Scalability


Skeepers Influence Workshop

Your HOsts Tonight


Alex Fernández (pinchito)
Director of Eng, Influence
 
Platform Lead, Influence

Alfredo López

What We Will See


What is Scalability?


Horizontal and Vertical Scaling


Scaling Strategies

🚀 What is  scalability


🪜 Definition of scalability


The capacity to be changed in size or scale.

The ability of a computing process to be used or produced in a range of capabilities.

Lexico (Oxford Dictionary)

⛷️ Scale Up and Down!

📚 Use in Literature

🐧 Example: Linux

From embedded

✋ What makes something Scalable?


The right question is: why doesn't it scale?

  • Scarcity of a resource
  • Growing wait times
  • Unability to answer
  • Blocking on a resource
  • ...

📝 Exercise: non Scalable Service


Install Node.js

Install loadtest
$ npm install -g loadtest 

Run the command
$ loadtest https://gorest.co.in/public/v1/users -n 2000 -c 100 --keepalive
Write down the average for requests per second (rps),
average latency and number of errors


📝 Exercise +


Adjust rps from 10 to 100 ±10

$ loadtest http://service.pinchito.es:3000/a -n 2000 -c 100 --rps 30 -k
$ loadtest http://service.pinchito.es:3000/a -n 2000 -c 100 --rps 40 -k
...
Write down rps, latency and errors

Go up to 100, then 1000
$ loadtest http://service.pinchito.es:3000/a -n 2000 -c 100 --rps 1000 -k


📝 Exercise +

Draw a graph with rps sent and result

Another graph with rps vs latency


📝 Exercise +


Now test against:
loadtest https://www.google.com -n 2000 -c 100 -k

What is the difference?

How do latency and rps behave now?



👍 Success!


 🥛 What Resource Run out?


Graph with CPU from AWS:

300 rps
400 rps
1000 rps

🚂 rps vs throughput



📈 Scalability Profiles


Brendan Gregg: Systems Performance

🚒 Latency vs rps



⚖️ Little's Law

Little, 1952 - 1960

The average number of requests in flight L equals:
the rate of requests per second λ
multiplied by the average request time W.



If we increase concurrency L,
the average time per request W grows proportionally.

v and ⇔ h Scaling

🧓 Hard Beginnings


IBM mainframe

💽 Specialized Servers


🗄️ The Usual Cabins

🤖 And then Google Arrived

⇕ vertical Scaling


Buy a bigger machine


And bigger


Until you run out of machines


Hard to go back to a smaller machine 😅

⇕ vertical Scaling


🤫 Sshhh...

For many decades now, supercomputers are just...
clusters of smaller machines
IBM Blue Gene/P: 164k cores, 2007

⇔ Horizontal Scaling


Use many similar machines for a given function

("provisioning")


Add or remove machines to scale


When one machine is failing it is removed from service

⇔ horizontal Scaling


📝 Exercise: Storage


Design a corporate storage system with 15 TB


Option 1 ⇕: storage area network (SAN)

Best option as of december 2008


Option 2 ⇔: raw hard drives

Best option as of july 2009


📝 EXERCISE +


Add controllers


RAID options (Redundant Array of Inexpensive Disks)


Measure the $ difference between option 1 and option 2

Final price?


📝 EXERCISE +


Consider redundancy strategies

Fault tolerance

Redundancy options: 2x, 3x, ?


Consider scaling strategies


How do they affect the price?



👍 success!



⇔ Horizontal Strategies


🤹 Balancing (server-side)

🕵️ Balancing (client-side)

💝 Affinity

🔱 Independence

🍇 Clustering

🔑 Sharding

🧬 Replication

⌛ Queues

🤹 Server-side Balancing



Example: AWS ELB, Google Cloud Load Balancing

🕵️ Client-Side Balancing


Example: Facebook client
Chooses the API endpoint in the browser

Example: DNS balancing

💝 Affinity ⇔


By cookie or geographical

Needs a sophisticated router
Client-side or server-side

🔱 Independence ⇔


Neutral (or blind) balancing

🍇 Clustering ⇔

Generic term "cluster":
create one machine out of many

In databases usually means having more than one server
all equivalent

🔑 Sharding ⇔


Balancing by key

Needs a sharding algorithm (usually with hashing)

🧬 Replication ⇔


A primary server (read + write) and several replicas (read-only)

Useful when reading > writing

🐫 Active REPLICAtion ⇔

Active-active, multiple primary...

Needs a conciliation algorithm

⌛ Queues ⇔


Production of tasks independent of consumption

Mechanism for polling
(NOT pooling 🙏)

📝 Exercise: Scalable Storage


You work for search engine Fooble in January 2000

You have to store the search index

Design a scaling strategy

Assume index = page sizes



📝 EXERCISE +


10 KB per page



10 search terms max

Target time of 0.1 seconds per search


📝 EXERCISE +


50M pages × 10 KB = 500 GB

Cheapest disk drive: Seagate ST317242A, 17.2 GB, $152

32 disks × 16 GB = 512 GB, $4864

8 servers × 4 disks = 32 disks

4M searches × 100 ms = 400k seconds = 4.6 servers
Adding peak time: at least 8 servers


📝 EXERCISE +



100 ms for ~5 search terms
Average query time to storage < 20 ms

📝 EXERCISE +


Query time: seek time + 1/2 turn + formatting

Seek time: ~8 ms
7200 rpm disk drive: 8 ms per turn
Query total: >12 ms

Seems doable; better add some caching



👍 Well done!



📚 Bibliography


Skeepers Influence Workshop: Distributed Systems

By Alex Fernández

Skeepers Influence Workshop: Distributed Systems

Workshop for Skeepers Influence: Distributed Systems

  • 369