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The fashion shopping future
Metail's Data Pipeline and
AWS
OCTOBER 2015

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Introduction
• Introduction to Metail (from BD shiny)
• Architecture Overview
• Event Tracking and Collection
• Extract Transform and Load (ETL)
• Getting Insights
• Managing The Pipeline

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The Metail Experience allows customer to…
Discover clothes on
your body shape
Create, save outfits
and share
Shop with
confidence of size
and fit

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1.6m MeModels created
Size & scale

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+
-
88 Countries
Size & scale

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Architecture Overview
• Our architecture is modelled on Nathan Marz’s Lambda Architecture: http://lambda-
architecture.net

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Architecture Overview
• Our architecture is modelled on Nathan Marz’s Lambda Architecture: http://lambda-
architecture.net

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New Data and Collection

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New Data and Collection
Batch Layer

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New Data and Collection
Batch Layer
Serving Layer

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Data Collection
• A significant part of our pipeline is powered by Snowplow:
http://snowplowanalytics.com
• We use their technology for tracking and setup for collection
– They have specified a tracking protocol, implementing it in many languages
– We’re using the JavaScript tracker
– Implementation very similar to Google Analytics (GA):
http://www.google.co.uk/analytics/
– But you have all the raw data 

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Data Collection
• Where does AWS come in?
– Snowplow Cloudfront Collector: https://github.com/snowplow/snowplow/wiki/Setting-
up-the-Cloudfront-collector
– Snowplow’s GIF, called i, we uploaded to an S3 bucket
– Cloudfront serves the content of the bucket
– To collect the events the tracker performs a GET request
– Query parameters of the GET request contain the payload
– E.g. GET http://d2sgzneryst63x.cloudfront.net/i?e=pv&url=...&page=...&...
– Cloudfront configured for http and https for only GET and HEAD with logging enabled
– Cloudfront requests, the events, are logged to our S3 bucket 
– In Lambda Architecture terms these Cloudfront logs are our master record and are
the raw data

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Extract Transform and Load (ETL)
• This is the batch layer of our architecture
• Runs over the raw (and enriched) data producing (further) enriched data sets
• Implemented using MapReduce technologies:
– Snowplow ETL written in Scalding
– Cascading (Java higher level MapReduce libraries) in Scala
https://github.com/twitter/scalding + http://www.cascading.org/
– Looks like Scala and Cascading
– Metail ETL written in Cascalog: http://cascalog.org
– Cascalog has been described as logic programming over Hadoop
– Cascading + Datalog = Cascalog
– Ridiculously compact and expressive – one of the steepest learning curve I’ve
encountered in software engineering but no hidden traps
– AWS’s Elastic MapReduce (EMR) https://aws.amazon.com/elasticmapreduce/
– AWS has done the hard/tedious work of deploying Hadoop to EC2

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Extract Transform and Load (ETL)
• Snowplow’s ETL https://github.com/snowplow/snowplow/wiki/setting-up-EmrEtlRunner
– Initial step executed outside of EMR
– Copy data in Cloudfront incoming log bucket to another S3 bucket for processing
– Next create EMR cluster
– To that cluster you add steps

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Extract Transform and Load (ETL)
• Metail’s ETL
– We run directly on the data in S3
– We store our JARs in S3 and have a process to deploy them
– We have several enrichment steps
– Our enrichment runs on Snowplow’s enriched events
– And further enrich our enriched events
– This is what is building our batch views for the serving layer

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Extract Transform and Load (ETL)
• EMR and S3 get on very well
– AWS have engineered S3 so that it can behave as a native HDFS file system with very
little loss of performance
– They recommend using S3 as permanent data store
– EMR cluster’s HDFS file system in my mind is a giant /tmp
– Encourages immutable infrastructure
– You don’t need your compute cluster running to hold your data
– Snowplow and Metail output directly to S3
– The only reason Snowplow copies to local HDFS is because they’re aggregating
the Cloudfront logs
– That’s transitory data
– You can archive S3 data to Glacier

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Getting Insights
• The work horse of Metail’s insights is Redshift: https://aws.amazon.com/redshift/
– I’d like it to be Cascalog but even I’d hate that :P
• Redshift is a “petabyte-scale data warehouse”
– Offers a Postgres like SQL dialect to query the data
– Uses a columnar distributed data store
– It’s very quick
– Currently we have a nine node compute cluster (9*160GB = 1.44TB)
– Thinking of switching to dense storage node or re-architecting
– Growing at 10GB a day

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Getting Insights
SELECT DATE_TRUNC('mon', collector_tstamp),
COUNT(event_id)
FROM events
GROUP BY DATE_TRUNC('mon', collector_tstamp)
ORDER BY DATE_TRUNC('mon', collector_tstamp);

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Getting Insights
• The Snowplow pipeline is setup to have Redshift as an endpoint:
https://github.com/snowplow/snowplow/wiki/setting-up-redshift
• The Snowplow events table is loaded into Redshift directly from S3
• The events we enrich in EMR are also loaded into Redshift again directly from S3

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Getting Insights
• A technology called Looker …
– This provides a powerful Excel like interface to the data
– While providing software engineering tools to manage the SQL used explore the data
• .. and R for the heavier stats
– Starting to interface directly to Redshift through a PostgreSQL driver
The analysis of this data is done using a combination of

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Managing the Pipeline
• I’ve almost certainly run out of time and not reached this slide 
• Lemur to submit ad-hoc Cascalog jobs
– The initial manual pipeline
– Clojure based
• Snowplow have written their configuration tools in Ruby and bash
• We use AWS’s Data Pipeline: https://aws.amazon.com/datapipeline/
– More flaws than advantages