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APIdays Barcelona 2019 - How to build a social network on Serverless with Yan Cui, Lumigo
1.
How to build
a social network on #serverless Yan Cui @theburningmonk
2.
Yan Cui http://theburningmonk.com @theburningmonk Independent Consultant
3.
Yan Cui http://theburningmonk.com @theburningmonk Developer Advocate
@
4.
5.
6.
7.
apr, 2016
8.
nov, 2016
9.
WHY?
10.
11.
hey guys, vote
on this post and I’ll announce a winner at 10PM tonight
12.
10PM traffic
13.
10PM traffic 70-100x
14.
low utilisation to
leave room for spikes EC2 scaling is slow, so scale earlier
15.
lots of $$$
for unused resources
16.
up to 30
mins for deployment deployment required downtime
17.
features took months
to develop
18.
- Dan North “lead
time to someone saying thank you is the only reputation metric that matters.”
19.
20.
WHY? to deliver better
UX
21.
WHY? to deliver better
UX to deliver value faster
22.
WHY? to deliver better
UX to deliver value faster to be more cost efficient
23.
WHY? to deliver better
UX to deliver value faster to be more cost efficient HOW?
24.
what would good
look like for us?
25.
small fast zero downtime no lock-step deployments
should be…
26.
features should be… deployable
independently loosely-coupled
27.
we want to… minimise
cost for unused resources
28.
we want to… minimise
cost for unused resources minimise ops effort
29.
we want to… minimise
cost for unused resources minimise ops effort reduce tech mess
30.
we want to… minimise
cost for unused resources minimise ops effort reduce tech mess deliver visible improvements faster
31.
WHY? to deliver better
UX to deliver value faster to be more cost efficient HOW? microservices
32.
WHY? to deliver better
UX to deliver value faster to be more cost efficient HOW? microservices event-driven
33.
WHY? to deliver better
UX to deliver value faster to be more cost efficient HOW? microservices event-driven serverless
34.
WHY? to deliver better
UX to deliver value faster to be more cost efficient HOW? microservices event-driven serverless WHAT? this talk!
35.
WHY? to deliver better
UX to deliver value faster to be more cost efficient HOW? microservices event-driven serverless WHAT? this talk!
36.
170 Lambda functions
in prod
37.
95% cost saving
vs. EC2
38.
15x no. of
prod releases per month
39.
15x no. of
prod releases per month (features were sometimes shipped on the same day)
40.
time is a good
fit
41.
1st function in
prod! time is a good fit
42.
? time is a good
fit 1st function in prod!
43.
CI/CD?
44.
CI/CD? testing?
45.
CI/CD? testing? logging, monitoring, alerting?
46.
time is a good
fit 1st function in prod! CI/CD, testing, logging, monitoring, alerting
47.
170 functions ? time is a
good fit 1st function in prod! CI/CD, testing, logging, monitoring, alerting
48.
tracing?
49.
tracing? config management?
50.
tracing? config management? security?
51.
170 functions time is a
good fit 1st function in prod! CI/CD, testing, logging, monitoring, alerting tracing, config management, security
52.
API Gateway and
Kinesis Authentication & authorisation (IAM, Cognito) Testing Running & Debugging functions locally Log aggregation Monitoring & Alerting X-Ray Correlation IDs CI/CD Performance and Cost optimisation Error Handling Configuration management VPC Security Leading practices (API Gateway, Kinesis, Lambda) Canary deployments http://bit.ly/production-ready-serverless get 40% off with: ytcui
53.
evolving the PLATFORM
54.
Legacy Monolith Amazon
Kinesis Step 1. ALL state changes!
55.
events are an
enabler for COMPOSABILITY
56.
AWS LAMBDA is the...
57.
Kinesis
58.
Kinesis API Gateway AWS
Lambda API GatewayAWS Lambda service-A service-B
59.
Kinesis API Gateway AWS
Lambda API GatewayAWS Lambda service-A service-B
60.
Kinesis API Gateway AWS
Lambda API GatewayAWS Lambda service-A service-B AWS Lambda AWS Lambda AWS Lambda
61.
Kinesis API Gateway AWS
Lambda API GatewayAWS Lambda service-A service-B AWS Lambda AWS Lambda AWS Lambda DynamoDBIOT
62.
Kinesis API Gateway AWS
Lambda API GatewayAWS Lambda service-A service-B AWS Lambda AWS Lambda AWS Lambda DynamoDBIOT
63.
Kinesis API Gateway AWS
Lambda API GatewayAWS Lambda service-A service-B AWS Lambda AWS Lambda AWS Lambda DynamoDBIOT AWS Lambda AWS Lambda
64.
build loosely-coupled system through
events
65.
service A service
B service C service D bounded context bounded context
66.
service A service
B service C service D bounded context bounded context
67.
service A service
B service C service D
68.
69.
there are no
silver bullets
70.
service A service
B service C service D
71.
service A service
B service C service D
72.
service A service
B service C service D update!
73.
service A service
B service C service Dbackward-compatible? update!
74.
75.
bounded context DON’T use
events to orchestrate workflows within the same bounded context
76.
bounded context adds unnecessary complexity
to logging, tracing, and end-to-end reporting
77.
bounded context the workflow
doesn’t exist as a standalone concept, but as the sum of a series of loosely connected parts
78.
Step Functions use Step
Functions instead
79.
Step Functions don’t forget
to emit events from the workflow
80.
Step Functions so others
can react to state changes that happened as part of the workflow
81.
“how do I
organize my functions into code repositories?”
82.
one repo per
function?
83.
github repo github repo github repo github repo github repo github repo github repo github repo github repo
84.
github repo github repo github repo github repo github repo github repo github repo github repo github repo
85.
monorepo?
86.
github repo
87.
https://lumigo.io/blog/mono-repo-vs-one-per-service/
88.
one repo per
service?
89.
90.
91.
92.
93.
94.
95.
96.
github repo github repo github repo github repo user-api timeline-api relationship-api search-api
97.
CI/CD pipeline per
service
98.
functions are deployed together,
as a stack
99.
Strangler Pattern incrementally migrate
the legacy system by gradually replacing pieces of functionalities to the new system
100.
101.
rebuilt search
102.
Legacy Monolith Amazon
Kinesis Amazon Lambda Amazon CloudSearch
103.
Legacy Monolith Amazon
Kinesis Amazon Lambda Amazon CloudSearchAmazon API Gateway Amazon Lambda
104.
proxy requests from
monolith to new service
105.
new analytics pipeline
106.
expensive ($3000/month) don’t understand
our domain JS based query language
107.
Legacy Monolith Amazon
Kinesis Amazon Lambda Google BigQuery
108.
Legacy Monolith Amazon
Kinesis Amazon Lambda Google BigQuery 1 developer, 2 days design production (his 1st serverless project)
109.
Legacy Monolith Amazon
Kinesis Amazon Lambda Google BigQuery “nothing ever got done this fast at Skype!” - Chris Twamley
110.
- Dan North “lead
time to someone saying thank you is the only reputation metric that matters.”
111.
$3000/month $0.03/month
112.
113.
Kinesis sink
114.
Kinesis Kinesis Firehose batch
Kinesis events
115.
Kinesis Kinesis Firehose
S3 data lake
116.
Kinesis Kinesis Firehose
S3 Glue analyze data schema, catalog data into tables
117.
Kinesis Kinesis Firehose
S3 Athena Glue query engine
118.
Kinesis Kinesis Firehose
S3 AthenaQuickSight Glue visualization, dashboards
119.
Kinesis Kinesis Firehose
S3 AthenaQuickSight Glue no code is required!
120.
Kinesis Kinesis Firehose
S3 AthenaQuickSight Glue no code is required! pay-per-use!
121.
user action business
intelligence
122.
user action business
intelligence
123.
124.
125.
Problem didn’t work…
126.
Problem didn’t work… over-engineered…
127.
128.
try figure out
what’s going on here…
129.
Problem didn’t work… over-engineered… didn’t scale…
130.
131.
Rebuilt with Lambda
132.
133.
134.
135.
136.
137.
built-in retry and DLQ
138.
built-in retry and DLQ avoid
repeating expensive work of fetching mils of relationships
139.
140.
141.
142.
github repo timeline-api service: timeline-api provider: name: aws runtime:
nodejs6.10 stage: dev region: us-east-1 functions: distribute-yubl: … undistribute-yubl: …
143.
144.
Problem didn’t work…
145.
“it returns the
first 30 users in the database, by creation time…”
146.
147.
Rebuilt with Lambda
148.
BigQuery
149.
BigQuery
150.
grapheneDB BigQuery
151.
grapheneDB BigQuery
152.
grapheneDB BigQuery
153.
grapheneDB BigQuery mostly built in
one sleepless night…
154.
155.
more Scalable (and scales
faster!)
156.
Cheaper (don’t pay for
idle servers)
157.
Resilience (built-in redundancy and
multi-AZ)
158.
Secure
159.
request blue-green deployment req/s auto-scaling us-east-1a us-east-1b us-east-1c multi-AZ
160.
idea production greater Velocity
from idea to product
161.
WHY? to deliver better
UX to deliver value faster to be more cost efficient
162.
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