Tag Archives: source code

Storing Encrypted Credentials In Git

Post Syndicated from Bozho original https://techblog.bozho.net/storing-encrypted-credentials-in-git/

We all know that we should not commit any passwords or keys to the repo with our code (no matter if public or private). Yet, thousands of production passwords can be found on GitHub (and probably thousands more in internal company repositories). Some have tried to fix that by removing the passwords (once they learned it’s not a good idea to store them publicly), but passwords have remained in the git history.

Knowing what not to do is the first and very important step. But how do we store production credentials. Database credentials, system secrets (e.g. for HMACs), access keys for 3rd party services like payment providers or social networks. There doesn’t seem to be an agreed upon solution.

I’ve previously argued with the 12-factor app recommendation to use environment variables – if you have a few that might be okay, but when the number of variables grow (as in any real application), it becomes impractical. And you can set environment variables via a bash script, but you’d have to store it somewhere. And in fact, even separate environment variables should be stored somewhere.

This somewhere could be a local directory (risky), a shared storage, e.g. FTP or S3 bucket with limited access, or a separate git repository. I think I prefer the git repository as it allows versioning (Note: S3 also does, but is provider-specific). So you can store all your environment-specific properties files with all their credentials and environment-specific configurations in a git repo with limited access (only Ops people). And that’s not bad, as long as it’s not the same repo as the source code.

Such a repo would look like this:

project
└─── production
|   |   application.properites
|   |   keystore.jks
└─── staging
|   |   application.properites
|   |   keystore.jks
└─── on-premise-client1
|   |   application.properites
|   |   keystore.jks
└─── on-premise-client2
|   |   application.properites
|   |   keystore.jks

Since many companies are using GitHub or BitBucket for their repositories, storing production credentials on a public provider may still be risky. That’s why it’s a good idea to encrypt the files in the repository. A good way to do it is via git-crypt. It is “transparent” encryption because it supports diff and encryption and decryption on the fly. Once you set it up, you continue working with the repo as if it’s not encrypted. There’s even a fork that works on Windows.

You simply run git-crypt init (after you’ve put the git-crypt binary on your OS Path), which generates a key. Then you specify your .gitattributes, e.g. like that:

secretfile filter=git-crypt diff=git-crypt
*.key filter=git-crypt diff=git-crypt
*.properties filter=git-crypt diff=git-crypt
*.jks filter=git-crypt diff=git-crypt

And you’re done. Well, almost. If this is a fresh repo, everything is good. If it is an existing repo, you’d have to clean up your history which contains the unencrypted files. Following these steps will get you there, with one addition – before calling git commit, you should call git-crypt status -f so that the existing files are actually encrypted.

You’re almost done. We should somehow share and backup the keys. For the sharing part, it’s not a big issue to have a team of 2-3 Ops people share the same key, but you could also use the GPG option of git-crypt (as documented in the README). What’s left is to backup your secret key (that’s generated in the .git/git-crypt directory). You can store it (password-protected) in some other storage, be it a company shared folder, Dropbox/Google Drive, or even your email. Just make sure your computer is not the only place where it’s present and that it’s protected. I don’t think key rotation is necessary, but you can devise some rotation procedure.

git-crypt authors claim to shine when it comes to encrypting just a few files in an otherwise public repo. And recommend looking at git-remote-gcrypt. But as often there are non-sensitive parts of environment-specific configurations, you may not want to encrypt everything. And I think it’s perfectly fine to use git-crypt even in a separate repo scenario. And even though encryption is an okay approach to protect credentials in your source code repo, it’s still not necessarily a good idea to have the environment configurations in the same repo. Especially given that different people/teams manage these credentials. Even in small companies, maybe not all members have production access.

The outstanding questions in this case is – how do you sync the properties with code changes. Sometimes the code adds new properties that should be reflected in the environment configurations. There are two scenarios here – first, properties that could vary across environments, but can have default values (e.g. scheduled job periods), and second, properties that require explicit configuration (e.g. database credentials). The former can have the default values bundled in the code repo and therefore in the release artifact, allowing external files to override them. The latter should be announced to the people who do the deployment so that they can set the proper values.

The whole process of having versioned environment-speific configurations is actually quite simple and logical, even with the encryption added to the picture. And I think it’s a good security practice we should try to follow.

The post Storing Encrypted Credentials In Git appeared first on Bozho's tech blog.

Backing Up Github to Backblaze B2

Post Syndicated from Roderick Bauer original https://www.backblaze.com/blog/backing-up-github-to-cloud-storage/

Github to B2

Github is the largest host of source code in the world, with over 26 million users and 57 million code repositories. That’s a lot of code.

If you’re using Github, you want to make sure that your hard work isn’t lost. What happens if a repository or gist is removed or changed due to involuntary deletion, a forced push, or you lose metadata information? There are some paid backup services for Github, and the Github Enterprise product includes backup, but what if you don’t use either? What’s the best way to make sure there’s a secure copy of your code in a place other than Github and your local computer?

Our VP of Sales, and inveterate code tinkerer, decided he could tackle this problem, so he forked a Github script to work with our B2 Cloud Storage. The script is below. All you need to do is sign up for a B2 account on our website. The first 10 GB of data on B2 is free, so if your backup is under that size, congratulations, this backup is on us.

The script is on Github at: Complete git repository backup script to Backblaze B2

Credit to Philipp Berner for the script this was based on.

For more information about backing up Github, see Github Help.

Github logo

The post Backing Up Github to Backblaze B2 appeared first on Backblaze Blog | Cloud Storage & Cloud Backup.

How to rotate your Twitter API key and bearer token automatically with AWS Secrets Manager

Post Syndicated from Apurv Awasthi original https://aws.amazon.com/blogs/security/how-to-rotate-your-twitter-api-key-and-bearer-token-automatically-with-aws-secrets-manager/

Previously, I showed you how to rotate Amazon RDS database credentials automatically with AWS Secrets Manager. In addition to database credentials, AWS Secrets Manager makes it easier to rotate, manage, and retrieve API keys, OAuth tokens, and other secrets throughout their lifecycle. You can configure Secrets Manager to rotate these secrets automatically, which can help you meet your compliance needs. You can also use Secrets Manager to rotate secrets on demand, which can help you respond quickly to security events. In this post, I show you how to store an API key in Secrets Manager and use a custom Lambda function to rotate the key automatically. I’ll use a Twitter API key and bearer token as an example; you can reference this example to rotate other types of API keys.

The instructions are divided into four main phases:

  1. Store a Twitter API key and bearer token in Secrets Manager.
  2. Create a custom Lambda function to rotate the bearer token.
  3. Configure your application to retrieve the bearer token from Secrets Manager.
  4. Configure Secrets Manager to use the custom Lambda function to rotate the bearer token automatically.

For the purpose of this post, I use the placeholder Demo/Twitter_Api_Key to denote the API key, the placeholder Demo/Twitter_bearer_token to denote the bearer token, and placeholder Lambda_Rotate_Bearer_Token to denote the custom Lambda function. Be sure to replace these placeholders with the resource names from your account.

Phase 1: Store a Twitter API key and bearer token in Secrets Manager

Twitter enables developers to register their applications and retrieve an API key, which includes a consumer_key and consumer_secret. Developers use these to generate a bearer token that applications can then use to authenticate and retrieve information from Twitter. At any given point of time, you can use an API key to create only one valid bearer token.

Start by storing the API key in Secrets Manager. Here’s how:

  1. Open the Secrets Manager console and select Store a new secret.
     
    Figure 1: The "Store a new secret" button in the AWS Secrets Manager console

    Figure 1: The “Store a new secret” button in the AWS Secrets Manager console

  2. Select Other type of secrets (because you’re storing an API key).
  3. Input the consumer_key and consumer_secret, and then select Next.
     
    Figure 2: Select the consumer_key and the consumer_secret

    Figure 2: Select the consumer_key and the consumer_secret

  4. Specify values for Secret Name and Description, then select Next. For this example, I use Demo/Twitter_API_Key.
     
    Figure 3: Set values for "Secret Name" and "Description"

    Figure 3: Set values for “Secret Name” and “Description”

  5. On the next screen, keep the default setting, Disable automatic rotation, because you’ll use the same API key to rotate bearer tokens programmatically and automatically. Applications and employees will not retrieve this API key. Select Next.
     
    Figure 4: Keep the default "Disable automatic rotation" setting

    Figure 4: Keep the default “Disable automatic rotation” setting

  6. Review the information on the next screen and, if everything looks correct, select Store. You’ve now successfully stored a Twitter API key in Secrets Manager.

Next, store the bearer token in Secrets Manager. Here’s how:

  1. From the Secrets Manager console, select Store a new secret, select Other type of secrets, input details (access_token, token_type, and ARN of the API key) about the bearer token, and then select Next.
     
    Figure 5: Add details about the bearer token

    Figure 5: Add details about the bearer token

  2. Specify values for Secret Name and Description, and then select Next. For this example, I use Demo/Twitter_bearer_token.
     
    Figure 6: Again set values for "Secret Name" and "Description"

    Figure 6: Again set values for “Secret Name” and “Description”

  3. Keep the default rotation setting, Disable automatic rotation, and then select Next. You’ll enable rotation after you’ve updated the application to use Secrets Manager APIs to retrieve secrets.
  4. Review the information and select Store. You’ve now completed storing the bearer token in Secrets Manager.
    I take note of the sample code provided on the review page. I’ll use this code to update my application to retrieve the bearer token using Secrets Manager APIs.
     
    Figure 7: The sample code you can use in your app

    Figure 7: The sample code you can use in your app

Phase 2: Create a custom Lambda function to rotate the bearer token

While Secrets Manager supports rotating credentials for databases hosted on Amazon RDS natively, it also enables you to meet your unique rotation-related use cases by authoring custom Lambda functions. Now that you’ve stored the API key and bearer token, you’ll create a Lambda function to rotate the bearer token. For this example, I’ll create my Lambda function using Python 3.6.

  1. Open the Lambda console and select Create function.
     
    Figure 8: In the Lambda console, select "Create function"

    Figure 8: In the Lambda console, select “Create function”

  2. Select Author from scratch. For this example, I use the name Lambda_Rotate_Bearer_Token for my Lambda function. I also set the Runtime environment as Python 3.6.
     
    Figure 9: Create a new function from scratch

    Figure 9: Create a new function from scratch

  3. This Lambda function requires permissions to call AWS resources on your behalf. To grant these permissions, select Create a custom role. This opens a console tab.
  4. Select Create a new IAM Role and specify the value for Role Name. For this example, I use Role_Lambda_Rotate_Twitter_Bearer_Token.
     
    Figure 10: For "IAM Role," select "Create a new IAM role"

    Figure 10: For “IAM Role,” select “Create a new IAM role”

  5. Next, to define the IAM permissions, copy and paste the following IAM policy in the View Policy Document text-entry field. Be sure to replace the placeholder ARN-OF-Demo/Twitter_API_Key with the ARN of your secret.
    
{
    "Version": "2012-10-17",
    "Statement": [
        {
            “Sid”: “AllowLambdaToStoreAndRetrieveSecrets”
"Effect": "Allow",
            "Action": [
                "secretsmanager:DescribeSecret",
                "secretsmanager:GetSecretValue",
                "secretsmanager:PutSecretValue",
                "secretsmanager:UpdateSecretVersionStage"
            ],
            "Resource": "*"
        },
        {
            “Sid”: “GenerateARandomStringToExecuteRotation”
            "Effect": "Allow",
            "Action": [
                "secretsmanager:GetRandomPassword"
            ],
            "Resource": "*"
        },
        {
            “Sid”: “AccessToNetworkInterface”
            "Action": [
                "ec2:CreateNetworkInterface",
                "ec2:DeleteNetworkInterface",
                "ec2:DescribeNetworkInterfaces",
                "ec2:DetachNetworkInterface"
            ],
            "Resource": "*",
            "Effect": "Allow"
        },
        {
            “Sid”: “RecordProgressInLogsForEasierDebugging”
            "Effect": "Allow",
            "Action": [
                "logs:CreateLogGroup",
                "logs:CreateLogStream",
                "logs:PutLogEvents"
            ],
            "Resource": "*"
        },
        {
            “Sid”: “GetMasterCredential”
            "Effect": "Allow",
            "Action": [
                "secretsmanager:GetSecretValue"
            ],
            "Resource": ARN-OF-Demo/Twitter_API_Key
        }
    ]
}

    Here’s what it will look like:
     

    Figure 11: The IAM policy pasted in the "View Policy Document" text-entry field

    Figure 11: The IAM policy pasted in the “View Policy Document” text-entry field

  6. Now, select Allow. This brings me back to the Lambda console with the appropriate Role selected.
  7. Select Create function.
     
    Figure 12: Select the "Create function" button in the lower-right corner

    Figure 12: Select the “Create function” button in the lower-right corner

  8. Copy the following Python code and paste it in the Function code section.
    
import base64
import json
import logging
import os

import boto3
from botocore.vendored import requests

logger = logging.getLogger()
logger.setLevel(logging.INFO)

def lambda_handler(event, context):
    """Secrets Manager Twitter Bearer Token Handler

    This handler uses the master-user rotation scheme to rotate a bearer token of a Twitter app.

    The Secret PlaintextString is expected to be a JSON string with the following format:
    {
        'access_token': ,
        'token_type': ,
        'masterarn': 
    }

    Args:
        event (dict): Lambda dictionary of event parameters. These keys must include the following:
            - SecretId: The secret ARN or identifier
            - ClientRequestToken: The ClientRequestToken of the secret version
            - Step: The rotation step (one of createSecret, setSecret, testSecret, or finishSecret)

        context (LambdaContext): The Lambda runtime information

    Raises:
        ResourceNotFoundException: If the secret with the specified arn and stage does not exist

        ValueError: If the secret is not properly configured for rotation

        KeyError: If the secret json does not contain the expected keys

    """
    arn = event['SecretId']
    token = event['ClientRequestToken']
    step = event['Step']

    # Setup the client and environment variables
    service_client = boto3.client('secretsmanager', endpoint_url=os.environ['SECRETS_MANAGER_ENDPOINT'])
    oauth2_token_url = os.environ['TWITTER_OAUTH2_TOKEN_URL']
    oauth2_invalid_token_url = os.environ['TWITTER_OAUTH2_INVALID_TOKEN_URL']
    tweet_search_url = os.environ['TWITTER_SEARCH_URL']

    # Make sure the version is staged correctly
    metadata = service_client.describe_secret(SecretId=arn)
    if not metadata['RotationEnabled']:
        logger.error("Secret %s is not enabled for rotation" % arn)
        raise ValueError("Secret %s is not enabled for rotation" % arn)
    versions = metadata['VersionIdsToStages']
    if token not in versions:
        logger.error("Secret version %s has no stage for rotation of secret %s." % (token, arn))
        raise ValueError("Secret version %s has no stage for rotation of secret %s." % (token, arn))
    if "AWSCURRENT" in versions[token]:
        logger.info("Secret version %s already set as AWSCURRENT for secret %s." % (token, arn))
        return
    elif "AWSPENDING" not in versions[token]:
        logger.error("Secret version %s not set as AWSPENDING for rotation of secret %s." % (token, arn))
        raise ValueError("Secret version %s not set as AWSPENDING for rotation of secret %s." % (token, arn))

    # Call the appropriate step
    if step == "createSecret":
        create_secret(service_client, arn, token, oauth2_token_url, oauth2_invalid_token_url)

    elif step == "setSecret":
        set_secret(service_client, arn, token, oauth2_token_url)

    elif step == "testSecret":
        test_secret(service_client, arn, token, tweet_search_url)

    elif step == "finishSecret":
        finish_secret(service_client, arn, token)

    else:
        logger.error("lambda_handler: Invalid step parameter %s for secret %s" % (step, arn))
        raise ValueError("Invalid step parameter %s for secret %s" % (step, arn))


def create_secret(service_client, arn, token, oauth2_token_url, oauth2_invalid_token_url):
    """Get a new bearer token from Twitter 

    This method invalidates existing bearer token for the Twitter app and retrieves a new one from Twitter.
    If a secret version with AWSPENDING stage exists, updates it with the newly retrieved bearer token and if 
    the AWSPENDING stage does not exist, creates a new version of the secret with that stage label. 

    Args:
        service_client (client): The secrets manager service client

        arn (string): The secret ARN or other identifier

        token (string): The ClientRequestToken associated with the secret version

        oauth2_token_url (string): The Twitter API endpoint to request a bearer token

        oauth2_invalid_token_url (string): The Twitter API endpoint to invalidate a bearer token

    Raises:
        ValueError: If the current secret is not valid JSON

        KeyError: If the secret json does not contain the expected keys

        ResourceNotFoundException: If the current secret is not found

    """
    # Make sure the current secret exists and try to get the master arn from the secret
    try:
      current_secret_dict = get_secret_dict(service_client, arn, "AWSCURRENT")
      master_arn = current_secret_dict['masterarn']
      logger.info("createSecret: Successfully retrieved secret for %s." % arn)
    except service_client.exceptions.ResourceNotFoundException:
      return
    # create bearer token credentials to be passed as authorization string to Twitter
    bearer_token_credentials = encode_credentials(service_client, master_arn, "AWSCURRENT")
    # get the bearer token from Twitter
    bearer_token_from_twitter = get_bearer_token(bearer_token_credentials,oauth2_token_url)
    # invalidate the current bearer token
    invalidate_bearer_token(oauth2_invalid_token_url,bearer_token_credentials,bearer_token_from_twitter)
    # get a new bearer token from Twitter
    new_bearer_token = get_bearer_token(bearer_token_credentials, oauth2_token_url)
    # if a secret version with AWSPENDING stage exists, update it with the lastest bearer token
    # if the AWSPENDING stage does not exist, then create the version with AWSPENDING stage
    try:
      pending_secret_dict = get_secret_dict(service_client, arn, "AWSPENDING", token)
      pending_secret_dict['access_token'] = new_bearer_token
      service_client.put_secret_value(SecretId=arn, ClientRequestToken=token, SecretString=json.dumps(pending_secret_dict), VersionStages=['AWSPENDING'])
      logger.info("createSecret: Successfully invalidated the bearer token of the secret %s and updated the pending version" % arn)
    except service_client.exceptions.ResourceNotFoundException:
      current_secret_dict['access_token'] = new_bearer_token
      service_client.put_secret_value(SecretId=arn, ClientRequestToken=token, SecretString=json.dumps(current_secret_dict), VersionStages=['AWSPENDING'])
      logger.info("createSecret: Successfully invalidated the bearer token of the secret %s and and created the pending version." % arn)

def set_secret(service_client, arn, token, oauth2_token_url):
    """Validate the pending secret with that in Twitter

    This method checks wether the bearer token in Twitter is the same as the one in the version with AWSPENDING stage.

    Args:
        service_client (client): The secrets manager service client

        arn (string): The secret ARN or other identifier

        token (string): The ClientRequestToken associated with the secret version

        oauth2_token_url (string): The Twitter API endopoint to get a bearer token

    Raises:
        ResourceNotFoundException: If the secret with the specified arn and stage does not exist

        ValueError: If the secret is not valid JSON or master credentials could not be used to login to DB

        KeyError: If the secret json does not contain the expected keys

    """
    # First get the pending version of the bearer token and compare it with that in Twitter
    pending_secret_dict = get_secret_dict(service_client, arn, "AWSPENDING")
    master_arn = pending_secret_dict['masterarn']
    # create bearer token credentials to be passed as authorization string to Twitter
    bearer_token_credentials = encode_credentials(service_client, master_arn, "AWSCURRENT")
    # get the bearer token from Twitter
    bearer_token_from_twitter = get_bearer_token(bearer_token_credentials, oauth2_token_url)
    # if the bearer tokens are same, invalidate the bearer token in Twitter
    # if not, raise an exception that bearer token in Twitter was changed outside Secrets Manager
    if pending_secret_dict['access_token'] == bearer_token_from_twitter:
      logger.info("createSecret: Successfully verified the bearer token of arn %s" % arn)
    else:
      raise ValueError("The bearer token of the Twitter app was changed outside Secrets Manager. Please check.")
    
def test_secret(service_client, arn, token, tweet_search_url):
    """Test the pending secret by calling a Twitter API

    This method tries to use the bearer token in the secret version with AWSPENDING stage and search for tweets
    with 'aws secrets manager' string. 

    Args:
        service_client (client): The secrets manager service client

        arn (string): The secret ARN or other identifier

        token (string): The ClientRequestToken associated with the secret version

    Raises:
        ResourceNotFoundException: If the secret with the specified arn and stage does not exist

        ValueError: If the secret is not valid JSON or pending credentials could not be used to login to the database

        KeyError: If the secret json does not contain the expected keys

    """
    # First get the pending version of the bearer token and compare it with that in Twitter
    pending_secret_dict = get_secret_dict(service_client, arn, "AWSPENDING", token)
    # Now verify you can search for tweets using the bearer token
    if verify_bearer_token(pending_secret_dict['access_token'], tweet_search_url):
        logger.info("testSecret: Successfully authorized with the pending secret in %s." % arn)
        return
    else:
        logger.error("testSecret: Unable to authorize with the pending secret of secret ARN %s" % arn)
        raise ValueError("Unable to connect to Twitter with pending secret of secret ARN %s" % arn)

def finish_secret(service_client, arn, token):
    """Finish the rotation by marking the pending secret as current

    This method moves the secret from the AWSPENDING stage to the AWSCURRENT stage.

    Args:
        service_client (client): The secrets manager service client

        arn (string): The secret ARN or other identifier

        token (string): The ClientRequestToken associated with the secret version

    Raises:
        ResourceNotFoundException: If the secret with the specified arn and stage does not exist

    """
    # First describe the secret to get the current version
    metadata = service_client.describe_secret(SecretId=arn)
    current_version = None
    for version in metadata["VersionIdsToStages"]:
        if "AWSCURRENT" in metadata["VersionIdsToStages"][version]:
            if version == token:
                # The correct version is already marked as current, return
                logger.info("finishSecret: Version %s already marked as AWSCURRENT for %s" % (version, arn))
                return
            current_version = version
            break

    # Finalize by staging the secret version current
    service_client.update_secret_version_stage(SecretId=arn, VersionStage="AWSCURRENT", MoveToVersionId=token, RemoveFromVersionId=current_version)
    logger.info("finishSecret: Successfully set AWSCURRENT stage to version %s for secret %s." % (version, arn))
def encode_credentials(service_client, arn, stage):
  """Encodes the Twitter credentials
  This helper function encodes the Twitter credentials (consumer_key and consumer_secret) 

  Args:
    service_client (client):The secrets manager service client

    arn (string): The secret ARN or other identifier

    stage (stage): The stage identifying the secret version
  
  Returns:
    encoded_credentials (string): base64 encoded authorization string for Twitter

  Raises:
    KeyError: If the secret json does not contain the expected keys
  """
  required_fields = ['consumer_key','consumer_secret']
  master_secret_dict = get_secret_dict(service_client, arn, stage)
  for field in required_fields:
    if field not in master_secret_dict:
      raise KeyError("%s key is missing from the secret JSON" % field)
  encoded_credentials = base64.urlsafe_b64encode(
    '{}:{}'.format(master_secret_dict['consumer_key'], master_secret_dict['consumer_secret']).encode('ascii')).decode('ascii')
  return encoded_credentials
def get_bearer_token(encoded_credentials, oauth2_token_url):
  """Gets a bearer token from Twitter

  This helper function retrieves the current bearer token from Twitter, given a set of credentials.

  Args:
    encoded_credentials (string): Twitter credentials for authentication

    oauth2_token_url (string): REST API endpoint to request a bearer token from Twitter

  Raises:
    KeyError: If the secret json does not contain the expected keys
  """
  headers = {
      'Authorization': 'Basic {}'.format(encoded_credentials),
      'Content-Type': 'application/x-www-form-urlencoded;charset=UTF-8',
  }
  data = 'grant_type=client_credentials'
  response = requests.post(oauth2_token_url, headers=headers, data=data)
  response_data = response.json()
  if response_data['token_type'] == 'bearer':
      bearer_token = response_data['access_token']
      return bearer_token
  else:
      raise RuntimeError('unexpected token type: {}'.format(response_data['token_type']))
def invalidate_bearer_token(oauth2_invalid_token_url, bearer_token_credentials, bearer_token):
    """Invalidates a Bearer Token of a Twitter App

    This helper function invalidates a bearer token of a Twitter app. 
    If successful, it returns the invalidated bearer token, else None

    Args:
        oauth2_invalid_token_url (string): The Twitter API endpoint to invalidate a bearer token

        bearer_token_credentials (string): encoded consumer key and consumer secret to authenticate with Twitter

        bearer_token (string): The bearer token to be invalidated

    Returns:
        invalidated_bearer_token: The invalidated bearer token

    Raises:
        ResourceNotFoundException: If the secret with the specified arn and stage does not exist

        ValueError: If the secret is not valid JSON

        KeyError: If the secret json does not contain the expected keys

    """
    headers = {
      'Authorization': 'Basic {}'.format(bearer_token_credentials),
      'Content-Type': 'application/x-www-form-urlencoded;charset=UTF-8',
    }
    data = 'access_token=' + bearer_token
    invalidate_response = requests.post(oauth2_invalid_token_url, headers=headers, data=data)
    invalidate_response_data = invalidate_response.json()
    if invalidate_response_data:
      return
    else:
      raise RuntimeError('Invalidate bearer token request failed')
def verify_bearer_token(bearer_token, tweet_search_url):
  """Verifies access to Twitter APIs using a bearer token

  This helper function verifies that the bearer token is valid by calling Twitter's search/tweets API endpoint

  Args:
      bearer_token (string): The current bearer token for the application

  Returns:
      True or False

  Raises:
      KeyError: If the response of search tweets API call fails
  """
  headers = {
    'Authorization' : 'Bearer {}'.format(bearer_token),
    'Content-Type': 'application/x-www-form-urlencoded;charset=UTF-8',
  }
  search_results = requests.get(tweet_search_url, headers=headers)
  try:
    search_results.json()['statuses']
    return True
  except:
    return False

def get_secret_dict(service_client, arn, stage, token=None):
    """Gets the secret dictionary corresponding for the secret arn, stage, and token

    This helper function gets credentials for the arn and stage passed in and returns the dictionary by parsing the JSON string

    Args:
        service_client (client): The secrets manager service client

        arn (string): The secret ARN or other identifier

        token (string): The ClientRequestToken associated with the secret version, or None if no validation is desired

        stage (string): The stage identifying the secret version

    Returns:
        SecretDictionary: Secret dictionary

    Raises:
        ResourceNotFoundException: If the secret with the specified arn and stage does not exist

        ValueError: If the secret is not valid JSON

    """
    # Only do VersionId validation against the stage if a token is passed in
    if token:
        secret = service_client.get_secret_value(SecretId=arn, VersionId=token, VersionStage=stage)
    else:
        secret = service_client.get_secret_value(SecretId=arn, VersionStage=stage)
    plaintext = secret['SecretString']

    # Parse and return the secret JSON string
    return json.loads(plaintext)

    Here’s what it will look like:
     

    Figure 13: The Python code pasted in the "Function code" section

    Figure 13: The Python code pasted in the “Function code” section

  9. On the same page, provide the following environment variables:

    Here’s what it will look like:
     

    Figure 14: Add the environment variables

    Figure 14: Add the environment variables

    Note: Resources used in this example are in US East (Ohio) region. If you intend to use another AWS Region, change the SECRETS_MANAGER_ENDPOINT set in the Environment variables to the appropriate region.

    You’ve now created a Lambda function that can rotate the bearer token:
     

    Figure 15: The new Lambda function

    Figure 15: The new Lambda function

  10. Before you can configure Secrets Manager to use this Lambda function, you need to update the function policy of the Lambda function. A function policy permits AWS services, such as Secrets Manager, to invoke a Lambda function on behalf of your application. You can attach a Lambda function policy from the AWS Command Line Interface (AWS CLI) or SDK. To attach a function policy, call the add-permission Lambda API from the AWS CLI.
    
>aws lambda add-permission --function-name Lambda_Rotate_Bearer_Token --principal secretsmanager.amazonaws.com --action lambda:InvokeFunction --statement-id SecretsManagerAccess

Phase 3: Configure your application to retrieve the bearer token from Secrets Manager

Now that you’ve stored the bearer token in Secrets Manager, update the application to retrieve the bearer token from Secrets Manager instead of hard-coding this information in a configuration file or source code. For this example, I show you how to configure a Python application to retrieve this secret from Secrets Manager.

  1. Connect to your Amazon EC2 instance via Secure Shell (SSH). Previously, you configured the application to retrieve the bearer token from a configuration file. Below is the source code for my application.
    
import config

def no_secrets_manager_sample()
    
    # Get the bearer token from a config file. 
    Bearer_token = config.bearer_token
    
    # Use the bearer token to authenticate requests to Twitter

  2. Use the sample code from section titled Phase 1 and update the application to retrieve the bearer token from Secrets Manager. The following code sets up the client and retrieves and decrypts the secret Demo/Twitter_bearer_token.
    
# Use this code snippet in your app.
import boto3
from botocore.exceptions import ClientError


def get_secret():
    secret_name = "Demo/Twitter_bearer_token"
    endpoint_url = "https://secretsmanager.us-east-2.amazonaws.com"
    region_name = "us-east-2"

    session = boto3.session.Session()
    client = session.client(
        service_name='secretsmanager',
        region_name=region_name,
        endpoint_url=endpoint_url
    )

    try:
        get_secret_value_response = client.get_secret_value(
            SecretId=secret_name
        )
    except ClientError as e:
        if e.response['Error']['Code'] == 'ResourceNotFoundException':
            print("The requested secret " + secret_name + " was not found")
        elif e.response['Error']['Code'] == 'InvalidRequestException':
            print("The request was invalid due to:", e)
        elif e.response['Error']['Code'] == 'InvalidParameterException':
            print("The request had invalid params:", e)
    else:
        # Decrypted secret using the associated KMS CMK
        # Depending on whether the secret was a string or binary, one of these fields will be populated
        if 'SecretString' in get_secret_value_response:
            secret = get_secret_value_response['SecretString']
        else:
            binary_secret_data = get_secret_value_response['SecretBinary']
            
        # Your code goes here.

  3. Applications require permissions to access Secrets Manager. My application runs on Amazon EC2 and uses an IAM role to get access to AWS services. I’ll attach the following policy to my IAM role, and you should take a similar action with your IAM role. This policy uses the GetSecretValue action to grant my application permissions to read secrets from Secrets Manager. This policy also uses the resource element to limit my application to read only the Demo/Twitter_bearer_token secret from Secrets Manager. Read the AWS Secrets Manager documentation to understand the minimum IAM permissions required to retrieve a secret.
    
{
"Version": "2012-10-17",
"Statement": {
"Sid": "RetrieveBearerToken",
"Effect": "Allow",
"Action": "secretsmanager:GetSecretValue",
"Resource": Input ARN of the secret Demo/Twitter_bearer_token here 
}
}

    Note: To improve the resiliency of your applications, associate your application with two API keys/bearer tokens. This is a higher availability option because you can continue to use one bearer token while Secrets Manager rotates the other token. Read the AWS documentation to learn how AWS Secrets Manager rotates your secrets.

Phase 4: Enable and verify rotation

Now that you’ve stored the secret in Secrets Manager and created a Lambda function to rotate this secret, configure Secrets Manager to rotate the secret Demo/Twitter_bearer_token.

  1. From the Secrets Manager console, go to the list of secrets and choose the secret you created in the first step (in my example, this is named Demo/Twitter_bearer_token).
  2. Scroll to Rotation configuration, and then select Edit rotation.
     
    Figure 16: Select the "Edit rotation" button

    Figure 16: Select the “Edit rotation” button

  3. To enable rotation, select Enable automatic rotation, and then choose how frequently you want Secrets Manager to rotate this secret. For this example, I set the rotation interval to 30 days. I also choose the rotation Lambda function, Lambda_Rotate_Bearer_Token, from the drop-down list.
     
    Figure 17: "Edit rotation configuration" options

    Figure 17: “Edit rotation configuration” options

The banner on the next screen confirms that I have successfully configured rotation and the first rotation is in progress, which enables you to verify that rotation is functioning as expected. Secrets Manager will rotate this credential automatically every 30 days.
 

Figure 18: Confirmation notice

Figure 18: Confirmation notice

Summary

In this post, I showed you how to configure Secrets Manager to manage and rotate an API key and bearer token used by applications to authenticate and retrieve information from Twitter. You can use the steps described in this blog to manage and rotate other API keys, as well.

Secrets Manager helps you protect access to your applications, services, and IT resources without the upfront investment and on-going maintenance costs of operating your own secrets management infrastructure. To get started, open the Secrets Manager console. To learn more, read the Secrets Manager documentation.

If you have comments about this post, submit them in the Comments section below. If you have questions about anything in this post, start a new thread on the Secrets Manager forum or contact AWS Support.

Want more AWS Security news? Follow us on Twitter.

Integrating JFrog Artifactory with AWS CodePipeline

Post Syndicated from Erin McGill original https://aws.amazon.com/blogs/devops/integrating-jfrog-artifactory-with-aws-codepipeline/

When I talk with customers and partners, I find that they are in different stages in the adoption of DevOps methodologies. They are automating the creation of application artifacts and the deployment of their applications to different infrastructure environments. In many cases, they are creating and supporting multiple applications using a variety of coding languages and artifacts.

The management of these processes and artifacts can be challenging, but using the right tools and methodologies can simplify the process.

In this post, I will show you how you can automate the creation and storage of application artifacts through the implementation of a pipeline and custom deploy action in AWS CodePipeline. The example includes a Node.js code base stored in an AWS CodeCommit repository. A Node Package Manager (npm) artifact is built from the code base, and the build artifact is published to a JFrog Artifactory npm repository.

I frequently recommend AWS CodePipeline, the AWS continuous integration and continuous delivery tool. You can use it to quickly innovate through integration and deployment of new features and bug fixes by building a workflow that automates the build, test, and deployment of new versions of your application. And, because AWS CodePipeline is extensible, it allows you to create a custom action that performs customized, automated actions on your behalf.

JFrog’s Artifactory is a universal binary repository manager where you can manage multiple applications, their dependencies, and versions in one place. Artifactory also enables you to standardize the way you manage your package types across all applications developed in your company, no matter the code base or artifact type.

If you already have a Node.js CodeCommit repository, a JFrog Artifactory host, and would like to automate the creation of the pipeline, including the custom action and CodeBuild project, you can use this AWS CloudFormation template to create your AWS CloudFormation stack.

The project code can be found in this GitHub repository: https://github.com/aws-samples/aws-codepipeline-custom-job-worker-for-jfrog-artifactory.

 

The AWS CodePipeline workflow

This figure shows the path defined in the pipeline for this project. It starts with a change to Node.js source code committed to a private code repository in AWS CodeCommit. With this change, CodePipeline triggers AWS CodeBuild to create the npm package from the node.js source code. After the build, CodePipeline triggers the custom action job worker to commit the build artifact to the designated artifact repository in Artifactory.

 

This blog post assumes you have already:

·      Created a CodeCommit repository that contains a Node.js project.

·      Configured a two-stage pipeline in AWS CodePipeline.

The Source stage of the pipeline is configured to poll the Node.js CodeCommit repository. The Build stage is configured to use a CodeBuild project to build the npm package using a buildspec.yml file located in the code repository.

If you do not have a Node.js repository, you can create a CodeCommit repository that contains this simple ‘Hello World’ project. This project also includes a buildspec.yml file that is used when you define your CodeBuild project. It defines the steps to be taken by CodeBuild to create the npm artifact.

If you do not already have a pipeline set up in CodePipeline, you can use this template to create a pipeline with a CodeCommit source action and a CodeBuild build action through the AWS Command Line Interface (AWS CLI). If you do not want to install the AWS CLI on your local machine, you can use AWS Cloud9, our managed integrated development environment (IDE), to interact with AWS APIs.

In your development environment, open your favorite editor and fill out the template with values appropriate to your project. For information, see the readme in the GitHub repository.

Use this CLI command to create the pipeline from the template:

aws codepipeline create-pipeline --cli-input-json file://source-build-actions-codepipeline.json --region 'us-west-2'

It creates a pipeline that has a CodeCommit source action and a CodeBuild build action.

Integrating JFrog Artifactory

JFrog Artifactory provides default repositories for your project needs. For my NPM package repository, I am using the default virtual npm repository (named npm) that is available in Artifactory Pro. You might want to consider creating a repository per project but for the example used in this post, using the default lets me get started without having to configure a new repository.

I can use the steps in the Set Me Up -> npm section on the landing page to configure my worker to interact with the default NPM repository.

 

 

 

Custom actions in AWS CodePipeline

A custom action in AWS CodePipeline contains:

·      custom action definition

Describes the required values to run the custom action. I will define my custom action in the ‘Deploy’ category, identify the provider as ‘Artifactory’, of version ‘1’, and specify a variety of configurationProperties whose values will be defined when this stage is added to my pipeline.

·      custom action job worker

Polls CodePipeline for a job, scanning for its action-definition properties. In this blog post, after a job has been found, the job worker does the work required to publish the npm artifact to the Artifactory repository.

 

My custom action definition in JSON:

{
    "category": "Deploy",
    "configurationProperties": [{
        "name": "TypeOfArtifact",
        "required": true,
        "key": true,
        "secret": false,
        "description": "Package type, ex. npm for node packages",
        "type": "String"
    },
    {   "name": "RepoKey",
        "required": true,
        "key": true,
	"secret": false,
	"type": "String",
	"description": "Name of the repository in which this artifact should be stored"
    },
    {   "name": "UserName",
        "required": true,
        "key": true,
	"secret": false,
	"type": "String",
	"description": "Username for authenticating with the repository"
    },
    {   "name": "Password",
        "required": true,
        "key": true,
	"secret": true,
	"type": "String",
	"description": "Password for authenticating with the repository"
    },
    {   "name": "EmailAddress",
        "required": true,
        "key": true,
	"secret": false,
	"type": "String",
	"description": "Email address used to authenticate with the repository"
    },
    {   "name": "ArtifactoryHost",
        "required": true,
        "key": true,
	"secret": false,
	"type": "String",
	"description": "Public address of Artifactory host, ex: https://myexamplehost.com or http://myexamplehost.com:8080"
    }],
    "provider": "Artifactory",
    "version": "1",
    "settings": {
        "entityUrlTemplate": "{Config:ArtifactoryHost}/artifactory/webapp/#/artifacts/browse/tree/General/{Config:RepoKey}"
    },
    "inputArtifactDetails": {
        "maximumCount": 5,
        "minimumCount": 1
    },
    "outputArtifactDetails": {
        "maximumCount": 5,
        "minimumCount": 0
    }
}

There are seven sections to the custom action definition:

  • category: This is the stage in which you will be creating this action. It can be Source, Build, Deploy, Test, Invoke, Approval. Except for source actions, the category section simply allows us to organize our actions. I am setting the category for my action as ‘Deploy’ because I’m using it to publish my node artifact to my Artifactory instance.
  • configurationProperties: These are the parameters or variables required for your project to authenticate and commit your artifact. In the case of my custom worker, I need:
    • TypeOfArtifact: In this case, npm, because it’s for the Node Package Manager.
    • RepoKey: The name of the repository. In this case, it’s the default npm.
    • UserName and Password for the user to authenticate with the Artifactory repository.
    • EmailAddress used to authenticate with the repository.
    • Artifactory host name or IP address.
  • provider: The name you define for your custom action stage. I have named the provider Artifactory.
  • version: Version number for the custom action. Because this is the first version, I set the version number to 1.
  • entityUrlTemplate: This URL is presented to your users for the deploy stage along with the title you define in your provider. The link takes the user to their artifact repository page in the Artifactory host.
  • inputArtifactDetails: The number of artifacts to expect from the previous stage in the pipeline.
  • outputArtifactDetails: The number of artifacts that should be the result from the custom action stage. Later in this blog post, I define 0 for my output artifacts because I am publishing the artifact to the Artifactory repository as the final action.

After I define the custom action in a JSON file, I use the AWS CLI to create the custom action type in CodePipeline:

aws codepipeline create-custom-action-type --cli-input-json file://artifactory_custom_action_deploy_npm.json --region='us-west-2'

After I create the custom action type in the same region as my pipeline, I edit the pipeline to add a Deploy stage and configure it to use the custom action I created for Artifactory:

 

 

 

 

I have created a custom worker for the actions required to commit the npm artifact to the Artifactory repository. The worker is in Python and it runs in a loop on an Amazon EC2 instance. My custom worker polls for a deploy job and publishes the NPM artifact to the Artifactory repository.

 

The EC2 instance is running Amazon Linux and has an IAM instance role attached that gives the worker permission to access CodePipeline. The worker process is as follows:

  1. Take the configuration properties from the custom worker and poll CodePipeline for a custom action job.
  2. After there is a job in the job queue with the appropriate category, provider, and version, acknowledge the job.
  3. Download the zipped artifact created in the previous Build stage from the provided S3 buckets with the provided temporary credentials.
  4. Unzip the artifact into a temporary directory.
  5. A user-defined Artifactory user name and password is used to receive a temporary API key from Artifactory.
  6. To avoid having to write the password to a file, use that temporary API key and user name to authenticate with the NPM repository.
  7. Publish the Node.js package to the specified repository.

Because I am running my custom worker on an Amazon Linux EC2 instance, I installed npm with the following command:

sudo yum install nodejs npm --enablerepo=epel

For my custom worker, I used pip to install the required Python libraries:

pip install boto3 requests

For a full Python package list, see requirements.txt in the GitHub repository.

Let’s take a look at some of the code snippets from the worker.

First, the worker polls for jobs:

def action_type():
    ActionType = {
        'category': 'Deploy',
        'owner': 'Custom',
        'provider': 'Artifactory',
        'version': '1' }
    return(ActionType)

def poll_for_jobs():
    try:
        artifactory_action_type = action_type()
        print(artifactory_action_type)
        jobs = codepipeline.poll_for_jobs(actionTypeId=artifactory_action_type)
        while not jobs['jobs']:
            time.sleep(10)
            jobs = codepipeline.poll_for_jobs(actionTypeId=artifactory_action_type)
            if jobs['jobs']:
                print('Job found')
        return jobs['jobs'][0]
    except ClientError as e:
        print("Received an error: %s" % str(e))
        raise

 

When there is a job in the queue, the poller returns a number of values from the queue such as jobId, the input and output S3 buckets for artifacts, temporary credentials to access the S3 buckets, and other configuration details from the stage in the pipeline.

 

Here is an example of the return response:

{
	'jobs': [
		{
			'nonce': '3',
			'data': {
				'inputArtifacts': [
					{
						'name': 'Output',
						'location': {
							'type': 'S3',
							's3Location': {
								'objectKey': 'ArtifactoryNPMwithCo/Output/Key,
							'bucketName': '123456789012-codepipelineartifact-us-west-2'
							}
						}
					}
				],
				'pipelineContext': {
						'action': {
							'name': 'Deploy'
						},
						'pipelineName': 'ArtifactoryNPMwithCodeDeploy',
						'stage': {
							'name': 'Deploy'
						}
				},
				'actionTypeId': {
					'category': 'Deploy',
					'owner': 'Custom',
					'version': '1',
					'provider': 'Artifactory'
				},
				'outputArtifacts': [
					{
						'name': 'ArtifactoryOut',
						'location': {
							'type': 'S3',
							's3Location': {
								'objectKey': 'ArtifactoryNPMwithCo/Artifactor/Key,
								'bucketName': '123456789012-codepipelineartifact-us-west-2'
							}
						}
					}
				],
				'actionConfiguration': {
					'configuration': {
						'UserName': 'admin',
						'ArtifactoryHost': 'https://artifactory.myexamplehost.com',
						'Password': 'xxx',
						'EmailAddress': '[email protected]',
						'TypeOfArtifact': 'npm',
						'RepoKey': 'npm'
					}
				},
				'artifactCredentials': {
					'secretAccessKey': 'SECRET',
			               'sessionToken':‘FQoDYXdz...XdndMF',
					'accessKeyId': 'ACCESSKEY'
					}
				},
				'id': 'a0eb',
				'accountId': '123456789012
			}
		],
		'ResponseMetadata': {
			'RetryAttempts': 0,
			'HTTPStatusCode': 200,
			'RequestId': a88b-cdbd5d08b9de',
			'HTTPHeaders': {
				'x-amzn-requestid': '77343c2d-eff4’,
				'content-length': '2461',
				'content-type': 'application/x-amz-json-1.1'
			}
		}
}

 

After successfully receiving the job details, the worker sends an acknowledgement to CodePipeline to ensure that the work on the job is not duplicated by other workers watching for the same job:

def job_acknowledge(jobId, nonce):
    try:
        print('Acknowledging job')
        result = codepipeline.acknowledge_job(jobId=jobId, nonce=nonce)
        return result
    except Exception as e:
        print("Received an error when trying to acknowledge the job: %s" % str(e))
        raise

With the job now acknowledged, the worker publishes the source code artifact into the desired repository. The worker gets the value of the artifact S3 bucket and objectKey from the inputArtifacts in the response from the poll_for_jobs API request. Next, the worker creates a new directory in /tmp and downloads the S3 object into this directory:

def get_bucket_location(bucketName, init_client):
    region = init_client.get_bucket_location(Bucket=bucketName)['LocationConstraint']
    if not region:
        region = 'us-east-1'
    return region


def get_s3_artifact(bucketName, objectKey, ak, sk, st):
    init_s3 = boto3.client('s3')
    region = get_bucket_location(bucketName, init_s3)
    session = Session(aws_access_key_id=ak,
                      aws_secret_access_key=sk,
                      aws_session_token=st)

    s3 = session.resource('s3',
                          region_name=region,
                          config=botocore.client.Config(signature_version='s3v4'))
    try:
        tempdirname = tempfile.mkdtemp()
    except OSError as e:
        print('Could not write temp directory %s' % tempdirname)
        raise
    bucket = s3.Bucket(bucketName)
    obj = bucket.Object(objectKey)
    filename = tempdirname + '/' + objectKey
    try:
        if os.path.dirname(objectKey):
            directory = os.path.dirname(filename)
            os.makedirs(directory)
        print('Downloading the %s object and writing it to disk in %s location' % (objectKey, tempdirname))
        with open(filename, 'wb') as data:
            obj.download_fileobj(data)
    except ClientError as e:
        print('Downloading the object and writing the file to disk raised this error: ' + str(e))
        raise
    return(filename, tempdirname)

 

 

Because the downloaded artifact from S3 is a zip file, the worker must unzip it first. To have a clean area in which to work, I extract the downloaded zip archive into a new directory:

def unzip_codepipeline_artifact(artifact, origtmpdir):
    # create a new temp directory
    # Unzip artifact into new directory
    try:
        newtempdir = tempfile.mkdtemp()
        print('Extracting artifact %s into temporary directory %s' % (artifact, newtempdir))
        zip_ref = zipfile.ZipFile(artifact, 'r')
        zip_ref.extractall(newtempdir)
        zip_ref.close()
        shutil.rmtree(origtmpdir)
        return(os.listdir(newtempdir), newtempdir)
    except OSError as e:
        if e.errno != errno.EEXIST:
            shutil.rmtree(newtempdir)
            raise

The worker now has the npm package that I want to store in my Artifactory NPM repository.

To authenticate with the NPM repository, the worker requests a temporary token from the Artifactory host. After receiving this temporary token, it creates a .npmrc file in the worker user’s home directory that includes a hash of the user name and temporary token. After it has authenticated, the worker runs npm config set registry <URL OF REPOSITORY> to configure the npm registry value to be the Artifactory host.  Next, the worker runs npm publish –registry <URL OF REPOSITORY>, which publishes the node package to the NPM repository in the Artifactory host.

def push_to_npm(configuration, artifact_list, temp_dir, jobId):
    reponame = configuration['RepoKey']
    art_type = configuration['TypeOfArtifact']
    print("Putting artifact into NPM repository " + reponame)
    token, hostname, username = gen_artifactory_auth_token(configuration)
    npmconfigfile = create_npmconfig_file(configuration, username, token)
    url = hostname + '/artifactory/api/' + art_type + '/' + reponame
    print("Changing directory to " + str(temp_dir))
    os.chdir(temp_dir)
    try:
        print("Publishing following files to the repository: %s " % os.listdir(temp_dir))
        print("Sending artifact to Artifactory NPM registry URL: " + url)
        subprocess.call(["npm", "config", "set", "registry", url])
        req = subprocess.call(["npm", "publish", "--registry", url])
        print("Return code from npm publish: " + str(req))
        if req != 0:
            err_msg = "npm ERR! Recieved non OK response while sending response to Artifactory. Return code from npm publish: " + str(req)
            signal_failure(jobId, err_msg)
        else:
            signal_success(jobId)
    except requests.exceptions.RequestException as e:
       print("Received an error when trying to commit artifact %s to repository %s: " % (str(art_type), str(configuration['RepoKey']), str(e)))
       raise
    return(req, npmconfigfile)

 

If the return value from publishing to the repository is not 0, the worker signals a failure to CodePipeline. If the value is 0, the worker signals success to CodePipeline to indicate that the stage of the pipeline has been completed successfully.

For the custom worker code, see npm_job_worker.py in the GitHub repository.

I run my custom worker on an EC2 instance using the command python npm_job_worker.py, with an optional --version flag that can be used to specify worker versions other than 1. Then I trigger a release change in my pipeline:

From my custom worker output logs, I have just committed a package named node_example at version 1.0.3:

On artifact: index.js
Committing to the repo: https://artifactory.myexamplehost.com/artifactory/api/npm/npm
Sending artifact to Artifactory URL: https:// artifactoryhost.myexamplehost.com/artifactory/api/npm/npm
npm config: 0
npm http PUT https://artifactory.myexamplehost.com/artifactory/api/npm/npm/node_example
npm http 201 https://artifactory.myexamplehost.com/artifactory/api/npm/npm/node_example
+ [email protected]
Return code from npm publish: 0
Signaling success to CodePipeline

After that has been built successfully, I can find my artifact in my Artifactory repository:

To help you automate this process, I have created this AWS CloudFormation template that automates the creation of the CodeBuild project, the custom action, and the CodePipeline pipeline. It also launches the Amazon EC2-based custom job worker in an AWS Auto Scaling group. This template requires you to have a VPC and CodeCommit repository for your Node.js project. If you do not currently have a VPC in which you want to run your custom worker EC2 instances, you can use this AWS QuickStart to create one. If you do not have an existing Node.js project, I’ve provided a sample project in the GitHub repository.

Conclusion

I‘ve shown you the steps to integrate your JFrog Artifactory repository with your CodePipeline workflow. I’ve shown you how to create a custom action in CodePipeline and how to create a custom worker that works in your CI/CD pipeline. To dig deeper into custom actions and see how you can integrate your Artifactory repositories into your AWS CodePipeline projects, check out the full code base on GitHub.

If you have any questions or feedback, feel free to reach out to us through the AWS CodePipeline forum.

Erin McGill is a Solutions Architect in the AWS Partner Program with a focus on DevOps and automation tooling.

AWS Online Tech Talks – May and Early June 2018

Post Syndicated from Devin Watson original https://aws.amazon.com/blogs/aws/aws-online-tech-talks-may-and-early-june-2018/

AWS Online Tech Talks – May and Early June 2018  

Join us this month to learn about some of the exciting new services and solution best practices at AWS. We also have our first re:Invent 2018 webinar series, “How to re:Invent”. Sign up now to learn more, we look forward to seeing you.

Note – All sessions are free and in Pacific Time.

Tech talks featured this month:

Analytics & Big Data

May 21, 2018 | 11:00 AM – 11:45 AM PT – Integrating Amazon Elasticsearch with your DevOps Tooling – Learn how you can easily integrate Amazon Elasticsearch Service into your DevOps tooling and gain valuable insight from your log data.

May 23, 2018 | 11:00 AM – 11:45 AM PT – Data Warehousing and Data Lake Analytics, Together – Learn how to query data across your data warehouse and data lake without moving data.

May 24, 2018 | 11:00 AM – 11:45 AM PT – Data Transformation Patterns in AWS – Discover how to perform common data transformations on the AWS Data Lake.

Compute

May 29, 2018 | 01:00 PM – 01:45 PM PT – Creating and Managing a WordPress Website with Amazon Lightsail – Learn about Amazon Lightsail and how you can create, run and manage your WordPress websites with Amazon’s simple compute platform.

May 30, 2018 | 01:00 PM – 01:45 PM PT – Accelerating Life Sciences with HPC on AWS – Learn how you can accelerate your Life Sciences research workloads by harnessing the power of high performance computing on AWS.

Containers

May 24, 2018 | 01:00 PM – 01:45 PM PT – Building Microservices with the 12 Factor App Pattern on AWS – Learn best practices for building containerized microservices on AWS, and how traditional software design patterns evolve in the context of containers.

Databases

May 21, 2018 | 01:00 PM – 01:45 PM PT – How to Migrate from Cassandra to Amazon DynamoDB – Get the benefits, best practices and guides on how to migrate your Cassandra databases to Amazon DynamoDB.

May 23, 2018 | 01:00 PM – 01:45 PM PT – 5 Hacks for Optimizing MySQL in the Cloud – Learn how to optimize your MySQL databases for high availability, performance, and disaster resilience using RDS.

DevOps

May 23, 2018 | 09:00 AM – 09:45 AM PT – .NET Serverless Development on AWS – Learn how to build a modern serverless application in .NET Core 2.0.

Enterprise & Hybrid

May 22, 2018 | 11:00 AM – 11:45 AM PT – Hybrid Cloud Customer Use Cases on AWS – Learn how customers are leveraging AWS hybrid cloud capabilities to easily extend their datacenter capacity, deliver new services and applications, and ensure business continuity and disaster recovery.

IoT

May 31, 2018 | 11:00 AM – 11:45 AM PT – Using AWS IoT for Industrial Applications – Discover how you can quickly onboard your fleet of connected devices, keep them secure, and build predictive analytics with AWS IoT.

Machine Learning

May 22, 2018 | 09:00 AM – 09:45 AM PT – Using Apache Spark with Amazon SageMaker – Discover how to use Apache Spark with Amazon SageMaker for training jobs and application integration.

May 24, 2018 | 09:00 AM – 09:45 AM PT – Introducing AWS DeepLens – Learn how AWS DeepLens provides a new way for developers to learn machine learning by pairing the physical device with a broad set of tutorials, examples, source code, and integration with familiar AWS services.

Management Tools

May 21, 2018 | 09:00 AM – 09:45 AM PT – Gaining Better Observability of Your VMs with Amazon CloudWatch – Learn how CloudWatch Agent makes it easy for customers like Rackspace to monitor their VMs.

Mobile

May 29, 2018 | 11:00 AM – 11:45 AM PT – Deep Dive on Amazon Pinpoint Segmentation and Endpoint Management – See how segmentation and endpoint management with Amazon Pinpoint can help you target the right audience.

Networking

May 31, 2018 | 09:00 AM – 09:45 AM PT – Making Private Connectivity the New Norm via AWS PrivateLink – See how PrivateLink enables service owners to offer private endpoints to customers outside their company.

Security, Identity, & Compliance

May 30, 2018 | 09:00 AM – 09:45 AM PT – Introducing AWS Certificate Manager Private Certificate Authority (CA) – Learn how AWS Certificate Manager (ACM) Private Certificate Authority (CA), a managed private CA service, helps you easily and securely manage the lifecycle of your private certificates.

June 1, 2018 | 09:00 AM – 09:45 AM PT – Introducing AWS Firewall Manager – Centrally configure and manage AWS WAF rules across your accounts and applications.

Serverless

May 22, 2018 | 01:00 PM – 01:45 PM PT – Building API-Driven Microservices with Amazon API Gateway – Learn how to build a secure, scalable API for your application in our tech talk about API-driven microservices.

Storage

May 30, 2018 | 11:00 AM – 11:45 AM PT – Accelerate Productivity by Computing at the Edge – Learn how AWS Snowball Edge support for compute instances helps accelerate data transfers, execute custom applications, and reduce overall storage costs.

June 1, 2018 | 11:00 AM – 11:45 AM PT – Learn to Build a Cloud-Scale Website Powered by Amazon EFS – Technical deep dive where you’ll learn tips and tricks for integrating WordPress, Drupal and Magento with Amazon EFS.

 

 

 

 

Announcing Local Build Support for AWS CodeBuild

Post Syndicated from Karthik Thirugnanasambandam original https://aws.amazon.com/blogs/devops/announcing-local-build-support-for-aws-codebuild/

Today, we’re excited to announce local build support in AWS CodeBuild.

AWS CodeBuild is a fully managed build service. There are no servers to provision and scale, or software to install, configure, and operate. You just specify the location of your source code, choose your build settings, and CodeBuild runs build scripts for compiling, testing, and packaging your code.

In this blog post, I’ll show you how to set up CodeBuild locally to build and test a sample Java application.

By building an application on a local machine you can:

  • Test the integrity and contents of a buildspec file locally.
  • Test and build an application locally before committing.
  • Identify and fix errors quickly from your local development environment.

Prerequisites

In this post, I am using AWS Cloud9 IDE as my development environment.

If you would like to use AWS Cloud9 as your IDE, follow the express setup steps in the AWS Cloud9 User Guide.

The AWS Cloud9 IDE comes with Docker and Git already installed. If you are going to use your laptop or desktop machine as your development environment, install Docker and Git before you start.

Steps to build CodeBuild image locally

Run git clone https://github.com/aws/aws-codebuild-docker-images.git to download this repository to your local machine.

$ git clone https://github.com/aws/aws-codebuild-docker-images.git

Lets build a local CodeBuild image for JDK 8 environment. The Dockerfile for JDK 8 is present in /aws-codebuild-docker-images/ubuntu/java/openjdk-8.

Edit the Dockerfile to remove the last line ENTRYPOINT [“dockerd-entrypoint.sh”] and save the file.

Run cd ubuntu/java/openjdk-8 to change the directory in your local workspace.

Run docker build -t aws/codebuild/java:openjdk-8 . to build the Docker image locally. This command will take few minutes to complete.

$ cd aws-codebuild-docker-images
$ cd ubuntu/java/openjdk-8
$ docker build -t aws/codebuild/java:openjdk-8 .

Steps to setup CodeBuild local agent

Run the following Docker pull command to download the local CodeBuild agent.

$ docker pull amazon/aws-codebuild-local:latest --disable-content-trust=false

Now you have the local agent image on your machine and can run a local build.

Run the following git command to download a sample Java project.

$ git clone https://github.com/karthiksambandam/sample-web-app.git

Steps to use the local agent to build a sample project

Let’s build the sample Java project using the local agent.

Execute the following Docker command to run the local agent and build the sample web app repository you cloned earlier.

$ docker run -it -v /var/run/docker.sock:/var/run/docker.sock -e "IMAGE_NAME=aws/codebuild/java:openjdk-8" -e "ARTIFACTS=/home/ec2-user/environment/artifacts" -e "SOURCE=/home/ec2-user/environment/sample-web-app" amazon/aws-codebuild-local

Note: We need to provide three environment variables namely  IMAGE_NAME, SOURCE and ARTIFACTS.

IMAGE_NAME: The name of your build environment image.

SOURCE: The absolute path to your source code directory.

ARTIFACTS: The absolute path to your artifact output folder.

When you run the sample project, you get a runtime error that says the YAML file does not exist. This is because a buildspec.yml file is not included in the sample web project. AWS CodeBuild requires a buildspec.yml to run a build. For more information about buildspec.yml, see Build Spec Example in the AWS CodeBuild User Guide.

Let’s add a buildspec.yml file with the following content to the sample-web-app folder and then rebuild the project.

version: 0.2

phases:
  build:
    commands:
      - echo Build started on `date`
      - mvn install

artifacts:
  files:
    - target/javawebdemo.war


$ docker run -it -v /var/run/docker.sock:/var/run/docker.sock -e "IMAGE_NAME=aws/codebuild/java:openjdk-8" -e "ARTIFACTS=/home/ec2-user/environment/artifacts" -e "SOURCE=/home/ec2-user/environment/sample-web-app" amazon/aws-codebuild-local

This time your build should be successful. Upon successful execution, look in the /artifacts folder for the final built artifacts.zip file to validate.

Conclusion:

In this blog post, I showed you how to quickly set up the CodeBuild local agent to build projects right from your local desktop machine or laptop. As you see, local builds can improve developer productivity by helping you identify and fix errors quickly.

I hope you found this post useful. Feel free to leave your feedback or suggestions in the comments.

CI/CD with Data: Enabling Data Portability in a Software Delivery Pipeline with AWS Developer Tools, Kubernetes, and Portworx

Post Syndicated from Kausalya Rani Krishna Samy original https://aws.amazon.com/blogs/devops/cicd-with-data-enabling-data-portability-in-a-software-delivery-pipeline-with-aws-developer-tools-kubernetes-and-portworx/

This post is written by Eric Han – Vice President of Product Management Portworx and Asif Khan – Solutions Architect

Data is the soul of an application. As containers make it easier to package and deploy applications faster, testing plays an even more important role in the reliable delivery of software. Given that all applications have data, development teams want a way to reliably control, move, and test using real application data or, at times, obfuscated data.

For many teams, moving application data through a CI/CD pipeline, while honoring compliance and maintaining separation of concerns, has been a manual task that doesn’t scale. At best, it is limited to a few applications, and is not portable across environments. The goal should be to make running and testing stateful containers (think databases and message buses where operations are tracked) as easy as with stateless (such as with web front ends where they are often not).

Why is state important in testing scenarios? One reason is that many bugs manifest only when code is tested against real data. For example, we might simply want to test a database schema upgrade but a small synthetic dataset does not exercise the critical, finer corner cases in complex business logic. If we want true end-to-end testing, we need to be able to easily manage our data or state.

In this blog post, we define a CI/CD pipeline reference architecture that can automate data movement between applications. We also provide the steps to follow to configure the CI/CD pipeline.

 

Stateful Pipelines: Need for Portable Volumes

As part of continuous integration, testing, and deployment, a team may need to reproduce a bug found in production against a staging setup. Here, the hosting environment is comprised of a cluster with Kubernetes as the scheduler and Portworx for persistent volumes. The testing workflow is then automated by AWS CodeCommit, AWS CodePipeline, and AWS CodeBuild.

Portworx offers Kubernetes storage that can be used to make persistent volumes portable between AWS environments and pipelines. The addition of Portworx to the AWS Developer Tools continuous deployment for Kubernetes reference architecture adds persistent storage and storage orchestration to a Kubernetes cluster. The example uses MongoDB as the demonstration of a stateful application. In practice, the workflow applies to any containerized application such as Cassandra, MySQL, Kafka, and Elasticsearch.

Using the reference architecture, a developer calls CodePipeline to trigger a snapshot of the running production MongoDB database. Portworx then creates a block-based, writable snapshot of the MongoDB volume. Meanwhile, the production MongoDB database continues serving end users and is uninterrupted.

Without the Portworx integrations, a manual process would require an application-level backup of the database instance that is outside of the CI/CD process. For larger databases, this could take hours and impact production. The use of block-based snapshots follows best practices for resilient and non-disruptive backups.

As part of the workflow, CodePipeline deploys a new MongoDB instance for staging onto the Kubernetes cluster and mounts the second Portworx volume that has the data from production. CodePipeline triggers the snapshot of a Portworx volume through an AWS Lambda function, as shown here

 

 

 

AWS Developer Tools with Kubernetes: Integrated Workflow with Portworx

In the following workflow, a developer is testing changes to a containerized application that calls on MongoDB. The tests are performed against a staging instance of MongoDB. The same workflow applies if changes were on the server side. The original production deployment is scheduled as a Kubernetes deployment object and uses Portworx as the storage for the persistent volume.

The continuous deployment pipeline runs as follows:

  • Developers integrate bug fix changes into a main development branch that gets merged into a CodeCommit master branch.
  • Amazon CloudWatch triggers the pipeline when code is merged into a master branch of an AWS CodeCommit repository.
  • AWS CodePipeline sends the new revision to AWS CodeBuild, which builds a Docker container image with the build ID.
  • AWS CodeBuild pushes the new Docker container image tagged with the build ID to an Amazon ECR registry.
  • Kubernetes downloads the new container (for the database client) from Amazon ECR and deploys the application (as a pod) and staging MongoDB instance (as a deployment object).
  • AWS CodePipeline, through a Lambda function, calls Portworx to snapshot the production MongoDB and deploy a staging instance of MongoDB• Portworx provides a snapshot of the production instance as the persistent storage of the staging MongoDB
    • The MongoDB instance mounts the snapshot.

At this point, the staging setup mimics a production environment. Teams can run integration and full end-to-end tests, using partner tooling, without impacting production workloads. The full pipeline is shown here.

 

Summary

This reference architecture showcases how development teams can easily move data between production and staging for the purposes of testing. Instead of taking application-specific manual steps, all operations in this CodePipeline architecture are automated and tracked as part of the CI/CD process.

This integrated experience is part of making stateful containers as easy as stateless. With AWS CodePipeline for CI/CD process, developers can easily deploy stateful containers onto a Kubernetes cluster with Portworx storage and automate data movement within their process.

The reference architecture and code are available on GitHub:

● Reference architecture: https://github.com/portworx/aws-kube-codesuite
● Lambda function source code for Portworx additions: https://github.com/portworx/aws-kube-codesuite/blob/master/src/kube-lambda.py

For more information about persistent storage for containers, visit the Portworx website. For more information about Code Pipeline, see the AWS CodePipeline User Guide.

Secure Build with AWS CodeBuild and LayeredInsight

Post Syndicated from Asif Khan original https://aws.amazon.com/blogs/devops/secure-build-with-aws-codebuild-and-layeredinsight/

This post is written by Asif Awan, Chief Technology Officer of Layered Insight, Subin Mathew – Software Development Manager for AWS CodeBuild, and Asif Khan – Solutions Architect

Enterprises adopt containers because they recognize the benefits: speed, agility, portability, and high compute density. They understand how accelerating application delivery and deployment pipelines makes it possible to rapidly slipstream new features to customers. Although the benefits are indisputable, this acceleration raises concerns about security and corporate compliance with software governance. In this blog post, I provide a solution that shows how Layered Insight, the pioneer and global leader in container-native application protection, can be used with seamless application build and delivery pipelines like those available in AWS CodeBuild to address these concerns.

Layered Insight solutions

Layered Insight enables organizations to unify DevOps and SecOps by providing complete visibility and control of containerized applications. Using the industry’s first embedded security approach, Layered Insight solves the challenges of container performance and protection by providing accurate insight into container images, adaptive analysis of running containers, and automated enforcement of container behavior.

 

AWS CodeBuild

AWS CodeBuild is a fully managed build service that compiles source code, runs tests, and produces software packages that are ready to deploy. With CodeBuild, you don’t need to provision, manage, and scale your own build servers. CodeBuild scales continuously and processes multiple builds concurrently, so your builds are not left waiting in a queue. You can get started quickly by using prepackaged build environments, or you can create custom build environments that use your own build tools.

 

Problem Definition

Security and compliance concerns span the lifecycle of application containers. Common concerns include:

Visibility into the container images. You need to verify the software composition information of the container image to determine whether known vulnerabilities associated with any of the software packages and libraries are included in the container image.

Governance of container images is critical because only certain open source packages/libraries, of specific versions, should be included in the container images. You need support for mechanisms for blacklisting all container images that include a certain version of a software package/library, or only allowing open source software that come with a specific type of license (such as Apache, MIT, GPL, and so on). You need to be able to address challenges such as:

·       Defining the process for image compliance policies at the enterprise, department, and group levels.

·       Preventing the images that fail the compliance checks from being deployed in critical environments, such as staging, pre-prod, and production.

Visibility into running container instances is critical, including:

·       CPU and memory utilization.

·       Security of the build environment.

·       All activities (system, network, storage, and application layer) of the application code running in each container instance.

Protection of running container instances that is:

·       Zero-touch to the developers (not an SDK-based approach).

·       Zero touch to the DevOps team and doesn’t limit the portability of the containerized application.

·       This protection must retain the option to switch to a different container stack or orchestration layer, or even to a different Container as a Service (CaaS ).

·       And it must be a fully automated solution to SecOps, so that the SecOps team doesn’t have to manually analyze and define detailed blacklist and whitelist policies.

 

Solution Details

In AWS CodeCommit, we have three projects:
●     “Democode” is a simple Java application, with one buildspec to build the app into a Docker container (run by build-demo-image CodeBuild project), and another to instrument said container (instrument-image CodeBuild project). The resulting container is stored in ECR repo javatestasjavatest:20180415-layered. This instrumented container is running in AWS Fargate cluster demo-java-appand can be seen in the Layered Insight runtime console as the javatestapplication in us-east-1.
●     aws-codebuild-docker-imagesis a clone of the official aws-codebuild-docker-images repo on GitHub . This CodeCommit project is used by the build-python-builder CodeBuild project to build the python 3.3.6 codebuild image and is stored at the codebuild-python ECR repo. We then manually instructed the Layered Insight console to instrument the image.
●     scan-java-imagecontains just a buildspec.yml file. This file is used by the scan-java-image CodeBuild project to instruct Layered Assessment to perform a vulnerability scan of the javatest container image built previously, and then run the scan results through a compliance policy that states there should be no medium vulnerabilities. This build fails — but in this case that is a success: the scan completes successfully, but compliance fails as there are medium-level issues found in the scan.

This build is performed using the instrumented version of the Python 3.3.6 CodeBuild image, so the activity of the processes running within the build are recorded each time within the LI console.

Build container image

Create or use a CodeCommit project with your application. To build this image and store it in Amazon Elastic Container Registry (Amazon ECR), add a buildspec file to the project and build a container image and create a CodeBuild project.

Scan container image

Once the image is built, create a new buildspec in the same project or a new one that looks similar to below (update ECR URL as necessary):

version: 0.2
phases:
  pre_build:
    commands:
      - echo Pulling down LI Scan API client scripts
      - git clone https://github.com/LayeredInsight/scan-api-example-python.git
      - echo Setting up LI Scan API client
      - cd scan-api-example-python
      - pip install layint_scan_api
      - pip install -r requirements.txt
  build:
    commands:
      - echo Scanning container started on `date`
      - IMAGEID=$(./li_add_image --name <aws-region>.amazonaws.com/javatest:20180415)
      - ./li_wait_for_scan -v --imageid $IMAGEID
      - ./li_run_image_compliance -v --imageid $IMAGEID --policyid PB15260f1acb6b2aa5b597e9d22feffb538256a01fbb4e5a95

Add the buildspec file to the git repo, push it, and then build a CodeBuild project using with the instrumented Python 3.3.6 CodeBuild image at <aws-region>.amazonaws.com/codebuild-python:3.3.6-layered. Set the following environment variables in the CodeBuild project:
●     LI_APPLICATIONNAME – name of the build to display
●     LI_LOCATION – location of the build project to display
●     LI_API_KEY – ApiKey:<key-name>:<api-key>
●     LI_API_HOST – location of the Layered Insight API service

Instrument container image

Next, to instrument the new container image:

  1. In the Layered Insight runtime console, ensure that the ECR registry and credentials are defined (click the Setup icon and the ‘+’ sign on the top right of the screen to add a new container registry). Note the name given to the registry in the console, as this needs to be referenced in the li_add_imagecommand in the script, below.
  2. Next, add a new buildspec (with a new name) to the CodeCommit project, such as the one shown below. This code will download the Layered Insight runtime client, and use it to instruct the Layered Insight service to instrument the image that was just built:
    version: 0.2
phases:
pre_build:
commands:
echo Pulling down LI API Runtime client scripts
git clone https://github.com/LayeredInsight/runtime-api-example-python
echo Setting up LI API client
cd runtime-api-example-python
pip install layint-runtime-api
pip install -r requirements.txt
build:
commands:
echo Instrumentation started on `date`
./li_add_image --registry "Javatest ECR" --name IMAGE_NAME:TAG --description "IMAGE DESCRIPTION" --policy "Default Policy" --instrument --wait --verbose
  3. Commit and push the new buildspec file.
  4. Going back to CodeBuild, create a new project, with the same CodeCommit repo, but this time select the new buildspec file. Use a Python 3.3.6 builder – either the AWS or LI Instrumented version.
  5. Click Continue
  6. Click Save
  7. Run the build, again on the master branch.
  8. If everything runs successfully, a new image should appear in the ECR registry with a -layered suffix. This is the instrumented image.

Run instrumented container image

When the instrumented container is now run — in ECS, Fargate, or elsewhere — it will log data back to the Layered Insight runtime console. It’s appearance in the console can be modified by setting the LI_APPLICATIONNAME and LI_LOCATION environment variables when running the container.

Conclusion

In the above blog we have provided you steps needed to embed governance and runtime security in your build pipelines running on AWS CodeBuild using Layered Insight.

 

 

 

Implement continuous integration and delivery of serverless AWS Glue ETL applications using AWS Developer Tools

Post Syndicated from Prasad Alle original https://aws.amazon.com/blogs/big-data/implement-continuous-integration-and-delivery-of-serverless-aws-glue-etl-applications-using-aws-developer-tools/

AWS Glue is an increasingly popular way to develop serverless ETL (extract, transform, and load) applications for big data and data lake workloads. Organizations that transform their ETL applications to cloud-based, serverless ETL architectures need a seamless, end-to-end continuous integration and continuous delivery (CI/CD) pipeline: from source code, to build, to deployment, to product delivery. Having a good CI/CD pipeline can help your organization discover bugs before they reach production and deliver updates more frequently. It can also help developers write quality code and automate the ETL job release management process, mitigate risk, and more.

AWS Glue is a fully managed data catalog and ETL service. It simplifies and automates the difficult and time-consuming tasks of data discovery, conversion, and job scheduling. AWS Glue crawls your data sources and constructs a data catalog using pre-built classifiers for popular data formats and data types, including CSV, Apache Parquet, JSON, and more.

When you are developing ETL applications using AWS Glue, you might come across some of the following CI/CD challenges:

  • Iterative development with unit tests
  • Continuous integration and build
  • Pushing the ETL pipeline to a test environment
  • Pushing the ETL pipeline to a production environment
  • Testing ETL applications using real data (live test)
  • Exploring and validating data

In this post, I walk you through a solution that implements a CI/CD pipeline for serverless AWS Glue ETL applications supported by AWS Developer Tools (including AWS CodePipeline, AWS CodeCommit, and AWS CodeBuild) and AWS CloudFormation.

Solution overview

The following diagram shows the pipeline workflow:

This solution uses AWS CodePipeline, which lets you orchestrate and automate the test and deploy stages for ETL application source code. The solution consists of a pipeline that contains the following stages:

1.) Source Control: In this stage, the AWS Glue ETL job source code and the AWS CloudFormation template file for deploying the ETL jobs are both committed to version control. I chose to use AWS CodeCommit for version control.

To get the ETL job source code and AWS CloudFormation template, download the gluedemoetl.zip file. This solution is developed based on a previous post, Build a Data Lake Foundation with AWS Glue and Amazon S3.

2.) LiveTest: In this stage, all resources—including AWS Glue crawlers, jobs, S3 buckets, roles, and other resources that are required for the solution—are provisioned, deployed, live tested, and cleaned up.

The LiveTest stage includes the following actions:

  • Deploy: In this action, all the resources that are required for this solution (crawlers, jobs, buckets, roles, and so on) are provisioned and deployed using an AWS CloudFormation template.
  • AutomatedLiveTest: In this action, all the AWS Glue crawlers and jobs are executed and data exploration and validation tests are performed. These validation tests include, but are not limited to, record counts in both raw tables and transformed tables in the data lake and any other business validations. I used AWS CodeBuild for this action.
  • LiveTestApproval: This action is included for the cases in which a pipeline administrator approval is required to deploy/promote the ETL applications to the next stage. The pipeline pauses in this action until an administrator manually approves the release.
  • LiveTestCleanup: In this action, all the LiveTest stage resources, including test crawlers, jobs, roles, and so on, are deleted using the AWS CloudFormation template. This action helps minimize cost by ensuring that the test resources exist only for the duration of the AutomatedLiveTest and LiveTestApproval

3.) DeployToProduction: In this stage, all the resources are deployed using the AWS CloudFormation template to the production environment.

Try it out

This code pipeline takes approximately 20 minutes to complete the LiveTest test stage (up to the LiveTest approval stage, in which manual approval is required).

To get started with this solution, choose Launch Stack:

This creates the CI/CD pipeline with all of its stages, as described earlier. It performs an initial commit of the sample AWS Glue ETL job source code to trigger the first release change.

In the AWS CloudFormation console, choose Create. After the template finishes creating resources, you see the pipeline name on the stack Outputs tab.

After that, open the CodePipeline console and select the newly created pipeline. Initially, your pipeline’s CodeCommit stage shows that the source action failed.

Allow a few minutes for your new pipeline to detect the initial commit applied by the CloudFormation stack creation. As soon as the commit is detected, your pipeline starts. You will see the successful stage completion status as soon as the CodeCommit source stage runs.

In the CodeCommit console, choose Code in the navigation pane to view the solution files.

Next, you can watch how the pipeline goes through the LiveTest stage of the deploy and AutomatedLiveTest actions, until it finally reaches the LiveTestApproval action.

At this point, if you check the AWS CloudFormation console, you can see that a new template has been deployed as part of the LiveTest deploy action.

At this point, make sure that the AWS Glue crawlers and the AWS Glue job ran successfully. Also check whether the corresponding databases and external tables have been created in the AWS Glue Data Catalog. Then verify that the data is validated using Amazon Athena, as shown following.

Open the AWS Glue console, and choose Databases in the navigation pane. You will see the following databases in the Data Catalog:

Open the Amazon Athena console, and run the following queries. Verify that the record counts are matching.

SELECT count(*) FROM "nycitytaxi_gluedemocicdtest"."data";
SELECT count(*) FROM "nytaxiparquet_gluedemocicdtest"."datalake";

The following shows the raw data:

The following shows the transformed data:

The pipeline pauses the action until the release is approved. After validating the data, manually approve the revision on the LiveTestApproval action on the CodePipeline console.

Add comments as needed, and choose Approve.

The LiveTestApproval stage now appears as Approved on the console.

After the revision is approved, the pipeline proceeds to use the AWS CloudFormation template to destroy the resources that were deployed in the LiveTest deploy action. This helps reduce cost and ensures a clean test environment on every deployment.

Production deployment is the final stage. In this stage, all the resources—AWS Glue crawlers, AWS Glue jobs, Amazon S3 buckets, roles, and so on—are provisioned and deployed to the production environment using the AWS CloudFormation template.

After successfully running the whole pipeline, feel free to experiment with it by changing the source code stored on AWS CodeCommit. For example, if you modify the AWS Glue ETL job to generate an error, it should make the AutomatedLiveTest action fail. Or if you change the AWS CloudFormation template to make its creation fail, it should affect the LiveTest deploy action. The objective of the pipeline is to guarantee that all changes that are deployed to production are guaranteed to work as expected.

Conclusion

In this post, you learned how easy it is to implement CI/CD for serverless AWS Glue ETL solutions with AWS developer tools like AWS CodePipeline and AWS CodeBuild at scale. Implementing such solutions can help you accelerate ETL development and testing at your organization.

If you have questions or suggestions, please comment below.

 

Additional Reading

If you found this post useful, be sure to check out Implement Continuous Integration and Delivery of Apache Spark Applications using AWS and Build a Data Lake Foundation with AWS Glue and Amazon S3.

 

About the Authors

Prasad Alle is a Senior Big Data Consultant with AWS Professional Services. He spends his time leading and building scalable, reliable Big data, Machine learning, Artificial Intelligence and IoT solutions for AWS Enterprise and Strategic customers. His interests extend to various technologies such as Advanced Edge Computing, Machine learning at Edge. In his spare time, he enjoys spending time with his family.

 
Luis Caro is a Big Data Consultant for AWS Professional Services. He works with our customers to provide guidance and technical assistance on big data projects, helping them improving the value of their solutions when using AWS.

 

 

 

Rotate Amazon RDS database credentials automatically with AWS Secrets Manager

Post Syndicated from Apurv Awasthi original https://aws.amazon.com/blogs/security/rotate-amazon-rds-database-credentials-automatically-with-aws-secrets-manager/

Recently, we launched AWS Secrets Manager, a service that makes it easier to rotate, manage, and retrieve database credentials, API keys, and other secrets throughout their lifecycle. You can configure Secrets Manager to rotate secrets automatically, which can help you meet your security and compliance needs. Secrets Manager offers built-in integrations for MySQL, PostgreSQL, and Amazon Aurora on Amazon RDS, and can rotate credentials for these databases natively. You can control access to your secrets by using fine-grained AWS Identity and Access Management (IAM) policies. To retrieve secrets, employees replace plaintext secrets with a call to Secrets Manager APIs, eliminating the need to hard-code secrets in source code or update configuration files and redeploy code when secrets are rotated.

In this post, I introduce the key features of Secrets Manager. I then show you how to store a database credential for a MySQL database hosted on Amazon RDS and how your applications can access this secret. Finally, I show you how to configure Secrets Manager to rotate this secret automatically.

Key features of Secrets Manager

These features include the ability to:

  • Rotate secrets safely. You can configure Secrets Manager to rotate secrets automatically without disrupting your applications. Secrets Manager offers built-in integrations for rotating credentials for Amazon RDS databases for MySQL, PostgreSQL, and Amazon Aurora. You can extend Secrets Manager to meet your custom rotation requirements by creating an AWS Lambda function to rotate other types of secrets. For example, you can create an AWS Lambda function to rotate OAuth tokens used in a mobile application. Users and applications retrieve the secret from Secrets Manager, eliminating the need to email secrets to developers or update and redeploy applications after AWS Secrets Manager rotates a secret.
  • Secure and manage secrets centrally. You can store, view, and manage all your secrets. By default, Secrets Manager encrypts these secrets with encryption keys that you own and control. Using fine-grained IAM policies, you can control access to secrets. For example, you can require developers to provide a second factor of authentication when they attempt to retrieve a production database credential. You can also tag secrets to help you discover, organize, and control access to secrets used throughout your organization.
  • Monitor and audit easily. Secrets Manager integrates with AWS logging and monitoring services to enable you to meet your security and compliance requirements. For example, you can audit AWS CloudTrail logs to see when Secrets Manager rotated a secret or configure AWS CloudWatch Events to alert you when an administrator deletes a secret.
  • Pay as you go. Pay for the secrets you store in Secrets Manager and for the use of these secrets; there are no long-term contracts or licensing fees.

Get started with Secrets Manager

Now that you’re familiar with the key features, I’ll show you how to store the credential for a MySQL database hosted on Amazon RDS. To demonstrate how to retrieve and use the secret, I use a python application running on Amazon EC2 that requires this database credential to access the MySQL instance. Finally, I show how to configure Secrets Manager to rotate this database credential automatically. Let’s get started.

Phase 1: Store a secret in Secrets Manager

  1. Open the Secrets Manager console and select Store a new secret.
     
    Secrets Manager console interface
     
  2. I select Credentials for RDS database because I’m storing credentials for a MySQL database hosted on Amazon RDS. For this example, I store the credentials for the database superuser. I start by securing the superuser because it’s the most powerful database credential and has full access over the database.
     
    Store a new secret interface with Credentials for RDS database selected
     

    Note: For this example, you need permissions to store secrets in Secrets Manager. To grant these permissions, you can use the AWSSecretsManagerReadWriteAccess managed policy. Read the AWS Secrets Manager Documentation for more information about the minimum IAM permissions required to store a secret.

  3. Next, I review the encryption setting and choose to use the default encryption settings. Secrets Manager will encrypt this secret using the Secrets Manager DefaultEncryptionKeyDefaultEncryptionKey in this account. Alternatively, I can choose to encrypt using a customer master key (CMK) that I have stored in AWS KMS.
     
    Select the encryption key interface
     
  4. Next, I view the list of Amazon RDS instances in my account and select the database this credential accesses. For this example, I select the DB instance mysql-rds-database, and then I select Next.
     
    Select the RDS database interface
     
  5. In this step, I specify values for Secret Name and Description. For this example, I use Applications/MyApp/MySQL-RDS-Database as the name and enter a description of this secret, and then select Next.
     
    Secret Name and description interface
     
  6. For the next step, I keep the default setting Disable automatic rotation because my secret is used by my application running on Amazon EC2. I’ll enable rotation after I’ve updated my application (see Phase 2 below) to use Secrets Manager APIs to retrieve secrets. I then select Next.

    Note: If you’re storing a secret that you’re not using in your application, select Enable automatic rotation. See our AWS Secrets Manager getting started guide on rotation for details.

     
    Configure automatic rotation interface
     

  7. Review the information on the next screen and, if everything looks correct, select Store. We’ve now successfully stored a secret in Secrets Manager.
  8. Next, I select See sample code.
     
    The See sample code button
     
  9. Take note of the code samples provided. I will use this code to update my application to retrieve the secret using Secrets Manager APIs.
     
    Python sample code
     

Phase 2: Update an application to retrieve secret from Secrets Manager

Now that I have stored the secret in Secrets Manager, I update my application to retrieve the database credential from Secrets Manager instead of hard coding this information in a configuration file or source code. For this example, I show how to configure a python application to retrieve this secret from Secrets Manager.

  1. I connect to my Amazon EC2 instance via Secure Shell (SSH).
  2. Previously, I configured my application to retrieve the database user name and password from the configuration file. Below is the source code for my application.
    import MySQLdb
    import config

    def no_secrets_manager_sample()

    # Get the user name, password, and database connection information from a config file.
    database = config.database
    user_name = config.user_name
    password = config.password

    # Use the user name, password, and database connection information to connect to the database
    db = MySQLdb.connect(database.endpoint, user_name, password, database.db_name, database.port)

  3. I use the sample code from Phase 1 above and update my application to retrieve the user name and password from Secrets Manager. This code sets up the client and retrieves and decrypts the secret Applications/MyApp/MySQL-RDS-Database. I’ve added comments to the code to make the code easier to understand.
    # Use the code snippet provided by Secrets Manager.
    import boto3
    from botocore.exceptions import ClientError

    def get_secret():
    #Define the secret you want to retrieve
    secret_name = "Applications/MyApp/MySQL-RDS-Database"
    #Define the Secrets mManager end-point your code should use.
    endpoint_url = "https://secretsmanager.us-east-1.amazonaws.com"
    region_name = "us-east-1"

    #Setup the client
    session = boto3.session.Session()
    client = session.client(
    service_name='secretsmanager',
    region_name=region_name,
    endpoint_url=endpoint_url
    )

    #Use the client to retrieve the secret
    try:
    get_secret_value_response = client.get_secret_value(
    SecretId=secret_name
    )
    #Error handling to make it easier for your code to tolerate faults
    except ClientError as e:
    if e.response['Error']['Code'] == 'ResourceNotFoundException':
    print("The requested secret " + secret_name + " was not found")
    elif e.response['Error']['Code'] == 'InvalidRequestException':
    print("The request was invalid due to:", e)
    elif e.response['Error']['Code'] == 'InvalidParameterException':
    print("The request had invalid params:", e)
    else:
    # Decrypted secret using the associated KMS CMK
    # Depending on whether the secret was a string or binary, one of these fields will be populated
    if 'SecretString' in get_secret_value_response:
    secret = get_secret_value_response['SecretString']
    else:
    binary_secret_data = get_secret_value_response['SecretBinary']

    # Your code goes here.

  4. Applications require permissions to access Secrets Manager. My application runs on Amazon EC2 and uses an IAM role to obtain access to AWS services. I will attach the following policy to my IAM role. This policy uses the GetSecretValue action to grant my application permissions to read secret from Secrets Manager. This policy also uses the resource element to limit my application to read only the Applications/MyApp/MySQL-RDS-Database secret from Secrets Manager. You can visit the AWS Secrets Manager Documentation to understand the minimum IAM permissions required to retrieve a secret.
    {
    "Version": "2012-10-17",
    "Statement": {
    "Sid": "RetrieveDbCredentialFromSecretsManager",
    "Effect": "Allow",
    "Action": "secretsmanager:GetSecretValue",
    "Resource": "arn:aws:secretsmanager:::secret:Applications/MyApp/MySQL-RDS-Database"
    }
    }

Phase 3: Enable Rotation for Your Secret

Rotating secrets periodically is a security best practice because it reduces the risk of misuse of secrets. Secrets Manager makes it easy to follow this security best practice and offers built-in integrations for rotating credentials for MySQL, PostgreSQL, and Amazon Aurora databases hosted on Amazon RDS. When you enable rotation, Secrets Manager creates a Lambda function and attaches an IAM role to this function to execute rotations on a schedule you define.

Note: Configuring rotation is a privileged action that requires several IAM permissions and you should only grant this access to trusted individuals. To grant these permissions, you can use the AWS IAMFullAccess managed policy.

Next, I show you how to configure Secrets Manager to rotate the secret Applications/MyApp/MySQL-RDS-Database automatically.

  1. From the Secrets Manager console, I go to the list of secrets and choose the secret I created in the first step Applications/MyApp/MySQL-RDS-Database.
     
    List of secrets in the Secrets Manager console
     
  2. I scroll to Rotation configuration, and then select Edit rotation.
     
    Rotation configuration interface
     
  3. To enable rotation, I select Enable automatic rotation. I then choose how frequently I want Secrets Manager to rotate this secret. For this example, I set the rotation interval to 60 days.
     
    Edit rotation configuration interface
     
  4. Next, Secrets Manager requires permissions to rotate this secret on your behalf. Because I’m storing the superuser database credential, Secrets Manager can use this credential to perform rotations. Therefore, I select Use the secret that I provided in step 1, and then select Next.
     
    Select which secret to use in the Edit rotation configuration interface
     
  5. The banner on the next screen confirms that I have successfully configured rotation and the first rotation is in progress, which enables you to verify that rotation is functioning as expected. Secrets Manager will rotate this credential automatically every 60 days.
     
    Confirmation banner message
     

Summary

I introduced AWS Secrets Manager, explained the key benefits, and showed you how to help meet your compliance requirements by configuring AWS Secrets Manager to rotate database credentials automatically on your behalf. Secrets Manager helps you protect access to your applications, services, and IT resources without the upfront investment and on-going maintenance costs of operating your own secrets management infrastructure. To get started, visit the Secrets Manager console. To learn more, visit Secrets Manager documentation.

If you have comments about this post, submit them in the Comments section below. If you have questions about anything in this post, start a new thread on the Secrets Manager forum.

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New – Encryption of Data in Transit for Amazon EFS

Post Syndicated from Jeff Barr original https://aws.amazon.com/blogs/aws/new-encryption-of-data-in-transit-for-amazon-efs/

Amazon Elastic File System was designed to be the file system of choice for cloud-native applications that require shared access to file-based storage. We launched EFS in mid-2016 and have added several important features since then including on-premises access via Direct Connect and encryption of data at rest. We have also made EFS available in additional AWS Regions, most recently US West (Northern California). As was the case with EFS itself, these enhancements were made in response to customer feedback, and reflect our desire to serve an ever-widening customer base.

Encryption in Transit
Today we are making EFS even more useful with the addition of support for encryption of data in transit. When used in conjunction with the existing support for encryption of data at rest, you now have the ability to protect your stored files using a defense-in-depth security strategy.

In order to make it easy for you to implement encryption in transit, we are also releasing an EFS mount helper. The helper (available in source code and RPM form) takes care of setting up a TLS tunnel to EFS, and also allows you to mount file systems by ID. The two features are independent; you can use the helper to mount file systems by ID even if you don’t make use of encryption in transit. The helper also supplies a recommended set of default options to the actual mount command.

Setting up Encryption
I start by installing the EFS mount helper on my Amazon Linux instance:

$ sudo yum install -y amazon-efs-utils

Next, I visit the EFS Console and capture the file system ID:

Then I specify the ID (and the TLS option) to mount the file system:

$ sudo mount -t efs fs-92758f7b -o tls /mnt/efs

And that’s it! The encryption is transparent and has an almost negligible impact on data transfer speed.

Available Now
You can start using encryption in transit today in all AWS Regions where EFS is available.

The mount helper is available for Amazon Linux. If you are running another distribution of Linux you will need to clone the GitHub repo and build your own RPM, as described in the README.

— Jeff;

EFS File Sync – Faster File Transfer To Amazon EFS File Systems

Post Syndicated from Jeff Barr original https://aws.amazon.com/blogs/aws/efs-file-sync-faster-file-transfer-to-amazon-efs-file-systems/

We launched EFS File Sync a few days before AWS re:Invent 2017 and I finally have time to tell you about it!

If you need to move a large collection of files from an on-premises or in-cloud file system to Amazon Elastic File System, this tool is for you. Simple, single-threaded command line tools such as cp and rsync predate the cloud and cannot deliver the throughput required to move massive amounts of data from place to place. These tools are generally used as building blocks, often within scripts that take care of scheduling, orchestration, and network security.

Secure & Parallel
EFS File Sync uses a secure, highly parallel data transfer mechanism that can run up to 5 times faster than the tools I mentioned above. It is available as an agent that runs within VMware ESXi or on an EC2 instance, and accesses the source file system via NFS (v3 and v4), and can be used in all AWS Regions where EFS is available. Because the agent is responsible for initiating all communication with AWS you don’t need to set up VPNs or allow inbound connections through your firewall.

You can launch, control, and monitor the agent and your sync tasks from the AWS Management Console. Jobs can specify the transfer of an entire file system or a specific directory tree, with the option to detect and skip files that are already present in the destination. File metadata (modification and access time, POSIX ownership and permissions, symbolic links, and hard links) is also copied.

Using EFS File Sync
In order to write this blog post, I launched an EC2 instance, exported an NFS file system (/data), and populated the file system with the Linux kernel source code.

I open the EFS Console in the same Region as my instance, and click File syncs:

I click on Get started, choose Amazon EC2 as my host platform and click Launch instance, and click Connect to agent to proceed:

Clicking Launch instance opens the EC2 console in a separate tab. I pick a Memory optimized instance type (xlarge or bigger), configure it with a public IP address and with a security group that allows inbound traffic on port 80, and launch it as I would any other EC2 instance. Then I wait a minute or two (time to water my plants or check on my dog), and wait until the status checks pass:

Then I capture the instance’s public IP address, return to the EFS tab, enter the address, and click on Activate agent:

This step retrieves the activation key from the sync agent. After it completes, I enter a name for it and click Activate agent to proceed:

Now that the agent is running and activated, I click on Create sync task to start moving some files to EFS:

I configure the source location (the EC2 instance that I mentioned at the start of this section):

I also choose the destination EFS file system and specify a target location within it for my files:

Then I select my sync options and click Next to review my configuration:

The review looks good and I click Create sync task to start copying my files:

After the sync task has been created and its status becomes Available, I can select it and choose Start from the Actions menu to initiate a sync:

I fine-tune the settings that I established when I created the task, and click Start to proceed:

I can track the status of the sync task on the History tab:

It completes within minutes and my EFS file system now includes the new files:

Available Now
EFS File Sync is available in all AWS Regions where EFS is available. You pay for the EFS and EC2 resources that you consume and $0.01 per GB of data copied (see the EFS Pricing page for more info).

— Jeff;

Power from wind: Open data on AWS

Post Syndicated from Caleb Phillips original https://aws.amazon.com/blogs/big-data/power-from-wind-open-data-on-aws/

Data that describe processes in a spatial context are everywhere in our day-to-day lives and they dominate big data problems. Map data, for instance, whether describing networks of roads or remote sensing data from satellites, get us where we need to go. Atmospheric data from simulations and sensors underlie our weather forecasts and climate models. Devices and sensors with GPS can provide a spatial context to nearly all mobile data.

In this post, we introduce the WIND toolkit, a huge (500 TB), open weather model dataset that’s available to the world on Amazon’s cloud services. We walk through how to access this data and some of the open-source software developed to make it easily accessible. Our solution considers a subset of geospatial data that exist on a grid (raster) and explores ways to provide access to large-scale raster data from weather models. The solution uses foundational AWS services and the Hierarchical Data Format (HDF), a well adopted format for scientific data.

The approach developed here can be extended to any data that fit in an HDF5 file, which can describe sparse and dense vectors and matrices of arbitrary dimensions. This format is already popular within the physical sciences for both experimental and simulation data. We discuss solutions to gridded data storage for a massive dataset of public weather model outputs called the Wind Integration National Dataset (WIND) toolkit. We also highlight strategies that are general to other large geospatial data management problems.

Wind Integration National Dataset

As variable renewable power penetration levels increase in power systems worldwide, the importance of renewable integration studies to ensure continued economic and reliable operation of the power grid is also increasing. The WIND toolkit is the largest freely available grid integration dataset to date.

The WIND toolkit was developed by 3TIER by Vaisala. They were under a subcontract to the National Renewable Energy Laboratory (NREL) to support studies on integration of wind energy into the existing US grid. NREL is a part of a network of national laboratories for the US Department of Energy and has a mission to advance the science and engineering of energy efficiency, sustainable transportation, and renewable power technologies.

The toolkit has been used by consultants, research groups, and universities worldwide to support grid integration studies. Less traditional uses also include resource assessments for wind plants (such as those powering Amazon data centers), and studying the effects of weather on California condor migrations in the Baja peninsula.

The diversity of applications highlights the value of accessible, open public data. Yet, there’s a catch: the dataset is huge. The WIND toolkit provides simulated atmospheric (weather) data at a two-km spatial resolution and five-minute temporal resolution at multiple heights for seven years. The entire dataset is half a petabyte (500 TB) in size and is stored in the NREL High Performance Computing data center in Golden, Colorado. Making this dataset publicly available easily and in a cost-effective manner is a major challenge.

As other laboratories and public institutions work to release their data to the world, they may face similar challenges to those that we experienced. Some prior, well-intentioned efforts to release huge datasets as-is have resulted in data resources that are technically available but fundamentally unusable. They may be stored in an unintuitive format or indexed and organized to support only a subset of potential uses. Downloading hundreds of terabytes of data is often impractical. Most users don’t have access to a big data cluster (or super computer) to slice and dice the data as they need after it’s downloaded.

We aim to provide a large amount of data (50 terabytes) to the public in a way that is efficient, scalable, and easy to use. In many cases, researchers can access these huge cloud-located datasets using the same software and algorithms they have developed for smaller datasets stored locally. Only the pieces of data they need for their individual analysis must be downloaded. To make this work in practice, we worked with the HDF Group and have built upon their forthcoming Highly Scalable Data Service.

In the rest of this post, we discuss how the HSDS software was developed to use Amazon EC2 and Amazon S3 resources to provide convenient and scalable access to these huge geospatial datasets. We describe how the HSDS service has been put to work for the WIND Toolkit dataset and demonstrate how to access it using the h5pyd Python library and the REST API. We conclude with information about our ongoing work to release more ‘open’ datasets to the public using AWS services, and ways to improve and extend the HSDS with newer Amazon services like Amazon ECS and AWS Lambda.

Developing a scalable service for big geospatial data

The HDF5 file format and API have been used for many years and is an effective means of storing large scientific datasets.  For example, NASA’s Earth Observing System (EOS) satellites collect more than 16 TBs of data per day using HDF5.

With the rise of the cloud, there are new challenges and opportunities to rethink how HDF5 can be enhanced to work effectively as a component in a cloud-native architecture.  For the HDF Group, working with NREL has been a great opportunity to put ideas into practice with a production-size dataset.

An HDF5 file consists of a directed graph of group and dataset objects.  Datasets can be thought of as a multidimensional array with support for user-defined metadata tags and compression.  Typical operations on datasets would be reading or writing data to a regular subregion (a hyperslab) or reading and writing individual elements (a point selection).   Also, group and dataset objects may each contain an arbitrary number of the user-defined metadata elements known as attributes.

Many people have used the HDF library in applications developed or ported to run on EC2 instances, but there are a number of constraints that often prove problematic:

  • The HDF5 library can’t read directly from HDF5 files stored as S3 objects. The entire file (often many GB in size) would need to be copied to local storage before the first byte can be read. Also, the instance must be configured with the appropriately sized EBS volume)
  • The HDF library only has access to the computational resources of the instance itself (as opposed to a cluster of instances), so many operations are bottlenecked by the library.
  • Any modifications to the HDF5 file would somehow have to be synchronized with changes that other instances have made to same file before writing back to S3.

Using a pattern common to many offerings from AWS, the solution to these constraints is to develop a service framework around the HDF data model. Using this model, the HDF Group has created the Highly Scalable Data Service (HSDS) that provides all the functionality that traditionally was provided by the HDF5 library.  By using the service, you don’t need to manage your own file volumes, but can just read and write whatever data that you need.

Because the service manages the actual data persistence to a durable medium (S3, in this case), you don’t need to worry about disk management. Simply stream the data you need from the service as you need it.  Secondly, putting the functionality behind a service allows some tricks to increase performance (described in more detail later). And lastly, HSDS allows any number of clients to access the data at the same time, enabling HDF5 to be used as a coordination mechanism for multiple readers and writers.

In designing the HSDS architecture, we gave much thought to how to achieve scalability of the HSDS service.  For accessing HDF5 data, there are two different types of scaling to consider:

  • Multiple clients making many requests to the service
  • Single requests that require a significant amount of data processing

To deal with the first scaling challenge, as with most services, we considered how the service responds as the request rate increases.  AWS provides some great tools that help in this regard:

  • Auto Scaling groups
  • Elastic Load Balancing load balancers
  • The ability of S3 to handle large aggregate throughput rates

By using a cluster of EC2 instances behind a load balancer, you can handle different client loads in a cost-effective manner.

The second scaling challenge concerns single requests that would take significant processing time with just one compute node.  One example of this from the WIND toolkit would be extracting all the values in the seven-year time span for a given geographic point and dataset.

In HDF5, large datasets are typically stored as “chunks”; that is, a regular partition of the array.  In HSDS, each chunk is stored as a binary object in S3.  The sequential approach to retrieving the time series values would be for the service to read each chunk needed from S3, extract the needed elements, and go on to the next chunk.  In this case, that would involve processing 2557 chunks, and would be quite slow.

Fortunately, with HSDS, you can speed this up quite a bit by exploiting the compute and I/O capabilities of the cluster.  Upon receiving the request, the receiving node can use other nodes in the cluster to read different portions of the selection.  With multiple nodes reading from S3 in parallel, performance improves as the cluster size increases.

The diagram below illustrates how this works in simplified case of four chunks and four nodes.

This architecture has worked in well in practice.  In testing with the WIND toolkit and time series extraction, we observed a request latency of ~60 seconds using four nodes vs. ~5 seconds with 40 nodes. Performance roughly scales with the size of the cluster.

A planned enhancement to this is to use AWS Lambda for the worker processing.  This enables 1000-way parallel reads at a reasonable cost, as you only pay for the milliseconds of CPU time used with AWS Lambda.

Public access to atmospheric data using HSDS and AWS

An early challenge in releasing the WIND toolkit data was in deciding how to subset the data for different use cases. In general, few researchers need access to the entire 0.5 PB of data and a great deal of efficiency and cost reduction can be gained by making directed constituent datasets.

NREL grid integration researchers initially extracted a 2-TB subset by selecting 120,000 points where the wind resource seemed appropriate for development. They also chose only those data important for wind applications (100-m wind speed, converted to power), the most interesting locations for those performing grid studies. To support the remaining users who needed more data resolution, we down-sampled the data to a 60-minute temporal resolution, keeping all the other variables and spatial resolution intact. This reduced dataset is 50 TB of data describing 30+ atmospheric variables of data for 7 years at a 60-minute temporal resolution.

The WindViz browser-based Gridded Wind Toolkit Visualizer was created as an example implementation of the HSDS REST API in JavaScript. The visualizer is written in the style of ECMAScript 2016 using a modern development toolchain that includes webpack and Babel. The source code is available through our GitHub repository. The demo page is hosted via GitHub pages, and we use a cross-origin AJAX request to fetch data from the HSDS service running on the EC2 infrastructure. The visualizer can be used to explore the gridded wind toolkit data on a map. Achieve full spatial resolution by zooming in to a specific region.

Programmatic access is possible using the h5pyd Python library, a distributed analog to the widely used h5py library. Users interact with the datasets (variables) and slice the data from its (time x longitude x latitude) cube form as they see fit.

Examples and use cases are described in a set of Jupyter notebooks and available on GitHub:

NREL/hsds-examples

To run these notebooks on an EC2 instance in the Oregon Region, run the following commands:

$ sudo yum install git gcc
$ sudo pip install –user jupyter
$ pip install --user git+http://github.com/HDFGroup/h5pyd.git
$ git clone https://github.com/NREL/hsds-examples.git
$ cd hsds-examples
$ jupyter notebook

Now you have a Jupyter notebook server running on your EC2 server.

From your laptop, create an SSH tunnel:

$ ssh –L 8888:localhost:8888 (IP address of the EC2 server)

Now, you can browse to localhost:8888 using the correct token, and interact with the notebooks as if they were local. Within the directory, there are examples for accessing the HSDS API and plotting wind and weather data using matplotlib.

Controlling access and defraying costs

A final concern is rate limiting and access control. Although the HSDS service is scalable and relatively robust, we had a few practical concerns:

  • How can we protect from malicious or accidental use that may lead to high egress fees (for example, someone who attempts to repeatedly download the entire dataset from S3)?
  • How can we keep track of who is using the data both to document the value of the data resource and to justify the costs?
  • If costs become too high, can we charge for some or all API use to help cover the costs?

To approach these problems, we investigated using Amazon API Gateway and its simplified integration with the AWS Marketplace for SaaS monetization as well as third-party API proxies.

In the end, we chose to use API Umbrella due to its close involvement with http://data.gov. While AWS Marketplace is a compelling option for future datasets, the decision was made to keep this dataset entirely open, at least for now. As community use and associated costs grow, we’ll likely revisit Marketplace. Meanwhile, API Umbrella provides controls for rate limiting and API key registration out of the box and was simple to implement as a front-end proxy to HSDS. Those applications that may want to charge for API use can accomplish a similar strategy using Amazon API Gateway and AWS Marketplace.

Ongoing work and other resources

As NREL and other government research labs, municipalities, and organizations try to share data with the public, we expect many of you will face similar challenges to those we have tried to approach with the architecture described in this post. Providing large datasets is one challenge. Doing so in a way that is affordable and convenient for users is an entirely more difficult goal. Using AWS cloud-native services and the existing foundation of the HDF file format has allowed us to tackle that challenge in a meaningful way.

 

Additional Reading

If you found this post useful, be sure to check out Perform Near Real-time Analytics on Streaming Data with Amazon Kinesis and Amazon Elasticsearch Service, Analyze OpenFDA Data in R with Amazon S3 and Amazon Athena and Querying OpenStreetMap with Amazon Athena.

 

About the Authors

Dr. Caleb Phillips is a senior scientist with the Data Analysis and Visualization Group within the Computational Sciences Center at the National Renewable Energy Laboratory. Caleb comes from a background in computer science systems, applied statistics, computational modeling, and optimization. His work at NREL spans the breadth of renewable energy technologies and focuses on applying modern data science techniques to data problems at scale.

 

 

 

Dr. Caroline Draxl is a senior scientist at NREL. She supports the research and modeling activities of the US Department of Energy from mesoscale to wind plant scale. Caroline uses mesoscale models to research wind resources in various countries, and participates in on- and offshore boundary layer research and in the coupling of the mesoscale flow features (kilometer scale) to the microscale (tens of meters). She holds a M.S. degree in Meteorology and Geophysics from the University of Innsbruck, Austria, and a PhD in Meteorology from the Technical University of Denmark.

 

 

 

John Readey has been a Senior Architect at The HDF Group since he joined in June 2014.  His interests include web services related to HDF, applications that support the use of HDF and data visualization.Before joining The HDF Group, John worked at Amazon.com from 2006–2014 where he developed service-based systems for eCommerce and AWS.

 

 

 

Jordan Perr-Sauer is an RPP intern with the Data Analysis and Visualization Group within the Computational Sciences Center at the National Renewable Energy Laboratory. Jordan hopes to use his professional background in software engineering and his academic training in applied mathematics to solve the challenging problems facing America and the world.

AWS Documentation is Now Open Source and on GitHub

Post Syndicated from Jeff Barr original https://aws.amazon.com/blogs/aws/aws-documentation-is-now-open-source-and-on-github/

Earlier this year we made the AWS SDK developer guides available as GitHub repos (all found within the awsdocs organization) and invited interested parties to contribute changes and improvements in the form of pull requests.

Today we are adding over 138 additional developer and user guides to the organization, and we are looking forward to receiving your requests. You can fix bugs, improve code samples (or submit new ones), add detail, and rewrite sentences and paragraphs in the interest of accuracy or clarity. You can also look at the commit history in order to learn more about new feature and service launches and to track improvements to the documents.

Making a Contribution
Before you get started, read the Amazon Open Source Code of Conduct and take a look at the Contributing Guidelines document (generally named CONTRIBUTING.md) for the AWS service of interest. Then create a GitHub account if you don’t already have one.

Once you find something to change or improve, visit the HTML version of the document and click on Edit on GitHub button at the top of the page:

This will allow you to edit the document in source form (typically Markdown or reStructuredText). The source code is used to produce the HTML, PDF, and Kindle versions of the documentation.

Once you are in GitHub, click on the pencil icon:

This creates a “fork” — a separate copy of the file that you can edit in isolation.

Next, make an edit. In general, as a new contributor to an open source project, you should gain experience and build your reputation by making small, high-quality edits. I’ll change “dozens of services” to “over one hundred services” in this document:

Then I summarize my change and click Propose file change:

I examine the differences to verify my changes and then click Create pull request:

Then I review the details and click Create pull request again:

The pull request (also known as a PR) makes its way to the Elastic Beanstalk documentation team and they get to decide if they want to accept it, reject it, or to engage in a conversation with me to learn more. The teams endeavor to respond to PRs within 48 hours, and I’ll be notified via GitHub whenever the status of the PR changes.

As is the case with most open source projects, a steady stream of focused, modest-sized pull requests is preferable to the occasional king-sized request with dozens of edits inside.

If I am interested in tracking changes to a repo over time, I can Watch and/or Star it:

If I Watch a repo, I’ll receive an email whenever there’s a new release, issue, or pull request for that service guide.

Go Fork It
This launch gives you another way to help us to improve AWS. Let me know what you think!

— Jeff;

One LED Matrix Table to rule them all

Post Syndicated from Alex Bate original https://www.raspberrypi.org/blog/led-matrix-table/

Germany-based Andreas Rottach’s multi-purpose LED table is an impressive build within a gorgeous-looking body. Play games, view (heavily pixelated) images, and become hypnotised by flashy lights, once you’ve built your own using his newly released tutorial.

LED-Matrix Table – 300 LEDs – Raspberry Pi – C++ Engine – Custom Controllers

This is a short presentation of my LED-Matrix Table. The table is controlled by a raspberry pi computer that executes a control engine, written in c++. It supports input from keyboards or custom made game controllers. A full list of all features as well as the source code is available on GitHub (https://github.com/rottaca/LEDTableEngine).

Much excitement

Andreas uploaded a video of his LED Matrix Table to YouTube back in February, with the promise of publishing a complete write-up within the coming weeks. And so the members of Pi Towers sat, eagerly waiting and watching. Now the write-up has arrived, to our cheers of acclaim for this beautful, shiny, flashy, LED-based wonderment.

Build your own LED table

In his GitHub tutorial, Andreas goes through all the stages of building the table, from the necessary components to coding the Raspberry Pi 3 and 3D printing your own controllers.

Raspberry Pi LED Table

Find files for the controllers on Thingiverse

Andreas created the table’s impressive light matrix using a strip of 300 LEDs, chained together and connected to the Raspberry Pi via an LED controller.

Raspberry Pi LED Table

The LEDs are set out in zigzags

For the code, he used several open-source tools, such as SDL for image and audio support, and CMake for building the project software.

Anyone planning to recreate Andreas’ table can compile its engine by downloading the project repository from GitHub. Again, find full instructions for this on his GitHub.

Features

The table boasts multiple cool features, including games and visualisation tools. Using the controllers, you can play simplified versions of Flappy Bird and Minesweeper, or go on a nostalgia trip with Tetris, Pong, and Snake.

Raspberry Pi LED Table

There’s also a version of Conway’s Game of Life. Andreas explains: “The lifespan of each cell is color-coded. If the game field gets static, the animation is automatically reset to a new random cell population.”

Raspberry Pi LED Table

The table can also display downsampled Bitmap images, or show clear static images such as a chess board, atop of which you can place physical game pieces.

Raspberry Pi LED Table
Raspberry Pi LED Table
Raspberry Pi LED Table

Find all the 3D-printable aspects of the LED table on Thingiverse here and here, and the full GitHub tutorial and repository here. If you build your own, or have already dabbled in LED tables and displays, be sure to share your project with us, either in the comments below or via our social media accounts. What other functions would you integrate into this awesome build?

The post One LED Matrix Table to rule them all appeared first on Raspberry Pi.

Voice-controlled magnification glasses

Post Syndicated from Alex Bate original https://www.raspberrypi.org/blog/voice-controlled-magnification-glasses/

Go hands-free in the laboratory or makerspace with Mauro Pichiliani’s voice-controlled magnification glasses.

Voice Controlled Glasses With Magnifying Lens

This video presents the project MoveLens: a voice controlled glasses with magnifying lens. It was the my entry for the Voice Activated context on unstructables. Check the step by step guide at Voice Controlled Glasses With Magnifying Lens. Source code: https://github.com/pichiliani/MoveLens Step by Step guide: https://www.instructables.com/id/Voice-Controlled-Glasses-With-Magnifying-Lens/

It’s a kind of magnification

We’ve all been there – that moment when you need another pair of hands to complete a task. And while these glasses may not hold all the answers, they’re a perfect addition to any hobbyist’s arsenal.

Introducing Mauro Pichilliani’s voice-activated glasses: a pair of frames with magnification lenses that can flip up and down in response to a voice command, depending on the task at hand. No more needing to put down your tools in order to put magnifying glasses on. No more trying to re-position a magnifying glass with the back of your left wrist, or getting grease all over your lenses.

As Mauro explains in his tutorial for the glasses:

Many professionals work for many hours looking at very small areas, such as surgeons, watchmakers, jewellery designers and so on. Most of the time these professionals use some kind of magnification glasses that helps them to see better the area they are working with and other tiny items used on the job. The devices that had magnifications lens on a form factor of a glass usually allow the professional to move the lens out of their eye sight, i.e. put aside the lens. However, in some scenarios touching the lens or the glass rim to move away the lens can contaminate the fingers. Also, it is cumbersome and can break the concentration of the professional.

Voice-controlled magnification glasses

Using a Raspberry Pi Zero W, a servo motor, a microphone, and the IBM Watson speech-to-text service, Mauro built a pair of glasses that lets users control the position of the magnification lenses with voice commands.

Magnification glasses, before modification and addition of Raspberry Pi

The glasses Mauro modified, before he started work on them; you have to move the lenses with your hands, like it’s October 2015

Mauro started by dismantling a pair of standard magnification glasses in order to modify the lens supports to allow them to move freely. He drilled a hole in one of the lens supports to provide a place to attach the servo, and used lollipop sticks and hot glue to fix the lenses relative to one another, so they would both move together under the control of the servo. Then, he set up a Raspberry Pi Zero, installing Raspbian and software to use a USB microphone; after connecting the servo to the Pi Zero’s GPIO pins, he set up the Watson speech-to-text service.

Finally, he wrote the code to bring the project together. Two Python scripts direct the servo to raise and lower the lenses, and a Node.js script captures audio from the microphone, passes it on to Watson, checks for an “up” or “down” command, and calls the appropriate Python script as required.

Your turn

You can follow the tutorial on the Instructables website, where Mauro entered the glasses into the Instructables Voice Activated Challenge. And if you’d like to take your first steps into digital making using the Raspberry Pi, take a look at our free online projects.

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