data - harvard seasdaslab.seas.harvard.edu/classes/cs265/files/... · architecture of a database...
TRANSCRIPT
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Stratos Idreos
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DATA
INDEX——HOW——
TO STORE ——DATA——
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DATA
INDEX
data structure decisions define the algorithms that access data
ALGORITHMS
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DATA
INDEX
[7,4,2,6,1,3,9,10,5,8]
ALGORITHMSunordered
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DATA
INDEX
[7,4,2,6,1,3,9,10,5,8]
ALGORITHMSunordered
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DATA
INDEX
[7,4,2,6,1,3,9,10,5,8]
ALGORITHMS[1,2,3,4,5,6,7,8,9,10]
unordered
ordered
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DATA
INDEX
ALGORITHMS
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DATA
INDEX
ALGORITHMS
DATA SYSTEMS
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DATA STRUCTURES
DEFINE PERFORMANCE
2018
spee
d COMPUTE
DATA MOVEMENT
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2018
spee
d COMPUTE
DATA MOVEMENT
register = this room
disk = Pluto memory = nearby city
Jim Gray, Turing Award 1998
caches = this city
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Read Update
Memory
no perfect structure
amplification
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Read Update
Memory
Mem
ory
Read
Upda
teno perfect structure
amplification
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Read Update
Memory
Mem
ory
Read
Upda
te
differential approximate
pointtree
no perfect structure
amplification
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Read Update
Memory
Mem
ory
Read
Upda
te
differential approximate
pointtree
no perfect structure
amplification
Array
Linked-List
Skip-List
Trie
Hash-Table
Sorted Array
B-tree
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How do I make my data system run x times as fast? (sql,nosql,bigdata, …)
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How do I make my data system run x times as fast?
How do I minimize my bill in the cloud?
(sql,nosql,bigdata, …)
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How do I make my data system run x times as fast?
How do I minimize my bill in the cloud?
(sql,nosql,bigdata, …)
How do I extend the lifetime of my hardware?
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How do I make my data system run x times as fast?
How do I minimize my bill in the cloud?
How to accelerate statistics computation for data science/ML?
(sql,nosql,bigdata, …)
How do I extend the lifetime of my hardware?
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How do I make my data system run x times as fast?
How do I minimize my bill in the cloud?
How do I train my neural network x times faster?
How to accelerate statistics computation for data science/ML?
(sql,nosql,bigdata, …)
How do I extend the lifetime of my hardware?
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NEW APPLICATIONS
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NEW APPLICATIONS
existing systems need to change too
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NEW APPLICATIONS
existing systems need to change too
WORKLOAD HARDWARE
ADAPT
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NEW APPLICATIONS
existing systems need to change too
WORKLOAD HARDWARE
ADAPT
IMPROVE WITHIN A BUDGET
WHAT WILL BREAK MY SYSTEM?
REASON
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more data
new applications
new h/w
continuous need
for newstorage solutions
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fundamental of storage learning outcome
software engineering data-driven startup researchdata structures, SQL, NoSQL, Big Data, Neural Networks, Statistics, Data Science
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interaction: in and out of class
There is no such thing as a
wrong question/answer!!!!
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Recent Research Papers
review and slides should focus on
what is the problem why is it important
why is it hard why existing solutions do not work
what is the core intuition for the solution solution step by step
does the paper prove its claims exact setup of analysis/experiments are there any gaps in the logic/proof
possible next steps
* follow a few citations to gain more background
Each student: 2 reviews per week/1 presentation
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Recent Research Papers
review and slides should focus on
what is the problem why is it important
why is it hard why existing solutions do not work
what is the core intuition for the solution solution step by step
does the paper prove its claims exact setup of analysis/experiments are there any gaps in the logic/proof
possible next steps
* follow a few citations to gain more background
Each student: 2 reviews per week/1 presentation
learn to judge constructively
learn to present
learn to prepare slides
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systems project research project
semester project: due in the end of semester + a midway check in (early March,10%)
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systems project
individual projectNoSQL, in c/c++
research project
semester project: due in the end of semester + a midway check in (early March,10%)
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systems project
individual projectNoSQL, in c/c++
research project
groups of threeNoSQL, Neural Networks
Periodic Table of Data Structures
only open to cs165 students (unless proven otherwise)
semester project: due in the end of semester + a midway check in (early March,10%)
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systems project
individual projectNoSQL, in c/c++
research project
groups of threeNoSQL, Neural Networks
Periodic Table of Data Structures
only open to cs165 students (unless proven otherwise)
semester project: due in the end of semester + a midway check in (early March,10%)
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ACM Special Interest Group In Data Management (SIGMOD)Undergrad Research Competition
first prize in 2016, 2017, 2018Adaptive Denormalization Evolving Trees Splaying LSM-Trees
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ACM Special Interest Group In Data Management (SIGMOD)Undergrad Research Competition
first prize in 2016, 2017, 2018Adaptive Denormalization Evolving Trees Splaying LSM-Trees
Design continuums at CIDR 2019, LSM/BTree hybrids in SIGMOD finals
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piazza forum
all announcements & discussions (link on class website - check out usage guidelines)
classes are recorded (links on class website)
NO LAPTOP/PHONE POLICYclass is based on participation!
Project: 40% Midway Check-in:10% Discussion: 20% Presentation: 15% Reviews: 15%
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Check out: syllabus, preparation readings, project 0, systems project, online sections
http://daslab.seas.harvard.edu/classes/cs265/
Get familiar with the very basics of traditional database architectures:Architecture of a Database System. By J. Hellerstein, M. Stonebraker and J. Hamilton. Foundations and Trends in Databases, 2007
Get familiar with very basics of modern database architectures:The Design and Implementation of Modern Column-store Database Systems. By D. Abadi, P. Boncz, S. Harizopoulos, S. Idreos, S. Madden. Foundations and Trends in Databases, 2013
Get familiar with the very basics of modern large scale systems:Massively Parallel Databases and MapReduce Systems. By Shivnath Babu and Herodotos Herodotou. Foundations and Trends in Databases, 2013
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subarna wilson wasay
Teaching Fellows:
Off class discussions are key! question on readings, ideas, help with code/analysis
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questions on logistics?
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BASICS of storageIntro to RESEACH topics
Discussion phase/presentation as of week 3
Next few classes:
ask/answer questions, keep notes/ideas
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registers
on chip cache
on board cache
memory
disk
CPU
memory wall
chea
per
fast
er
SRAM
DRAM
~1ns
~10ns
~100ns
cache miss: looking for something which is not in the cache
memory miss: looking for something which is not in memory
time
speed cpu
mem
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registers
on chip cache
on board cache
memory
disk
CPU
memory wall
chea
per
fast
er
SRAM
DRAM
~1ns
~10ns
~100ns
cache miss: looking for something which is not in the cache
memory miss: looking for something which is not in memory
time
speed cpu
mem
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Jim Gray, IBM, Tandem, DEC, Microsoft ACM Turing award ACM SIGMOD Edgar F. Codd Innovations award
disk100Kx Pluto
2 years
memory100x New York1.5 hours
on board cache10x this building
10 min
on chip cache2x this room
1 min
registers my head~0
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…
need to only read x… but have to read all of page 1
page1 page2 page3
data value x
registers
on chip cache
on board cachememory
disk
CPU
data
mov
e
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…5 10 6 4 12 2 8 9 7 6 7 11 3 9 6
memory level N
memory level N-1
query x<5
page size: 5x8 bytes
5 10 6 4 12
(size=120 bytes)
2 8 9 7 6 7 11 3 9 6
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…5 10 6 4 12 2 8 9 7 6 7 11 3 9 6
memory level N
memory level N-1
query x<5
page size: 5x8 bytes
scan
5 10 6 4 12(size=120 bytes)
2 8 9 7 6 7 11 3 9 6
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…5 10 6 4 12 2 8 9 7 6 7 11 3 9 6
memory level N
memory level N-1
query x<5
page size: 5x8 bytes
scan
5 10 6 4 12(size=120 bytes)
2 8 9 7 6
4
7 11 3 9 6
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…5 10 6 4 12 2 8 9 7 6 7 11 3 9 6
memory level N
memory level N-1
query x<5
page size: 5x8 bytes
scan
40 bytes
5 10 6 4 12(size=120 bytes)
2 8 9 7 6
4
7 11 3 9 6
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…5 10 6 4 12 2 8 9 7 6 7 11 3 9 6
memory level N
memory level N-1
query x<5
page size: 5x8 bytes
scan scan
40 bytes
5 10 6 4 12(size=120 bytes) 2 8 9 7 6 4
7 11 3 9 6
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…5 10 6 4 12 2 8 9 7 6 7 11 3 9 6
memory level N
memory level N-1
query x<5
page size: 5x8 bytes
scan scan
40 bytes
5 10 6 4 12(size=120 bytes) 2 8 9 7 6 4 2
7 11 3 9 6
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…5 10 6 4 12 2 8 9 7 6 7 11 3 9 6
memory level N
memory level N-1
query x<5
page size: 5x8 bytes
scan scan
5 10 6 4 12(size=120 bytes) 2 8 9 7 6 4 2
7 11 3 9 6
80 bytes
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…5 10 6 4 12 2 8 9 7 6 7 11 3 9 6
memory level N
memory level N-1
query x<5
page size: 5x8 bytes
(size=120 bytes) 2 8 9 7 6 4 2
7 11 3 9 6
80 bytes
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…5 10 6 4 12 2 8 9 7 6 7 11 3 9 6
memory level N
memory level N-1
query x<5
page size: 5x8 bytes
(size=120 bytes) 2 8 9 7 6 4 27 11 3 9 6
scan
80 bytes
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…5 10 6 4 12 2 8 9 7 6 7 11 3 9 6
memory level N
memory level N-1
query x<5
page size: 5x8 bytes
(size=120 bytes) 2 8 9 7 6 4 27 11 3 9 6
scan
3
80 bytes
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…5 10 6 4 12 2 8 9 7 6 7 11 3 9 6
memory level N
memory level N-1
query x<5
page size: 5x8 bytes
(size=120 bytes) 2 8 9 7 6 4 27 11 3 9 6
scan
3
120 bytes
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…5 10 6 4 12 2 8 9 7 6 7 11 3 9 6
memory level N
memory level N-1
query x<5
page size: 5x8 bytes
5 10 6 4 12
(size=120 bytes)
2 8 9 7 6 7 11 3 9 6
an oracle gives us the positions
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…5 10 6 4 12 2 8 9 7 6 7 11 3 9 6
memory level N
memory level N-1
query x<5
page size: 5x8 bytes
oracle
5 10 6 4 12(size=120 bytes)
2 8 9 7 6 7 11 3 9 6
an oracle gives us the positions
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…5 10 6 4 12 2 8 9 7 6 7 11 3 9 6
memory level N
memory level N-1
query x<5
page size: 5x8 bytes
oracle
5 10 6 4 12(size=120 bytes)
2 8 9 7 6
4
7 11 3 9 6
an oracle gives us the positions
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…5 10 6 4 12 2 8 9 7 6 7 11 3 9 6
memory level N
memory level N-1
query x<5
page size: 5x8 bytes
oracle
40 bytes
5 10 6 4 12(size=120 bytes)
2 8 9 7 6
4
7 11 3 9 6
an oracle gives us the positions
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…5 10 6 4 12 2 8 9 7 6 7 11 3 9 6
memory level N
memory level N-1
query x<5
page size: 5x8 bytes
oracle oracle
40 bytes
5 10 6 4 12(size=120 bytes) 2 8 9 7 6 4
7 11 3 9 6
an oracle gives us the positions
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…5 10 6 4 12 2 8 9 7 6 7 11 3 9 6
memory level N
memory level N-1
query x<5
page size: 5x8 bytes
oracle oracle
40 bytes
5 10 6 4 12(size=120 bytes) 2 8 9 7 6 4 2
7 11 3 9 6
an oracle gives us the positions
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…5 10 6 4 12 2 8 9 7 6 7 11 3 9 6
memory level N
memory level N-1
query x<5
page size: 5x8 bytes
oracle oracle
5 10 6 4 12(size=120 bytes) 2 8 9 7 6 4 2
7 11 3 9 6
80 bytesan oracle gives us the positions
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…5 10 6 4 12 2 8 9 7 6 7 11 3 9 6
memory level N
memory level N-1
query x<5
page size: 5x8 bytes
(size=120 bytes) 2 8 9 7 6 4 2
7 11 3 9 6
80 bytesan oracle gives us the positions
![Page 64: DATA - Harvard SEASdaslab.seas.harvard.edu/classes/cs265/files/... · Architecture of a Database System. By J. Hellerstein, M. Stonebraker and J. Hamilton. Foundations and Trends](https://reader034.vdocuments.mx/reader034/viewer/2022042223/5ec975a53194143a89736cb1/html5/thumbnails/64.jpg)
…5 10 6 4 12 2 8 9 7 6 7 11 3 9 6
memory level N
memory level N-1
query x<5
page size: 5x8 bytes
(size=120 bytes) 2 8 9 7 6 4 27 11 3 9 6
oracle
80 bytesan oracle gives us the positions
![Page 65: DATA - Harvard SEASdaslab.seas.harvard.edu/classes/cs265/files/... · Architecture of a Database System. By J. Hellerstein, M. Stonebraker and J. Hamilton. Foundations and Trends](https://reader034.vdocuments.mx/reader034/viewer/2022042223/5ec975a53194143a89736cb1/html5/thumbnails/65.jpg)
…5 10 6 4 12 2 8 9 7 6 7 11 3 9 6
memory level N
memory level N-1
query x<5
page size: 5x8 bytes
(size=120 bytes) 2 8 9 7 6 4 27 11 3 9 6
oracle
3
80 bytesan oracle gives us the positions
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…5 10 6 4 12 2 8 9 7 6 7 11 3 9 6
memory level N
memory level N-1
query x<5
page size: 5x8 bytes
(size=120 bytes) 2 8 9 7 6 4 27 11 3 9 6
oracle
3
120 bytesan oracle gives us the positions
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when does it make sense to have an oraclehow can we minimize the cost
…5 10 6 4 12 2 8 9 7 6 7 11 3 9 65 10 6 4 12 2 8 9 7 6 7 11 3 9 6
e.g., query x<5
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CPU DATA MOVEMENT
MEMORY REQUIREMENT
SPACE REQUIREMENT
(ENERGY)
ROBUSTNESS
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CPU DATA MOVEMENT
MEMORY REQUIREMENT
SPACE REQUIREMENT
(ENERGY)
SQL, NoSQL, Graph, Neural Nets, Statistics —> Data to Processing
ROBUSTNESS
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CPU DATA MOVEMENT
MEMORY REQUIREMENT
SPACE REQUIREMENT
(ENERGY)
SQL, NoSQL, Graph, Neural Nets, Statistics —> Data to Processing
TIME —— CLOUD COSTS
ROBUSTNESS
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Stratos Idreos