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SPOT 401 - Leading the NoSQL
Revolution:
under the covers of Distributed
Systems @ scale
@swami_79

@ksshams
what are we covering?
The evolution of large scale
distributed systems @ Amazon from
the 90’s to today

The lessons we
learned and insights
you can employ in
your own distributed
systems
@swami_79

@ksshams
let’s start with a story about a little
company called amazon.com

@swami_79

@ksshams
episode 1
once upon a time...
(in 2000)

@swami_79

@ksshams
a thousand miles
away...
(seattle)

@swami_79

@ksshams
amazon.com - a rapidly growing Internet based retail
business relied on relational databases

@swami_79

@ksshams
we had 1000s of independent services

@swami_79

@ksshams
each service managed its own state in RDBMs

@swami_79

@ksshams
RDBMs are actually kind of cool

@swami_79

@ksshams
first of all... SQL!!

@swami_79

@ksshams
so it is easier to query..

@swami_79

@ksshams
easier to learn

@swami_79

@ksshams
they are as versatile as a swiss army knife

complex
queries

key-value
access

analytics

transactions
@swami_79

@ksshams
RDBMs are *very* similar to
Swiss Army Knives

@swami_79

@ksshams
but sometimes.. swiss army knifes..
can be more than what you bargained for

@swami_79

@ksshams
repartitioning
HARD..

partitioning

easy

@swami_79

@ksshams
so we bought

bigger boxes...
@swami_79

@ksshams
benchmark
new
hardware

migrate to
new
hardware

Q4 was hard-work at Amazon
repartition
databases

pray
...
@swami_79

@ksshams
RDBMs availability challenges..

@swami_79

@ksshams
episode 2
then.. (in 2005)

@swami_79

@ksshams
amazon dynamo
predecessor to
dynamoDB
replicated DHT with consistent
hashing
optimistic replication
“sloppy quorum”
anti-entropy mechanism
object versioning
specialist tool :
•limited querying capabilities
•simpler consistency
@swami_79

@ksshams
dynamo had many benefits
• higher availability
• we traded it off for eventual consistency

•
•
•
•

incremental scalability
no more repartitioning
no need to architect apps for peak
just add boxes

• simpler querying model ==>> predictable performance
@swami_79

@ksshams
but dynamo was not perfect...
lacked strong consistency

@swami_79

@ksshams
but dynamo was not perfect...
scaling was easier, but...

@swami_79

@ksshams
but dynamo was not perfect...
steep learning curve

@swami_79

@ksshams
but dynamo was not perfect...
dynamo was a product ... ==>> not
a service...

@swami_79

@ksshams
episode 3
then.. (in 2012)

@swami_79

@ksshams
DynamoDB
• NoSQL database
• fast & predictable

performance
• seamless scalability
• easy administration

ADMIN

“Even though we have years of experience with large, complex
NoSQL architectures, we are happy to be finally out of the
business of managing it ourselves.” - Don MacAskill, CEO
@swami_79

@ksshams
services, services, services

@swami_79

@ksshams
amazon.com’s experience with services

@swami_79

@ksshams
how do you create a successful service?

@swami_79

@ksshams
with great services, comes great responsibility

@swami_79

@ksshams
Architect

Customer

@swami_79

@ksshams
DynamoDB Goals and
Philosophies
never compromise on
scale is our
durability
problem
easy to use
consistent and low
scale in rps
latencies
@swami_79

@ksshams
how to build these large scale services?

@swami_79

@ksshams
don’t compromise on durability…

@swami_79

@ksshams
don’t compromise on… availability

@swami_79

@ksshams
plan for success, plan for scalability

@swami_79

@ksshams
@swami_79

@ksshams
Fault tolerant design
is key..
• Everything fails all the time
• Planning for failures is not easy
• How do you ensure your recovery strategies work correctly?

@swami_79

@ksshams
Byzantine General Problem
@swami_79

@ksshams
A simple 2-way replication system of a
traditional database…
Writes

Primary

Standby

@swami_79

@ksshams
P is dead, need to
promote myself

S is dead, need
to trigger new
replica

P

P’
S

@swami_79

@ksshams
Improved
Replication: Quorum
Replica

Replica

Writes

Replica

Quorum: Successful write on a majority

@swami_79

@ksshams
Not so easy..
New member in the
group

Replica D

Replica A

Replica B

Reads and
Writes from
client B

Replica C

Should I continue to serve reads?
Should I start a new quorum?
Replica E

Writes from
client A

Replica F

Classic Split Brain Issue in Replicated systems leading to lost writes!
Building correct distributed systems is
not straight forward..
• How do you handle replica failures?
• How do you ensure there is not a parallel

quorum?
• How do you handle partial failures of replicas?
• How do you handle concurrent failures?

@swami_79

@ksshams
correctness is hard, but necessary
Formal Methods
Formal Methods
to minimize bugs, we must have a precise description
of the design
Formal Methods
code is too detailed

design documents and diagrams are vague &
imprecise
how would you express
partial failures or
concurrency?
Formal Methods
law of large numbers is your friend,

until you hit large
numbers
so design for scale
TLA+ to the rescue?

@swami_79

@ksshams
PlusCal

@swami_79

@ksshams
formal methods are necessary

but not sufficient..
@swami_79

@ksshams
customer

@swami_79

@ksshams
don’t forget to test - no, serious ly

@swami_79

@ksshams
simulate
failures at unit
test level

fault injection
testing
scale testing

embrace failure and don’t be
surprised
datacenter
testing

network brown out
testing
testing is a lifelong journey
testing is necessary
but not sufficient..
@swami_79

@ksshams
Customer

Architect

@swami_79

@ksshams
gamma
simulate real
world

one box
does it work?

release cycle
phased
deployment
treading lightly

monitor
does it still
work?
@swami_79

@ksshams
Monitor customer behavior

@swami_79

@ksshams
measuring customer experience is key

don’t be satisfied by average - look at
99 percentile
@swami_79

@ksshams
understand the scaling dimensions

@swami_79

@ksshams
understand how your service will be abused
@swami_79

@ksshams
let’s see these rules in action through a true story

@swami_79

@ksshams
we were building distributed systems all over
amazon.com

@swami_79

@ksshams
we needed a uniform and correct way to do
consensus..

@swami_79

@ksshams
service
so we built a paxos lock library
@swami_79

@ksshams
such a service is so much more useful than just
leader election..
it became a distributed
state store

@swami_79

@ksshams
such a service is so much more useful than just
leader election..
or a distributed state
store
wait wait.. you’re telling me
if I poll,
I can detect node failure?
@swami_79

@ksshams
we acted quickly - and scaled up our entire fleet
with more nodes

doh!!!!

we slowed
consensus...
@swami_79

@ksshams
understand the scaling dimensions

& scale them
independently...

@swami_79

@ksshams
a lock service has 3 components..

State Store

@swami_79

@ksshams
they must be scaled independently..

State Store
@swami_79

@ksshams
they must be scaled independently..

State Store
@swami_79

@ksshams
they must be scaled independently..

State Store
@swami_79

@ksshams
Let’s Go Over The demo from this morning
stream ingestion
stream ingestion
stream ingestion
Real-time tweet analytics using DynamoDB
• Stream from Kinesis to DynamoDB

• What data do want in real-time?
• (per-second, top words)
• How does DynamoDB help?
• Atomic counters (per-word counts in that second)
• Indexed queries (top N word-counts in that second
WordCount Table

Local Secondary Index

Time

Word

Count

Time

Count

Word

2013-10-13T12:00
2013-10-13T12:00
2013-10-13T12:00
2013-10-13T12:03

Earth
Mars
Pluto
Earth

9
10
5
8

2013-10-13T12:00
2013-10-13T12:00
2013-10-13T12:00
2013-10-13T12:03

5
9
10
8

Pluto
Earth
Mars
Earth
DynamoDB cost: $0.25 / hr
stream ingestion
Aggregate queries using Redshift
• Simple Redshift connector (buffer files, store in s3, call copy

command)
• Manifest copy connector
• 2 streams
• transaction table for deduplication
• manifest copy
Right tool for right job…
• Canal -> DynamoDB -> Redshift -> Glacier…
You are not done yet..
• Listen to customer feedback
• Iterate..
Example: DynamoDB
• Start with immediate needs of reliable, super scalable, low latency

datastore
• Iterate
• Developers wanted flexible query: Local Secondary Indexes
• Developers wanted parallel loads: Parallel Scans
• Mobile developers wanted direct access to their datastore: Fine-grained

Access Control
• Mobile developers wanted geo-awareness: Geospatial library
• Developers wanted DynamoDB on their laptop: DynamoDB Local
• Developers wanted richer query: Global Secondary Indexes
• We will continue to innovate..
Sacred Tenets in
Distributed Systems
don’t compromise durability
for performance

plan for success –
plan for scalability

plan for failures - fault tolerance is key
consistent performance
is important
release - think of blast radius
insist on correctness
@swami_79

@ksshams
understand
scaling
dimensions

observe
how service is
used
monitor
like a hawk

relentlessly
test
scalability
over features
@swami_79

strive
for
correctness

@ksshams
Please give us your feedback on this
presentation

SPOT 401

Don’t miss SPOT 201!!!

@swami_79

@ksshams
@swami_79

@ksshams

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NoSQL Revolution: Under the Covers of Distributed Systems at Scale (SPOT401) | AWS re:Invent 2013