This document summarizes Sony's development of a scalable search system using Apache Solr for user-generated content on the PlayStation platform. PlayStation users can easily share media like broadcasts, screenshots, and videos to third-party networks. Sony built a SolrCloud-based system to provide a central place to search for this content across millions of users. The system uses three Solr clusters to handle different media types and supports over 20 languages. It processes over 1 billion search requests per day with low latency.

1. Developing
Scalable
Search
for
User
Generated
Content
at
PlaySta:on
Alvin
Peng
Sr.
So=ware
Engineer
Sony
Interac:ve
Entertainment

2. User
Generated
Content
(UGC)
in
PlaySta:on
• PlaySta:on
users
can
easily
share
awesome
medias
• Media
types
– Broadcasts
– Screenshots
– Videos
• Medias
are
posted
to
third
party
networks
– Facebook
– TwiKer
– YouTube
– Dailymo:on
– Twitch
– Ustream
– Niconico

3. However…
• There
was
no
central
place
to
show
or
search
for
all
these
awesome
contents
• Only
shown
up
in
users’
Ac:vity
Feed
and
Profile
• Only
sent
to
friends
• Basically
not
visible
to
majority
of
our
millions
of
users

4. Difficulties of UGC System
• Searchable
• Scalability
• Performance
• Dynamic
content
• A
lot
of
read
• A
lot
write
• Various
searching
requirements

5. Solution
• SolrCloud
based
scalable
search
system
for
public
UGC
by
users
of
PlaySta:on

6. Why Solr?
• Widely
used
open
source
search
plaTorm
• Scalable
• Stable
• Feature
rich
• Not
just
a
search
plaTorm
• Great
Solr
community,
both
individuals
and
companies

7. Developers of UGC backend
system
• Alvin
Peng
• David
Herrera
Rosales

8. Live From PlayStation and More

9. Live From PlayStation and More

10. Live From PlayStation and More

11. Live From PlayStation and More

12. System Architecture

13. System Architecture

14. System Architecture

15. System Architecture

16. System Architecture

17. System Architecture

18. System Architecture

19. System Architecture

20. UGC SolrCloud System Design
• Solr
5.2.1
• SolrJ
CloudSolrClient
• Single
collec:on
• 3
clusters
in
produc:on
environment
– Broadcasts
– Screenshots
– Videos
• 5
zookeeper
nodes
• Single
shard
• 16
Solr
nodes
per
cluster

21. Solr Schema
• Field
types
– Class
• StrField
• TextField
• TrieLongField
• TrieDateField
• etc.
– Analyzer
• Char
filter
– MappingCharFilterFactory
– HTMLStripCharFilterFactory
– PaKernReplaceCharFilterFactory
– etc.
• Tokenizer
– StandardTokenizerFactory
– NGramTokenizerFactory
– KeywordTokenizerFactory
– etc.
• Filter
– LowerCaseFilterFactory
– PorterStemFilterFactory
– StopFilterFactory
– etc.
– Index
Analyzer
and
Query
Analyzer

22. Solr Schema
• Fields
– Number
of
fields
– Field
type
– Indexed
– Stored
– etc.
• copyField
– <copyField
source="*_t"
dest=”anything"
maxChars="25000"
/>
• dynamicField
– <dynamicField
name="*_t"
type=”text"
indexed="true"
stored="true"/>

23. UGC Multilingual Support
• Supports
about
20
languages
– English
– Spanish
– Japanese
– etc.
• Different
field
types
for
different
languages
• Different
tokenizers
and
filters

24. UGC Solr Configuration
• Hard
commit:
15
minutes
– Hard
commits
are
about
durability
• So=
commit:
1
minute
– So=
commits
are
about
visibility
– Less
expensive,
but
not
free
– Use
the
longest
so=
commit
interval
that’s
acceptable
for
best
performance

25. UGC Stats
• Online
since
last
Sept.
• Number
of
documents
– Broadcasts:
26K
– Screenshots:
5M
– Videos:
20M
• Average
request
RPS
– Total
UGC
query
requests
per
day
>
1B
– Average
Solr
query
RPS:
• Broadcasts:
1600
• Screenshots:
250
• Videos:
250
– Average
Solr
update
RPS:
• Broadcasts:
500
• Screenshots:
250
• Videos:
500
• Average
query
latency
– Average
Solr
query
latency:
• Broadcasts:
4ms
(16ms
for
leader)
• Screenshots:
14ms
(16ms
for
leader)
• Videos:
60ms
(210ms
for
leader)
– Average
Solr
update
latency:
• Broadcasts:
8ms
(60ms
for
leader)
• Screenshots:
1ms
(10ms
for
leader)
• Videos:
2ms
(24ms
for
leader)

26. Finally
• Happy
searching
with
Solr!

27. Q/A