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Netflix OSS, meet Docker!

[fa icon="calendar'] Oct 16, 2013 3:01:43 AM / by Ritesh Patel posted in Engineering

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Background

At Nirmata, we are building a cloud services platform to help customers rapidly build cloud ready applications. We believe that next generation of cloud applications will be composed from stateless, loosely-coupled, fine-grained services. In this architecture, each service can be independently developed, deployed, managed and scaled. The Nirmata Platform, itself, is built using the same architectural principles. Such an architecture requires a set of core, infrastructure services. Since Netflix has open sourced their components [1] we decided to evaluate and extend them. The components that best meet our needs were Eureka (a registry for inter-service communication), Ribbon (a client-side load balancer and SDK for service to service communication), Zuul (a gateway service) and Archaius (a configuration framework). In a few days we had the Netflix OSS components working with our services and things looked good!

Challenges in dev/test

Our application now was now made up of six independent services and we could develop and test these services locally on our laptops fairly easily. Next we decided to move our platform to Amazon AWS for testing and integrate with our Jenkins continuous integration server. This required automation to deploy these various services in our test environment. We considered various options such as creating and launching AMI’s, or using Puppet/Chef. But these approaches would require each service to be installed on a separate EC2 instance and the number of EC2 instances would quickly grow as we add more services. Being a startup, we started looking for more efficient alternatives for ourselves as well as our customers..

Why Docker

This is when we started looking at Docker [2]. We knew about Linux containers [3] but didn’t feel we could invest time and effort to directly use them. However, Docker made using Linux containers easy! With a few hours of prototyping with Docker we were able to get an application service up and running. Also, once the Docker images were created, running them was a snap, and unlike launching VM instances there was hardly any startup time penalty. This meant that we could now launch our entire application, very quickly on a single medium EC2 instance vs launching multiple micro or small instances. This was amazing!

Using Netflix OSS with Docker

The next step was to get all our services, including the Netflix OSS services running in Docker (v0.6.3) containers. So, first we created the base image for all our services by installing jdk 7 and tomcat 7. For test environment, we wanted to make sure that the base container image can be use for any service, including Netflix OSS services so we added a short startup script to the container base image to copy the service war file from a mounted location, setup some environment variables (explanation later) and start the tomcat service.

#!/bin/bash
echo Starting $1 on port $2
#Copy the war file from mounted directory to tomcat webapps directory
if [ $1 ]
then
cp /var/lib/webapps/$1.war /var/lib/tomcat7/webapps/$1.war
fi
#Add the port to the JVM args
if [ $2 ]
then
echo "export JAVA_OPTS=\"-Xms512m -Xmx1024m -Dport.http.nonssl=$2 -Darchaius.configurationSource.additionalUrls=file:///home/nirmata/dynamic.properties\"" >> /usr/share/tomcat7/bin/setenv.sh
else
echo "export JAVA_OPTS=\"-Xms512m -Xmx1024m -Dport.http.nonssl=8080\"" >> /usr/share/tomcat7/bin/setenv.sh
fi
#Setup dynamic properties
echo "eureka.port=$2" >> /home/nirmata/dynamic.properties
if [ $3 ]
then
echo "eureka.serviceUrl.defaultZone=$3" >> /home/nirmata/dynamic.properties
echo "eureka.serviceUrl.default.defaultZone=$3" >> /home/nirmata/dynamic.properties
fi
echo "eureka.environment=" >> /home/nirmata/dynamic.properties
if [ $4 ]
then
echo "default.host=$4" >> /home/nirmata/dynamic.properties
fi
service tomcat7 start
tail -F /var/lib/tomcat7/logs/catalina.out

 

Following are some key considerations in deployed Netflix OSS with Docker:

Ports – With Docker, ports used by the application in the container need to be specified when launching a container so that the port can be mapped to the host port. Docker automatically assigns the host port. On startup various Nirmata services register with the service registry, Eureka. Service running within a Docker container needs to register using the host port so that other services can communicate with it. To solve this, we specified the same host and container port when launch a container. For example, Eureka would be launched using port 8080 on the container as well as the host. One challenge this introduced was the need to automatically configure Tomcat port for various services. This is easily done by specifying the port as an environment variable and modifying the server.xml file to use the environment variable instead of a hard coded value.

IP Address – Each application typically registers with Eureka using its IP address. We noticed that our services running in Docker containers were registering with Eureka using the loopback (127.0.0.1) IP instead of the container IP. This required a change in the Eureka client code to use the container virtual NIC IP address instead of the loopback IP.

Hostname – Another challenge was hostname resolution. Various Nirmata services register with Eureka using the hostname and IP address but Ribbon just used the hostname to communicate with the services. This proves problematic as there is no DNS service available to resolve the container hostname to IP address. For our existing deployment since Zuul is the only service that communicates with the various other services, we were able to get past this issue by using the same hostname as the Docker host for various service containers (other than the Zuul container). This is not an elegant solution by any means and may not work for all scenarios. My understanding is that Docker 0.7.0 will address this problem with the new links feature.

Dependencies – In our application, there are a few dependencies like each service needs to know the database URL and the Eureka server URL. Docker container IP address is assigned at launch and we couldn’t use the hostname (as described above) to inject this information to our services. We addressed this by launching our services in a predetermined order and by passing in the relevant information at runtime to our container startup script. We used Archaius to load runtime properties dynamically from a file url.

To bring it all together we automated the deployment of our application by developing a basic orchestrator using the docker-java client library. Now we can easily trigger the deployment of entire application from our Jenkins continuous integration server within minutes and test our services on AWS.

Whats next..

The combination of Netflix OSS and Docker makes it really easy to develop, deploy and test distributed applications in the cloud. Our current focus is on building a flexible, application-aware orchestration layer that can make deploying & managing complex applications using Docker that addresses some of the current challenges with using Docker. We would love to hear how you are using Docker, and potentially collaborate on Docker & Netflix OSS related projects.

Ritesh Patel

Follow us on Twitter @NirmataCloud

References

[1] Netflix OSS, http://netflix.github.io/

[2] Docker, http://www.docker.io/

[3] LXC, http://en.wikipedia.org/wiki/LXC

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Netflix OSS, meet Docker!

[fa icon="calendar'] Oct 16, 2013 3:01:43 AM / by Ritesh Patel posted in Containers, Engineering, AWS, Cloud Architecture

[fa icon="comment"] 0 Comments

Background

At Nirmata, we are building a cloud services platform to help customers rapidly build cloud ready applications. We believe that next generation of cloud applications will be composed from stateless, loosely-coupled, fine-grained services. In this architecture, each service can be independently developed, deployed, managed and scaled. The Nirmata Platform, itself, is built using the same architectural principles. Such an architecture requires a set of core, infrastructure services. Since Netflix has open sourced their components [1] we decided to evaluate and extend them. The components that best meet our needs were Eureka (a registry for inter-service communication), Ribbon (a client-side load balancer and SDK for service to service communication), Zuul (a gateway service) and Archaius (a configuration framework). In a few days we had the Netflix OSS components working with our services and things looked good!

Challenges in dev/test

Our application now was now made up of six independent services and we could develop and test these services locally on our laptops fairly easily. Next we decided to move our platform to Amazon AWS for testing and integrate with our Jenkins continuous integration server. This required automation to deploy these various services in our test environment. We considered various options such as creating and launching AMI’s, or using Puppet/Chef. But these approaches would require each service to be installed on a separate EC2 instance and the number of EC2 instances would quickly grow as we add more services. Being a startup, we started looking for more efficient alternatives for ourselves as well as our customers..

Why Docker

This is when we started looking at Docker [2]. We knew about Linux containers [3] but didn’t feel we could invest time and effort to directly use them. However, Docker made using Linux containers easy! With a few hours of prototyping with Docker we were able to get an application service up and running. Also, once the Docker images were created, running them was a snap, and unlike launching VM instances there was hardly any startup time penalty. This meant that we could now launch our entire application, very quickly on a single medium EC2 instance vs launching multiple micro or small instances. This was amazing!

Using Netflix OSS with Docker

The next step was to get all our services, including the Netflix OSS services running in Docker (v0.6.3) containers. So, first we created the base image for all our services by installing jdk 7 and tomcat 7. For test environment, we wanted to make sure that the base container image can be use for any service, including Netflix OSS services so we added a short startup script to the container base image to copy the service war file from a mounted location, setup some environment variables (explanation later) and start the tomcat service.

#!/bin/bash
echo Starting $1 on port $2
#Copy the war file from mounted directory to tomcat webapps directory
if [ $1 ]
then
cp /var/lib/webapps/$1.war /var/lib/tomcat7/webapps/$1.war
fi
#Add the port to the JVM args
if [ $2 ]
then
echo "export JAVA_OPTS=\"-Xms512m -Xmx1024m -Dport.http.nonssl=$2 -Darchaius.configurationSource.additionalUrls=file:///home/nirmata/dynamic.properties\"" >> /usr/share/tomcat7/bin/setenv.sh
else
echo "export JAVA_OPTS=\"-Xms512m -Xmx1024m -Dport.http.nonssl=8080\"" >> /usr/share/tomcat7/bin/setenv.sh
fi
#Setup dynamic properties
echo "eureka.port=$2" >> /home/nirmata/dynamic.properties
if [ $3 ]
then
echo "eureka.serviceUrl.defaultZone=$3" >> /home/nirmata/dynamic.properties
echo "eureka.serviceUrl.default.defaultZone=$3" >> /home/nirmata/dynamic.properties
fi
echo "eureka.environment=" >> /home/nirmata/dynamic.properties
if [ $4 ]
then
echo "default.host=$4" >> /home/nirmata/dynamic.properties
fi
service tomcat7 start
tail -F /var/lib/tomcat7/logs/catalina.out

 

Following are some key considerations in deployed Netflix OSS with Docker:

Ports – With Docker, ports used by the application in the container need to be specified when launching a container so that the port can be mapped to the host port. Docker automatically assigns the host port. On startup various Nirmata services register with the service registry, Eureka. Service running within a Docker container needs to register using the host port so that other services can communicate with it. To solve this, we specified the same host and container port when launch a container. For example, Eureka would be launched using port 8080 on the container as well as the host. One challenge this introduced was the need to automatically configure Tomcat port for various services. This is easily done by specifying the port as an environment variable and modifying the server.xml file to use the environment variable instead of a hard coded value.

IP Address – Each application typically registers with Eureka using its IP address. We noticed that our services running in Docker containers were registering with Eureka using the loopback (127.0.0.1) IP instead of the container IP. This required a change in the Eureka client code to use the container virtual NIC IP address instead of the loopback IP.

Hostname – Another challenge was hostname resolution. Various Nirmata services register with Eureka using the hostname and IP address but Ribbon just used the hostname to communicate with the services. This proves problematic as there is no DNS service available to resolve the container hostname to IP address. For our existing deployment since Zuul is the only service that communicates with the various other services, we were able to get past this issue by using the same hostname as the Docker host for various service containers (other than the Zuul container). This is not an elegant solution by any means and may not work for all scenarios. My understanding is that Docker 0.7.0 will address this problem with the new links feature.

Dependencies – In our application, there are a few dependencies like each service needs to know the database URL and the Eureka server URL. Docker container IP address is assigned at launch and we couldn’t use the hostname (as described above) to inject this information to our services. We addressed this by launching our services in a predetermined order and by passing in the relevant information at runtime to our container startup script. We used Archaius to load runtime properties dynamically from a file url.

To bring it all together we automated the deployment of our application by developing a basic orchestrator using the docker-java client library. Now we can easily trigger the deployment of entire application from our Jenkins continuous integration server within minutes and test our services on AWS.

Whats next..

The combination of Netflix OSS and Docker makes it really easy to develop, deploy and test distributed applications in the cloud. Our current focus is on building a flexible, application-aware orchestration layer that can make deploying & managing complex applications using Docker that addresses some of the current challenges with using Docker. We would love to hear how you are using Docker, and potentially collaborate on Docker & Netflix OSS related projects.

Ritesh Patel

Follow us on Twitter @NirmataCloud

References

[1] Netflix OSS, http://netflix.github.io/

[2] Docker, http://www.docker.io/

[3] LXC, http://en.wikipedia.org/wiki/LXC

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REST is not about APIs, Part 1

[fa icon="calendar'] Oct 1, 2013 3:05:11 AM / by admin posted in Engineering

[fa icon="comment"] 0 Comments

Most articles on REST seem to focus only on APIs. This view misses several key benefits of a RESTful system. The true potential of REST is to build systems as scalable, distributed, resilient, and composable as the Web. Yes, APIs play a role in this but by themselves are not enough. In this two-part post, I will discuss how you can leverage all REST architecture constraints in your systems:

Rest is not about APIs, Part 1: Description of REST and the API-centric view

Rest is not about APIs, Part 2: The true power of REST, and examples of its application

Brief Description of REST

REST (short for REpresentational State Transfer) is an architectural style that describes how a distributed hypermedia system works. The internet (or “the web”) is the best known example of a distributed hypermedia system. The term REST was introduced by Roy Fielding. His doctoral dissertation [1] remains the go-to source on REST from which I have summarized below.

REST is described using 6 architectural constraints, 4 interface constraints, and 3 architectural components.

Architectural Constraints

  1. Client-server: separation of client & server roles i.e. the representation of resources from their stored state.
  2. Stateless: the server should not store or cache client state across requests. Each client request should be transition the stored data from one valid state to another. This allows any available server instance to be used to fulfill any request.
  3. Cache: the server should indicate if data can be cached and reused across requests.
  4. Uniform Interface: all server data can be manipulated using the same interface. This constraint further expands into four interface constraints:
    1. identification of resources: all resources have one or more names (e.g. HTTP URIs) managed by the naming authority (typically the server).
    2. manipulation of resources through representations: The representation of a resource is separated from its identify and can change over time.
    3. self-descriptive messages: the messages should contain metadata that describes how to read the message (e.g. HTTP MIME types and other headers).
    4. hypermedia as the engine of application state (HATEOS): Representations should also contain data to drive application state. This allows clients to be loosely coupled to servers, and require no prior (hard-coded) knowledge of how to interact with a particular resource.
  5. Layered System: each layer deals with the one below it, and has no direct visibility to other layers.
  6. Code on Demand (Optional): the server can extend the client functionality by sending back scripts or code.

Architectural Elements

  • Data Elements: data elements allow information to be moved from where it is stored to where it will be used. Data Elements are described by Resources, Resource Identifiers and Representations.
    1. Resources: are things that can be uniquely named (e.g. a HTML page or image on the Web, or an Object instance in an application.) A request for a resource can return a representation or a set of resource identifiers, or a combination of the two.
    2. Resource Identifiers: are the names given to resources (e.g. a HTTP URL.)
    3. Representations: a representation is what gets transferred between REST components.
  • Connectors: connectors encapsulate the activities of accessing resources and transferring representations.
    1. Client: a Client initiates requests for information.
    2. Server: a Server listens for, and responds to, requests
    3. Cache: a Cache can be attached to clients or servers and is used to speed up interactions.
    4. Resolver: a Resolver helps find resources to establish inter-component communication (e.g. DNS bind)
    5. Tunnel: a Tunnel allows interactions across network boundaries like firewalls.
  • Components: components are the different roles in a system. Components use one or more Connectors for interactions with other components.
    1. Origin server: uses a server connector to manage a collection of resources.
    2. Gateway: a gateway component is a reverse proxy. It performs common functions across servers, such as authentication.
    3. Proxy: a proxy is an intermediary component selected by the client, to perform common functions.
    4. User agent: uses client connectors to initiate requests, and receive resource representations from servers.

The API-centric view of REST

The API centric view on REST focuses only on the uniform interface constraint. For the most when APIs are discussed the assumption is that these are external facing APIs (northbound from the perspective of the application). A RESTful external API is a nice addition for software products, and can fulfill a business need. However, it does not address product maintainability and other architectural challenges like elasticity, resilience, and composability.

The Richardson Maturity Model (RMM) [2] is a popular way of measuring how RESTful an API is. The RMM is useful to qualify whether the API has REST characteristics, but does not imply a RESTful system. ip domain info In a blog post describing the RMM [3], Martin Fowler notes:

“I should stress that the RMM, while a good way to think about what the elements of REST, is not a definition of levels of REST itself. Roy Fielding has made is clear that level 3 RMM is a pre-condition of REST. Like many terms in software, REST gets lots of definitions, but since Roy Fielding coined the term, his definition should carry more weight than most.”
-- Martin Fowler, Richardson Maturity Model

(See [4] for Roy Fielding’s post that Martin is referring to.)

Most RESTful API implementations layered on top of existing systems, have a difficult time with the HATEOS interface constraint. They end up adopting “pragmatic REST” [5]. Getting the HATEOS constraint right requires both the server and client to be designed for this type of interaction, like a web browser and a web server.

If you are trying to add an RESTful API to an existing application, this will be hard to do. However, if you can design for HATEOS the potential payoff is huge as you will have a loosely coupled system, where server-side changes do not easily break the client. [6]

Summary

In this part, we discussed what REST is and why the API centric view is not sufficient. In the next part we will cover how to use all of REST to build a systems as flexible as the web.

References

[1] Representational State Transfer (REST), Roy Fielding, <a href="http://www.ics.uci.edu/~fielding/pubs/dissertation/rest_arch_style.htm">http://www.ics.uci.edu/~fielding/pubs/dissertation/rest_arch_style.htm</a>
[2] Richardson Maturity Model, Leonard Richardson Presentation 2008, <a href="http://www.crummy.com/writing/speaking/2008-QCon/act3.html">http://www.crummy.com/writing/speaking/2008-QCon/act3.html</a>
[3] Richardson Maturity Model, Martin Fowler, <a href="http://martinfowler.com/articles/richardsonMaturityModel.html">http://martinfowler.com/articles/richardsonMaturityModel.html</a>
[4] REST APIs must be hypertext-driven, Roy Fielding, <a href="http://roy.gbiv.com/untangled/2008/rest-apis-must-be-hypertext-driven">http://roy.gbiv.com/untangled/2008/rest-apis-must-be-hypertext-driven</a>
[5] API Design: Harnessing HATEOAS, Part 2, <a href="https://blog.apigee.com/detail/api_design_harnessing_hateoas_part_2">https://blog.apigee.com/detail/api_design_harnessing_hateoas_part_2</a>
[6] Haters gonna HATEOAS, <a href="http://timelessrepo.com/haters-gonna-hateoas">http://timelessrepo.com/haters-gonna-hateoas</a>
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REST is not about APIs, Part 1

[fa icon="calendar'] Oct 1, 2013 3:05:11 AM / by admin posted in REST, Engineering, Cloud Architecture

[fa icon="comment"] 0 Comments

Most articles on REST seem to focus only on APIs. This view misses several key benefits of a RESTful system. The true potential of REST is to build systems as scalable, distributed, resilient, and composable as the Web. Yes, APIs play a role in this but by themselves are not enough. In this two-part post, I will discuss how you can leverage all REST architecture constraints in your systems:

Rest is not about APIs, Part 1: Description of REST and the API-centric view

Rest is not about APIs, Part 2: The true power of REST, and examples of its application

Brief Description of REST

REST (short for REpresentational State Transfer) is an architectural style that describes how a distributed hypermedia system works. The internet (or “the web”) is the best known example of a distributed hypermedia system. The term REST was introduced by Roy Fielding. His doctoral dissertation [1] remains the go-to source on REST from which I have summarized below.

REST is described using 6 architectural constraints, 4 interface constraints, and 3 architectural components.

Architectural Constraints

  1. Client-server: separation of client & server roles i.e. the representation of resources from their stored state.
  2. Stateless: the server should not store or cache client state across requests. Each client request should be transition the stored data from one valid state to another. This allows any available server instance to be used to fulfill any request.
  3. Cache: the server should indicate if data can be cached and reused across requests.
  4. Uniform Interface: all server data can be manipulated using the same interface. This constraint further expands into four interface constraints:
    1. identification of resources: all resources have one or more names (e.g. HTTP URIs) managed by the naming authority (typically the server).
    2. manipulation of resources through representations: The representation of a resource is separated from its identify and can change over time.
    3. self-descriptive messages: the messages should contain metadata that describes how to read the message (e.g. HTTP MIME types and other headers).
    4. hypermedia as the engine of application state (HATEOS): Representations should also contain data to drive application state. This allows clients to be loosely coupled to servers, and require no prior (hard-coded) knowledge of how to interact with a particular resource.
  5. Layered System: each layer deals with the one below it, and has no direct visibility to other layers.
  6. Code on Demand (Optional): the server can extend the client functionality by sending back scripts or code.

Architectural Elements

  • Data Elements: data elements allow information to be moved from where it is stored to where it will be used. Data Elements are described by Resources, Resource Identifiers and Representations.
    1. Resources: are things that can be uniquely named (e.g. a HTML page or image on the Web, or an Object instance in an application.) A request for a resource can return a representation or a set of resource identifiers, or a combination of the two.
    2. Resource Identifiers: are the names given to resources (e.g. a HTTP URL.)
    3. Representations: a representation is what gets transferred between REST components.
  • Connectors: connectors encapsulate the activities of accessing resources and transferring representations.
    1. Client: a Client initiates requests for information.
    2. Server: a Server listens for, and responds to, requests
    3. Cache: a Cache can be attached to clients or servers and is used to speed up interactions.
    4. Resolver: a Resolver helps find resources to establish inter-component communication (e.g. DNS bind)
    5. Tunnel: a Tunnel allows interactions across network boundaries like firewalls.
  • Components: components are the different roles in a system. Components use one or more Connectors for interactions with other components.
    1. Origin server: uses a server connector to manage a collection of resources.
    2. Gateway: a gateway component is a reverse proxy. It performs common functions across servers, such as authentication.
    3. Proxy: a proxy is an intermediary component selected by the client, to perform common functions.
    4. User agent: uses client connectors to initiate requests, and receive resource representations from servers.

The API-centric view of REST

The API centric view on REST focuses only on the uniform interface constraint. For the most when APIs are discussed the assumption is that these are external facing APIs (northbound from the perspective of the application). A RESTful external API is a nice addition for software products, and can fulfill a business need. However, it does not address product maintainability and other architectural challenges like elasticity, resilience, and composability.

The Richardson Maturity Model (RMM) [2] is a popular way of measuring how RESTful an API is. The RMM is useful to qualify whether the API has REST characteristics, but does not imply a RESTful system. ip domain info In a blog post describing the RMM [3], Martin Fowler notes:

“I should stress that the RMM, while a good way to think about what the elements of REST, is not a definition of levels of REST itself. Roy Fielding has made is clear that level 3 RMM is a pre-condition of REST. Like many terms in software, REST gets lots of definitions, but since Roy Fielding coined the term, his definition should carry more weight than most.”
-- Martin Fowler, Richardson Maturity Model

(See [4] for Roy Fielding’s post that Martin is referring to.)

Most RESTful API implementations layered on top of existing systems, have a difficult time with the HATEOS interface constraint. They end up adopting “pragmatic REST” [5]. Getting the HATEOS constraint right requires both the server and client to be designed for this type of interaction, like a web browser and a web server.

If you are trying to add an RESTful API to an existing application, this will be hard to do. However, if you can design for HATEOS the potential payoff is huge as you will have a loosely coupled system, where server-side changes do not easily break the client. [6]

Summary

In this part, we discussed what REST is and why the API centric view is not sufficient. In the next part we will cover how to use all of REST to build a systems as flexible as the web.

References

[1] Representational State Transfer (REST), Roy Fielding, <a href="http://www.ics.uci.edu/~fielding/pubs/dissertation/rest_arch_style.htm">http://www.ics.uci.edu/~fielding/pubs/dissertation/rest_arch_style.htm</a>
[2] Richardson Maturity Model, Leonard Richardson Presentation 2008, <a href="http://www.crummy.com/writing/speaking/2008-QCon/act3.html">http://www.crummy.com/writing/speaking/2008-QCon/act3.html</a>
[3] Richardson Maturity Model, Martin Fowler, <a href="http://martinfowler.com/articles/richardsonMaturityModel.html">http://martinfowler.com/articles/richardsonMaturityModel.html</a>
[4] REST APIs must be hypertext-driven, Roy Fielding, <a href="http://roy.gbiv.com/untangled/2008/rest-apis-must-be-hypertext-driven">http://roy.gbiv.com/untangled/2008/rest-apis-must-be-hypertext-driven</a>
[5] API Design: Harnessing HATEOAS, Part 2, <a href="https://blog.apigee.com/detail/api_design_harnessing_hateoas_part_2">https://blog.apigee.com/detail/api_design_harnessing_hateoas_part_2</a>
[6] Haters gonna HATEOAS, <a href="http://timelessrepo.com/haters-gonna-hateoas">http://timelessrepo.com/haters-gonna-hateoas</a>
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