Tag Archives: Containers

Containers Will Not Fix Your Broken Culture (and Other Hard Truths) (ACMQueue)

Post Syndicated from jake original https://lwn.net/Articles/747020/rss

In ACMQueue magazine, Bridget Kromhout writes about containers and why they are not the solution to every problem. The article is subtitled:
“Complex socio-technical systems are hard;
film at 11.”
Don’t get me wrong—containers are delightful! But let’s be real: we’re unlikely to solve the vast majority of problems in a given organization via the judicious application of kernel features. If you have contention between your ops team and your dev team(s)—and maybe they’re all facing off with some ill-considered DevOps silo inexplicably stuck between them—then cgroups and namespaces won’t have a prayer of solving that.

Development teams love the idea of shipping their dependencies bundled with their apps, imagining limitless portability. Someone in security is weeping for the unpatched CVEs, but feature velocity is so desirable that security’s pleas go unheard. Platform operators are happy (well, less surly) knowing they can upgrade the underlying infrastructure without affecting the dependencies for any applications, until they realize the heavyweight app containers shipping a full operating system aren’t being maintained at all.”

Migrating Your Amazon ECS Containers to AWS Fargate

Post Syndicated from Tiffany Jernigan original https://aws.amazon.com/blogs/compute/migrating-your-amazon-ecs-containers-to-aws-fargate/

AWS Fargate is a new technology that works with Amazon Elastic Container Service (ECS) to run containers without having to manage servers or clusters. What does this mean? With Fargate, you no longer need to provision or manage a single virtual machine; you can just create tasks and run them directly!

Fargate uses the same API actions as ECS, so you can use the ECS console, the AWS CLI, or the ECS CLI. I recommend running through the first-run experience for Fargate even if you’re familiar with ECS. It creates all of the one-time setup requirements, such as the necessary IAM roles. If you’re using a CLI, make sure to upgrade to the latest version

In this blog, you will see how to migrate ECS containers from running on Amazon EC2 to Fargate.

Getting started

Note: Anything with code blocks is a change in the task definition file. Screen captures are from the console. Additionally, Fargate is currently available in the us-east-1 (N. Virginia) region.

Launch type

When you create tasks (grouping of containers) and clusters (grouping of tasks), you now have two launch type options: EC2 and Fargate. The default launch type, EC2, is ECS as you knew it before the announcement of Fargate. You need to specify Fargate as the launch type when running a Fargate task.

Even though Fargate abstracts away virtual machines, tasks still must be launched into a cluster. With Fargate, clusters are a logical infrastructure and permissions boundary that allow you to isolate and manage groups of tasks. ECS also supports heterogeneous clusters that are made up of tasks running on both EC2 and Fargate launch types.

The optional, new requiresCompatibilities parameter with FARGATE in the field ensures that your task definition only passes validation if you include Fargate-compatible parameters. Tasks can be flagged as compatible with EC2, Fargate, or both.

"requiresCompatibilities": [
    "FARGATE"
]

Networking

"networkMode": "awsvpc"

In November, we announced the addition of task networking with the network mode awsvpc. By default, ECS uses the bridge network mode. Fargate requires using the awsvpc network mode.

In bridge mode, all of your tasks running on the same instance share the instance’s elastic network interface, which is a virtual network interface, IP address, and security groups.

The awsvpc mode provides this networking support to your tasks natively. You now get the same VPC networking and security controls at the task level that were previously only available with EC2 instances. Each task gets its own elastic networking interface and IP address so that multiple applications or copies of a single application can run on the same port number without any conflicts.

The awsvpc mode also provides a separation of responsibility for tasks. You can get complete control of task placement within your own VPCs, subnets, and the security policies associated with them, even though the underlying infrastructure is managed by Fargate. Also, you can assign different security groups to each task, which gives you more fine-grained security. You can give an application only the permissions it needs.

"portMappings": [
    {
        "containerPort": "3000"
    }
 ]

What else has to change? First, you only specify a containerPort value, not a hostPort value, as there is no host to manage. Your container port is the port that you access on your elastic network interface IP address. Therefore, your container ports in a single task definition file need to be unique.

"environment": [
    {
        "name": "WORDPRESS_DB_HOST",
        "value": "127.0.0.1:3306"
    }
 ]

Additionally, links are not allowed as they are a property of the “bridge” network mode (and are now a legacy feature of Docker). Instead, containers share a network namespace and communicate with each other over the localhost interface. They can be referenced using the following:

localhost/127.0.0.1:<some_port_number>

CPU and memory

"memory": "1024",
 "cpu": "256"

"memory": "1gb",
 "cpu": ".25vcpu"

When launching a task with the EC2 launch type, task performance is influenced by the instance types that you select for your cluster combined with your task definition. If you pick larger instances, your applications make use of the extra resources if there is no contention.

In Fargate, you needed a way to get additional resource information so we created task-level resources. Task-level resources define the maximum amount of memory and cpu that your task can consume.

  • memory can be defined in MB with just the number, or in GB, for example, “1024” or “1gb”.
  • cpu can be defined as the number or in vCPUs, for example, “256” or “.25vcpu”.
    • vCPUs are virtual CPUs. You can look at the memory and vCPUs for instance types to get an idea of what you may have used before.

The memory and CPU options available with Fargate are:

CPU Memory
256 (.25 vCPU) 0.5GB, 1GB, 2GB
512 (.5 vCPU) 1GB, 2GB, 3GB, 4GB
1024 (1 vCPU) 2GB, 3GB, 4GB, 5GB, 6GB, 7GB, 8GB
2048 (2 vCPU) Between 4GB and 16GB in 1GB increments
4096 (4 vCPU) Between 8GB and 30GB in 1GB increments

IAM roles

Because Fargate uses awsvpc mode, you need an Amazon ECS service-linked IAM role named AWSServiceRoleForECS. It provides Fargate with the needed permissions, such as the permission to attach an elastic network interface to your task. After you create your service-linked IAM role, you can delete the remaining roles in your services.

"executionRoleArn": "arn:aws:iam::<your_account_id>:role/ecsTaskExecutionRole"

With the EC2 launch type, an instance role gives the agent the ability to pull, publish, talk to ECS, and so on. With Fargate, the task execution IAM role is only needed if you’re pulling from Amazon ECR or publishing data to Amazon CloudWatch Logs.

The Fargate first-run experience tutorial in the console automatically creates these roles for you.

Volumes

Fargate currently supports non-persistent, empty data volumes for containers. When you define your container, you no longer use the host field and only specify a name.

Load balancers

For awsvpc mode, and therefore for Fargate, use the IP target type instead of the instance target type. You define this in the Amazon EC2 service when creating a load balancer.

If you’re using a Classic Load Balancer, change it to an Application Load Balancer or a Network Load Balancer.

Tip: If you are using an Application Load Balancer, make sure that your tasks are launched in the same VPC and Availability Zones as your load balancer.

Let’s migrate a task definition!

Here is an example NGINX task definition. This type of task definition is what you’re used to if you created one before Fargate was announced. It’s what you would run now with the EC2 launch type.

{
    "containerDefinitions": [
        {
            "name": "nginx",
            "image": "nginx",
            "memory": "512",
            "cpu": "100",
            "essential": true,
            "portMappings": [
                {
                    "hostPort": "80",
                    "containerPort": "80",
                    "protocol": "tcp"
                }
            ],
            "logConfiguration": {
                "logDriver": "awslogs",
                "options": {
                    "awslogs-group": "/ecs/",
                    "awslogs-region": "us-east-1",
                    "awslogs-stream-prefix": "ecs"
                }
            }
        }
    ],
    "family": "nginx-ec2"
}

OK, so now what do you need to do to change it to run with the Fargate launch type?

  • Add FARGATE for requiredCompatibilities (not required, but a good safety check for your task definition).
  • Use awsvpc as the network mode.
  • Just specify the containerPort (the hostPortvalue is the same).
  • Add a task executionRoleARN value to allow logging to CloudWatch.
  • Provide cpu and memory limits for the task.
{
    "requiresCompatibilities": [
        "FARGATE"
    ],
    "containerDefinitions": [
        {
            "name": "nginx",
            "image": "nginx",
            "memory": "512",
            "cpu": "100",
            "essential": true,
            "portMappings": [
                {
                    "containerPort": "80",
                    "protocol": "tcp"
                }
            ],
            "logConfiguration": {
                "logDriver": "awslogs",
                "options": {
                    "awslogs-group": "/ecs/",
                    "awslogs-region": "us-east-1",
                    "awslogs-stream-prefix": "ecs"
                }
            }
        }
    ],
    "networkMode": "awsvpc",
    "executionRoleArn": "arn:aws:iam::<your_account_id>:role/ecsTaskExecutionRole",
    "family": "nginx-fargate",
    "memory": "512",
    "cpu": "256"
}

Are there more examples?

Yep! Head to the AWS Samples GitHub repo. We have several sample task definitions you can try for both the EC2 and Fargate launch types. Contributions are very welcome too :).

 

tiffany jernigan
@tiffanyfayj

Reactive Microservices Architecture on AWS

Post Syndicated from Sascha Moellering original https://aws.amazon.com/blogs/architecture/reactive-microservices-architecture-on-aws/

Microservice-application requirements have changed dramatically in recent years. These days, applications operate with petabytes of data, need almost 100% uptime, and end users expect sub-second response times. Typical N-tier applications can’t deliver on these requirements.

Reactive Manifesto, published in 2014, describes the essential characteristics of reactive systems including: responsiveness, resiliency, elasticity, and being message driven.

Being message driven is perhaps the most important characteristic of reactive systems. Asynchronous messaging helps in the design of loosely coupled systems, which is a key factor for scalability. In order to build a highly decoupled system, it is important to isolate services from each other. As already described, isolation is an important aspect of the microservices pattern. Indeed, reactive systems and microservices are a natural fit.

Implemented Use Case
This reference architecture illustrates a typical ad-tracking implementation.

Many ad-tracking companies collect massive amounts of data in near-real-time. In many cases, these workloads are very spiky and heavily depend on the success of the ad-tech companies’ customers. Typically, an ad-tracking-data use case can be separated into a real-time part and a non-real-time part. In the real-time part, it is important to collect data as fast as possible and ask several questions including:,  “Is this a valid combination of parameters?,””Does this program exist?,” “Is this program still valid?”

Because response time has a huge impact on conversion rate in advertising, it is important for advertisers to respond as fast as possible. This information should be kept in memory to reduce communication overhead with the caching infrastructure. The tracking application itself should be as lightweight and scalable as possible. For example, the application shouldn’t have any shared mutable state and it should use reactive paradigms. In our implementation, one main application is responsible for this real-time part. It collects and validates data, responds to the client as fast as possible, and asynchronously sends events to backend systems.

The non-real-time part of the application consumes the generated events and persists them in a NoSQL database. In a typical tracking implementation, clicks, cookie information, and transactions are matched asynchronously and persisted in a data store. The matching part is not implemented in this reference architecture. Many ad-tech architectures use frameworks like Hadoop for the matching implementation.

The system can be logically divided into the data collection partand the core data updatepart. The data collection part is responsible for collecting, validating, and persisting the data. In the core data update part, the data that is used for validation gets updated and all subscribers are notified of new data.

Components and Services

Main Application
The main application is implemented using Java 8 and uses Vert.x as the main framework. Vert.x is an event-driven, reactive, non-blocking, polyglot framework to implement microservices. It runs on the Java virtual machine (JVM) by using the low-level IO library Netty. You can write applications in Java, JavaScript, Groovy, Ruby, Kotlin, Scala, and Ceylon. The framework offers a simple and scalable actor-like concurrency model. Vert.x calls handlers by using a thread known as an event loop. To use this model, you have to write code known as “verticles.” Verticles share certain similarities with actors in the actor model. To use them, you have to implement the verticle interface. Verticles communicate with each other by generating messages in  a single event bus. Those messages are sent on the event bus to a specific address, and verticles can register to this address by using handlers.

With only a few exceptions, none of the APIs in Vert.x block the calling thread. Similar to Node.js, Vert.x uses the reactor pattern. However, in contrast to Node.js, Vert.x uses several event loops. Unfortunately, not all APIs in the Java ecosystem are written asynchronously, for example, the JDBC API. Vert.x offers a possibility to run this, blocking APIs without blocking the event loop. These special verticles are called worker verticles. You don’t execute worker verticles by using the standard Vert.x event loops, but by using a dedicated thread from a worker pool. This way, the worker verticles don’t block the event loop.

Our application consists of five different verticles covering different aspects of the business logic. The main entry point for our application is the HttpVerticle, which exposes an HTTP-endpoint to consume HTTP-requests and for proper health checking. Data from HTTP requests such as parameters and user-agent information are collected and transformed into a JSON message. In order to validate the input data (to ensure that the program exists and is still valid), the message is sent to the CacheVerticle.

This verticle implements an LRU-cache with a TTL of 10 minutes and a capacity of 100,000 entries. Instead of adding additional functionality to a standard JDK map implementation, we use Google Guava, which has all the features we need. If the data is not in the L1 cache, the message is sent to the RedisVerticle. This verticle is responsible for data residing in Amazon ElastiCache and uses the Vert.x-redis-client to read data from Redis. In our example, Redis is the central data store. However, in a typical production implementation, Redis would just be the L2 cache with a central data store like Amazon DynamoDB. One of the most important paradigms of a reactive system is to switch from a pull- to a push-based model. To achieve this and reduce network overhead, we’ll use Redis pub/sub to push core data changes to our main application.

Vert.x also supports direct Redis pub/sub-integration, the following code shows our subscriber-implementation:

vertx.eventBus().<JsonObject>consumer(REDIS_PUBSUB_CHANNEL_VERTX, received -> {

JsonObject value = received.body().getJsonObject("value");

String message = value.getString("message");

JsonObject jsonObject = new JsonObject(message);

eb.send(CACHE_REDIS_EVENTBUS_ADDRESS, jsonObject);

});

redis.subscribe(Constants.REDIS_PUBSUB_CHANNEL, res -> {

if (res.succeeded()) {

LOGGER.info("Subscribed to " + Constants.REDIS_PUBSUB_CHANNEL);

} else {

LOGGER.info(res.cause());

}

});

The verticle subscribes to the appropriate Redis pub/sub-channel. If a message is sent over this channel, the payload is extracted and forwarded to the cache-verticle that stores the data in the L1-cache. After storing and enriching data, a response is sent back to the HttpVerticle, which responds to the HTTP request that initially hit this verticle. In addition, the message is converted to ByteBuffer, wrapped in protocol buffers, and send to an Amazon Kinesis Data Stream.

The following example shows a stripped-down version of the KinesisVerticle:

public class KinesisVerticle extends AbstractVerticle {

private static final Logger LOGGER = LoggerFactory.getLogger(KinesisVerticle.class);

private AmazonKinesisAsync kinesisAsyncClient;

private String eventStream = "EventStream";

@Override

public void start() throws Exception {

EventBus eb = vertx.eventBus();

kinesisAsyncClient = createClient();

eventStream = System.getenv(STREAM_NAME) == null ? "EventStream" : System.getenv(STREAM_NAME);

eb.consumer(Constants.KINESIS_EVENTBUS_ADDRESS, message -> {

try {

TrackingMessage trackingMessage = Json.decodeValue((String)message.body(), TrackingMessage.class);

String partitionKey = trackingMessage.getMessageId();

byte [] byteMessage = createMessage(trackingMessage);

ByteBuffer buf = ByteBuffer.wrap(byteMessage);

sendMessageToKinesis(buf, partitionKey);

message.reply("OK");

}

catch (KinesisException exc) {

LOGGER.error(exc);

}

});

}

Kinesis Consumer
This AWS Lambda function consumes data from an Amazon Kinesis Data Stream and persists the data in an Amazon DynamoDB table. In order to improve testability, the invocation code is separated from the business logic. The invocation code is implemented in the class KinesisConsumerHandler and iterates over the Kinesis events pulled from the Kinesis stream by AWS Lambda. Each Kinesis event is unwrapped and transformed from ByteBuffer to protocol buffers and converted into a Java object. Those Java objects are passed to the business logic, which persists the data in a DynamoDB table. In order to improve duration of successive Lambda calls, the DynamoDB-client is instantiated lazily and reused if possible.

Redis Updater
From time to time, it is necessary to update core data in Redis. A very efficient implementation for this requirement is using AWS Lambda and Amazon Kinesis. New core data is sent over the AWS Kinesis stream using JSON as data format and consumed by a Lambda function. This function iterates over the Kinesis events pulled from the Kinesis stream by AWS Lambda. Each Kinesis event is unwrapped and transformed from ByteBuffer to String and converted into a Java object. The Java object is passed to the business logic and stored in Redis. In addition, the new core data is also sent to the main application using Redis pub/sub in order to reduce network overhead and converting from a pull- to a push-based model.

The following example shows the source code to store data in Redis and notify all subscribers:

public void updateRedisData(final TrackingMessage trackingMessage, final Jedis jedis, final LambdaLogger logger) {

try {

ObjectMapper mapper = new ObjectMapper();

String jsonString = mapper.writeValueAsString(trackingMessage);

Map<String, String> map = marshal(jsonString);

String statusCode = jedis.hmset(trackingMessage.getProgramId(), map);

}

catch (Exception exc) {

if (null == logger)

exc.printStackTrace();

else

logger.log(exc.getMessage());

}

}

public void notifySubscribers(final TrackingMessage trackingMessage, final Jedis jedis, final LambdaLogger logger) {

try {

ObjectMapper mapper = new ObjectMapper();

String jsonString = mapper.writeValueAsString(trackingMessage);

jedis.publish(Constants.REDIS_PUBSUB_CHANNEL, jsonString);

}

catch (final IOException e) {

log(e.getMessage(), logger);

}

}

Similarly to our Kinesis Consumer, the Redis-client is instantiated somewhat lazily.

Infrastructure as Code
As already outlined, latency and response time are a very critical part of any ad-tracking solution because response time has a huge impact on conversion rate. In order to reduce latency for customers world-wide, it is common practice to roll out the infrastructure in different AWS Regions in the world to be as close to the end customer as possible. AWS CloudFormation can help you model and set up your AWS resources so that you can spend less time managing those resources and more time focusing on your applications that run in AWS.

You create a template that describes all the AWS resources that you want (for example, Amazon EC2 instances or Amazon RDS DB instances), and AWS CloudFormation takes care of provisioning and configuring those resources for you. Our reference architecture can be rolled out in different Regions using an AWS CloudFormation template, which sets up the complete infrastructure (for example, Amazon Virtual Private Cloud (Amazon VPC), Amazon Elastic Container Service (Amazon ECS) cluster, Lambda functions, DynamoDB table, Amazon ElastiCache cluster, etc.).

Conclusion
In this blog post we described reactive principles and an example architecture with a common use case. We leveraged the capabilities of different frameworks in combination with several AWS services in order to implement reactive principles—not only at the application-level but also at the system-level. I hope I’ve given you ideas for creating your own reactive applications and systems on AWS.

About the Author

Sascha Moellering is a Senior Solution Architect. Sascha is primarily interested in automation, infrastructure as code, distributed computing, containers and JVM. He can be reached at [email protected]

 

 

[$] Containers from user space

Post Syndicated from corbet original https://lwn.net/Articles/745820/rss

In a linux.conf.au 2018 keynote called “Containers from user space” — an
explicit reference to the cult film “Plan 9 from Outer Space” — Jessie
Frazelle took the audience on a fast-moving tour of the past, present, and
possible future of container technology. Describing the container craze as
“amazing”, she covered topics like the definition of a container, security,
runtimes, container concepts in programming languages, multi-tenancy, and
more.

Task Networking in AWS Fargate

Post Syndicated from Nathan Peck original https://aws.amazon.com/blogs/compute/task-networking-in-aws-fargate/

AWS Fargate is a technology that allows you to focus on running your application without needing to provision, monitor, or manage the underlying compute infrastructure. You package your application into a Docker container that you can then launch using your container orchestration tool of choice.

Fargate allows you to use containers without being responsible for Amazon EC2 instances, similar to how EC2 allows you to run VMs without managing physical infrastructure. Currently, Fargate provides support for Amazon Elastic Container Service (Amazon ECS). Support for Amazon Elastic Container Service for Kubernetes (Amazon EKS) will be made available in the near future.

Despite offloading the responsibility for the underlying instances, Fargate still gives you deep control over configuration of network placement and policies. This includes the ability to use many networking fundamentals such as Amazon VPC and security groups.

This post covers how to take advantage of the different ways of networking your containers in Fargate when using ECS as your orchestration platform, with a focus on how to do networking securely.

The first step to running any application in Fargate is defining an ECS task for Fargate to launch. A task is a logical group of one or more Docker containers that are deployed with specified settings. When running a task in Fargate, there are two different forms of networking to consider:

  • Container (local) networking
  • External networking

Container Networking

Container networking is often used for tightly coupled application components. Perhaps your application has a web tier that is responsible for serving static content as well as generating some dynamic HTML pages. To generate these dynamic pages, it has to fetch information from another application component that has an HTTP API.

One potential architecture for such an application is to deploy the web tier and the API tier together as a pair and use local networking so the web tier can fetch information from the API tier.

If you are running these two components as two processes on a single EC2 instance, the web tier application process could communicate with the API process on the same machine by using the local loopback interface. The local loopback interface has a special IP address of 127.0.0.1 and hostname of localhost.

By making a networking request to this local interface, it bypasses the network interface hardware and instead the operating system just routes network calls from one process to the other directly. This gives the web tier a fast and efficient way to fetch information from the API tier with almost no networking latency.

In Fargate, when you launch multiple containers as part of a single task, they can also communicate with each other over the local loopback interface. Fargate uses a special container networking mode called awsvpc, which gives all the containers in a task a shared elastic network interface to use for communication.

If you specify a port mapping for each container in the task, then the containers can communicate with each other on that port. For example the following task definition could be used to deploy the web tier and the API tier:

{
  "family": "myapp"
  "containerDefinitions": [
    {
      "name": "web",
      "image": "my web image url",
      "portMappings": [
        {
          "containerPort": 80
        }
      ],
      "memory": 500,
      "cpu": 10,
      "esssential": true
    },
    {
      "name": "api",
      "image": "my api image url",
      "portMappings": [
        {
          "containerPort": 8080
        }
      ],
      "cpu": 10,
      "memory": 500,
      "essential": true
    }
  ]
}

ECS, with Fargate, is able to take this definition and launch two containers, each of which is bound to a specific static port on the elastic network interface for the task.

Because each Fargate task has its own isolated networking stack, there is no need for dynamic ports to avoid port conflicts between different tasks as in other networking modes. The static ports make it easy for containers to communicate with each other. For example, the web container makes a request to the API container using its well-known static port:

curl 127.0.0.1:8080/my-endpoint

This sends a local network request, which goes directly from one container to the other over the local loopback interface without traversing the network. This deployment strategy allows for fast and efficient communication between two tightly coupled containers. But most application architectures require more than just internal local networking.

External Networking

External networking is used for network communications that go outside the task to other servers that are not part of the task, or network communications that originate from other hosts on the internet and are directed to the task.

Configuring external networking for a task is done by modifying the settings of the VPC in which you launch your tasks. A VPC is a fundamental tool in AWS for controlling the networking capabilities of resources that you launch on your account.

When setting up a VPC, you create one or more subnets, which are logical groups that your resources can be placed into. Each subnet has an Availability Zone and its own route table, which defines rules about how network traffic operates for that subnet. There are two main types of subnets: public and private.

Public subnets

A public subnet is a subnet that has an associated internet gateway. Fargate tasks in that subnet are assigned both private and public IP addresses:


A browser or other client on the internet can send network traffic to the task via the internet gateway using its public IP address. The tasks can also send network traffic to other servers on the internet because the route table can route traffic out via the internet gateway.

If tasks want to communicate directly with each other, they can use each other’s private IP address to send traffic directly from one to the other so that it stays inside the subnet without going out to the internet gateway and back in.

Private subnets

A private subnet does not have direct internet access. The Fargate tasks inside the subnet don’t have public IP addresses, only private IP addresses. Instead of an internet gateway, a network address translation (NAT) gateway is attached to the subnet:

 

There is no way for another server or client on the internet to reach your tasks directly, because they don’t even have an address or a direct route to reach them. This is a great way to add another layer of protection for internal tasks that handle sensitive data. Those tasks are protected and can’t receive any inbound traffic at all.

In this configuration, the tasks can still communicate to other servers on the internet via the NAT gateway. They would appear to have the IP address of the NAT gateway to the recipient of the communication. If you run a Fargate task in a private subnet, you must add this NAT gateway. Otherwise, Fargate can’t make a network request to Amazon ECR to download the container image, or communicate with Amazon CloudWatch to store container metrics.

Load balancers

If you are running a container that is hosting internet content in a private subnet, you need a way for traffic from the public to reach the container. This is generally accomplished by using a load balancer such as an Application Load Balancer or a Network Load Balancer.

ECS integrates tightly with AWS load balancers by automatically configuring a service-linked load balancer to send network traffic to containers that are part of the service. When each task starts, the IP address of its elastic network interface is added to the load balancer’s configuration. When the task is being shut down, network traffic is safely drained from the task before removal from the load balancer.

To get internet traffic to containers using a load balancer, the load balancer is placed into a public subnet. ECS configures the load balancer to forward traffic to the container tasks in the private subnet:

This configuration allows your tasks in Fargate to be safely isolated from the rest of the internet. They can still initiate network communication with external resources via the NAT gateway, and still receive traffic from the public via the Application Load Balancer that is in the public subnet.

Another potential use case for a load balancer is for internal communication from one service to another service within the private subnet. This is typically used for a microservice deployment, in which one service such as an internet user account service needs to communicate with an internal service such as a password service. Obviously, it is undesirable for the password service to be directly accessible on the internet, so using an internet load balancer would be a major security vulnerability. Instead, this can be accomplished by hosting an internal load balancer within the private subnet:

With this approach, one container can distribute requests across an Auto Scaling group of other private containers via the internal load balancer, ensuring that the network traffic stays safely protected within the private subnet.

Best Practices for Fargate Networking

Determine whether you should use local task networking

Local task networking is ideal for communicating between containers that are tightly coupled and require maximum networking performance between them. However, when you deploy one or more containers as part of the same task they are always deployed together so it removes the ability to independently scale different types of workload up and down.

In the example of the application with a web tier and an API tier, it may be the case that powering the application requires only two web tier containers but 10 API tier containers. If local container networking is used between these two container types, then an extra eight unnecessary web tier containers would end up being run instead of allowing the two different services to scale independently.

A better approach would be to deploy the two containers as two different services, each with its own load balancer. This allows clients to communicate with the two web containers via the web service’s load balancer. The web service could distribute requests across the eight backend API containers via the API service’s load balancer.

Run internet tasks that require internet access in a public subnet

If you have tasks that require internet access and a lot of bandwidth for communication with other services, it is best to run them in a public subnet. Give them public IP addresses so that each task can communicate with other services directly.

If you run these tasks in a private subnet, then all their outbound traffic has to go through an NAT gateway. AWS NAT gateways support up to 10 Gbps of burst bandwidth. If your bandwidth requirements go over this, then all task networking starts to get throttled. To avoid this, you could distribute the tasks across multiple private subnets, each with their own NAT gateway. It can be easier to just place the tasks into a public subnet, if possible.

Avoid using a public subnet or public IP addresses for private, internal tasks

If you are running a service that handles private, internal information, you should not put it into a public subnet or use a public IP address. For example, imagine that you have one task, which is an API gateway for authentication and access control. You have another background worker task that handles sensitive information.

The intended access pattern is that requests from the public go to the API gateway, which then proxies request to the background task only if the request is from an authenticated user. If the background task is in a public subnet and has a public IP address, then it could be possible for an attacker to bypass the API gateway entirely. They could communicate directly to the background task using its public IP address, without being authenticated.

Conclusion

Fargate gives you a way to run containerized tasks directly without managing any EC2 instances, but you still have full control over how you want networking to work. You can set up containers to talk to each other over the local network interface for maximum speed and efficiency. For running workloads that require privacy and security, use a private subnet with public internet access locked down. Or, for simplicity with an internet workload, you can just use a public subnet and give your containers a public IP address.

To deploy one of these Fargate task networking approaches, check out some sample CloudFormation templates showing how to configure the VPC, subnets, and load balancers.

If you have questions or suggestions, please comment below.

Building Blocks of Amazon ECS

Post Syndicated from Tiffany Jernigan original https://aws.amazon.com/blogs/compute/building-blocks-of-amazon-ecs/

So, what’s Amazon Elastic Container Service (ECS)? ECS is a managed service for running containers on AWS, designed to make it easy to run applications in the cloud without worrying about configuring the environment for your code to run in. Using ECS, you can easily deploy containers to host a simple website or run complex distributed microservices using thousands of containers.

Getting started with ECS isn’t too difficult. To fully understand how it works and how you can use it, it helps to understand the basic building blocks of ECS and how they fit together!

Let’s begin with an analogy

Imagine you’re in a virtual reality game with blocks and portals, in which your task is to build kingdoms.

In your spaceship, you pull up a holographic map of your upcoming destination: Nozama, a golden-orange planet. Looking at its various regions, you see that the nearest one is za-southwest-1 (SW Nozama). You set your destination, and use your jump drive to jump to the outer atmosphere of za-southwest-1.

As you approach SW Nozama, you see three portals, 1a, 1b, and 1c. Each portal lets you transport directly to an isolated zone (Availability Zone), where you can start construction on your new kingdom (cluster), Royaume.

With your supply of blocks, you take the portal to 1b, and erect the surrounding walls of your first territory (instance)*.

Before you get ahead of yourself, there are some rules to keep in mind. For your territory to be a part of Royaume, the land ordinance requires construction of a building (container), specifically a castle, from which your territory’s lord (agent)* rules.

You can then create architectural plans (task definitions) to build your developments (tasks), consisting of up to 10 buildings per plan. A development can be built now within this or any territory, or multiple territories.

If you do decide to create more territories, you can either stay here in 1b or take a portal to another location in SW Nozama and start building there.

Amazon EC2 building blocks

We currently provide two launch types: EC2 and Fargate. With Fargate, the Amazon EC2 instances are abstracted away and managed for you. Instead of worrying about ECS container instances, you can just worry about tasks. In this post, the infrastructure components used by ECS that are handled by Fargate are marked with a *.

Instance*

EC2 instances are good ol’ virtual machines (VMs). And yes, don’t worry, you can connect to them (via SSH). Because customers have varying needs in memory, storage, and computing power, many different instance types are offered. Just want to run a small application or try a free trial? Try t2.micro. Want to run memory-optimized workloads? R3 and X1 instances are a couple options. There are many more instance types as well, which cater to various use cases.

AMI*

Sorry if you wanted to immediately march forward, but before you create your instance, you need to choose an AMI. An AMI stands for Amazon Machine Image. What does that mean? Basically, an AMI provides the information required to launch an instance: root volume, launch permissions, and volume-attachment specifications. You can find and choose a Linux or Windows AMI provided by AWS, the user community, the AWS Marketplace (for example, the Amazon ECS-Optimized AMI), or you can create your own.

Region

AWS is divided into regions that are geographic areas around the world (for now it’s just Earth, but maybe someday…). These regions have semi-evocative names such as us-east-1 (N. Virginia), us-west-2 (Oregon), eu-central-1 (Frankfurt), ap-northeast-1 (Tokyo), etc.

Each region is designed to be completely isolated from the others, and consists of multiple, distinct data centers. This creates a “blast radius” for failure so that even if an entire region goes down, the others aren’t affected. Like many AWS services, to start using ECS, you first need to decide the region in which to operate. Typically, this is the region nearest to you or your users.

Availability Zone

AWS regions are subdivided into Availability Zones. A region has at minimum two zones, and up to a handful. Zones are physically isolated from each other, spanning one or more different data centers, but are connected through low-latency, fiber-optic networking, and share some common facilities. EC2 is designed so that the most common failures only affect a single zone to prevent region-wide outages. This means you can achieve high availability in a region by spanning your services across multiple zones and distributing across hosts.

Amazon ECS building blocks

Container

Well, without containers, ECS wouldn’t exist!

Are containers virtual machines?
Nope! Virtual machines virtualize the hardware (benefits), while containers virtualize the operating system (even more benefits!). If you look inside a container, you would see that it is made by processes running on the host, and tied together by kernel constructs like namespaces, cgroups, etc. But you don’t need to bother about that level of detail, at least not in this post!

Why containers?
Containers give you the ability to build, ship, and run your code anywhere!

Before the cloud, you needed to self-host and therefore had to buy machines in addition to setting up and configuring the operating system (OS), and running your code. In the cloud, with virtualization, you can just skip to setting up the OS and running your code. Containers make the process even easier—you can just run your code.

Additionally, all of the dependencies travel in a package with the code, which is called an image. This allows containers to be deployed on any host machine. From the outside, it looks like a host is just holding a bunch of containers. They all look the same, in the sense that they are generic enough to be deployed on any host.

With ECS, you can easily run your containerized code and applications across a managed cluster of EC2 instances.

Are containers a fairly new technology?
The concept of containerization is not new. Its origins date back to 1979 with the creation of chroot. However, it wasn’t until the early 2000s that containers became a major technology. The most significant milestone to date was the release of Docker in 2013, which led to the popularization and widespread adoption of containers.

What does ECS use?
While other container technologies exist (LXC, rkt, etc.), because of its massive adoption and use by our customers, ECS was designed first to work natively with Docker containers.

Container instance*

Yep, you are back to instances. An instance is just slightly more complex in the ECS realm though. Here, it is an ECS container instance that is an EC2 instance running the agent, has a specifically defined IAM policy and role, and has been registered into your cluster.

And as you probably guessed, in these instances, you are running containers. 

AMI*

These container instances can use any AMI as long as it has the following specifications: a modern Linux distribution with the agent and the Docker Daemon with any Docker runtime dependencies running on it.

Want it more simplified? Well, AWS created the Amazon ECS-Optimized AMI for just that. Not only does that AMI come preconfigured with all of the previously mentioned specifications, it’s tested and includes the recommended ecs-init upstart process to run and monitor the agent.

Cluster

An ECS cluster is a grouping of (container) instances* (or tasks in Fargate) that lie within a single region, but can span multiple Availability Zones – it’s even a good idea for redundancy. When launching an instance (or tasks in Fargate), unless specified, it registers with the cluster named “default”. If “default” doesn’t exist, it is created. You can also scale and delete your clusters.

Agent*

The Amazon ECS container agent is a Go program that runs in its own container within each EC2 instance that you use with ECS. (It’s also available open source on GitHub!) The agent is the intermediary component that takes care of the communication between the scheduler and your instances. Want to register your instance into a cluster? (Why wouldn’t you? A cluster is both a logical boundary and provider of pool of resources!) Then you need to run the agent on it.

Task

When you want to start a container, it has to be part of a task. Therefore, you have to create a task first. Succinctly, tasks are a logical grouping of 1 to N containers that run together on the same instance, with N defined by you, up to 10. Let’s say you want to run a custom blog engine. You could put together a web server, an application server, and an in-memory cache, each in their own container. Together, they form a basic frontend unit.

Task definition

Ah, but you cannot create a task directly. You have to create a task definition that tells ECS that “task definition X is composed of this container (and maybe that other container and that other container too!).” It’s kind of like an architectural plan for a city. Some other details it can include are how the containers interact, container CPU and memory constraints, and task permissions using IAM roles.

Then you can tell ECS, “start one task using task definition X.” It might sound like unnecessary planning at first. As soon as you start to deal with multiple tasks, scaling, upgrades, and other “real life” scenarios, you’ll be glad that you have task definitions to keep track of things!

Scheduler*

So, the scheduler schedules… sorry, this should be more helpful, huh? The scheduler is part of the “hosted orchestration layer” provided by ECS. Wait a minute, what do I mean by “hosted orchestration”? Simply put, hosted means that it’s operated by ECS on your behalf, without you having to care about it. Your applications are deployed in containers running on your instances, but the managing of tasks is taken care of by ECS. One less thing to worry about!

Also, the scheduler is the component that decides what (which containers) gets to run where (on which instances), according to a number of constraints. Say that you have a custom blog engine to scale for high availability. You could create a service, which by default, spreads tasks across all zones in the chosen region. And if you want each task to be on a different instance, you can use the distinctInstance task placement constraint. ECS makes sure that not only this happens, but if a task fails, it starts again.

Service

To ensure that you always have your task running without managing it yourself, you can create a service based on the task that you defined and ECS ensures that it stays running. A service is a special construct that says, “at any given time, I want to make sure that N tasks using task definition X1 are running.” If N=1, it just means “make sure that this task is running, and restart it if needed!” And with N>1, you’re basically scaling your application until you hit N, while also ensuring each task is running.

So, what now?

Hopefully you, at the very least, learned a tiny something. All comments are very welcome!

Want to discuss ECS with others? Join the amazon-ecs slack group, which members of the community created and manage.

Also, if you’re interested in learning more about the core concepts of ECS and its relation to EC2, here are some resources:

Pages
Amazon ECS landing page
AWS Fargate landing page
Amazon ECS Getting Started
Nathan Peck’s AWSome ECS

Docs
Amazon EC2
Amazon ECS

Blogs
AWS Compute Blog
AWS Blog

GitHub code
Amazon ECS container agent
Amazon ECS CLI

AWS videos
Learn Amazon ECS
AWS videos
AWS webinars

 

— tiffany

 @tiffanyfayj

 

Containers, the GPL, and copyleft: No reason for concern (opensource.com)

Post Syndicated from corbet original https://lwn.net/Articles/745245/rss

Richard Fontana explores
the intersection of containers and copyleft licensing
on
opensource.com.
One imperfect way of framing the question is whether GPL-licensed
code, when combined in some sense with proprietary code, forms a single
modified work such that the proprietary code could be interpreted as being
subject to the terms of the GPL. While we haven’t yet seen much of that
concern directed to Linux containers, we expect more questions to be raised
as adoption of containers continues to grow. But it’s fairly
straightforward to show that containers do not raise new or concerning GPL
scope issues.

Migrating .NET Classic Applications to Amazon ECS Using Windows Containers

Post Syndicated from Sundar Narasiman original https://aws.amazon.com/blogs/compute/migrating-net-classic-applications-to-amazon-ecs-using-windows-containers/

This post contributed by Sundar Narasiman, Arun Kannan, and Thomas Fuller.

AWS recently announced the general availability of Windows container management for Amazon Elastic Container Service (Amazon ECS). Docker containers and Amazon ECS make it easy to run and scale applications on a virtual machine by abstracting the complex cluster management and setup needed.

Classic .NET applications are developed with .NET Framework 4.7.1 or older and can run only on a Windows platform. These include Windows Communication Foundation (WCF), ASP.NET Web Forms, and an ASP.NET MVC web app or web API.

Why classic ASP.NET?

ASP.NET MVC 4.6 and older versions of ASP.NET occupy a significant footprint in the enterprise web application space. As enterprises move towards microservices for new or existing applications, containers are one of the stepping stones for migrating from monolithic to microservices architectures. Additionally, the support for Windows containers in Windows 10, Windows Server 2016, and Visual Studio Tooling support for Docker simplifies the containerization of ASP.NET MVC apps.

Getting started

In this post, you pick an ASP.NET 4.6.2 MVC application and get step-by-step instructions for migrating to ECS using Windows containers. The detailed steps, AWS CloudFormation template, Microsoft Visual Studio solution, ECS service definition, and ECS task definition are available in the aws-ecs-windows-aspnet GitHub repository.

To help you getting started running Windows containers, here is the reference architecture for Windows containers on GitHub: ecs-refarch-cloudformation-windows. This reference architecture is the layered CloudFormation stack, in that it calls the other stacks to create the environment. The CloudFormation YAML template in this reference architecture is referenced to create a single JSON CloudFormation stack, which is used in the steps for the migration.

Steps for Migration

The code and templates to implement this migration can be found on GitHub: https://github.com/aws-samples/aws-ecs-windows-aspnet.

  1. Your development environment needs to have the latest version and updates for Visual Studio 2017, Windows 10, and Docker for Windows Stable.
  2. Next, containerize the ASP.NET application and test it locally. The size of Windows container application images is generally larger compared to Linux containers. This is because the base image of the Windows container itself is large in size, typically greater than 9 GB.
  3. After the application is containerized, the container image needs to be pushed to Amazon Elastic Container Registry (Amazon ECR). Images stored in ECR are compressed to improve pull times and reduce storage costs. In this case, you can see that ECR compresses the image to around 1 GB, for an optimization factor of 90%.
  4. Create a CloudFormation stack using the template in the ‘CloudFormation template’ folder. This creates an ECS service, task definition (referring the containerized ASP.NET application), and other related components mentioned in the ECS reference architecture for Windows containers.
  5. After the stack is created, verify the successful creation of the ECS service, ECS instances, running tasks (with the threshold mentioned in the task definition), and the Application Load Balancer’s successful health check against running containers.
  6. Navigate to the Application Load Balancer URL and see the successful rendering of the containerized ASP.NET MVC app in the browser.

Key Notes

  • Generally, Windows container images occupy large amount of space (in the order of few GBs).
  • All the task definition parameters for Linux containers are not available for Windows containers. For more information, see Windows Task Definitions.
  • An Application Load Balancer can be configured to route requests to one or more ports on each container instance in a cluster. The dynamic port mapping allows you to have multiple tasks from a single service on the same container instance.
  • IAM roles for Windows tasks require extra configuration. For more information, see Windows IAM Roles for Tasks. For this post, configuration was handled by the CloudFormation template.
  • The ECS container agent log file can be accessed for troubleshooting Windows containers: C:\ProgramData\Amazon\ECS\log\ecs-agent.log

Summary

In this post, you migrated an ASP.NET MVC application to ECS using Windows containers.

The logical next step is to automate the activities for migration to ECS and build a fully automated continuous integration/continuous deployment (CI/CD) pipeline for Windows containers. This can be orchestrated by leveraging services such as AWS CodeCommit, AWS CodePipeline, AWS CodeBuild, Amazon ECR, and Amazon ECS. You can learn more about how this is done in the Set Up a Continuous Delivery Pipeline for Containers Using AWS CodePipeline and Amazon ECS post.

If you have questions or suggestions, please comment below.

Optimize Delivery of Trending, Personalized News Using Amazon Kinesis and Related Services

Post Syndicated from Yukinori Koide original https://aws.amazon.com/blogs/big-data/optimize-delivery-of-trending-personalized-news-using-amazon-kinesis-and-related-services/

This is a guest post by Yukinori Koide, an the head of development for the Newspass department at Gunosy.

Gunosy is a news curation application that covers a wide range of topics, such as entertainment, sports, politics, and gourmet news. The application has been installed more than 20 million times.

Gunosy aims to provide people with the content they want without the stress of dealing with a large influx of information. We analyze user attributes, such as gender and age, and past activity logs like click-through rate (CTR). We combine this information with article attributes to provide trending, personalized news articles to users.

In this post, I show you how to process user activity logs in real time using Amazon Kinesis Data Firehose, Amazon Kinesis Data Analytics, and related AWS services.

Why does Gunosy need real-time processing?

Users need fresh and personalized news. There are two constraints to consider when delivering appropriate articles:

  • Time: Articles have freshness—that is, they lose value over time. New articles need to reach users as soon as possible.
  • Frequency (volume): Only a limited number of articles can be shown. It’s unreasonable to display all articles in the application, and users can’t read all of them anyway.

To deliver fresh articles with a high probability that the user is interested in them, it’s necessary to include not only past user activity logs and some feature values of articles, but also the most recent (real-time) user activity logs.

We optimize the delivery of articles with these two steps.

  1. Personalization: Deliver articles based on each user’s attributes, past activity logs, and feature values of each article—to account for each user’s interests.
  2. Trends analysis/identification: Optimize delivering articles using recent (real-time) user activity logs—to incorporate the latest trends from all users.

Optimizing the delivery of articles is always a cold start. Initially, we deliver articles based on past logs. We then use real-time data to optimize as quickly as possible. In addition, news has a short freshness time. Specifically, day-old news is past news, and even the news that is three hours old is past news. Therefore, shortening the time between step 1 and step 2 is important.

To tackle this issue, we chose AWS for processing streaming data because of its fully managed services, cost-effectiveness, and so on.

Solution

The following diagrams depict the architecture for optimizing article delivery by processing real-time user activity logs

There are three processing flows:

  1. Process real-time user activity logs.
  2. Store and process all user-based and article-based logs.
  3. Execute ad hoc or heavy queries.

In this post, I focus on the first processing flow and explain how it works.

Process real-time user activity logs

The following are the steps for processing user activity logs in real time using Kinesis Data Streams and Kinesis Data Analytics.

  1. The Fluentd server sends the following user activity logs to Kinesis Data Streams:
{"article_id": 12345, "user_id": 12345, "action": "click"}
{"article_id": 12345, "user_id": 12345, "action": "impression"}
...
  1. Map rows of logs to columns in Kinesis Data Analytics.

  1. Set the reference data to Kinesis Data Analytics from Amazon S3.

a. Gunosy has user attributes such as gender, age, and segment. Prepare the following CSV file (user_id, gender, segment_id) and put it in Amazon S3:

101,female,1
102,male,2
103,female,3
...

b. Add the application reference data source to Kinesis Data Analytics using the AWS CLI:

$ aws kinesisanalytics add-application-reference-data-source \
  --application-name <my-application-name> \
  --current-application-version-id <version-id> \
  --reference-data-source '{
  "TableName": "REFERENCE_DATA_SOURCE",
  "S3ReferenceDataSource": {
    "BucketARN": "arn:aws:s3:::<my-bucket-name>",
    "FileKey": "mydata.csv",
    "ReferenceRoleARN": "arn:aws:iam::<account-id>:role/..."
  },
  "ReferenceSchema": {
    "RecordFormat": {
      "RecordFormatType": "CSV",
      "MappingParameters": {
        "CSVMappingParameters": {"RecordRowDelimiter": "\n", "RecordColumnDelimiter": ","}
      }
    },
    "RecordEncoding": "UTF-8",
    "RecordColumns": [
      {"Name": "USER_ID", "Mapping": "0", "SqlType": "INTEGER"},
      {"Name": "GENDER",  "Mapping": "1", "SqlType": "VARCHAR(32)"},
      {"Name": "SEGMENT_ID", "Mapping": "2", "SqlType": "INTEGER"}
    ]
  }
}'

This application reference data source can be referred on Kinesis Data Analytics.

  1. Run a query against the source data stream on Kinesis Data Analytics with the application reference data source.

a. Define the temporary stream named TMP_SQL_STREAM.

CREATE OR REPLACE STREAM "TMP_SQL_STREAM" (
  GENDER VARCHAR(32), SEGMENT_ID INTEGER, ARTICLE_ID INTEGER
);

b. Insert the joined source stream and application reference data source into the temporary stream.

CREATE OR REPLACE PUMP "TMP_PUMP" AS
INSERT INTO "TMP_SQL_STREAM"
SELECT STREAM
  R.GENDER, R.SEGMENT_ID, S.ARTICLE_ID, S.ACTION
FROM      "SOURCE_SQL_STREAM_001" S
LEFT JOIN "REFERENCE_DATA_SOURCE" R
  ON S.USER_ID = R.USER_ID;

c. Define the destination stream named DESTINATION_SQL_STREAM.

CREATE OR REPLACE STREAM "DESTINATION_SQL_STREAM" (
  TIME TIMESTAMP, GENDER VARCHAR(32), SEGMENT_ID INTEGER, ARTICLE_ID INTEGER, 
  IMPRESSION INTEGER, CLICK INTEGER
);

d. Insert the processed temporary stream, using a tumbling window, into the destination stream per minute.

CREATE OR REPLACE PUMP "STREAM_PUMP" AS
INSERT INTO "DESTINATION_SQL_STREAM"
SELECT STREAM
  ROW_TIME AS TIME,
  GENDER, SEGMENT_ID, ARTICLE_ID,
  SUM(CASE ACTION WHEN 'impression' THEN 1 ELSE 0 END) AS IMPRESSION,
  SUM(CASE ACTION WHEN 'click' THEN 1 ELSE 0 END) AS CLICK
FROM "TMP_SQL_STREAM"
GROUP BY
  GENDER, SEGMENT_ID, ARTICLE_ID,
  FLOOR("TMP_SQL_STREAM".ROWTIME TO MINUTE);

The results look like the following:

  1. Insert the results into Amazon Elasticsearch Service (Amazon ES).
  2. Batch servers get results from Amazon ES every minute. They then optimize delivering articles with other data sources using a proprietary optimization algorithm.

How to connect a stream to another stream in another AWS Region

When we built the solution, Kinesis Data Analytics was not available in the Asia Pacific (Tokyo) Region, so we used the US West (Oregon) Region. The following shows how we connected a data stream to another data stream in the other Region.

There is no need to continue containing all components in a single AWS Region, unless you have a situation where a response difference at the millisecond level is critical to the service.

Benefits

The solution provides benefits for both our company and for our users. Benefits for the company are cost savings—including development costs, operational costs, and infrastructure costs—and reducing delivery time. Users can now find articles of interest more quickly. The solution can process more than 500,000 records per minute, and it enables fast and personalized news curating for our users.

Conclusion

In this post, I showed you how we optimize trending user activities to personalize news using Amazon Kinesis Data Firehose, Amazon Kinesis Data Analytics, and related AWS services in Gunosy.

AWS gives us a quick and economical solution and a good experience.

If you have questions or suggestions, please comment below.


Additional Reading

If you found this post useful, be sure to check out Implement Serverless Log Analytics Using Amazon Kinesis Analytics and Joining and Enriching Streaming Data on Amazon Kinesis.


About the Authors

Yukinori Koide is the head of development for the Newspass department at Gunosy. He is working on standardization of provisioning and deployment flow, promoting the utilization of serverless and containers for machine learning and AI services. His favorite AWS services are DynamoDB, Lambda, Kinesis, and ECS.

 

 

 

Akihiro Tsukada is a start-up solutions architect with AWS. He supports start-up companies in Japan technically at many levels, ranging from seed to later-stage.

 

 

 

 

Yuta Ishii is a solutions architect with AWS. He works with our customers to provide architectural guidance for building media & entertainment services, helping them improve the value of their services when using AWS.

 

 

 

 

 

Kim Dotcom Loses Megaupload Domain Names, Gets “Destroyed” Gaming Chair Back

Post Syndicated from Ernesto original https://torrentfreak.com/kim-dotcom-loses-megaupload-domain-names-gets-destroyed-gaming-chair-back-180117/

Following the 2012 raid on Megaupload and Kim Dotcom, U.S. and New Zealand authorities seized millions of dollars in cash and other property, located around the world.

Claiming the assets were obtained through copyright and money laundering crimes, the U.S. government launched separate civil cases in which it asked the court to forfeit bank accounts, servers, domain names, and other seized possessions of the Megaupload defendants.

One of these cases was lost after the U.S. branded Dotcom and his colleagues as “fugitives”.The defense team appealed the ruling, but lost again, and a subsequent petition at the Supreme Court was denied.

Following this lost battle, the U.S. also moved to conclude a separate civil forfeiture case, which was still pending at a federal court in Virginia.

The assets listed in this case are several bank accounts, including several at PayPal, as well as 60 servers Megaupload bought at Leaseweb. What has the most symbolic value, however, are the domain names that were seized, including Megaupload.com, Megaporn.com and Megavideo.com.

Mega’s domains

This week a U.S. federal court decided that all claims of Kim Dotcom, his former colleague Mathias Ortman, and several Megaupload-related companies should be stricken. A default was entered against them on Tuesday.

The same fugitive disentitlement argument was used in this case. This essentially means that someone who’s considered to be a fugitive from justice is not allowed to get relief from the judicial system he or she evades.

“Claimants Kim Dotcom and Mathias Ortmann have deliberately avoided prosecution by declining to enter or reenter the United States,” Judge Liam O’Grady writes in his order to strike the claims.

“Because Claimant Kim Dotcom, who is himself a fugitive under Section 2466, is the Corporate Claimants’ controlling shareholder and, in particular, because he signed the claims on behalf of the corporations, a presumption of disentitlement applies to the corporations as well.”

As a result, the domain names which once served 50 million users per day, are now lost to the US Government. The court records list 18 domains in total, which were registered through Godaddy, DotRegistrar, and Fabulous.

Given the legal history, the domains and other assets are likely lost for good. However, Megaupload defense lawyer Ira Rothken is not giving up yet.

“We are still evaluating the legal options in a climate where Kim Dotcom is being labeled a fugitive in a US criminal copyright case even though he has never been to the US, is merely asserting his US-NZ extradition treaty rights, and the NZ High Court has ruled that he and his co-defendants did not commit criminal copyright infringement under NZ law,” Rothken tells TorrentFreak.

There might be a possibility that assets located outside the US could be saved. Foreign courts are more open to defense arguments, it seems, as a Hong Kong court previously ordered the US to return several assets belonging to Kim Dotcom.

The Hong Kong case also brought some good news this week. At least, something that was supposed to be positive. On Twitter, Dotcom writes that two containers with seized assets were returned, but in a “rotten and destroyed” state.

“A shipment of 2 large containers just arrived in New Zealand. This is how all my stuff looks now. Rotten & destroyed. Photo: My favorite gaming chair,” Dotcom wrote.

According to Dotcom, the US Government asked him to pay for ‘climate controlled’ storage for more than half a decade to protect the seized goods. However, judging from the look of the chair and the state of some other belongings, something clearly went wrong.

Rotten & destroyed

Source: TF, for the latest info on copyright, file-sharing, torrent sites and more. We also have VPN discounts, offers and coupons

Continuous Deployment to Kubernetes using AWS CodePipeline, AWS CodeCommit, AWS CodeBuild, Amazon ECR and AWS Lambda

Post Syndicated from Chris Barclay original https://aws.amazon.com/blogs/devops/continuous-deployment-to-kubernetes-using-aws-codepipeline-aws-codecommit-aws-codebuild-amazon-ecr-and-aws-lambda/

Thank you to my colleague Omar Lari for this blog on how to create a continuous deployment pipeline for Kubernetes!


You can use Kubernetes and AWS together to create a fully managed, continuous deployment pipeline for container based applications. This approach takes advantage of Kubernetes’ open-source system to manage your containerized applications, and the AWS developer tools to manage your source code, builds, and pipelines.

This post describes how to create a continuous deployment architecture for containerized applications. It uses AWS CodeCommit, AWS CodePipeline, AWS CodeBuild, and AWS Lambda to deploy containerized applications into a Kubernetes cluster. In this environment, developers can remain focused on developing code without worrying about how it will be deployed, and development managers can be satisfied that the latest changes are always deployed.

What is Continuous Deployment?

There are many articles, posts and even conferences dedicated to the practice of continuous deployment. For the purposes of this post, I will summarize continuous delivery into the following points:

  • Code is more frequently released into production environments
  • More frequent releases allow for smaller, incremental changes reducing risk and enabling simplified roll backs if needed
  • Deployment is automated and requires minimal user intervention

For a more information, see “Practicing Continuous Integration and Continuous Delivery on AWS”.

How can you use continuous deployment with AWS and Kubernetes?

You can leverage AWS services that support continuous deployment to automatically take your code from a source code repository to production in a Kubernetes cluster with minimal user intervention. To do this, you can create a pipeline that will build and deploy committed code changes as long as they meet the requirements of each stage of the pipeline.

To create the pipeline, you will use the following services:

  • AWS CodePipeline. AWS CodePipeline is a continuous delivery service that models, visualizes, and automates the steps required to release software. You define stages in a pipeline to retrieve code from a source code repository, build that source code into a releasable artifact, test the artifact, and deploy it to production. Only code that successfully passes through all these stages will be deployed. In addition, you can optionally add other requirements to your pipeline, such as manual approvals, to help ensure that only approved changes are deployed to production.
  • AWS CodeCommit. AWS CodeCommit is a secure, scalable, and managed source control service that hosts private Git repositories. You can privately store and manage assets such as your source code in the cloud and configure your pipeline to automatically retrieve and process changes committed to your repository.
  • AWS CodeBuild. AWS CodeBuild is a fully managed build service that compiles source code, runs tests, and produces artifacts that are ready to deploy. You can use AWS CodeBuild to both build your artifacts, and to test those artifacts before they are deployed.
  • AWS Lambda. AWS Lambda is a compute service that lets you run code without provisioning or managing servers. You can invoke a Lambda function in your pipeline to prepare the built and tested artifact for deployment by Kubernetes to the Kubernetes cluster.
  • Kubernetes. Kubernetes is an open-source system for automating deployment, scaling, and management of containerized applications. It provides a platform for running, deploying, and managing containers at scale.

An Example of Continuous Deployment to Kubernetes:

The following example illustrates leveraging AWS developer tools to continuously deploy to a Kubernetes cluster:

  1. Developers commit code to an AWS CodeCommit repository and create pull requests to review proposed changes to the production code. When the pull request is merged into the master branch in the AWS CodeCommit repository, AWS CodePipeline automatically detects the changes to the branch and starts processing the code changes through the pipeline.
  2. AWS CodeBuild packages the code changes as well as any dependencies and builds a Docker image. Optionally, another pipeline stage tests the code and the package, also using AWS CodeBuild.
  3. The Docker image is pushed to Amazon ECR after a successful build and/or test stage.
  4. AWS CodePipeline invokes an AWS Lambda function that includes the Kubernetes Python client as part of the function’s resources. The Lambda function performs a string replacement on the tag used for the Docker image in the Kubernetes deployment file to match the Docker image tag applied in the build, one that matches the image in Amazon ECR.
  5. After the deployment manifest update is completed, AWS Lambda invokes the Kubernetes API to update the image in the Kubernetes application deployment.
  6. Kubernetes performs a rolling update of the pods in the application deployment to match the docker image specified in Amazon ECR.
    The pipeline is now live and responds to changes to the master branch of the CodeCommit repository. This pipeline is also fully extensible, you can add steps for performing testing or adding a step to deploy into a staging environment before the code ships into the production cluster.

An example pipeline in AWS CodePipeline that supports this architecture can be seen below:

Conclusion

We are excited to see how you leverage this pipeline to help ease your developer experience as you develop applications in Kubernetes.

You’ll find an AWS CloudFormation template with everything necessary to spin up your own continuous deployment pipeline at the CodeSuite – Continuous Deployment Reference Architecture for Kubernetes repo on GitHub. The repository details exactly how the pipeline is provisioned and how you can use it to deploy your own applications. If you have any questions, feedback, or suggestions, please let us know!

Wanted: Sales Engineer

Post Syndicated from Yev original https://www.backblaze.com/blog/wanted-sales-engineer/

At inception, Backblaze was a consumer company. Thousands upon thousands of individuals came to our website and gave us $5/mo to keep their data safe. But, we didn’t sell business solutions. It took us years before we had a sales team. In the last couple of years, we’ve released products that businesses of all sizes love: Backblaze B2 Cloud Storage and Backblaze for Business Computer Backup. Those businesses want to integrate Backblaze deeply into their infrastructure, so it’s time to hire our first Sales Engineer!

Company Description:
Founded in 2007, Backblaze started with a mission to make backup software elegant and provide complete peace of mind. Over the course of almost a decade, we have become a pioneer in robust, scalable low cost cloud backup. Recently, we launched B2 – robust and reliable object storage at just $0.005/gb/mo. Part of our differentiation is being able to offer the lowest price of any of the big players while still being profitable.

We’ve managed to nurture a team oriented culture with amazingly low turnover. We value our people and their families. Don’t forget to check out our “About Us” page to learn more about the people and some of our perks.

We have built a profitable, high growth business. While we love our investors, we have maintained control over the business. That means our corporate goals are simple – grow sustainably and profitably.

Some Backblaze Perks:

  • Competitive healthcare plans
  • Competitive compensation and 401k
  • All employees receive Option grants
  • Unlimited vacation days
  • Strong coffee
  • Fully stocked Micro kitchen
  • Catered breakfast and lunches
  • Awesome people who work on awesome projects
  • Childcare bonus
  • Normal work hours
  • Get to bring your pets into the office
  • San Mateo Office – located near Caltrain and Highways 101 & 280.

Backblaze B2 cloud storage is a building block for almost any computing service that requires storage. Customers need our help integrating B2 into iOS apps to Docker containers. Some customers integrate directly to the API using the programming language of their choice, others want to solve a specific problem using ready made software, already integrated with B2.

At the same time, our computer backup product is deepening it’s integration into enterprise IT systems. We are commonly asked for how to set Windows policies, integrate with Active Directory, and install the client via remote management tools.

We are looking for a sales engineer who can help our customers navigate the integration of Backblaze into their technical environments.

Are you 1/2” deep into many different technologies, and unafraid to dive deeper?

Can you confidently talk with customers about their technology, even if you have to look up all the acronyms right after the call?

Are you excited to setup complicated software in a lab and write knowledge base articles about your work?

Then Backblaze is the place for you!

Enough about Backblaze already, what’s in it for me?
In this role, you will be given the opportunity to learn about the technologies that drive innovation today; diverse technologies that customers are using day in and out. And more importantly, you’ll learn how to learn new technologies.

Just as an example, in the past 12 months, we’ve had the opportunity to learn and become experts in these diverse technologies:

  • How to setup VM servers for lab environments, both on-prem and using cloud services.
  • Create an automatically “resetting” demo environment for the sales team.
  • Setup Microsoft Domain Controllers with Active Directory and AD Federation Services.
  • Learn the basics of OAUTH and web single sign on (SSO).
  • Archive video workflows from camera to media asset management systems.
  • How upload/download files from Javascript by enabling CORS.
  • How to install and monitor online backup installations using RMM tools, like JAMF.
  • Tape (LTO) systems. (Yes – people still use tape for storage!)

How can I know if I’ll succeed in this role?

You have:

  • Confidence. Be able to ask customers questions about their environments and convey to them your technical acumen.
  • Curiosity. Always want to learn about customers’ situations, how they got there and what problems they are trying to solve.
  • Organization. You’ll work with customers, integration partners, and Backblaze team members on projects of various lengths. You can context switch and either have a great memory or keep copious notes. Your checklists have their own checklists.

You are versed in:

  • The fundamentals of Windows, Linux and Mac OS X operating systems. You shouldn’t be afraid to use a command line.
  • Building, installing, integrating and configuring applications on any operating system.
  • Debugging failures – reading logs, monitoring usage, effective google searching to fix problems excites you.
  • The basics of TCP/IP networking and the HTTP protocol.
  • Novice development skills in any programming/scripting language. Have basic understanding of data structures and program flow.
  • Your background contains:

  • Bachelor’s degree in computer science or the equivalent.
  • 2+ years of experience as a pre or post-sales engineer.
  • The right extra credit:
    There are literally hundreds of previous experiences you can have had that would make you perfect for this job. Some experiences that we know would be helpful for us are below, but make sure you tell us your stories!

  • Experience using or programming against Amazon S3.
  • Experience with large on-prem storage – NAS, SAN, Object. And backing up data on such storage with tools like Veeam, Veritas and others.
  • Experience with photo or video media. Media archiving is a key market for Backblaze B2.
  • Program arduinos to automatically feed your dog.
  • Experience programming against web or REST APIs. (Point us towards your projects, if they are open source and available to link to.)
  • Experience with sales tools like Salesforce.
  • 3D print door stops.
  • Experience with Windows Servers, Active Directory, Group policies and the like.
  • What’s it like working with the Sales team?
    The Backblaze sales team collaborates. We help each other out by sharing ideas, templates, and our customer’s experiences. When we talk about our accomplishments, there is no “I did this,” only “we”. We are truly a team.

    We are honest to each other and our customers and communicate openly. We aim to have fun by embracing crazy ideas and creative solutions. We try to think not outside the box, but with no boxes at all. Customers are the driving force behind the success of the company and we care deeply about their success.

    If this all sounds like you:

    1. Send an email to [email protected] with the position in the subject line.
    2. Tell us a bit about your Sales Engineering experience.
    3. Include your resume.

    The post Wanted: Sales Engineer appeared first on Backblaze Blog | Cloud Storage & Cloud Backup.

    AWS Online Tech Talks – January 2018

    Post Syndicated from Ana Visneski original https://aws.amazon.com/blogs/aws/aws-online-tech-talks-january-2018/

    Happy New Year! Kick of 2018 right by expanding your AWS knowledge with a great batch of new Tech Talks. We’re covering some of the biggest launches from re:Invent including Amazon Neptune, Amazon Rekognition Video, AWS Fargate, AWS Cloud9, Amazon Kinesis Video Streams, AWS PrivateLink, AWS Single-Sign On and more!

    January 2018– Schedule

    Noted below are the upcoming scheduled live, online technical sessions being held during the month of January. Make sure to register ahead of time so you won’t miss out on these free talks conducted by AWS subject matter experts.

    Webinars featured this month are:

    Monday January 22

    Analytics & Big Data
    11:00 AM – 11:45 AM PT Analyze your Data Lake, Fast @ Any Scale  Lvl 300

    Database
    01:00 PM – 01:45 PM PT Deep Dive on Amazon Neptune Lvl 200

    Tuesday, January 23

    Artificial Intelligence
    9:00 AM – 09:45 AM PT  How to get the most out of Amazon Rekognition Video, a deep learning based video analysis service Lvl 300

    Containers

    11:00 AM – 11:45 AM Introducing AWS Fargate Lvl 200

    Serverless
    01:00 PM – 02:00 PM PT Overview of Serverless Application Deployment Patterns Lvl 400

    Wednesday, January 24

    DevOps
    09:00 AM – 09:45 AM PT Introducing AWS Cloud9  Lvl 200

    Analytics & Big Data
    11:00 AM – 11:45 AM PT Deep Dive: Amazon Kinesis Video Streams
    Lvl 300
    Database
    01:00 PM – 01:45 PM PT Introducing Amazon Aurora with PostgreSQL Compatibility Lvl 200

    Thursday, January 25

    Artificial Intelligence
    09:00 AM – 09:45 AM PT Introducing Amazon SageMaker Lvl 200

    Mobile
    11:00 AM – 11:45 AM PT Ionic and React Hybrid Web/Native Mobile Applications with Mobile Hub Lvl 200

    IoT
    01:00 PM – 01:45 PM PT Connected Product Development: Secure Cloud & Local Connectivity for Microcontroller-based Devices Lvl 200

    Monday, January 29

    Enterprise
    11:00 AM – 11:45 AM PT Enterprise Solutions Best Practices 100 Achieving Business Value with AWS Lvl 100

    Compute
    01:00 PM – 01:45 PM PT Introduction to Amazon Lightsail Lvl 200

    Tuesday, January 30

    Security, Identity & Compliance
    09:00 AM – 09:45 AM PT Introducing Managed Rules for AWS WAF Lvl 200

    Storage
    11:00 AM – 11:45 AM PT  Improving Backup & DR – AWS Storage Gateway Lvl 300

    Compute
    01:00 PM – 01:45 PM PT  Introducing the New Simplified Access Model for EC2 Spot Instances Lvl 200

    Wednesday, January 31

    Networking
    09:00 AM – 09:45 AM PT  Deep Dive on AWS PrivateLink Lvl 300

    Enterprise
    11:00 AM – 11:45 AM PT Preparing Your Team for a Cloud Transformation Lvl 200

    Compute
    01:00 PM – 01:45 PM PT  The Nitro Project: Next-Generation EC2 Infrastructure Lvl 300

    Thursday, February 1

    Security, Identity & Compliance
    09:00 AM – 09:45 AM PT  Deep Dive on AWS Single Sign-On Lvl 300

    Storage
    11:00 AM – 11:45 AM PT How to Build a Data Lake in Amazon S3 & Amazon Glacier Lvl 300

    timeShift(GrafanaBuzz, 1w) Issue 28

    Post Syndicated from Blogs on Grafana Labs Blog original https://grafana.com/blog/2018/01/05/timeshiftgrafanabuzz-1w-issue-28/

    Happy new year! Grafana Labs is getting back in the swing of things after taking some time off to celebrate 2017, and spending time with family and friends. We’re diligently working on the new Grafana v5.0 release (planning v5.0 beta release by end of January), which includes a ton of new features, a new layout engine, and a polished UI. We’d love to hear your feedback!


    Latest Stable Release

    Grafana 4.6.3 is now available. Latest bugfixes include:

    • Gzip: Fixes bug Gravatar images when gzip was enabled #5952
    • Alert list: Now shows alert state changes even after adding manual annotations on dashboard #99513
    • Alerting: Fixes bug where rules evaluated as firing when all conditions was false and using OR operator. #93183
    • Cloudwatch: CloudWatch no longer display metrics’ default alias #101514, thx @mtanda

    Download Grafana 4.6.3 Now


    From the Blogosphere

    Why Observability Matters – Now and in the Future: Our own Carl Bergquist teamed up with Neil Gehani, Director of Product at Weaveworks to discuss best practices on how to get started with monitoring your application and infrastructure. This video focuses on modern containerized applications instrumented to use Prometheus to generate metrics and Grafana to visualize them.

    How to Install and Secure Grafana on Ubuntu 16.04: In this tutorial, you’ll learn how to install and secure Grafana with a SSL certificate and a Nginx reverse proxy, then you’ll modify Grafana’s default settings for even tighter security.

    Monitoring Informix with Grafana: Ben walks us through how to use Grafana to visualize data from IBM Informix and offers a practical demonstration using Docker containers. He also talks about his philosophy of sharing dashboards across teams, important metrics to collect, and how he would like to improve his monitoring stack.

    Monitor your hosts with Glances + InfluxDB + Grafana: Glances is a cross-platform system monitoring tool written in Python. This article takes you step by step through the pieces of the stack, installation, confirguration and provides a sample dashboard to get you up and running.


    GrafanaCon Tickets are Going Fast!

    Lock in your seat for GrafanaCon EU while there are still tickets avaialable! Join us March 1-2, 2018 in Amsterdam for 2 days of talks centered around Grafana and the surrounding monitoring ecosystem including Graphite, Prometheus, InfluxData, Elasticsearch, Kubernetes, and more.

    We have some exciting talks lined up from Google, CERN, Bloomberg, eBay, Red Hat, Tinder, Fastly, Automattic, Prometheus, InfluxData, Percona and more! You can see the full list of speakers below, but be sure to get your ticket now.

    Get Your Ticket Now

    GrafanaCon EU will feature talks from:

    “Google Bigtable”
    Misha Brukman
    PROJECT MANAGER,
    GOOGLE CLOUD
    GOOGLE

    “Monitoring at Bloomberg”
    Stig Sorensen
    HEAD OF TELEMETRY
    BLOOMBERG

    “Monitoring at Bloomberg”
    Sean Hanson
    SOFTWARE DEVELOPER
    BLOOMBERG

    “Monitoring Tinder’s Billions of Swipes with Grafana”
    Utkarsh Bhatnagar
    SR. SOFTWARE ENGINEER
    TINDER

    “Grafana at CERN”
    Borja Garrido
    PROJECT ASSOCIATE
    CERN

    “Monitoring the Huge Scale at Automattic”
    Abhishek Gahlot
    SOFTWARE ENGINEER
    Automattic

    “Real-time Engagement During the 2016 US Presidential Election”
    Anna MacLachlan
    CONTENT MARKETING MANAGER
    Fastly

    “Real-time Engagement During the 2016 US Presidential Election”
    Gerlando Piro
    FRONT END DEVELOPER
    Fastly

    “Grafana v5 and the Future”
    Torkel Odegaard
    CREATOR | PROJECT LEAD
    GRAFANA

    “Prometheus for Monitoring Metrics”
    Brian Brazil
    FOUNDER
    ROBUST PERCEPTION

    “What We Learned Integrating Grafana with Prometheus”
    Peter Zaitsev
    CO-FOUNDER | CEO
    PERCONA

    “The Biz of Grafana”
    Raj Dutt
    CO-FOUNDER | CEO
    GRAFANA LABS

    “What’s New In Graphite”
    Dan Cech
    DIR, PLATFORM SERVICES
    GRAFANA LABS

    “The Design of IFQL, the New Influx Functional Query Language”
    Paul Dix
    CO-FOUNTER | CTO
    INFLUXDATA

    “Writing Grafana Dashboards with Jsonnet”
    Julien Pivotto
    OPEN SOURCE CONSULTANT
    INUITS

    “Monitoring AI Platform at eBay”
    Deepak Vasthimal
    MTS-2 SOFTWARE ENGINEER
    EBAY

    “Running a Power Plant with Grafana”
    Ryan McKinley
    DEVELOPER
    NATEL ENERGY

    “Performance Metrics and User Experience: A “Tinder” Experience”
    Susanne Greiner
    DATA SCIENTIST
    WÜRTH PHOENIX S.R.L.

    “Analyzing Performance of OpenStack with Grafana Dashboards”
    Alex Krzos
    SENIOR SOFTWARE ENGINEER
    RED HAT INC.

    “Storage Monitoring at Shell Upstream”
    Arie Jan Kraai
    STORAGE ENGINEER
    SHELL TECHNICAL LANDSCAPE SERVICE

    “The RED Method: How To Instrument Your Services”
    Tom Wilkie
    FOUNDER
    KAUSAL

    “Grafana Usage in the Quality Assurance Process”
    Andrejs Kalnacs
    LEAD SOFTWARE DEVELOPER IN TEST
    EVOLUTION GAMING

    “Using Prometheus and Grafana for Monitoring my Power Usage”
    Erwin de Keijzer
    LINUX ENGINEER
    SNOW BV

    “Weather, Power & Market Forecasts with Grafana”
    Max von Roden
    DATA SCIENTIST
    ENERGY WEATHER

    “Weather, Power & Market Forecasts with Grafana”
    Steffen Knott
    HEAD OF IT
    ENERGY WEATHER

    “Inherited Technical Debt – A Tale of Overcoming Enterprise Inertia”
    Jordan J. Hamel
    HEAD OF MONITORING PLATFORMS
    AMGEN

    “Grafanalib: Dashboards as Code”
    Jonathan Lange
    VP OF ENGINEERING
    WEAVEWORKS

    “The Journey of Shifting the MQTT Broker HiveMQ to Kubernetes”
    Arnold Bechtoldt
    SENIOR SYSTEMS ENGINEER
    INOVEX

    “Graphs Tell Stories”
    Blerim Sheqa
    SENIOR DEVELOPER
    NETWAYS

    [email protected] or How to Store Millions of Metrics per Second”
    Vladimir Smirnov
    SYSTEM ADMINISTRATOR
    Booking.com


    Upcoming Events:

    In between code pushes we like to speak at, sponsor and attend all kinds of conferences and meetups. We also like to make sure we mention other Grafana-related events happening all over the world. If you’re putting on just such an event, let us know and we’ll list it here.

    FOSDEM | Brussels, Belgium – Feb 3-4, 2018: FOSDEM is a free developer conference where thousands of developers of free and open source software gather to share ideas and technology. There is no need to register; all are welcome.

    Jfokus | Stockholm, Sweden – Feb 5-7, 2018:
    Carl Bergquist – Quickie: Monitoring? Not OPS Problem

    Why should we monitor our system? Why can’t we just rely on the operations team anymore? They use to be able to do that. What’s currently changing? Presentation content: – Why do we monitor our system – How did it use to work? – Whats changing – Why do we need to shift focus – Everyone should be on call. – Resilience is the goal (Best way of having someone care about quality is to make them responsible).

    Register Now

    Jfokus | Stockholm, Sweden – Feb 5-7, 2018:
    Leonard Gram – Presentation: DevOps Deconstructed

    What’s a Site Reliability Engineer and how’s that role different from the DevOps engineer my boss wants to hire? I really don’t want to be on call, should I? Is Docker the right place for my code or am I better of just going straight to Serverless? And why should I care about any of it? I’ll try to answer some of these questions while looking at what DevOps really is about and how commodisation of servers through “the cloud” ties into it all. This session will be an opinionated piece from a developer who’s been on-call for the past 6 years and would like to convince you to do the same, at least once.

    Register Now

    Tweet of the Week

    We scour Twitter each week to find an interesting/beautiful dashboard and show it off! #monitoringLove

    Awesome! Let us know if you have any questions – we’re happy to help out. We also have a bunch of screencasts to help you get going.


    Grafana Labs is Hiring!

    We are passionate about open source software and thrive on tackling complex challenges to build the future. We ship code from every corner of the globe and love working with the community. If this sounds exciting, you’re in luck – WE’RE HIRING!

    Check out our Open Positions


    How are we doing?

    That’s a wrap! Let us know what you think about timeShift. Submit a comment on this article below, or post something at our community forum. See you next year!

    Follow us on Twitter, like us on Facebook, and join the Grafana Labs community.

    Set Up a Continuous Delivery Pipeline for Containers Using AWS CodePipeline and Amazon ECS

    Post Syndicated from Nathan Taber original https://aws.amazon.com/blogs/compute/set-up-a-continuous-delivery-pipeline-for-containers-using-aws-codepipeline-and-amazon-ecs/

    This post contributed by Abby FullerAWS Senior Technical Evangelist

    Last week, AWS announced support for Amazon Elastic Container Service (ECS) targets (including AWS Fargate) in AWS CodePipeline. This support makes it easier to create a continuous delivery pipeline for container-based applications and microservices.

    Building and deploying containerized services manually is slow and prone to errors. Continuous delivery with automated build and test mechanisms helps detect errors early, saves time, and reduces failures, making this a popular model for application deployments. Previously, to automate your container workflows with ECS, you had to build your own solution using AWS CloudFormation. Now, you can integrate CodePipeline and CodeBuild with ECS to automate your workflows in just a few steps.

    A typical continuous delivery workflow with CodePipeline, CodeBuild, and ECS might look something like the following:

    • Choosing your source
    • Building your project
    • Deploying your code

    We also have a continuous deployment reference architecture on GitHub for this workflow.

    Getting Started

    First, create a new project with CodePipeline and give the project a name, such as “demo”.

    Next, choose a source location where the code is stored. This could be AWS CodeCommit, GitHub, or Amazon S3. For this example, enter GitHub and then give CodePipeline access to the repository.

    Next, add a build step. You can import an existing build, such as a Jenkins server URL or CodeBuild project, or create a new step with CodeBuild. If you don’t have an existing build project in CodeBuild, create one from within CodePipeline:

    • Build provider: AWS CodeBuild
    • Configure your project: Create a new build project
    • Environment image: Use an image managed by AWS CodeBuild
    • Operating system: Ubuntu
    • Runtime: Docker
    • Version: aws/codebuild/docker:1.12.1
    • Build specification: Use the buildspec.yml in the source code root directory

    Now that you’ve created the CodeBuild step, you can use it as an existing project in CodePipeline.

    Next, add a deployment provider. This is where your built code is placed. It can be a number of different options, such as AWS CodeDeploy, AWS Elastic Beanstalk, AWS CloudFormation, or Amazon ECS. For this example, connect to Amazon ECS.

    For CodeBuild to deploy to ECS, you must create an image definition JSON file. This requires adding some instructions to the pre-build, build, and post-build phases of the CodeBuild build process in your buildspec.yml file. For help with creating the image definition file, see Step 1 of the Tutorial: Continuous Deployment with AWS CodePipeline.

    • Deployment provider: Amazon ECS
    • Cluster name: enter your project name from the build step
    • Service name: web
    • Image filename: enter your image definition filename (“web.json”).

    You are almost done!

    You can now choose an existing IAM service role that CodePipeline can use to access resources in your account, or let CodePipeline create one. For this example, use the wizard, and go with the role that it creates (AWS-CodePipeline-Service).

    Finally, review all of your changes, and choose Create pipeline.

    After the pipeline is created, you’ll have a model of your entire pipeline where you can view your executions, add different tests, add manual approvals, or release a change.

    You can learn more in the AWS CodePipeline User Guide.

    Happy automating!

    [$] Containers without Docker at Red Hat

    Post Syndicated from jake original https://lwn.net/Articles/741841/rss

    The Docker (now Moby) project has
    done a lot to popularize containers in recent years. Along the way,
    though, it has generated concerns about its concentration of functionality
    into a single, monolithic system under the control of a single daemon
    running with root privileges: dockerd. Those concerns were
    reflected in a talk
    by Dan Walsh, head of the container team at Red Hat, at KubeCon +
    CloudNativeCon
    . Walsh spoke about the work the container team is doing
    to replace Docker with a set of smaller, interoperable components. His rallying cry is “no big fat
    daemons” as he finds them to be contrary to the venerated Unix philosophy.

    [$] Demystifying container runtimes

    Post Syndicated from jake original https://lwn.net/Articles/741897/rss

    As we briefly mentioned in our overview article about
    KubeCon + CloudNativeCon, there are multiple container “runtimes”, which are
    programs that can create and execute containers that are typically fetched
    from online
    images. That space is slowly reaching maturity both in terms
    of standards and implementation: Docker’s containerd 1.0 was released
    during KubeCon, CRI-O 1.0 was released a few months ago, and rkt is
    also still in the game. With all of those runtimes, it may be a confusing
    time for those looking at deploying their own container-based system
    or Kubernetes cluster from
    scratch. This article will try to explain
    what container runtimes are, what they do, how they compare with each other, and
    how to choose the right one. It also provides a primer on container
    specifications and standards.

    timeShift(GrafanaBuzz, 1w) Issue 26

    Post Syndicated from Blogs on Grafana Labs Blog original https://grafana.com/blog/2017/12/15/timeshiftgrafanabuzz-1w-issue-26/

    Welcome to TimeShift

    Big news this week: Grafana v5.0 has been merged into master and is available in the nightly builds! We are really excited to share this with the community, and look forward to receiving community feedback (good or bad) on the new features and enhancements. As you see in the video below, there are some big changes that aim to improve workflow, team organization, permissions, and overall user experience. Check out the video below to see it in action, and give it a spin yourself.

    • New Grid Layout Engine: Make it easier to build dashboards and enable more complex layouts
    • Dashboard Folders & Permissions
    • User Teams
    • Improved Dashboard Settings UX
    • Improved Page Design and Navigation

    NOTE: That’s actually Torkel Odegaard, creator of Grafana shredding on the soundtrack!


    Latest Stable Release

    Grafana 4.6.3 is available and includes some bug fixes:

    • Gzip: Fixes bug Gravatar images when gzip was enabled #5952
    • Alert list: Now shows alert state changes even after adding manual annotations on dashboard #99513
    • Alerting: Fixes bug where rules evaluated as firing when all conditions was false and using OR operator. #93183
    • Cloudwatch: CloudWatch no longer display metrics’ default alias #101514, thx @mtanda

    Download Grafana 4.6.3 Now


    From the Blogosphere

    Monitoring MySQL with Prometheus and Grafana: Julien Pivotto (who will be speaking at GrafanaCon EU), gave a great presentation last month on Monitoring MySQL with Prometheus and Grafana. You can also check out his slides.

    Monitor your Docker Containers: docker stats doesn’t often give you the level of insight you need to effectively manage your containers. This article discuses how to use cAdvisor, Prometheus and Grafana to get a handle on your Docker performance.

    Magento Performance Monitoring with Grafana Dashboards and Alerts: This Christmas-themed post walks you through how to monitor the performance of Magento, start building dashboards, and setup Slack alerts, all while sitting in your rocking chair, sipping eggnog.

    Icinga Web2 and Grafana Working Together: This is a follow-up post about displaying service performance data from Icinga2 in Grafana. Now that we know how to list the services on a dashboard, it would be helpful to filter this list so that specific teams can know the status of services they specifically manage.

    Setup of sitespeed in AWS with Peter Hedenskog: In this video, Peter Hedenskop from Wikimedia and Stefan Judis set up a video call to go over setting up sitespeed in AWS. They create a fully functional Grafana dashboard, including web performance metrics from Stefan’s personal website running in the cloud.

    Deploying Grafana to Access Zabbix in Alibaba Cloud ECS: This article walks you through how to deploy Grafana on Alibaba Cloud ECS to access Zabbix to visualize performance data for your website or application.

    Let’s Summarize the Test Results with Grafana Annotations + Prometheus: The engineers of NTT Communications Corporation have created something of an Advent Calendar, with new posts each day. December 14th’s post focused on Grafana’s new annotation functionality via the UI and the API.


    New Speakers Added!

    We have added new speakers, and talk titles to the lineup at grafanacon.org. Only a few left to include, which should be added in the next few days.

    Join us March 1-2, 2018 in Amsterdam for 2 days of talks centered around Grafana and the surrounding monitoring ecosystem including Graphite, Prometheus, InfluxData, Elasticsearch, Kubernetes, and many other topics.

    This year we have speakers from Bloomberg, CERN, Tinder, Red Hat, Prometheus, InfluxData, Fastly, Automattic, Percona, and more!

    Get Your Ticket Now


    Grafana Plugins

    This week we have a new plugin for the popular IoT platform DeviceHive, and an update to our own Kubernetes App. To install or update any plugin in an on-prem Grafana instance, use the Grafana-cli tool, or install and update with 1 click on Hosted Grafana.

    NEW PLUGIN

    DeviceHive is an IOT Platform and now has a data source plugin, which means you can visualize the live commands and notifications from a device.


    Install Now

    UPDATED PLUGIN

    Kubernetes App – The Grafana Kubernetes App allows you to monitor your Kubernetes cluster’s performance. It includes 4 dashboards, Cluster, Node, Pod/Container and Deployment, and also comes with Intel Snap collectors that are deployed to your cluster to collect health metrics.


    Update


    Upcoming Events:

    In between code pushes we like to speak at, sponsor and attend all kinds of conferences and meetups. We also like to make sure we mention other Grafana-related events happening all over the world. If you’re putting on just such an event, let us know and we’ll list it here.

    FOSDEM | Brussels, Belgium – Feb 3-4, 2018: FOSDEM is a free developer conference where thousands of developers of free and open source software gather to share ideas and technology. Carl Bergquist is managing the Cloud and Monitoring Devroom, and we’ve heard there were some great talks submitted. There is no need to register; all are welcome.


    Tweet of the Week

    We scour Twitter each week to find an interesting/beautiful dashboard and show it off! #monitoringLove


    Ok, ok – This tweet isn’t showing a off a dashboard, but we can’t help but be thrilled when someone post about our poster series. We’ll be working on the fourth poster to be unveiled at GrafanaCon EU!


    Grafana Labs is Hiring!

    We are passionate about open source software and thrive on tackling complex challenges to build the future. We ship code from every corner of the globe and love working with the community. If this sounds exciting, you’re in luck – WE’RE HIRING!

    Check out our Open Positions


    How are we doing?

    Let us know what you think about timeShift. Submit a comment on this article below, or post something at our community forum. Find an article I haven’t included? Send it my way. Help us make timeShift better!

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