Jennifer Rawlins, a software engineer with extensive experience, discusses real-time streaming using Kafka in her presentation. She outlines the functionalities of messaging systems, particularly Kafka's role in handling data pipelines, real-time processing, and scalability. The document also covers the integration of Spark and R for data analysis and machine learning within Kafka streams.

1. Jennifer Rawlins
Real Time Streaming with Kafka - for the data scientist

2. About Me
Jenn Rawlins has been creating software solutions for 19 years. She began her
career at Microsoft as an engineer in test, and as an international program
manager. This was followed by management and consultant roles, working with
VPs and Directors across multiple industries to create custom software solutions.
She then changed her focus to software engineering roles.
Recently Jenn has created Big Data solutions using Hadoop, Yarn, Kafka, and
Cassandra, writing real time streaming solutions in Java and Scala. Her current
focus is a solution in AWS for IoT devices.

3. AGENDA
❖ Messaging Systems
❖ Kafka
❖ SparkR
❖ Data Processing Pipelines

4. What is a message queueing system
Messages are sent to a queue. Messages are read from a queue. The queue is independent of the
senders or receivers (Publishers/Subscribers or Producers/Consumers). Fast, Predictable, easy to scale.
Cloud solutions
Amazon SQS - Simple Queue Service
Azure service bus
Server Solutions
Kafka
IBM WebSphere MQ
RabbitMQ

5. Kafka
LinkedIn uses Apache Kafka as a central publish-subscribe log for integrating data
between applications, stream processing, and Hadoop data ingestion.
REAL-TIME STREAMING
1. Data pipelines that reliably get data between systems or applications.
2. Applications to transform or react to streams of data.

6. Real Time Process streams of records as they occur. Data in, Data out.
Fault Tolerant Store streams of records in a fault-tolerant way.
Highly Scalable (Horizontal) Nodes can be added and removed from a
Kafka Cluster and the cluster will rebalance itself. High Availability begins at 5 Nodes.

7. Ordering guaranteed within a partition as it was received
Parallel processing of partitioned topics
Multi publisher (producer) - kafka writes message as received to a specific topic,
balancing across multiple partitions.
Multi subscriber (consumer) - Partitions assigned to specific subscriber.

8. Producer
Producer
Producer
ProducerProducer
Producer
Consumer
Consumer
ConsumerConsumerConsumer
Consumer
Kafka
Producer
Consumer Consumer
Kafka
Cluster
Producer Producer
Consumer

9. Record consists of a key, a value, and a timestamp. (message)
Topic kafka stores streams of records in categories called topics.
Cluster Kafka is run as a cluster on one or more servers.
Broker The actual server, and synchronization layer between server instances.
Node The logical kafka entity or ‘worker’ on each server.
Publish and subscribe to streams of records. Similar to a message queue or
enterprise messaging system.

10. Publish and Consume streams of records.
Process streams of records efficiently and in real time.
Store streams of records safely in a distributed, replicated cluster. Fault Tolerant.

11. A Stream is an unbounded, continuously updating data set. A stream is an
ordered, replayable, and fault-tolerant sequence of immutable data records.
A Stream DSL is stateful, and is a processor topology.
# Example: a record stream for page view events
1 => {"time":1440557383335, "user_id":1, "url":"/home?user=1"}
5 => {"time":1440557383345, "user_id":5, "url":"/home?user=5"}
2 => {"time":1440557383456, "user_id":2, "url":"/profile?user=2"}
1 => {"time":1440557385365, "user_id":1, "url":"/profile?user=1"}

12. Typical Use Cases
Message Broker ActiveMQ or RabbitMQ
Website Activity Tracking
Metrics - monitoring
Log Aggregation
Stream Processing

13. Website Activity Tracking
TRACKING- Web Site Activity
Add clicks
page views,
searches,
or other actions users may take
Record of each activity is published to central topics, with one topic per activity
type.

14. Application
Connector
RealTime
Processor
Application Application
Connector
Kafka
Cluster
Data
Store
Data
Store
Application
Producers (write)
Data
Store
Processor
Real Time
Consumers (read)
Connectors Stream
Processors
User
Action

15. Platforms Spark runs on Hadoop Yarn, Apache Mesos, in Standalone cluster
mode, or in the on EC2.
Languages Can be used from Scala, Python, and R shells
Processing optimizes jobs running on Hadoop in memory by 100x, or 10X
faster on disk.

16. R limitations
R is a popular statistical programming language used for data processing and
machine learning tasks.
Data Analysis is usually limited to a single thread, and the memory available on a
single computer.

17. Developed at the AMPLab, it was accepted and merged into Spark version 1.4
Provides an R frontend to apache Spark
Uses the Sparks data sources API to read from a variety of sources:
Hive(Hadoop), Json Files, Parquet Files.
Uses Spark’s distributed computation engine to run large scale data analysis from
the R shell on a cluster: Many Cores, Many Machines.
SparkDataFrame (distributed collection of data organized in named columns)
inherit optimizations from the computation engine.
SparkR: R package for Apache Spark

18. MLib and SparkR
Machine Learning algorithms currently supported:
Generalized Linear Model
Accelerated Failure Time (AFT)
Survival Regression Model
Naive Bayes Model
KMeans Model

19. Real Time Record Processing
Example Real Time Scenario: Serve up related ads to user that are more likely to
be clicked
Kafka Data Stream Spark StreamingWebsite
User Clicks Ad
Record added to
AdClick Topic
AdClick run Ad
through model to
update predictive
score
Application
Log Click Record
Use AdClick to
find related ads
to serve to user
using predictive
scoring.
Display New
Ads to User

20. Real-time process user data using an R model in a Spark job.
Batch process data from Kafka, Hadoop HDFS, SQL, Cassandra, HBase
Model Training multiple times with SparkR from multiple data sources

21. Historical Record Batch Processing
SparkRKafka Data Streams
AdClick
HomePageView
Spark job
AdClick topic: run
recent records
through model
RSpark & SparkHadoop
Hive AdClick model
training on historical
data
Cassandra
SQL
Pull Topics to create
stores of data for
many related
features
AdView
Kafka
Topic

22. Language
Kafka is written in Java
In Kafka the communication between the clients and the servers is done with a simple, high-performance,
language agnostic TCP protocol. This protocol is versioned and maintains backwards compatibility with
older version. We provide a Java client for Kafka, but clients are available in many languages0
. Java
C/C++
Python
Go (AKA golang)
Erlang
.NET
Clojure
Ruby
Node.js
Proxy (HTTP REST, etc)
Perl
stdin/stdout
PHP
Rust
Alternative Java
Storm
Scala DSL
Clojure

23. Kafka http://kafka.apache.org/
Free and Open Source Software under the Apache License
Github code repo: https://github.com/apache/kafka
Confluent http://www.confluent.io/
Open Source offering Consulting, Training, Support, Monitoring Tools
Confluent Docs: http://docs.confluent.io/3.0.0/streams/developer-guide.html
Examples: https://github.com/confluentinc/examples/tree/kafka-0.10.0.0-cp-3.0.0/kafka-
streams/src/main/java/io/confluent/examples/streams

25. Resources
LinkedIn Story:
https://engineering.linkedin.com/distributed-systems/log-what-every-software-engineer-should-know-
about-real-time-datas-unifying
Benchmarking:
https://engineering.linkedin.com/kafka/benchmarking-apache-kafka-2-million-writes-second-three-
cheap-machines
SparkR
https://spark.apache.org/docs/latest/sparkr.html
https://databricks.com/blog/2015/06/09/announcing-sparkr-r-on-spark.html
https://cs.stanford.edu/~matei/papers/2016/sigmod_sparkr.pdf