Leveraging Kafka for Big Data in Real Time Bidding, Analytics, ML & Campaign Management for Globally Distributed Data Flows

@helenaedelson #kafkasummit 1
Leveraging Kafka for Big Data in Real Time Bidding,
Analytics, ML & Campaign Management
for Globally Distributed
Data Flows
Helena Edelson @helenaedelson Kafka Summit 2016

@helenaedelson #kafkasummit
VP of Engineering, Tuplejump
Previously: Sr Cloud / Big Data / Analytics
Engineer: DataStax, CrowdStrike, VMware,
SpringSource...
Event-Driven systems, Analytics, Machine
Learning, Scala
Committer: Kafka Connect Cassandra,
Spark Cassandra Connector
Contributor: Akka, previously: Spring
Integration
Speaker: Kafka Summit, Spark Summit, Strata,
QCon, Scala Days, Scala World, Philly ETE
2
twitter.com/helenaedelson
github.com/helena
slideshare.net/helenaedelson

The Real Topic
3
http://www.slideshare.net/palvaro/ricon-keynote-outwards-from-the-middle-of-the-maze/42

Chaos Of
Distribution
One of the more
fascinating problems is
that of solving the chaos
of distributed systems.
Regardless of the
domain.
4

Aproaching this
within the use case of:
High-Level
Landscape
Platform &
Infrastructure
Strategies and Patterns
Four-Letter Acronyms
Can't Touch This
Architecture
5

The Landscape

The Digital Ad Industry

An RTB Drive-By
Real time auction for ad
spaces, all devices
High throughput, low-Latency
(similar to FIN Tech but not
quite)
OpenRTB API Spec - but not
everyone uses it
8
Open protocol for
automated trading of
digital media across
platforms, devices, and
advertising solutions

Ad
Delivered
to User
In A Nutshell
User
hits a
Publisher's
page
Advertiser
Advertiser
Advertisers
send Bid
Requests
Highest
Bid
Accepted

Site:
Ad supported
content
Real Time Exchange &
Auction (SSP):
OpenRTB Server used to
bid
Bidder Service
(DSP):
OpenRTB client
Advertiser:Buyer
wants ad
impressions. Uses
bidders to bid on
behalf
Publisher:Seller
has ad spaces to
sell to highest
bidders
User Devices
ad
request
winning
ad
bid
request
win notice &
settlement price
insert orders
bid
response
winning
ad
RTB Auction for Impressions

Time Is Money
RTB:
Maximum response latency of 100 ms

Time Is Money
Assume some network latency!

Sampling of RTB
Events
Ad Request
Bid Request - JSON 100 bytes
Compute optimal bid for advertiser
Bid Response - JSON 1000 bytes (may include ad metadata)
Win Notification (may or may not exist) with settlement price
Ad Impression - when the ad is viewed
Ad Click
Ad Conversion
13

Event Streams
Auctions: auction data + bid requests
Ad Impressions: which ad ids were shown
Ad Clicks: which auction ids resulted in a
click
Ad Conversions: streams joined on auction id
Analytics Aggregations & ML to derive
hundreds of metrics and dimensions
14

Real Time
Just means Event-Driven or processing events as they arrive.
Does not automatically equal sub-second latency
requirements.
Seen / Ingestion Time
When an event is ingested into the system
Event Time
When an event is created, e.g. on a device.

The Platform

Platform Requirements
24 / 7 Uptime
Brokerage model: DSPs only make $ on
successful ad deliveries, so uptime is critical
Security
Enable service across the globe
Handle thousands of concurrent requests per second
Scale to traffic of 700TB per day
Manage 700TB per day of data
Derive Metrics
17

Business Requirements
Support SLAs for bid transactions
Legal constraints - user data crossing borders
The critical path must be fast to win
No data loss on ingestion path
Bid & Campaign Optimization
Frequency Capping
Management UI for Publishers & Advertisers
18

Questions To Answer
% Writes on ingestion, analytics pre-aggregation, etc.
% Reads of raw data by analytics, aggregated views by customer
management UI
How much in memory on RTB app nodes?
Dimensions of data in analytics queries
Optimization Algos
What needs real time feedback loops, what does not
Which data flows are low-lateny/high frequency, which not
Where are potential bottlenecks
19

Constraints
Resources - I need to build highly
functioning teams that are psyched
about the work and working together
Budget
Cloud Resources
JDK Version (What?!)
Existing infrastructure & technologies
that will be replaced later but you have to
deal with now :(
20
Pro Tip:
Pay well,
Allow people
to grow & be
creative

Strategies
To Avoid

Beware of the C word
Consistency?
22
Convergence?

http://www.slideshare.net/palvaro/ricon-keynote-outwards-from-the-middle-of-the-maze/39
he went
there
@palvaro

Complexity
24
Can't
Ops
your
way
out
of
that

Occam's razor:
Simpler theories are preferable to more complex

Strategies

Approaches
Eventual/Tunable consistency
Time & Clocks in globally-distributed
systems
Location Transparency
Asynchrony
Pub-Sub
Design for scale
Design for Failure
27

Kafka as Platform Fabric
28

From MVP to Scalable with Kafka
Microservices
Does One Thing, Knows One Thing
Separate low-latency hot path
Separate deploy artifacts
Separate data mgmt clusters by
concern
analytics, timeseries, etc.
CQRS: Separate Read Write paths
29
Scalpel...
Separate The Monolith

Immutable events stream to Kafka, partitioned
by event type, time, etc.
Subscribers & Publishers
RTB microservices - receives raw, receives
Analytics cluster - receives raw, publishes
aggregates
Management / Reporting nodes
30
Services communicate
indirectly via Kafka

CQRS: Command Query
Responsibility Segregation
Decouple Write streams from Read streams
Different schemas / data structures
Writers (Publishers) publish without having
awareness who needs to receive it or how
to reach them (location, protocol...)
Readers (Subscribers) should be able to
subscribe and asynchronously receive
from topics of interest
31

Eventually Consistent Across DCs
US-East-1
MirrorMaker
EU-west-1
RTB
micro
services
RTB
micro
services
RTB
micro
services
Publishers
Subscribers
Subscribers
Publishers
Kafka Cluster Per Region
ZK
ZK
Mgmt
micro
services
Mgmt
micro
services
Mgmt
micro
services
Query Layer
Analytics & ML Cluster
Timeseries Cluster
Spark
Streaming
& ML
Cassandra
Cross DC
Replication
Topology
Aware
Spark
Streaming
& ML
Cassandra
Spark
Streaming
& ML
Cassandra
Cross DC
Replication
Topology
Aware
Spark
Streaming
& ML
Cassandra
Compute Layer

MirrorMaker
RTB
micro
services
RTB
micro
services
RTB
micro
services
Publishers
Subscribers
Subscribers
Publishers
C*
C*
Mgmt
micro
services
Mgmt
micro
services
Mgmt
micro
services
US-East-1
EU-west-1
Timeseries Cluster
Spark
Streaming
& ML
Cassandra
Cross DC
Replication
Topology
Aware
Spark
Streaming
& ML
Cassandra
Spark
Streaming
& ML
Cassandra
Cross DC
Replication
Topology
Aware
Spark
Streaming
& ML
Cassandra
Compute Layer
Query Layer

Kafka Cross Datacenter
Mirroring
bin/kafka-run-class.sh kafka.tools.MirrorMaker --consumer.config config/
consumer_source_cluster.properties --producer.config config/
producer_target_cluster.properties --whitelist bidrequests --num.producers 2 --
num.streams 4
34
Publish messages from various datacenters
around the world

Users in the US and UK connect DCs in their geo region for
lower latency
Both DCs are part of the same cluster for X-DC Replication
Configure LB policies to prefer local DC
LOCAL_QUORUM reads
Data is available cluster-wide for backup, analytics, and to
account for user travel across regions
35
Cassandra Cross DC Replication
It's out of the box. Multi-region live backups for free:
[ NetworkTopologyStrategy ]

Cassandra Cross DC Replication
Keep EU User Data in the EU
CREATE KEYSPACE rtb WITH REPLICATION = {
‘class’: ‘NetworkTopologyStrategy’,
‘eu-east-dc’: ‘3’,‘eu-west-dc’: ‘3’
};

Cassandra Time Windowed Buckets with TTL
CREATE TABLE rtb.fu_events (
id int,
seen_time timeuuid,
event_time timestamp,
PRIMARY KEY (id,date)
) WITH CLUSTERING ORDER BY (event_time DESC)
AND compaction = {
'compaction_window_unit': 'DAY',
'compaction_window_size': '3',
'class':'com.jeffjirsa.cassandra.db.compaction.TimeWindowCompactionStrategy'
}
AND compression = {
'crc_check_chance': '0.5',
'sstable_compression': 'org.apache.cassandra.io.compress.LZ4Compressor'
}
AND bloom_filter_fp_chance = 0.01
AND caching = '{"keys":"ALL", "rows_per_partition":"100"}'
AND dclocal_read_repair_chance = 0.0
AND default_time_to_live = 60
AND gc_grace_seconds = 0
AND max_index_interval = 2048
AND memtable_flush_period_in_ms = 0
AND min_index_interval = 128
AND read_repair_chance = 0.0
AND speculative_retry = '99.0PERCENTILE';
3 DAY buckets -
larger SSTables on
disk minimizes
bootstrapping issues
when adding nodes
to a cluster
3 MINUTE buckets
1 HOUR buckets
1 DAY buckets
MICROSECOND resolution:

Want Can Or Currently Use Status But
Kafka Security Kafka Security TLS, Kerberos, SASL, Auth,
Encryption, Authentication
v0.9.0
Thanks Jun!
Integrated Streaming Kafka Streams processing inside Kafka, no alternate
cluster setup or ops.
v0.10
Thanks Guozhang!
It's java :( Iw
Cassandra CDC Cassandra CDC.
Triggers? Tiggers are a pre-commit
hook :(
The Epic JIRA:
https://issues.apache.org/jira/browse/
CASSANDRA-8844
no comment
And... Kafka Streams &
Kafka Connect Integration
..wait for it..
no comment
Always on, X-DC
Replication, Flexible
Topologies
Kafka, Cassandra
OOTB
Fault Tolerance Kafka, Spark, Mesos, Cassandra,
Akka
Baked In
Location Transparency Kafka, Cassandra, Akka Check!
Asynchrony Kafka, Cassandra, Akka Check!
Decoupling Kafka, Akka Check!
Pub-Sub Kafka, Cassandra, Akka Check!
Immutability Kafka, Akka, Scala Check!
My Nerdy Chart v2.0

Kafka Streams
in v 0.10
39
val builder = new KStreamBuilder() 
val stream: KStream[K,V] = builder.stream(des, des, "raw.data.topic")
.flatMapValues(value -> Arrays.asList(value.toLowerCase.split(" ")
.map((k,v) -> new KeyValue(k,v))
.countByKey(ser, ser, des, des, "kTable")
.toStream
stream.to("results.topic", ...)
val streams = new KafkaStreams(builder, props)
streams.start()

Kafka Streams &
Kafka Connect?
40
val builder = new KStreamBuilder()
val stream1: KStream[K,V] = builder.stream(new CassandraConnect(configs))
.flatMapValues(..)
.map((k,v) -> new KeyValue(k,v))
.countByKey(ser, ser, des, des, "kTable")
.toStream
stream.to("results.topic", ...)
val streams = new KafkaStreams(builder, props)
streams.start()
YES

/** Writes records from Kafka to Cassandra asynchronously and non-blocking. */
override def put(records: JCollection[SinkRecord]): Unit
/** Returns a list of records when available by polling for new records. */
override def poll: JList[SourceRecord])
https://github.com/tuplejump/kafka-connect-cassandra

Frequency Capping
1. Count the number of times user X has seen ad Y from
Advertiser A's Campaign C
2. Limit the max number of impressions of an ad within
T1...T2
42
Use Case:
Continuously count impressions grouped by campaign across DCs
low-latency reads & writes
Must scale
Cross DC Counters
Translation: Distributed Counters

Redis? Broke under the load
Aerospike? Great candidate
Eventuate? Interesting, much lighter
Kafka streams when it's out? Interesting, already in the
infra
Flink? Very interesting but...
Cassandra Counters - not applicable for this
43
Frequency Capping

As a distributed counting microservice
As a key-value store for in-memory caching
Fast reads - Very read heavy
99% reads are < 1 ms latency (sweet)
30,000 writes per second
350,000 reads per second on 7 nodes
Replication factor 2:
Cross datacenter replication (XDC), SSD-backed
Excellent few posts by Dag, Tapads CTO on in-memory
infrastructure + Ad Tech: (see resources slide)
44
Aerospike

CRDT: Conflict Free
Replicated Data Type
State-based: objects require only eventual communication
between pairs of replicas
Operation-based: replication requires reliable broadcast
communication with delivery in a well-defined delivery
order
Both guaranteed to converge towards common, correct state
Keep replicas available for writes during a network partition
requires resolution of conflicting writes when the partition
heals
45

Eventuate
A toolkit for building distributed, HA & partition-tolerant event-sourced applications.
Developed by Martin Krasser (@mrt1nz) for Red Bull Media (open source)
Interactive, automated conflict resolution (via op-based CRDTs)
Separates command side of an app from its query side (CQRS)
Primary Goals: preserving causality, idempotency & event ordering guarantees even under
chaotic conditions
AP of CAP - conflicts cannot be prevented & must be resolved.
Causality - tracked with Vector Clocks
Adapters provide connectivity to other stream processing solutions
Can currently chose Cassandra if desired
Kafka coming soon!
46

Replication of application state through
async event replication across locations
Locations consume replicated events to re-
construct application state locally
Multiple locations concurrently update as
multi-master
47
Eventuate as
Distributed CRDT Microservice

Applications can continue
writing to a local replica during
a network partition
-> To Cassandra
-> To Kafka
(soon)
Pass To Pipeline:

import scala.concurrent.Future 
import akka.actor.{ActorRef, ActorSystem} 
import com.rbmhtechnology.eventuate.crdt.{CRDTServiceOps, Counter, CounterService}
class CappingService(val id: String, override val log: ActorRef) 
(implicit val system: ActorSystem, 
val integral: Integral[Int], 
override val ops: CRDTServiceOps[Counter[Int], Int]) 
extends CounterService[Int](id, log) { 
 
/** Increment only op: adds `delta` to the counter identified by `id`
* and returns the updated counter value.
*/ 
def increment(id: String, delta: Int): Future[Int] = 
value(id) flatMap { 
case v if v >= 0 && (delta > 0 || delta > v) => 
update(id, delta) 
case v => 
Future.successful(v) 
} 
 
start() 
 
}
import scala.concurrent.Future
import akka.actor.ActorSystem
val a = new CappingService(id1, eventLog) 
a.increment(id1, 3) // Future(3) 3 impressions 
a.value(id1) // Future(3) 3 impressions 
a.increment(id1, -2) // increments only, idempotent.
val b = new CappingService(id2, eventLog)
b.value(id1) // Future(a.value(id1))
Knows the same count over n-instances,
all geo-locations, for the same id
class CounterService[A : Integral](val replicaId: String, val log: ActorRef) {
def value(id: String): Future[A] = { ... }
def update(id: String, delta: A): Future[A] = { ... }
}

Eventuate

Eventuate Takeaway
It's just a jar!
OOTB async internal component messaging
and fault tolerance
Integrate with relevant microservices
No store/cache cluster to deploy, just keep
monitoring your apps
Written in Scala
Built on Akka - a toolkit for building highly
concurrent, distributed, and resilient event-
driven applications on the JVM
51

Analytics & ML

Refresher: Sampling of
RTB Events
Ad Request
Bid Request - JSON 100 bytes
Compute optimal bid for advertiser
Bid Response - JSON 1000 bytes (may include ad metadata)
Win Notification (may or may not exist) with settlement price
Ad Impression - when the ad is viewed
Ad Click
Ad Conversion
53

OpenRTB: objects in the Bid Request model

TopK most high performing
campaigns
Number of views served in the last 7
days, by country, by city
What determined successful ad
conversions
Age distribution per campaign
55
Streaming Analytics

Spark Streaming Kafka
class KafkaStreamingActor(ssc: StreamingContext) extends MyAggregationActor {
val stream = KafkaUtils.createDirectStream(...).map(RawData(_))
stream
.foreachRDD(_.toDF.write.format("filodb.spark")
.option("dataset", "rawdata")
.save()) 
/* Pre-Aggregate data in the stream for fast querying and aggregation later
stream.map(hour => 
(hour.wsid, hour.year, hour.month, hour.day, hour.oneHourPrecip)
).saveToCassandra(timeseriesKeyspace, dailyPrecipTable)
}
56
Can write to
Cassandra,
FiloDB...

Machine Learning
Train on 1+ week of data for
Recommendations
Bid Optimization
Campaign Optimization
Consumer Profiling
...and much more
57

Machine Learning
The probability of an ad, from a specific
ISP, OS, website, demographic, etc.
resulting in a conversion
Which attributes of impressions are good
predictors of better ad performance?
58

Bid Optimization &
Predictive Models
Which impressions should an Advertiser bid for?
Per campaign, per country it may run in..?
What is the best bid for each impression
59

Compute
optimal bid
price
Train the
model
Score
bid requests
Determine
value of
bid reqest
Train on every
bid req attribute
Based on Campaign
Objectives
Against Budget Send bid decision
to bidder
Machine Learning

Spark Streaming, MLLib & FiloDB
61
val ssc = new StreamingContext(sparkConf, Seconds(5)) 
val kafkaStream = KafkaUtils.createDirectStream[..](..)
.map(transformFunc)
.map(LabeledPoint.parse)
kafkaStream.foreachRDD(_.toDF.write.format("filodb.spark")
.option("dataset", "training").save())
 
val model = new StreamingLinearRegressionWithSGD() 
.setInitialWeights(Vectors.dense(weights))
.trainOn(dataStream.join(historicalEvents))
model.predictOnValues(dataStream.map(lp => (lp.label, lp.features)))
.insertIntoFilo("predictions")

700 Queries Per Second:
Spark Streaming & FiloDB
Even for datasets with 15 million rows! Using FiloDB's
InMemoryColumnStore
Single host / MBP
5GB RAM
SQL to DataFrame caching
https://github.com/tuplejump/FiloDB
Evan Chan's (@velvia) blog post
NoLambda: A new architecture combining streaming, ad hoc,
machine-learning, and batch analytics
62

US-East-1
MirrorMaker
EU-west-1
RTB
micro
services
RTB
micro
services
RTB
micro
services
Publishers
Subscribers
Subscribers
Publishers
ZK
ZK
Mgmt
micro
services
Mgmt
micro
services
Mgmt
micro
services
Query Layer
Timeseries Cluster
Spark
Streaming
& ML
Cassandra
Cross DC
Replication
Topology
Aware
Spark
Streaming
& ML
Cassandra
Spark
Streaming
& ML
Cassandra
Cross DC
Replication
Topology
Aware
Spark
Streaming
& ML
Cassandra
Compute Layer

Self-Healing Systems
Massive event spikes & bursty traffic
Fast producers / slow consumers
Network partitioning & out of sync
systems
DC down
Not DDOS'ing ourselves from fast
streams No data loss when auto-
scaling down
64

Byzantine Fault Tolerance?
65
Looks like I'll
miss standup

Everything fails, all the time
Monitor
Everything
66

Non-Monotonic Snapshot Isolation: scalable and strong consistency
for geo-replicated transactional systems
Conflict-free Replicated Data Types
Implementing operation-based CRDTs
http://codebetter.com/gregyoung/2010/02/16/cqrs-task-based-uis-event-sourcing-agh
http://martinfowler.com/bliki/CQRS.html
http://github.com/openrtb/OpenRTB
http://akka.io
http://rbmhtechnology.github.io/eventuate
https://github.com/RBMHTechnology/eventuate
http://rbmhtechnology.github.io/eventuate/user-guide.html#commutative-replicated-data-types
http://www.planetcassandra.org/data-replication-in-nosql-databases-explained
http://wikibon.org/wiki/v/Optimizing_Infrastructure_for_Analytics-Driven_Real-Time_Decision_Making
Resources
67

twitter.com/helenaedelson
github.com/helena
slideshare.net/helenaedelson
Thanks!

Leveraging Kafka for Big Data in Real Time Bidding, Analytics, ML & Campaign Management for Globally Distributed Data Flows

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Leveraging Kafka for Big Data in Real Time Bidding, Analytics, ML & Campaign Management for Globally Distributed Data Flows