Divolte collector overview

GoDataDriven
PROUDLY PART OF THE XEBIA GROUP
@asnare / @fzk / @godatadriven
signal@godatadriven.com
Divolte Collector
Andrew Snare / Friso van Vollenhoven
Because life’s too short for log ﬁle parsing

99% of all data in Hadoop
156.68.7.63 - - [28/Jul/1995:11:53:28 -0400] "GET /images/WORLD-logosmall.gif HTTP/1.0" 200 669
137.244.160.140 - - [28/Jul/1995:11:53:29 -0400] "GET /images/WORLD-logosmall.gif HTTP/1.0" 304 0
163.205.160.5 - - [28/Jul/1995:11:53:31 -0400] "GET /shuttle/countdown/ HTTP/1.0" 200 4324
163.205.160.5 - - [28/Jul/1995:11:53:40 -0400] "GET /shuttle/countdown/count70.gif HTTP/1.0" 200 46573
140.229.50.189 - - [28/Jul/1995:11:53:54 -0400] "GET /shuttle/missions/sts-67/images/images.html HTTP/1.0"
163.206.89.4 - - [28/Jul/1995:11:54:02 -0400] "GET /shuttle/technology/sts-newsref/sts-mps.html HTTP/1.0" 2
163.206.89.4 - - [28/Jul/1995:11:54:05 -0400] "GET /images/KSC-logosmall.gif HTTP/1.0" 200 1204
163.206.89.4 - - [28/Jul/1995:11:54:05 -0400] "GET /images/shuttle-patch-logo.gif HTTP/1.0" 200 891
131.110.53.48 - - [28/Jul/1995:11:54:07 -0400] "GET /shuttle/technology/sts-newsref/stsref-toc.html HTTP/1.
130.160.196.81 - - [28/Jul/1995:11:54:15 -0400] "GET /shuttle/resources/orbiters/challenger.html HTTP/1.0"
131.110.53.48 - - [28/Jul/1995:11:54:16 -0400] "GET /images/shuttle-patch-small.gif HTTP/1.0" 200 4179
137.244.160.140 - - [28/Jul/1995:11:54:16 -0400] "GET /shuttle/missions/sts-69/mission-sts-69.html HTTP/1.0
131.110.53.48 - - [28/Jul/1995:11:54:19 -0400] "GET /images/launch-logo.gif HTTP/1.0" 200 1713
130.160.196.81 - - [28/Jul/1995:11:54:19 -0400] "GET /shuttle/resources/orbiters/challenger-logo.gif HTTP/1
163.205.160.5 - - [28/Jul/1995:11:54:25 -0400] "GET /shuttle/missions/sts-70/images/images.html HTTP/1.0" 2
130.181.4.158 - - [28/Jul/1995:11:54:26 -0400] "GET /history/rocket-history.txt HTTP/1.0" 200 26990
137.244.160.140 - - [28/Jul/1995:11:54:31 -0400] "GET /images/launch-logo.gif HTTP/1.0" 304 0
137.244.160.140 - - [28/Jul/1995:11:54:38 -0400] "GET /history/apollo/images/apollo-logo1.gif HTTP/1.0" 304
168.178.17.149 - - [28/Jul/1995:11:54:48 -0400] "GET /shuttle/missions/sts-65/mission-sts-65.html HTTP/1.0"
140.229.50.189 - - [28/Jul/1995:11:54:53 -0400] "GET /shuttle/missions/sts-67/images/KSC-95EC-0390.jpg HTTP
131.110.53.48 - - [28/Jul/1995:11:54:58 -0400] "GET /shuttle/missions/missions.html HTTP/1.0" 200 8677
131.110.53.48 - - [28/Jul/1995:11:55:02 -0400] "GET /images/launchmedium.gif HTTP/1.0" 200 11853
131.110.53.48 - - [28/Jul/1995:11:55:05 -0400] "GET /images/NASA-logosmall.gif HTTP/1.0" 200 786
128.159.111.141 - - [28/Jul/1995:11:55:09 -0400] "GET /procurement/procurement.html HTTP/1.0" 200 3499
128.159.111.141 - - [28/Jul/1995:11:55:10 -0400] "GET /images/op-logo-small.gif HTTP/1.0" 200 14915
128.159.111.141 - - [28/Jul/1995:11:55:11 -0400] "GET /images/NASA-logosmall.gif HTTP/1.0" 200 786

How do we use our data?
•Ad hoc
•Batch
•Streaming

USER
HTTP request:
/org/apache/hadoop/io/IOUtils.html
log transport
service
log event:
2012-07-01T06:00:02.500Z /org/apache/hadoop/io/IOUtils.html
transport logs to
compute cluster
off line analytics /
model training
batch update
model state
serve model result
(e.g. recommendations) streaming log
processing
streaming update
model state
Typical web optimization architecture

Parse HTTP server logs
access.log

How did it get there?
Option 1: parse HTTP server logs
•Ship log ﬁles on a schedule
•Parse using MapReduce jobs
•Batch analytics jobs feed online systems

HTTP server log parsing
•Inherently batch oriented
•Schema-less (URL format is the schema)
•Initial job to parse logs into structured format
•Usually multiple versions of parsers required
•Requires sessionizing
•Logs usually have more than you ask for (bots,
image requests, spiders, health check, etc.)

Stream HTTP server logs
access.log
Message Queue or Event Transport
(Kafka, Flume, etc.)
EVENTS
tail -F
EVENTS
OTHER
CONSUMERS

Option 2: stream HTTP server logs
•tail -F logﬁles
•Use a queue for transport (e.g. Flume or Kafka)
•Parse logs on the ﬂy
•Or write semi-schema’d logs, like JSON
•Parse again for batch work load

Stream HTTP server logs
•Allows for near real-time event handling when
consuming from queues
•Sessionizing? Duplicates? Bots?
•Still requires parser logic
•No schema

Tagging
index.
html
script.
js
web server
access.log
tracking server
EVENTS
OTHER
CONSUMERS
web page trafﬁc
tracking trafﬁc
(asynchronous)
structured events
structured events

Option 3: tagging
•Instrument pages with special ‘tag’, i.e. special
JavaScript or image just for logging the request
•Create special endpoint that handles the tag
request in a structured way
•Tag endpoint handles logging the events

Tagging
•Not a new idea (Google Analytics, Omniture,
etc.)
•Less garbage trafﬁc, because a browser is
required to evaluate the tag
•Event logging is asynchronous
•Easier to do inﬂight processing (apply a schema,
add enrichments, etc.)
•Allows for custom events (other than page view)

Also…
•Manage session through cookies on the client
side
•Incoming data is already sessionized
•Extract additional information from clients
•Screen resolution
•Viewport size
•Timezone

Looks familiar?
<script>
(function(i,s,o,g,r,a,m){i['GoogleAnalyticsObject']=r;i[r]=i[r]||function(){
(i[r].q=i[r].q||[]).push(arguments)},i[r].l=1*new Date();a=s.createElement(o),
m=s.getElementsByTagName(o)[0];a.async=1;a.src=g;m.parentNode.insertBefore(a,m)
})(window,document,'script','//www.google-analytics.com/analytics.js','ga');
ga('create', 'UA-40578233-2', 'godatadriven.com');
ga('send', 'pageview');
</script>

Divolte Collector
Click stream
data collection
for Hadoop
and Kafka.

Divolte Collector
index.
html
script.
js
web server
access.log
tracking server
EVENTS
OTHER
CONSUMERS
web page trafﬁc
tracking trafﬁc
(asynchronous)
structured events
structured events

Divolte Collector:Vision
•Focus purely on collection
•Processing is a separate concern
•Minimal on the ﬂy enrichment
•The Hadoop tools ecosystem evolves too fast to compete
(SQL solutions, streaming, machine learning, etc.)
•Just provide data
•Data source for custom data science solutions
•Not a web analytics solution per se; descriptive web
analytics is a side effect
•Use cases will vary, try not too many assumptions about
users’ needs

Divolte Collector:Vision
•Solve the web speciﬁc tricky parts
•ID generation on client side (JavaScript)
•In-stream duplicate detection
•Schema!
•Data will be written in a schema-evolution-
friendly open format (Apache Avro)
•No arbitrary (JSON) objects

Javascript based tag
<body>


<script src="//example.com/divolte.js"
defer async>
</script>
</body>

Data with a schema in Avro
{
"namespace": "com.example.record",
"type": "record",
"name": "MyEventRecord",
"fields": [
{ "name": "location", "type": "string" },
{ "name": "pageType", "type": "string" },
{ "name": "timestamp", "type": "long" }
]
}

Map incoming data onto Avro records
mapping {
map clientTimestamp() onto 'timestamp'
map location() onto 'location'
def u = parse location() to uri
section {
when u.path().equalTo('/checkout') apply {
map 'checkout' onto 'pageType'
exit()
}
map 'normal' onto 'pageType'
}
}

User agent parsing
map userAgent().family() onto 'browserName'
map userAgent().osFamily() onto 'operatingSystemName'
map userAgent().osVersion() onto 'operatingSystemVersion'
// Etc... More fields available

Useful performance
Requests per second: 14010.80 [#/sec] (mean)
Time per request: 0.571 [ms] (mean)
Time per request: 0.071 [ms] (mean, across all concurrent requests)
Transfer rate: 4516.55 [Kbytes/sec] received
Connection Times (ms)
min mean[+/-sd] median max
Connect: 0 0 0.1 0 1
Processing: 0 0 0.2 0 3
Waiting: 0 0 0.2 0 3
Total: 0 1 0.2 1 3
Percentage of the requests served within a certain time (ms)
50% 1
66% 1
75% 1
80% 1
90% 1
95% 1
98% 1
99% 1
100% 3 (longest request)

Custom events
divolte.signal('addToBasket', {
productId: 309125,
count: 1
})
In the page (Javascript)
map eventParameter('productId') onto 'basketProductId'
map eventParameter('count') onto 'basketNumProducts'
In the mapping (Groovy)

Divolte Collector
•http://divolte.io
•Apache License,Version 2.0

Approach
1. Pick n images randomly
2. Optimise displayed image using bandit optimisation
3. After X iterations:
•Pick n / 2 new images randomly
•Select n / 2 images from existing set using learned
distribution
•Construct new set of images using half of existing
set and newly selected random images
4. Goto 2

Bayesian Bandits
•For each image, keep track of:
•Number of impressions
•Number of clicks
•When serving an image:
•Draw a random number from a Beta
distribution with parameters alpha = # of clicks,
beta = # of impressions, for each image
•Show image where sample value is largest

Bayesian Bandits
•https://en.wikipedia.org/wiki/Multi-armed_bandit
•http://tdunning.blogspot.nl/2012/02/bayesian-
bandits.html
•https://www.chrisstucchio.com/blog/2013/
bayesian_bandit.html

Prototype UI
class HomepageHandler(ShopHandler):
@coroutine
def get(self):
# Hard-coded ID for a pretty flower.
# Later this ID will be decided by the bandit optmization.
winner = '15442023790'
# Grab the item details from our catalog service.
top_item = yield self._get_json('catalog/item/%s' % winner)
# Render the homepage
self.render(
'index.html',
top_item=top_item)

Prototype UI
<div class="col-md-6">
<h4>Top pick:</h4>
<p>

<a href="/product/{{ top_item['id'] }}/#/?source=top_pick">
<img class="img-responsive img-rounded" src="{{ top_item['variants']['Medium']['img_source'] }}">

<script>divolte.signal('impression', { source: 'top_pick', productId: '{{ top_item['id'] }}'})</script>
</a>
</p>
<p>
Photo by {{ top_item['owner']['real_name'] or top_item['owner']['user_name']}}
</p>
</div>

Data collection in Divolte Collector
{
"name": "source",
"type": ["null", "string"],
"default": null
}
def locationUri = parse location() to uri
when eventType().equalTo('pageView') apply {
def fragmentUri = parse locationUri.rawFragment() to uri
map fragmentUri.query().value('source') onto 'source'
}
when eventType().equalTo('impression') apply {
map eventParameters().value('productId') onto 'productId'
map eventParameters().value('source') onto 'source'
}

Keep counts in Redis
{
'c|14502147379': '2',
'c|15106342717': '2',
'c|15624953471': '1',
'c|9609633287': '1',
'i|14502147379': '2',
'i|15106342717': '3',
'i|15624953471': '2',
'i|9609633287': '3'
}

Consuming Kafka in Python
def start_consumer(args):
# Load the Avro schema used for serialization.
schema = avro.schema.Parse(open(args.schema).read())
# Create a Kafka consumer and Avro reader. Note that
# it is trivially possible to create a multi process
# consumer.
consumer = KafkaConsumer(args.topic,
client_id=args.client,
group_id=args.group,
metadata_broker_list=args.brokers)
reader = avro.io.DatumReader(schema)
# Consume messages.
for message in consumer:
handle_event(message, reader)

Consuming Kafka in Python
def handle_event(message, reader):
# Decode Avro bytes into a Python dictionary.
message_bytes = io.BytesIO(message.value)
decoder = avro.io.BinaryDecoder(message_bytes)
event = reader.read(decoder)
# Event logic.
if 'top_pick' == event['source'] and 'pageView' == event['eventType']:
# Register a click.
redis_client.hincrby(
ITEM_HASH_KEY,
CLICK_KEY_PREFIX + ascii_bytes(event['productId']),
1)
elif 'top_pick' == event['source'] and 'impression' == event['eventType']:
# Register an impression and increment experiment count.
p = redis_client.pipeline()
p.incr(EXPERIMENT_COUNT_KEY)
p.hincrby(
ITEM_HASH_KEY,
IMPRESSION_KEY_PREFIX + ascii_bytes(event['productId']),
1)
experiment_count, ingnored = p.execute()
if experiment_count == REFRESH_INTERVAL:
refresh_items()

def refresh_items():
# Fetch current model state. We convert everything to str.
current_item_dict = redis_client.hgetall(ITEM_HASH_KEY)
current_items = numpy.unique([k[2:] for k in current_item_dict.keys()])
# Fetch random items from ElasticSearch. Note we fetch more than we need,
# but we filter out items already present in the current set and truncate
# the list to the desired size afterwards.
random_items = [
ascii_bytes(item)
for item in random_item_set(NUM_ITEMS + NUM_ITEMS - len(current_items) // 2)
if not item in current_items][:NUM_ITEMS - len(current_items) // 2]
# Draw random samples.
samples = [
numpy.random.beta(
int(current_item_dict[CLICK_KEY_PREFIX + item]),
int(current_item_dict[IMPRESSION_KEY_PREFIX + item]))
for item in current_items]
# Select top half by sample values. current_items is conveniently
# a Numpy array here.
survivors = current_items[numpy.argsort(samples)[len(current_items) // 2:]]
# New item set is survivors plus the random ones.
new_items = numpy.concatenate([survivors, random_items])
# Update model state to reflect new item set. This operation is atomic
# in Redis.
p = redis_client.pipeline(transaction=True)
p.set(EXPERIMENT_COUNT_KEY, 1)
p.delete(ITEM_HASH_KEY)
for item in new_items:
p.hincrby(ITEM_HASH_KEY, CLICK_KEY_PREFIX + item, 1)
p.hincrby(ITEM_HASH_KEY, IMPRESSION_KEY_PREFIX + item, 1)
p.execute()

Serving a recommendation
class BanditHandler(web.RequestHandler):
redis_client = None
def initialize(self, redis_client):
self.redis_client = redis_client
@gen.coroutine
def get(self):
# Fetch model state.
item_dict = yield gen.Task(self.redis_client.hgetall, ITEM_HASH_KEY)
items = numpy.unique([k[2:] for k in item_dict.keys()])
# Draw random samples.
samples = [
numpy.random.beta(
int(item_dict[CLICK_KEY_PREFIX + item]),
int(item_dict[IMPRESSION_KEY_PREFIX + item]))
for item in items]
# Select item with largest sample value.
winner = items[numpy.argmax(samples)]
self.write(winner)

Integrate
class HomepageHandler(ShopHandler):
@coroutine
def get(self):
http = AsyncHTTPClient()
request = HTTPRequest(url='http://localhost:8989/item', method='GET')
response = yield http.fetch(request)
winner = json_decode(response.body)
top_item = yield self._get_json('catalog/item/%s' % winner)
self.render(
'index.html',
top_item=top_item)

Server side - short term
•Allow multiple sources / sink channels
•With different input → schema mappings
•Server side events
•Support for server side event logging (JSON
endpoint)
•Enabler for mobile SDKs
•Trivial to add pixel based end-point (server
managed cookies)

Client side
•Speciﬁc browser related bug ﬁxes (IE9)
•Allow for setting session scoped parameters
•JavaScript Data Layer

Collector next steps
•Integrate with Planout (https://facebook.github.io/
planout/)
•Allow deﬁnition of online experiments in one
place
•All event logging automatically includes random
parameters generated for experiment selection
•Single solution for data collection for online
experimentation / optimization

References
•http://blog.godatadriven.com/rapid-prototyping-
online-machine-learning-divolte-collector.html
•http://divolte.io
•https://github.com/divolte/divolte-collector
•https://github.com/divolte/divolte-examples

GoDataDriven
We’re hiring / Questions? / Thank you!
@asnare / @fzk / @godatadriven
signal@godatadriven.com
Andrew Snare / Friso van Vollenhoven

Divolte collector overview

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