This document provides an introduction and overview of Apache Mesos. It begins by describing Mesos' origins at companies like Google and how it enables fine-grained resource sharing in data centers. It then discusses concepts like schedulers, executors, frameworks and how Mesos allows building distributed applications and data center infrastructure. The document also covers Mesos concepts such as resources, attributes, roles, constraints and how tools like Marathon interact with Mesos. Finally, it provides examples of Mesos frameworks for technologies like Kafka and Cassandra.

1. Introduction
To Apache Mesos

2. Joe Stein
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!
LinkedIn: http://linkedin.com/in/charmalloc
Twitter : @allthingshadoop

3. Overview
◉Life without Apache Mesos
◉Hit the ground running with Mesos
◉Schedulers & Executors (Frameworks)
◉Building Data Center Applications

4. Origins
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

9. static vs elastic

10. Data Center Kernel

11. Data Center Operating System
Mesosphere’s Data Center Operating System (DCOS) is an operating
system that spans all of the machines in a datacenter or cloud and treats
them as a single computer, providing a highly elastic and highly scalable
way of deploying applications, services, and big data infrastructure on
shared resources. DCOS is based on Apache Mesos and includes a
distributed systems kernel with enterprise-grade security. It also includes
a set of core system services, such as a native Marathon instance to
manage processes and installable services, and Mesos-DNS for service
discovery. DCOS provides a web interface and a command-line interface
(CLI) to manage the deployment and scale of applications.

15. Resources & Attributes
The Mesos system has two basic methods to describe the
Agent (fka slave) that comprise a cluster. One of these is
managed by the Mesos master, the other is simply passed
onwards to the frameworks using the cluster.
Attributes
The attributes are simply key value string pairs that Mesos passes along when it sends offers to frameworks.
attributes : attribute ( ";" attribute )*
attribute : labelString ":" ( labelString | "," )+

16. Resources
The Mesos master has a few resources that it pre-defines in how it handles them. At the current time, this
list consist of:
● cpu
● mem
● disk
● ports
In particular, a slave without cpu and mem resources will never have its resources advertised to any
frameworks. Also, the Master’s user interface interprets the scalars inmem and disk in terms of MB. IE: the
value 15000 is displayed as 14.65GB.

17. Here are some examples for configuring the Mesos slaves.
--resources='cpu:24;mem:24576;disk:409600;ports:[21000-24000];bugs:{a,b,c}'
--attributes='rack:abc;zone:west;os:centos5,full'
In this case, we have three different types of resources, scalars, a range, and a set. They are called cpu,
mem, disk, and the range type is ports.
● scalar called cpu, with the value 24
● scalar called mem, with the value 24576
● scalar called disk, with the value 409600
● range called ports, with values 21000 through 24000 (inclusive)
● set called bugs, with the values a, b and c
In the case of attributes, we end up with three attributes:
● rack with value abc
● zone with value west
● os with value centos5,full

18. Roles
Total consumable resources per slave, in the form 'name(role):value;name(role):value...'. This
value can be set to limit resources per role, or to overstate the number of resources that are
available to the slave.
--resources="cpus(*):8; mem(*):15360; disk(*):710534; ports(*):[31000-32000]"
--resources="cpus(prod):8; cpus(stage):2 mem(*):15360; disk(*):710534;
ports(*):[31000-32000]"
All * roles will be detected, so you can specify only the resources that are not all roles (*). --
resources="cpus(prod):8; cpus(stage)"
Frameworks bind a specific roles or any. A default roll (instead of *) can also be
configured.
Roles can be used to isolate and segregate frameworks.

19. Dynamic Reservations
{
"type": Offer::Operation::RESERVE,
"reserve": {
"resources": [
{
"name": "cpus",
"type": "SCALAR",
"scalar": { "value": 8 },
"role": <framework_role>,
"reservation": {
"principal": <framework_principal>
}
},
{
"name": "mem",
"type": "SCALAR",
"scalar": { "value": 4096 },
"role": <framework_role>,
"reservation": {
"principal": <framework_principal>

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

21. Constraints
Constraints control where apps run to allow optimizing for fault tolerance or locality. Constraints are made up of three parts: a field
name, an operator, and an optional parameter. The field can be the slave hostname or any Mesos slave attribute.
Fields
Hostname field
hostname field matches the slave hostnames, see UNIQUE operator for usage example.
hostname field supports all operators of Marathon.
Attribute field
If the field name is none of the above, it will be treated as a Mesos slave attribute. Mesos slave attribute is a way to tag a slave node,
see mesos-slave --help to learn how to set the attributes.

22. Unique
UNIQUE tells Marathon to enforce uniqueness of the attribute across all of an app's tasks. For example the
following constraint ensures that there is only one app task running on each host:
via the Marathon gem:
$ marathon start -i sleep -C 'sleep 60' -n 3 --constraint hostname:UNIQUE
via curl:
$ curl -X POST -H "Content-type: application/json" localhost:8080/v1/apps/start -d '{
"id": "sleep-unique",
"cmd": "sleep 60",
"instances": 3,
"constraints": [["hostname", "UNIQUE"]]
}'

23. Cluster
CLUSTER allows you to run all of your app's tasks on slaves that share a certain attribute.
$ curl -X POST -H "Content-type: application/json" localhost:8080/v1/apps/start -d '{
"id": "sleep-cluster",
"cmd": "sleep 60",
"instances": 3,
"constraints": [["rack_id", "CLUSTER", "rack-1"]]
}'
You can also use this attribute to tie an application to a specific node by using the hostname property:
$ curl -X POST -H "Content-type: application/json" localhost:8080/v1/apps/start -d '{
"id": "sleep-cluster",
"cmd": "sleep 60",
"instances": 3,
"constraints": [["hostname", "CLUSTER", "a.specific.node.com"]]
}'

24. Group By
GROUP_BY can be used to distribute tasks evenly across racks or datacenters for high availability.
via the Marathon gem:
$ marathon start -i sleep -C 'sleep 60' -n 3 --constraint rack_id:GROUP_BY
via curl:
$ curl -X POST -H "Content-type: application/json" localhost:8080/v1/apps/start -d '{
"id": "sleep-group-by",
"cmd": "sleep 60",
"instances": 3,
"constraints": [["rack_id", "GROUP_BY"]]
}'

25. Like
LIKE accepts a regular expression as parameter, and allows you to run your tasks only on the slaves
whose field values match the regular expression.
via the Marathon gem:
$ marathon start -i sleep -C 'sleep 60' -n 3 --constraint rack_id:LIKE:rack-[1-3]
via curl:
$ curl -X POST -H "Content-type: application/json" localhost:8080/v1/apps/start -d '{
"id": "sleep-group-by",
"cmd": "sleep 60",
"instances": 3,
"constraints": [["rack_id", "LIKE", "rack-[1-3]"]]
}'

26. Unlike
Just like LIKE operator, but only run tasks on slaves whose field values don't match the regular expression.
via the Marathon gem:
$ marathon start -i sleep -C 'sleep 60' -n 3 --constraint rack_id:UNLIKE:rack-[7-9]
via curl:
$ curl -X POST -H "Content-type: application/json" localhost:8080/v1/apps/start -d '{
"id": "sleep-group-by",
"cmd": "sleep 60",
"instances": 3,
"constraints": [["rack_id", "UNLIKE", "rack-[7-9]"]]
}'

27. Working with Marathon
TAG=sample
APP=xyz
ID=$TAG-$APP
CMD=”./yourScript "'$HOST'" "'$PORT0'" "'$PORT1'
JSON=$(printf '{ "id": "%s", "cmd": "%s", "cpus": %s, "mem": %s, "instances": %s,
"uris": ["%s"], "ports": [0,0] , “env”:{ “JAVA_OPTS”,”%s”}' "$ID" “$CMD" "0.1" "256"
"1" "http://dns/path/yourScriptAndStuff.tgz" “-Xmx 128”)
curl -i -X POST -H "Content-Type: application/json" -d "$JSON"
http://localhost:8080/v2/apps

28. ./yourScript
#think of it as a distributed application launching in the cloud
HOST=$1
PORT0=$2
PORT1=$3
#talk to zookeeper
#call marathon rest api
#spawn another process, they are all in your cgroup =8^) woot woot

29. Framework = (Scheduler + Executor)

31. Scheduler

32. Executors

33. mesos/kafka
https://github.com/mesos/kafka

34. Scheduler
◉ Provides the operational automation for a Kafka Cluster.
◉ Manages the changes to the broker's configuration.
◉ Exposes a REST API for the CLI to use or any other
client.
◉ Runs on Marathon for high availability.
◉ Broker Failure Management “stickiness”
Executor
◉ The executor interacts with the kafka broker as an
intermediary to the scheduler
Scheduler & Executor

35. CLI & REST API
◉ scheduler - starts the scheduler.
◉ 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)
◉ 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}

36. Launch 20 brokers in seconds
./kafka-mesos.sh add 1000..1019 --cpus 0.01 --heap 128 --mem 256 --options num.io.threads=1
./kafka-mesos.sh start 1000..1019

37. Sawfly (Elodina Software Suite of Schedulers)
Sawfly is the suite of Elodina’s proprietary and open source
schedulers Apache Mesos and DCOS. Sawfly schedulers
break out the configuration, artifact management (including
Docker containers) and service discovery from each stack
being built on Mesos. The management of both very small
and large complex stacks are managed within these
schedulers. You can have very complex stateless and
stateful stacks that when deployed be namespaced with
different resources and configurations passed to the micro
services. You can run thousands of development and
testing services as it would be configured in production (but
just with less resources). This brings the power of the
underlying fine grained resource management to reduce
risks.

38. Productivity
Our platform
automates
infrastructure,
testing and
deployment which
promotes efficient
product
development.
Compatibility
Customers can
easily add data
ingestion,
processing and
analysis
capabilities to
their current
systems.
ELODINA PLATFORM
CUSTOMER BENEFITS
Efficiency
Using the power
of distributed
computing we
enable customers
to take advantage
of idle data center
resources.

39. Built on Mesos & Marathon

40. The Elodina platform
can also be implemented
on premise, or in a
managed data center
The Elodina Platform can run on multiple cloud services at the same time
COMPATIBLE WITH THE CLOUD

41. CORE TECH & PARTNERS
Open Source Projects Languages Companies

42. MULTIPLE STACKS DIFFERENT
CONFIGURATIONS AND RESOURCE

43. RUN AS MANY STACKS WITH AS MANY CONFIGURATION AND
RESOURCE PERMUTATIONS HAS YOU HAVE COMPUTE AVAILABLE

44. BLUE / GREEN DEPLOYMENTS

45. Metric & Log Ingestion & Analytics
Telemetry from every service within the
infrastructure is vital to long term sustained
production operations. Collecting,
measuring and effectively communicating
this information often creates a repeated
effort for what is rarely business domain
specific. Unlike traditional log and metric
analysis products which rely heavily on
human management, Elodina’s real-time
monitoring finds errors before they become
critical and offers subscribers proactive
solutions.

46. Distributed Trace
The Elodina platform automates the process to manage and run distributed trace

47. ● Real-time, distributed concurrent and parallelized response for requests
● Plug and play data analysis jobs
Distributed Remote Procedure Calls

48. DataStax Enterprise Mesos Scheduler
DataStax delivers Apache Cassandra™ in a database platform that
meets the performance and availability demands of Internet-of-things
(IoT), Web, and Mobile applications. It gives enterprises a secure, fast,
always-on database that remains operationally simple when scaled in
a single datacenter or across multiple datacenters and clouds.
Running on the Mesosphere Datacenter Operating System brings
DSE to new levels of operational efficiencies, risk mitigation in release
cycles and overall system stability out of the box on your Mesos
cluster.
DCOS

49. Questions?
http://www.elodina.net