1 jim gray & gordon bell: vldb 95 parallel database systems survey parallel database systems 101...
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1Jim Gray & Gordon Bell: VLDB 95 Parallel Database Systems Survey
Parallel Database Systems 101
Jim Gray & Gordon BellMicrosoft Corporation
presented at VLDB 95, Zurich Switzerland, Sept 1995
• Detailed notes available from [email protected] – this presentation is 120 of the 174 slides (time limit)
– Notes in PowerPoint7 and Word7
2Jim Gray & Gordon Bell: VLDB 95 Parallel Database Systems Survey
Outline
• Why Parallelism: –technology push
–application pull• Benchmark Buyer’s Guide
– metrics
– simple tests
• Parallel Database Techniques– partitioned data
– partitioned and pipelined execution
– parallel relational operators
• Parallel Database Systems– Teradata. Tandem, Oracle, Informix, Sybase, DB2, ‘RedBrick
3Jim Gray & Gordon Bell: VLDB 95 Parallel Database Systems Survey
Kinds Of Information Processing
Point-to-Point Broadcast
Immediate
TimeShifted
conversationmoney
lectureconcert
mail booknewspaper
NetNetworkwork
DataDataBaseBase
Its ALL going electronicImmediate is being stored for analysis (so ALL database)Analysis & Automatic Processing are being added
4Jim Gray & Gordon Bell: VLDB 95 Parallel Database Systems Survey
Why Put Everything in Cyberspace?
Low rentmin $/byte
Shrinks timenow or later
Shrinks spacehere or there
Automate processingknowbots
Point-to-Point OR Broadcast
Imm
edia
te O
R T
ime
Del
ayed
NetworkNetwork
DataDataBaseBase
LocateLocateProcessProcessAnalyzeAnalyzeSummarizeSummarize
5Jim Gray & Gordon Bell: VLDB 95 Parallel Database Systems Survey
Databases: Information At Your Fingertips™
Information Network™Knowledge Navigator™
• All information will be in an online database (somewhere)• You might record everything you
• read: 10MB/day, 400 GB/lifetime (two tapes)• hear: 400MB/day, 16 TB/lifetime (a tape per decade)• see: 1MB/s, 40GB/day, 1.6 PB/lifetime (maybe someday)
• Data storage, organization, and analysis is a challenge.• That is what databases are about• DBs do a good job on “records”• Now working on text, spatial, image, and sound.
6Jim Gray & Gordon Bell: VLDB 95 Parallel Database Systems Survey
Database Store ALL Data Types
• The New World:•Billions of objects•Big objects (1MB)•Objects have behavior
(methods)
• The Old World:
– Millions of objects
– 100-byte objects
Mike
Won
David NY
Berk
Austin
People
Name Address
Mike
Won
David NY
Berk
Austin Paperless officeLibrary of congress onlineAll information online entertainment publishing businessInformation Network, Knowledge Navigator, Information at your fingertips
Name Address Papers Picture Voice
People
7Jim Gray & Gordon Bell: VLDB 95 Parallel Database Systems Survey
Magnetic Storage Cheaper than Paper
• File Cabinet: cabinet (4 drawer) 250$paper (24,000 sheets) 250$space (2x3 @ 10$/ft2) 180$total 700$
3 ¢/sheet
•Disk: disk (8 GB =) 2,000$ASCII: 4 m pages
0.05 ¢/sheet (60x cheaper)• Image: 200 k pages
1 ¢/sheet (3x cheaper than paper)• Store everything on disk
10Jim Gray & Gordon Bell: VLDB 95 Parallel Database Systems Survey
Moore’s Law
128KB
128MB
20008KB
1MB
8MB
1GB
1970 1980 1990
1M 16Mbits: 1K 4K 16K 64K 256K 4M 64M 256M
1 chip memory size ( 2 MB to 32 MB)
•XXX doubles every 18 months 60% increase per year–Micro Processor speeds–chip density–Magnetic disk density–Communications bandwidthWAN bandwidth approaching LANs
•Exponential Growth:
–The past does not matter
–10x here, 10x there, soon you're talking REAL change.
•PC costs decline faster than any other platform
–Volume & learning curves
–PCs will be the building bricks of all future systems
14Jim Gray & Gordon Bell: VLDB 95 Parallel Database Systems Survey
In The Limit: The Pico Processor
1 M SPECmarks, 1TFLOP
106 clocks to bulk ram
Event-horizon on chip.
VM reincarnated
Multi-program cacheOn-Chip SMP
Terror Bytes!
10 microsecond ram
10 millisecond disc
10 second tape archive
10 nano-second ram
Pico Processor
10 pico-second ram
1 MM 3
100 TB
1 TB
10 GB
1 MB
100 MB
23
Jim Gray & Gordon Bell: VLDB 95 Parallel Database Systems Survey
What's a Terabyte? (250 K$ of Disk @ .25$/MB)
1 Terabyte 1,000,000,000 business letters 100,000,000 book pages 50,000,000 FAX images 10,000,000 TV pictures (mpeg) 4,000 LandSat images
Library of Congress (in ASCII) is 25 TB 1980: 200 M$ of disc 10,000 discs 5 M$ of tape silo 10,000 tapes
1995: 250 K$ of magnetic disc 70 discs 500 K$ of optical disc robot 250 platters 50 K$ of tape silo 50 tapes
Terror Byte !!
150 miles of bookshelf 15 miles of bookshelf 7 miles of bookshelf 10 days of video
30Jim Gray & Gordon Bell: VLDB 95 Parallel Database Systems Survey
Summary (of storage)• Capacity and cost are improving fast (100x per decade)
• Accesses are getting larger (MOX, GOX, SCANS)
• BUT Latencies and bandwidth are not improving much• (3x per decade)
• How to deal with this???
• Bandwidth:
– Use partitioned parallel access (disk & tape farms)
• Latency
– Pipeline data up storage hierarchy (next section)
31Jim Gray & Gordon Bell: VLDB 95 Parallel Database Systems Survey
Interesting Storage Ratios
• Disk is back to 100x cheaper than RAM
• Nearline tape is only 10x cheaper than disk
– and the gap is closing!
100:1
10:1
1:1
1960 1970 1980 1990 2000
RAM $/MBDisk $/MB
30:1
?Disk $/MBNearline Tape
??? Why bother with Tape
Disk & DRAM look good
32Jim Gray & Gordon Bell: VLDB 95 Parallel Database Systems Survey
Performance =Storage Accesses
not Instructions Executed• In the “old days” we counted instructions and IO’s• Now we count memory references• Processors wait most of the time
Where the time goes: clock ticks used by AlphaSort Components
SortDisc Wait SortDisc Wait OS
Memory Wait
D-Cache Miss
I-Cache MissB-Cache
Data Miss
70 MIPS“real” apps have worse Icache misses so run at 60 MIPSif well tuned, 20 MIPS if not
33Jim Gray & Gordon Bell: VLDB 95 Parallel Database Systems Survey
Storage Latency: How Far Away is the Data?
RegistersOn Chip CacheOn Board Cache
Memory
Disk
12
10
100
Tape /Optical Robot
10 9
106
Sacramento
This CampusThis Room
My Head
10 min
1.5 hr
2 Years
1 min
Pluto
2,000 YearsAndromdeda
Clo
ck T
icks
34Jim Gray & Gordon Bell: VLDB 95 Parallel Database Systems Survey
Network Speeds
• Network speeds grow 60% / year• WAN speeds limited by politics
• if voice is X$/minute, how much is video?
• Switched 100Mb Ethernet•1,000x more bandwidth
• ATM is a scaleable net:•1 Gb/s to desktop & wall plug•commodity: same for LAN, WAN
• 1Tb/s fibers in laboratory
1e 9
1e 8
1e 7
1e 6
1e 5
1e 4
1e 3
1960 1970 1980 1990 2000
Processors (i/s)
Year
Comm Speedups
LANs &WANs (b/s)
35Jim Gray & Gordon Bell: VLDB 95 Parallel Database Systems Survey
Network Trends & Challenge• Bandwidth UP 104 Price DOWN
• Speed-of-light unchanged
• Software got worse
• Standard Fast Nets» ATM
» PCI
» Myrinet
» Tnet
• HOPE:
– Commodity Net
– Good software
• Then clusters become a SNAP!
• commodity: 10k$/slice
102
103
104
105
106
107
108
109
1010
POTS
WAN
LAN
CAN PC Bus
1 Mb/s
1 Gb/s
1 Kb/s
20001995198519751965
WAN Data Rates (fiber)
0.01
0.1
1
10
100
1000
1970 1980 1990 2000
Year
36Jim Gray & Gordon Bell: VLDB 95 Parallel Database Systems Survey
The Seven Price Tiers
• 10$: wrist watch computers• 100$: pocket/ palm computers• 1,000$: portable computers• 10,000$: personal computers (desktop)• 100,000$: departmental computers (closet)• 1,000,000$: site computers (glass house)• 10,000,000$: regional computers (glass castle)
SuperServer: Costs more than 100,000 $“Mainframe” Costs more than 1M$Must be an array of processors,
disks, tapescomm ports
38Jim Gray & Gordon Bell: VLDB 95 Parallel Database Systems Survey
The New Computer Industry
• Horizontal integrationis new structure
• Each layer picks best from lower layer.
• Desktop (C/S) market•1991: 50%•1995: 75%
Intel & SeagateSilicon & Oxide
SystemsBaseware
MiddlewareApplications SAP
OracleMicrosoftCompaq
Integration EDS
Operation AT&TFunction Example
40Jim Gray & Gordon Bell: VLDB 95 Parallel Database Systems Survey
Software Economics: Bill’s Law
•Bill Joy’s law (Sun): Don’t write software for less than 100,000 platforms.
@10M$ engineering expense, 1,000$ price
•Bill Gate’s law:Don’t write software for less than 1,000,000 platforms.
@10M$ engineering expense, 100$ price• Examples:
•UNIX vs NT: 3,500$ vs 500$•UNIX-Oracle vs SQL-Server: 100,000$ vs 1,000$•No Spreadsheet or Presentation pack on UNIX/VMS/...
• Commoditization of base Software & Hardware
44Jim Gray & Gordon Bell: VLDB 95 Parallel Database Systems Survey
ThesisMany Little will Win over Few Big
1 M$100 K$ 10 K$
Mainframe MiniMicro Nano
14"9"
5.25" 3.5" 2.5" 1.8"
45Jim Gray & Gordon Bell: VLDB 95 Parallel Database Systems Survey
Year 2000 4B Machine
• The Year 2000 commodity PC (3K$)
•Billion Instructions/Sec
•Billion Bytes RAM
•Billion Bits/s Net
• 10 B Bytes Disk
•Billion Pixel display• 3000 x 3000 x 24 pixel
10 B byte Disk
.1 B byte RAM
1 Bips Processor
1 B
bits
/sec
LA
N/W
AN
46Jim Gray & Gordon Bell: VLDB 95 Parallel Database Systems Survey
4 B PC’s: The Bricks of Cyberspace
• Cost 3,000 $• Come with
•OS (NT, POSIX,..)•DBMS•High speed Net•System management•GUI / OOUI •Tools
• Compatible with everyone else• CyberBricks
47Jim Gray & Gordon Bell: VLDB 95 Parallel Database Systems Survey
Implications of Hardware TrendsLarge Disc Farms will be inexpensive ( 100$/GB)
Large RAM databases will be inexpensive (1,000$/GB)
Processors will be inexpensive
So The building block will be a processor with large RAM lots of Disc
1k SPECintCPU
50 GB Disc
5 GB RAM
48Jim Gray & Gordon Bell: VLDB 95 Parallel Database Systems Survey
Implication of Hardware Trends: Clusters
Future Servers are CLUSTERSof processors, discs
Distributed Database techniquesmake clusters work
CPU
50 GB Disc
5 GB RAM
49
Hig
h S
pe
ed N
etw
ork
( 10
Gb
/s)
Jim Gray & Gordon Bell: VLDB 95 Parallel Database Systems Survey
Future SuperServer4T Machine
Array of 1,000 4B machinesprocessors, disks, tapescomm lines
A few MegaBucksChallenge:
ManageabilityProgrammabilitySecurityAvailabilityScaleabilityAffordability
As easy as a single system
1,000 discs = 10 Terrorbytes
100 Tape Transports= 1,000 tapes = 1 PetaByte
100 Nodes1 Tips
50Jim Gray & Gordon Bell: VLDB 95 Parallel Database Systems Survey
Great Debate: Shared What?
Shared Memory (SMP)
Shared Disk Shared Nothing (network)
CLIENTS CLIENTSCLIENTS
MemoryProcessors
Easy to programDifficult to buildDifficult to scaleup
Hard to programEasy to buildEasy to scaleup
Sequent, SGI, Sun VMScluster, Sysplex Tandem, Teradata, SP2
Winner will be a synthesis of these ideasDistributed shared memory (DASH, Encore) blurs distinction between Network and Bus (locality still important)
But gives Shared memory message cost.
51Jim Gray & Gordon Bell: VLDB 95 Parallel Database Systems Survey
Scaleables: Uneconomic So Far• A Slice is a processor, memory, and a few disks.
• Slice Price of Scaleables so far is 5x to 10x markup
– Teradata: 70K$ for a Intel 486 + 32MB + 4 disk.
– Tandem: 100k$ for a MipsCo R4000 + 64MB + 4 disk
– Intel: 75k$ for an I860 +32MB + 2 disk
– TMC: 75k$ for a SPARC 3 + 32MB + 2 disk.
– IBM/SP2: 100k$ for a R6000 + 64MB + 8 disk
• Compaq Slice Price is less than 10k$
• What is the problem?
– Proprietary interconnect
– Proprietary packaging
– Proprietary software (vendorIX)
52Jim Gray & Gordon Bell: VLDB 95 Parallel Database Systems Survey
Summary• Storage trends force pipeline & partition parallelism
– Lots of bytes & bandwidth per dollar
– Lots of latency
• Processor trends force pipeline & partition
– Lots of MIPS per dollar
– Lots of processors
• Putting it together Scaleable Networks and Platforms)
– Build clusters of commodity processors & storage
– Commodity interconnect is key (S of PMS)» Traditional interconnects give 100k$/slice.
– Commodity Cluster Operating System is key
– Fault isolation and tolerance is key
– Automatic Parallel Programming is key
53Jim Gray & Gordon Bell: VLDB 95 Parallel Database Systems Survey
The Hardware is in Place and Then A Miracle Occurs
SNAPSNAPScaleable Network And Platforms
Commodity Distributed OSCommodity Distributed OS built onCommodity PlatformsCommodity Network Interconnect
?
Enables Parallel Applications
56Jim Gray & Gordon Bell: VLDB 95 Parallel Database Systems Survey
Why Parallel Access To Data?
1 Terabyte
10 MB/s
At 10 MB/s1.2 days to scan
1 Terabyte
1,000 x parallel1.5 minute SCAN.
Parallelism: divide a big problem into many smaller ones to be solved in parallel.
Bandwidth
57Jim Gray & Gordon Bell: VLDB 95 Parallel Database Systems Survey
DataFlow ProgrammingPrefetch & Postwrite Hide Latency
• Can't wait for the data to arrive (2,000 years!)• Need a memory that gets the data in advance ( 100MB/S)
• Solution:•Pipeline from source (tape, disc, ram...) to cpu cache•Pipeline results to destination
Latency
58Jim Gray & Gordon Bell: VLDB 95 Parallel Database Systems Survey
Why are Relational OperatorsSo Successful for Parallelism?
Relational data model uniform operatorson uniform data streamClosed under composition
Each operator consumes 1 or 2 input streamsEach stream is a uniform collection of dataSequential data in and out: Pure dataflow
partitioning some operators (e.g. aggregates, non-equi-join, sort,..)
requires innovation
AUTOMATIC PARALLELISM
59Jim Gray & Gordon Bell: VLDB 95 Parallel Database Systems Survey
Database Systems “Hide” Parallelism
•Automate system management via tools•data placement•data organization (indexing)•periodic tasks (dump / recover / reorganize)
•Automatic fault tolerance•duplex & failover• transactions
•Automatic parallelism•among transactions (locking)•within a transaction (parallel execution)
60Jim Gray & Gordon Bell: VLDB 95 Parallel Database Systems Survey
Automatic Parallel OR DB
Select imagefrom landsatwhere date between 1970 and 1990and overlaps(location, :Rockies) and snow_cover(image) >.7;
Temporal
Spatial
Image
date loc image
Landsat
1/2/72.........4/8/95
33N120W.......34N120W
Assign one process per processor/disk:find images with right data & locationanalyze image, if 70% snow, return it
image
Answer
date, location, & image tests
61Jim Gray & Gordon Bell: VLDB 95 Parallel Database Systems Survey
Outline• Why Parallelism:
– technology push
– application pull
• Benchmark Buyer’s Guide
–metrics
–simple tests• Parallel Database Techniques
– partitioned data
– partitioned and pipelined execution
– parallel relational operators
• Parallel Database Systems– Teradata. Tandem, Oracle, Informix, Sybase, DB2, RedBrick
62Jim Gray & Gordon Bell: VLDB 95 Parallel Database Systems Survey
Parallelism: Speedup & Scaleup
100GB 100GBSpeedup: Same Job, More Hardware Less time
Scaleup: Bigger Job, More Hardware Same time
100GB 1 TB
100GB 1 TB
Server Server
1 k clients 10 k clientsTransactionScaleup: more clients/servers Same response time
63Jim Gray & Gordon Bell: VLDB 95 Parallel Database Systems Survey
The New Law of Computing
Grosch's Law:
Parallel Law: Needs
Linear Speedup and Linear ScaleupNot always possible
1 MIPS1 $
1,000 $1,000 MIPS
2x $ is 2x performance
1 MIPS1 $
1,000 MIPS 32 $.03$/MIPS
2x $ is 4x performance
64Jim Gray & Gordon Bell: VLDB 95 Parallel Database Systems Survey
Parallelism: Performance is the Goal
Goal is to get 'good' performance.
Law 1: parallel system should be faster than serial system
Law 2: parallel system should give near-linear scaleup or
near-linear speedup orboth.
65Jim Gray & Gordon Bell: VLDB 95 Parallel Database Systems Survey
The New Performance Metrics• Transaction Processing Performance Council:
– TPC-A: simple transaction
– TPC-B: server only, about 3x lighter than TPC-A– Both obsoleted by TPC-C (no new results after 6/7/95)
• TPC-C (revision 3) Transactions Per Minute tpm-C
– Mix of 5 transactions: query, update, minibatch
– Terminal price eliminated
– about 5x heavier than tpcA (so 3.5 ktpcA 20 ktpmC)
• TPC-D approved in March 1995 - Transactions Per Hour
– Scaleable database (30 GB, 100GB, 300GB,... ) – 17 complex SQL queries (no rewrites, no hints without permission)
– 2 load/purge queries
– No official results yet, many “customer” results.
66Jim Gray & Gordon Bell: VLDB 95 Parallel Database Systems Survey
TPC-C Results 12/94
22000200001800016000140001200010000800060004000200000
1000
2000
3000
Tandem
HP-H70
AS400
HP9000
RS6000
Sun
DG
HP-H70
HP T500-8
PERFORMANCE (TPMC)
CO
ST
($
/TP
MC
) AS400
RS6000
HP9000
Tandem Himalaya Server
16 cpus 32 cpus 64 cpus 112 cpus
HP 9000 E55, H70
SUN
HP T500
Courtesy of Charles Levine of Tandem (of course)
67Jim Gray & Gordon Bell: VLDB 95 Parallel Database Systems Survey
Success Stories
•Online Transaction Processing •many little jobs•SQL systems support 3700 tps-A
(24 cpu, 240 disk)
•SQL systems support 21,000 tpm-C (112 cpu,670 disks)
•Batch (decision support and Utility)• few big jobs, parallelism inside•Scan data at 100 MB/s•Linear Scaleup to 500 processors
tran
sact
ion
s /
sec
hardware
recs
/ se
c
hardware
68Jim Gray & Gordon Bell: VLDB 95 Parallel Database Systems Survey
The Perils of ParallelismO
ldTi
me
Ne
wTi
me
Spe
edup
=
Processors & Discs
The Good Speedup Curve
Linearity
Processors & Discs
A Bad Speedup Curve
Linearity
No Parallelism Benefit
Processors & Discs
A Bad Speedup Curve3-Factors
Sta
rtu
p
Inte
rfe
ren
ce
Ske
w
Startup: Creating processesOpening filesOptimization
Interference: Device (cpu, disc, bus)logical (lock, hotspot, server, log,...)
Skew: If tasks get very small, variance > service time
69Jim Gray & Gordon Bell: VLDB 95 Parallel Database Systems Survey
Benchmark Buyer's Guide
The Whole Story (for any system)
Th
rou
gh
pu
t
Processors & Discs
The Benchmark Report
Things to ask
When does it stop scaling?
Throughput numbers,Not ratios.
Standard benchmarks allowComparison to others
Comparison to sequential
Ratios and non-standard benchmarks are red flags.
70Jim Gray & Gordon Bell: VLDB 95 Parallel Database Systems Survey
AggCount
Performance 101: Scan RateDisk is 3MB/s to 10MB/s
Record is 100B to 200B (TPC-D 110...160, Wisconsin 204)So should be able to read 10kr/s to 100kr/s
Simple test: Time this on a 1M record tableSELECT count(*) FROM T WHERE x < :infinity;(table on one disk, turn off parallelism)
Typical problems:disk or controller is an antiqueno read-ahead in operating system or DBsmall page reads (2kb)data not clustered on disk big cpu overhead in record movement
Parallelism is not the cure for these problems
Scan
71Jim Gray & Gordon Bell: VLDB 95 Parallel Database Systems Survey
Parallel Scan Rate
AggCount
Scan
AggCount
Scan
AggCount
Scan
AggCount
Scan
AggSum
Simplest parallel test:Scaleup previous test:
4 disks, 4 controllers, 4 processors4 times as many records
partitioned 4 ways.Same query
Should have same elapsed time.
Some systems do.
72Jim Gray & Gordon Bell: VLDB 95 Parallel Database Systems Survey
Parallel Update Rate
UPDATELog
Test: UPDATE TSET x = x + :one;
Test for million row T on 1 disk
Test for four million row T on 4 disks
Look for bottlenecks.
After each call, execute ROLLBACK WORK
See if UNDO runs at the DO speed
See if UNDO is parallel (scales up)
74Jim Gray & Gordon Bell: VLDB 95 Parallel Database Systems Survey
The records/$/second Metric• parallel database systems scan data
• An interesting metric (100 byte record):
– Record Scan Rate / System Cost
• Typical scan rates: 1k records/s to 30k records/s
• Each Scaleable system has a “slice price” guess:– Gateway: 15k$ (P5 + ATM + 2 disks +NT + SQLserver or Informix or
Oracle)– Teradata: 75k$– Sequent: 75k$ (P5+2 disks+Dynix+Informix)– Tandem: 100k$– IBM SP2: 130k$ (RS6000+2 disks, AIX, DB2)
• You can compute slice price for systems later in presentation
• BAD: 0.1 records/s/$ (there is one of these)
• GOOD: 0.33 records/s/$ (there is one of these)
• Super! 1.00 records/s/$ (there is one of these)
• We should aim at 10 records/s/$ with P6.
75Jim Gray & Gordon Bell: VLDB 95 Parallel Database Systems Survey
Embarrassing Questions to Ask Your PDB Vendor
How are constraints checked?ask about unique secondary indicesask about deferred constraintsask about referential integrity
How does parallelism interact withtriggersStored proceduresOO extensions
How can I change my 10 TB database design in an hour?add index add constraintreorganize / repartition
These are hard problems.
76Jim Gray & Gordon Bell: VLDB 95 Parallel Database Systems Survey
Outline• Why Parallelism:
– technology push
– application pull
• Benchmark Buyer’s Guide– metrics
– simple tests
• Parallel Database Techniques–partitioned data
–partitioned and pipelined execution
–parallel relational operators• Parallel Database Systems
– Teradata. Tandem, Oracle, Informix, Sybase, DB2, RedBrick
77Jim Gray & Gordon Bell: VLDB 95 Parallel Database Systems Survey
Automatic Data Partitioning
Split a SQL table to subset of nodes & disks
Partition within set:Range Hash Round Robin
Shared disk and memory less sensitive to partitioning, Shared nothing benefits from "good" partitioning
A...E F...J K...N O...S T...Z A...E F...J K...N O...S T...Z A...E F...J K...N O...S T...Z
Good for equijoins, range queriesgroup-by
Good for equijoins Good to spread load
78Jim Gray & Gordon Bell: VLDB 95 Parallel Database Systems Survey
Index Partitioning
Hash indices partition by hash
B-tree indices partition as a forest of trees.One tree per range
Primary index clusters data
0...9 10..19 20..29 30..39 40..
A..C D..F G...M N...R S..Z
79Jim Gray & Gordon Bell: VLDB 95 Parallel Database Systems Survey
Secondary Index Partitioning
In shared nothing, secondary indices are Problematic
Partition by base table key rangesInsert: completely local (but what about unique?)Lookup: examines ALL trees (see figure)
Unique index involves lookup on insert.
Partition by secondary key rangesInsert: two nodes (base and index)Lookup: two nodes (index -> base)Uniqueness is easy
Teradata solution
A..C D..F G...M N...R S..
Base Table
A..Z
Base Table
A..Z A..Z A..Z A..Z
80Jim Gray & Gordon Bell: VLDB 95 Parallel Database Systems Survey
Kinds of Parallel Execution
Pipeline
Partition outputs split N ways inputs merge M ways
Any Sequential Program
Any Sequential Program
Any Sequential
Any Sequential Program Program
81Jim Gray & Gordon Bell: VLDB 95 Parallel Database Systems Survey
Data Rivers Split + Merge Streams
River
M ConsumersN producers
Producers add records to the river, Consumers consume records from the riverPurely sequential programming.River does flow control and buffering
does partition and merge of data records River = Split/Merge in Gamma = Exchange operator in Volcano.
N X M Data Streams
82Jim Gray & Gordon Bell: VLDB 95 Parallel Database Systems Survey
Partitioned Execution
A...E F...J K...N O...S T...Z
A Table
Count Count Count Count Count
Count
Spreads computation and IO among processors
Partitioned data gives NATURAL parallelism
83Jim Gray & Gordon Bell: VLDB 95 Parallel Database Systems Survey
N x M way Parallelism
A...E F...J K...N O...S T...Z
Merge
Join
Sort
Join
Sort
Join
Sort
Join
Sort
Join
Sort
Merge Merge
N inputs, M outputs, no bottlenecks.
Partitioned DataPartitioned and Pipelined Data Flows
84Jim Gray & Gordon Bell: VLDB 95 Parallel Database Systems Survey
Picking Data Ranges
Disk PartitioningFor range partitioning, sample load on disks.
Cool hot disks by making range smallerFor hash partitioning,
Cool hot disks by mapping some buckets to others
River PartitioningUse hashing and assume uniform If range partitioning, sample data and use
histogram to level the bulk
Teradata, Tandem, Oracle use these tricks
85Jim Gray & Gordon Bell: VLDB 95 Parallel Database Systems Survey
Blocking Operators = Short Pipelines
An operator is blocking, if it does not produce any output, until it has consumed all its input
Examples:Sort, Aggregates, Hash-Join (reads all of one operand)
Blocking operators kill pipeline parallelismMake partition parallelism all the more important.
Sort RunsScan
Sort Runs
Sort Runs
Sort Runs
Tape File SQL Table Process
Merge Runs
Merge Runs
Merge Runs
Merge Runs
Table Insert
Index Insert
Index Insert
Index Insert
SQL Table
Index 1
Index 2
Index 3
Database LoadTemplate hasthree blocked phases
86Jim Gray & Gordon Bell: VLDB 95 Parallel Database Systems Survey
Simple Aggregates (sort or hash?)
Simple aggregates (count, min, max, ...) can use indicesMore compactSometimes have aggregate info.
GROUP BY aggregatesscan in category order if possible (use indices)Else If categories fit in RAM use RAM category hash table
Elsemake temp of <category, item>sort by category,do math in merge step.
88Jim Gray & Gordon Bell: VLDB 95 Parallel Database Systems Survey
Sort
Used forloading and reorganization (sort makes them sequential)
build B-treesreports
non-equijoinsRarely used for aggregates or equi-joins (if hash available
SortRunsInput
DataSortedData
Merge
89Jim Gray & Gordon Bell: VLDB 95 Parallel Database Systems Survey
Sub-sortsgenerateruns
Mergeruns
Range or Hash Partition River
River is range or hash partitioned
Scan or other source
Parallel Sort
M input N output Sort design
Disk and mergenot needed if sort fits in memory
Scales linearly because6
12= => 2x slowerlog(10 ) 6
log(10 ) 12
Sort is benchmark from hell for shared nothing machinesnet traffic = disk bandwidth, no data filtering at the source
90Jim Gray & Gordon Bell: VLDB 95 Parallel Database Systems Survey
SIGMOD Sort AwardDatamation Sort: 1M records (100 B recs)
1000 seconds 1986
60 seconds 1990
7 seconds 1994
3.5 seconds 1995 (SGI challenge)
micros finally beat the mainframe!
finally! a UNIX system that does IO
SIGMOD MinuteSort1.1GB, Nyberg, 1994
Alpha 3cpu
1.6GB, Nyberg, 1995 SGI Challenge (12 cpu)
no SIGMOD PennySort record Threads (Sprocs) devoted to sorting
Ela
ps
ed
Tim
e (
se
co
nd
s)
0
50
100
150
200
250
1 2 4 6 10
write done
lists merged
lists-sorted
read-done
pin
Sort Time on an SGI Challenge
1.6 GB (16 M 100-byte records)12 cpu, 2.2 GB, 96 disk
1.0E+02
1.0E+03
1.0E+04
1.0E+05
1.0E+06
1985 1990 1995
Sort Records/second vs Time
M68000
Cray YMP
IBM 3090
Tandem
Hardware Sorter
Sequent
Alpha
Intel
HyperCube
SGI
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Nested Loops Join
OuterTable
InnerTable
If inner table indexed on join cols (b-tree or hash)then sequential scan outer (from start key)For each outer record
probe inner table for matching recs
Works best if inner is in RAM (=> small inner
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Merge Join (and sort-merge join)
LeftTable
RightTable
NxM caseCartesian product
Partitions well: partition smaller to larger partition.
Works for all joins (outer, non-equijoins, Cartesian, exclusion,...)
If tables sorted on join cols (b-tree or hash)then sequential scan each (from start key)left < right left=right left > rightadvance left match advance right
Nice sequential scan of data (disk speed)(MxN case may cause backwards rescan)
Sort-merge join sorts before doing the merge
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Hash Join
Hash smaller table into N buckets (hope N=1)
If N=1 read larger table, hash to smallerElse, hash outer to disk then
bucket-by-bucket hash join.
Purely sequential data behavior
Always beats sort-merge and nestedunless data is clustered.
Good for equi, outer, exclusion joinLots of papers,
products just appearing (what went wrong?)
Hash reduces skew
Right Table
LeftTable
HashBuckets
95Jim Gray & Gordon Bell: VLDB 95 Parallel Database Systems Survey
Parallel Hash JoinICL implemented hash join with bitmaps in CAFS machine
(1976)!
Kitsuregawa pointed out the parallelism benefits of hashjoin in early 1980’s (it partitions beautifully)
We ignored them! (why?) But now, Everybody's doing it.(or promises to do it).
Hashing minimizes skew, requires little thinking for redistribution
Hashing uses massive main memory
98Jim Gray & Gordon Bell: VLDB 95 Parallel Database Systems Survey
ObservationsIt is easy to build a fast parallel execution environment
(no one has done it, but it is just programming)
It is hard to write a robust and world-class query optimizer.There are many tricksOne quickly hits the complexity barrier
Common approach:Pick best sequential planPick degree of parallelism based on bottleneck analysis
Bind operators to process
99Jim Gray & Gordon Bell: VLDB 95 Parallel Database Systems Survey
What’s Wrong With That?Why isn’t the best serial plan, the best parallel plan?
Counter example:Table partitioned with local secondary index at two nodesRange query selects all of node 1 and 1% of node 2.Node 1 should do a scan of its partition.Node 2 should use secondary index.
SELECT * FROM telephone_book WHERE name < “NoGood”;
Sybase Navigator & DB2 PE should get this right.
We need theorems here (practitioners do not have them)
N..Z
TableScan
A..M
Index Scan
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What Systems Work This Way
Shared NothingTeradata: 400 nodesTandem: 110 nodesIBM / SP2 / DB2: 128 nodesInformix/SP2 48 nodesATT & Sybase 8x14 nodes
Shared DiskOracle 170 nodesRdb 24 nodes
Shared MemoryInformix 9 nodes RedBrick ? nodes
CLIENTS
MemoryProcessors
CLIENTS
CLIENTS
102Jim Gray & Gordon Bell: VLDB 95 Parallel Database Systems Survey
Outline• Why Parallelism:
– technology push
– application pull
• Benchmark Buyer’s Guide– metrics
– simple tests
• Parallel Database Techniques– partitioned data
– partitioned and pipelined execution
– parallel relational operators
• Parallel Database Systems–Teradata - Oracle -DB2
–Tandem - Informix -RedBrick- Sybase
103Jim Gray & Gordon Bell: VLDB 95 Parallel Database Systems Survey
System Survey Ground Rules
Premise: The world does not need yet another PDB survey
It would be nice to have a survey of “real” systems
Visited each parallel DB vendor I could (time limited)
Asked not to be given confidential info.
Asked for public manuals and benchmarks
Asked that my notes be reviewed
I say only nice things (I am a PDB booster)
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AcknowledgmentsTeradata
Todd Walter and Carrie BallingerTandem
Susanne Englert, Don Slutz, HansJorge Zeller, Mike PongOracle
Gary Hallmark, Bill WiddingtonInformix
Gary Kelley, Hannes Spintzik, Frank Symonds, Dave ClayNavigator
Rick Stellwagen, Brian Hart, Ilya Listvinsky, Bill Huffman , Bob McDonald, Jan Graveson Ron Chung Hu, Stuart Thompto
DB2 Chaitan Baru, Gilles Fecteau, James Hamilton, Hamid Pirahesh
RedbrickPhil Fernandez, Donovan Schneider
105Jim Gray & Gordon Bell: VLDB 95 Parallel Database Systems Survey
Teradata • Ship 1984, now an ATT GIS brand name
• Parallel DB server for decision support SQL in, tables out
• Support Heterogeneous data (convert to client format)
Data hash partitioned among AMPswith fallback (mirror) hash.
Applications run on clients
Biggest installation: 476 nodes, 2.4 TB
Ported to UNIX base
Application Processor
AMP
IBM
PC
MAC
UNIX
VMS
AS400
Mac
PEP
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Application Processor
AMP
IBM
PC
MAC
UNIX
VMS
AS400
Mac
PEP
Parsing EnginesInterface to IBM or Ethernet or...Accept SQL, return records and status.Support SQL 89, moving to SQL92
Parse, Plan & authorize SQL cost based optimizerIssue requests to AMPsMerge AMP results to requester.Some global load control based on client priority
(adaptive and GREAT!)
Access ModulesAlmost all work done in AMPsA shared nothing SQL engine
scans, inserts, joins, log, lock,....Manages up to 4 disks (as one logical volume)Easy design, manage, grow (just add disk)
107Jim Gray & Gordon Bell: VLDB 95 Parallel Database Systems Survey
Data Layout: Hash PartitioningAll data declustered to all nodesEach table has a hash key (may be compound)Key maps to one of 4,000 bucketsBuckets map to one of the AMPsNon-Unique secondary index partitioned by table criterionFallback bucket maps to second AMP in cluster.
Typical cluster is 6 nodes (2 is mirroring).Cluster limits failure scope:
2 failures only cause data outage if both in same cluster.
Within a node, each hash to cylinder then hash to “page”
Page is a heap with a sorted directory
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Teradata Optimization & Execution
Sophisticated query optimizer(many tricks) Great emphasis on Joins & Aggregates.
Nested, merge, product, bitmap join (no hash join)
Automatic load balancing from hashing & load control
Excellent utilities for data loading, reorganize
Move > 1TB database from old to new in 6 days, in background while old system running
Old hardware, 3.8B row table (1TB), >300 AMPstypical scan, sort, join averages 30 minutes
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Query ExecutionProtocol
PE requests workAMP responds OK (or pushback)AMP works (if all OK)AMP declares finishedWhen all finished, PE does 2PC and starts pull
Simple scan: PE broadcasts scan to each AMPEach AMP scans produces answer spool filePE pulls spool file from AMPs via Ynet
If scan were ordered, sort “catcher” would be forkedat each AMP pipelined to scansYnet and PE would do merge of merges from AMPs
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Aggregates, Updates
Aggregate of Scan:Scan’s produce local sub-aggregatesHash sub-aggregates to YnetEach AMP “catches” its sub-aggregate hash bucketsConsolidate sub-aggregates.PE pulls aggregates from AMPs via Ynet.Note: fully scaleable design
Insert / Update / Delete at a AMP nodegenerates insert / update /delete messages to
unique-secondary indicesfallback bucket of base table.messages saved in spool if node is down
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Query Execution: Joins
Great emphasis on Joins.Includes small-table large-table optimization
cheapest triple, then cheapest in triple.
If equi-partitioned, do locallyIf not equi-partitioned,
May replicate small table to large partition (Ynet shines) May repartition one if other is already partitioned on joinMay repartition both (in parallel)
Join algorithm within node is ProductNestedSort-mergeHash bit map of secondary indices, intersected.
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Utilities
Bulk Data Load, Fast Data Load, Multi-load, Blast 32KB of data to an AMPMultiple sessions by multiple clients can drive 200x parallelDouble bufferAMP unpacks, and puts “upsert”onto YnetOne record can generate multiple upserts
(transaction-> inventory, store-sales, ...)Catcher on Ynet, grabs relevant “upserts” to temp file.Sorts and then batches inserts (survives restarts).Online and restartable.Customers cite this as Teradata strength.
Fast Export (similar to bulk data load)
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Utilities II
Backup / Restore: Rarely needed because of fallback.Cluster is unit of recoveryBackup is online, Restore is offline
Reorganize:Rarely needed, add disk is just restartAdd node:
rehash all buckets that go to that node:(Ynet has old and new bucket map)
Fully parallel and fault tolerant, takes minutes
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Port To UNIXNew design (3700 series) described in VLDB 93
Ported to UNIX platforms (3600 AP, PE, AMP)
Moved Teradata to Software Ynet on SMPs
Based on Bullet-Proof UNIX with TOS layer atop.message system
communications stacks
raw disk & virtual processors
virtual partitions (buckets go to virtual partitions)
removes many TOS limits
Result is 10x to 60x faster
than an AMP
Compiled expression evaluation(gives 50x speedup on scans)
Large main memory helps
UNIX 5.4 (SMP, RAS, virtual Ynet)UNIX PDE: TOS adapterTeradata SQL (AMP logic)Parsing engine (parallelism)
ApplicationsSQL
HARDWARE
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Customer BenchmarksStandard Benchmarks
Only old Boral/DeWitt Wisconsin numbers.Nothing public.
Moving > 1TB database from one old to new in 6 days, in background while old system runningSo: unload-load rate > 2MB/s sustained
Background task (speed limited by host speed/space)
Old hardware, 3.8B row table, >300 AMPstypical scan, sort, join averages 30 minutes
rates (rec size not cited): krec/s/AMP k rec/s
scan: 9 2.7 mr/s !!!!!! clustered join: 2 600 kr/s insert-select: .39 120 kr/s Hash index build: 3.3 100 kr/s
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UNIX/SMP Port of Teradata
op rows seconds k r/s MB/s
scan 50000000 737 67.8 11.0
copy 5000000 1136 4.4 0.7
aggregate 50000000 788 63.5 10.3
Join 50x2M (clustered) 52000000 768 67.7 11.0
Join 5x5 (unclustered) 10000000 237 42.2 6.8
Join 50Mx.1K 50000100 1916 26.1 4.2
Times to process a Teradata Test DB on a 8 Pentium, 3650. These numbers are 10 to 150x better than a single AMP Compiled expression handling
more memory
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Teradata Good Things
Scaleable to large (multi-terabyte) databases
Available TODAY!
It is VERY real: in production in many large sites
Robust and complete set of utilities
Automatic management.
Integrates with the IBM mainframe OLTP world
Heterogeneous data support is good data warehouse
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TandemMessage-based OS (Guardian): (1) location transparency(2) fault isolation (failover to other nodes).
Expand software 255 Systems WAN
Classic shared-nothing system (like Teradata except applicationsrun inside DB machine.
4 node System
8 x1M B/S
30MB/S
1-16 MIPS R4400 cpusdual port controllers,dual 30MB/s LAN
224PROCESSORS
1974-1985: Encompass: Fault-tolerant Distributed OLTP1986: NonStopSQL: First distributed and high-performance SQL (200 tps)
1989: Parallel NonStopSQL: Parallel query optimizer/executor1994: Parallel and Online SQL (utilities, DDL, recovery, ....)1995: Moving to ServerNet: shared disk model
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Tandem Data LayoutEach table or index range partitioned to a set of disks
(anywhere in network)
Index is B-tree per partitionclustering index is B+ tree
Table fragments are files (extent based).
Descriptors for all local files live in local catalog (node autonomy)
Tables can be distributed in network (lan or wan)
Duplexed disks and disk processes for failover
PartitionBlock
Extents may be added
File= {parts}
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Tandem Software (Process Structure)
Disk Server Pair
Data partition
C/COBOL/..Application
SQLSQL engineJoins, Sortsglobal aggstriggersindex maintenanceviewssecurity
Query Compiler
Utilities
TransactionsHelperProcesses
GUI
SelectsUpdate, DeleteRecord/Set insertAggregatesAssertionsLocking Logging
bufferpool
Disk Pairor Array
Hardware & OS move data at 4MB/s with >1 ins/byte
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OLTP FeaturesInsert / Update / Delete index in parallel with base table
If 5 indices, 5x faster response time.
Record and key-value range locking, SQL92 isolation levels
Undo scanner per log: double-buffers undo to each server
21 k tpc-C (WOW!!) with 110 node server (800GB db)
Can mix OLTP and batch.Priority serving to avoid priority inversion problem
Buffer management prevents sequential buffer pollution
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Tandem Query Plan & Execution
SQL subsystem
Application
Executors
DiskServers
Simple selects & aggregates done in disk servers
Parallelism chosen: scan: table fragmentation hash: # processors or Outer table fragments
Sorts: redistribution, sort in executors (N-M)Joins done in executors (nest, sort-merge, hash).
Redistribution is always a hash (minimize skew)Pipeline as deep as possible (use lots of processes)
Multiple logs & parallel UNDO avoid bottlenecks
Can mix OLTP and batch.Priority serving to avoid priority inversion problem
Buffer management prevents sequential buffer pollution
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Parallel OperatorsInitially just inserted rivers between sequential operatorsParallel query optimizerCreated executors at all clustering nodes or
at all nodes, repartitioned via hash to themGave parallel select, insert, update, delete
join, sort, aggregates,...correlated subqueries are blocking
Got linear speedup/scaleup on Wisconsin.Marketing never noticed, product slept from 1989-1993
Developers added: Hash Joinaggregates in disk processSQL92 featuresparallel utilitiesonline everythingconverted to MIPScofixed bugs
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Join StrategiesNested loopSort mergeBoth can work off index-only accessReplicate small to all partitions (when one small)Small-table Cartesian product large-table optimizationNow hybrid-hash join
uses many small bucketstuned to memory demand tuned to sequential disk performanceno bitmaps because (1) parallel hash
(2) equijoins usually do not benefit
When both large, and unclustered (rare case)N+M scanners, 16 catchers: sortmerge or hybrid hash
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Administration (Parallel & Online everything)All utilities are online (claim to reduce outages by 40%):
Add table, column,...Add index:
builds index from stale copyuses log for catchupin final minute, gets lock, completes index.
Reorg B-tree while it is accessedAdd / split/ merge/ reorg partitionBackupRecover page, partition, file.Add, alter logs, disks, processors, ...
You need this: Terabyte operations take a long time!
Parallel Utilities:load (M to N)index build (M scanners, N inserters, in background)recovery:
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BenchmarksNo official DSS benchmark reports
Unofficial results1 to 16 R4400 class processors, 64MB each (Himalayas)
3 disks, 3 ctlrs each
Sequential 16x Parallel rec/s MB/s rec/s MB/s speedup
Load Wisc 1.6 kr/s 321 Kb/s 28 kr/s 5.4 MB/s 16Parallel Index build 1.5 kr/s 15Kb/s 24 kr/s 240 KB/s 16SCAN 28 kr/s5.8 MB/s 470 kr/s 94 MB/s 16 !!!!!!!Aggregate (1 col) 25 kr/s 4.9 MB/s 400 kr/s 58 MB/s 16Aggregate (6 col) 18 kr/s 3.6 MB/s 300 kr/s 60 MB/s 162-Way hash Join 13 kr/s 2.6 MB/s 214 kr/s 42 MB/s 163-Way hash Join ? kr/s ? Mb/s ? kr/s ? MB/s ?
1x and 16x rates are best I’ve seen anywhere.
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Tandem Good Things21 K TPM-C (WOW!)
It is available TODAY!
Online everything
Fault tolerant, distributed, high availability
Mix OLTP and batch
Great Hash Join Algorithm
Probably the best peak performance available
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OracleParallel Server (V7): Multiple threads in a server
Multiple servers in a cluster Client/server, OLTP & clusters (TP lite)
Parallel Query (V7.1) Parallel SELECT (and sub-selects)
Parallel Recovery: (V7.1) @ restart, one log scanner, multiple redoers
Beta in 1993, Ship 6/94.More Parallel (create table): V7.2, 6/95
Shared disk implementation ported to most platforms
Parallel SELECT (no parallel INSERT, UPDATE, DELETE, DDL) except for sub-selects inside these verbs.
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Tabl
e or
Inde
x
SegmentBlock
Extents may be added
= File SetTable Space
Ext
ent
s
Oracle Data LayoutHomogenous:
one table (index) per segmentextents picked from a TableSpace
Files may be raw disk Segments are B-trees or heaps.
data -> disk map is automaticNo range / hash / round-robin partitioning
ROWID can be used as scan partitioning on base tables.
Guiding principal:If its not organized, it can’t get disorganized,
and doesn’t need to be reorganized.
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Oracle Parallel Query Product ConceptConvert serial SELECT plan to parallel plan
If Table scan or HINT then consider parallel planTable has default degree of parallelism (explicitly set)Overridden by system limits and hints.Use max degree of all participating tables.Intermediate results are hash partitionedNested Loop Join and Merge Join
User hints can (must?) specify join order, join strategy, index, degree of parallelism,...
DBMulti-process & thread Client Query
Coordinator
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Query PlanningQuery Coordinator starts with Oracle Cost-Based plan
If plan requests Table scan or HINT then consider parallel plan
Table has default degree of parallelism (explicitly set)Overridden by system limits and hints.Use max degree of all participating tables.
Shared disk makes temp space allocation easy
Planner picks degree of parallelism and river partitioning.
Proud of their OR optimization.
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Query ExecutionCoordinator does extra work to
merge the outputs of several sortssubsorts pushed to servers
aggregate the outputs of several aggregatesaggregates pushed to servers
Parallel function invocation is potentially a big win.
SELECT COUNT ( f(a,b,c,...)) FROM T;
Invokes function f on each element of T, 100x parallel.
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Join Strategies
Oracle has (1) Nested Loop Join (2) Merge Join
Replicate inner to outer partition automatic in shared disk (looks like partition outer).
Has small-table large-table optimization (Cartesian product join)
User hints can specify join order, join strategy, indexdegree of parallelism,...
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Transactions & RecoveryTransactions and transaction save points (linear nest).
ReadOnly snapshots for decision support.
SQL92 isolation levels (ACID = Snapshot isolation)
Database has multiple rollback segments UNDO log,
Transaction has one commit/REDO log so may be a bottleneck
Parallel recovery at restart:One log scanner,
DEGREE REDO streams, typically one per diskINSTANCE REDO streams, typically two-deep per disk
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Oracle Utilities User can write parallel load / unload utility
Index build, Constraints, are separate stepsNot incremental or online or restartable.
Update Statistics (Analyze) is not parallelIndex build is a N-1 parallel: N scanner/sorter, 1 inserter.Parallel recovery at restart:
One log scanner, DEGREE REDO streams, typically one per diskINSTANCE REDO streams, typically two-deep per disk
AdministrationNot much special:
Limit degree of parallelism at a serverSet default parallelism of a tableQuery can only lower these limits
No special tools, meters, monitors,... Just ordinary Parallel Server
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BenchmarksSequent 20x 50MHz 486, .5GB RAM, 20 disk
Sequential 20x Parallel rec/s krecr/s KB/s rec/s MB/s speedupLoad 5M Wisc .5 kr/s 113 KB/s 8.8 kr/s 1.8 MB/s 16Parallel Index load 2.2 kr/s 18 Kb/s 29 kr/s 235 KB/s 13SCAN 1.7 kr/s 364 KB/s 26 kr/s 5.3 MB/s 15Agg MJ 3.3 kr/s 660 KB/s 45 kr/s 9.3 MB/s 14Agg NJ 1.4 kr/s 290 KB/s 26 kr/s 5.4 MB/s 19
Same benchmark on 16x SP1 (a shared nothing machine), got similar results.168x N-cube ( 16MB/node), 4 lock nodes, 64 disk nodes got good scaleup
Oracle has published details on all these benchmarks.
20 Pentium, 40 disk system, SCAN at 44 MB/s 55% cpuSept 1994 news:
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Oracle Good ThingsAvailable now!
Parallel Everywhere (on everybody’s box)
A HIGH FUNCTION SQL
No restrictions (triggers, indices,...)
Very easy to use (almost no knobs or options)
Parallel invocation of stored procedures
Near-linear scaleup and speedup of SELECTs.
Respectable performance on Sequent
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InformixDSA (Dynamic Scaleable Architecture) describes redesign to
thread-based, server-based system. V6 - 1993 - : DSA -- rearchitecture (threads, OLTP focus)V7 - 1994 - : PDQ -- Parallel Data Query (SMP)V8 - 1995 - : XMP -- Cluster parallelism (shared disk/nothing).
Parallelism is a MAJOR focus now that SQL92 under control
Other major focus is TOOLS (ODBC, DRDA, NewEra 4GL).
Informix is a UNIX SQL system: AIX (IBM), HP/UX (HP), OSF/1 (DEC, HP), SCO/UNIX, Sequent/DYNIX, SUN (SunOS, Solaris)
Today shared nothing parallelism on IBM SP2, ATT3650, ICL, (beta)
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Informix Data Layout
DBspaceBlock
Chunks may be added
File
Table or index maps to homogeneous set of DB spaces contains “chunks” (extents)
Partition by: range, round robinexpressionhash (V8)
Access via B+Tree, B* tree, and hash (V8)
Built an extent-based file system on raw disks or files
High speed sequential, clustering, async IO,...
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Informix Execution
Completely parallel DML, some parallel DDLParallel SELECT, UPDATE, DELETE
Executor per partition in all cases.Parallel sort,
joins (nest, merge, hash)aggregates, union
Whenever an operator has input and a free output buffer, it can work
to fill the output buffer.Natural flow control
Blocking operators (sort, hash join, aggregates, correlated subqueries)Spool to a buffer (if small), else spool to disk.
Shared buffer pool minimizes data copies.
scan
M join
scan
scan
M join
Client
Buffer Pool
Virtual Processes
helpers
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Parallel PlansQuery plan is parallelized by
scanner per table partition (does select, project)sub-aggregates per partition (hash or sort)
If clustered join (nested loop or merge) then operator per outer or per partition
If hash-join, parallel scan smaller first, build bitmap and hash buckets
then scan larger and:join to smaller if it fits in memoryelse filter via bitmap and build larger buckets
then join bucket by bucketHybrid hash join with bitmaps and bucket tuning.
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Parallel Operators
Parallel SELECT, UPDATE, DELETEExecutor per partition in all cases.
Parallel sort, joins, aggregates, union
Only correlated subqueries are blocking
Completely parallel DML, some parallel DDL
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Transactions & Recovery
SQL 2 isolation levels allow DSS to run in background
Transaction save points
Separate logical and physical logs.Bulk updates could bottleneck on single log.
Recovery unit is data partition (DBspace)
Parallel recovery: thread per DBspace
If DB fragment
unavailable, DSS readers can skip it
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Informix AdministrationCan assign % of processors, memory, IO to DSS
(parallel query)
Sum of all parallel queries live within this quota
Each query can specify the % of the total that it wishes.(0 means sequential execution)
Parallel Data load (SMP only)Parallel Index Build (N - M)Parallel recoveryOnline backup / restore
Utilities
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Benchmarks
Sequent system: 9 Pentium processors1 GB main memoryBase tables on 16 disk (FWD SCSI)Indices on 10 discsTemp space on 10 disks
Sequential Parallel rec/s MB/s rec/s MB/s speedup
Load 300M Wisc 3kr/s 600Kb/sParallel Index load 48kr/s 1MB/sSCAN 17kr/s 3.5MB/s 147kr/s 30MB/s 8.3Aggregate 11kr/s 2.3MB/s 113kr/s 23MB/s 10.12-Way hash Join 18kr/s 3.2MB/s 242kr/s 31MB/s 9.73-Way hash Join 25kr/s 3.5Mb/s 239kr/s 33MB/s 9.5
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Informix Shared Nothing Benchmark IBM SP2 - : TPC-D-like database 48 SP2 ProcessorsCustomer Benchmark, Not audited benchmark.
Load 60 GB in 40 minutes,
250 GB in 140 min about 100 GB/hr ! 2GB/node/hr
Scan & Aggregate (#6) 60 GB in 7 min = 140 MB/s = 3 MB/s/node = 30 kr/s 260 GB in 24 min = 180 MB/s = 4 MB/s/node = 40 kr/s
Power Test (17 complex queries and 2 load/purge ops) 60 GB in 5 hrs 260 GB in 18 hrs
Multiuser Test: 1 user, 12 queries: 10 hrs, 4 users, 3 queries: 10 hrs
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Informix Good Things
A full function SQL
Available today on Sequent
Beautiful manuals
Linear speedup and scaleup
Best published performance on UNIX systemsProbably best price performance.
(but things are changing fast!)
Some mechanisms to mix OLTP and batch.
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Sybase Navigator Product conceptTwo layer software architecture: (1) Navigator
drives array of shared-nothing SQL engines. (2) Array of SQL engines, each unaware of others.
similar to Tandem disk processesSQL engine is COTS.
Goal: linear scaleup and speedup, plus good OLTP support
Emphasize WHOLE LIFECYCLEConfigurator: tools to design a parallel systemAdministrator: tools to manage a parallel system
(install/upgrade, start/stop, backup/restore, monitor/tune)
Optimizer: execute requests in parallel.
SQLSQL
SQL
SQLSQL
SQL
SQLSQL
SQL
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ConfiguratorFully graphical design tool
Given ER model and dataflow model of the application workload characteristicsresponse time requirements,hardware components(heavy into circles and arrows)
Recommends hardware configuration/ Table definitions (SQL)table partitioning
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AdministratorMade HUGE investments in this area.
Truly industry leadinggraphical tools make MPP configuration “doable”.
GUI interface to manage:startup / shutdown of clusterbackup / restore / manage logsconfigure (install, add nodes, configure and tune servers)Manage / consolidate system event logs System stored procedures (global operations)
(e.g. aggregate statistics from local to global cat)Monitor SQL Server events
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Data Layout
Pure shared nothingNavigator partitions data among SQL servers
• map to a subset of the servers • range partition or hash partition.
Secondary indices are partitioned with base table No Unique secondary indicesOnly shorthand views, no protection views Schema server stores global data definition for all nodes.Each partition server has
schema for its partitiondata for its partition.log for its partition
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Sybase SQL Server BackgrounderRecently became SQL89 compliant (cursors, nulls, etc)Stored procedures, multi-threaded, internationalized, B*-tree centric (clustering index is B+tree)Use nested loops, sort-merge join (sort is index build).Page locking, 2K disk IO, ... other little-endian design decisions.Respectable TPC-C results (AIX RS/6000).UNIX raw disks or files are base (also on OS/2, NetWare,...).table->disk mapping
CREATE DATABASE name ON {device...} LOG ON {device...}SP_ADDSEGMENT segment, deviceCREATE TABLE name(cols) [ ON segment]
Microsoft has a copy of the code, deep ported to NT
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Navigator Extension Mechanisms
Navigator extended Sybase TDS byAdding stored procedures to do thingsExtending the syntax (e.g. see data placement syntax below)
Sybase TDS and OpenServer design are great for thisAll “front ends based on OpenServer and threads”
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Process Structure - Pure Shared Nothing
Control(1/node)
Clients
SQLSplit
DBAserver
= catalogs database in a SQL server
= system manager monitor& SQL optimizer
GUINavigatorManager
schemaserver
DBA Server does everything: SQL compilationSystem managementCatalog managementSQL server restart (in 2nd node)DBA fallback detects deadlock does DBA takeover on fail
Control server at each node manages SQL servers there(security, request caching, 2PC, final merge /aggregate,...
parallel stored procedures (SMID) )Split server manages re-partitioning of dataSQL Server is unit of query parallelism, (one per cpu per node)
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Simple Request Processing
Control(1/node)
Client
SQLSplit
DBAserver
schemaserver
Client connects to Navigator (a Control Server) usingstandard Sybase TDS protocol.
SQL request flows to DBA server that compiles itsends stored procedures (plans) to all control servers
plans to all relevant SQL serversControl server executes plan.Pass to SQL server, returns results.
Plan cached on second call, DBA server not invoked.Good for OLTP
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Parallel Request Processing
Control
Split
Control
Client
SQLSplit
DBAserver
schemaserver
Control
Split
If query involves multiple nodes, then command sent to each one (diagram shows secondary index lookup)
Query sent to SQL servers that may have relevant data.
If data needs to be redistributed or aggregated, split servers issue queries and inserts
(that is their only role)
split servers have no persistent storage.
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Data ManipulationSQL server is unit of parallelism
"Parallelized EVERYTHING in the T-SQL language" Includes SIMD execution of T-SQL procedures, plus N-M data move operations.
Two-level optimization: DBA Server has optimizer
(BIG investment, all new code, NOT the infamous Sybase optimizer)
Each SQL server has Sybase optimizer If extreme skew, different servers have different plansDBA optimizer shares code with SQL server
(so they do not play chess with one another).Very proud of their optimizer.
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Query Execution
Classic Sellinger cost-based optimizer.SELECT, UPDATE, DELETE N-to-M parallelBulk and async INSERT interface.N-M Parallel sortAggregate (hash/sort)select and join can do index-only access if data is there.eliminate correlated subqueries (convert to join).
(Gansky&Wong. SIGMOD87 extended)Join: nested-loop, sort-merge, index only
Sybase often dynamically builds index tosupport nested loop (fake sort-merge)
Typically left-deep sequence of binary joins.
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Join and Partition Strategy
Partition strategiesIf already partitioned on join, then no splittingElse Move subset of T1 to T2 partitions.or Replicate T1 to all T2 partitionsor repartition both T1 and T2 to width of home nodes
or target.No hash join, but
all (re) partitioning is range or hash based.
Not aggressive parallelism/pipelining: 2 op at a time.Pipeline to disk via split server (not local to disk and then split).Split servers fake subtables for SQL engines.Top level aggregates merged by control, others done by split.
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Utilities
Bulk data load (N-M) async calls
GUI managesBackup all SQL serves in parallel
Reorg via CREATE TABLE <new> , INSERT INTO <new> SELECT * FROM <old>
Utilities are mostly offline (as per Sybase)
Nice EXPLAIN utility
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Futures
Hash join within split servers
Shared memory optimizations
Full support for unique secondary indices Full trigger support (cross-server triggers)
Full security and view support.
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BenchmarksPreliminary: 8x8 3600 - Ynet.
node: 8 x (50MHz 486 256k local cache) 512MB main memory, 2 x 10 disk arrays, @ 2GB 4 MB/s per disk.6 x Sybase servers
Scaleup & speedup tests of 1, 4, and 8 nodes.Numbers (except loading) reported as ratios of elapsed times
S&S tests show a >7x speedup of 8-way over 1-way
Tests cover insert, select, update, delete, join, aggregate, load
Reference Account: Chase Manahattan Bank14x8 P5 ATT 3600 cluster: (112 processors)56 SQL servers, 10GB each = 560 GB 100x faster than DB2/MVS (minutes vs days)
Linearity is great.
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Navigator Good ThingsConcern for lifecycle
design, install,manage, operate, use
Good optimization techniques
Fully parallel, including stored procedures!
Scaleup and Speedup are near linear.
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Sybase IQ
Sybase bought Expressway
Expressway evolved from Model 204
bitmap technology: index duplicates with bitmap
compress bitmap.
Can give 10x or 100x speedup.
Can save space and IO bandwidth
Currently, two products (Sybase and IQ) not integrated
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DB2DB2/VM: = SQL/DS: System R gone public
DB2/MVS (classic Parallel Sysplex, Parallel Query Server, ...)Parallel and async IO into one process (on mainframe)Parallel execution in next release (late next year?)MVS PQS now withdrawn?
DB2/AS400: Home grown
DB2-2-PE: OS2/DM grown large. First moved to AIXBeing extended parallelismParallelism based on SP/2 -- shared nothing done right.Benchmarks today - Beta everywhere
DB2++: separate code path has OO extensions, good TPC-C Ported to HP/UX, Solaris, NT in beta
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DB2/2 Data Layout• DATABASE: a collection of nodes (up to 128 SP2s so far)
• NODEGROUP: a collection of logical nodes (a 4k hash map
• LOGICAL NODE: A DB2 instance (segments, log, locks...)
• PHYSICAL NODE: A box.
• Logical Node: Segments of 4 k pages
– Segments allocated in units (64K default)
– Tables stripe across all segments
• Table created in NodeGroup:
– Hash (partition key) across all members of group
• Cluster has single system Image
Segments
Nodes:Group 1
Group 2
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DB2/2 Query Execution• Each node maintains pool of AIX server processes
• Query optimizer does query decomposition to node plans (like R* distributed query decomposition)
• Parallel Optimization is 1Ø (not like Wai Hong’s work)
• Sends sub-plans to nodes to be executed by servers
• Node binds plan to server process
• Intermediate results hashed
• Proud that Optimizer does not need hints.
• “Standard” join strategies (except no hash join).
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DB2/2 Utilities• 4 loaders:
– import
– raw-insert (fabricates raw blocks, no checks)
– insert
– bulk insert
• Reorganize hash map, add / drop nodes, add devices– Table unavailable during these operations
• Online & Incremental backup
• Fault tolerance via HACMP
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DB2/2 Performance: Good performance Great Scaling
Wisconsin scaleups
big = 4.8 M rec = 1 GB
small = 1.2 M rec = 256MB
scan rate ~12 kr/s/node
raw load: 2.5 kr/s/node
see notes for more data
0.0
5.0
10.0
15.0
20.0
25.0
0 2 4 6 8 10 12 14 16
Load
Scan
Agg
SMJ
NLJ
SMJ2
Index1
Index2
MJ
Speedup vs NodesDB2/2 PE on SP2
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DB2/2 Good Things• Scaleable to 128 nodes (or more)
• From IBM
• Good performance
• Complete SQL (update, insert,...)
• Will converge with DB2/3 (OO and TPC-C stuff)
• Will be available off AIX someday – (aix is slow and SP2 is very expensive)
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RedBrick• Read-only (LOAD then SELECT only) Database system
– Load is incremental and sophisticated
• Precompute indices to make small-large joins run fast– Indices use compression techniques.
– Only join via indices
• Many aggregate functions to make DSS reports easy
• Parallelism:
– Pipeline IO
– Typically a thread per processor (works on index partition)
– Piggyback many queries on one scan
– Parallel utilities (index in parallel, etc)
– SP2 implementation uses shared disk model.
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SummaryThere is a LOT of activity
(many products coming to market)
Query optimization is near the complexity barrierNeeds a new approach?
All have good speedup & scaleup if they can find a plan
Managing huge processor / disk / tape arrays is hard.
I am working on commoditizing these ideas:low $/record/sec (scaleup PC technology)low Admin $/node (automate, automate, automate,...)Continuous availability (online & fault tolerant)