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Introduction To Apache Mesos
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
Overview
◉Life without Apache Mesos◉Hit the ground running with Mesos◉Schedulers & Executors (Frameworks)◉Building Data Center Applications
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
static vs elastic
Data Center Kernel
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.
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.
AttributesThe attributes are simply key value string pairs that Mesos passes along when it sends offers to frameworks.
attributes : attribute ( ";" attribute )*
attribute : labelString ":" ( labelString | "," )+
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.
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
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.
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> } } ] } }
Marathonhttps://github.com/mesosphere/marathonCluster-wide init and control system for services in cgroups or docker based on Apache Mesos
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 fieldhostname field matches the slave hostnames, see UNIQUE operator for usage example.
hostname field supports all operators of Marathon.
Attribute fieldIf 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.
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"]] }'
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"]] }'
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"]] }'
Optionally, you can specify a minimum number of groups to try and achieve.
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]"]] }'
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]"]] }'
Working with Marathon
TAG=sampleAPP=xyzID=$TAG-$APPCMD=”./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
./yourScript
#think of it as a distributed application launching in the cloud
HOST=$1PORT0=$2PORT1=$3
#talk to zookeeper
#call marathon rest api
#spawn another process, they are all in your cgroup =8^) woot woot
Framework = (Scheduler + Executor)
Scheduler
Executors
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
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}
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
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.
ProductivityOur platform automates infrastructure, testing and deployment which promotes efficient product development.
CompatibilityCustomers can easily add data ingestion, processing and analysis capabilities to their current systems.
ELODINA PLATFORMCUSTOMER BENEFITS
EfficiencyUsing the power of distributed computing we enable customers to take advantage of idle data center resources.
Built on Mesos & Marathon
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
CORE TECH & PARTNERS
Open Source Projects Languages Companies
MULTIPLE STACKS DIFFERENT CONFIGURATIONS AND RESOURCE
RUN AS MANY STACKS WITH AS MANY CONFIGURATION AND RESOURCE PERMUTATIONS HAS YOU HAVE COMPUTE AVAILABLE
BLUE / GREEN DEPLOYMENTS
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.
Distributed TraceThe Elodina platform automates the process to manage and run distributed trace
● Real-time, distributed concurrent and parallelized response for requests● Plug and play data analysis jobs
Distributed Remote Procedure Calls
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
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