h. pang / nus principles of query processing pang hwee hwa school of computing, nus cs5226 week 5
TRANSCRIPT
H. Pang / NUS
ApplicationProgrammer
(e.g., business analyst,Data architect)
SophisticatedApplicationProgrammer
(e.g., SAP admin)
DBA,Tuner
Hardware[Processor(s), Disk(s), Memory]
Operating System
Concurrency Control Recovery
Storage SubsystemIndexes
Query Processor
Application
H. Pang / NUS
Overview of Query Processing
Parser QueryOptimizer
Statistics Cost Model
QEPParsed Query
Database
High Level Query Query Result
QueryEvaluator
H. Pang / NUS
Projection Operator
R.attrib, .. (R)
• Implementation is straightforward
SELECT bidFROM Reserves RWHERE R.rname < ‘C%’
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Selection Operator
R.attr op value (R)
• Size of result = R * selectivity • Scan• Clustered index: Good• Non-clustered index:
– Good for low selectivity– Worse than scan for high selectivity
SELECT *FROM Reserves RWHERE R.rname < ‘C%’
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Example of Join
sid sname rating age22 dustin 7 45.028 yuppy 9 35.031 lubber 8 55.544 guppy 5 35.058 rusty 10 35.0
sid bid day rname
31 101 10/11/96 lubber58 103 11/12/96 dustin
sid sname rating age bid day rname
31 lubber 8 55.5 101 10/11/96 lubber58 rusty 10 35.0 103 11/12/96 dustin
SELECT *FROM Sailors R, Reserve SWHERE R.sid=S.sid
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Notations
• |R| = number of pages in outer table R• ||R|| = number of tuples in outer table R• |S| = number of pages in inner table S• ||S|| = number of tuples in inner table S• M = number of main memory pages allocated
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Simple Nested Loop Join
• Scan inner table S per R tuple: ||R|| * |S|– Each scan costs |S| pages– For ||R|| tuples
• |R| pages for outer table R• Total cost = |R| + ||R|| * |S| pages• Not optimal!
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Block Nested Loop Join
R S
M – 2 pages
1 scan per R block
|S| pages per scan|R| / (M – 2) blocks
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Block Nested Loop Join
• Scan inner table S per block of (M – 2) pages of R tuples– Each scan costs |S| pages– |R| / (M – 2) blocks of R tuples
• |R| pages for outer table R
• Total cost = |R| + |R| / (M – 2) * |S| pages
• R should be the smaller table
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Index Nested Loop Join
• Probe S index for matching S tuples per R tuple– Probe hash index: 1.2 I/Os– Probe B+ tree: 2-4 I/Os, plus retrieve matching S
tuples: 1 I/O– For ||R|| tuples
• |R| pages for outer table R• Total cost = |R| + ||R|| * index retrieval• Better than Block NL join only for small number
of R tuples
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External Sort R
R0,M-1 R0,M… …
R1,2 R1,M-1…Merge pass 1 R1,1
Merge pass 2 R2,1
Split pass R R0,1
# merge passes = logM-1 |R|/M
Cost per pass = |R| input + |R| output = 2 |R|
Total cost = 2 |R| (logM-1 |R|/M + 1) including split pass
Size of R0,i = M, # R0,i’s = |R|/M
(m-1)-waymerge
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Sort Merge Join
• External-sort R: 2 |R| * (logM-1 |R|/M + 1)– Split R into |R|/M sorted runs each of size M: 2 |R|– Merge up to (M – 1) runs repeatedly logM-1 |R|/M passes, each costing 2 |R|
• External-sort S: 2 |S| * (logM-1 |S|/M + 1)• Merge matching tuples from sorted R and S: |R|
+ |S|• Total cost = 2 |R| * (logM-1 |R|/M + 1) + 2 |S| *
(logM-1 |S|/M + 1) + |R| + |S|– If |R| < M*(M-1), cost = 5 * (|R| + |S|)
H. Pang / NUS
GRACE Hash Join
X X XX X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X
R
S
0
1
2
3
0 1 2 3
bucketID = X mod 4Join on R.X = S.X
R S = R0 S0 + R1 S1 + R2 S2 + R3 S3
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GRACE Hash Join – Partition Phase
M main memory buffers DiskDisk
Original Relation OUTPUT
2INPUT
1
hashfunction
h1M-1
Partitions
1
2
M-1
. . .
R (M – 1) partitions, each of size |R| / (M – 1)
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GRACE Hash Join – Join Phase
Partitionsof R & S
Input bufferfor Si
Hash table for partitionRi (< M-1 pages)
B main memory buffersDisk
Output buffer
Disk
Join Result
hashfnh2
h2
Partition must fit in memory: |R| / (M – 1) < M -1
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GRACE Hash Join Algorithm
• Partition phase: 2 (|R| + |S|)– Partition table R using hash function h1: 2 |R|– Partition table S using hash function h1: 2 |S|– R tuples in partition i will match only S tuples in partition I– R (M – 1) partitions, each of size |R| / (M – 1)
• Join phase: |R| + |S|– Read in a partition of R (|R| / (M – 1) < M -1)– Hash it using function h2 (<> h1!)– Scan corresponding S partition, search for matches
• Total cost = 3 (|R| + |S|) pages
• Condition: M > √f|R|, f ≈ 1.2 to account for hash table
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Summary of Join Operator
• Simple nested loop: |R| + ||R|| * |S|
• Block nested loop: |R| + |R| / (M – 2) * |S|
• Index nested loop: |R| + ||R|| * index retrieval
• Sort-merge: 2 |R| * (logM-1 |R|/M + 1) + 2 |S| *
(logM-1 |S|/M + 1) + |R| + |S|
• GRACE hash: 3 * (|R| + |S|)– Condition: M > √f|R|
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Overview of Query Processing
Parser QueryOptimizer
Statistics Cost Model
QEPParsed Query
Database
High Level Query Query Result
QueryEvaluator
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Query Optimization
• Given: An SQL query joining n tables• Dream: Map to most efficient plan• Reality: Avoid rotten plans• State of the art:
– Most optimizers follow System R’s technique– Works fine up to about 10 joins
SELECT S.snameFROM Reserves R, Sailors SWHERE R.sid=S.sid AND R.bid=100 AND S.rating>5
Reserves Sailors
sid=sid
bid=100 rating > 5
sname
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Complexity of Query Optimization
• Many degrees of freedom– Selection: scan versus
(clustered, non-clustered) index
– Join: block nested loop, sort-merge, hash
– Relative order of the operators
– Exponential search space!
• Heuristics– Push the selections down– Push the projections down– Delay Cartesian products– System R: Only left-deep
trees
BA
C
D
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• Selection: - cascade
- commutative
• Projection: - cascade
• Join: - associative
- commutative
Equivalences in Relational Algebra
c cn c cnR R1 1 ... . . .
c c c cR R1 2 2 1
a a anR R1 1 . . .
R (S T) (R S) T
(R S) (S R)
H. Pang / NUS
Equivalences in Relational Algebra
• A projection commutes with a selection that only uses attributes retained by the projection
• Selection between attributes of the two arguments of a cross-product converts cross-product to a join
• A selection on just attributes of R commutes with join R S (i.e., (R S) (R) S )
• Similarly, if a projection follows a join R S, we can `push’ it by retaining only attributes of R (and S) that are needed for the join or are kept by the projection
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System R Optimizer
1. Find all plans for accessing each base table2. For each table
• Save cheapest unordered plan• Save cheapest plan for each interesting order• Discard all others
3. Try all ways of joining pairs of 1-table plans; save cheapest unordered + interesting ordered plans
4. Try all ways of joining 2-table with 1-table5. Combine k-table with 1-table till you have full plan tree6. At the top, to satisfy GROUP BY and ORDER BY
• Use interesting ordered plan• Add a sort node to unordered plan
H. Pang / NUS Source: Selinger et al, “Access Path Selection in a Relational Database Management System”
H. Pang / NUS
Note: Only branches for NL join are shown here. Additional branches for other join methods (e.g. sort-merge) are not shown.
Source: Selinger et al, “Access Path Selection in a Relational Database Management System”
H. Pang / NUS
What is “Cheapest”?
• Need information about the relations and indexes involved
• Catalogs typically contain at least:– # tuples (NTuples) and # pages (NPages) for each relation.– # distinct key values (NKeys) and NPages for each index.– Index height, low/high key values (Low/High) for each tree index.
• Catalogs updated periodically.– Updating whenever data changes is too expensive; lots of
approximation anyway, so slight inconsistency ok.
• More detailed information (e.g., histograms of the values in some field) are sometimes stored.
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Estimating Result Size
• Consider a query block:
• Maximum # tuples in result is the product of the cardinalities of relations in the FROM clause.
• Reduction factor (RF) associated with each termi reflects the impact of the term in reducing result size– Term col=value has RF 1/NKeys(I)– Term col1=col2 has RF 1/MAX(NKeys(I1), NKeys(I2))– Term col>value has RF (High(I)-value)/(High(I)-Low(I))
• Result cardinality = Max # tuples * product of all RF’s.– Implicit assumption that terms are independent!
SELECT attribute listFROM relation listWHERE term1 AND ... AND termk
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Cost Estimates for Single-Table Plans
• Index I on primary key matches selection:– Cost is Height(I)+1 for a B+ tree, about 1.2 for hash index.
• Clustered index I matching one or more selects:– (NPages(I)+NPages(R)) * product of RF’s of matching selects.
• Non-clustered index I matching one or more selects:– (NPages(I)+NTuples(R)) * product of RF’s of matching selects.
• Sequential scan of file:– NPages(R).
Note: Typically, no duplicate elimination on projections! (Exception: Done on answers if user says DISTINCT.)
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Counting the Costs
• With 5 buffers, cost of plan:– Scan Reserves (1000) + write temp
T1 (10 pages, if we have 100 boats, uniform distribution)
– Scan Sailors (500) + write temp T2 (250 pages, if we have 10 ratings).
– Sort T1 (2*10*2), sort T2 (2*250*4), merge (10+250), total=2300
– Total: 4060 page I/Os
• If we used BNL join, join cost = 10+4*250, total cost = 2770
• If we ‘push’ projections, T1 has only sid, T2 only sid and sname:– T1 fits in 3 pages, cost of BNL
drops to under 250 pages, total < 2000
Reserves Sailors
sid=sid
bid=100
sname(On-the-fly)
rating > 5(Scan;write to temp T1)
(Scan;write totemp T2)
(Sort-Merge Join)
SELECT S.snameFROM Reserves R, Sailors SWHERE R.sid=S.sid AND R.bid=100 AND S.rating>5
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Exercise
• Reserves: 100,000 tuples, 100 tuples per page
• With clustered index on bid of Reserves, we get 100,000/100 = 1000 tuples on 1000/100 = 10 pages
• Join column sid is a key for Sailors - at most one matching tuple
• Decision not to push rating>5 before the join is based on availability of sid index on Sailors
• Cost: Selection of Reserves tuples (10 I/Os); for each tuple, must get matching Sailors tuple (1000*1.2); total 1210 I/Os
Reserves
Sailors
sid=sid
bid=100
sname(On-the-fly)
rating > 5
(Use clustered index on sid)
(Index Nested Loops,with pipelining )
(On-the-fly)
(Use hashIndex on sid)
H. Pang / NUS
Avoid Redundant DISTINCT
• DISTINCT usually entails a sort operation• Slow down query optimization because one
more “interesting” order to consider• Remove if you know the result has no duplicates
SELECT DISTINCT ssnumFROM EmployeeWHERE dept = ‘information systems’
H. Pang / NUS
Change Nested Queries to Join
• Might not use index on Employee.dept
• Need DISTINCT if an employee might belong to multiple departments
SELECT ssnumFROM EmployeeWHERE dept IN (SELECT dept FROM Techdept)
SELECT ssnumFROM Employee, TechdeptWHERE Employee.dept = Techdept.dept
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Avoid Unnecessary Temp Tables
• Creating temp table causes update to catalog• Cannot use any index on original table
SELECT * INTO TempFROM EmployeeWHERE salary > 40000
SELECT ssnumFROM TempWHERE Temp.dept = ‘information systems’
SELECT ssnumFROM EmployeeWHERE Employee.dept = ‘information systems’AND salary > 40000
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Avoid Complicated Correlation Subqueries
• Search all of e2 for each e1 record!
SELECT ssnumFROM Employee e1WHERE salary = (SELECT MAX(salary) FROM Employee e2 WHERE e2.dept = e1.dept
SELECT MAX(salary) as bigsalary, dept INTO TempFROM EmployeeGROUP BY dept
SELECT ssnumFROM Employee, TempWHERE salary = bigsalaryAND Employee.dept = Temp.dept
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Avoid Complicated Correlation Subqueries
• SQL Server 2000 does a good job at handling the correlated subqueries (a hash join is used as opposed to a nested loop between query blocks)– The techniques
implemented in SQL Server 2000 are described in “Orthogonal Optimization of Subqueries and Aggregates” by C.Galindo-Legaria and M.Joshi, SIGMOD 2001.-10
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correlated subquery
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SQLServer 2000
Oracle 8i
DB2 V7.1
> 10000> 1000
H. Pang / NUS
Join on Clustering and Integer Attributes
• Employee is clustered on ssnum• ssnum is an integer
SELECT Employee.ssnumFROM Employee, StudentWHERE Employee.name = Student.name
SELECT Employee.ssnumFROM Employee, StudentWHERE Employee.ssnum = Student.ssnum
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Avoid HAVING when WHERE is enough
• May first perform grouping for all departments!
SELECT AVG(salary) as avgsalary, deptFROM EmployeeGROUP BY deptHAVING dept = ‘information systems’
SELECT AVG(salary) as avgsalaryFROM EmployeeWHERE dept = ‘information systems’GROUP BY dept
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Avoid Views with unnecessary Joins
• Join with Techdept unnecessarily
CREATE VIEW TechlocationAS SELECT ssnum, Techdept.dept, locationFROM Employee, TechdeptWHERE Employee.dept = Techdept.dept
SELECT deptFROM TechlocationWHERE ssnum = 4444
SELECT deptFROM EmployeeWHERE ssnum = 4444
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Aggregate Maintenance
• Materialize an aggregate if needed “frequently”• Use trigger to update
create trigger updateVendorOutstanding on orders for insert asupdate vendorOutstandingset amount =
(select vendorOutstanding.amount+sum(inserted.quantity*item.price)from inserted,itemwhere inserted.itemnum = item.itemnum)
where vendor = (select vendor from inserted) ;
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Avoid External Loops
• No loop:sqlStmt = “select * from lineitem where l_partkey <=
200;”odbc->prepareStmt(sqlStmt);odbc->execPrepared(sqlStmt);
• Loop:sqlStmt = “select * from lineitem where l_partkey = ?;”odbc->prepareStmt(sqlStmt);for (int i=1; i<200; i++){
odbc->bindParameter(1, SQL_INTEGER, i);odbc->execPrepared(sqlStmt);
}
H. Pang / NUS
Avoid External Loops
• SQL Server 2000 on Windows 2000
• Crossing the application interface has a significant impact on performance
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loop no loop
thro
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hp
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(rec
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s/se
c)
Let the DBMS optimizeset operations
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Avoid Cursors
• No cursorselect * from employees;
• CursorDECLARE d_cursor CURSOR FOR select * from employees;OPEN d_cursorwhile (@@FETCH_STATUS = 0)BEGIN
FETCH NEXT from d_cursorENDCLOSE d_cursorgo
H. Pang / NUS
Avoid Cursors
• SQL Server 2000 on Windows 2000
• Response time is a few seconds with a SQL query and more than an hour iterating over a cursor
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cursor SQL
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Retrieve Needed Columns Only
– All
Select * from lineitem;
– Covered subset
Select l_orderkey, l_partkey, l_suppkey, l_shipdate, l_commitdate from lineitem;
• Avoid transferring unnecessary data
• May enable use of a covering index.
0
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0.5
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1.25
1.5
1.75
no index index
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all
covered subset
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Use Direct Path for Bulk Loading
sqlldr directpath=true control=load_lineitem.ctl data=E:\Data\lineitem.tbl
load data infile "lineitem.tbl"into table LINEITEM appendfields terminated by '|' (
L_ORDERKEY, L_PARTKEY, L_SUPPKEY, L_LINENUMBER, L_QUANTITY, L_EXTENDEDPRICE, L_DISCOUNT, L_TAX, L_RETURNFLAG, L_LINESTATUS, L_SHIPDATE DATE "YYYY-MM-DD", L_COMMITDATE DATE "YYYY-MM-DD", L_RECEIPTDATE DATE "YYYY-MM-DD", L_SHIPINSTRUCT, L_SHIPMODE, L_COMMENT
)
H. Pang / NUS
Use Direct Path for Bulk Loading
• Direct path loading bypasses the query engine and the storage manager. It is orders of magnitude faster than for conventional bulk load (commit every 100 records) and inserts (commit for each record).
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conventional direct path insert
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Some Idiosyncrasies
• OR may stop the index being used– break the query and use UNION
• Order of tables may affect join implementation
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Query Tuning – Thou Shalt …
• Avoid redundant DISTINCT• Change nested queries to join• Avoid unnecessary temp tables• Avoid complicated correlation subqueries• Join on clustering and integer attributes• Avoid HAVING when WHERE is enough• Avoid views with unnecessary joins• Maintain frequently used aggregates• Avoid external loops