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The Evolution of Spotify Home
Architecture
Emily
Staff Engineer
Anil
Data Engineer
@anilmuppallar@emilymsa
Our mission is to unlock the potential of human creativity — by giving a million
creative artists the opportunity to live off their art and billions of fans the
opportunity to enjoy and be inspired by it.
The Evolution of Spotify Home Architecture - Qcon 2019
The Evolution of Spotify Home Architecture - Qcon 2019
shelf
shelf name
card
Overview
● Started with a Batch architecture
● Used services to hide complexity and be more reactive
● Leveraged GCP and added streaming pipelines to build
a product based on user activity
Batch
2016
Batch
Songs
Played
Logs
Word2Vec
word2vec
A natural language processing model to learn
vector representations of words
(“embeddings”) from text.
https://www.tensorflow.org/tutorials/word2vec
word2vec
Input:
Playlists
Output:
Vector representation of tracks
word2vec
Input:
Playlists
Output:
Vector representation of tracks
2Pac
Bach
Mozart
Batch
Songs
Played
Logs
Word2Vec
Batch
Songs
Played
Logs
Hadoop
Jobs
Word2Vec
Batch
Songs
Played
Logs
Hadoop
Jobs Cassandra
Word2Vec
Batch
Songs
Played
Logs
Hadoop
Jobs Cassandra
Word2Vec
Batch
Songs
Played
Logs
Hadoop
Jobs Cassandra
Word2Vec
CMS
Batch
Songs
Played
Logs
Hadoop
Jobs
Fetch Shelf
for HomeCassandra
Word2Vec
CMS
Pros & Cons
+ Low latency to load Home
+ Fallback to old data if it fails to
generate recommendations
- Recommendations updated
once every 24 hours
- Calculate recommendations
for every user, even if they
aren’t active
- Experimentation can be
difficult
- Operational overhead to
maintain Cassandra and
Hadoop
Batch
Songs
Played
Logs
Hadoop
Jobs
Fetch Shelf
for HomeCassandra
Word2Vec
CMS
Batch
Songs
Played
Logs
Hadoop
Jobs
Fetch Shelf
for HomeCassandra
Word2Vec
CMS
Services
2017
Services
Songs
Played
Service
Word2Vec
Service
Services
Songs
Played
Service
CMS
Word2Vec
Service
Services
Create Shelf
for Home
CMS
Songs
Played
Service
Word2Vec
Service
Services
CMS
Songs
Played
Service
Word2Vec
Service
Create Shelf
for Home
Services
CMS
Songs
Played
Service
Word2Vec
Service
Create Shelf
for HomeCreate Shelf
for Home
Services
CMS
Songs
Played
Service
Word2Vec
Service
Create Shelf
for HomeCreate Shelf
for HomeCreate Shelf
for Home
Pros & Cons
+ Updates recommendations at
request time
+ Calculate recommendations
for Home users only
+ Simplified stack
+ Easier to Experiment
+ Google managed
infrastructure
- High latency to load Home
- No fallback if request fails
Streaming ++ Services
2018 - Present
Streaming Pipelines
● Google Dataflow pipelines using Spotify Scio - scala wrapper on Apache
Beam
● Real time data - Unbounded stream of user events
○ All user events are available as Google Pubsub topics
● Perform aggregation operations using time based windows
○ groupBy, countBy, join...
● Store the results
○ Pubsub, BigQuery, GCS, Bigtable
follow
Real time Signals
Real time Signals
follow
pubsub
pubsub
pubsub
Streaming
Pipeline
Real time Signals
follow
pubsub
pubsub
pubsub
Streaming
Pipeline
Real time Signals
follow pubsub
pubsub
pubsub
pubsub
pubsub
Streaming
Pipeline
Real time Signals
follow Create
Shelves
pubsub
Streaming
Pipeline
Real time Signals
follow Create
Shelves
pubsub
Streaming
Pipeline
Real time Signals
follow
Songs
Played
Service
Word2Vec
Service
Create
Shelves
BT
pubsub
Streaming
Pipeline
BT
WriteWrite
Shelf
Real time Signals
follow
Fetch
Shelf
Songs
Played
Service
Word2Vec
Service
Create
Shelves
pubsub
Streaming
Pipeline
CMS
Real time Signals
follow
BTBT
WriteWrite
Shelf
Fetch
Shelf
Songs
Played
Service
Word2Vec
Service
Create
Shelves
Pros & Cons
+ Updates recommendations based
on user events
+ Computing recommendations out
of request path
+ Fresher content, driven by user
sessions
+ Fallback to previously generated
recommendations
+ Easy to experiment
- More complex stack
- More tuning in the system
- Event spikes
+ Guardrails
- Debugging is more complicated
Lessons Learned
Batch
+ Fallback to old
recommendations
+ Low latency to load
Home
- Updates are slow
Services
+ Updates are fast
- High Latency to load
Home
- No fallback if
request fails
Streaming ++ Services
+ Updates are
frequent/fast
+ Low latency to load
Home
+ Fallback to old
recommendations
- Balance computation
frequency and
downstream system
load
Lessons Learned
Batch
+ Fallback to old
recommendations
+ Low latency to load
Home
- Updates are slow
Services
+ Updates are fast
- High Latency to load
Home
- No fallback if
request fails
Streaming ++ Services
+ Updates are
frequent/fast
+ Low latency to load
Home
+ Fallback to old
recommendations
- Balance computation
frequency and
downstream system
load
Takeaways
● Less overhead with managed infrastructure. Focus more on
product
● If you care about timeliness, then adopt streaming pipelines
○ Beware of event spikes
● Optimize for developer productivity and ease of experimentation
○ Creating a new shelf is as simple as writing a new function.
Hi! I’m Luna,
Any questions?

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