This document summarizes Gojek's product launches and growth over time. It describes their transformations from a monolithic backend architecture to a microservices architecture using technologies like Kafka, Redis, and gRPC to improve scalability and resilience. It shows how these architectural changes helped reduce response times and increase throughput as Gojek's orders and users grew exponentially.

1. Bergas Bimo Branarto
Arlinda Juwitasari
Rama Notowidigdo
SCALING GOJEK

2. WHAT MAKES US WHO WE ARE

3. SPEED INNOVATION SOCIAL
IMPACT
OUR VALUES

4. Product Releases
2011: go-ride, go-send, go-shop (all phone order)
January 2015: app release with go-ride, go-send, go-shop
April 2015: go-food
September 2015: go-mart
Okt 2015: go-box, go-massage, go-clean, go-glam, go-busway
Desember 2015: go-tix
Januari 2016: go-kilat (e-commerce partnership, not on app)
April 2016: go-car

5. 11,000,000 downloads in 15 months
0
3000000
6000000
9000000
12000000
January February March April May June July August MARCH
cumulative total app downloads

6. JABODETABEK
123.500 drivers
BANDUNG
37,000 drivers
BALI
11,200 drivers
surabaya
23,000 drivers
MAKASSAR
7,100 drivers
ARE WE?
WHERE
PALEMBANG
510 drivers
MEDAN
440 drivers
BALIKPAPAN
11,200 drivers
YOGYAKARTA
690 drivers
SEMARANG
370 drivers

7. The Growth of Driver Gojek
400
800
1,200
1,600
10 11 12 1 2 3 4 5 6 7 8 9 10 11 12 1 2 3 4 5 6 7

8. The Growth of Customer
100
200
300
400
500
1 2 3 4 5 6 7 8 9 10 11 12 1 2 3 4 5 6

9. The growth of Order
350
700
1,050
1,400
1 2 3 4 5 6 7 8 9 10 11 12 1 2 3 4 5 6
number of order requestmultipliers

10. CONWAY’S LAW
Any Organization that designs a system (defined
broadly) will produce a design whose structure is a copy
of the organization’s communication structure

11. SOCIAL
Monolithic
SOCIAL
Backend
Driver
Customer
Internal User

12. Issue :
• Bugs
• Unexpected Load growth
• Long process time which resulted with server crash during peak
Challenge :
- Small tech team (5 devs: for mobile apps (cust&driver), portals (3 different portals),
backend) vs 4 (or more) divisions
- Keep server alive in unexpected high loads
- So many features (and products) need to be released to keep business running

13. Transform 1
backend - 1
Disk
backend - n
backend - 2proxy

14. Transform 1
Plus :
At least we’re still alive Minus:
Long process time
DB bottleneck

15. Transform 2
Disk
proxy
service C
core backend
core backend
service C
service B
core backend
core backend
service B
service A
core backend
service A
service A
redis
queue

16. Transform 2
Plus :
- Splitting functionalities to services
makes code more efficient (at least
for new services)
- Queue enables core backend to
push and forget for one way
communication
- Process time reduced
- Enable throttling in queue workers
Minus:
• Process time still long enough for
incoming traffic since it only split
non-transactional functionalities
• DB bottleneck

17. Transform 3 Disk
proxy
service C
core backend
core backend
service C
redis queue
service B
core backend
core backend
service B
Disk
service A
core backend
service A
service A
Disk
rest api
service D
core backend
core backend
service D
Disk
rest api
redis cache
service E
core backend
core backend
service E
Disk

18. Transform 3
Plus :
- Split some transaction processes
to another services: load splitted,
process time reduced
- Redis cache: reduce db bottleneck
- Each service owns their own db:
reduce db bottleneck
Minus:
• API calls in a flow of more than 2
services cause cascading failures

19. Transform
4 Disk
proxy
service C
core backend
core backend
service C
kafka queue
service B
core backend
core backend
service B
Disk
service A
core backend
service A
service A
Diskservice D
core backend
core backend
service D
Disk
inline redis
cache
service E
core backend
core backend
service E
Disk
service F
core backend
service A
service F
Disk
grpc (http/2)

20. Transform 4
Plus :
- Asynchronous communication
between services via kafka: reduce
api calls between services, reduces
cascade failures
- Shared redis (inline) cache: reduce
db queries, reduce api calls
between services, reduce cascade
failures
- grpc (uses http/2) should reduce
network time
Minus:
?

21. Stack :
• Java: Spring MVC and Spark
• Go
• Jruby on Rails
• AngularJS
• MySQL
• PostgreSQL
• MongoDB
• Elasticsearch
• Redis
• Kafka
• RabbitMQ

22. Response Time vs Throughput
25,000
50,000
75,000
100,000
0
400
800
1200
1600
4 5 6 7 8 9 10 11 12 1 2 3 4 5 6
response time (ms) throughput (rpm)

23. Order Growth vs Response Time
0
400
800
1,200
1,600
0
350
700
1,050
1,400
order response time (ms)

24. Order Growth vs Throughput
0
25,000
50,000
75,000
100,000
0
350
700
1,050
1,400
order throughput (rpm)

25. TRUE HAPPINESS IS THE JOURNEY, NOT THE DESTINATION
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