Distributed Systems In One Lesson

DistributedSystems

OneLessonin

@tlberglund

What are ?Distributed Systems

Any system too large to fit on one computer.

What are

• Storage • Transac6ons • Computa6on • Coordina6on

?Distributed Systems

Distributed Storage

Distributed Storage

• Read replica6on • Sharding • Consistent hashing • Distributed filesystems

Distributed Storage

• MongoDB • Cassandra • HDFS

When Life is Good

When Life is Good

Tim:Americano

When Life is Good

AmericanoTim?

When Life is Good

Americano

When Life is Busier

Tim:Triple Skinny

When Life is Busier

Tim:Triple SkinnyTim:Triple Skinny

When Life is Busier

Tim:Triple Skinny

Tim:Triple Skinny

When Life is Busier

Tim?

ReplicaCon Problems

• Complexity • Consistency

When Life is REALLY busy

Tim:Triple SkinnyAaron-‐ Faye



Faye-‐ Nancy



Faye-‐ Nancy

Nancy-‐ Zed

Sharding Problems

• More Complexity • Broken data model • Limited data access paEerns

Consistent Hashing

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Consistent Hashing

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Tim:Americano

Consistent Hashing

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9F72:Americano

Consistent Hashing

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Tim?

Consistent Hashing

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9F72?

Consistent Hashing

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Consistent Hashing

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CAP Theorem

C

PA

CAP Theorem

• Consistency • Availability • Par66on Tolerance

CAP Theorem

• Shared wri6ng project • Coffee shop closes • Synchronizing over the phone • BaEery dies • Status report!

CAP Theorem

C

PA

Distributed TransacCons

ACID TransacCons• Atomic • Consistent • Isolated • Durable • One barista • Mul6ple baristas

Ordering Coffee• Receive order • Process payment • Enqueue order • Make coffee • Deliver drink

Ordering Coffee• Receive order • Process payment • Enqueue order • Make coffee • Deliver drink

{Diffe

rent

Actors

Ordering Coffee

• Why split up order processing? • What can fail? • What are the consequences of failure? • How do we repair failure?

Ordering Coffee

• How can we design a coffee shop with atomic transac6ons?

• How does that limit the business? • Why give up atomicity?

Distributed ComputaCon

Distributed ComputaCon

• ScaEer/Gather • MapReduce • Hadoop • Spark

MapReduce

• All computa6on in two func6ons: Map and Reduce • Keep data (mostly) where it is • Move compute to data

MapReduce

Mapper

Reducer

(k ,v) [(k ,v), (k ,v), (k ,v), …]

Shuffle(k , [v, v, v, …])

[(k ,v), (k ,v), (k ,v), …]

MapReduce

Once upon a midnight dreary, while I pondered, weak and weary, Over many a quaint and curious volume of forgotten lore,

While I nodded, nearly napping, suddenly there came a tapping, As of some one gently rapping, rapping at my chamber door. "'Tis some visitor," I muttered, "tapping at my chamber door-

Only this, and nothing more."

Ah, distinctly I remember it was in the bleak December, And each separate dying ember wrought its ghost upon the floor.

Eagerly I wished the morrow;- vainly I had sought to borrow From my books surcease of sorrow- sorrow for the lost Lenore-

poems/raven.txt:

MapReduceIt shall clasp a sainted maiden whom the angels name Lenore-

Clasp a rare and radiant maiden whom the angels name Lenore." Quoth the Raven, "Nevermore."

"Be that word our sign in parting, bird or fiend," I shrieked, upstarting- "Get thee back into the tempest and the Night's Plutonian shore! Leave no black plume as a token of that lie thy soul hath spoken!

Leave my loneliness unbroken!- quit the bust above my door! Take thy beak from out my heart, and take thy form from off my door!"

Quoth the Raven, "Nevermore."

And the Raven, never flitting, still is sitting, still is sitting On the pallid bust of Pallas just above my chamber door;

And his eyes have all the seeming of a demon's that is dreaming, And the lamp-light o'er him streaming throws his shadow on the floor;

And my soul from out that shadow that lies floating on the floor Shall be lifted- nevermore!

poems/raven.txt:

MapReduce

Once

upon

a

midnightdrearywhile

pondered

suddenly

there

came a

tapping

gently

rapping

rapping

tapping

at

my

chamber

door

Map

MapReduce

Once:1

upon:1

a:1

midnight:1dreary:1while:1

pondered:1

suddenly:1

there:1

came:1 a:1

tapping:1

gently:1

rapping:1

rapping:1

tapping:1

at:1

my:1

chamber:1

door:1

Map

MapReduce

Once:1

upon:1a:1

midnight:1

dreary:1while:1

pondered:1suddenly:1 there:1came:1a:1

tapping:1gently:1

rapping:1

rapping:1 tapping:1

at:1

my:1chamber:1

door:1

Shuffle

MapReduce

Once:[1]

upon:[1]

midnight:[1]

dreary:[1]while:[1]

pondered:[1]suddenly:[1] there:[1]came:[1]a:[1,1]

gently:[1]

rapping:[1,1]

tapping:[1,1]

at:[1]

my:[1]chamber:[1]

door:[1]

Reduce

MapReduce

Once:1

upon:1

midnight:1

dreary:1while:1

pondered:1suddenly:1 there:1came:1a:2

gently:1

rapping:2

tapping:2

at:1

my:1chamber:1

door:1

Reduce

Real-‐World MapReduce• Hadoop • Cloudera, Hortonworks, MapR • Nobody write map, reduce func6ons • Hive (SQL-‐like interface) • Integra6on with BI front-‐ends

Hadoop

• MapReduce API • Job Tracker, Task Tracker • Distributed Filesystem (HDFS) • Enormous ecosystem

Spark

• ScaEer/gather paradigm (similar to MapReduce) • More general data model (RDDs) • More general programming model (transform/ac6on) • Storage agnos6c

Spark Architecture2000

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Spark Client Spark

Context

Driver

Spark Client Spark

ContextJob

Spark Architecture

Spark Client Spark

Context

Job

Cluster Manager

Spark Architecture

Spark Client Spark

Context

Cluster Manager

JobTaskTaskTaskTask

Spark Architecture

Spark Client Spark

Context

Job

TaskTask

Task

Task

Spark ArchitectureCluster Manager

Executor

Task Task

Cache

Spark Worker Node

Executor

Task Task

Cache

Executor

Task Task

CacheRDD RDD

RDD

RDDRDD

RDD

Spark Worker Node

What’s an RDD?

What’s an RDD?

This is

What’s an RDD?• Bigger than a computer • Read from an input source • Output of a pure func6on • Immutable • Typed • Ordered • Lazily evaluated • Par66oned • Collec6on of things

Distributed, how?

5444 5676 0686 8389: List(xn-1, xn-2, xn-3)

4532 4569 7030 1191: List(xn-4, xn-5, xn-6)

5444 5607 6517 6027: List(xn-7, xn-8, xn-9)

4532 4577 0122 2189: List(xn-10, xn-11, xn-12)

Hash these

Distributed, how?

Distributed CoordinaCon

Distributed CoordinaCon

• NTP • Epidemic Protocols • Paxos

NTP

• Distributed 6me synchroniza6on • A network of 6ered clocks • Synchroniza6on algorithm • O`en accurate to ±10ms

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Paxos

• Distributed log protocol • Assumes distributed, unreliable nodes • Performs leader elec6on

Review

• Storage • Transac6ons • Computa6on • Coordina6on

ThankYou!@tlberglund

Distributed Systems In One Lesson

Software

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