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Session Replays: Unlocking
User Experience with Data
Saurabh Mathur, Founder, browsee.io
About Me
● Founder Browsee.io
● Previously, build a Customer Data Platform startup called
Connecto. Acquired by Cardekho.com
● Ex - Google
Session Replays
● Video Like Replay of User Behavior
● How is this made possible ?
Mutation Observers
● Starts with the initial page HTML
● Record all changes on a page
● Record all mouse movements, scroll and
clicks with their elements and positions
https://developer.mozilla.org/en-US/docs/Web/API/MutationObserver
Websockets vs HTTP
● Even transmitting this
information over HTTP would
have significant overhead
● Websockets allow fast
transmission of this data over
a persistent connection
Source: http://blog.arungupta.me/rest-vs-websocket-comparison-
benchmarks/
Data for User Experience
● About a few PBs of recordings data every
day persisted upto a year
● For every heatmap, we aggregate every
click and every scroll along with time
spent by the user in their heatmaps along
with.
● About a few TBs of time series data
● About a few PBs of Full Page snapshots
Data Storage
● Initially we used Cassandra as our
primary storage for replay data
● We soon moved to ScyllaDB and
were getting better throughput per
node as well as read latency. Source: https://www.scylladb.com/2021/08/24/apache-cassandra-4-
0-vs-scylla-4-4-comparing-performance//
Size Tiered Compaction
● The default replication strategies is
the Size Tiered Compaction
Strategy
● This is the oldest and the default
strategy for both ScyllaDB and
Cassandra.
Size Tiered Compaction
● Major Drawback is it requires about
25-50% free space for compaction.
● Storage is one of our largest cost
head.
● 25-50% disk wastage directly impacts
cost
Level Tiered Compaction
● Keeps small SStables each of size
160MB
● Keeps adding data to next level
sstables. Creates new table if size
is breached.
Level Tiered Compaction
● In our experience it significantly
increased the write processing.
● No improvement in read latency
● The cost we were saving in disk
was getting lost in compute cycles
Incremental Compaction
Strategy
● A variant of Size Tiered Compaction. It
only addresses the largest compaction
step which requires free space.
● At that step, it breaks the compacting
sstables into runs and keeps compacting
the runs while deleting the completed
ones. So much lesser.
Data Security at Rest
● Users can choose which data
center to save and process their
data.
● We also have blacklist caches at
queueing as well as rest to
immediately respond to Data
cleanup requests.
Session Tagging
● Browsee marks several user insights
on session recordings depending on
the user’s Behavior
● Temporal click events like rage clicks
● Repeated events or quickly browsing
through several pages
Live Sessions
● Kafka’s Real time processors allow us to
process session tagging in near real time.
● Users need real time session views
● We also need incremental session
updates on client side to fetch new data
as the users are watching a live session
Roadmap
● Navigation graph - A graph based time series data which scales
exponentially with site’s pages
● Bot Detection Models
● More User Behavior Models like Multi User Analysis, User Defined Tags
Thanks!

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Session Recordings - Unlocking the user experience with Data

  • 1. Session Replays: Unlocking User Experience with Data Saurabh Mathur, Founder, browsee.io
  • 2. About Me ● Founder Browsee.io ● Previously, build a Customer Data Platform startup called Connecto. Acquired by Cardekho.com ● Ex - Google
  • 3. Session Replays ● Video Like Replay of User Behavior ● How is this made possible ?
  • 4. Mutation Observers ● Starts with the initial page HTML ● Record all changes on a page ● Record all mouse movements, scroll and clicks with their elements and positions https://developer.mozilla.org/en-US/docs/Web/API/MutationObserver
  • 5. Websockets vs HTTP ● Even transmitting this information over HTTP would have significant overhead ● Websockets allow fast transmission of this data over a persistent connection Source: http://blog.arungupta.me/rest-vs-websocket-comparison- benchmarks/
  • 6. Data for User Experience ● About a few PBs of recordings data every day persisted upto a year ● For every heatmap, we aggregate every click and every scroll along with time spent by the user in their heatmaps along with. ● About a few TBs of time series data ● About a few PBs of Full Page snapshots
  • 7. Data Storage ● Initially we used Cassandra as our primary storage for replay data ● We soon moved to ScyllaDB and were getting better throughput per node as well as read latency. Source: https://www.scylladb.com/2021/08/24/apache-cassandra-4- 0-vs-scylla-4-4-comparing-performance//
  • 8. Size Tiered Compaction ● The default replication strategies is the Size Tiered Compaction Strategy ● This is the oldest and the default strategy for both ScyllaDB and Cassandra.
  • 9. Size Tiered Compaction ● Major Drawback is it requires about 25-50% free space for compaction. ● Storage is one of our largest cost head. ● 25-50% disk wastage directly impacts cost
  • 10. Level Tiered Compaction ● Keeps small SStables each of size 160MB ● Keeps adding data to next level sstables. Creates new table if size is breached.
  • 11. Level Tiered Compaction ● In our experience it significantly increased the write processing. ● No improvement in read latency ● The cost we were saving in disk was getting lost in compute cycles
  • 12. Incremental Compaction Strategy ● A variant of Size Tiered Compaction. It only addresses the largest compaction step which requires free space. ● At that step, it breaks the compacting sstables into runs and keeps compacting the runs while deleting the completed ones. So much lesser.
  • 13. Data Security at Rest ● Users can choose which data center to save and process their data. ● We also have blacklist caches at queueing as well as rest to immediately respond to Data cleanup requests.
  • 14. Session Tagging ● Browsee marks several user insights on session recordings depending on the user’s Behavior ● Temporal click events like rage clicks ● Repeated events or quickly browsing through several pages
  • 15. Live Sessions ● Kafka’s Real time processors allow us to process session tagging in near real time. ● Users need real time session views ● We also need incremental session updates on client side to fetch new data as the users are watching a live session
  • 16. Roadmap ● Navigation graph - A graph based time series data which scales exponentially with site’s pages ● Bot Detection Models ● More User Behavior Models like Multi User Analysis, User Defined Tags