1. CS 257 Chapter – 15.9 Summary of Query Execution Database Systems: The Complete Book Krishna Vellanki 124

2. Introduction What is Query Processor? ◦ Group of components of a DBMS that converts a user queries and data-modification commands into a sequence of database operations ◦ It also executes those operations ◦ Must supply detail regarding how the query is to be executed

3. Building Blocks of Query processing 3 Query Execution:  The algorithms that manipulate the data of the database.  Focus on the operations of extended relational algebra.

4. Outline of Query Compilation Query compilation Parsing: A parse tree for the query is constructed Query Rewrite: The parse tree is converted to an initial query plan and transformed into logical query plan (less time) Physical Plan Generation : Logical Q Plan is converted into physical query plan by selecting algorithms and order of execution of these operator. 4

5. Scanning Tables One of the basic thing we can do in a Physical query plan is to read the entire contents of a relation R. Variation of this operator involves simple predicate, read only those tuples of the relation R that satisfy the predicate. Basic approaches to locate the tuples of a relation R  Table Scan  Relation R is stored in secondary memory with its tuples arranged in blocks  It is possible to get the blocks one by one  Index-Scan  If there is an index on any attribute of Relation R, we can use this index to get all the tuples of Relation R 5

6. Sorting While Scanning Tables Number of reasons to sort a relation  Query could include an ORDER BY clause, requiring that a relation be sorted.  Algorithms to implement relational algebra operations requires one or both arguments to be sorted relations.  Physical-query-plan operator sort-scan takes a relation R, attributes on which the sort is to be made, and produces R in that sorted order 6

7. Parameters for Measuring Costs Parameters that affect the performance of a query  Buffer space availability in the main memory at the time of execution of the query  Size of input and the size of the output generated  The size of memory block on the disk and the size in the main memory also affects the performance  B: The number of blocks are needed to hold all tuples of relation R. Also denoted as B(R).  T is the number of tuples in relation R, also denoted as T(R).  V: The number of distinct values that appear in a column of a relation R  V(R, a)- is the number of distinct values of column for a in relation R 7

8. One-Pass Algorithms for Database Operations The choice of an algorithm for each operator is an essential part of the process of transforming a logical query plan into a physical query plan. Main classes of Algorithms: ◦ Sorting-based methods ◦ Hash-based methods ◦ Index-based methods Division based on degree difficulty and cost: ◦ 1-pass algorithms ◦ 2-pass algorithms ◦ 3 or more pass algorithms

9. One-Pass Algorithm Methods 1. One-Pass Algorithms for Tuple-at-a-Time Operations: 1. One-Pass Algorithms for Tuple-at-a-Time Operations: selection and projection 2. One-Pass Algorithms for Unary, fill-Relation Operations: 2. One-Pass Algorithms for Unary, fill-Relation Operations: Duplicate Elimination and Grouping 3. One-Pass Algorithms for Unary, fill-Relation Operations: 3. One-Pass Algorithms for Unary, fill-Relation Operations: Binary operations including Union, Intersection, Difference, Product and Join 9

10. Nested Loop Joins  Used for relations of any side.  Not necessary that relation fits in main memory  Uses “One-and-a-half” pass method in which for each variation:  One argument read just once.  Other argument read repeatedly.  Two kinds:  Tuple-Based Nested Loop Join  Block-Based Nested Loop Join

11. Improvement & Modification To decrease the cost  Method 1: Use algorithm for Index-Based joins  We find tuple of R that matches given tuple of S  We need not to read entire relation R  Method 2: Use algorithm for Block-Based joins  Tuples of R & S are divided into blocks  Uses enough memory to store blocks in order to reduce the number of disk I/O ’ s.

12. Physically Unrealizable Behaviors Transaction T tries to read too late Read too Late

13. Write too Late Transaction T tries to write too late

14. Problem with dirty data T could perform a dirty read if it is reads X

15. A write is cancelled because of a write with a later timestamp, but the writer then aborts

16. Timestamps Vs Locks TimestampsLocks Superior if most transactions are read-only rare that concurrent transactions will read or write the same element Superior in high-conflict situations In high-conflict situations, rollback will be frequent, introducing more delays than a locking system Frequently delay transactions as they wait for locks

17. Two passed Algorithm based on hashing Hashing is done if the data is too big to store in main memory buffers. ◦ Hash all the tuples of the argument(s) using an appropriate hash key. ◦ For all the common operations, there is a way to select the hash key so all the tuples that need to be considered together when we perform the operation have the same hash value. ◦ This reduces the size of the operand(s) by a factor equal to the number of buckets.

18. Steps to be followed for a Two passed Algorithm based on hashing Duplicate Elimination Duplicate Elimination Grouping and Aggregation Grouping and Aggregation Union, Intersection, and Difference Union, Intersection, and Difference Hash-Join Algorithm Hash-Join Algorithm

19. Sort based Vs Hash based For binary operations, hash-based only limits size to min of arguments, not sum Sort-based can produce output in sorted order, which can be helpful Hash-based depends on buckets being of equal size Sort-based algorithms can experience reduced rotational latency or seek time

20. 15.6 Index based Algorithms Clustered Relation: Tuples are packed into roughly as few blocks as can possibly hold those tuples Clustering indexes: Indexes on attributes that all the tuples with a fixed value for the search key of this index appear on roughly as few blocks as can hold them A relation that isn’t clustered cannot have a clustering index A clustered relation can have nonclustering indexes

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