4. Some of these questions
are ”relatively easy”
to answer…
5. 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
6. ■■ 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
8. Question
Who was the most frequently streamed female artist in 2013?
Answer?
A) Katy Perry
B) Lady Gaga
C) Madonna
D) Rihanna
Popular Artists
9. Question
Who was the most frequently streamed female artist in 2013?
Popular Artists
10. ■■ The Most Popular Male Artist - Macklemore
■■ The Most Popular Band - Imagine Dragons
■■ The Most Popular Track - “Can't Hold Us”
Popular Artists In 2013
11. ■■ UsersUsers love local artists!
-- Berlin - Sido
-- London - Coldplay
-- Singapore – Vanessa-Mae
-- Stockholm - Avicii
Popular Artists In 2013
12. ■■ 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
18. Artist Analytics – Daft Punk
“Random Access Memories” was
released on May, 17th
2013.
19. ■■ 09.08.63 – 11.02.2012
Artist Analytics – Whitney Houston
20. ■■ One of the most popular Polish rock bands ever
Artist Analytics – Budka Suflera
What happened?
21. ■■ One of the most popular Polish rock bands ever
Artist Analytics – Budka Suflera
Information about
the retirement was
announced...
22. 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
23. ■■ 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
25. 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
26. ■■ 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
27. ■■ 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
28. 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
29. ■■ 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
30. ■■ Sign-up Button On FacebookSign-up Button On Facebook
A/B Test Use Case
Sign-up button on the
landing page
31. Sign-up Button On FacebookSign-up Button On Facebook
Layouts of
sign-up
button
B – Test Group (50%)
A – Control Group (50%)
32. 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
33. 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%)
34. ■■ “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
35. In the past,
we guesstimated a bit
(common sense, intuition,
gut feeling, observations,
inspirations)
36. 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!”
39. To make data-driven decision
data and data-infrastructure
are required (among the others)
40. ■■ 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
41. ■■ 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
42. ■■ 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
43. ■■ 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
44. ■■ 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
45. ■■ 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?
46. ■■ 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?
47. ■■ 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
48. ■■ 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
49. ■■ 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
50. 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
52. 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
53. ■ 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
54. ■■ Latency analysis
- msec to wait for music after pressing the “Play” button
■■ CCapacity planning
- servers, bandwidth, data-center space and more
Operational Metrics
55. ■■ Hadoop provides tons of metrics, logs and files
■■ They can beThey can be analyzed by … Hadoop
Operational Metrics For Hadoop
56. ■ 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
61. ■ 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
62. ■ You can also use ''trend feature'' in Ganglia
Simplified HDFS Capacity Planning
If we do
NOTHING, we will
fill the cluster in
September...
63. What will we do
to surviver longer than September?
64. ■ We introduced an automatic retention policy
- An owner of the dataset specifies a retention period
- If needed, a retention period can be calculated empirically
66. ■ 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!
67. ■ 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
68. ■ 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
71. 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?
72. 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!
73.
74. Check out spotify.com/jobs or @Spotifyjobs
for more information
kawaa@spotify.com
Check out my blog: HakunaMapData.com
Want to join the band?
