big data at spotify

Adam KawaData Engineer @ Spotify

(Big) Data At Spotify

At Spotify,

important questions are being

asked all the time!

Some of these questions

are ”relatively easy”

to answer…

1. How many times has Coldplay been streamed this month?2. Who was the most popular artist in NYC last week?3. How many times was “Get Lucky” streamed during first 24h?

Labels, Licensor, Partners, Advertisers

■■ Very granular reports are required-- Divided Divided by gender, age, location and more

■■ We have been delivering various reportsWe have been delivering various reports from day 1-- Too much data for traditional solutions Too much data for traditional solutions

Reporting

QUIZ!

QuestionWho was the most frequently streamed female artist in 2013?

Answer?A) Katy PerryB) Lady GagaC) MadonnaD) Rihanna

Popular Artists

QuestionWho was the most frequently streamed female artist in 2013?

Popular Artists

■■ The Most Popular Male Artist - Macklemore■■ The Most Popular Band - Imagine Dragons■■ The Most Popular Track - “Can't Hold Us”

Popular Artists In 2013

■■ UsersUsers love local artists!-- Berlin - Sido-- London - Coldplay-- Singapore – Vanessa-Mae-- Stockholm - Avicii

Popular Artists In 2013

■■ UsersUsers love local artists! love local artists!-- NYC listens to Jay-Z 88% more than rest of the world-- Stockholm listens to ABBA 110% more than the rest of the world

Popular Artists In 2013

QuestionWhat was the most “viral” track in 2013?

Popular Tracks

QuestionWhat was the most “viral” track in 2013?

Answer“Get Lucky” by Daft Punk feat. Pharrell Williams

Popular Tracks

Artist Analytics – Daft Punk

“Get Lucky” was released on April, 19th

2013.

Artist Analytics – Daft Punk

Around 5x more streams comparing a day “before” and “after” “Get Lucky”

Artist Analytics – Daft Punk

What happened that day?

Artist Analytics – Daft Punk

“Random Access Memories” was released on May, 17th 2013.

■■ 09.08.63 – 11.02.2012

Artist Analytics – Whitney Houston

■■ One of the most popular Polish rock bands ever

Artist Analytics – Budka Suflera

What happened?

■■ One of the most popular Polish rock bands ever

Artist Analytics – Budka Suflera

Information about the retirement was

announced...

1. What was the number of daily active users (DAU) yesterday?2. How many users have signed up this week?3. Which country to launch Spotify next?

Management And Investors

■■ Analyzing Analyzing growthgrowth-- Number of Number of aactive usersctive users, , streamed songsstreamed songs, sign-ups and more-- Where to launch Spotify next Where to launch Spotify next

■■ Company KPIs

Business Analytics

However,

some of the questions are

really tricky to answer!

1. What song to stream to Jay-Z when he wakes up?2. Is Adam Kawa bored with Timbuktu today?3. How to encourage Jeff to go for the Premium Account?

Data Scientists, Researchers

■■ Recommendations-- Powering features like Powering features like Discover, Radio-- “ “Perfect music for every moment ♪♫ ♬ ♯Perfect music for every moment ♪♫ ♬ ♯””

■■ Classification of songs and playlists by genre or mood

■■ Top listsTop lists per country

Product Features

■■ Overall, in 2013Overall, in 2013-- Best Hangover Cure - “The Lazy Song”-- Best Song To Get Over An Ex - “Someone like you”-- Best Party Starter - “Levels”-- Best Driving Song – “Bohemian Rhapsody”-- Best Work Out Song - “Eye of the Tiger”

Perfect Music For Every Moment

1. Is this button nicer that the previous one?2. How to personalize the messages displayed to users?3. How should the results of search be displayed?

Designers, Feature's Owners

■■ A/B Test-- Come with promising “look-and-feels” and do A/B tests Come with promising “look-and-feels” and do A/B tests

■■ Explicit Explicit ffeedback from users-- But But users usually do not like to rateusers usually do not like to rate things things-- But But users usually do not like to customizeusers usually do not like to customize things things

Designers, Feature's Owners

■■ Sign-up Button On FacebookSign-up Button On Facebook

A/B Test Use CaseSign-up button on the

landing page

Sign-up Button On FacebookSign-up Button On FacebookLayouts of sign-up button

B – Test Group (50%)

A – Control Group (50%)

Sign-up Button On FacebookSign-up Button On Facebook

Which one performed better?

B – Test Group (50%)

A – Control Group (50%)

Layouts of sign-up button

Sign-up Button On FacebookSign-up Button On FacebookLayouts of

sign-up button

Much more sign-ups!

A – Control Group (50%)

B – Test Group (50%)

■■ “Only 10% are likely to cause a true uplif” - Google after 12K tests-- Be able to i Be able to iterate fast!

■■ “80% of the times, we are wrong about what consumers want”-- The truth is in data!The truth is in data!

A/B Tests

In the past,

we guesstimated a bit

(common sense, intuition,

gut feeling, observations,

inspirations)

Isn't it inspired by the Window's

Menu Start button? ;)

Isn't it inspired by the Window's

Menu Start button? ;)

“KöP!” means “BUY!”“KöP!” means “BUY!”

Today,

we make data-driven decisions

To make data-driven decision

data and data-infrastructure

are required (among the others)

■ ■ Over Over 6 million of paying subscribers6 million of paying subscribers ■ ■ Over Over 24 million of MAU24 million of MAU (monthly active users) (monthly active users) ■ ■ 1.5 billion playlists1.5 billion playlists created so far created so far ■ ■ Available in Available in 55 countries55 countries ■ ■ Over Over 20 million of songs20 million of songs ■ ■ 4,5 billion hours streamed4,5 billion hours streamed in 2013 in 2013

Users At Spotify

■ ■ Data generated Data generated by usersby users and and for usersfor users!!-- 1.5 1.5 TB of compressed data from users per day TB of compressed data from users per day-- 64 TB of data generated in Hadoop each day (triplicated)64 TB of data generated in Hadoop each day (triplicated)

(Big) Data At Spotify

■■ Apache Apache Hadoop YARNHadoop YARN ■■ Many other systems including:Many other systems including:-- KafkaKafka, , LuigiLuigi, , Cassandra, Cassandra, PostgreSQLPostgreSQL in production in production

-- Giraph, Tez, Spark in the evaluation modeGiraph, Tez, Spark in the evaluation mode

Data Infrastructure At Spotify

■■ Probably Probably the largest commercial Hadoop cluster in Europe!the largest commercial Hadoop cluster in Europe!-- 694 heterogeneous nodes-- 12.63 PB of data used12.63 PB of data used-- ~7.000 job each day~7.000 job each day

Apache Hadoop

■■ Used for Used for “off-line” processing“off-line” processing-- When Hadoop is down, Spotify still plays music!When Hadoop is down, Spotify still plays music!-- When Hadoop is down, Data Analysts play FIFA, table tennisWhen Hadoop is down, Data Analysts play FIFA, table tennis

or … run queries locallyor … run queries locally■■ We We mostly analyze logsmostly analyze logs from users' activity from users' activity

Apache Hadoop

■■ Get insights to Get insights to offer a better productoffer a better product-- “More data usually beats better algorithms”“More data usually beats better algorithms”

■■ Get insights to Get insights to make better decisionsmake better decisions-- Avoid “guesstimates”Avoid “guesstimates”

■■ Take a competitive advantageTake a competitive advantage-- More companies have started offering music streamingMore companies have started offering music streaming

What Does Hadoop Allow Us To Do?

■■ We We use multiple tools and languagesuse multiple tools and languages-- HiveHive is very popular among our data analysts is very popular among our data analysts-- CrunchCrunch for core pipeline jobs for core pipeline jobs-- Some Some legacy code in Hadoop Streaminglegacy code in Hadoop Streaming with Python with Python-- A number of A number of PigPig, , Java MapReduceJava MapReduce jobs jobs-- AvroAvro as storage format (but we start considering columnar as storage format (but we start considering columnar

formats)formats)

How Do We Use Hadoop?

■■ Primarily Primarily uused to transport logs-- from multiple servers-- to a central location for storage and analysis

■■ A better fit for us than FlumeA better fit for us than Flume-- We got higher throughput with Kafka We got higher throughput with Kafka

■■ We added more features to KafkaWe added more features to Kafka-- E End-to-end deliverynd-to-end delivery-- Encryption Encryption

Apache Kafka

■■ A scalable and distributed key-value store■■ Provides fast read-write access for many Provides fast read-write access for many small pieces of datasmall pieces of data-- We use it for playlists, user profiles, We use it for playlists, user profiles,

popularity countpopularity count

■■ Was a better fit for us than HBaseWas a better fit for us than HBase-- The NN was the SPOF at that time The NN was the SPOF at that time

Apache Cassandra

■■ Allows us to build complex pipelines of batch jobs■■ HHandles dependency resolution, workflow management, visualization and more

■■ Our alternative to Oozie and AzkabanOur alternative to Oozie and Azkaban-- Spotify, Spotify, Foursquare, Bitly and more contributeFoursquare, Bitly and more contribute

Luigi

We still use them!■■ Powering features that require Powering features that require transactions support, integrity transactions support, integrity constraintsconstraints-- e.g. e.g. ordering Spotify gift-cardsordering Spotify gift-cards

■■ Semi-aggregated data for Semi-aggregated data for dashboardsdashboards■■ Semi-aggregated data for Semi-aggregated data for quick analysisquick analysis

RDBMS

March 2013

Tricky questions were asked!

1. How many servers do you need to buy to survive one year?2. If we agree, what will you do to use them efficiently?3. If we agree, do not come back to us this year, OK?

Finance Department

■ Partially responsible for answering these questions!■ One of Data Engineers who - takes care of 694-node Hadoop-YARN cluster - implements and troubleshoots users' jobs- works in a team with Josh, Marcin, Rafal, Fabian and Wouter

■ Hadoop instructor for almost 2 years■ Co-organizer of Warsaw and Stockholm HUGs■ Blogger at HakunaMapData.com

Adam Kawa

■■ Latency analysis- msec to wait for music after pressing the “Play” button

■■ CCapacity planning- servers, bandwidth, data-center space and more

Operational Metrics

■■ Hadoop provides tons of metrics, logs and files■■ They can beThey can be analyzed by … Hadoop

Operational Metrics For Hadoop

■ This knowledge can be useful to learn how to- measure how fast our HDFS is growing- calculate the empirical retention policy for datasets- optimize the scheduler- benchmark the cluster- and more

What Hadoop Can Tell About Itself

Let's see

a couple of examples

5.000 TB of data created before October 1, 2013

Could we Archive data accessed

before this day?

■ You can analyze FsImage file to learn how fast you grow■ You can even correlate this data with - number of DAU - total size of logs generated by users - activity of users e.g. hours streamed - number of queries / day run by analysts

Advanced HDFS Capacity Planning

■ You can also use ''trend feature'' in Ganglia

Simplified HDFS Capacity Planning

If we do NOTHING, we will fill the cluster in September...

What will we do

to surviver longer than September?

■ We introduced an automatic retention policy- An owner of the dataset specifies a retention period- If needed, a retention period can be calculated empirically

We continuously improve

our MapReduce jobs

■ We schedule some jobs each hour, day or week e.g.:- Top lists for each country- Reports for the labels, partners, advertisers

Idea■ Use job statistics from the previous executions of a job- to optimize the current execution of this job- to learn about the history of performance of a given job

Recurring MapReduce Jobs

Even perfect manual settingmay become eventually outdatedwhen an input dataset grows!

■ A tiny PoC ;)■ The average task time set to 10 minutes (inspired by LinkedIn)

■ It should help in extreme cases: very short and long living tasks

type # map # reduce avg map time avg reduce time job execution time

old_1 4826 25 46sec 1hrs, 52mins, 14sec 2hrs, 52mins, 16sec

new_1 391 294 4mins, 46sec 8mins, 24sec 23mins, 12sec

type # map # reduce avg map time avg reduce time job execution time

old_2 4936 800 7mins, 30sec 22mins, 18sec 5hrs, 20mins, 1sec

new_2 4936 1893 8mins, 52sec 7mins, 35sec 1hrs, 18mins, 29sec

MapReduce Jobs Autotuning

■ We make data-driven decisions to improve our product■ Scalable and open-source projects allows us to do that■ Hadoop, Cassandra, Kafka need love and care- And passionate people who give it to them

■ Hadoop is like a salutary virus- It quickly spreads across people and projects

Summary

Questions?

BONUS!

One Question:One Question: What could happen after some time of simultaneousWhat could happen after some time of simultaneous development of MapReduce jobs,development of MapReduce jobs, maintenance of a large cluster,maintenance of a large cluster, and listening to perfect music for every moment?and listening to perfect music for every moment?

A Possible Answer:A Possible Answer: You may discover Hadoop in the lyrics of many popular songs!You may discover Hadoop in the lyrics of many popular songs!

Check out spotify.com/jobs or @Spotifyjobs for more information

kawaa@spotify.comCheck out my blog: HakunaMapData.com

Want to join the band?

Thank you!