Slides for our solution we developed for using Mesos, Docker, Kafka, Spark, Cassandra and Solr (DataStax Enterprise Edition) all developed in Go for doing realtime log analysis at scale. Many organizations either need or want log analysis in real time where you can see within a second what is happening within your entire infrastructure. Today, with the hardware available and software systems we have in place, you can develop, build and use as a service these solutions.

1. Real-Time log analysis with
Mesos, Docker, Kafka, Spark, Cassandra and Solr at scale

2. whoami
CEO of Elodina http://www.elodina.net/ a big data as a service platform built on top open source
software. The Elodina platform enables customers to analyze data streams and programmatically
react to the results in real-time. We solve today’s data analytics needs by providing the tools and
support necessary to utilize open source technologies. As users, contributors and committers,
Elodina also provides support for frameworks that run on Mesos including Apache Kafka,
Exhibitor (Zookeeper), Apache Storm, Apache Cassandra and a whole lot more!
Apache Kafka Committer & PMC Member
LinkedIn: http://linkedin.com/in/charmalloc
Twitter : @allthingshadoop
2© 2015. All Rights Reserved.

3. 1 Intro To Mesos, Kafka, Etc
2 Architecture Overview
3 Breaking it down into pieces
4 Questions?
3© 2015. All Rights Reserved.

4. Apache Mesos
4© 2015. All Rights Reserved.

5. Mesos Papers
Mesos: A Platform for Fine-Grained Resource Sharing in the Data Center
http://static.usenix.org/event/nsdi11/tech/full_papers/Hindman_new.pdf
Google Borg - https://research.google.com/pubs/pub43438.html
Google Omega: flexible, scalable schedulers for large compute clusters
http://eurosys2013.tudos.org/wp-content/uploads/2013/paper/Schwarzkopf.pdf
5

6. Static Partitioning
6

7. Static Partitioning
7

8. Static Partitioning
8

9. Static Partitioning
9

10. Fine Grained Resource Elasticity
"If people knew how low it really is, we’d all get fired."
https://gigaom.com/2013/11/30/the-sorry-state-of-server-utilization-and-the-impending-post-hypervisor-era/
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11. An operating system for your data center
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12. EVERYTHING ON MESOS
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13. How it works
13

14. Marathon
14
https://github.com/mesosphere/marathon
Cluster-wide init and control system for
services in cgroups or docker based on
Apache Mesos

15. Docker on Marathon
{
"id": "basic-3",
"cmd": "python3 -m http.server 8080",
"cpus": 0.5,
"mem": 32.0,
"container": {
"type": "DOCKER",
"docker": {
"image": "python:3",
"network": "BRIDGE",
"portMappings": [
{ "containerPort": 8080, "hostPort": 0 }
]
}
}
}
15

16. Apache Kafka
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17. Kafka papers
Apache Kafka was first open sourced by LinkedIn in 2011
Papers
● Building a Replicated Logging System with Apache Kafka http://www.vldb.org/pvldb/vol8/p1654-wang.pdf
● Kafka: A Distributed Messaging System for Log Processing http://research.microsoft.com/en-
us/um/people/srikanth/netdb11/netdb11papers/netdb11-final12.pdf
● Building LinkedIn’s Real-time Activity Data Pipeline http://sites.computer.org/debull/A12june/pipeline.pdf
● The Log: What Every Software Engineer Should Know About Real-time Data's Unifying Abstraction
http://engineering.linkedin.com/distributed-systems/log-what-every-software-engineer-should-know-about-real-time-datas-
unifying
http://kafka.apache.org/
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18. How Big Data Starts
18

19. More Big Data! More!
19

20. uhhhh
20

21. eeesh
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22. Kafka de-couples data pipelines
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23. Distributed Replicated Log
Read & Write
In real time
As much as you want
As fast as your network
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24. Reference Architecture
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25. Producers
syslog → Kafka via docker https://hub.docker.com/r/stealthly/syslog/
syslog → Kafka scheduler https://github.com/stealthly/syslog-service
statsd → Kafka scheduler https://github.com/stealthly/statsd-mesos-kafka
system stats collection → Kafka scheduler https://github.com/stealthly/syscol
tailf → Kafka https://github.com/stealthly/go_kafka_client/tree/master/producers/tailf
Any language https://cwiki.apache.org/confluence/display/KAFKA/Clients
25

26. Reference Architecture
26

27. Kafka on Mesos
https://github.com/mesos/kafka
27

28. Kafka on Mesos
• smart broker.id assignment.
• preservation of broker placement (through constraints and/or new
features).
• ability to-do configuration changes.
• rolling restarts (for things like configuration changes).
• scaling the cluster up and down with automatic, programmatic and manual
options.
• smart partition assignment via constraints visa vi roles, resources and
attributes.
28

29. CLI & REST API
• scheduler - starts the scheduler.
• broker
– add - adds one more more brokers to the cluster.
– update - changes resources, constraints or broker properties one or more brokers.
– remove - take a broker out of the cluster.
– start - starts a broker up.
– stop - this can either a graceful shutdown or will force kill it (./kafka-mesos.sh help stop)
• topic
– list - list topics in cluster
– add - add new topics in cluster
– update - change topics in cluster
– rebalance - allows you to rebalance a cluster either by selecting the brokers or topics to rebalance.
Manual assignment is still possible using the Apache Kafka project tools. Rebalance can also change the
replication factor on a topic.
• help - ./kafka-mesos.sh help || ./kafka-mesos.sh help {command}
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30. Reference Architecture
30

31. Schema Avro or ProtoBuff
• https://github.com/stealthly/go_kafka_client/blob/master/syslog/syslog_proto/logline.proto
• https://github.com/stealthly/go_kafka_client/blob/master/logline.avsc
logline
• line
• logtypeid
• source
• tags (k/v pairs)
• timings (k/v pairs)
31

32. Consume from Kafka → Write to Cassandra
Implement CQL write here
https://github.com/stealthly/go_kafka_client/blob/master/consu
mers/consumers.go#L186-L194 with
https://github.com/gocql/gocql
Go Kafka Client does fan out work processing, rebalance
doesn’t upset consumers that are reading already.
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33. Reference Architecture
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34. Sample Spark Job → Cassandra
https://github.com/stealthly/gauntlet
Uses the Cassandra Spark Connector
https://github.com/datastax/spark-cassandra-connector
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35. Use DataStax Enterprise to enable Search
http://docs.datastax.com/en/datastax_enterprise/4.8/datastax_e
nterprise/srch/srchOverview.html
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36. Questions?
36
http://www.elodina.net

37. Thank you