To support 6 million on-demand rides per day, a lot has to happen in near-real time. Latency translates into missed rides and monetary losses. Grab relies data streaming in Apache Kafka, with Scylla to tie it all together. This presentation details how Grab uses Scylla as a high throughput, low-latency aggregation store to combine multiple Kafka streams in near real-time, highlighting impressive characteristics of Scylla and how it fared against other databases in Grab’s exhaustive evaluations.

1. Grab & Scylla:
Driving SEA Forward
Aravind Velamur Srinivasan
Software Engineer, Grab Technology Corp

2. Presenter bio
▪ Lead Software Engineer at Grab Technologies
• Streaming Platform
• Data Platform Infrastructure
▪ Worked at Uber Inc prior to Grab
▪ Masters in Computer Science from University of
Virginia (UVa)
▪ Contact: aravindvelamur@gmail.com

3. Agenda
▪ Background
▪ Motivation for using ScyllaDB
▪ ScyllaDB use at Grab
▪ Conclusion
▪ Q&A

4. Background

5. Introduction to Grab
▪ One of the most frequently used mobile platforms in
Southeast Asia
▪ Multiple services (Multi-million users per day and growing!)
• Transport
• Food
• Payment
• Shopping
• Package Delivery
• ...

6. Motivation

7. Why ScyllaDB
▪ Grab Tech
• Microservices (Golang)
• Kafka for streaming
• Why? Loose coupling and Scale! doh! :)

8. Why ScyllaDB
● Stream Processing
● Transforms
● Aggregates
● Joins
● ...

9. Why ScyllaDB
▪ Required a State store (metadata store) which has the
following characteristics:
• Ability to handle very high throughput
• Ability to handle bursts (doh!)
• Ability to scale out (i.e, handle hockey stick like growth)
• Very low latencies (both write and reads) - Near RealTime
• Low operational overhead
• Cost efficient

10. Why ScyllaDB

11. ScyllaDB USAGE

12. Number of Use cases:
▪ Fraud detection
▪ Stream Aggregation &
▪ Stream Statistics
▪ ...
ScyllaDB Usage

13. ▪ Background
https://www.scmp.com/week-asia/business/article/2154770/who-pays-when-indonesian-ride-
sharing-fraud-goes-full-throttle
https://www.techrepublic.com/article/seven-ride-sharing-scams-to-watch-out-for/
ScyllaDB Usage - Fraud Detection

14. ▪ Use Kafka streams to do real-time flagging of rides
▪ Might require combining multiple streams together
▪ Requires
• Low latency
• Ability to handle high throughput
ScyllaDB Usage - Fraud Detection

15. ScyllaDB Usage - Stream Statistics

16. ScyllaDB Usage - Stream Statistics
▪ Teams like to find out:
• Counts of … (eg: count of rides in the past month, etc)
• Raw stats particular to a key for help in debugging
• Business users - require stats on city/country data

17. ScyllaDB Usage - Stream Statistics
▪ Requires a fast store for raw stats
▪ Ability to build Time series on top of it.
• Hello KairosDB :) ...

18. ScyllaDB Usage - Numbers
▪ One 15 node cluster (version 2.2)
▪ Running on AWS - EC2
▪ Single DC (may be multi-DC in the future)
▪ Overall ~15TB data across all nodes

19. ScyllaDB Usage - Numbers

20. Conclusion

21. ScyllaDB at Grab So far..
▪ Overall
• Great experience
• Cost effective
• Very responsive team
• As good as advertised :)
• Growing within the company…

22. ScyllaDB at Grab So far..
▪ Some hiccups
• nodetool repair - Writes and read timeouts every time when the run
finishes (even with -pr)
• Wish nodes can join the cluster faster
• Right now takes a really long time (approx 1TB data on each node across all
keyspaces) - takes ~2.5 hours to add a node
• Better error logging

23. Thank You
Any Questions ?
Please stay in touch
aravindvelamur@gmail.com