Query Compiler: 16.7 Completing the Physical Query-Plan CS257 Spring 2009 Professor Tsau Lin Student: Suntorn Sae-Eung ID: 212

Outline 16.7 Completing the Physical-Query-Plan I. Choosing a Selection Method II. Choosing a Join Method III. Pipelining Versus Materialization IV. Pipelining Unary Operations V. Pipelining Binary Operations

Before complete Physical-Query-Plan A query previously has been Parsed and Preprocessed (16.1) Converted to Logical Query Plans (16.3) Estimated the Costs of Operations (16.4) Determined costs by Cost-Based Plan Selection (16.5) Weighed costs of join operations by choosing an Order for Joins

16.7 Completing the Physical-Query-Plan 3 topics related to turning LP into a complete physical plan 1. Choosing of physical implementations such as Selection and Join methods 2. Decisions regarding to intermediate results (Materialized or Pipelined) 3. Notation for physical-query-plan operators

I. Choosing a Selection Method (A) Algorithms for each selection operators 1. Can we use an created index on an attribute? If yes, index-scan. Otherwise table-scan) 2. After retrieve all condition-satisfied tuples in (1), then filter them with the rest selection conditions

Choosing a Selection Method(A) (cont.) Recall  Cost of query = # disk I/O’s How costs for various plans are estimated from σ C (R) operation 1. Cost of table-scan algorithm a) B(R) if R is clustered b) T(R) if R is not clustered 2. Cost of a plan picking an equality term (e.g. a = 10) w/ index-scan a) B(R) / V(R, a) clustering index b) T(R) / V(R, a) nonclustering index 3. Cost of a plan picking an inequality term (e.g. b < 20) w/ index-scan a) B(R) / 3 clustering index b) T(R) / 3 nonclustering index

Example Selection: σ x=1 AND y=2 AND z<5 (R) - Where paremeters of R(x, y, z) are : T(R)=5000,B(R)=200, V(R,x)=100, andV(R, y)=500 - Relation R is clustered - x, y have nonclustering indexes, only index on z is clustering.

Example (cont.) Selection options: 1. Table-scan  filter x, y, z. Cost is B(R) = 200 since R is clustered. 2. Use index on x =1  filter on y, z. Cost is 50 since T(R) / V(R, x) is (5000/100) = 50 tuples, index is not clustering. 3. Use index on y =2  filter on x, z. Cost is 10 since T(R) / V(R, y) is (5000/500) = 10 tuples using nonclustering index. 4. Index-scan on clustering index w/ z < 5  filter x, y. Cost is about B(R) /3 = 67

Example (cont.) Costs option 1 = 200 option 2 = 50 option 3 = 10 option 4 = 67 The lowest Cost is option 3. Therefore, the preferred physical plan 1. retrieves all tuples with y = 2 2. then filters for the rest two conditions (x, z).

II. Choosing a Join Method Determine costs associated with each join algorithms: 1. One-pass join, and nested-loop join devotes enough buffer to joining 2. Sort-join is preferred when attributes are pre-sorted or two or more join on the same attribute such as ( R(a, b) S(a, c)) T(a, d) - where sorting R and S on a will produce result of R S to be sorted on a and used directly in next join

3. Index-join for a join with high chance of using index created on the join attribute such as R(a, b) S(b, c) 4. Hashing join is the best choice for unsorted or non-indexing relations which needs multipass join. Choosing a Join Method (cont.)

III. Pipelining Versus Materialization Materialization (naïve way) store (intermediate) result of each operations on disk Pipelining (more efficient way) Interleave the execution of several operations, the tuples produced by one operation are passed directly to the operations that used it store (intermediate) result of each operations on buffer, which is implemented on main memory

Unary = a-tuple-at-a-time or full relation selection and projection are the best candidates for pipelining. IV. Pipelining Unary Operations R In buf Unary operation Out buf In buf Unary operation Out buf M-1 buffers

Pipelining Unary Operations (cont.) Pipelining Unary Operations are implemented by iterators

V. Pipelining Binary Operations Binary operations : , , -,, x The results of binary operations can also be pipelined. Use one buffer to pass result to its consumer, one block at a time. The extended example shows tradeoffs and opportunities

Example Consider physical query plan for the expression ( R(w, x) S(x, y)) U(y, z) Assumption R occupies 5,000 blocks, S and U each 10,000 blocks. The intermediate result R S occupies k blocks for some k. Both joins will be implemented as hash-joins, either one-pass or two-pass depending on k There are 101 buffers available.

Example (cont.) First consider join R S, neither relations fits in buffers Needs two-pass hash-join to partition R into 100 buckets (maximum possible) each bucket has 50 blocks The 2 nd pass hash-join uses 51 buffers, leaving the rest 50 buffers for joining result of R S with U.

Example (cont.) Case 1: suppose k  49, the result of R S occupies at most 49 blocks. Steps 1. Pipeline in R S into 49 buffers 2. Organize them for lookup as a hash table 3. Use one buffer left to read each block of U in turn 4. Execute the second join as one-pass join.

Example (cont.) The total number of I/O’s is 55,000 45,000 for two-pass hash join of R and S 10,000 to read U for one-pass hash join of (R S) U.

Example (cont.) Case 2: suppose k > 49 but < 5,000, we can still pipeline, but need another strategy which intermediate results join with U in a 50- bucket, two-pass hash-join. Steps are: 1. Before start on R S, we hash U into 50 buckets of 200 blocks each. 2. Perform two-pass hash join of R and U using 51 buffers as case 1, and placing results in 50 remaining buffers to form 50 buckets for the join of R S with U. 3. Finally, join R S with U bucket by bucket.

Example (cont.) The number of disk I/O’s is: 20,000 to read U and write its tuples into buckets 45,000 for two-pass hash-join R S k to write out the buckets of R S k+10,000 to read the buckets of R S and U in the final join The total cost is 75,000+2k.

Example (cont.) Compare Increasing I/O’s between case 1 and case 2 k  49 (case 1) Disk I/O’s is 55,000 k > 50  5000 (case 2) k=50, I/O’s is 75,000+(2*50) = 75,100 k=51, I/O’s is 75,000+(2*51) = 75,102 k=52, I/O’s is 75,000+(2*52) = 75,104 Notice: I/O’s discretely grows as k increases from 49  50.

Example (cont.) Case 3: k > 5,000, we cannot perform two-pass join in 50 buffers available if result of R S is pipelined. Steps are 1. Compute R S using two-pass join and store the result on disk. 2. Join result on (1) with U, using two-pass join.

Example (cont.) The number of disk I/O’s is: 45,000 for two-pass hash-join R and S k to store R S on disk 30,000 + k for two-pass join of U in R S The total cost is 75,000+4k.

Example (cont.) In summary, costs of physical plan as function of R S size.

Questions & Answers

For your attention

Reference [1] H. Garcia-Molina, J. Ullman, and J. Widom, “Database System: The Complete Book,” second edition: p , Prentice Hall, New Jersy, 2008