Analytics on write is a four-step process where we: 1. Read our events from our event stream 2. Analyze our events using a stream processing framework 3. Write the summarized output of our analysis to some form of storage target 4. Serve the summarized output into real-time dashboards, reports and similar In this talk I introduced the concept of analytics on write, compared it to analytics on read and then talked through an example implementation using Amazon Kinesis and AWS Lambda.

1. Analytics on write with AWS
Lambda
Unified Log London, 4th November 2015

2. Introducing myself
• Alex Dean
• Co-founder and technical lead at Snowplow,
the open-source event analytics platform
based here in London [1]
• Weekend writer of Unified Log Processing,
available on the Manning Early Access Program
[2]
[1] https://github.com/snowplow/snowplow
[2] http://manning.com/dean

3. Analytics on read, analytics
on write

4. It’s easier to start by explaining analytics on read, which
is much more widely practised and understood
1. Write all of our events to some kind of event store
2. Read the events from our event store to perform some analysis

5. In analytics on write, the analysis is performed on the
events in-stream (i.e. before reaching storage)
• Read our events from our event stream
• Analyze our events using a stream processing framework
• Write the summarized output of our analysis to some storage target
• Serve the summarized output into real-time dashboards, reports etc

6. Analytics on write and analytics on read are good at
different things, and leverage different technologies

7. With a unified log powered by Kafka or Kinesis, you can
apply both analytical approaches to your event stream
• Apache Kafka and Amazon Kinesis make it easy to have multiple
consuming apps on the same event stream
• Each consuming app can maintain its own “cursor position” on the
stream

8. Getting started with
analytics on write

9. What are some good use cases for getting started with
analytics on write?
Low-latency operational reporting, which must be fed
from the incoming event streams in as close to real-
time as possible
Dashboards to support thousands of simultaneous
users, for example a freight company might share a a
parcel tracker on its website for customers
Others? Please share your thoughts! 
1
2
?

10. Analytics on write is a very immature space – there’s only
a handful of tools and frameworks available so far…
PipelineDB
• Analytics on write (“continuous views”) using SQL
• Implemented as a Postgres fork
• Supports Kafka but no sharding yet (I believe)
amazon-kinesis-aggregators
• Reads from Kinesis streams and outputs to DynamoDB & CloudWatch
• JSON-based query recipes
• Written by Ian Meyers here in London
Druid
• Hybrid analytics on write, analytics on read
• Very rich JSON-based query language
• Supports Kafka

11. … or we can implement a bespoke analytics on write
solution – for example with AWS Lambda
• The central idea of AWS Lambda is that
developers should be writing functions
not servers
• With Lambda, we write self-contained
functions to process events, and then
we publish those functions to Lambda
to run
• We don’t worry about developing,
deploying or managing servers –
instead, Lambda takes care of auto-
scaling our functions to meet the
incoming event volumes

12. An AWS Lambda function is stateless and exists only for the side
effects that it performs

13. Designing an analytics on
write solution for OOPS

14. Let’s imagine that we have a global delivery company called
OOPS, which has five event types

15. OOPS management want a near-real time dashboard to tell
them two things
Where are each of our delivery trucks now?
How many miles has each of our delivery trucks
driven since its last oil change?
1
2

16. In DynamoDB, we could represent this as a simple table

17. All we need to do is write an AWS Lambda function to populate
this DynamoDB table from our event stream…

18. … however a more efficient approach is to apply some old-
school map-reduce to the micro-batch first

19. What do we mean when we talk about conditional writes in
DynamoDB?

20. Bonus: DynamoDB’s conditional write syntax is very readable
def conditionalWrite(row: Row) {
val vin = AttrVal.toJavaValue(row.vin)
updateIf(vin, "SET #m = :m",
"attribute_not_exists(#m) OR #m < :m",
Map(":m" -> AttrVal.toJavaValue(row.mileage)),
Map("#m" -> "mileage"))
for (maoc <- row.mileageAtOilChange) {
updateIf(vin, "SET #maoc = :maoc",
"attribute_not_exists(#maoc) OR #maoc < :maoc",
Map(":maoc" -> AttrVal.toJavaValue(maoc)),
Map("#maoc" -> "mileage-at-oil-change"))
}
for ((loc, ts) <- row.locationTs) {
updateIf(vin, "SET #ts = :ts, #lat = :lat, #long = :long",
"attribute_not_exists(#ts) OR #ts < :ts",
Map(":ts" -> AttrVal.toJavaValue(ts.toString),
":lat" -> AttrVal.toJavaValue(loc.latitude),
":long" -> AttrVal.toJavaValue(loc.longitude)),
Map("#ts" -> "location-timestamp", "#lat" -> "latitude",
"#long" -> "longitude"))
}
}

21. Demo

22. To simplify the demo, I performed some configuration
steps already (1/2)
1. Downloaded the Scala code from
https://github.com/alexanderdean/Unified-Log-
Processing/tree/master/ch11/11.2/aow-lambda
2. Built a “fatjar” for my Lambda function ($ sbt assembly)
3. Uploaded my fatjar to Amazon S3 ($ aws s3 cp …)
4. Ran a CloudFormation template to setup permissions for my
Lambda, available herehttps://ulp-
assets.s3.amazonaws.com/ch11/cf/aow-lambda.template
5. Registered my Lambda function with AWS Lambda ($ aws
lambda create-function …)

23. To simplify the demo, I performed some configuration
steps already (2/2)
6. Created a Kinesis stream ($ aws kinesis create-stream
…)
7. Created a DynamoDB table ($ aws dynamodb create-
table …)
8. Configured the registered Lambda function to use the Kinesis
stream as its input ($ aws lambda create-event-
source-mapping --event-source-arn
${stream_arn} --function-name AowLambda --
enabled --batch-size 100 --starting-position
TRIM_HORIZON)

24. Finally, let’s feed in some OOPS events…
host$ vagrant ssh
guest$ cd /vagrant/ch11/11.1
guest$ ./generate.py
Wrote DriverDeliversPackage with timestamp 2015-01-11 00:49:00
Wrote DriverMissesCustomer with timestamp 2015-01-11 04:07:00
Wrote TruckArrivesEvent with timestamp 2015-01-11 04:56:00
Wrote DriverDeliversPackage with timestamp 2015-01-11 06:16:00
Wrote TruckArrivesEvent with timestamp 2015-01-11 07:35:00

25. … and check our Kinesis stream, Lambda function and
DynamoDB table

26. Resources and further
reading

27. Further reading
Chapter 11, Analytics on write
Manning Deal of the Day today!
Discount code: dotd110415au (50% off
just today)
• https://www.pipelinedb.com/
• https://github.com/awslabs/amazon-kinesis-aggregators/
• http://druid.io/
• https://github.com/snowplow/aws-lambda-nodejs-example-project
• https://github.com/snowplow/aws-lambda-scala-example-project
• https://github.com/snowplow/spark-streaming-example-project

28. Questions?
http://snowplowanalytics.com
https://github.com/snowplow/snowplow
@snowplowdata
To meet up or chat, @alexcrdean on Twitter or
alex@snowplowanalytics.com