Database tuning principles experiments and troubleshooting techniques
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Database tuning principles experiments and troubleshooting techniques

Database TuningPrinciples, Experiments and Troubleshooting Techniques

http://www.mkp.com/dbtune

Dennis Shasha ([email protected])

Philippe Bonnet ([email protected])


Availability of materials

Availability of Materials

  • Power Point presentation is available on my web site (and Philippe Bonnet’s). Just type our names into google

  • Experiments available from Denmark site maintained by Philippe (site in a few slides)

  • Book with same title available from Morgan Kaufmann.


Database tuning

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.


Database tuning principles experiments and troubleshooting techniques

ApplicationProgrammer(e.g., business analyst,

Data architect)

Application

SophisticatedApplicationProgrammer(e.g., SAP admin)

QueryProcessor

Indexes

Storage Subsystem

Concurrency Control

Recovery

DBA,Tuner

Operating System

Hardware[Processor(s), Disk(s), Memory]


Outline of tutorial

Outline of Tutorial

  • Basic Principles

  • Tuning the guts

  • Indexes

  • Relational Systems

  • Application Interface

  • Ecommerce Applications

  • Data warehouse Applications

  • Distributed Applications

  • Troubleshooting


Goal of the tutorial

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.


Tuning principles leitmotifs

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)


Experiments why and where

Experiments -- why and where

  • Simple experiments to illustrate the performance impact of tuning principles.

  • http://www.diku.dk/dbtune/experiments to get the SQL scripts, the data and a tool to run the experiments.


Experimental dbms and hardware

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:

    • Dual Xeon (550MHz,512Kb), 1Gb RAM, Internal RAID controller from Adaptec (80Mb) 2 Ultra 160 channels, 4x18Gb drives (10000RPM), Windows 2000.

    • Dual Pentium II (450MHz, 512Kb), 512 Mb RAM, 3x18Gb drives (10000RPM), Windows 2000.

    • Pentium III (1 GHz, 256 Kb), 1Gb RAM, Adapter 39160 with 2 channels, 3x18Gb drives (10000RPM), Linux Debian 2.4.


Tuning the guts

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?


Isolation

Isolation

  • Correctness vs. Performance

    • Number of locks held by each transaction

    • Kind of locks

    • Length of time a transaction holds locks


Isolation levels

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


Snapshot isolation

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.

R(Y) returns 1

R(Z) returns 0

R(X) returns 0

T1

W(Y:=1)

W(X:=2, Z:=3)

T2

T3

TIME

X=Y=Z=0

Snapshot isolation


Value of serializability data

Value of Serializability -- Data

Settings:

accounts( number, branchnum, balance);

create clustered index c on accounts(number);

  • 100000 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.


Value of serializability transactions

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;


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).

Value of Serializability -- results


Cost of serializability

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

Cost of Serializability


Locking overhead data

Locking Overhead -- data

Settings:

accounts( number, branchnum, balance);

create clustered index c on accounts(number);

  • 100000 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.


Locking overhead transactions

Locking Overhead -- transactions

No Concurrent Transactions:

  • Update [10 000 updates]update accounts set balance = Val;

  • Insert [10 000 transactions], e.g. typical one:insert into accounts values(664366,72255,2296.12);


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.

Locking Overhead


Logical bottleneck sequential key generation

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).


Counter facility data

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 2000.

accounts( number, branchnum, balance);

create clustered index c on accounts(number);

counter ( nextkey );

insert into counter values (1);


Counter facility transactions

Counter Facility -- transactions

No Concurrent Transactions:

  • System [100 000 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 [100 000 inserts, N threads]begin transactionNextKey:=select nextkey from counter; update counter set nextkey = NextKey+1; insert into accounts values(NextKey,?,?);commit transaction


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.

Avoid Bottlenecks: Counters


Semantics altering chopping

Semantics-Altering Chopping

You call up an airline and want to reserve a seat. You talk to the agent, find a seat and then reserve it.

Should all of this be a single transaction?


Semantics altering chopping ii

Semantics-Altering Chopping II

Probably not. You don’t want to hold locks through human interaction.

Transaction redesign: read is its own transaction. Get seat is another.

Consequences?


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.

Atomicity and Durability

COMMITTED

COMMIT

Ø

ACTIVE

(running, waiting)

BEGINTRANS

ABORTED

ROLLBACK


Database tuning principles experiments and troubleshooting techniques

UNSTABLE STORAGE

DATABASE BUFFER

LOG BUFFER

lri

lrj

WRITE log records before commit

WRITE

modified pages after commit

LOG

DATA

DATA

DATA

RECOVERY

STABLE STORAGE


Log io data

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.


Log io transactions

Log IO -- transactions

No Concurrent Transactions:

Insertions [300 000 inserts, 10 threads], e.g.,

insert into lineitem values (1,7760,401,1,17,28351.92,0.04,0.02,'N','O','1996-03-13','1996-02-12','1996-03-22','DELIVER IN PERSON','TRUCK','blithely regular ideas caj');


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.

Group Commits


Put the log on a separate disk

DB2 UDB v7.1 on Windows 2000

5 % 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.

Put the Log on a Separate Disk


Tuning database writes

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.


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

Tune Checkpoint Intervals


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.

DATABASE PROCESSES

RAM

DATABASEBUFFER

Paging Disk

LOG

DATA

DATA

Database Buffer Size


Buffer size data

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

  • 20000000 rows (630 Mb);

  • Warm Buffer

  • Dual Xeon (550MHz,512Kb), 1Gb RAM, Internal RAID controller from Adaptec (80Mb), 4x18Gb drives (10000 RPM), Windows 2000.


Buffer size queries

Buffer Size -- queries

Queries:

  • Scan Query

    select sum(long) from employees;

  • Multipoint query

    select * from employees where lat = ?;


Database buffer size1

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.

Database Buffer Size


Scan performance data

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 );

  • 600 000 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.


Scan performance queries

Scan Performance -- queries

Queries:

select avg(l_discount) from lineitem;


Prefetching

DB2 UDB v7.1 on Windows 2000

Throughput increases up to a certain point when prefetching size increases.

Prefetching


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.

Usage Factor


Tuning the storage subsystem

Tuning the Storage Subsystem


Outline

Outline

  • Storage Subsystem Components

    • Moore’s law and consequences

    • Magnetic disk performances

  • From SCSI to SAN, NAS and Beyond

    • Storage virtualization

  • Tuning the Storage Subsystem

    • RAID levels

    • RAID controller cache


Exponential growth

Exponential Growth

Moore’s law

  • Every 18 months:

    • New processing = sum of all existing processing

    • New storage = sum of all existing storage

  • 2x / 18 months ~ 100x / 10 years

    http://www.intel.com/research/silicon/moorespaper.pdf


Consequences of moore s law

Consequences of “Moore’s 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)


Memory hierarchy

Memory Hierarchy

Access Time

Price $/ Mb

1 ns

Processor cache

100

x10

RAM/flash

10

6

0.2

x10

Disks

0.2 (nearline)

10

Tapes / Optical Disks

x10


Magnetic disks

1956: IBM (RAMAC) first disk drive

5 Mb – 0.002 Mb/in235000$/year9 Kb/sec

1980: SEAGATE

first 5.25’’ disk drive

5 Mb – 1.96 Mb/in2625 Kb/sec

1999: IBM MICRODRIVE

first 1’’ disk drive340Mb 6.1 MB/sec

tracks

spindle

platter

read/write head

actuator

disk arm

Controller

disk interface

Magnetic Disks


Magnetic disks1

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 (Ultra3 - 80 MHz).

http://www.pcguide.com/ref/hdd/

Magnetic Disks


Raid levels

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 server’s CPU

    • Hardware RAID: run on the RAID controller’s CPU


Why 4 read writes when updating a single stripe using raid 5

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).


Raid levels data

RAID Levels -- data

Settings:

accounts( number, branchnum, balance);

create clustered index c on accounts(number);

  • 100000 rows

  • Cold Buffer

  • Dual Xeon (550MHz,512Kb), 1Gb RAM, Internal RAID controller from Adaptec (80Mb), 4x18Gb drives (10000RPM), Windows 2000.


Raid levels transactions

RAID Levels -- transactions

No Concurrent Transactions:

  • Read Intensive:

    select avg(balance) from accounts;

  • Write Intensive, e.g. typical insert:

    insert into accounts values (690466,6840,2272.76);

    Writes are uniformly distributed.


Raid levels1

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.

RAID Levels


Raid levels2

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.


Controller prefetching no write back yes

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.


Scsi controller cache data

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).

  • 200 000 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.


Scsi not disk controller cache transactions

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)


Scsi controller cache

SQL Server 7 on Windows 2000.

Adaptec ServerRaid controller:

80 Mb RAM

Write-back mode

Updates

Controller cache increases throughput whether operation is cache friendly or not.

Efficient replacement policy!

SCSI Controller Cache


Index tuning

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


Index

Index

An index is a data structure that supports efficient access to data

Set ofRecords

Matchingrecords

Conditiononattributevalue

index

(search key)


Index1

Index

An index is a data structure that supports efficient access to data

Set ofRecords

Matchingrecords

Conditiononattributevalue

index

(search key)


Search keys

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)


Data structures

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.


B tree

B+-Tree

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

96

75 83

107

33 48 69

75 80 81

83 92 95

96 98 103

107 110 120


B tree performance

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


B tree performance1

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.


Hash index

values

Hashed key

key

R1 R5

Hashfunction

0

1

R3 R6 R9

R14 R17 R21

R25

2341

n

Hash Index

  • A hash index stores key-value pairs based on a pseudo-randomizing function called a hash function.

The length of

these chains impacts performance


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.

Clustered / Non clustered index

Records

Records


Dense sparse index

Sparse index

Pointers are associated to pages

Dense index

Pointers are associated to records

Non clustered indexes are dense

Dense / Sparse Index

P1

P2

Pi

record

record

record


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.

Index-organized tables organize data according to index.

Index Implementations in some major DBMS


Types of queries

Point QuerySELECT balanceFROM accountsWHERE number = 1023;

Multipoint QuerySELECT balanceFROM accountsWHERE branchnum = 100;

Range QuerySELECT numberFROM accountsWHERE balance > 10000 and balance <= 20000;

Prefix Match QuerySELECT *FROM employeesWHERE name = ‘J*’ ;

Types of Queries


More types of queries

Extremal QuerySELECT *FROM accountsWHERE balance = max(select balance from accounts)

Ordering QuerySELECT *FROM accountsORDER BY balance;

Grouping QuerySELECT branchnum, avg(balance)FROM accountsGROUP BY branchnum;

Join QuerySELECT distinct branch.adresseFROM accounts, branchWHERE accounts.branchnum = branch.numberand accounts.balance > 10000;

More Types of Queries


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);

1000000 rows ; Cold buffer

Dual Xeon (550MHz,512Kb), 1Gb RAM, Internal RAID controller from Adaptec (80Mb), 4x18Gb drives (10000RPM), Windows 2000.

Index Tuning -- data


Index tuning operations

Operations:

Update: update employees set name = ‘XXX’ where ssnum = ?;

Insert: insert into employees values (1003505,'polo94064',97.48,84.03,4700.55,3987.2);

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 = ?

Index Tuning -- operations


Clustered index

Multipoint query that returns 100 records out of 1000000.

Cold buffer

Clustered index is twice as fast as non-clustered index and orders of magnitude faster than a scan.

Clustered Index


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.

Index “Face Lifts”


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

Index Maintenance


Covering index defined

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.


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.

Covering Index - impact


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.

Scan Can Sometimes Win


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.

Index on Small Tables


Bitmap vs hash vs b tree

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 1000000 SIZE 60;

create bitmap index b on employees (hundreds2);

create bitmap index b2 on employees (ssnum);

  • 1000000 rows ; Cold buffer

  • Dual Xeon (550MHz,512Kb), 1Gb RAM, Internal RAID controller from Adaptec (80Mb), 4x18Gb drives (10000RPM), Windows 2000.


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.

Multipoint query: B-Tree, Hash Tree, Bitmap


Database tuning principles experiments and troubleshooting techniques

Hash indexes don’t help when evaluating range queries

Hash index outperforms B-tree on point queries

B-Tree, Hash Tree, Bitmap


Tuning relational systems

Tuning Relational Systems

  • Schema Tuning

    • Denormalization, Vertical Partitioning

  • Query Tuning

    • Query rewriting

    • Materialized views


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);

600000 rows in lineitem, 25 nations, 5 regions, 500 suppliers

Denormalizing -- data


Denormalizing transactions

Denormalizing -- transactions

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);

  • 600000 rows in lineitemdenormalized

  • Cold Buffer

  • Dual Pentium II (450MHz, 512Kb), 512 Mb RAM, 3x18Gb drives (10000RPM), Windows 2000.


Queries on normalized vs denormalized schemas

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';


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%

Denormalization


Vertical partitioning

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.


Vertical partitioning1

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.

Vertical Partitioning


Tuning normalization

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


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.

Vertical Partitioningand Scan


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.

Vertical Partitioningand Point Queries


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);

100000 rows in employee, 100000 students, 10 departments; Cold buffer

Dual Pentium II (450MHz, 512Kb), 512 Mb RAM, 3x18Gb drives (10000RPM), Windows 2000.

Queries


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 = ?;

Queries – View on Join


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.

Query Rewriting - Views


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;

Queries – Correlated Subqueries


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 2001.

Query Rewriting – Correlated Subqueries

> 1000

> 10000


Eliminate unneeded distincts

Eliminate unneeded DISTINCTs

  • Query: Find employees who work in the information systems department. There should be no duplicates.

    SELECT distinct ssnumFROM employeeWHERE dept = ‘information systems’

  • DISTINCT is unnecessary, since ssnum is a key of employee so certainly is a key of a subset of employee.


Eliminate unneeded distincts1

Eliminate unneeded DISTINCTs

  • Query: Find social security numbers of employees in the technical departments. There should be no duplicates.

    SELECT DISTINCT ssnumFROM employee, techWHERE employee.dept = tech.dept

  • Is DISTINCT needed?


Distinct unnecessary here too

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.


Reaching

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.


Reaches main theorem

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.


Reaches proof sketch

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.


Reaches example 1

Reaches: Example 1

  • SELECT ssnumFROM employee, techWHERE employee.manager = tech.manager

  • 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.


Reaches example 2

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.deptFROM employee, techWHERE employee.manager = tech.manager


Reaches example 3

Reaches: Example 3

SELECT student.ssnumFROM student, employee, techWHERE student.name = employee.name AND employee.dept = tech.dept;

  • Student is priviledged

  • Employee does not reach student (name is not a key of employee)

  • DISTINCT is needed to avoid duplicates.


Aggregate maintenance data

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);

  • 1000000 orders, 10000 stores, 400000 items; Cold buffer

  • Dual Pentium II (450MHz, 512Kb), 512 Mb RAM, 3x18Gb drives (10000RPM), Windows 2000.


Aggregate maintenance triggers

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)


Aggregate maintenance transactions

Aggregate Maintenance -- transactions

Concurrent Transactions:

  • Insertions

    insert into orders values (1000350,7825,562,'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;


Aggregate maintenance

SQLServer 2000 on Windows 2000

Using triggers for view maintenance

If queries frequent or important, then aggregate maintenance is good.

Aggregate Maintenance


Superlinearity data

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);


Superlinearity indexes

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);

  • 1000000 sales, 400000 customers, 40000 items, 1000 stores

  • Cold buffer

  • Dual Pentium II (450MHz, 512Kb), 512 Mb RAM, 3x18Gb drives (10000RPM), Windows 2000.


Superlinearity queries

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)


Superlinearity batch queries

Superlinearity -- batch queries

Queries:

  • Small batches

    DECLARE @Nlow INT;

    DECLARE @Nhigh INT;

    DECLARE @INCR INT;

    set @INCR = 100000

    set @NLow = 0

    set @Nhigh = @INCR

    WHILE (@NLow <= 500000)

    BEGIN

    insert into tempsales

    select * from sales

    where id between @NLow and @Nhigh

    set @Nlow = @Nlow + @INCR

    set @Nhigh = @Nhigh + @INCR

    delete from tempsales

    where id in (select id from successfulsales);

    insert into unsuccessfulsales

    select * from tempsales;

    delete from tempsales;

    END


Superlinearity outer join

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


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.

Circumventing Superlinearity


Tuning the application interface

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.


Looping can hurt data

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 );

  • 600 000 rows; warm buffer.

  • Dual Pentium II (450MHz, 512Kb), 512 Mb RAM, 3x18Gb drives (10000RPM), Windows 2000.


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);

}

Looping can hurt -- queries


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?

Looping can Hurt


Cursors are death data

Cursors are Death -- data

Settings:

employees(ssnum, name, lat, long, hundreds1,

hundreds2);

  • 100000 rows ; Cold buffer

  • Dual Pentium II (450MHz, 512Kb), 512 Mb RAM, 3x18Gb drives (10000RPM), Windows 2000.


Cursors are death queries

Cursors are Death -- queries

Queries:

  • No cursor

    select * from employees;

  • Cursor

    DECLARE d_cursor CURSOR FOR select * from employees;

    OPEN d_cursorwhile (@@FETCH_STATUS = 0)

    BEGIN

    FETCH NEXT from d_cursorEND

    CLOSE d_cursor

    go


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.

Cursors are Death


Retrieve needed columns only data

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);

  • 600 000 rows; warm buffer.

  • Lineitem records are ~ 10 bytes long

  • Dual Pentium II (450MHz, 512Kb), 512 Mb RAM, 3x18Gb drives (10000RPM), Windows 2000.


Retrieve needed columns only queries

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;


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.

Retrieve Needed Columns Only

Experiment performed on Oracle8iEE on Windows 2000.


Bulk loading data

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; 600 000 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.


Bulk loading queries

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

)


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).

Direct Path

Experiment performed on Oracle8iEE on Windows 2000.


Batch size

Throughput increases steadily when the batch size increases to 100000 records.Throughput remains constant afterwards.

Trade-off between performance and amount of data that has to be reloaded in case of problem.

Batch Size

Experiment performed on SQL Server 2000 on Windows 2000.


Tuning e commerce applications

Tuning E-Commerce Applications

Database-backed web-sites:

  • Online shops

  • Shop comparison portals

  • MS TerraServer


E commerce application architecture

E-commerce Application Architecture

Clients

Web servers

Application servers

Database server

Web cache

DB cache

Web cache

DB cache

Web cache


E commerce application workload

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., men’s clothing.

  • Keyword searching (frequent, uncached, need for timeliness guarantees)

  • Shopping cart interactions (rare, but transactional)

  • Electronic purchasing (rare, but transactional)


Design issues

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)


Caching

Caching

  • Web cache:

    • Static web pages

    • Caching fragments of dynamically created web pages

  • Database cache (Oracle9iAS, TimesTen’s 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.


Ecommerce setting

Ecommerce -- setting

Settings:

shoppingcart( shopperid, itemid, price, qty);

  • 500000 rows; warm buffer

  • Dual Pentium II (450MHz, 512Kb), 512 Mb RAM, 3x18Gb drives (10000RPM), Windows 2000.


Ecommerce transactions

Ecommerce -- transactions

Concurrent Transactions:

  • Mix

    insert into shoppingcart values (107999,914,870,214);

    update shoppingcart set Qty = 10 where shopperid = 95047 and itemid = 88636;

    delete from shoppingcart where shopperid = 86123 and itemid = 8321;

    select shopperid, itemid, qty, price from shoppingcart where shopperid = ?;

  • Queries Only

    select shopperid, itemid, qty, price from shoppingcart where shopperid = ?;


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.

Connection Pooling (no refusals)

Experiment performed on Oracle8i on Windows 2000


Indexing

Using a clustered index on shopperid in the shopping cart provides:

Query speed-up

Update/Deletion speed-up

Indexing

Experiment performed on SQL Server 2000 on Windows 2000


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)

Capacity Planning

Entry (S1)

0.4

0.5

Search (S2)

0.1

Checkout (S3)

Getting the demand assumptions right

is what makes capacity planning hard


How to handle multiple servers

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.


Rough estimate for multiple servers

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.


Approximating waiting time for n parallel servers

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.


Approximating response time for n parallel servers

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.


Example

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 = 0.033. So the response time is 0.133.

  • For a 2-times faster server, S = 0.05, U = 0.4, so response time is 0.05/0.6 = 0.0833


Example continued

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 = 0.00625 So response time is 0.10625.


Datawarehouse tuning

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


Data warehouse workload

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?


Tuning knobs

Tuning Knobs

  • Indexes

  • Materialized views

  • Approximation


Bitmaps data

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);

  • 100000 rows ; cold buffer

  • Dual Pentium II (450MHz, 512Kb), 512 Mb RAM, 3x18Gb drives (10000RPM), Windows 2000.


Bitmaps queries

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';


Bitmaps

Order of magnitude improvement compared to scan, because summing up the 1s is very fast.

Bitmaps are best suited for multiple conditions on several attributes, each having a low selectivity.

ANR

l_returnflag

O

F

l_linestatus

Bitmaps


Vertical partitioning revisited

Vertical Partitioning Revisited

In the same settings that bit vectors are useful – lots of attributes, unpredictable combinations queried – so is vertical partitioning more attractive.

If a table has 200 attributes, no reason to retrieve 200 field rows if only three attributes are needed.

Sybase IQ, KDB, Vertica all use column-oriented tables.


Multidimensional indexes data

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);

  • 500000 rows ; cold buffer

  • Dual Pentium II (450MHz, 512Kb), 512 Mb RAM, 3x18Gb drives (10000RPM), Windows 2000.


Multidimensional indexes queries

Multidimensional Indexes -- queries

Queries:

  • Point Queries

    select count(*) from fact where a3 = 694014 and a7 = 928878;

    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(10,800000,1000000,1000000)), 'mask=inside querytype=WINDOW') = 'TRUE';

    select count(*) from spatial_facts where a3 > 10 and a3 < 1000000 and a7 > 800000 and a7 < 1000000;


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.

Multidimensional Indexes


Multidimensional indexes1

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

Multidimensional Indexes


Materialized views data

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);

  • 1000000 orders, 10000 stores, 400000 items; Cold buffer

  • Oracle 9i

  • Pentium III (1 GHz, 256 Kb), 1Gb RAM, Adapter 39160 with 2 channels, 3x18Gb drives (10000RPM), Linux Debian 2.4.


Materialized views data1

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;


Materialized views transactions

Materialized Views -- transactions

Concurrent Transactions:

  • Insertions

    insert into orders values (1000350,7825,562,'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;


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.

Materialized Views


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.

Materialized View Maintenance


Materialized view example

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)


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;

Approximations -- data


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;

Approximations -- queries


Approximations more queries

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, '1998-12-01') <= '120'

    group by l_returnflag, l_linestatus

    order by l_returnflag, l_linestatus;


Approximations still more

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 >= '1993-01-01'

    and datediff(year, o_orderdate,'1993-01-01') < 1

    group by n_name

    order by revenue desc;


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.

Approximation accuracy


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.

Approximation Speedup


Approximating count distinct

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?


Approximating count distinct1

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?


Tuning distributed applications

Tuning Distributed Applications

  • Queries across multiple databases

    • Federated Datawarehouse

    • IBM’s 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


A puzzle

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.


Two phase commit

Two-Phase Commit

commit

X

  • 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

commit

Source(coordinator

& participant)

Y

Destination

(participant)


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!

Replication Server

X

Source

Y

Destination


Staging tables

No specific mechanism is necessary at source or destination

Coordination of transactions on X and Y

Staging Tables

X

Staging table

Source

Y

Destination


Issues in solution

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.


States

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 tuple’s operation is done exactly once.


Staging tables1

Staging Tables

Write to destination

Read from source table

M2

M1

Yunprocessed

Yunprocessed

Yunprocessed

Yunprocessed

unprocessed

unprocessed

unprocessed

Yunprocessed

Yunprocessed

Table I

Table S

Table I

Table S

Database X

Database Y

Database X

Database Y

STEP 1

STEP 2


Staging tables2

Staging Tables

Delete source; then dest Y

Xact: Update source site

Xact:update destination site

Update on destination

M3

M4

Yunprocessed

Yunprocessed

processed

unprocessed

unprocessed

Yunprocessed

Yunprocessed

processed

unprocessed

unprocessed

Yunprocessed

Yunprocessed

Table I

Table S

Table I

Table S

Database X

Database Y

Database X

Database Y

STEP 3

STEP 4


What to look for

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?


Troubleshooting techniques

Troubleshooting Techniques(*)

  • Extraction and Analysis of Performance Indicators

    • Consumer-producer chain framework

    • Tools

      • Query plan monitors

      • Performance monitors

      • Event monitors

        (*) From Alberto Lerner’s chapter


A consumer producer chain of a dbms s resources

High LevelConsumers

IntermediateResources/Consumers

probingspots

for

indicators

PrimaryResources

A Consumer-Producer Chain of a DBMS’s Resources

sql

commands

PARSEROPTIMIZER

EXECUTIONSUBSYSTEM

DISKSYBSYSTEM

LOCKINGSUBSYSTEM

CACHEMANAGER

LOGGINGSUBSYSTEM

MEMORY

CPU

DISK/CONTROLLER

NETWORK


Recurrent patterns of problems

Recurrent Patterns of Problems

Effects are not always felt first where the cause is!

  • An overloading high-level consumer

  • A poorly parameterized subsystem

  • An overloaded primary resource


A systematic approach to monitoring

A Systematic Approach to Monitoring

Extract indicators to answer the following questions

  • 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?


Investigating high level consumers

Investigating High Level Consumers

  • Answer question 1: “Are critical queries being served in the most efficient manner?”

    • Identify the critical queries

    • Analyze their access plans

    • Profile their execution


Identifying critical queries

Identifying Critical Queries

Critical queries are usually those that:

  • Take a long time

  • Are frequently executed

    Often, a user complaint will tip us off.


Event monitors to identify critical queries

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.


An example event monitor

An example Event Monitor

  • CPU indicatorssorted by Oracle’sTrace Data Viewer

  • Similar tools: DB2’s Event Monitor and MSSQL’s Server Profiler


An example plan explainer

An example Plan Explainer

  • Access plan according to MSSQL’s Query Analyzer

  • Similar tools: DB2’s Visual Explain and Oracle’s SQL AnalyzeTool


Finding strangeness in access plans

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


To index or not to index

To Index or not to index?

select c_name, n_name from CUSTOMER join NATIONon 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)


Non clustering indexes can be trouble

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 Scan

Index Scan

76 sec272,618 pages131,425 pages273,173 pages552 pages

CPU timedata logical readsdata physical readsindex logical readsindex physical reads

5 sec143,075 pages6,777 pages136,319 pages7 pages


An example performance monitor query level

An example Performance Monitor (query level)

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: 136308

Number of rows sent to output is: 0

Elapsed Time is: 76.349 seconds

Buffer pool data logical reads = 272618

Buffer pool data physical reads = 131425

Buffer pool data writes = 0

Buffer pool index logical reads = 273173

Buffer pool index physical reads = 552

Buffer pool index writes = 0

Total buffer pool read time (ms) = 71352

Total buffer pool write time (ms) = 0

Summary of Results

==================

Elapsed Agent CPU Rows Rows

Statement # Time (s) Time (s) Fetched Printed

1 76.349 6.670 136308 0

  • Buffer and CPU consumption for a query according to DB2’s Benchmark tool

  • Similar tools: MSSQL’s SET STATISTICS switch and Oracle’s SQL Analyze Tool


An example performance monitor system level

An example Performance Monitor (system level)

  • An IO indicator’s consumption evolution (qualitative and quantitative) according to DB2’s System Monitor

  • Similar tools: Window’s Performance Monitor and Oracle’s Performance Manager


Investigating high level consumers summary

Investigating High Level Consumers: Summary

Find criticalqueries

Investigatelower levels

Found any?

Answer Q1over them

no

yes

Overcon-sumption?

Tune problematicqueries

no

yes


Investigating primary resources

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


Cpu consumption indicators at the os level

CPU Consumption Indicators at the OS Level

Sustained utilizationover 70% should trigger the alert.

System utilization shouldn’t be more than 40%.DBMS (on a non-dedicated machine)should be getting a decent time share.

100%

CPU% ofutilization

total usage

70%

system usage

time


Disk performance indicators at the os level

Average Queue Size

Disk Performance Indicators at the OS Level

Should beclose to zero

New requests

Wait queue

Disk Transfers/second

Wait timesshould also be close to zero

Idle disk with pending requests?Check controller contention.Also, transfersshould be balanced amongdisks/controllers


Memory consumption indicators at the os level

Memory Consumption Indicators at the OS Level

Page faults/timeshould be closeto zero. If paginghappens, at least not DB cache pages.

virtualmemory

real memory

% of pagefile inuse will tellyou how much memory is “needed.”

pagefile


Investigating intermediate resources consumers

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


Cache manager performance indicators

Cache Manager Performance Indicators

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)

Tablescan

readpage()

CacheManager

Pickvictimstrategy

Data Pages

Pages are regularly saved to disk to make free space.# of free slots should always be > 0

Free Page slots

Page reads/writes


Disk manager performance indicators

Disk Manager Performance Indicators

Displaced rows (moved to other pages) should be kept under 5% of rows

rows

Free space fragmentation: pages with little space should not be in the free list

page

Storage Hierarchy (simplified)

extent

Data fragmentation: ideally files that store DB objects (table, index) should be in one or few (<5) contiguous extents

file

File position: should balance workload evenly among all disks

disk


Lock manager performance indicators

Object

Lock Type

TXN ID

Lock Manager Performance Indicators

Lock wait time for a transaction should be a small fraction of the whole transaction time.Number of lock waitsshould be a small fraction of the number of locks on the lock list.

Lock request

Lockspending list

LockList

Deadlocks and timeouts should seldom happen (no more then 1% of the transactions)


Investigating intermediate and primary resources summary

Investigating Intermediate and Primary Resources: Summary

Answer Q3

Problems at

OS level?

Answer Q2

Tune low-levelresources

no

yes

Problematic

subsystems?

Tune subsystems

Investigateupper level

yes

no


Troubleshooting techniques1

Troubleshooting Techniques

  • Monitoring a DBMS’s 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


Recall tuning principles

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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