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Routing billions of
analytics events
with high
deliverability
Calvin French-Owen
@calvinfo
This talk
- Constraints
- Architecture
- Monitoring and microservices
What problem are
we solving?
- Thousands of incoming requests/second
- Need to for real-time
- Reliable fan-out
- Hundreds of unreliable APIs
Our Constraints
- Delivery rate
- End-to-end latency
- Data fidelity
Golden Metrics
The Lifecycle of an
Event
3 months post
launch: 80 req/s
Segment V1
- Node everywhere
- RabbitMQ
- EC2/VPC
- Mongo
- Redis
API
Layer
3 months post
launch: 80 req/s
API Scaling
- Rabbit → NSQ
- Node → Go
- Making the edge stateless
- Removed AMQP (segmentio/nsq.js)
- Co-located
- Distributed
- Simple and rock solid
Rabbit → NSQ
But there were still
issues...
// parse the body
var body = JSON.parse(req.body);
// elsewhere...
clone(body);
// parse the body
var body = JSON.parse(req.body);
// elsewhere...
clone(body); //
stateless!!!
stateless!!!
- Edge nodes are stateless
- NSQ for simplicity and reliability
- Go for parallelism
Scaling the API
Ingestion and
fanout
- Queueing topology
- Abstract everything
- Sanely retry
Scaling the fanout
Queues give you
flexibility and
scheduling
Queues give you
flexibility and
scheduling
If you’re building
150 of anything, err
on the side of over-
abstraction
// Integration Factory
function createIntegration(name){
// Create the constructor to be passed back
function Integration(settings){
this.debug = debug('segmentio:integration:' + this.slug());
this.settings = settings;
this.initialize();
}
Integration.prototype.name = name; // set the name
merge(Integration.prototype, proto); // add prototype methods
merge(Integration, statics); // add static methods
return Integration; // return the constructor
}
var MailChimp = module.exports = integration('MailChimp')
.channels(['server', 'mobile', 'client'])
.endpoint('https://api.mailchimp.com/')
.ensure('settings.datacenter')
.ensure('settings.apiKey')
.ensure('settings.listId', { methods: ['identify'] })
.ensure('message.email')
.mapper(mapper) // map our input to our output
.retries(2);
Integration.prototype.track = function track(payload, fn){
var self = this;
return this
.get('/httpapi') // common request handling
.type('json')
.query({ api_key: this.settings.apiKey })
.query({ event: JSON.stringify(payload) })
.end(function(err, res){
if (err) return fn(err, res);
if ('invalid api_key' == res.text) return fn(self.error('invalid api_key'));
fn(null, res);
});
}
Retries
function status(err){
return err.status == 500
|| err.status == 502
|| err.status == 503
|| err.status == 504
|| err.status == 429;
}
function network(err){
return err.code == 'ECONNRESET'
|| err.code == 'ECONNREFUSED'
|| err.code == 'ECONNABORTED'
|| err.code == 'ETIMEDOUT'
|| err.code == 'EADDRINFO'
|| err.code == 'EHOSTUNREACH'
|| err.code == 'ENOTFOUND';
}
API Errors Network Errors
// retry strategy with exponential backoff
if (err.retry) {
var attempts = msg.attempts;
var timeout = jitter(15*Math.pow(attempts, 3));
msg.requeue(timeout);
return;
}
Microservices &
Monitoring
- Microservices everywhere
- Docker for isolation
- Use metrics religiously
Microservices & Monitoring
module "google-analytics" {
source = "./worker"
cluster = "integration-worker"
memory = "256"
cpu = "128"
name = "google-analytics"
version = "latest"
count = "${var.count}"
}
The more surface
area you have, the
more visibility you
need.
Scaling your data pipeline
1. Queues not only define service boundaries, but
scheduling
2. Microservices and workers can provide great visibility
and scalability–as long as they are easy to boot
3. The bigger your surface area, the more visibility and
metrics you will need to provide
What’s next?
- In search of fairness
- Moving to Kafka
- Standard microservice toolkit
- Custom data transforms
What’s next?
Fin
Questions?
calvin@segment.com
@calvinfo

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DataEngConf SF16 - Routing Billions of Analytics Events with High Deliverability

  • 1. Routing billions of analytics events with high deliverability Calvin French-Owen @calvinfo
  • 2. This talk - Constraints - Architecture - Monitoring and microservices
  • 4.
  • 5.
  • 6.
  • 7.
  • 8. - Thousands of incoming requests/second - Need to for real-time - Reliable fan-out - Hundreds of unreliable APIs Our Constraints
  • 9. - Delivery rate - End-to-end latency - Data fidelity Golden Metrics
  • 10. The Lifecycle of an Event
  • 11.
  • 13. Segment V1 - Node everywhere - RabbitMQ - EC2/VPC - Mongo - Redis
  • 14.
  • 17.
  • 18. API Scaling - Rabbit → NSQ - Node → Go - Making the edge stateless
  • 19. - Removed AMQP (segmentio/nsq.js) - Co-located - Distributed - Simple and rock solid Rabbit → NSQ
  • 20.
  • 21.
  • 22.
  • 23.
  • 24. But there were still issues...
  • 25.
  • 26.
  • 27.
  • 28. // parse the body var body = JSON.parse(req.body); // elsewhere... clone(body);
  • 29. // parse the body var body = JSON.parse(req.body); // elsewhere... clone(body); //
  • 30.
  • 33. - Edge nodes are stateless - NSQ for simplicity and reliability - Go for parallelism Scaling the API
  • 34.
  • 35.
  • 37. - Queueing topology - Abstract everything - Sanely retry Scaling the fanout
  • 38. Queues give you flexibility and scheduling
  • 39. Queues give you flexibility and scheduling
  • 40.
  • 41.
  • 42.
  • 43.
  • 44.
  • 45.
  • 46.
  • 47. If you’re building 150 of anything, err on the side of over- abstraction
  • 48.
  • 49. // Integration Factory function createIntegration(name){ // Create the constructor to be passed back function Integration(settings){ this.debug = debug('segmentio:integration:' + this.slug()); this.settings = settings; this.initialize(); } Integration.prototype.name = name; // set the name merge(Integration.prototype, proto); // add prototype methods merge(Integration, statics); // add static methods return Integration; // return the constructor }
  • 50. var MailChimp = module.exports = integration('MailChimp') .channels(['server', 'mobile', 'client']) .endpoint('https://api.mailchimp.com/') .ensure('settings.datacenter') .ensure('settings.apiKey') .ensure('settings.listId', { methods: ['identify'] }) .ensure('message.email') .mapper(mapper) // map our input to our output .retries(2);
  • 51. Integration.prototype.track = function track(payload, fn){ var self = this; return this .get('/httpapi') // common request handling .type('json') .query({ api_key: this.settings.apiKey }) .query({ event: JSON.stringify(payload) }) .end(function(err, res){ if (err) return fn(err, res); if ('invalid api_key' == res.text) return fn(self.error('invalid api_key')); fn(null, res); }); }
  • 52. Retries function status(err){ return err.status == 500 || err.status == 502 || err.status == 503 || err.status == 504 || err.status == 429; } function network(err){ return err.code == 'ECONNRESET' || err.code == 'ECONNREFUSED' || err.code == 'ECONNABORTED' || err.code == 'ETIMEDOUT' || err.code == 'EADDRINFO' || err.code == 'EHOSTUNREACH' || err.code == 'ENOTFOUND'; } API Errors Network Errors
  • 53.
  • 54. // retry strategy with exponential backoff if (err.retry) { var attempts = msg.attempts; var timeout = jitter(15*Math.pow(attempts, 3)); msg.requeue(timeout); return; }
  • 56. - Microservices everywhere - Docker for isolation - Use metrics religiously Microservices & Monitoring
  • 57.
  • 58.
  • 59. module "google-analytics" { source = "./worker" cluster = "integration-worker" memory = "256" cpu = "128" name = "google-analytics" version = "latest" count = "${var.count}" }
  • 60.
  • 61.
  • 62. The more surface area you have, the more visibility you need.
  • 63.
  • 64.
  • 65.
  • 66.
  • 67. Scaling your data pipeline 1. Queues not only define service boundaries, but scheduling 2. Microservices and workers can provide great visibility and scalability–as long as they are easy to boot 3. The bigger your surface area, the more visibility and metrics you will need to provide
  • 69. - In search of fairness - Moving to Kafka - Standard microservice toolkit - Custom data transforms What’s next?