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1 Database Tuning Principles, Experiments and Troubleshooting Techniques Dennis Shasha Philippe Bonnet

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1 1 Database Tuning Principles, Experiments and Troubleshooting Techniques Dennis Shasha Philippe Bonnet

2 2 Availability of Materials 1.Power Point presentation is available on my web site (and Philippe Bonnets). Just type our names into google 2.Experiments available from Denmark site maintained by Philippe (site in a few slides) 3.Book with same title available from Morgan Kaufmann.

3 3 Database Tuning Database Tuning is the activity of making a database application run more quickly. More quickly usually means higher throughput, though it may mean lower response time for time-critical applications.

4 4 Application Programmer (e.g., business analyst, Data architect) Sophisticated Application Programmer (e.g., SAP admin) DBA, Tuner Hardware [Processor(s), Disk(s), Memory] Operating System Concurrency ControlRecovery Storage Subsystem Indexes Query Processor Application

5 5 Outline of Tutorial 1.Basic Principles 2.Tuning the guts 3.Indexes 4.Relational Systems 5.Application Interface 6.Ecommerce Applications 7.Data warehouse Applications 8.Distributed Applications 9.Troubleshooting

6 6 Goal of the Tutorial To show: –Tuning principles that port from one system to the other and to new technologies –Experimental results to show the effect of these tuning principles. –Troubleshooting techniques for chasing down performance problems.

7 7 Tuning Principles Leitmotifs Think globally, fix locally (does it matter?) Partitioning breaks bottlenecks (temporal and spatial) Start-up costs are high; running costs are low (disk transfer, cursors) Be prepared for trade-offs (indexes and inserts)

8 8 Experiments -- why and where Simple experiments to illustrate the performance impact of tuning principles. to get the SQL scripts, the data and a tool to run the experiments.

9 9 Experimental DBMS and Hardware Results presented throughout this tutorial obtained with: –SQL Server 7, SQL Server 2000, Oracle 8i, Oracle 9i, DB2 UDB 7.1 –Three configurations: 1.Dual Xeon (550MHz,512Kb), 1Gb RAM, Internal RAID controller from Adaptec (80Mb) 2 Ultra 160 channels, 4x18Gb drives (10000RPM), Windows Dual Pentium II (450MHz, 512Kb), 512 Mb RAM, 3x18Gb drives (10000RPM), Windows Pentium III (1 GHz, 256 Kb), 1Gb RAM, Adapter with 2 channels, 3x18Gb drives (10000RPM), Linux Debian 2.4.

10 10 Tuning the Guts Concurrency Control –How to minimize lock contention? Recovery –How to manage the writes to the log (to dumps)? OS –How to optimize buffer size, process scheduling, … Hardware –How to allocate CPU, RAM and disk subsystem resources?

11 11 Isolation Correctness vs. Performance –Number of locks held by each transaction –Kind of locks –Length of time a transaction holds locks

12 12 Isolation Levels Read Uncommitted (No lost update) –Exclusive locks for write operations are held for the duration of the transactions –No locks for read Read Committed (No dirty retrieval) –Shared locks are released as soon as the read operation terminates. Repeatable Read (no unrepeatable reads for read/write ) –Two phase locking Serializable (read/write/insert/delete model) –Table locking or index locking to avoid phantoms

13 13 Snapshot isolation T1 T2 T3 TIME R(Y) returns 1 R(Z) returns 0 R(X) returns 0 W(Y:=1) W(X:=2, Z:=3) X=Y=Z=0 Each transaction executes against the version of the data items that was committed when the transaction started: –No locks for read –Costs space (old copy of data must be kept) Almost serializable level: –T1: x:=y –T2: y:= x –Initially x=3 and y =17 –Serial execution: x,y=17 or x,y=3 –Snapshot isolation: x=17, y=3 if both transactions start at the same time.

14 14 Value of Serializability -- Data Settings: accounts( number, branchnum, balance); create clustered index c on accounts(number); – rows –Cold buffer; same buffer size on all systems. –Row level locking –Isolation level (SERIALIZABLE or READ COMMITTED) –SQL Server 7, DB2 v7.1 and Oracle 8i on Windows 2000 –Dual Xeon (550MHz,512Kb), 1Gb RAM, Internal RAID controller from Adaptec (80Mb), 4x18Gb drives (10000RPM), Windows 2000.

15 15 Value of Serializability -- transactions Concurrent Transactions: –T1: summation query [1 thread] select sum(balance) from accounts; –T2: swap balance between two account numbers (in order of scan to avoid deadlocks) [N threads] T1: valX:=select balance from accounts where number=X; valY:=select balance from accounts where number=Y; T2: update accounts set balance=valX where number=Y; update accounts set balance=valY where number=X;

16 16 Value of Serializability -- results With SQL Server and DB2 the scan returns incorrect answers if the read committed isolation level is used (default setting) With Oracle correct answers are returned (snapshot isolation).

17 17 Cost of Serializability Because the update conflicts with the scan, correct answers are obtained at the cost of decreased concurrency and thus decreased throughput.

18 18 Locking Overhead -- data Settings: accounts( number, branchnum, balance); create clustered index c on accounts(number); – rows –Cold buffer –SQL Server 7, DB2 v7.1 and Oracle 8i on Windows 2000 –No lock escalation on Oracle; Parameter set so that there is no lock escalation on DB2; no control on SQL Server. –Dual Xeon (550MHz,512Kb), 1Gb RAM, Internal RAID controller from Adaptec (80Mb), 4x18Gb drives (10000RPM), Windows 2000.

19 19 Locking Overhead -- transactions No Concurrent Transactions: –Update [ updates] update accounts set balance = Val; –Insert [ transactions], e.g. typical one: insert into accounts values(664366,72255, );

20 20 Locking Overhead Row locking is barely more expensive than table locking because recovery overhead is higher than row locking overhead –Exception is updates on DB2 where table locking is distinctly less expensive than row locking.

21 21 Logical Bottleneck: Sequential Key generation Consider an application in which one needs a sequential number to act as a key in a table, e.g. invoice numbers for bills. Ad hoc approach: a separate table holding the last invoice number. Fetch and update that number on each insert transaction. Counter approach: use facility such as Sequence (Oracle)/Identity(MSSQL).

22 22 Counter Facility -- data Settings: –default isolation level: READ COMMITTED; Empty tables –Dual Xeon (550MHz,512Kb), 1Gb RAM, Internal RAID controller from Adaptec (80Mb), 4x18Gb drives (10000RPM), Windows accounts( number, branchnum, balance); create clustered index c on accounts(number); counter ( nextkey ); insert into counter values (1);

23 23 Counter Facility -- transactions No Concurrent Transactions: –System [ inserts, N threads] SQL Server 7 (uses Identity column) insert into accounts values (94496,2789); Oracle 8i insert into accounts values (seq.nextval,94496,2789); –Ad-hoc [ inserts, N threads] begin transaction NextKey:=select nextkey from counter; update counter set nextkey = NextKey+1; insert into accounts values(NextKey,?,?); commit transaction

24 24 Avoid Bottlenecks: Counters System generated counter (system) much better than a counter managed as an attribute value within a table (ad hoc). Counter is separate transaction. The Oracle counter can become a bottleneck if every update is logged to disk, but caching many counter numbers is possible. Counters may miss ids.

25 25 Insertion Points -- transactions No Concurrent Transactions: –Sequential [ inserts, N threads] Insertions into account table with clustered index on ssnum Data is sorted on ssnum Single insertion point –Non Sequential [ inserts, N threads] Insertions into account table with clustered index on ssnum Data is not sorted (uniform distribution) insertion points –Hashing Key [ inserts, N threads] Insertions into account table with extra attribute att with clustered index on (ssnum, att) Extra attribute att contains hash key (1021 possible values) 1021 insertion points

26 26 Insertion Points Page locking: single insertion point is a source of contention (sequential key with clustered index, or heap) Row locking: No contention between successive insertions. DB2 v7.1 and Oracle 8i do not support page locking.

27 27 Atomicity and Durability Every transaction either commits or aborts. It cannot change its mind Even in the face of failures: –Effects of committed transactions should be permanent; –Effects of aborted transactions should leave no trace. ACTIVE (running, waiting) ABORTED COMMITTED COMMIT ROLLBACK Ø BEGIN TRANS

28 28 LOGDATA STABLE STORAGE UNSTABLE STORAGE WRITE log records before commit WRITE modified pages after commit RECOVERY Pi Pj DATABASE BUFFER LOG BUFFER lrilrj

29 29 Log IO -- data Settings: lineitem ( L_ORDERKEY, L_PARTKEY, L_SUPPKEY, L_LINENUMBER, L_QUANTITY, L_EXTENDEDPRICE, L_DISCOUNT, L_TAX, L_RETURNFLAG, L_LINESTATUS, L_SHIPDATE, L_COMMITDATE, L_RECEIPTDATE, L_SHIPINSTRUCT, L_SHIPMODE, L_COMMENT ); –READ COMMITTED isolation level –Empty table –Dual Xeon (550MHz,512Kb), 1Gb RAM, Internal RAID controller from Adaptec (80Mb), 4x18Gb drives (10000RPM), Windows 2000.

30 30 Log IO -- transactions No Concurrent Transactions: Insertions [ inserts, 10 threads], e.g., insert into lineitem values (1,7760,401,1,17, ,0.04,0.02,'N','O', ' ',' ',' ','DELIVER IN PERSON','TRUCK','blithely regular ideas caj');

31 31 Group Commits DB2 UDB v7.1 on Windows 2000 Log records of many transactions are written together –Increases throughput by reducing the number of writes –at cost of increased minimum response time.

32 32 Put the Log on a Separate Disk DB2 UDB v7.1 on Windows % performance improvement if log is located on a different disk Controller cache hides negative impact –mid-range server, with Adaptec RAID controller (80Mb RAM) and 2x18Gb disk drives.

33 33 Tuning Database Writes Dirty data is written to disk –When the number of dirty pages is greater than a given parameter (Oracle 8) –When the number of dirty pages crosses a given threshold (less than 3% of free pages in the database buffer for SQL Server 7) –When the log is full, a checkpoint is forced. This can have a significant impact on performance.

34 34 Tune Checkpoint Intervals Oracle 8i on Windows 2000 A checkpoint (partial flush of dirty pages to disk) occurs at regular intervals or when the log is full: –Impacts the performance of on-line processing +Reduces the size of log +Reduces time to recover from a crash

35 35 Database Buffer Size Buffer too small, then hit ratio too small hit ratio = (logical acc. - physical acc.) / (logical acc.) Buffer too large, paging Recommended strategy: monitor hit ratio and increase buffer size until hit ratio flattens out. If there is still paging, then buy memory. LOGDATA RAM Paging Disk DATABASE PROCESSES DATABASE BUFFER

36 36 Buffer Size -- data Settings: employees(ssnum, name, lat, long, hundreds1, hundreds2); clustered index c on employees(lat); (unused) –10 distinct values of lat and long, 100 distinct values of hundreds1 and hundreds2 – rows (630 Mb); –Warm Buffer –Dual Xeon (550MHz,512Kb), 1Gb RAM, Internal RAID controller from Adaptec (80Mb), 4x18Gb drives (10000 RPM), Windows 2000.

37 37 Buffer Size -- queries Queries: –Scan Query select sum(long) from employees; –Multipoint query select * from employees where lat = ?;

38 38 Database Buffer Size SQL Server 7 on Windows 2000 Scan query: –LRU (least recently used) does badly when table spills to disk as Stonebraker observed 20 years ago. Multipoint query: –Throughput increases with buffer size until all data is accessed from RAM.

39 39 Scan Performance -- data Settings: lineitem ( L_ORDERKEY, L_PARTKEY, L_SUPPKEY, L_LINENUMBER, L_QUANTITY, L_EXTENDEDPRICE, L_DISCOUNT, L_TAX, L_RETURNFLAG, L_LINESTATUS, L_SHIPDATE, L_COMMITDATE, L_RECEIPTDATE, L_SHIPINSTRUCT, L_SHIPMODE, L_COMMENT ); – rows –Lineitem tuples are ~ 160 bytes long –Cold Buffer –Dual Xeon (550MHz,512Kb), 1Gb RAM, Internal RAID controller from Adaptec (80Mb), 4x18Gb drives (10000RPM), Windows 2000.

40 40 Scan Performance -- queries Queries: select avg(l_discount) from lineitem;

41 41 Prefetching DB2 UDB v7.1 on Windows 2000 Throughput increases up to a certain point when prefetching size increases.

42 42 Usage Factor DB2 UDB v7.1 on Windows 2000 Usage factor is the percentage of the page used by tuples and auxilliary data structures (the rest is reserved for future) Scan throughput increases with usage factor.

43 43 Tuning the Storage Subsystem

44 44 Outline Storage Subsystem Components –Moores law and consequences –Magnetic disk performances From SCSI to SAN, NAS and Beyond –Storage virtualization Tuning the Storage Subsystem –RAID levels –RAID controller cache

45 45 Exponential Growth Moores law –Every 18 months: New processing = sum of all existing processing New storage = sum of all existing storage –2x / 18 months ~ 100x / 10 years

46 46 Consequences of Moores law Over the last decade: –10x better access time –10x more bandwidth –100x more capacity –4000x lower media price –Scan takes 10x longer (3 min vs 45 min) –Data on disk is accessed 25x less often (on average)

47 47 Data Flood Disk Sales double every nine months –Because volume of stored data increases Data Warehouses Internet Logs Web Archives Sky Survey –Because media price drops much faster than areal density. Graph courtesy of Joe Hellerstein Source: J. Porter, Disk/Trend, Inc.

48 48 Memory Hierarchy Processor cache RAM Disks Tapes / Optical Disks Access Time Price $/ Mb 1 ns x (nearline)

49 49 Magnetic Disks 1956: IBM (RAMAC) first disk drive 5 Mb – Mb/in $/year 9 Kb/sec 1980: SEAGATE first 5.25 disk drive 5 Mb – 1.96 Mb/in2 625 Kb/sec 1999: IBM MICRODRIVE first 1 disk drive 340Mb 6.1 MB/sec Controller read/write head disk arm tracks platter spindle actuator disk interface

50 50 Magnetic Disks Access Time (2001) –Controller overhead (0.2 ms) –Seek Time (4 to 9 ms) –Rotational Delay (2 to 6 ms) –Read/Write Time (10 to 500 KB/ms) Disk Interface –IDE (16 bits, Ultra DMA - 25 MHz) –SCSI: width (narrow 8 bits vs. wide 16 bits) - frequency (Ultra MHz).

51 51 RAID Levels RAID 0: striping (no redundancy) RAID 1: mirroring (2 disks) RAID 5: parity checking –Read: stripes read from multiple disks (in parallel) –Write: 2 reads + 2 writes RAID 10: striping and mirroring Software vs. Hardware RAID: –Software RAID: run on the servers CPU –Hardware RAID: run on the RAID controllers CPU

52 52 Why 4 read/writes when updating a single stripe using RAID 5? Read old data stripe; read parity stripe (2 reads) XOR old data stripe with replacing one. Take result of XOR and XOR with parity stripe. Write new data stripe and new parity stripe (2 writes).

53 53 RAID Levels -- data Settings: accounts( number, branchnum, balance); create clustered index c on accounts(number); – rows –Cold Buffer –Dual Xeon (550MHz,512Kb), 1Gb RAM, Internal RAID controller from Adaptec (80Mb), 4x18Gb drives (10000RPM), Windows 2000.

54 54 RAID Levels -- transactions No Concurrent Transactions: –Read Intensive: select avg(balance) from accounts; –Write Intensive, e.g. typical insert: insert into accounts values (690466,6840, ); Writes are uniformly distributed.

55 55 RAID Levels SQL Server7 on Windows 2000 (SoftRAID means striping/parity at host) Read-Intensive: –Using multiple disks (RAID0, RAID 10, RAID5) increases throughput significantly. Write-Intensive: –Without cache, RAID 5 suffers. With cache, it is ok.

56 56 RAID Levels Log File –RAID 1 is appropriate Fault tolerance with high write throughput. Writes are synchronous and sequential. No benefits in striping. Temporary Files –RAID 0 is appropriate. No fault tolerance. High throughput. Data and Index Files –RAID 5 is best suited for read intensive apps or if the RAID controller cache is effective enough. –RAID 10 is best suited for write intensive apps.

57 57 Controller Prefetching no, Write-back yes. Read-ahead: –Prefetching at the disk controller level. –No information on access pattern. –Better to let database management system do it. Write-back vs. write through: –Write back: transfer terminated as soon as data is written to cache. Batteries to guarantee write back in case of power failure –Write through: transfer terminated as soon as data is written to disk.

58 58 SCSI Controller Cache -- data Settings: employees(ssnum, name, lat, long, hundreds1, hundreds2); create clustered index c on employees(hundreds2); –Employees table partitioned over two disks; Log on a separate disk; same controller (same channel). – rows per table –Database buffer size limited to 400 Mb. –Dual Xeon (550MHz,512Kb), 1Gb RAM, Internal RAID controller from Adaptec (80Mb), 4x18Gb drives (10000RPM), Windows 2000.

59 59 SCSI (not disk) Controller Cache -- transactions No Concurrent Transactions: update employees set lat = long, long = lat where hundreds2 = ?; –cache friendly: update of 20,000 rows (~90Mb) –cache unfriendly: update of 200,000 rows (~900Mb)

60 60 SCSI Controller Cache SQL Server 7 on Windows Adaptec ServerRaid controller: –80 Mb RAM –Write-back mode Updates Controller cache increases throughput whether operation is cache friendly or not. –Efficient replacement policy!

61 61 Index Tuning Index issues –Indexes may be better or worse than scans –Multi-table joins that run on for hours, because the wrong indexes are defined –Concurrency control bottlenecks –Indexes that are maintained and never used

62 62 Index An index is a data structure that supports efficient access to data Set of Records index Condition on attribute value Matching records (search key)

63 63 Index An index is a data structure that supports efficient access to data Set of Records index Condition on attribute value Matching records (search key)

64 64 Search Keys A (search) key is a sequence of attributes. create index i1 on accounts(branchnum, balance); Types of keys –Sequential: the value of the key is monotonic with the insertion order (e.g., counter or timestamp) –Non sequential: the value of the key is unrelated to the insertion order (e.g., social security number)

65 65 Data Structures Most index data structures can be viewed as trees. In general, the root of this tree will always be in main memory, while the leaves will be located on disk. –The performance of a data structure depends on the number of nodes in the average path from the root to the leaf. –Data structure with high fan-out (maximum number of children of an internal node) are thus preferred.

66 66 B+-Tree A B+-Tree is a balanced tree whose leaves contain a sequence of key-pointer pairs

67 67 B+-Tree Performance Tree levels –Tree Fanout Size of key Page utilization Tree maintenance –Online On inserts On deletes –Offline Tree locking Tree root in main memory

68 68 B+-Tree Performance Key length influences fanout –Choose small key when creating an index –Key compression Prefix compression (Oracle 8, MySQL): only store that part of the key that is needed to distinguish it from its neighbors: Smi, Smo, Smy for Smith, Smoot, Smythe. Front compression (Oracle 5): adjacent keys have their front portion factored out: Smi, (2)o, (2)y. There are problems with this approach: –Processor overhead for maintenance –Locking Smoot requires locking Smith too.

69 69 Hash Index A hash index stores key-value pairs based on a pseudo-randomizing function called a hash function. Hashed key values 0 1 n R1 R5 R3 R6 R9R14 R17 R21R25 Hash function key 2341 The length of these chains impacts performance

70 70 Clustered / Non clustered index Clustered index (primary index) –A clustered index on attribute X co-locates records whose X values are near to one another. Non-clustered index (secondary index) –A non clustered index does not constrain table organization. –There might be several non- clustered indexes per table. Records

71 71 Dense / Sparse Index Sparse index –Pointers are associated to pages Dense index –Pointers are associated to records –Non clustered indexes are dense P1PiP2 record

72 72 Index Implementations in some major DBMS SQL Server –B+-Tree data structure –Clustered indexes are sparse –Indexes maintained as updates/insertions/deletes are performed DB2 –B+-Tree data structure, spatial extender for R-tree –Clustered indexes are dense –Explicit command for index reorganization Oracle –B+-tree, hash, bitmap, spatial extender for R-Tree –clustered index Index organized table (unique/clustered) Clusters used when creating tables. TimesTen (Main-memory DBMS) –T-tree

73 73 Types of Queries Point Query SELECT balance FROM accounts WHERE number = 1023; Multipoint Query SELECT balance FROM accounts WHERE branchnum = 100; Range Query SELECT number FROM accounts WHERE balance > and balance <= 20000; Prefix Match Query SELECT * FROM employees WHERE name = J* ;

74 74 More Types of Queries Extremal Query SELECT * FROM accounts WHERE balance = max(select balance from accounts) Ordering Query SELECT * FROM accounts ORDER BY balance; Grouping Query SELECT branchnum, avg(balance) FROM accounts GROUP BY branchnum; Join Query SELECT distinct branch.adresse FROM accounts, branch WHERE accounts.branchnum = branch.number and accounts.balance > 10000;

75 75 Index Tuning -- data Settings: employees(ssnum, name, lat, long, hundreds1, hundreds2); clustered index c on employees(hundreds1) with fillfactor = 100; nonclustered index nc on employees (hundreds2); index nc3 on employees (ssnum, name, hundreds2); index nc4 on employees (lat, ssnum, name); – rows ; Cold buffer –Dual Xeon (550MHz,512Kb), 1Gb RAM, Internal RAID controller from Adaptec (80Mb), 4x18Gb drives (10000RPM), Windows 2000.

76 76 Index Tuning -- operations Operations: –Update: update employees set name = XXX where ssnum = ?; –Insert: insert into employees values ( ,'polo94064',97.48,84.03, ,3987.2); –Multipoint query: select * from employees where hundreds1= ?; select * from employees where hundreds2= ?; –Covered query: select ssnum, name, lat from employees; –Range Query: select * from employees where long between ? and ?; –Point Query: select * from employees where ssnum = ?

77 77 Clustered Index Multipoint query that returns 100 records out of Cold buffer Clustered index is twice as fast as non- clustered index and orders of magnitude faster than a scan.

78 78 Index Face Lifts Index is created with fillfactor = 100. Insertions cause page splits and extra I/O for each query Maintenance consists in dropping and recreating the index With maintenance performance is constant while performance degrades significantly if no maintenance is performed.

79 79 Index Maintenance In Oracle, clustered index are approximated by an index defined on a clustered table No automatic physical reorganization Index defined with pctfree = 0 Overflow pages cause performance degradation

80 80 Covering Index - defined Select name from employee where department = marketing Good covering index would be on (department, name) Index on (name, department) less useful. Index on department alone moderately useful.

81 81 Covering Index - impact Covering index performs better than clustering index when first attributes of index are in the where clause and last attributes in the select. When attributes are not in order then performance is much worse.

82 82 Scan Can Sometimes Win IBM DB2 v7.1 on Windows 2000 Range Query If a query retrieves 10% of the records or more, scanning is often better than using a non- clustering non-covering index. Crossover > 10% when records are large or table is fragmented on disk – scan cost increases.

83 83 Index on Small Tables Small table: 100 records, i.e., a few pages. Two concurrent processes perform updates (each process works for 10ms before it commits) No index: the table is scanned for each update. No concurrent updates. A clustered index allows to take advantage of row locking.

84 84 Bitmap vs. Hash vs. B+-Tree Settings: employees(ssnum, name, lat, long, hundreds1, hundreds2); create cluster c_hundreds (hundreds2 number(8)) PCTFREE 0; create cluster c_ssnum(ssnum integer) PCTFREE 0 size 60; create cluster c_hundreds(hundreds2 number(8)) PCTFREE 0 HASHKEYS 1000 size 600; create cluster c_ssnum(ssnum integer) PCTFREE 0 HASHKEYS SIZE 60; create bitmap index b on employees (hundreds2); create bitmap index b2 on employees (ssnum); – rows ; Cold buffer –Dual Xeon (550MHz,512Kb), 1Gb RAM, Internal RAID controller from Adaptec (80Mb), 4x18Gb drives (10000RPM), Windows 2000.

85 85 Multipoint query: B-Tree, Hash Tree, Bitmap There is an overflow chain in a hash index because hundreds2 has few values In a clustered B-Tree index records are on contiguous pages. Bitmap is proportional to size of table and non- clustered for record access.

86 86 Hash indexes dont help when evaluating range queries Hash index outperforms B-tree on point queries B-Tree, Hash Tree, Bitmap

87 87 Tuning Relational Systems Schema Tuning –Denormalization, Vertical Partitioning Query Tuning –Query rewriting –Materialized views

88 88 Denormalizing -- data Settings: lineitem ( L_ORDERKEY, L_PARTKEY, L_SUPPKEY, L_LINENUMBER, L_QUANTITY, L_EXTENDEDPRICE, L_DISCOUNT, L_TAX, L_RETURNFLAG, L_LINESTATUS, L_SHIPDATE, L_COMMITDATE, L_RECEIPTDATE, L_SHIPINSTRUCT, L_SHIPMODE, L_COMMENT ); region( R_REGIONKEY, R_NAME, R_COMMENT ); nation( N_NATIONKEY, N_NAME, N_REGIONKEY, N_COMMENT,); supplier( S_SUPPKEY, S_NAME, S_ADDRESS, S_NATIONKEY, S_PHONE, S_ACCTBAL, S_COMMENT); – rows in lineitem, 25 nations, 5 regions, 500 suppliers

89 89 Denormalizing -- transactions L_REGIONNAME lineitemdenormalized ( L_ORDERKEY, L_PARTKEY, L_SUPPKEY, L_LINENUMBER, L_QUANTITY, L_EXTENDEDPRICE, L_DISCOUNT, L_TAX, L_RETURNFLAG, L_LINESTATUS, L_SHIPDATE, L_COMMITDATE, L_RECEIPTDATE, L_SHIPINSTRUCT, L_SHIPMODE, L_COMMENT, L_REGIONNAME); – rows in lineitemdenormalized –Cold Buffer –Dual Pentium II (450MHz, 512Kb), 512 Mb RAM, 3x18Gb drives (10000RPM), Windows 2000.

90 90 Queries on Normalized vs. Denormalized Schemas Queries: select L_ORDERKEY, L_PARTKEY, L_SUPPKEY, L_LINENUMBER, L_QUANTITY, L_EXTENDEDPRICE, L_DISCOUNT, L_TAX, L_RETURNFLAG, L_LINESTATUS, L_SHIPDATE, L_COMMITDATE, L_RECEIPTDATE, L_SHIPINSTRUCT, L_SHIPMODE, L_COMMENT, R_NAME from LINEITEM, REGION, SUPPLIER, NATION where L_SUPPKEY = S_SUPPKEY and S_NATIONKEY = N_NATIONKEY and N_REGIONKEY = R_REGIONKEY and R_NAME = 'EUROPE'; select L_ORDERKEY, L_PARTKEY, L_SUPPKEY, L_LINENUMBER, L_QUANTITY, L_EXTENDEDPRICE, L_DISCOUNT, L_TAX, L_RETURNFLAG, L_LINESTATUS, L_SHIPDATE, L_COMMITDATE, L_RECEIPTDATE, L_SHIPINSTRUCT, L_SHIPMODE, L_COMMENT, L_REGIONNAME from LINEITEMDENORMALIZED where L_REGIONNAME = 'EUROPE';

91 91 Denormalization TPC-H schema Query: find all lineitems whose supplier is in Europe. With a normalized schema this query is a 4-way join. If we denormalize lineitem and add the name of the region for each lineitem (foreign key denormalization) throughput improves 30%

92 92 Vertical Partitioning Consider account(id, balance, homeaddress) When might it be a good idea to do a vertical partitioning into account1(id,balance) and account2(id,homeaddress)? Join vs. size.

93 93 Vertical Partitioning Which design is better depends on the query pattern: –The application that sends a monthly statement is the principal user of the address of the owner of an account –The balance is updated or examined several times a day. The second schema might be better because the relation (account_ID, balance) can be made smaller: –More account_ID, balance pairs fit in memory, thus increasing the hit ratio –A scan performs better because there are fewer pages.

94 94 Tuning Normalization A single normalized relation XYZ is better than two normalized relations XY and XZ if the single relation design allows queries to access X, Y and Z together without requiring a join. The two-relation design is better iff: –Users access tend to partition between the two sets Y and Z most of the time –Attributes Y or Z have large values

95 95 Vertical Partitioning and Scan R (X,Y,Z) –X is an integer –YZ are large strings Scan Query Vertical partitioning exhibits poor performance when all attributes are accessed. Vertical partitioning provides a sped up if only two of the attributes are accessed.

96 96 Vertical Partitioning and Point Queries R (X,Y,Z) –X is an integer –YZ are large strings A mix of point queries access either XYZ or XY. Vertical partitioning gives a performance advantage if the proportion of queries accessing only XY is greater than 20%. The join is not expensive compared to a simple look-up.

97 97 Queries Settings: employee(ssnum, name, dept, salary, numfriends); student(ssnum, name, course, grade); techdept(dept, manager, location); clustered index i1 on employee (ssnum); nonclustered index i2 on employee (name); nonclustered index i3 on employee (dept); clustered index i4 on student (ssnum); nonclustered index i5 on student (name); clustered index i6 on techdept (dept); – rows in employee, students, 10 departments; Cold buffer –Dual Pentium II (450MHz, 512Kb), 512 Mb RAM, 3x18Gb drives (10000RPM), Windows 2000.

98 98 Queries – View on Join View Techlocation: create view techlocation as select ssnum, techdept.dept, location from employee, techdept where employee.dept = techdept.dept; Queries: –Original: select dept from techlocation where ssnum = ?; –Rewritten: select dept from employee where ssnum = ?;

99 99 Query Rewriting - Views All systems expand the selection on a view into a join The difference between a plain selection and a join (on a primary key-foreign key) followed by a projection is greater on SQL Server than on Oracle and DB2 v7.1.

100 100 Queries – Correlated Subqueries Queries: –Original: select ssnum from employee e1 where salary = (select max(salary) from employee e2 where e2.dept = e1.dept); –Rewritten: select max(salary) as bigsalary, dept into TEMP from employee group by dept; select ssnum from employee, TEMP where salary = bigsalary and employee.dept = temp.dept;

101 101 Query Rewriting – Correlated 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 > 10000> 1000

102 102 Eliminate unneeded DISTINCTs Query: Find employees who work in the information systems department. There should be no duplicates. SELECT distinct ssnum FROM employee WHERE dept = information systems DISTINCT is unnecessary, since ssnum is a key of employee so certainly is a key of a subset of employee.

103 103 Eliminate unneeded DISTINCTs Query: Find social security numbers of employees in the technical departments. There should be no duplicates. SELECT DISTINCT ssnum FROM employee, tech WHERE employee.dept = tech.dept Is DISTINCT needed?

104 104 Distinct Unnecessary Here Too Since dept is a key of the tech table, each employee record will join with at most one record in tech. Because ssnum is a key for employee, distinct is unnecessary.

105 105 Reaching The relationship among DISTINCT, keys and joins can be generalized: –Call a table T privileged if the fields returned by the select contain a key of T –Let R be an unprivileged table. Suppose that R is joined on equality by its key field to some other table S, then we say R reaches S. –Now, define reaches to be transitive. So, if R1 reaches R2 and R2 reaches R3 then say that R1 reaches R3.

106 106 Reaches: Main Theorem There will be no duplicates among the records returned by a selection, even in the absence of DISTINCT if one of the two following conditions hold: –Every table mentioned in the FROM clause is privileged –Every unprivileged table reaches at least one privileged table.

107 107 Reaches: Proof Sketch If every relation is privileged then there are no duplicates –The keys of those relations are in the from clause Suppose some relation T is not privileged but reaches at least one privileged one, say R. Then the qualifications linking T with R ensure that each distinct combination of privileged records is joined with at most one record of T.

108 108 Reaches: Example 1 The same employee record may match several tech records (because manager is not a key of tech), so the ssnum of that employee record may appear several times. Tech does not reach the privileged relation employee. SELECT ssnum FROM employee, tech WHERE employee.manager = tech.manager

109 109 Reaches: Example 2 Each repetition of a given ssnum vlaue would be accompanied by a new tech.dept since tech.dept is a key of tech Both relations are privileged. SELECT ssnum, tech.dept FROM employee, tech WHERE employee.manager = tech.manager

110 110 Reaches: Example 3 Student is priviledged Employee does not reach student (name is not a key of employee) DISTINCT is needed to avoid duplicates. SELECT student.ssnum FROM student, employee, tech WHERE student.name = employee.name AND employee.dept = tech.dept;

111 111 Aggregate Maintenance -- data Settings: orders( ordernum, itemnum, quantity, storeid, vendorid ); create clustered index i_order on orders(itemnum); store( storeid, name ); item(itemnum, price); create clustered index i_item on item(itemnum); vendorOutstanding( vendorid, amount); storeOutstanding( storeid, amount); – orders, stores, items; Cold buffer –Dual Pentium II (450MHz, 512Kb), 512 Mb RAM, 3x18Gb drives (10000RPM), Windows 2000.

112 112 Aggregate Maintenance -- triggers Triggers for Aggregate Maintenance create trigger updateVendorOutstanding on orders for insert as update vendorOutstanding set amount = (select vendorOutstanding.amount+sum(inserted.quantity*item.price) from inserted,item where inserted.itemnum = item.itemnum ) where vendorid = (select vendorid from inserted) ; create trigger updateStoreOutstanding on orders for insert as update storeOutstanding set amount = (select storeOutstanding.amount+sum(inserted.quantity*item.price) from inserted,item where inserted.itemnum = item.itemnum ) where storeid = (select storeid from inserted)

113 113 Aggregate Maintenance -- transactions Concurrent Transactions: –Insertions insert into orders values ( ,7825,562,'xxxxxx6944','vendor4'); –Queries (first without, then with redundant tables) select orders.vendor, sum(orders.quantity*item.price) from orders,item where orders.itemnum = item.itemnum group by orders.vendorid; vs. select * from vendorOutstanding; select store.storeid, sum(orders.quantity*item.price) from orders,item, store where orders.itemnum = item.itemnum and orders.storename = store.name group by store.storeid; vs. select * from storeOutstanding;

114 114 Aggregate Maintenance SQLServer 2000 on Windows 2000 Using triggers for view maintenance If queries frequent or important, then aggregate maintenance is good.

115 115 Superlinearity -- data Settings: sales( id, itemid, customerid, storeid, amount, quantity); item (itemid); customer (customerid); store (storeid); A sale is successful if all foreign keys are present. successfulsales(id, itemid, customerid, storeid, amount, quantity); unsuccessfulsales(id, itemid, customerid, storeid, amount, quantity); tempsales( id, itemid, customerid, storeid, amount,quantity);

116 116 Superlinearity -- indexes Settings (non-clustering, dense indexes): index s1 on item(itemid); index s2 on customer(customerid); index s3 on store(storeid); index succ on successfulsales(id); – sales, customers, items, 1000 stores –Cold buffer –Dual Pentium II (450MHz, 512Kb), 512 Mb RAM, 3x18Gb drives (10000RPM), Windows 2000.

117 117 Superlinearity -- queries Queries: –Insert/create indexdelete insert into successfulsales select sales.id, sales.itemid, sales.customerid, sales.storeid, sales.amount, sales.quantity from sales, item, customer, store where sales.itemid = item.itemid and sales.customerid = customer.customerid and sales.storeid = store.storeid; insert into unsuccessfulsales select * from sales; go delete from unsuccessfulsales where id in (select id from successfulsales)

118 118 Superlinearity -- batch queries Queries: –Small batches INT; INT; INT; = = 0 WHILE <= ) BEGIN insert into tempsales select * from sales where id delete from tempsales where id in (select id from successfulsales); insert into unsuccessfulsales select * from tempsales; delete from tempsales; END

119 119 Superlinearity -- outer join Queries: –outerjoin insert into successfulsales select sales.id, item.itemid, customer.customerid, store.storeid, sales.amount, sales.quantity from ((sales left outer join item on sales.itemid = item.itemid) left outer join customer on sales.customerid = customer.customerid) left outer join store on sales.storeid = store.storeid; insert into unsuccessfulsales select * from successfulsales where itemid is null or customerid is null or storeid is null; go delete from successfulsales where itemid is null or customerid is null or storeid is null

120 120 Circumventing Superlinearity SQL Server 2000 Outer join achieves the best response time. Small batches do not help because overhead of crossing the application interface is higher than the benefit of joining with smaller tables.

121 121 Tuning the Application Interface 4GL –Power++, Visual basic Programming language + Call Level Interface –ODBC: Open DataBase Connectivity –JDBC: Java based API –OCI (C++/Oracle), CLI (C++/ DB2), Perl/DBI In the following experiments, the client program is located on the database server site. Overhead is due to crossing the application interface.

122 122 Looping can hurt -- data Settings: lineitem ( L_ORDERKEY, L_PARTKEY, L_SUPPKEY, L_LINENUMBER, L_QUANTITY, L_EXTENDEDPRICE, L_DISCOUNT, L_TAX, L_RETURNFLAG, L_LINESTATUS, L_SHIPDATE, L_COMMITDATE, L_RECEIPTDATE, L_SHIPINSTRUCT, L_SHIPMODE, L_COMMENT ); – rows; warm buffer. –Dual Pentium II (450MHz, 512Kb), 512 Mb RAM, 3x18Gb drives (10000RPM), Windows 2000.

123 123 Looping can hurt -- queries Queries: –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<100; i++) { odbc->bindParameter(1, SQL_INTEGER, i); odbc->execPrepared(sqlStmt); }

124 124 Looping can Hurt SQL Server 2000 on Windows 2000 Crossing the application interface has a significant impact on performance. Why would a programmer use a loop instead of relying on set-oriented operations: object- orientation?

125 125 Cursors are Death -- data Settings: employees(ssnum, name, lat, long, hundreds1, hundreds2); – rows ; Cold buffer –Dual Pentium II (450MHz, 512Kb), 512 Mb RAM, 3x18Gb drives (10000RPM), Windows 2000.

126 126 Cursors are Death -- queries Queries: –No cursor select * from employees; –Cursor DECLARE d_cursor CURSOR FOR select * from employees; OPEN d_cursor while = 0) BEGIN FETCH NEXT from d_cursor END CLOSE d_cursor go

127 127 Cursors are Death 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.

128 128 Retrieve Needed Columns Only - data Settings: lineitem ( L_ORDERKEY, L_PARTKEY, L_SUPPKEY, L_LINENUMBER, L_QUANTITY, L_EXTENDEDPRICE, L_DISCOUNT, L_TAX, L_RETURNFLAG, L_LINESTATUS, L_SHIPDATE, L_COMMITDATE, L_RECEIPTDATE, L_SHIPINSTRUCT, L_SHIPMODE, L_COMMENT ); create index i_nc_lineitem on lineitem (l_orderkey, l_partkey, l_suppkey, l_shipdate, l_commitdate); – rows; warm buffer. –Lineitem records are ~ 10 bytes long –Dual Pentium II (450MHz, 512Kb), 512 Mb RAM, 3x18Gb drives (10000RPM), Windows 2000.

129 129 Retrieve Needed Columns Only - queries Queries: –All Select * from lineitem; –Covered subset Select l_orderkey, l_partkey, l_suppkey, l_shipdate, l_commitdate from lineitem;

130 130 Retrieve Needed Columns Only Avoid transferring unnecessary data May enable use of a covering index. In the experiment the subset contains ¼ of the attributes. –Reducing the amount of data that crosses the application interface yields significant performance improvement. Experiment performed on Oracle8iEE on Windows 2000.

131 131 Bulk Loading Data Settings: lineitem ( L_ORDERKEY, L_PARTKEY, L_SUPPKEY, L_LINENUMBER, L_QUANTITY, L_EXTENDEDPRICE, L_DISCOUNT, L_TAX, L_RETURNFLAG, L_LINESTATUS, L_SHIPDATE, L_COMMITDATE, L_RECEIPTDATE, L_SHIPINSTRUCT, L_SHIPMODE, L_COMMENT ); –Initially the table is empty; rows to be inserted (138Mb) –Table sits one disk. No constraint, index is defined. –Dual Pentium II (450MHz, 512Kb), 512 Mb RAM, 3x18Gb drives (10000RPM), Windows 2000.

132 132 Bulk Loading Queries Oracle 8i sqlldr directpath=true control=load_lineitem.ctl data=E:\Data\lineitem.tbl load data infile "lineitem.tbl" into table LINEITEM append fields 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 )

133 133 Direct Path 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). Experiment performed on Oracle8iEE on Windows 2000.

134 134 Batch Size Throughput increases steadily when the batch size increases to records.Throughput remains constant afterwards. Trade-off between performance and amount of data that has to be reloaded in case of problem. Experiment performed on SQL Server 2000 on Windows 2000.

135 135 Tuning E-Commerce Applications Database-backed web-sites: –Online shops –Shop comparison portals –MS TerraServer

136 136 E-commerce Application Architecture ClientsWeb serversApplication serversDatabase server Web cache DB cache

137 137 E-commerce Application Workload Touristic searching (frequent, cached) –Access the top few pages. Pages may be personalized. Data may be out-of-date. Category searching (frequent, partly cached and need for timeliness guarantees) –Down some hierarchy, e.g., mens clothing. Keyword searching (frequent, uncached, need for timeliness guarantees) Shopping cart interactions (rare, but transactional) Electronic purchasing (rare, but transactional)

138 138 Design Issues Need to keep historic information –Electronic payment acknowledgements get lost. Preparation for variable load –Regular patterns of web site accesses during the day, and within a week. Possibility of disconnections –State information transmitted to the client (cookies) Special consideration for low bandwidth Schema evolution –Representing e-commerce data as attribute-value pairs (IBM Websphere)

139 139 Caching Web cache: –Static web pages –Caching fragments of dynamically created web pages Database cache (Oracle9iAS, TimesTens FrontTier) –Materialized views to represent cached data. –Queries are executed either using the database cache or the database server. Updates are propagated to keep the cache(s) consistent. –Note to vendors: It would be good to have queries distributed between cache and server.

140 140 Ecommerce -- setting Settings: shoppingcart( shopperid, itemid, price, qty); – rows; warm buffer –Dual Pentium II (450MHz, 512Kb), 512 Mb RAM, 3x18Gb drives (10000RPM), Windows 2000.

141 141 Ecommerce -- transactions Concurrent Transactions: –Mix insert into shoppingcart values (107999,914,870,214); update shoppingcart set Qty = 10 where shopperid = and itemid = 88636; delete from shoppingcart where shopperid = and itemid = 8321; select shopperid, itemid, qty, price from shoppingcart where shopperid = ?; –Queries Only select shopperid, itemid, qty, price from shoppingcart where shopperid = ?;

142 142 Connection Pooling (no refusals) Each thread establishes a connection and performs 5 insert statements. If a connection cannot be established the thread waits 15 secs before trying again. The number of connection is limited to 60 on the database server. Using connection pooling, the requests are queued and serviced when possible. There are no refused connections. Experiment performed on Oracle8i on Windows 2000

143 143 Indexing Using a clustered index on shopperid in the shopping cart provides: –Query speed-up –Update/Deletion speed- up Experiment performed on SQL Server 2000 on Windows 2000

144 144 Capacity Planning Arrival Rate –A1 is given as an assumption –A2 = (0.4 A1) + (0.5 A2) –A3 = 0.1 A2 Service Time (S) –S1, S2, S3 are measured Utilization –U = A x S Response Time –R = U/(A(1-U)) = S/(1-U) (assuming Poisson arrivals) Entry (S1) 0.4 Search (S2) Checkout (S3) Getting the demand assumptions right is what makes capacity planning hard

145 145 How to Handle Multiple Servers Suppose one has n servers for some task that requires S time for a single server to perform. The perfect parallelism model is that it is as if one has a single server that is n times as fast. However, this overstates the advantage of parallelism, because even if there were no waiting, single tasks require S time.

146 146 Rough Estimate for Multiple Servers There are two components to response time: waiting time + service time. In the parallel setting, the service time is still S. The waiting time however can be well estimated by a server that is n times as fast.

147 147 Approximating waiting time for n parallel servers. Recall: R = U/(A(1-U)) = S/(1-U) On an n-times faster server, service time is divided by n, so the single processor utilization U is also divided by n. So we would get: Rideal = (S/n)/(1 – (U/n)). That Rideal = serviceideal + waitideal. So waitideal = Rideal – S/n Our assumption: waitideal ~ wait for n processors.

148 148 Approximating response time for n parallel servers Waiting time for n parallel processors ~ (S/n)/(1 – (U/n)) – S/n = (S/n) ( 1/(1-(U/n)) – 1) = (S/(n(1 – U/n)))(U/n) = (S/(n – U))(U/n) So, response time for n parallel processors is above waiting time + S.

149 149 Example A = 8 per second. S = 0.1 second. U = 0.8. Single server response time = S/(1-U) = 0.1/0.2 = 0.5 seconds. If we have 2 servers, then we estimate waiting time to be (0.1/(2-0.8))(0.4) = 0.04/1.2 = So the response time is For a 2-times faster server, S = 0.05, U = 0.4, so response time is 0.05/0.6 =

150 150 Example -- continued A = 8 per second. S = 0.1 second. U = 0.8. If we have 4 servers, then we estimate waiting time to be (S/(n – U))(U/n) = 0.1/(3.2) * (0.8/4) = 0.02/3.2 = So response time is

151 151 Datawarehouse Tuning Aggregate (strategic) targeting: –Aggregates flow up from a wide selection of data, and then –Targeted decisions flow down Examples: –Riding the wave of clothing fads –Tracking delays for frequent-flyer customers

152 152 Data Warehouse Workload Broad –Aggregate queries over ranges of values, e.g., find the total sales by region and quarter. Deep –Queries that require precise individualized information, e.g., which frequent flyers have been delayed several times in the last month? Dynamic (vs. Static) –Queries that require up-to-date information, e.g. which nodes have the highest traffic now?

153 153 Tuning Knobs Indexes Materialized views Approximation

154 154 Bitmaps -- data Settings: lineitem ( L_ORDERKEY, L_PARTKEY, L_SUPPKEY, L_LINENUMBER, L_QUANTITY, L_EXTENDEDPRICE, L_DISCOUNT, L_TAX, L_RETURNFLAG, L_LINESTATUS, L_SHIPDATE, L_COMMITDATE, L_RECEIPTDATE, L_SHIPINSTRUCT, L_SHIPMODE, L_COMMENT ); create bitmap index b_lin_2 on lineitem(l_returnflag); create bitmap index b_lin_3 on lineitem(l_linestatus); create bitmap index b_lin_4 on lineitem(l_linenumber); – rows ; cold buffer –Dual Pentium II (450MHz, 512Kb), 512 Mb RAM, 3x18Gb drives (10000RPM), Windows 2000.

155 155 Bitmaps -- queries Queries: –1 attribute select count(*) from lineitem where l_returnflag = 'N'; –2 attributes select count(*) from lineitem where l_returnflag = 'N' and l_linenumber > 3; –3 attributes select count(*) from lineitem where l_returnflag = 'N' and l_linenumber > 3 and l_linestatus = 'F';

156 156 Bitmaps Order of magnitude improvement compared to scan. Bitmaps are best suited for multiple conditions on several attributes, each having a low selectivity. ANRANR l_returnflag O F l_linestatus

157 157 Multidimensional Indexes -- data Settings: create table spatial_facts ( a1 int, a2 int, a3 int, a4 int, a5 int, a6 int, a7 int, a8 int, a9 int, a10 int, geom_a3_a7 mdsys.sdo_geometry ); create index r_spatialfacts on spatial_facts(geom_a3_a7) indextype is mdsys.spatial_index; create bitmap index b2_spatialfacts on spatial_facts(a3,a7); – rows ; cold buffer –Dual Pentium II (450MHz, 512Kb), 512 Mb RAM, 3x18Gb drives (10000RPM), Windows 2000.

158 158 Multidimensional Indexes -- queries Queries: –Point Queries select count(*) from fact where a3 = and a7 = ; select count(*) from spatial_facts where SDO_RELATE(geom_a3_a7, MDSYS.SDO_GEOMETRY(2001, NULL, MDSYS.SDO_POINT_TYPE(694014,928878, NULL), NULL, NULL), 'mask=equal querytype=WINDOW') = 'TRUE'; –Range Queries select count(*) from spatial_facts where SDO_RELATE(geom_a3_a7, mdsys.sdo_geometry(2003,NULL,NULL, mdsys.sdo_elem_info_array(1,1003,3),mdsys.sdo_ordinate_array (10,800000, , )), 'mask=inside querytype=WINDOW') = 'TRUE'; select count(*) from spatial_facts where a3 > 10 and a and a7 < ;

159 159 Multidimensional Indexes Oracle 8i on Windows 2000 Spatial Extension: –2-dimensional data –Spatial functions used in the query R-tree does not perform well because of the overhead of spatial extension.

160 160 Multidimensional Indexes R-Tree SELECT STATEMENT SORT AGGREGATE TABLE ACCESS BY INDEX ROWID SPATIAL_FACTS DOMAIN INDEX R_SPATIALFACTS Bitmaps SELECT STATEMENT SORT AGGREGATE BITMAP CONVERSION COUNT BITMAP AND BITMAP INDEX SINGLE VALUE B_FACT7 BITMAP INDEX SINGLE VALUE B_FACT3

161 161 Materialized Views -- data Settings: orders( ordernum, itemnum, quantity, storeid, vendor ); create clustered index i_order on orders(itemnum); store( storeid, name ); item(itemnum, price); create clustered index i_item on item(itemnum); – orders, stores, items; Cold buffer –Oracle 9i –Pentium III (1 GHz, 256 Kb), 1Gb RAM, Adapter with 2 channels, 3x18Gb drives (10000RPM), Linux Debian 2.4.

162 162 Materialized Views -- data Settings: create materialized view vendorOutstanding build immediate refresh complete enable query rewrite as select orders.vendor, sum(orders.quantity*item.price) from orders,item where orders.itemnum = item.itemnum group by orders.vendor;

163 163 Materialized Views -- transactions Concurrent Transactions: –Insertions insert into orders values ( ,7825,562,'xxxxxx6944','vendor4'); –Queries select orders.vendor, sum(orders.quantity*item.price) from orders,item where orders.itemnum = item.itemnum group by orders.vendor; select * from vendorOutstanding;

164 164 Materialized Views Graph: –Oracle9i on Linux –Total sale by vendor is materialized Trade-off between query speed-up and view maintenance: –The impact of incremental maintenance on performance is significant. –Rebuild maintenance achieves a good throughput. –A static data warehouse offers a good trade-off.

165 165 Materialized View Maintenance Problem when large number of views to maintain. The order in which views are maintained is important: –A view can be computed from an existing view instead of being recomputed from the base relations (total per region can be computed from total per nation). Let the views and base tables be nodes v_i Let there be an edge from v_1 to v_2 if it possible to compute the view v_2 from v_1. Associate the cost of computing v_2 from v_1 to this edge. Compute all pairs shortest path where the start nodes are the set of base tables. The result is an acyclic graph A. Take a topological sort of A and let that be the order of view construction.

166 166 Materialized View Example Detail(storeid, item, qty, price, date) Materialized view1(storeid, category, qty, month) Materializedview2(city, category, qty, month) Materializedview3(storeid, category, qty, year)

167 167 Approximations -- data Settings: –TPC-H schema –Approximations insert into approxlineitem select top 6000 * from lineitem where l_linenumber = 4; insert into approxorders select O_ORDERKEY, O_CUSTKEY, O_ORDERSTATUS, O_TOTALPRICE, O_ORDERDATE, O_ORDERPRIORITY, O_CLERK, O_SHIPPRIORITY, O_COMMENT from orders, approxlineitem where o_orderkey = l_orderkey;

168 168 Approximations -- queries insert into approxsupplier select distinct S_SUPPKEY, S_NAME, S_ADDRESS, S_NATIONKEY, S_PHONE, S_ACCTBAL, S_COMMENT from approxlineitem, supplier where s_suppkey = l_suppkey; insert into approxpart select distinct P_PARTKEY, P_NAME, P_MFGR, P_BRAND, P_TYPE, P_SIZE, P_CONTAINER, P_RETAILPRICE, P_COMMENT from approxlineitem, part where p_partkey = l_partkey; insert into approxpartsupp select distinct PS_PARTKEY, PS_SUPPKEY, PS_AVAILQTY, PS_SUPPLYCOST, PS_COMMENT from partsupp, approxpart, approxsupplier where ps_partkey = p_partkey and ps_suppkey = s_suppkey; insert into approxcustomer select distinct C_CUSTKEY, C_NAME, C_ADDRESS, C_NATIONKEY, C_PHONE, C_ACCTBAL, C_MKTSEGMENT, C_COMMENT from customer, approxorders where o_custkey = c_custkey; insert into approxregion select * from region; insert into approxnation select * from nation;

169 169 Approximations -- more queries Queries: –Single table query on lineitem select l_returnflag, l_linestatus, sum(l_quantity) as sum_qty, sum(l_extendedprice) as sum_base_price, sum(l_extendedprice * (1 - l_discount)) as sum_disc_price, sum(l_extendedprice * (1 - l_discount) * (1 + l_tax)) as sum_charge, avg(l_quantity) as avg_qty, avg(l_extendedprice) as avg_price, avg(l_discount) as avg_disc, count(*) as count_order from lineitem where datediff(day, l_shipdate, ' ') <= '120' group by l_returnflag, l_linestatus order by l_returnflag, l_linestatus;

170 170 Approximations -- still more Queries: –6-way join select n_name, avg(l_extendedprice * (1 - l_discount)) as revenue from customer, orders, lineitem, supplier, nation, region where c_custkey = o_custkey and l_orderkey = o_orderkey and l_suppkey = s_suppkey and c_nationkey = s_nationkey and s_nationkey = n_nationkey and n_regionkey = r_regionkey and r_name = 'AFRICA' and o_orderdate >= ' ' and datediff(year, o_orderdate,' ') < 1 group by n_name order by revenue desc;

171 171 Approximation accuracy Good approximation for query Q1 on lineitem The aggregated values obtained on a query with a 6-way join are significantly different from the actual values -- for some applications may still be good enough.

172 172 Approximation Speedup Aqua approximation on the TPC-H schema –1% and 10% lineitem sample propagated. The query speed-up obtained with approximated relations is significant.

173 173 Approximating Count Distinct Suppose you have one or more columns with duplicates. You want to know how many distinct values there are. Imagine that you could hash into (0,1). (Simulate this by a very large integer range). Hashing will put duplicates in the same location and, if done right, will distribute values uniformly. How can we use this?

174 174 Approximating Count Distinct Suppose you took the minimum 1000 hash values. Suppose that highest value of the 1000 is f. What is a good approximation of the number of distinct values?

175 175 Tuning Distributed Applications Queries across multiple databases –Federated Datawarehouse –IBMs DataJoiner now integrated at DB2 v7.2 The source should perform as much work as possible and return as few data as possible for processing at the federated server Processing data across multiple databases

176 176 A puzzle Two databases X and Y –X records inventory data to be used for restocking –Y contains delivery data about shipments Improve the speed of shipping by sharing data –Certain data of X should be postprocessed on Y shortly after it enters X –You want to avoid losing data from X and you want to avoid double-processing data on Y, even in the face of failures.

177 177 Two-Phase Commit X Y commit Source (coordinator & participant) Destination (participant) +Commits are coordinated between the source and the destination -If one participant fails then blocking can occur -Not all db systems support prepare-to- commit interface

178 178 Replication Server +Destination within a few seconds of being up-to-date +Decision support queries can be asked on destination db -Administrator is needed when network connection breaks! X Y Source Destination

179 179 Staging Tables +No specific mechanism is necessary at source or destination -Coordination of transactions on X and Y X Y Source Destination Staging table

180 180 Issues in Solution A single thread can invoke a transaction on X, Y or both, but the thread may fail in the middle. A new thread will regain state by looking at the database. We want to delete data from the staging tables when we are finished. The tuples in the staging tables will represent an operation as well as data.

181 181 States A tuple (operation plus data) will start in state unprocessed, then state processed, and then deleted. The same transaction that processes the tuple on the destination site also changes its state. This is important so each tuples operation is done exactly once.

182 182 Database XDatabase Y Table S Table I Yunprocessed M1 Read from source table M2 Write to destination Staging Tables Table S Yunprocessed Database Y Table I unprocessed Database X STEP 1 STEP 2

183 183 Staging Tables Database XDatabase Y Table S Table I Yunprocessed Table S Yunprocessed Database Y Table I processed unprocessed Database X STEP 3 STEP 4 M3 Update on destination processed unprocessed M4 Delete source; then dest Y Xact: Update source site Xact:update destination site

184 184 What to Look For Transactions are atomic but threads, remember, are not. So a replacement thread will look to the database for information. In which order should the final step do its transactions? Should it delete the unprocessed tuple from X as the first transaction or as the second?

185 185 Troubleshooting Techniques (*) Extraction and Analysis of Performance Indicators –Consumer-producer chain framework –Tools Query plan monitors Performance monitors Event monitors (*) From Alberto Lerners chapter

186 186 A Consumer-Producer Chain of a DBMSs Resources High Level Consumers Intermediate Resources/ Consumers Primary Resources PARSER OPTIMIZER EXECUTION SUBSYSTEM DISK SYBSYSTEM CACHE MANAGER LOGGING SUBSYSTEM LOCKING SUBSYSTEM NETWORK DISK/ CONTROLLER CPUMEMORY sqlcommands probing spots for indicators

187 187 Recurrent Patterns of Problems An overloading high-level consumer A poorly parameterized subsystem An overloaded primary resource Effects are not always felt first where the cause is!

188 188 A Systematic Approach to Monitoring Question 1: Are critical queries being served in the most efficient manner? Question 2: Are subsystems making optimal use of resources? Question 3: Are there enough primary resources available? Extract indicators to answer the following questions

189 189 Investigating High Level Consumers Answer question 1: Are critical queries being served in the most efficient manner? 1.Identify the critical queries 2.Analyze their access plans 3.Profile their execution

190 190 Identifying Critical Queries Critical queries are usually those that: Take a long time Are frequently executed Often, a user complaint will tip us off.

191 191 Event Monitors to Identify Critical Queries If no user complains... Capture usage measurements at specific events (e.g., end of each query) and then sort by usage Less overhead than other type of tools because indicators are usually by-product of events monitored Typical measures include CPU used, IO used, locks obtained etc.

192 192 An example Event Monitor CPU indicators sorted by Oracles Trace Data Viewer Similar tools: DB2s Event Monitor and MSSQLs Server Profiler

193 193 An example Plan Explainer Access plan according to MSSQLs Query Analyzer Similar tools: DB2s Visual Explain and Oracles SQL Analyze Tool

194 194 Finding Strangeness in Access Plans What to pay attention to in a plan Access paths for each table Sorts or intermediary results (join, group by, distinct, order by) Order of operations Algorithms used in the operators

195 195 To Index or not to index? select c_name, n_name from CUSTOMER join NATION on c_nationkey=n_nationkey where c_acctbal > 0 Which plan performs best? (nation_pk is an non-clustered index over n_nationkey, and similarly for acctbal_ix over c_acctbal)

196 196 Non-clustering indexes can be trouble For a low selectivity predicate, each access to the index generates a random access to the table – possibly duplicate! It ends up that the number of pages read from the table is greater than its size, i.e., a table scan is way better Table ScanIndex Scan 5 sec 143,075 pages 6,777 pages 136,319 pages 7 pages 76 sec 272,618 pages 131,425 pages 273,173 pages 552 pages CPU time data logical reads data physical reads index logical reads index physical reads

197 197 An example Performance Monitor (query level) Buffer and CPU consumption for a query according to DB2s Benchmark tool Similar tools: MSSQLs SET STATISTICS switch and Oracles SQL Analyze Tool Statement number: 1 select C_NAME, N_NAME from DBA.CUSTOMER join DBA.NATION on C_NATIONKEY = N_NATIONKEY where C_ACCTBAL > 0 Number of rows retrieved is: Number of rows sent to output is: 0 Elapsed Time is: seconds … Buffer pool data logical reads = Buffer pool data physical reads = Buffer pool data writes = 0 Buffer pool index logical reads = Buffer pool index physical reads = 552 Buffer pool index writes = 0 Total buffer pool read time (ms) = Total buffer pool write time (ms) = 0 … Summary of Results ================== Elapsed Agent CPU Rows Rows Statement # Time (s) Time (s) Fetched Printed Statement number: 1 select C_NAME, N_NAME from DBA.CUSTOMER join DBA.NATION on C_NATIONKEY = N_NATIONKEY where C_ACCTBAL > 0 Number of rows retrieved is: Number of rows sent to output is: 0 Elapsed Time is: seconds … Buffer pool data logical reads = Buffer pool data physical reads = Buffer pool data writes = 0 Buffer pool index logical reads = Buffer pool index physical reads = 552 Buffer pool index writes = 0 Total buffer pool read time (ms) = Total buffer pool write time (ms) = 0 … Summary of Results ================== Elapsed Agent CPU Rows Rows Statement # Time (s) Time (s) Fetched Printed

198 198 An example Performance Monitor (system level) An IO indicators consumption evolution (qualitative and quantitative) according to DB2s System Monitor Similar tools: Windows Performance Monitor and Oracles Performance Manager

199 199 Investigating High Level Consumers: Summary Find critical queries Found any? Investigate lower levels Answer Q1 over them Overcon- sumption? Tune problematic queries yes no

200 200 Investigating Primary Resources Answer question 3: Are there enough primary resources available for a DBMS to consume? Primary resources are: CPU, disk & controllers, memory, and network Analyze specific OS-level indicators to discover bottlenecks. A system-level Performance Monitor is the right tool here

201 201 CPU Consumption Indicators at the OS Level 100% CPU % of utilization 70% time Sustained utilization over 70% should trigger the alert. System utilization shouldnt be more than 40%. DBMS (on a non- dedicated machine) should be getting a decent time share. total usage system usage

202 202 Disk Performance Indicators at the OS Level Wait queue Average Queue Size New requests Disk Transfers /second Should be close to zero Wait times should also be close to zero Idle disk with pending requests? Check controller contention. Also, transfers should be balanced among disks/controllers

203 203 Memory Consumption Indicators at the OS Level pagefile real memory virtual memory Page faults/time should be close to zero. If paging happens, at least not DB cache pages. % of pagefile in use will tell you how much memory is needed.

204 204 Investigating Intermediate Resources/Consumers Answer question 2: Are subsystems making optimal use of resources? Main subsystems: Cache Manager, Disk subsystem, Lock subsystem, and Log/Recovery subsystem Similarly to Q3, extract and analyze relevant Performance Indicators

205 205 Cache Manager Performance Indicators Table scan readpage () Free Page slots Page reads/ writes Pick victim strategy Data Pages Cache Manager If page is not in the cache, readpage (logical) generates an actual IO (physical). Fraction of readpages that did not generate physical IO should be 90% or more (hit ratio) Pages are regularly saved to disk to make free space. # of free slots should always be > 0

206 206 Disk Manager Performance Indicators rows page extent file Storage Hierarchy (simplified) disk Displaced rows (moved to other pages) should be kept under 5% of rows Free space fragmentation: pages with little space should not be in the free list Data fragmentation: ideally files that store DB objects (table, index) should be in one or few (<5) contiguous extents File position: should balance workload evenly among all disks

207 207 Lock Manager Performance Indicators Lock request ObjectLock TypeTXN ID Lock List Locks pending list Deadlocks and timeouts should seldom happen (no more then 1% of the transactions) Lock wait time for a transaction should be a small fraction of the whole transaction time. Number of lock waits should be a small fraction of the number of locks on the lock list.

208 208 Investigating Intermediate and Primary Resources: Summary Answer Q3 Problems at OS level? Answer Q2 Tune low-level resources yesno Problematic subsystems? Tune subsystems Investigate upper level yesno

209 209 Troubleshooting Techniques Monitoring a DBMSs performance should be based on queries and resources. –The consumption chain helps distinguish problems causes from their symptoms –Existing tools help extracting relevant performance indicators

210 210 Recall Tuning Principles Think globally, fix locally (troubleshoot to see what matters) Partitioning breaks bottlenecks (find parallelism in processors, controllers, caches, and disks) Start-up costs are high; running costs are low (batch size, cursors) Be prepared for trade-offs (unless you can rethink the queries)


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