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TVAddons Suffers Big Setback as Court Completely Overturns Earlier Ruling

Post Syndicated from Andy original https://torrentfreak.com/tvaddons-suffers-big-setback-as-court-completely-overturns-earlier-ruling-180221/

On June 2, 2017 a group of Canadian telecoms giants including Bell Canada, Bell ExpressVu, Bell Media, Videotron, Groupe TVA, Rogers Communications and Rogers Media, filed a complaint in Federal Court against Montreal resident, Adam Lackman.

Better known as the man behind Kodi addon repository TVAddons, Lackman was painted as a serial infringer in the complaint. The telecoms companies said that, without gaining permission from rightsholders, Lackman communicated copyrighted TV shows including Game of Thrones, Prison Break, The Big Bang Theory, America’s Got Talent, Keeping Up With The Kardashians and dozens more, by developing, hosting, distributing and promoting infringing Kodi add-ons.

To limit the harm allegedly caused by TVAddons, the complaint demanded interim, interlocutory, and permanent injunctions restraining Lackman from developing, promoting or distributing any of the allegedly infringing add-ons or software. On top, the plaintiffs requested punitive and exemplary damages, plus costs.

On June 9, 2017 the Federal Court handed down a time-limited interim injunction against Lackman ex parte, without Lackman being able to mount a defense. Bailiffs took control of TVAddons’ domains but the most controversial move was the granting of an Anton Piller order, a civil search warrant which granted the plaintiffs no-notice permission to enter Lackman’s premises to secure evidence before it could be tampered with.

The order was executed June 12, 2017, with Lackman’s home subjected to a lengthy search during which the Canadian was reportedly refused his right to remain silent. Non-cooperation with an Anton Piller order can amount to a contempt of court, he was told.

With the situation seemingly spinning out of Lackman’s control, unexpected support came from the Honourable B. Richard Bell during a subsequent June 29, 2017 Federal Court hearing to consider the execution of the Anton Piller order.

The Judge said that Lackman had been subjected to a search “without any of the protections normally afforded to litigants in such circumstances” and took exception to the fact that the plaintiffs had ordered Lackman to spill the beans on other individuals in the Kodi addon community. He described this as a hunt for further evidence, not the task of preserving evidence it should’ve been.

Justice Bell concluded by ruling that while the prima facie case against Lackman may have appeared strong before the judge who heard the matter ex parte, the subsequent adversarial hearing undermined it, to the point that it no longer met the threshold.

As a result of these failings, Judge Bell vacated the Anton Piller order and dismissed the application for interlocutory injunction.

While this was an early victory for Lackman and TVAddons, the plaintiffs took the decision to an appeal which was heard November 29, 2017. Determined by a three-judge panel and signed by Justice Yves de Montigny, the decision was handed down Tuesday and it effectively turns the earlier ruling upside down.

The appeal had two matters to consider: whether Justice Bell made errors when he vacated the Anton Piller order, and whether he made errors when he dismissed the application for an interlocutory injunction. In short, the panel found that he did.

In a 27-page ruling, the first key issue concerns Justice Bell’s understanding of the nature of both Lackman and TVAddons.

The telecoms companies complained that the Judge got it wrong when he characterized Lackman as a software developer who came up with add-ons that permit users to access material “that is for the most part not infringing on the rights” of the telecoms companies.

The companies also challenged the Judge’s finding that the infringing add-ons offered by the site represented “just over 1%” of all the add-ons developed by Lackman.

“I agree with the [telecoms companies] that the Judge misapprehended the evidence and made palpable and overriding errors in his assessment of the strength of the appellants’ case,” Justice Yves de Montigny writes in the ruling.

“Nowhere did the appellants actually state that only a tiny proportion of the add-ons found on the respondent’s website are infringing add-ons.”

The confusion appears to have arisen from the fact that while TVAddons offered 1,500 add-ons in total, the heavily discussed ‘featured’ addon category on the site contained just 22 add-ons, 16 of which were considered to be infringing according to the original complaint. So, it was 16 add-ons out of 22 being discussed, not 16 add-ons out of a possible 1,500.

“[Justice Bell] therefore clearly misapprehended the evidence in this regard by concluding that just over 1% of the add-ons were purportedly infringing,” the appeals Judge adds.

After gaining traction with Justice Bell in the previous hearing, Lackman’s assertion that his add-ons were akin to a “mini Google” was fiercely contested by the telecoms companies. They also fell flat before the appeal hearing.

Justice de Montigny says that Justice Bell “had been swayed” when Lackman’s expert replicated the discovery of infringing content using Google but had failed to grasp the important differences between a general search engine and a dedicated Kodi add-on.

“While Google is an indiscriminate search engine that returns results based on relevance, as determined by an algorithm, infringing add-ons target predetermined infringing content in a manner that is user-friendly and reliable,” the Judge writes.

“The fact that a search result using an add-on can be replicated with Google is of little consequence. The content will always be found using Google or any other Internet search engine because they search the entire universe of all publicly available information. Using addons, however, takes one to the infringing content much more directly, effortlessly and safely.”

With this in mind, Justice de Montigny says there is a “strong prima facie case” that Lackman, by hosting and distributing infringing add-ons, made the telecoms companies’ content available to the public “at a time of their choosing”, thereby infringing paragraph 2.4(1.1) and section 27 of the Copyright Act.

On TVAddons itself, the Judge said that the platform is “clearly designed” to facilitate access to infringing material since it targets “those who want to circumvent the legal means of watching television programs and the related costs.”

Turning to Lackman, the Judge said he could not claim to have no knowledge of the infringing content delivered by the add-ons distributed on this site, since they were purposefully curated prior to distribution.

“The respondent cannot credibly assert that his participation is content neutral and that he was not negligent in failing to investigate, since at a minimum he selects and organizes the add-ons that find their way onto his website,” the Judge notes.

In a further setback, the Judge draws clear parallels with another case before the Canadian courts involving pre-loaded ‘pirate’ set-top boxes. Justice de Montigny says that TVAddons itself bears “many similarities” with those devices that are already subjected to an interlocutory injunction in Canada.

“The service offered by the respondent through the TVAddons website is no different from the service offered through the set-top boxes. The means through which access is provided to infringing content is different (one relied on hardware while the other relied on a website), but they both provided unauthorized access to copyrighted material without authorization of the copyright owners,” the Judge finds.

Continuing, the Judge makes some pointed remarks concerning the execution of the Anton Piller order. In short, he found little wrong with the way things went ahead and also contradicted some of the claims and beliefs circulated in the earlier hearing.

Citing the affidavit of an independent solicitor who monitored the order’s execution, the Judge said that the order was explained to Lackman in plain language and he was informed of his right to remain silent. He was also told that he could refuse to answer questions other than those specified in the order.

The Judge said that Lackman was allowed to have counsel present, “with whom he consulted throughout the execution of the order.” There was nothing, the Judge said, that amounted to the “interrogation” alluded to in the earlier hearing.

Justice de Montigny also criticized Justice Bell for failing to take into account that Lackman “attempted to conceal crucial evidence and lied to the independent supervising solicitor regarding the whereabouts of that evidence.”

Much was previously made of Lackman apparently being forced to hand over personal details of third-parties associated directly or indirectly with TVAddons. The Judge clarifies what happened in his ruling.

“A list of names was put to the respondent by the plaintiffs’ solicitors, but it was apparently done to expedite the questioning process. In any event, the respondent did not provide material information on the majority of the aliases put to him,” the Judge reveals.

But while not handing over evidence on third-parties will paint Lackman in a better light with concerned elements of the add-on community, the Judge was quick to bring up the Canadian’s history and criticized Justice Bell for not taking it into account when he vacated the Anton Piller order.

“[T]he respondent admitted that he was involved in piracy of satellite television signals when he was younger, and there is evidence that he was involved in the configuration and sale of ‘jailbroken’ Apple TV set-top boxes,” Justice de Montigny writes.

“When juxtaposed to the respondent’s attempt to conceal relevant evidence during the execution of the Anton Piller order, that contextual evidence adds credence to the appellants’ concern that the evidence could disappear without a comprehensive order.”

Dismissing Justice Bell’s findings as “fatally flawed”, Justice de Montigny allowed the appeal of the telecoms companies, set aside the order of June 29, 2017, declared the Anton Piller order and interim injunctions legal, and granted an interlocutory injunction to remain valid until the conclusion of the case in Federal Court. The telecoms companies were also awarded costs of CAD$50,000.

It’s worth noting that despite all the detail provided up to now, the case hasn’t yet got to the stage where the Court has tested any of the claims put forward by the telecoms companies. Everything reported to date is pre-trial and has been taken at face value.

TorrentFreak spoke with Adam Lackman but since he hadn’t yet had the opportunity to discuss the matter with his lawyers, he declined to comment further on the record. There is a statement on the TVAddons website which gives his position on the story so far.

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

Sharing Secrets with AWS Lambda Using AWS Systems Manager Parameter Store

Post Syndicated from Chris Munns original https://aws.amazon.com/blogs/compute/sharing-secrets-with-aws-lambda-using-aws-systems-manager-parameter-store/

This post courtesy of Roberto Iturralde, Sr. Application Developer- AWS Professional Services

Application architects are faced with key decisions throughout the process of designing and implementing their systems. One decision common to nearly all solutions is how to manage the storage and access rights of application configuration. Shared configuration should be stored centrally and securely with each system component having access only to the properties that it needs for functioning.

With AWS Systems Manager Parameter Store, developers have access to central, secure, durable, and highly available storage for application configuration and secrets. Parameter Store also integrates with AWS Identity and Access Management (IAM), allowing fine-grained access control to individual parameters or branches of a hierarchical tree.

This post demonstrates how to create and access shared configurations in Parameter Store from AWS Lambda. Both encrypted and plaintext parameter values are stored with only the Lambda function having permissions to decrypt the secrets. You also use AWS X-Ray to profile the function.

Solution overview

This example is made up of the following components:

  • An AWS SAM template that defines:
    • A Lambda function and its permissions
    • An unencrypted Parameter Store parameter that the Lambda function loads
    • A KMS key that only the Lambda function can access. You use this key to create an encrypted parameter later.
  • Lambda function code in Python 3.6 that demonstrates how to load values from Parameter Store at function initialization for reuse across invocations.

Launch the AWS SAM template

To create the resources shown in this post, you can download the SAM template or choose the button to launch the stack. The template requires one parameter, an IAM user name, which is the name of the IAM user to be the admin of the KMS key that you create. In order to perform the steps listed in this post, this IAM user will need permissions to execute Lambda functions, create Parameter Store parameters, administer keys in KMS, and view the X-Ray console. If you have these privileges in your IAM user account you can use your own account to complete the walkthrough. You can not use the root user to administer the KMS keys.

SAM template resources

The following sections show the code for the resources defined in the template.
Lambda function

ParameterStoreBlogFunctionDev:
    Type: 'AWS::Serverless::Function'
    Properties:
      FunctionName: 'ParameterStoreBlogFunctionDev'
      Description: 'Integrating lambda with Parameter Store'
      Handler: 'lambda_function.lambda_handler'
      Role: !GetAtt ParameterStoreBlogFunctionRoleDev.Arn
      CodeUri: './code'
      Environment:
        Variables:
          ENV: 'dev'
          APP_CONFIG_PATH: 'parameterStoreBlog'
          AWS_XRAY_TRACING_NAME: 'ParameterStoreBlogFunctionDev'
      Runtime: 'python3.6'
      Timeout: 5
      Tracing: 'Active'

  ParameterStoreBlogFunctionRoleDev:
    Type: AWS::IAM::Role
    Properties:
      AssumeRolePolicyDocument:
        Version: '2012-10-17'
        Statement:
          -
            Effect: Allow
            Principal:
              Service:
                - 'lambda.amazonaws.com'
            Action:
              - 'sts:AssumeRole'
      ManagedPolicyArns:
        - 'arn:aws:iam::aws:policy/service-role/AWSLambdaBasicExecutionRole'
      Policies:
        -
          PolicyName: 'ParameterStoreBlogDevParameterAccess'
          PolicyDocument:
            Version: '2012-10-17'
            Statement:
              -
                Effect: Allow
                Action:
                  - 'ssm:GetParameter*'
                Resource: !Sub 'arn:aws:ssm:${AWS::Region}:${AWS::AccountId}:parameter/dev/parameterStoreBlog*'
        -
          PolicyName: 'ParameterStoreBlogDevXRayAccess'
          PolicyDocument:
            Version: '2012-10-17'
            Statement:
              -
                Effect: Allow
                Action:
                  - 'xray:PutTraceSegments'
                  - 'xray:PutTelemetryRecords'
                Resource: '*'

In this YAML code, you define a Lambda function named ParameterStoreBlogFunctionDev using the SAM AWS::Serverless::Function type. The environment variables for this function include the ENV (dev) and the APP_CONFIG_PATH where you find the configuration for this app in Parameter Store. X-Ray tracing is also enabled for profiling later.

The IAM role for this function extends the AWSLambdaBasicExecutionRole by adding IAM policies that grant the function permissions to write to X-Ray and get parameters from Parameter Store, limited to paths under /dev/parameterStoreBlog*.
Parameter Store parameter

SimpleParameter:
    Type: AWS::SSM::Parameter
    Properties:
      Name: '/dev/parameterStoreBlog/appConfig'
      Description: 'Sample dev config values for my app'
      Type: String
      Value: '{"key1": "value1","key2": "value2","key3": "value3"}'

This YAML code creates a plaintext string parameter in Parameter Store in a path that your Lambda function can access.
KMS encryption key

ParameterStoreBlogDevEncryptionKeyAlias:
    Type: AWS::KMS::Alias
    Properties:
      AliasName: 'alias/ParameterStoreBlogKeyDev'
      TargetKeyId: !Ref ParameterStoreBlogDevEncryptionKey

  ParameterStoreBlogDevEncryptionKey:
    Type: AWS::KMS::Key
    Properties:
      Description: 'Encryption key for secret config values for the Parameter Store blog post'
      Enabled: True
      EnableKeyRotation: False
      KeyPolicy:
        Version: '2012-10-17'
        Id: 'key-default-1'
        Statement:
          -
            Sid: 'Allow administration of the key & encryption of new values'
            Effect: Allow
            Principal:
              AWS:
                - !Sub 'arn:aws:iam::${AWS::AccountId}:user/${IAMUsername}'
            Action:
              - 'kms:Create*'
              - 'kms:Encrypt'
              - 'kms:Describe*'
              - 'kms:Enable*'
              - 'kms:List*'
              - 'kms:Put*'
              - 'kms:Update*'
              - 'kms:Revoke*'
              - 'kms:Disable*'
              - 'kms:Get*'
              - 'kms:Delete*'
              - 'kms:ScheduleKeyDeletion'
              - 'kms:CancelKeyDeletion'
            Resource: '*'
          -
            Sid: 'Allow use of the key'
            Effect: Allow
            Principal:
              AWS: !GetAtt ParameterStoreBlogFunctionRoleDev.Arn
            Action:
              - 'kms:Encrypt'
              - 'kms:Decrypt'
              - 'kms:ReEncrypt*'
              - 'kms:GenerateDataKey*'
              - 'kms:DescribeKey'
            Resource: '*'

This YAML code creates an encryption key with a key policy with two statements.

The first statement allows a given user (${IAMUsername}) to administer the key. Importantly, this includes the ability to encrypt values using this key and disable or delete this key, but does not allow the administrator to decrypt values that were encrypted with this key.

The second statement grants your Lambda function permission to encrypt and decrypt values using this key. The alias for this key in KMS is ParameterStoreBlogKeyDev, which is how you reference it later.

Lambda function

Here I walk you through the Lambda function code.

import os, traceback, json, configparser, boto3
from aws_xray_sdk.core import patch_all
patch_all()

# Initialize boto3 client at global scope for connection reuse
client = boto3.client('ssm')
env = os.environ['ENV']
app_config_path = os.environ['APP_CONFIG_PATH']
full_config_path = '/' + env + '/' + app_config_path
# Initialize app at global scope for reuse across invocations
app = None

class MyApp:
    def __init__(self, config):
        """
        Construct new MyApp with configuration
        :param config: application configuration
        """
        self.config = config

    def get_config(self):
        return self.config

def load_config(ssm_parameter_path):
    """
    Load configparser from config stored in SSM Parameter Store
    :param ssm_parameter_path: Path to app config in SSM Parameter Store
    :return: ConfigParser holding loaded config
    """
    configuration = configparser.ConfigParser()
    try:
        # Get all parameters for this app
        param_details = client.get_parameters_by_path(
            Path=ssm_parameter_path,
            Recursive=False,
            WithDecryption=True
        )

        # Loop through the returned parameters and populate the ConfigParser
        if 'Parameters' in param_details and len(param_details.get('Parameters')) > 0:
            for param in param_details.get('Parameters'):
                param_path_array = param.get('Name').split("/")
                section_position = len(param_path_array) - 1
                section_name = param_path_array[section_position]
                config_values = json.loads(param.get('Value'))
                config_dict = {section_name: config_values}
                print("Found configuration: " + str(config_dict))
                configuration.read_dict(config_dict)

    except:
        print("Encountered an error loading config from SSM.")
        traceback.print_exc()
    finally:
        return configuration

def lambda_handler(event, context):
    global app
    # Initialize app if it doesn't yet exist
    if app is None:
        print("Loading config and creating new MyApp...")
        config = load_config(full_config_path)
        app = MyApp(config)

    return "MyApp config is " + str(app.get_config()._sections)

Beneath the import statements, you import the patch_all function from the AWS X-Ray library, which you use to patch boto3 to create X-Ray segments for all your boto3 operations.

Next, you create a boto3 SSM client at the global scope for reuse across function invocations, following Lambda best practices. Using the function environment variables, you assemble the path where you expect to find your configuration in Parameter Store. The class MyApp is meant to serve as an example of an application that would need its configuration injected at construction. In this example, you create an instance of ConfigParser, a class in Python’s standard library for handling basic configurations, to give to MyApp.

The load_config function loads the all the parameters from Parameter Store at the level immediately beneath the path provided in the Lambda function environment variables. Each parameter found is put into a new section in ConfigParser. The name of the section is the name of the parameter, less the base path. In this example, the full parameter name is /dev/parameterStoreBlog/appConfig, which is put in a section named appConfig.

Finally, the lambda_handler function initializes an instance of MyApp if it doesn’t already exist, constructing it with the loaded configuration from Parameter Store. Then it simply returns the currently loaded configuration in MyApp. The impact of this design is that the configuration is only loaded from Parameter Store the first time that the Lambda function execution environment is initialized. Subsequent invocations reuse the existing instance of MyApp, resulting in improved performance. You see this in the X-Ray traces later in this post. For more advanced use cases where configuration changes need to be received immediately, you could implement an expiry policy for your configuration entries or push notifications to your function.

To confirm that everything was created successfully, test the function in the Lambda console.

  1. Open the Lambda console.
  2. In the navigation pane, choose Functions.
  3. In the Functions pane, filter to ParameterStoreBlogFunctionDev to find the function created by the SAM template earlier. Open the function name to view its details.
  4. On the top right of the function detail page, choose Test. You may need to create a new test event. The input JSON doesn’t matter as this function ignores the input.

After running the test, you should see output similar to the following. This demonstrates that the function successfully fetched the unencrypted configuration from Parameter Store.

Create an encrypted parameter

You currently have a simple, unencrypted parameter and a Lambda function that can access it.

Next, you create an encrypted parameter that only your Lambda function has permission to use for decryption. This limits read access for this parameter to only this Lambda function.

To follow along with this section, deploy the SAM template for this post in your account and make your IAM user name the KMS key admin mentioned earlier.

  1. In the Systems Manager console, under Shared Resources, choose Parameter Store.
  2. Choose Create Parameter.
    • For Name, enter /dev/parameterStoreBlog/appSecrets.
    • For Type, select Secure String.
    • For KMS Key ID, choose alias/ParameterStoreBlogKeyDev, which is the key that your SAM template created.
    • For Value, enter {"secretKey": "secretValue"}.
    • Choose Create Parameter.
  3. If you now try to view the value of this parameter by choosing the name of the parameter in the parameters list and then choosing Show next to the Value field, you won’t see the value appear. This is because, even though you have permission to encrypt values using this KMS key, you do not have permissions to decrypt values.
  4. In the Lambda console, run another test of your function. You now also see the secret parameter that you created and its decrypted value.

If you do not see the new parameter in the Lambda output, this may be because the Lambda execution environment is still warm from the previous test. Because the parameters are loaded at Lambda startup, you need a fresh execution environment to refresh the values.

Adjust the function timeout to a different value in the Advanced Settings at the bottom of the Lambda Configuration tab. Choose Save and test to trigger the creation of a new Lambda execution environment.

Profiling the impact of querying Parameter Store using AWS X-Ray

By using the AWS X-Ray SDK to patch boto3 in your Lambda function code, each invocation of the function creates traces in X-Ray. In this example, you can use these traces to validate the performance impact of your design decision to only load configuration from Parameter Store on the first invocation of the function in a new execution environment.

From the Lambda function details page where you tested the function earlier, under the function name, choose Monitoring. Choose View traces in X-Ray.

This opens the X-Ray console in a new window filtered to your function. Be aware of the time range field next to the search bar if you don’t see any search results.
In this screenshot, I’ve invoked the Lambda function twice, one time 10.3 minutes ago with a response time of 1.1 seconds and again 9.8 minutes ago with a response time of 8 milliseconds.

Looking at the details of the longer running trace by clicking the trace ID, you can see that the Lambda function spent the first ~350 ms of the full 1.1 sec routing the request through Lambda and creating a new execution environment for this function, as this was the first invocation with this code. This is the portion of time before the initialization subsegment.

Next, it took 725 ms to initialize the function, which includes executing the code at the global scope (including creating the boto3 client). This is also a one-time cost for a fresh execution environment.

Finally, the function executed for 65 ms, of which 63.5 ms was the GetParametersByPath call to Parameter Store.

Looking at the trace for the second, much faster function invocation, you see that the majority of the 8 ms execution time was Lambda routing the request to the function and returning the response. Only 1 ms of the overall execution time was attributed to the execution of the function, which makes sense given that after the first invocation you’re simply returning the config stored in MyApp.

While the Traces screen allows you to view the details of individual traces, the X-Ray Service Map screen allows you to view aggregate performance data for all traced services over a period of time.

In the X-Ray console navigation pane, choose Service map. Selecting a service node shows the metrics for node-specific requests. Selecting an edge between two nodes shows the metrics for requests that traveled that connection. Again, be aware of the time range field next to the search bar if you don’t see any search results.

After invoking your Lambda function several more times by testing it from the Lambda console, you can view some aggregate performance metrics. Look at the following:

  • From the client perspective, requests to the Lambda service for the function are taking an average of 50 ms to respond. The function is generating ~1 trace per minute.
  • The function itself is responding in an average of 3 ms. In the following screenshot, I’ve clicked on this node, which reveals a latency histogram of the traced requests showing that over 95% of requests return in under 5 ms.
  • Parameter Store is responding to requests in an average of 64 ms, but note the much lower trace rate in the node. This is because you only fetch data from Parameter Store on the initialization of the Lambda execution environment.

Conclusion

Deduplication, encryption, and restricted access to shared configuration and secrets is a key component to any mature architecture. Serverless architectures designed using event-driven, on-demand, compute services like Lambda are no different.

In this post, I walked you through a sample application accessing unencrypted and encrypted values in Parameter Store. These values were created in a hierarchy by application environment and component name, with the permissions to decrypt secret values restricted to only the function needing access. The techniques used here can become the foundation of secure, robust configuration management in your enterprise serverless applications.

timeShift(GrafanaBuzz, 1w) Issue 30

Post Syndicated from Blogs on Grafana Labs Blog original https://grafana.com/blog/2018/01/19/timeshiftgrafanabuzz-1w-issue-30/

Welcome to TimeShift

We’re only 6 weeks away from the next GrafanaCon and here at Grafana Labs we’re buzzing with excitement. We have some great talks lined up that you won’t want to miss.

This week’s TimeShift covers Grafana’s annotation functionality, monitoring with Prometheus, integrating Grafana with NetFlow and a peek inside Stream’s monitoring stack. Enjoy!


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

Walkthrough: Watch your Ansible deployments in Grafana!: Your graphs start spiking and your platform begins behaving abnormally. Did the config change in a deployment, causing the problem? This article covers Grafana’s new annotation functionality, and specifically, how to create deployment annotations via Ansible playbooks.

Application Monitoring in OpenShift with Prometheus and Grafana: There are many article describing how to monitor OpenShift with Prometheus running in the same cluster, but what if you don’t have admin permissions to the cluster you need to monitor?

Spring Boot Metrics Monitoring Using Prometheus & Grafana: As the title suggests, this post walks you through how to configure Prometheus and Grafana to monitor you Spring Boot application metrics.

How to Integrate Grafana with NetFlow: Learn how to monitor NetFlow from Scrutinizer using Grafana’s SimpleJSON data source.

Stream & Go: News Feeds for Over 300 Million End Users: Stream lets you build scalable newsfeeds and activity streams via their API, which is used by more than 300 million end users. In this article, they discuss their monitoring stack and why they chose particular components and technologies.


GrafanaCon EU Tickets are Going Fast!

We’re six weeks from kicking off GrafanaCon EU! Join us for talks from Google, Bloomberg, Tinder, eBay and more! You won’t want to miss two great days of open source monitoring talks and fun in Amsterdam. Get your tickets before they sell out!

Get Your Ticket Now


Grafana Plugins

We have a couple of plugin updates to share this week that add some new features and improvements. Updating your plugins is easy. For on-prem Grafana, use the Grafana-cli tool, or update with 1 click on your Hosted Grafana.

UPDATED PLUGIN

Druid Data Source – This new update is packed with new features. Notable enhancement include:

  • Post Aggregation feature
  • Support for thetaSketch
  • Improvements to the Query editor

Update Now

UPDATED PLUGIN

Breadcrumb Panel – The Breadcrumb Panel is a small panel you can include in your dashboard that tracks other dashboards you have visited – making it easy to navigate back to a previously visited dashboard. The latest release adds support for dashboards loaded from a file.

Update Now


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.

SnowCamp 2018: Yves Brissaud – Application metrics with Prometheus and Grafana | Grenoble, France – Jan 24, 2018:
We’ll take a look at how Prometheus, Grafana and a bit of code make it possible to obtain temporal data to visualize the state of our applications as well as to help with development and debugging.

Register Now

Women Who Go Berlin: Go Workshop – Monitoring and Troubleshooting using Prometheus and Grafana | Berlin, Germany – Jan 31, 2018: In this workshop we will learn about one of the most important topics in making apps production ready: Monitoring. We will learn how to use tools you’ve probably heard a lot about – Prometheus and Grafana, and using what we learn we will troubleshoot a particularly buggy Go app.

Register Now

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

Stockholm Metrics and Monitoring | Stockholm, Sweden – Feb 7, 2018:
Observability 3 ways – Logging, Metrics and Distributed Tracing

Let’s talk about often confused telemetry tools: Logging, Metrics and Distributed Tracing. We’ll show how you capture latency using each of the tools and how they work differently. Through examples and discussion, we’ll note edge cases where certain tools have advantages over others. By the end of this talk, we’ll better understand how each of Logging, Metrics and Distributed Tracing aids us in different ways to understand our applications.

Register Now

OpenNMS – Introduction to “Grafana” | Webinar – Feb 21, 2018:
IT monitoring helps detect emerging hardware damage and performance bottlenecks in the enterprise network before any consequential damage or disruption to business processes occurs. The powerful open-source OpenNMS software monitors a network, including all connected devices, and provides logging of a variety of data that can be used for analysis and planning purposes. In our next OpenNMS webinar on February 21, 2018, we introduce “Grafana” – a web-based tool for creating and displaying dashboards from various data sources, which can be perfectly combined with OpenNMS.

Register Now


Tweet of the Week

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

As we say with pie charts, use emojis wisely 😉


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 wraps up our 30th issue of TimeShift. What do you think? Are there other types of content you’d like to see here? Submit a comment on this issue below, or post something at our community forum.

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

timeShift(GrafanaBuzz, 1w) Issue 29

Post Syndicated from Blogs on Grafana Labs Blog original https://grafana.com/blog/2018/01/12/timeshiftgrafanabuzz-1w-issue-29/

Welcome to TimeShift

intro paragraph


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

Graphite 1.1: Teaching an Old Dog New Tricks: Grafana Labs’ own Dan Cech is a contributor to the Graphite project, and has been instrumental in the addition of some of the newest features. This article discusses five of the biggest additions, how they work, and what you can expect for the future of the project.

Instrument an Application Using Prometheus and Grafana: Chris walks us through how easy it is to get useful metrics from an application to understand bottlenecks and performace. In this article, he shares an application he built that indexes your Gmail account into Elasticsearch, and sends the metrics to Prometheus. Then, he shows you how to set up Grafana to get meaningful graphs and dashboards.

Visualising Serverless Metrics With Grafana Dashboards: Part 3 in this series of blog posts on “Monitoring Serverless Applications Metrics” starts with an overview of Grafana and the UI, covers queries and templating, then dives into creating some great looking dashboards. The series plans to conclude with a post about setting up alerting.

Huawei FAT WLAN Access Points in Grafana: Huawei’s FAT firmware for their WLAN Access points lacks central management overview. To get a sense of the performance of your AP’s, why not quickly create a templated dashboard in Grafana? This article quickly steps your through the process, and includes a sample dashboard.


Grafana Plugins

Lots of updated plugins this week. Plugin authors add new features and fix bugs often, to make your plugin perform better – so it’s important to keep your plugins up to date. We’ve made updating easy; for on-prem Grafana, use the Grafana-cli tool, or update with 1 click if you’re using Hosted Grafana.

UPDATED PLUGIN

Clickhouse Data Source – The Clickhouse Data Source plugin has been updated a few times with small fixes during the last few weeks.

  • Fix for quantile functions
  • Allow rounding with round option for both time filters: $from and $to

Update

UPDATED PLUGIN

Zabbix App – The Zabbix App had a release with a redesign of the Triggers panel as well as support for Multiple data sources for the triggers panel

Update

UPDATED PLUGIN

OpenHistorian Data Source – this data source plugin received some new query builder screens and improved documentation.

Update

UPDATED PLUGIN

BT Status Dot Panel – This panel received a small bug fix.

Update

UPDATED PLUGIN

Carpet Plot Panel – A recent update for this panel fixes a D3 import bug.

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.

Women Who Go Berlin: Go Workshop – Monitoring and Troubleshooting using Prometheus and Grafana | Berlin, Germany – Jan 31, 2018: In this workshop we will learn about one of the most important topics in making apps production ready: Monitoring. We will learn how to use tools you’ve probably heard a lot about – Prometheus and Grafana, and using what we learn we will troubleshoot a particularly buggy Go app.

Register Now

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

Stockholm Metrics and Monitoring | Stockholm, Sweden – Feb 7, 2018:
Observability 3 ways – Logging, Metrics and Distributed Tracing

Let’s talk about often confused telemetry tools: Logging, Metrics and Distributed Tracing. We’ll show how you capture latency using each of the tools and how they work differently. Through examples and discussion, we’ll note edge cases where certain tools have advantages over others. By the end of this talk, we’ll better understand how each of Logging, Metrics and Distributed Tracing aids us in different ways to understand our applications.

Register Now

OpenNMS – Introduction to “Grafana” | Webinar – Feb 21, 2018:
IT monitoring helps detect emerging hardware damage and performance bottlenecks in the enterprise network before any consequential damage or disruption to business processes occurs. The powerful open-source OpenNMS software monitors a network, including all connected devices, and provides logging of a variety of data that can be used for analysis and planning purposes. In our next OpenNMS webinar on February 21, 2018, we introduce “Grafana” – a web-based tool for creating and displaying dashboards from various data sources, which can be perfectly combined with OpenNMS.

Register Now

GrafanaCon EU | Amsterdam, Netherlands – March 1-2, 2018:
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, Automattic, Prometheus, InfluxData, Percona and more! Be sure to get your ticket before they’re sold out.

Learn More


Tweet of the Week

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

Nice hack! I know I like to keep one eye on server requests when I’m dropping beats. 😉


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?

Thanks for reading another issue of timeShift. Let us know what you think! Submit a comment on this article below, or post something at our community forum.

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

Graphite 1.1: Teaching an Old Dog New Tricks

Post Syndicated from Blogs on Grafana Labs Blog original https://grafana.com/blog/2018/01/11/graphite-1.1-teaching-an-old-dog-new-tricks/

The Road to Graphite 1.1

I started working on Graphite just over a year ago, when @obfuscurity asked me to help out with some issues blocking the Graphite 1.0 release. Little did I know that a year later, that would have resulted in 262 commits (and counting), and that with the help of the other Graphite maintainers (especially @deniszh, @iksaif & @cbowman0) we would have added a huge amount of new functionality to Graphite.

There are a huge number of new additions and updates in this release, in this post I’ll give a tour of some of the highlights including tag support, syntax and function updates, custom function plugins, and python 3.x support.

Tagging!

The single biggest feature in this release is the addition of tag support, which brings the ability to describe metrics in a much richer way and to write more flexible and expressive queries.

Traditionally series in Graphite are identified using a hierarchical naming scheme based on dot-separated segments called nodes. This works very well and is simple to map into a hierarchical structure like the whisper filesystem tree, but it means that the user has to know what each segment represents, and makes it very difficult to modify or extend the naming scheme since everything is based on the positions of the segments within the hierarchy.

The tagging system gives users the ability to encode information about the series in a collection of tag=value pairs which are used together with the series name to uniquely identify each series, and the ability to query series by specifying tag-based matching expressions rather than constructing glob-style selectors based on the positions of specific segments within the hierarchy. This is broadly similar to the system used by Prometheus and makes it possible to use Graphite as a long-term storage backend for metrics gathered by Prometheus with full tag support.

When using tags, series names are specified using the new tagged carbon format: name;tag1=value1;tag2=value2. This format is backward compatible with most existing carbon tooling, and makes it easy to adapt existing tools to produce tagged metrics simply by changing the metric names. The OpenMetrics format is also supported for ingestion, and is normalized into the standard Graphite format internally.

At its core, the tagging system is implemented as a tag database (TagDB) alongside the metrics that allows them to be efficiently queried by individual tag values rather than having to traverse the metrics tree looking for series that match the specified query. Internally the tag index is stored in one of a number of pluggable tag databases, currently supported options are the internal graphite-web database, redis, or an external system that implements the Graphite tagging HTTP API. Carbon automatically keeps the index up to date with any tagged series seen.

The new seriesByTag function is used to query the TagDB and will return a list of all the series that match the expressions passed to it. seriesByTag supports both exact and regular expression matches, and can be used anywhere you would previously have specified a metric name or glob expression.

There are new dedicated functions for grouping and aliasing series by tag (groupByTags and aliasByTags), and you can also use tags interchangeably with node numbers in the standard Graphite functions like aliasByNode, groupByNodes, asPercent, mapSeries, etc.

Piping Syntax & Function Updates

One of the huge strengths of the Graphite render API is the ability to chain together multiple functions to process data, but until now (unless you were using a tool like Grafana) writing chained queries could be painful as each function had to be wrapped around the previous one. With this release it is now possible to “pipe” the output of one processing function into the next, and to combine piped and nested functions.

For example:

alias(movingAverage(scaleToSeconds(sumSeries(stats_global.production.counters.api.requests.*.count),60),30),'api.avg')

Can now be written as:

sumSeries(stats_global.production.counters.api.requests.*.count)|scaleToSeconds(60)|movingAverage(30)|alias('api.avg')

OR

stats_global.production.counters.api.requests.*.count|sumSeries()|scaleToSeconds(60)|movingAverage(30)|alias('api.avg')

Another source of frustration with the old function API was the inconsistent implementation of aggregations, with different functions being used in different parts of the API, and some functions simply not being available. In 1.1 all functions that perform aggregation (whether across series or across time intervals) now support a consistent set of aggregations; average, median, sum, min, max, diff, stddev, count, range, multiply and last. This is part of a new approach to implementing functions that emphasises using shared building blocks to ensure consistency across the API and solve the problem of a particular function not working with the aggregation needed for a given task.

To that end a number of new functions have been added that each provide the same functionality as an entire family of “old” functions; aggregate, aggregateWithWildcards, movingWindow, filterSeries, highest, lowest and sortBy.

Each of these functions accepts an aggregation method parameter, for example aggregate(some.metric.*, 'sum') implements the same functionality as sumSeries(some.metric.*).

It can also be used with different aggregation methods to replace averageSeries, stddevSeries, multiplySeries, diffSeries, rangeOfSeries, minSeries, maxSeries and countSeries. All those functions are now implemented as aliases for aggregate, and it supports the previously-missing median and last aggregations.

The same is true for the other functions, and the summarize, smartSummarize, groupByNode, groupByNodes and the new groupByTags functions now all support the standard set of aggregations. Gone are the days of wishing that sortByMedian or highestRange were available!

For more information on the functions available check the function documentation.

Custom Functions

No matter how many functions are available there are always going to be specific use-cases where a custom function can perform analysis that wouldn’t otherwise be possible, or provide a convenient alias for a complicated function chain or specific set of parameters.

In Graphite 1.1 we added support for easily adding one-off custom functions, as well as for creating and sharing plugins that can provide one or more functions.

Each function plugin is packaged as a simple python module, and will be automatically loaded by Graphite when placed into the functions/custom folder.

An example of a simple function plugin that translates the name of every series passed to it into UPPERCASE:

from graphite.functions.params import Param, ParamTypes

def toUpperCase(requestContext, seriesList):
  """Custom function that changes series names to UPPERCASE"""
  for series in seriesList:
    series.name = series.name.upper()
  return seriesList

toUpperCase.group = 'Custom'
toUpperCase.params = [
  Param('seriesList', ParamTypes.seriesList, required=True),
]

SeriesFunctions = {
  'upper': toUpperCase,
}

Once installed the function is not only available for use within Grpahite, but is also exposed via the new Function API which allows the function definition and documentation to be automatically loaded by tools like Grafana. This means that users will be able to select and use the new function in exactly the same way as the internal functions.

More information on writing and using custom functions is available in the documentation.

Clustering Updates

One of the biggest changes from the 0.9 to 1.0 releases was the overhaul of the clustering code, and with 1.1.1 that process has been taken even further to optimize performance when using Graphite in a clustered deployment. In the past it was common for a request to require the frontend node to make multiple requests to the backend nodes to identify matching series and to fetch data, and the code for handling remote vs local series was overly complicated. In 1.1.1 we took a new approach where all render data requests pass through the same path internally, and multiple backend nodes are handled individually rather than grouped together into a single finder. This has greatly simplified the codebase, making it much easier to understand and reason about, while allowing much more flexibility in design of the finders. After these changes, render requests can now be answered with a single internal request to each backend node, and all requests for both remote and local data are executed in parallel.

To maintain the ability of graphite to scale out horizontally, the tagging system works seamlessly within a clustered environment, with each node responsible for the series stored on that node. Calls to load tagged series via seriesByTag are fanned out to the backend nodes and results are merged on the query node just like they are for non-tagged series.

Python 3 & Django 1.11 Support

Graphite 1.1 finally brings support for Python 3.x, both graphite-web and carbon are now tested against Python 2.7, 3.4, 3.5, 3.6 and PyPy. Django releases 1.8 through 1.11 are also supported. The work involved in sorting out the compatibility issues between Python 2.x and 3.x was quite involved, but it is a huge step forward for the long term support of the project! With the new Django 2.x series supporting only Python 3.x we will need to evaluate our long-term support for Python 2.x, but the Django 1.11 series is supported through 2020 so there is time to consider the options there.

Watch This Space

Efforts are underway to add support for the new functionality across the ecosystem of tools that work with Graphite, adding collectd tagging support, prometheus remote read & write with tags (and native Prometheus remote read/write support in Graphite) and last but not least Graphite tag support in Grafana.

We’re excited about the possibilities that the new capabilities in 1.1.x open up, and can’t wait to see how the community puts them to work.

Download the 1.1.1 release and check out the release notes here.

Judge Issues Devastating Order Against BitTorrent Copyright Troll

Post Syndicated from Ernesto original https://torrentfreak.com/judge-issues-devastating-order-bittorrent-copyright-troll-180110/

In recent years, file-sharers around the world have been pressured to pay significant settlement fees, or face legal repercussions.

These so-called “copyright trolling” efforts have been a common occurrence in the United States since the turn of the last decade.

Increasingly, however, courts are growing weary of these cases. Many districts have turned into no-go zones for copyright trolls and the people behind Prenda law were arrested and are being prosecuted in a criminal case.

In the Western District of Washington, the tide also appears to have turned. After Venice PI, a copyright holder of the film “Once Upon a Time in Venice”, sued a man who later passed away, concerns were raised over the validity of the evidence.

Venice PI responded to the concerns with a declaration explaining its data gathering technique and assuring the Court that false positives are out of the question.

That testimony didn’t help much though, as a recently filed minute order shows this week. The order applies to a dozen cases and prohibits the company from reaching out to any defendants until further notice, as there are several alarming issues that have to be resolved first.

One of the problems is that Venice PI declared that it’s owned by a company named Lost Dog Productions, which in turn is owned by Voltage Productions. Interestingly, these companies don’t appear in the usual records.

“A search of the California Secretary of State’s online database, however, reveals no registered entity with the name ‘Lost Dog’ or ‘Lost Dog Productions’,” the Court notes.

“Moreover, although ‘Voltage Pictures, LLC’ is registered with the California Secretary of State, and has the same address as Venice PI, LLC, the parent company named in plaintiff’s corporate disclosure form, ‘Voltage Productions, LLC,’ cannot be found in the California Secretary of State’s online database and does not appear to exist.”

In other words, the company that filed the lawsuit, as well as its parent company, are extremely questionable.

While the above is a reason for concern, it’s just the tip of the iceberg. The Court not only points out administrative errors, but it also has serious doubts about the evidence collection process. This was carried out by the German company MaverickEye, which used the tracking technology of another German company, GuardaLey.

GuardaLey CEO Benjamin Perino, who claims that he coded the tracking software, wrote a declaration explaining that the infringement detection system at issue “cannot yield a false positive.” However, the Court doubts this statement and Perino’s qualifications in general.

“Perino has been proffered as an expert, but his qualifications consist of a technical high school education and work experience unrelated to the peer-to-peer file-sharing technology known as BitTorrent,” the Court writes.

“Perino does not have the qualifications necessary to be considered an expert in the field in question, and his opinion that the surveillance program is incapable of error is both contrary to common sense and inconsistent with plaintiff’s counsel’s conduct in other matters in this district. Plaintiff has not submitted an adequate offer of proof”

It seems like the Court would prefer to see an assessment from a qualified independent expert instead of the person who wrote the software. For now, this means that the IP-address evidence, in these cases, is not good enough. That’s quite a blow for the copyright holder.

If that wasn’t enough the Court also highlights another issue that’s possibly even more problematic. When Venice PI requested the subpoenas to identify alleged pirates, they relied on declarations from Daniel Arheidt, a consultant for MaverickEye.

These declarations fail to mention, however, that MaverickEye has the proper paperwork to collect IP addresses.

“Nowhere in Arheidt’s declarations does he indicate that either he or MaverickEye is licensed in Washington to conduct private investigation work,” the order reads.

This is important, as doing private investigator work without a license is a gross misdemeanor in Washington. The copyright holder was aware of this requirement because it was brought up in related cases in the past.

“Plaintiff’s counsel has apparently been aware since October 2016, when he received a letter concerning LHF Productions, Inc. v. Collins, C16-1017 RSM, that Arheidt might be committing a crime by engaging in unlicensed surveillance of Washington citizens, but he did not disclose this fact to the Court.”

The order is very bad news for Venice PI. The company had hoped to score a few dozen easy settlements but the tables have now been turned. The Court instead asks the company to explain the deficiencies and provide additional details. In the meantime, the copyright holder is urged not to spend or transfer any of the settlement money that has been collected thus far.

The latter indicates that Venice PI might have to hand defendants their money back, which would be pretty unique.

The order suggests that the Judge is very suspicious of these trolling activities. In a footnote there’s a link to a Fight Copyright Trolls article which revealed that the same counsel dismissed several cases, allegedly to avoid having IP-address evidence scrutinized.

Even more bizarrely, in another footnote the Court also doubts if MaverickEye’s aforementioned consultant, Daniel Arheidt, actually exists.

“The Court has recently become aware that Arheidt is the latest in a series of German declarants (Darren M. Griffin, Daniel Macek, Daniel Susac, Tobias Fieser, Michael Patzer) who might be aliases or even fictitious.

“Plaintiff will not be permitted to rely on Arheidt’s declarations or underlying data without explaining to the Court’s satisfaction Arheidt’s relationship to the above-listed declarants and producing proof beyond a reasonable doubt of Arheidt’s existence,” the court adds.

These are serious allegations, to say the least.

If a copyright holder uses non-existent companies and questionable testimony from unqualified experts after obtaining evidence illegally to get a subpoena backed by a fictitious person….something’s not quite right.

A copy of the minute order, which affects a series of cases, is available here (pdf).

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

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.

Hijacker – Reaver For Android Wifi Hacker App

Post Syndicated from Darknet original https://www.darknet.org.uk/2018/01/hijacker-reaver-android-wifi-hacker-app/?utm_source=rss&utm_medium=social&utm_campaign=darknetfeed

Hijacker – Reaver For Android Wifi Hacker App

Hijacker is a native GUI which provides Reaver for Android along with Aircrack-ng, Airodump-ng and MDK3 making it a powerful Wifi hacker app.

It offers a simple and easy UI to use these tools without typing commands in a console and copy & pasting MAC addresses.

Features of Hijacker Reaver For Android Wifi Hacker App
Information Gathering

  • View a list of access points and stations (clients) around you (even hidden ones)
  • View the activity of a specific network (by measuring beacons and data packets) and its clients
  • Statistics about access points and stations
  • See the manufacturer of a device (AP or station) from the OUI database
  • See the signal power of devices and filter the ones that are closer to you
  • Save captured packets in .cap file

Reaver for Android Wifi Cracker Attacks

  • Deauthenticate all the clients of a network (either targeting each one or without specific target)
  • Deauthenticate a specific client from the network it’s connected
  • MDK3 Beacon Flooding with custom options and SSID list
  • MDK3 Authentication DoS for a specific network or to every nearby AP
  • Capture a WPA handshake or gather IVs to crack a WEP network
  • Reaver WPS cracking (pixie-dust attack using NetHunter chroot and external adapter)

Other Wifi Hacker App Features

  • Leave the app running in the background, optionally with a notification
  • Copy commands or MAC addresses to clipboard
  • Includes the required tools, no need for manual installation
  • Includes the nexmon driver and management utility for BCM4339 devices
  • Set commands to enable and disable monitor mode automatically
  • Crack .cap files with a custom wordlist
  • Create custom actions and run them on an access point or a client easily
  • Sort and filter Access Points and Stations with many parameters
  • Export all gathered information to a file
  • Add a persistent alias to a device (by MAC) for easier identification

Requirements to Crack Wifi Password with Android

This application requires an ARM Android device with an internal wireless adapter that supports Monitor Mode.

Read the rest of Hijacker – Reaver For Android Wifi Hacker App now! Only available at Darknet.

Serverless @ re:Invent 2017

Post Syndicated from Chris Munns original https://aws.amazon.com/blogs/compute/serverless-reinvent-2017/

At re:Invent 2014, we announced AWS Lambda, what is now the center of the serverless platform at AWS, and helped ignite the trend of companies building serverless applications.

This year, at re:Invent 2017, the topic of serverless was everywhere. We were incredibly excited to see the energy from everyone attending 7 workshops, 15 chalk talks, 20 skills sessions and 27 breakout sessions. Many of these sessions were repeated due to high demand, so we are happy to summarize and provide links to the recordings and slides of these sessions.

Over the course of the week leading up to and then the week of re:Invent, we also had over 15 new features and capabilities across a number of serverless services, including AWS Lambda, Amazon API Gateway, AWS [email protected], AWS SAM, and the newly announced AWS Serverless Application Repository!

AWS Lambda

Amazon API Gateway

  • Amazon API Gateway Supports Endpoint Integrations with Private VPCs – You can now provide access to HTTP(S) resources within your VPC without exposing them directly to the public internet. This includes resources available over a VPN or Direct Connect connection!
  • Amazon API Gateway Supports Canary Release Deployments – You can now use canary release deployments to gradually roll out new APIs. This helps you more safely roll out API changes and limit the blast radius of new deployments.
  • Amazon API Gateway Supports Access Logging – The access logging feature lets you generate access logs in different formats such as CLF (Common Log Format), JSON, XML, and CSV. The access logs can be fed into your existing analytics or log processing tools so you can perform more in-depth analysis or take action in response to the log data.
  • Amazon API Gateway Customize Integration Timeouts – You can now set a custom timeout for your API calls as low as 50ms and as high as 29 seconds (the default is 30 seconds).
  • Amazon API Gateway Supports Generating SDK in Ruby – This is in addition to support for SDKs in Java, JavaScript, Android and iOS (Swift and Objective-C). The SDKs that Amazon API Gateway generates save you development time and come with a number of prebuilt capabilities, such as working with API keys, exponential back, and exception handling.

AWS Serverless Application Repository

Serverless Application Repository is a new service (currently in preview) that aids in the publication, discovery, and deployment of serverless applications. With it you’ll be able to find shared serverless applications that you can launch in your account, while also sharing ones that you’ve created for others to do the same.

AWS [email protected]

[email protected] now supports content-based dynamic origin selection, network calls from viewer events, and advanced response generation. This combination of capabilities greatly increases the use cases for [email protected], such as allowing you to send requests to different origins based on request information, showing selective content based on authentication, and dynamically watermarking images for each viewer.

AWS SAM

Twitch Launchpad live announcements

Other service announcements

Here are some of the other highlights that you might have missed. We think these could help you make great applications:

AWS re:Invent 2017 sessions

Coming up with the right mix of talks for an event like this can be quite a challenge. The Product, Marketing, and Developer Advocacy teams for Serverless at AWS spent weeks reading through dozens of talk ideas to boil it down to the final list.

From feedback at other AWS events and webinars, we knew that customers were looking for talks that focused on concrete examples of solving problems with serverless, how to perform common tasks such as deployment, CI/CD, monitoring, and troubleshooting, and to see customer and partner examples solving real world problems. To that extent we tried to settle on a good mix based on attendee experience and provide a track full of rich content.

Below are the recordings and slides of breakout sessions from re:Invent 2017. We’ve organized them for those getting started, those who are already beginning to build serverless applications, and the experts out there already running them at scale. Some of the videos and slides haven’t been posted yet, and so we will update this list as they become available.

Find the entire Serverless Track playlist on YouTube.

Talks for people new to Serverless

Advanced topics

Expert mode

Talks for specific use cases

Talks from AWS customers & partners

Looking to get hands-on with Serverless?

At re:Invent, we delivered instructor-led skills sessions to help attendees new to serverless applications get started quickly. The content from these sessions is already online and you can do the hands-on labs yourself!
Build a Serverless web application

Still looking for more?

We also recently completely overhauled the main Serverless landing page for AWS. This includes a new Resources page containing case studies, webinars, whitepapers, customer stories, reference architectures, and even more Getting Started tutorials. Check it out!

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!

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

timeShift(GrafanaBuzz, 1w) Issue 25

Post Syndicated from Blogs on Grafana Labs Blog original https://grafana.com/blog/2017/12/08/timeshiftgrafanabuzz-1w-issue-25/

Welcome to TimeShift

This week, a few of us from Grafana Labs, along with 4,000 of our closest friends, headed down to chilly Austin, TX for KubeCon + CloudNativeCon North America 2017. We got to see a number of great talks and were thrilled to see Grafana make appearances in some of the presentations. We were also a sponsor of the conference and handed out a ton of swag (we overnighted some of our custom Grafana scarves, which came in handy for Thursday’s snow).

We also announced Grafana Labs has joined the Cloud Native Computing Foundation as a Silver member! We’re excited to share our expertise in time series data visualization and open source software with the CNCF community.


Latest Release

Grafana 4.6.2 is available and includes some bug fixes:

  • Prometheus: Fixes bug with new Prometheus alerts in Grafana. Make sure to download this version if you’re using Prometheus for alerting. More details in the issue. #9777
  • Color picker: Bug after using textbox input field to change/paste color string #9769
  • Cloudwatch: build using golang 1.9.2 #9667, thanks @mtanda
  • Heatmap: Fixed tooltip for “time series buckets” mode #9332
  • InfluxDB: Fixed query editor issue when using > or < operators in WHERE clause #9871

Download Grafana 4.6.2 Now


From the Blogosphere

Grafana Labs Joins the CNCF: Grafana Labs has officially joined the Cloud Native Computing Foundation (CNCF). We look forward to working with the CNCF community to democratize metrics and help unify traditionally disparate information.

Automating Web Performance Regression Alerts: Peter and his team needed a faster and easier way to find web performance regressions at the Wikimedia Foundation. Grafana 4’s alerting features were exactly what they needed. This post covers their journey on setting up alerts for both RUM and synthetic testing and shares the alerts they’ve set up on their dashboards.

How To Install Grafana on Ubuntu 17.10: As you probably guessed from the title, this article walks you through installing and configuring Grafana in the latest version of Ubuntu (or earlier releases). It also covers installing plugins using the Grafana CLI tool.

Prometheus: Starting the Server with Alertmanager, cAdvisor and Grafana: Learn how to monitor Docker from scratch using cAdvisor, Prometheus and Grafana in this detailed, step-by-step walkthrough.

Monitoring Java EE Servers with Prometheus and Payara: In this screencast, Adam uses firehose; a Java EE 7+ metrics gateway for Prometheus, to convert the JSON output into Prometheus statistics and visualizes the data in Grafana.

Monitoring Spark Streaming with InfluxDB and Grafana: This article focuses on how to monitor Apache Spark Streaming applications with InfluxDB and Grafana at scale.


GrafanaCon EU, March 1-2, 2018

We are currently reaching out to everyone who submitted a talk to GrafanaCon and will soon publish the final schedule at grafanacon.org.

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.

Get Your Ticket Now


Grafana Plugins

Lots of plugin updates and a new OpenNMS Helm App plugin to announce! 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

OpenNMS Helm App – The new OpenNMS Helm App plugin replaces the old OpenNMS data source. Helm allows users to create flexible dashboards using both fault management (FM) and performance management (PM) data from OpenNMS® Horizon™ and/or OpenNMS® Meridian™. The old data source is now deprecated.


Install Now

UPDATED PLUGIN

PNP Data Source – This data source plugin (that uses PNP4Nagios to access RRD files) received a small, but important update that fixes template query parsing.


Update

UPDATED PLUGIN

Vonage Status Panel – The latest version of the Status Panel comes with a number of small fixes and changes. Below are a few of the enhancements:

  • Threshold settings – removed Show Always option, and replaced it with 2 options:
    • Display Alias – Select when to show the metric alias.
    • Display Value – Select when to show the metric value.
  • Text format configuration (bold / italic) for warning / critical / disabled states.
  • Option to change the corner radius of the panel. Now you can change the panel’s shape to have rounded corners.

Update

UPDATED PLUGIN

Google Calendar Plugin – This plugin received a small update, so be sure to install version 1.0.4.


Update

UPDATED PLUGIN

Carpet Plot Panel – The Carpet Plot Panel received a fix for IE 11, and also added the ability to choose custom colors.


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.

Docker Meetup @ Tuenti | Madrid, Spain – Dec 12, 2017: Javier Provecho: Intro to Metrics with Swarm, Prometheus and Grafana

Learn how to gain visibility in real time for your micro services. We’ll cover how to deploy a Prometheus server with persistence and Grafana, how to enable metrics endpoints for various service types (docker daemon, traefik proxy and postgres) and how to scrape, visualize and set up alarms based on those metrics.

RSVP

Grafana Lyon Meetup n ° 2 | Lyon, France – Dec 14, 2017: This meetup will cover some of the latest innovations in Grafana and discussion about automation. Also, free beer and chips, so – of course you’re going!

RSVP

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

We were thrilled to see our dashboards bigger than life at KubeCon + CloudNativeCon this week. Thanks for snapping a photo and sharing!


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?

Hard to believe this is the 25th issue of Timeshift! I have a blast writing these roundups, but Let me know what you think. 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!

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

Implementing Canary Deployments of AWS Lambda Functions with Alias Traffic Shifting

Post Syndicated from Chris Munns original https://aws.amazon.com/blogs/compute/implementing-canary-deployments-of-aws-lambda-functions-with-alias-traffic-shifting/

This post courtesy of Ryan Green, Software Development Engineer, AWS Serverless

The concepts of blue/green and canary deployments have been around for a while now and have been well-established as best-practices for reducing the risk of software deployments.

In a traditional, horizontally scaled application, copies of the application code are deployed to multiple nodes (instances, containers, on-premises servers, etc.), typically behind a load balancer. In these applications, deploying new versions of software to too many nodes at the same time can impact application availability as there may not be enough healthy nodes to service requests during the deployment. This aggressive approach to deployments also drastically increases the blast radius of software bugs introduced in the new version and does not typically give adequate time to safely assess the quality of the new version against production traffic.

In such applications, one commonly accepted solution to these problems is to slowly and incrementally roll out application software across the nodes in the fleet while simultaneously verifying application health (canary deployments). Another solution is to stand up an entirely different fleet and weight (or flip) traffic over to the new fleet after verification, ideally with some production traffic (blue/green). Some teams deploy to a single host (“one box environment”), where the new release can bake for some time before promotion to the rest of the fleet. Techniques like this enable the maintainers of complex systems to safely test in production while minimizing customer impact.

Enter Serverless

There is somewhat of an impedance mismatch when mapping these concepts to a serverless world. You can’t incrementally deploy your software across a fleet of servers when there are no servers!* In fact, even the term “deployment” takes on a different meaning with functions as a service (FaaS). In AWS Lambda, a “deployment” can be roughly modeled as a call to CreateFunction, UpdateFunctionCode, or UpdateAlias (I won’t get into the semantics of whether updating configuration counts as a deployment), all of which may affect the version of code that is invoked by clients.

The abstractions provided by Lambda remove the need for developers to be concerned about servers and Availability Zones, and this provides a powerful opportunity to greatly simplify the process of deploying software.
*Of course there are servers, but they are abstracted away from the developer.

Traffic shifting with Lambda aliases

Before the release of traffic shifting for Lambda aliases, deployments of a Lambda function could only be performed in a single “flip” by updating function code for version $LATEST, or by updating an alias to target a different function version. After the update propagates, typically within a few seconds, 100% of function invocations execute the new version. Implementing canary deployments with this model required the development of an additional routing layer, further adding development time, complexity, and invocation latency.
While rolling back a bad deployment of a Lambda function is a trivial operation and takes effect near instantaneously, deployments of new versions for critical functions can still be a potentially nerve-racking experience.

With the introduction of alias traffic shifting, it is now possible to trivially implement canary deployments of Lambda functions. By updating additional version weights on an alias, invocation traffic is routed to the new function versions based on the weight specified. Detailed CloudWatch metrics for the alias and version can be analyzed during the deployment, or other health checks performed, to ensure that the new version is healthy before proceeding.

Note: Sometimes the term “canary deployments” refers to the release of software to a subset of users. In the case of alias traffic shifting, the new version is released to some percentage of all users. It’s not possible to shard based on identity without adding an additional routing layer.

Examples

The simplest possible use of a canary deployment looks like the following:

# Update $LATEST version of function
aws lambda update-function-code --function-name myfunction ….

# Publish new version of function
aws lambda publish-version --function-name myfunction

# Point alias to new version, weighted at 5% (original version at 95% of traffic)
aws lambda update-alias --function-name myfunction --name myalias --routing-config '{"AdditionalVersionWeights" : {"2" : 0.05} }'

# Verify that the new version is healthy
…
# Set the primary version on the alias to the new version and reset the additional versions (100% weighted)
aws lambda update-alias --function-name myfunction --name myalias --function-version 2 --routing-config '{}'

This is begging to be automated! Here are a few options.

Simple deployment automation

This simple Python script runs as a Lambda function and deploys another function (how meta!) by incrementally increasing the weight of the new function version over a prescribed number of steps, while checking the health of the new version. If the health check fails, the alias is rolled back to its initial version. The health check is implemented as a simple check against the existence of Errors metrics in CloudWatch for the alias and new version.

GitHub aws-lambda-deploy repo

Install:

git clone https://github.com/awslabs/aws-lambda-deploy
cd aws-lambda-deploy
export BUCKET_NAME=[YOUR_S3_BUCKET_NAME_FOR_BUILD_ARTIFACTS]
./install.sh

Run:

# Rollout version 2 incrementally over 10 steps, with 120s between each step
aws lambda invoke --function-name SimpleDeployFunction --log-type Tail --payload \
  '{"function-name": "MyFunction",
  "alias-name": "MyAlias",
  "new-version": "2",
  "steps": 10,
  "interval" : 120,
  "type": "linear"
  }' output

Description of input parameters

  • function-name: The name of the Lambda function to deploy
  • alias-name: The name of the alias used to invoke the Lambda function
  • new-version: The version identifier for the new version to deploy
  • steps: The number of times the new version weight is increased
  • interval: The amount of time (in seconds) to wait between weight updates
  • type: The function to use to generate the weights. Supported values: “linear”

Because this runs as a Lambda function, it is subject to the maximum timeout of 5 minutes. This may be acceptable for many use cases, but to achieve a slower rollout of the new version, a different solution is required.

Step Functions workflow

This state machine performs essentially the same task as the simple deployment function, but it runs as an asynchronous workflow in AWS Step Functions. A nice property of Step Functions is that the maximum deployment timeout has now increased from 5 minutes to 1 year!

The step function incrementally updates the new version weight based on the steps parameter, waiting for some time based on the interval parameter, and performing health checks between updates. If the health check fails, the alias is rolled back to the original version and the workflow fails.

For example, to execute the workflow:

export STATE_MACHINE_ARN=`aws cloudformation describe-stack-resources --stack-name aws-lambda-deploy-stack --logical-resource-id DeployStateMachine --output text | cut  -d$'\t' -f3`

aws stepfunctions start-execution --state-machine-arn $STATE_MACHINE_ARN --input '{
  "function-name": "MyFunction",
  "alias-name": "MyAlias",
  "new-version": "2",
  "steps": 10,
  "interval": 120,
  "type": "linear"}'

Getting feedback on the deployment

Because the state machine runs asynchronously, retrieving feedback on the deployment requires polling for the execution status using DescribeExecution or implementing an asynchronous notification (using SNS or email, for example) from the Rollback or Finalize functions. A CloudWatch alarm could also be created to alarm based on the “ExecutionsFailed” metric for the state machine.

A note on health checks and observability

Weighted rollouts like this are considerably more successful if the code is being exercised and monitored continuously. In this example, it would help to have some automation continuously invoking the alias and reporting metrics on these invocations, such as client-side success rates and latencies.

The absence of Lambda Errors metrics used in these examples can be misleading if the function is not getting invoked. It’s also recommended to instrument your Lambda functions with custom metrics, in addition to Lambda’s built-in metrics, that can be used to monitor health during deployments.

Extensibility

These examples could be easily extended in various ways to support different use cases. For example:

  • Health check implementations: CloudWatch alarms, automatic invocations with payload assertions, querying external systems, etc.
  • Weight increase functions: Exponential, geometric progression, single canary step, etc.
  • Custom success/failure notifications: SNS, email, CI/CD systems, service discovery systems, etc.

Traffic shifting with SAM and CodeDeploy

Using the Lambda UpdateAlias operation with additional version weights provides a powerful primitive for you to implement custom traffic shifting solutions for Lambda functions.

For those not interested in building custom deployment solutions, AWS CodeDeploy provides an intuitive turn-key implementation of this functionality integrated directly into the Serverless Application Model. Traffic-shifted deployments can be declared in a SAM template, and CodeDeploy manages the function rollout as part of the CloudFormation stack update. CloudWatch alarms can also be configured to trigger a stack rollback if something goes wrong.

i.e.

MyFunction:
  Type: AWS::Serverless::Function
  Properties:
    FunctionName: MyFunction
    AutoPublishAlias: MyFunctionInvokeAlias
    DeploymentPreference:
      Type: Linear10PercentEvery1Minute
      Role:
        Fn::GetAtt: [ DeploymentRole, Arn ]
      Alarms:
       - { Ref: MyFunctionErrorsAlarm }
...

For more information about using CodeDeploy with SAM, see Automating Updates to Serverless Apps.

Conclusion

It is often the simple features that provide the most value. As I demonstrated in this post, serverless architectures allow the complex deployment orchestration used in traditional applications to be replaced with a simple Lambda function or Step Functions workflow. By allowing invocation traffic to be easily weighted to multiple function versions, Lambda alias traffic shifting provides a simple but powerful feature that I hope empowers you to easily implement safe deployment workflows for your Lambda functions.

Using AWS Step Functions State Machines to Handle Workflow-Driven AWS CodePipeline Actions

Post Syndicated from Marcilio Mendonca original https://aws.amazon.com/blogs/devops/using-aws-step-functions-state-machines-to-handle-workflow-driven-aws-codepipeline-actions/

AWS CodePipeline is a continuous integration and continuous delivery service for fast and reliable application and infrastructure updates. It offers powerful integration with other AWS services, such as AWS CodeBuildAWS CodeDeployAWS CodeCommit, AWS CloudFormation and with third-party tools such as Jenkins and GitHub. These services make it possible for AWS customers to successfully automate various tasks, including infrastructure provisioning, blue/green deployments, serverless deployments, AMI baking, database provisioning, and release management.

Developers have been able to use CodePipeline to build sophisticated automation pipelines that often require a single CodePipeline action to perform multiple tasks, fork into different execution paths, and deal with asynchronous behavior. For example, to deploy a Lambda function, a CodePipeline action might first inspect the changes pushed to the code repository. If only the Lambda code has changed, the action can simply update the Lambda code package, create a new version, and point the Lambda alias to the new version. If the changes also affect infrastructure resources managed by AWS CloudFormation, the pipeline action might have to create a stack or update an existing one through the use of a change set. In addition, if an update is required, the pipeline action might enforce a safety policy to infrastructure resources that prevents the deletion and replacement of resources. You can do this by creating a change set and having the pipeline action inspect its changes before updating the stack. Change sets that do not conform to the policy are deleted.

This use case is a good illustration of workflow-driven pipeline actions. These are actions that run multiple tasks, deal with async behavior and loops, need to maintain and propagate state, and fork into different execution paths. Implementing workflow-driven actions directly in CodePipeline can lead to complex pipelines that are hard for developers to understand and maintain. Ideally, a pipeline action should perform a single task and delegate the complexity of dealing with workflow-driven behavior associated with that task to a state machine engine. This would make it possible for developers to build simpler, more intuitive pipelines and allow them to use state machine execution logs to visualize and troubleshoot their pipeline actions.

In this blog post, we discuss how AWS Step Functions state machines can be used to handle workflow-driven actions. We show how a CodePipeline action can trigger a Step Functions state machine and how the pipeline and the state machine are kept decoupled through a Lambda function. The advantages of using state machines include:

  • Simplified logic (complex tasks are broken into multiple smaller tasks).
  • Ease of handling asynchronous behavior (through state machine wait states).
  • Built-in support for choices and processing different execution paths (through state machine choices).
  • Built-in visualization and logging of the state machine execution.

The source code for the sample pipeline, pipeline actions, and state machine used in this post is available at https://github.com/awslabs/aws-codepipeline-stepfunctions.

Overview

This figure shows the components in the CodePipeline-Step Functions integration that will be described in this post. The pipeline contains two stages: a Source stage represented by a CodeCommit Git repository and a Prod stage with a single Deploy action that represents the workflow-driven action.

This action invokes a Lambda function (1) called the State Machine Trigger Lambda, which, in turn, triggers a Step Function state machine to process the request (2). The Lambda function sends a continuation token back to the pipeline (3) to continue its execution later and terminates. Seconds later, the pipeline invokes the Lambda function again (4), passing the continuation token received. The Lambda function checks the execution state of the state machine (5,6) and communicates the status to the pipeline. The process is repeated until the state machine execution is complete. Then the Lambda function notifies the pipeline that the corresponding pipeline action is complete (7). If the state machine has failed, the Lambda function will then fail the pipeline action and stop its execution (7). While running, the state machine triggers various Lambda functions to perform different tasks. The state machine and the pipeline are fully decoupled. Their interaction is handled by the Lambda function.

The Deploy State Machine

The sample state machine used in this post is a simplified version of the use case, with emphasis on infrastructure deployment. The state machine will follow distinct execution paths and thus have different outcomes, depending on:

  • The current state of the AWS CloudFormation stack.
  • The nature of the code changes made to the AWS CloudFormation template and pushed into the pipeline.

If the stack does not exist, it will be created. If the stack exists, a change set will be created and its resources inspected by the state machine. The inspection consists of parsing the change set results and detecting whether any resources will be deleted or replaced. If no resources are being deleted or replaced, the change set is allowed to be executed and the state machine completes successfully. Otherwise, the change set is deleted and the state machine completes execution with a failure as the terminal state.

Let’s dive into each of these execution paths.

Path 1: Create a Stack and Succeed Deployment

The Deploy state machine is shown here. It is triggered by the Lambda function using the following input parameters stored in an S3 bucket.

Create New Stack Execution Path

{
    "environmentName": "prod",
    "stackName": "sample-lambda-app",
    "templatePath": "infra/Lambda-template.yaml",
    "revisionS3Bucket": "codepipeline-us-east-1-418586629775",
    "revisionS3Key": "StepFunctionsDrivenD/CodeCommit/sjcmExZ"
}

Note that some values used here are for the use case example only. Account-specific parameters like revisionS3Bucket and revisionS3Key will be different when you deploy this use case in your account.

These input parameters are used by various states in the state machine and passed to the corresponding Lambda functions to perform different tasks. For example, stackName is used to create a stack, check the status of stack creation, and create a change set. The environmentName represents the environment (for example, dev, test, prod) to which the code is being deployed. It is used to prefix the name of stacks and change sets.

With the exception of built-in states such as wait and choice, each state in the state machine invokes a specific Lambda function.  The results received from the Lambda invocations are appended to the state machine’s original input. When the state machine finishes its execution, several parameters will have been added to its original input.

The first stage in the state machine is “Check Stack Existence”. It checks whether a stack with the input name specified in the stackName input parameter already exists. The output of the state adds a Boolean value called doesStackExist to the original state machine input as follows:

{
  "doesStackExist": true,
  "environmentName": "prod",
  "stackName": "sample-lambda-app",
  "templatePath": "infra/lambda-template.yaml",
  "revisionS3Bucket": "codepipeline-us-east-1-418586629775",
  "revisionS3Key": "StepFunctionsDrivenD/CodeCommit/sjcmExZ",
}

The following stage, “Does Stack Exist?”, is represented by Step Functions built-in choice state. It checks the value of doesStackExist to determine whether a new stack needs to be created (doesStackExist=true) or a change set needs to be created and inspected (doesStackExist=false).

If the stack does not exist, the states illustrated in green in the preceding figure are executed. This execution path creates the stack, waits until the stack is created, checks the status of the stack’s creation, and marks the deployment successful after the stack has been created. Except for “Stack Created?” and “Wait Stack Creation,” each of these stages invokes a Lambda function. “Stack Created?” and “Wait Stack Creation” are implemented by using the built-in choice state (to decide which path to follow) and the wait state (to wait a few seconds before proceeding), respectively. Each stage adds the results of their Lambda function executions to the initial input of the state machine, allowing future stages to process them.

Path 2: Safely Update a Stack and Mark Deployment as Successful

Safely Update a Stack and Mark Deployment as Successful Execution Path

If the stack indicated by the stackName parameter already exists, a different path is executed. (See the green states in the figure.) This path will create a change set and use wait and choice states to wait until the change set is created. Afterwards, a stage in the execution path will inspect  the resources affected before the change set is executed.

The inspection procedure represented by the “Inspect Change Set Changes” stage consists of parsing the resources affected by the change set and checking whether any of the existing resources are being deleted or replaced. The following is an excerpt of the algorithm, where changeSetChanges.Changes is the object representing the change set changes:

...
var RESOURCES_BEING_DELETED_OR_REPLACED = "RESOURCES-BEING-DELETED-OR-REPLACED";
var CAN_SAFELY_UPDATE_EXISTING_STACK = "CAN-SAFELY-UPDATE-EXISTING-STACK";
for (var i = 0; i < changeSetChanges.Changes.length; i++) {
    var change = changeSetChanges.Changes[i];
    if (change.Type == "Resource") {
        if (change.ResourceChange.Action == "Delete") {
            return RESOURCES_BEING_DELETED_OR_REPLACED;
        }
        if (change.ResourceChange.Action == "Modify") {
            if (change.ResourceChange.Replacement == "True") {
                return RESOURCES_BEING_DELETED_OR_REPLACED;
            }
        }
    }
}
return CAN_SAFELY_UPDATE_EXISTING_STACK;

The algorithm returns different values to indicate whether the change set can be safely executed (CAN_SAFELY_UPDATE_EXISTING_STACK or RESOURCES_BEING_DELETED_OR_REPLACED). This value is used later by the state machine to decide whether to execute the change set and update the stack or interrupt the deployment.

The output of the “Inspect Change Set” stage is shown here.

{
  "environmentName": "prod",
  "stackName": "sample-lambda-app",
  "templatePath": "infra/lambda-template.yaml",
  "revisionS3Bucket": "codepipeline-us-east-1-418586629775",
  "revisionS3Key": "StepFunctionsDrivenD/CodeCommit/sjcmExZ",
  "doesStackExist": true,
  "changeSetName": "prod-sample-lambda-app-change-set-545",
  "changeSetCreationStatus": "complete",
  "changeSetAction": "CAN-SAFELY-UPDATE-EXISTING-STACK"
}

At this point, these parameters have been added to the state machine’s original input:

  • changeSetName, which is added by the “Create Change Set” state.
  • changeSetCreationStatus, which is added by the “Get Change Set Creation Status” state.
  • changeSetAction, which is added by the “Inspect Change Set Changes” state.

The “Safe to Update Infra?” step is a choice state (its JSON spec follows) that simply checks the value of the changeSetAction parameter. If the value is equal to “CAN-SAFELY-UPDATE-EXISTING-STACK“, meaning that no resources will be deleted or replaced, the step will execute the change set by proceeding to the “Execute Change Set” state. The deployment is successful (the state machine completes its execution successfully).

"Safe to Update Infra?": {
      "Type": "Choice",
      "Choices": [
        {
          "Variable": "$.taskParams.changeSetAction",
          "StringEquals": "CAN-SAFELY-UPDATE-EXISTING-STACK",
          "Next": "Execute Change Set"
        }
      ],
      "Default": "Deployment Failed"
 }

Path 3: Reject Stack Update and Fail Deployment

Reject Stack Update and Fail Deployment Execution Path

If the changeSetAction parameter is different from “CAN-SAFELY-UPDATE-EXISTING-STACK“, the state machine will interrupt the deployment by deleting the change set and proceeding to the “Deployment Fail” step, which is a built-in Fail state. (Its JSON spec follows.) This state causes the state machine to stop in a failed state and serves to indicate to the Lambda function that the pipeline deployment should be interrupted in a fail state as well.

 "Deployment Failed": {
      "Type": "Fail",
      "Cause": "Deployment Failed",
      "Error": "Deployment Failed"
    }

In all three scenarios, there’s a state machine’s visual representation available in the AWS Step Functions console that makes it very easy for developers to identify what tasks have been executed or why a deployment has failed. Developers can also inspect the inputs and outputs of each state and look at the state machine Lambda function’s logs for details. Meanwhile, the corresponding CodePipeline action remains very simple and intuitive for developers who only need to know whether the deployment was successful or failed.

The State Machine Trigger Lambda Function

The Trigger Lambda function is invoked directly by the Deploy action in CodePipeline. The CodePipeline action must pass a JSON structure to the trigger function through the UserParameters attribute, as follows:

{
  "s3Bucket": "codepipeline-StepFunctions-sample",
  "stateMachineFile": "state_machine_input.json"
}

The s3Bucket parameter specifies the S3 bucket location for the state machine input parameters file. The stateMachineFile parameter specifies the file holding the input parameters. By being able to specify different input parameters to the state machine, we make the Trigger Lambda function and the state machine reusable across environments. For example, the same state machine could be called from a test and prod pipeline action by specifying a different S3 bucket or state machine input file for each environment.

The Trigger Lambda function performs two main tasks: triggering the state machine and checking the execution state of the state machine. Its core logic is shown here:

exports.index = function (event, context, callback) {
    try {
        console.log("Event: " + JSON.stringify(event));
        console.log("Context: " + JSON.stringify(context));
        console.log("Environment Variables: " + JSON.stringify(process.env));
        if (Util.isContinuingPipelineTask(event)) {
            monitorStateMachineExecution(event, context, callback);
        }
        else {
            triggerStateMachine(event, context, callback);
        }
    }
    catch (err) {
        failure(Util.jobId(event), callback, context.invokeid, err.message);
    }
}

Util.isContinuingPipelineTask(event) is a utility function that checks if the Trigger Lambda function is being called for the first time (that is, no continuation token is passed by CodePipeline) or as a continuation of a previous call. In its first execution, the Lambda function will trigger the state machine and send a continuation token to CodePipeline that contains the state machine execution ARN. The state machine ARN is exposed to the Lambda function through a Lambda environment variable called stateMachineArn. Here is the code that triggers the state machine:

function triggerStateMachine(event, context, callback) {
    var stateMachineArn = process.env.stateMachineArn;
    var s3Bucket = Util.actionUserParameter(event, "s3Bucket");
    var stateMachineFile = Util.actionUserParameter(event, "stateMachineFile");
    getStateMachineInputData(s3Bucket, stateMachineFile)
        .then(function (data) {
            var initialParameters = data.Body.toString();
            var stateMachineInputJSON = createStateMachineInitialInput(initialParameters, event);
            console.log("State machine input JSON: " + JSON.stringify(stateMachineInputJSON));
            return stateMachineInputJSON;
        })
        .then(function (stateMachineInputJSON) {
            return triggerStateMachineExecution(stateMachineArn, stateMachineInputJSON);
        })
        .then(function (triggerStateMachineOutput) {
            var continuationToken = { "stateMachineExecutionArn": triggerStateMachineOutput.executionArn };
            var message = "State machine has been triggered: " + JSON.stringify(triggerStateMachineOutput) + ", continuationToken: " + JSON.stringify(continuationToken);
            return continueExecution(Util.jobId(event), continuationToken, callback, message);
        })
        .catch(function (err) {
            console.log("Error triggering state machine: " + stateMachineArn + ", Error: " + err.message);
            failure(Util.jobId(event), callback, context.invokeid, err.message);
        })
}

The Trigger Lambda function fetches the state machine input parameters from an S3 file, triggers the execution of the state machine using the input parameters and the stateMachineArn environment variable, and signals to CodePipeline that the execution should continue later by passing a continuation token that contains the state machine execution ARN. In case any of these operations fail and an exception is thrown, the Trigger Lambda function will fail the pipeline immediately by signaling a pipeline failure through the putJobFailureResult CodePipeline API.

If the Lambda function is continuing a previous execution, it will extract the state machine execution ARN from the continuation token and check the status of the state machine, as shown here.

function monitorStateMachineExecution(event, context, callback) {
    var stateMachineArn = process.env.stateMachineArn;
    var continuationToken = JSON.parse(Util.continuationToken(event));
    var stateMachineExecutionArn = continuationToken.stateMachineExecutionArn;
    getStateMachineExecutionStatus(stateMachineExecutionArn)
        .then(function (response) {
            if (response.status === "RUNNING") {
                var message = "Execution: " + stateMachineExecutionArn + " of state machine: " + stateMachineArn + " is still " + response.status;
                return continueExecution(Util.jobId(event), continuationToken, callback, message);
            }
            if (response.status === "SUCCEEDED") {
                var message = "Execution: " + stateMachineExecutionArn + " of state machine: " + stateMachineArn + " has: " + response.status;
                return success(Util.jobId(event), callback, message);
            }
            // FAILED, TIMED_OUT, ABORTED
            var message = "Execution: " + stateMachineExecutionArn + " of state machine: " + stateMachineArn + " has: " + response.status;
            return failure(Util.jobId(event), callback, context.invokeid, message);
        })
        .catch(function (err) {
            var message = "Error monitoring execution: " + stateMachineExecutionArn + " of state machine: " + stateMachineArn + ", Error: " + err.message;
            failure(Util.jobId(event), callback, context.invokeid, message);
        });
}

If the state machine is in the RUNNING state, the Lambda function will send the continuation token back to the CodePipeline action. This will cause CodePipeline to call the Lambda function again a few seconds later. If the state machine has SUCCEEDED, then the Lambda function will notify the CodePipeline action that the action has succeeded. In any other case (FAILURE, TIMED-OUT, or ABORT), the Lambda function will fail the pipeline action.

This behavior is especially useful for developers who are building and debugging a new state machine because a bug in the state machine can potentially leave the pipeline action hanging for long periods of time until it times out. The Trigger Lambda function prevents this.

Also, by having the Trigger Lambda function as a means to decouple the pipeline and state machine, we make the state machine more reusable. It can be triggered from anywhere, not just from a CodePipeline action.

The Pipeline in CodePipeline

Our sample pipeline contains two simple stages: the Source stage represented by a CodeCommit Git repository and the Prod stage, which contains the Deploy action that invokes the Trigger Lambda function. When the state machine decides that the change set created must be rejected (because it replaces or deletes some the existing production resources), it fails the pipeline without performing any updates to the existing infrastructure. (See the failed Deploy action in red.) Otherwise, the pipeline action succeeds, indicating that the existing provisioned infrastructure was either created (first run) or updated without impacting any resources. (See the green Deploy stage in the pipeline on the left.)

The Pipeline in CodePipeline

The JSON spec for the pipeline’s Prod stage is shown here. We use the UserParameters attribute to pass the S3 bucket and state machine input file to the Lambda function. These parameters are action-specific, which means that we can reuse the state machine in another pipeline action.

{
  "name": "Prod",
  "actions": [
      {
          "inputArtifacts": [
              {
                  "name": "CodeCommitOutput"
              }
          ],
          "name": "Deploy",
          "actionTypeId": {
              "category": "Invoke",
              "owner": "AWS",
              "version": "1",
              "provider": "Lambda"
          },
          "outputArtifacts": [],
          "configuration": {
              "FunctionName": "StateMachineTriggerLambda",
              "UserParameters": "{\"s3Bucket\": \"codepipeline-StepFunctions-sample\", \"stateMachineFile\": \"state_machine_input.json\"}"
          },
          "runOrder": 1
      }
  ]
}

Conclusion

In this blog post, we discussed how state machines in AWS Step Functions can be used to handle workflow-driven actions. We showed how a Lambda function can be used to fully decouple the pipeline and the state machine and manage their interaction. The use of a state machine greatly simplified the associated CodePipeline action, allowing us to build a much simpler and cleaner pipeline while drilling down into the state machine’s execution for troubleshooting or debugging.

Here are two exercises you can complete by using the source code.

Exercise #1: Do not fail the state machine and pipeline action after inspecting a change set that deletes or replaces resources. Instead, create a stack with a different name (think of blue/green deployments). You can do this by creating a state machine transition between the “Safe to Update Infra?” and “Create Stack” stages and passing a new stack name as input to the “Create Stack” stage.

Exercise #2: Add wait logic to the state machine to wait until the change set completes its execution before allowing the state machine to proceed to the “Deployment Succeeded” stage. Use the stack creation case as an example. You’ll have to create a Lambda function (similar to the Lambda function that checks the creation status of a stack) to get the creation status of the change set.

Have fun and share your thoughts!

About the Author

Marcilio Mendonca is a Sr. Consultant in the Canadian Professional Services Team at Amazon Web Services. He has helped AWS customers design, build, and deploy best-in-class, cloud-native AWS applications using VMs, containers, and serverless architectures. Before he joined AWS, Marcilio was a Software Development Engineer at Amazon. Marcilio also holds a Ph.D. in Computer Science. In his spare time, he enjoys playing drums, riding his motorcycle in the Toronto GTA area, and spending quality time with his family.

VHostScan – Virtual Host Scanner With Alias & Catch-All Detection

Post Syndicated from Darknet original https://www.darknet.org.uk/2017/10/vhostscan-virtual-host-scanner-with-alias-catch-all-detection/?utm_source=rss&utm_medium=social&utm_campaign=darknetfeed

VHostScan – Virtual Host Scanner With Alias & Catch-All Detection

VHostScan is a Python-based virtual host scanner that can be used with pivot tools, detect catch-all scenarios, aliases and dynamic default pages.

Features of VHostScan Virtual Host Scanner

  • Quickly highlight unique content in catch-all scenarios
  • Locate the outliers in catch-all scenarios where results have dynamic content on the page (such as the time)
  • Identify aliases by tweaking the unique depth of matches
  • Wordlist supports standard words and a variable to input a base hostname (for e.g.

Read the rest of VHostScan – Virtual Host Scanner With Alias & Catch-All Detection now! Only available at Darknet.

Department of Homeland Security to Collect Social Media of Immigrants and Citizens

Post Syndicated from Bruce Schneier original https://www.schneier.com/blog/archives/2017/09/department_of_h_3.html

New rules give the DHS permission to collect “social media handles, aliases, associated identifiable information, and search results” as part of people’s immigration file. The Federal Register has the details, which seems to also include US citizens that communicate with immigrants.

This is part of the general trend to srcrutinize people coming into the US more, but it’s hard to get too worked up about the DHS accessing publicly available information. More disturbing is the trend of occasonally asking for social media passwords at the border.

TVAddons and ZemTV Operators Named in US Lawsuit

Post Syndicated from Ernesto original https://torrentfreak.com/tvaddons-and-zemtv-operators-named-in-us-lawsuit-170926/

Earlier this year, American satellite and broadcast provider Dish Network targeted two well-known players in the third-party Kodi add-on ecosystem.

In a complaint filed in a federal court in Texas, add-on ZemTV and the TVAddons library were accused of copyright infringement. As a result, both are facing up to $150,000 for each offense.

Initially, the true identities of the defendants unknown and listed as John Does, but an amended complaint that was submitted yesterday reveal their alleged names and hometowns.

The Texas court previously granted subpoenas which allowed Dish to request information from the defendants’ accounts on services including Amazon, Github, Google, Twitter, Facebook and PayPal, which likely helped with the identification.

According to Dish ZemTV was developed by Shahjahan Durrani, who’s based in London, UK. He allegedly controlled and maintained the addon which was used to stream infringing broadcasts of Dish content.

“Durrani developed the ZemTV add-on and managed and operated the ZemTV service. Durrani used the aliases ‘Shani’ and ‘Shani_08′ to communicate with users of the ZemTV service,” the complaint reads.

The owner and operator of TVAddons is listed as Adam Lackman, who resides in Montreal, Canada. This doesn’t really come as a surprise, since Lackman is publicly listed as TVAddons’ owner on Linkedin and was previously named in a Canadian lawsuit.

While both defendants are named, the allegations against them haven’t changed substantially. Both face copyright infringement charges and potentially risk millions of dollars in damages.

Durrani directly infringed Dish’s copyrights by making the streams available, the plaintiffs note. Lackman subsequently profited from this and failed to take any action in response.

“Lackman had the legal right and actual ability to supervise and control this infringing activity because Lackman made the ZemTV add-on, which is necessary to access the ZemTV service, available for download on his websites.

“Lackman refused to take any action to stop the infringement of DISH’s exclusive rights in the programs transmitted through the ZemTV service,” the complaint adds.

TorrentFreak spoke to a TVAddons representative who refutes the copyright infringement allegations. The website sees itself as a platform for user-generated content and cites the DMCA’s safe harbor as a defense.

“TV ADDONS is not a piracy site, it’s a platform for developers of open source add-ons for the Kodi media center. As a community platform filled with user-generated content, we have always acted in accordance with the law and swiftly complied whenever we received a DMCA takedown notice.”

The representative states that it will be very difficult for them to defend themselves against a billion dollar company with unlimited resources, but hopes that the site will prevail.

The new TVAddons

After the original TVAddons.ag domain was seized in the Canadian lawsuit the site returned on TVaddons.co. However, hundreds of allegedly infringing add-ons are no longer listed.

The site previously relied on the DMCA to shield it from liability but apparently, that wasn’t enough. As a result, they now check all submitted add-ons carefully.

“Since complying with the law is clearly not enough to prevent frivolous legal action from being taken against you, we have been forced to implement a more drastic code vetting process,” the TVAddons representative says.

If it’s not entirely clear that an add-on is properly licensed, it won’t be submitted for the time being. This hampers innovation, according to TVAddons, and threatens many communities that rely on user-generated content.

“When you visit any given web site, how can you be certain that every piece of media you see is licensed by the website displaying it? You can assume, but it’s very difficult to be certain. That’s why the DMCA is critical to the existence of online communities.”

Now that both defendants have been named the case will move forward. This may eventually lead to an in-depth discovery process where Dish will try to find more proof that both were knowingly engaging in infringing activity.

Durrani and Lackman, on the other hand, will try to prove their innocence.

A copy of the amended complaint is available here (pdf).

Source: TF, for the latest info on copyright, file-sharing, torrent sites and ANONYMOUS VPN services.

How to Configure an LDAPS Endpoint for Simple AD

Post Syndicated from Cameron Worrell original https://aws.amazon.com/blogs/security/how-to-configure-an-ldaps-endpoint-for-simple-ad/

Simple AD, which is powered by Samba  4, supports basic Active Directory (AD) authentication features such as users, groups, and the ability to join domains. Simple AD also includes an integrated Lightweight Directory Access Protocol (LDAP) server. LDAP is a standard application protocol for the access and management of directory information. You can use the BIND operation from Simple AD to authenticate LDAP client sessions. This makes LDAP a common choice for centralized authentication and authorization for services such as Secure Shell (SSH), client-based virtual private networks (VPNs), and many other applications. Authentication, the process of confirming the identity of a principal, typically involves the transmission of highly sensitive information such as user names and passwords. To protect this information in transit over untrusted networks, companies often require encryption as part of their information security strategy.

In this blog post, we show you how to configure an LDAPS (LDAP over SSL/TLS) encrypted endpoint for Simple AD so that you can extend Simple AD over untrusted networks. Our solution uses Elastic Load Balancing (ELB) to send decrypted LDAP traffic to HAProxy running on Amazon EC2, which then sends the traffic to Simple AD. ELB offers integrated certificate management, SSL/TLS termination, and the ability to use a scalable EC2 backend to process decrypted traffic. ELB also tightly integrates with Amazon Route 53, enabling you to use a custom domain for the LDAPS endpoint. The solution needs the intermediate HAProxy layer because ELB can direct traffic only to EC2 instances. To simplify testing and deployment, we have provided an AWS CloudFormation template to provision the ELB and HAProxy layers.

This post assumes that you have an understanding of concepts such as Amazon Virtual Private Cloud (VPC) and its components, including subnets, routing, Internet and network address translation (NAT) gateways, DNS, and security groups. You should also be familiar with launching EC2 instances and logging in to them with SSH. If needed, you should familiarize yourself with these concepts and review the solution overview and prerequisites in the next section before proceeding with the deployment.

Note: This solution is intended for use by clients requiring an LDAPS endpoint only. If your requirements extend beyond this, you should consider accessing the Simple AD servers directly or by using AWS Directory Service for Microsoft AD.

Solution overview

The following diagram and description illustrates and explains the Simple AD LDAPS environment. The CloudFormation template creates the items designated by the bracket (internal ELB load balancer and two HAProxy nodes configured in an Auto Scaling group).

Diagram of the the Simple AD LDAPS environment

Here is how the solution works, as shown in the preceding numbered diagram:

  1. The LDAP client sends an LDAPS request to ELB on TCP port 636.
  2. ELB terminates the SSL/TLS session and decrypts the traffic using a certificate. ELB sends the decrypted LDAP traffic to the EC2 instances running HAProxy on TCP port 389.
  3. The HAProxy servers forward the LDAP request to the Simple AD servers listening on TCP port 389 in a fixed Auto Scaling group configuration.
  4. The Simple AD servers send an LDAP response through the HAProxy layer to ELB. ELB encrypts the response and sends it to the client.

Note: Amazon VPC prevents a third party from intercepting traffic within the VPC. Because of this, the VPC protects the decrypted traffic between ELB and HAProxy and between HAProxy and Simple AD. The ELB encryption provides an additional layer of security for client connections and protects traffic coming from hosts outside the VPC.

Prerequisites

  1. Our approach requires an Amazon VPC with two public and two private subnets. The previous diagram illustrates the environment’s VPC requirements. If you do not yet have these components in place, follow these guidelines for setting up a sample environment:
    1. Identify a region that supports Simple AD, ELB, and NAT gateways. The NAT gateways are used with an Internet gateway to allow the HAProxy instances to access the internet to perform their required configuration. You also need to identify the two Availability Zones in that region for use by Simple AD. You will supply these Availability Zones as parameters to the CloudFormation template later in this process.
    2. Create or choose an Amazon VPC in the region you chose. In order to use Route 53 to resolve the LDAPS endpoint, make sure you enable DNS support within your VPC. Create an Internet gateway and attach it to the VPC, which will be used by the NAT gateways to access the internet.
    3. Create a route table with a default route to the Internet gateway. Create two NAT gateways, one per Availability Zone in your public subnets to provide additional resiliency across the Availability Zones. Together, the routing table, the NAT gateways, and the Internet gateway enable the HAProxy instances to access the internet.
    4. Create two private routing tables, one per Availability Zone. Create two private subnets, one per Availability Zone. The dual routing tables and subnets allow for a higher level of redundancy. Add each subnet to the routing table in the same Availability Zone. Add a default route in each routing table to the NAT gateway in the same Availability Zone. The Simple AD servers use subnets that you create.
    5. The LDAP service requires a DNS domain that resolves within your VPC and from your LDAP clients. If you do not have an existing DNS domain, follow the steps to create a private hosted zone and associate it with your VPC. To avoid encryption protocol errors, you must ensure that the DNS domain name is consistent across your Route 53 zone and in the SSL/TLS certificate (see Step 2 in the “Solution deployment” section).
  2. Make sure you have completed the Simple AD Prerequisites.
  3. We will use a self-signed certificate for ELB to perform SSL/TLS decryption. You can use a certificate issued by your preferred certificate authority or a certificate issued by AWS Certificate Manager (ACM).
    Note: To prevent unauthorized connections directly to your Simple AD servers, you can modify the Simple AD security group on port 389 to block traffic from locations outside of the Simple AD VPC. You can find the security group in the EC2 console by creating a search filter for your Simple AD directory ID. It is also important to allow the Simple AD servers to communicate with each other as shown on Simple AD Prerequisites.

Solution deployment

This solution includes five main parts:

  1. Create a Simple AD directory.
  2. Create a certificate.
  3. Create the ELB and HAProxy layers by using the supplied CloudFormation template.
  4. Create a Route 53 record.
  5. Test LDAPS access using an Amazon Linux client.

1. Create a Simple AD directory

With the prerequisites completed, you will create a Simple AD directory in your private VPC subnets:

  1. In the Directory Service console navigation pane, choose Directories and then choose Set up directory.
  2. Choose Simple AD.
    Screenshot of choosing "Simple AD"
  3. Provide the following information:
    • Directory DNS – The fully qualified domain name (FQDN) of the directory, such as corp.example.com. You will use the FQDN as part of the testing procedure.
    • NetBIOS name – The short name for the directory, such as CORP.
    • Administrator password – The password for the directory administrator. The directory creation process creates an administrator account with the user name Administrator and this password. Do not lose this password because it is nonrecoverable. You also need this password for testing LDAPS access in a later step.
    • Description – An optional description for the directory.
    • Directory Size – The size of the directory.
      Screenshot of the directory details to provide
  4. Provide the following information in the VPC Details section, and then choose Next Step:
    • VPC – Specify the VPC in which to install the directory.
    • Subnets – Choose two private subnets for the directory servers. The two subnets must be in different Availability Zones. Make a note of the VPC and subnet IDs for use as CloudFormation input parameters. In the following example, the Availability Zones are us-east-1a and us-east-1c.
      Screenshot of the VPC details to provide
  5. Review the directory information and make any necessary changes. When the information is correct, choose Create Simple AD.

It takes several minutes to create the directory. From the AWS Directory Service console , refresh the screen periodically and wait until the directory Status value changes to Active before continuing. Choose your Simple AD directory and note the two IP addresses in the DNS address section. You will enter them when you run the CloudFormation template later.

Note: Full administration of your Simple AD implementation is out of scope for this blog post. See the documentation to add users, groups, or instances to your directory. Also see the previous blog post, How to Manage Identities in Simple AD Directories.

2. Create a certificate

In the previous step, you created the Simple AD directory. Next, you will generate a self-signed SSL/TLS certificate using OpenSSL. You will use the certificate with ELB to secure the LDAPS endpoint. OpenSSL is a standard, open source library that supports a wide range of cryptographic functions, including the creation and signing of x509 certificates. You then import the certificate into ACM that is integrated with ELB.

  1. You must have a system with OpenSSL installed to complete this step. If you do not have OpenSSL, you can install it on Amazon Linux by running the command, sudo yum install openssl. If you do not have access to an Amazon Linux instance you can create one with SSH access enabled to proceed with this step. Run the command, openssl version, at the command line to see if you already have OpenSSL installed.
    [[email protected] ~]$ openssl version
    OpenSSL 1.0.1k-fips 8 Jan 2015

  2. Create a private key using the command, openssl genrsa command.
    [[email protected] tmp]$ openssl genrsa 2048 > privatekey.pem
    Generating RSA private key, 2048 bit long modulus
    ......................................................................................................................................................................+++
    ..........................+++
    e is 65537 (0x10001)

  3. Generate a certificate signing request (CSR) using the openssl req command. Provide the requested information for each field. The Common Name is the FQDN for your LDAPS endpoint (for example, ldap.corp.example.com). The Common Name must use the domain name you will later register in Route 53. You will encounter certificate errors if the names do not match.
    [[email protected] tmp]$ openssl req -new -key privatekey.pem -out server.csr
    You are about to be asked to enter information that will be incorporated into your certificate request.

  4. Use the openssl x509 command to sign the certificate. The following example uses the private key from the previous step (privatekey.pem) and the signing request (server.csr) to create a public certificate named server.crt that is valid for 365 days. This certificate must be updated within 365 days to avoid disruption of LDAPS functionality.
    [[email protected] tmp]$ openssl x509 -req -sha256 -days 365 -in server.csr -signkey privatekey.pem -out server.crt
    Signature ok
    subject=/C=XX/L=Default City/O=Default Company Ltd/CN=ldap.corp.example.com
    Getting Private key

  5. You should see three files: privatekey.pem, server.crt, and server.csr.
    [[email protected] tmp]$ ls
    privatekey.pem server.crt server.csr

    Restrict access to the private key.

    [[email protected] tmp]$ chmod 600 privatekey.pem

    Keep the private key and public certificate for later use. You can discard the signing request because you are using a self-signed certificate and not using a Certificate Authority. Always store the private key in a secure location and avoid adding it to your source code.

  6. In the ACM console, choose Import a certificate.
  7. Using your favorite Linux text editor, paste the contents of your server.crt file in the Certificate body box.
  8. Using your favorite Linux text editor, paste the contents of your privatekey.pem file in the Certificate private key box. For a self-signed certificate, you can leave the Certificate chain box blank.
  9. Choose Review and import. Confirm the information and choose Import.

3. Create the ELB and HAProxy layers by using the supplied CloudFormation template

Now that you have created your Simple AD directory and SSL/TLS certificate, you are ready to use the CloudFormation template to create the ELB and HAProxy layers.

  1. Load the supplied CloudFormation template to deploy an internal ELB and two HAProxy EC2 instances into a fixed Auto Scaling group. After you load the template, provide the following input parameters. Note: You can find the parameters relating to your Simple AD from the directory details page by choosing your Simple AD in the Directory Service console.
Input parameter Input parameter description
HAProxyInstanceSize The EC2 instance size for HAProxy servers. The default size is t2.micro and can scale up for large Simple AD environments.
MyKeyPair The SSH key pair for EC2 instances. If you do not have an existing key pair, you must create one.
VPCId The target VPC for this solution. Must be in the VPC where you deployed Simple AD and is available in your Simple AD directory details page.
SubnetId1 The Simple AD primary subnet. This information is available in your Simple AD directory details page.
SubnetId2 The Simple AD secondary subnet. This information is available in your Simple AD directory details page.
MyTrustedNetwork Trusted network Classless Inter-Domain Routing (CIDR) to allow connections to the LDAPS endpoint. For example, use the VPC CIDR to allow clients in the VPC to connect.
SimpleADPriIP The primary Simple AD Server IP. This information is available in your Simple AD directory details page.
SimpleADSecIP The secondary Simple AD Server IP. This information is available in your Simple AD directory details page.
LDAPSCertificateARN The Amazon Resource Name (ARN) for the SSL certificate. This information is available in the ACM console.
  1. Enter the input parameters and choose Next.
  2. On the Options page, accept the defaults and choose Next.
  3. On the Review page, confirm the details and choose Create. The stack will be created in approximately 5 minutes.

4. Create a Route 53 record

The next step is to create a Route 53 record in your private hosted zone so that clients can resolve your LDAPS endpoint.

  1. If you do not have an existing DNS domain for use with LDAP, create a private hosted zone and associate it with your VPC. The hosted zone name should be consistent with your Simple AD (for example, corp.example.com).
  2. When the CloudFormation stack is in CREATE_COMPLETE status, locate the value of the LDAPSURL on the Outputs tab of the stack. Copy this value for use in the next step.
  3. On the Route 53 console, choose Hosted Zones and then choose the zone you used for the Common Name box for your self-signed certificate. Choose Create Record Set and enter the following information:
    1. Name – The label of the record (such as ldap).
    2. Type – Leave as A – IPv4 address.
    3. Alias – Choose Yes.
    4. Alias Target – Paste the value of the LDAPSURL on the Outputs tab of the stack.
  4. Leave the defaults for Routing Policy and Evaluate Target Health, and choose Create.
    Screenshot of finishing the creation of the Route 53 record

5. Test LDAPS access using an Amazon Linux client

At this point, you have configured your LDAPS endpoint and now you can test it from an Amazon Linux client.

  1. Create an Amazon Linux instance with SSH access enabled to test the solution. Launch the instance into one of the public subnets in your VPC. Make sure the IP assigned to the instance is in the trusted IP range you specified in the CloudFormation parameter MyTrustedNetwork in Step 3.b.
  2. SSH into the instance and complete the following steps to verify access.
    1. Install the openldap-clients package and any required dependencies:
      sudo yum install -y openldap-clients.
    2. Add the server.crt file to the /etc/openldap/certs/ directory so that the LDAPS client will trust your SSL/TLS certificate. You can copy the file using Secure Copy (SCP) or create it using a text editor.
    3. Edit the /etc/openldap/ldap.conf file and define the environment variables BASE, URI, and TLS_CACERT.
      • The value for BASE should match the configuration of the Simple AD directory name.
      • The value for URI should match your DNS alias.
      • The value for TLS_CACERT is the path to your public certificate.

Here is an example of the contents of the file.

BASE dc=corp,dc=example,dc=com
URI ldaps://ldap.corp.example.com
TLS_CACERT /etc/openldap/certs/server.crt

To test the solution, query the directory through the LDAPS endpoint, as shown in the following command. Replace corp.example.com with your domain name and use the Administrator password that you configured with the Simple AD directory

$ ldapsearch -D "[email protected]corp.example.com" -W sAMAccountName=Administrator

You should see a response similar to the following response, which provides the directory information in LDAP Data Interchange Format (LDIF) for the administrator distinguished name (DN) from your Simple AD LDAP server.

# extended LDIF
#
# LDAPv3
# base <dc=corp,dc=example,dc=com> (default) with scope subtree
# filter: sAMAccountName=Administrator
# requesting: ALL
#

# Administrator, Users, corp.example.com
dn: CN=Administrator,CN=Users,DC=corp,DC=example,DC=com
objectClass: top
objectClass: person
objectClass: organizationalPerson
objectClass: user
description: Built-in account for administering the computer/domain
instanceType: 4
whenCreated: 20170721123204.0Z
uSNCreated: 3223
name: Administrator
objectGUID:: l3h0HIiKO0a/ShL4yVK/vw==
userAccountControl: 512
…

You can now use the LDAPS endpoint for directory operations and authentication within your environment. If you would like to learn more about how to interact with your LDAPS endpoint within a Linux environment, here are a few resources to get started:

Troubleshooting

If you receive an error such as the following error when issuing the ldapsearch command, there are a few things you can do to help identify issues.

ldap_sasl_bind(SIMPLE): Can't contact LDAP server (-1)
  • You might be able to obtain additional error details by adding the -d1 debug flag to the ldapsearch command in the previous section.
    $ ldapsearch -D "[email protected]" -W sAMAccountName=Administrator –d1

  • Verify that the parameters in ldap.conf match your configured LDAPS URI endpoint and that all parameters can be resolved by DNS. You can use the following dig command, substituting your configured endpoint DNS name.
    $ dig ldap.corp.example.com

  • Confirm that the client instance from which you are connecting is in the CIDR range of the CloudFormation parameter, MyTrustedNetwork.
  • Confirm that the path to your public SSL/TLS certificate configured in ldap.conf as TLS_CAERT is correct. You configured this in Step 5.b.3. You can check your SSL/TLS connection with the command, substituting your configured endpoint DNS name for the string after –connect.
    $ echo -n | openssl s_client -connect ldap.corp.example.com:636

  • Verify that your HAProxy instances have the status InService in the EC2 console: Choose Load Balancers under Load Balancing in the navigation pane, highlight your LDAPS load balancer, and then choose the Instances

Conclusion

You can use ELB and HAProxy to provide an LDAPS endpoint for Simple AD and transport sensitive authentication information over untrusted networks. You can explore using LDAPS to authenticate SSH users or integrate with other software solutions that support LDAP authentication. This solution’s CloudFormation template is available on GitHub.

If you have comments about this post, submit them in the “Comments” section below. If you have questions about or issues implementing this solution, start a new thread on the Directory Service forum.

– Cameron and Jeff