Lecture 19 CS 3200 Northeastern University 1 Slide Content Courtesy of R. Ramakrishnan, J. Gehrke, and J. Hellerstein Overview of Query Evaluation Query

### Review. Cost-based Query Sub-System. Schema for Examples. Relational Query Optimization. Query Optimization Overview

Review Relational Query Optimization CS186 R & G Chapters 12/15 Implementation of single Relational Operations Choices depend on indexes, memory, stats, Joins Blocked nested loops: simple, exploits extra

### Sorting - Iterative Algorithms

Sorting - Iterative Algorithms Problem Definition Input: A sequence of n elements Output: A permutation of such elements, so that a 1 a 2 a n 2 1 Types of Ordering Internal

### Outline BST Operations Worst case Average case Balancing AVL Red-black B-trees. Binary Search Trees. Lecturer: Georgy Gimel farb

Binary Search Trees Lecturer: Georgy Gimel farb COMPSCI 220 Algorithms and Data Structures 1 / 27 1 Properties of Binary Search Trees 2 Basic BST operations The worst-case time complexity of BST operations

### Query processing using HP NonStop SQL/MP software

Query processing using HP NonStop SQL/MP software Introduction... 2 Query processing components... 2 Query optimization... 3 Generating the plan... 3 Serial execution plans... 4 Parallel execution plans...

### B-Trees. Algorithms and data structures for external memory as opposed to the main memory B-Trees. B -trees

B-Trees Algorithms and data structures for external memory as opposed to the main memory B-Trees Previous Lectures Height balanced binary search trees: AVL trees, red-black trees. Multiway search trees:

### Life of a dirty page InnoDB disk IO. Mark Callaghan

Life of a dirty page InnoDB disk IO Mark Callaghan URLs slides at http://tinyurl.com/dzshl2 v3 Google patch at code.google.com many similar features in Percona binaries soon to be many similar features

### Analysis of Algorithms I: Binary Search Trees

Analysis of Algorithms I: Binary Search Trees Xi Chen Columbia University Hash table: A data structure that maintains a subset of keys from a universe set U = {0, 1,..., p 1} and supports all three dictionary

### Chapter 4: SQL. Basic Structure

Chapter 4: SQL Basic Structure Set Operations Aggregate Functions Null Values Nested Subqueries Derived Relations Views Modification of the Database Joined Relations Data Definition Language Embedded SQL

### Analysis of Binary Search algorithm and Selection Sort algorithm

Analysis of Binary Search algorithm and Selection Sort algorithm In this section we shall take up two representative problems in computer science, work out the algorithms based on the best strategy to

### Content: TDDB56 DALGOPT-D Algorithms and optimization. Lecture 4. Sets and Dictionaries Binary Search, Hashing, Skip Lists. ADT Set / Ordered Set

Content: TDDB56 DALGOPT-D Algorithms and optimization Lecture 4 Sets and Dictionaries Binary Search, Hashing, Skip Lists ADTs Set, Ordered Set, Dictionary/Map Implementation of Set and Dictionary using

### Understanding Query Processing and Query Plans in SQL Server. Craig Freedman Software Design Engineer Microsoft SQL Server

Understanding Query Processing and Query Plans in SQL Server Craig Freedman Software Design Engineer Microsoft SQL Server Outline SQL Server engine architecture Query execution overview Showplan Common