1.
Adam Kawa
Data Engineer @ Spotify
(Big) Data At Spotify

2.
At Spotify,
important questions are being
asked all the time!

3.
Some of these questions
are ”relatively easy”
to answer…

4.
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

5.
■■ Very granular reports are required
-- DividedDivided 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 solutionsToo much data for traditional solutions
Reporting

6.
QUIZ!

7.
Question
Who was the most frequently streamed female artist in 2013?
Answer?
A) Katy Perry
B) Lady Gaga
C) Madonna
D) Rihanna
Popular Artists

8.
Question
Who was the most frequently streamed female artist in 2013?
Popular Artists

9.
■■ The Most Popular Male Artist - Macklemore
■■ The Most Popular Band - Imagine Dragons
■■ The Most Popular Track - “Can't Hold Us”
Popular Artists In 2013

10.
■■ UsersUsers love local artists!
-- Berlin - Sido
-- London - Coldplay
-- Singapore – Vanessa-Mae
-- Stockholm - Avicii
Popular Artists In 2013

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■■ 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

12.
Question
What was the most “viral” track in 2013?
Popular Tracks

13.
Question
What was the most “viral” track in 2013?
Answer
“Get Lucky” by Daft Punk feat. Pharrell Williams
Popular Tracks

14.
Artist Analytics – Daft Punk
“Get Lucky” was
released on April, 19th
2013.

15.
Artist Analytics – Daft Punk
Around 5x more
streams comparing a
day “before” and
“after” “Get Lucky”

16.
Artist Analytics – Daft Punk
What happened that
day?

17.
Artist Analytics – Daft Punk
“Random Access Memories” was
released on May, 17th
2013.

18.
■■ 09.08.63 – 11.02.2012
Artist Analytics – Whitney Houston

19.
■■ One of the most popular Polish rock bands ever
Artist Analytics – Budka Suflera
What happened?

20.
■■ One of the most popular Polish rock bands ever
Artist Analytics – Budka Suflera
Information about
the retirement was
announced...

21.
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

22.
■■ AnalyzingAnalyzing growthgrowth
-- Number ofNumber of aactive usersctive users,, streamed songsstreamed songs, sign-ups and more
-- Where to launch Spotify nextWhere to launch Spotify next
■■ Company KPIs
Business Analytics

23.
However,
some of the questions are
really tricky to answer!

24.
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

25.
■■ Recommendations
-- Powering features likePowering 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

26.
■■ 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

27.
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

28.
■■ A/B Test
-- Come with promising “look-and-feels” and do A/B testsCome with promising “look-and-feels” and do A/B tests
■■ ExplicitExplicit ffeedback from users
-- ButBut users usually do not like to rateusers usually do not like to rate thingsthings
-- ButBut users usually do not like to customizeusers usually do not like to customize thingsthings
Designers, Feature's Owners

29.
■■ Sign-up Button On FacebookSign-up Button On Facebook
A/B Test Use Case
Sign-up button on the
landing page

30.
Sign-up Button On FacebookSign-up Button On Facebook
Layouts of
sign-up
button
B – Test Group (50%)
A – Control Group (50%)

31.
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

32.
Sign-up Button On FacebookSign-up Button On Facebook
Layouts of
sign-up button
Much more
sign-ups!
A – Control Group (50%)
B – Test Group (50%)

33.
■■ “Only 10% are likely to cause a true uplif” - Google after 12K tests
-- Be able to iBe 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

34.
In the past,
we guesstimated a bit
(common sense, intuition,
gut feeling, observations,
inspirations)

35.
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!”

36.
Today,
we make data-driven decisions

37.
To make data-driven decision
data and data-infrastructure
are required (among the others)

38.
■■ OverOver 6 million of paying subscribers6 million of paying subscribers
■■ OverOver 24 million of MAU24 million of MAU (monthly active users)(monthly active users)
■■ 1.5 billion playlists1.5 billion playlists created so farcreated so far
■■ Available inAvailable in 55 countries55 countries
■■ OverOver 20 million of songs20 million of songs
■■ 4,5 billion hours streamed4,5 billion hours streamed in 2013in 2013
Users At Spotify

39.
■■ Data generatedData generated by usersby users andand for usersfor users!!
-- 1.51.5 TB of compressed data from users per dayTB 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

40.
■■ ApacheApache Hadoop YARNHadoop YARN
■■ Many other systems including:Many other systems including:
-- KafkaKafka,, LuigiLuigi,, Cassandra,Cassandra, PostgreSQLPostgreSQL in productionin production
-- Giraph, Tez, Spark in the evaluation modeGiraph, Tez, Spark in the evaluation mode
Data Infrastructure At Spotify

41.
■■ ProbablyProbably 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

42.
■■ Used forUsed 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
■■ WeWe mostly analyze logsmostly analyze logs from users' activityfrom users' activity
Apache Hadoop

43.
■■ Get insights toGet insights to offer a better productoffer a better product
-- “More data usually beats better algorithms”“More data usually beats better algorithms”
■■ Get insights toGet 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?

44.
■■ WeWe use multiple tools and languagesuse multiple tools and languages
-- HiveHive is very popular among our data analystsis very popular among our data analysts
-- CrunchCrunch for core pipeline jobsfor core pipeline jobs
-- SomeSome legacy code in Hadoop Streaminglegacy code in Hadoop Streaming with Pythonwith Python
-- A number ofA number of PigPig,, Java MapReduceJava MapReduce jobsjobs
-- AvroAvro as storage format (but we start considering columnaras storage format (but we start considering columnar
formats)formats)
How Do We Use Hadoop?

45.
■■ PrimarilyPrimarily 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 KafkaWe got higher throughput with Kafka
■■ We added more features to KafkaWe added more features to Kafka
-- EEnd-to-end deliverynd-to-end delivery
-- EncryptionEncryption
Apache Kafka

46.
■■ A scalable and distributed key-value
store
■■ Provides fast read-write access for manyProvides 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 timeThe NN was the SPOF at that time
Apache Cassandra

47.
■■ 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

48.
We still use them!
■■ Powering features that requirePowering features that require transactions support, integritytransactions support, integrity
constraintsconstraints
-- e.g.e.g. ordering Spotify gift-cardsordering Spotify gift-cards
■■ Semi-aggregated data forSemi-aggregated data for dashboardsdashboards
■■ Semi-aggregated data forSemi-aggregated data for quick analysisquick analysis
RDBMS

49.
March 2013
Tricky questions were asked!

50.
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

51.
■ 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

52.
■■ Latency analysis
- msec to wait for music after pressing the “Play” button
■■ CCapacity planning
- servers, bandwidth, data-center space and more
Operational Metrics

53.
■■ Hadoop provides tons of metrics, logs and files
■■ They can beThey can be analyzed by … Hadoop
Operational Metrics For Hadoop

54.
■ 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

55.
Let's see
a couple of examples

56.
5.000 TB of data
created before
October 1, 2013

57.
Could we
Archive data accessed
before this day?

58.
■ 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

59.
■ You can also use ''trend feature'' in Ganglia
Simplified HDFS Capacity Planning
If we do
NOTHING, we will
fill the cluster in
September...

60.
What will we do
to surviver longer than September?

61.
■ We introduced an automatic retention policy
- An owner of the dataset specifies a retention period
- If needed, a retention period can be calculated empirically

62.
We continuously improve
our MapReduce jobs

63.
■ 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 setting
may become eventually outdated
when an input dataset grows!

64.
■ 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

65.
■ 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

66.
Questions?

67.
BONUS!

68.
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?

69.
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!

70.
Check out spotify.com/jobs or @Spotifyjobs
for more information
kawaa@spotify.com
Check out my blog: HakunaMapData.com
Want to join the band?

71.
Thank you!