1. Logging and Monitoring Pipelines with MongoDB Atlas and Splunk


    Logging and monitoring are essential practices for maintaining the health and performance of your applications and infrastructure. In this context, we will use Pulumi to set up a logging and monitoring pipeline that leverages MongoDB Atlas as a data source and Splunk as a logging and analysis platform.

    MongoDB Atlas is a cloud database service that provides scalable database solutions, and it includes features for monitoring the performance of your databases. Splunk, on the other hand, is a tool for searching, monitoring, and analyzing machine-generated data through a Web-style interface.

    To achieve logging and monitoring with MongoDB Atlas and Splunk, we need to configure a way for logs and metrics from MongoDB Atlas to be sent to Splunk. This typically involves setting up a connector or integration that can forward logs from MongoDB Atlas to the Splunk instance.

    Here is a Pulumi Python program that demonstrates how you might set up such a pipeline. This program assumes you have Pulumi, Python, and the necessary Pulumi providers installed and configured for use with your cloud environment.

    The program uses two key resources from the Pulumi Registry Results:

    • mongodbatlas.Cluster: This resource creates and manages a MongoDB Atlas Cluster. We will define a cluster and assume that it's configured to generate logs and metrics as per our requirements.
    • harness.platform.SplunkConnector: This resource represents a connector in the Harness platform, which can be used to configure a connection to Splunk. Even though this doesn't integrate directly with MongoDB Atlas, we'll include it to demonstrate how you could begin to set up a Splunk connection.

    Let's see how this could look in code:

    import pulumi import pulumi_mongodbatlas as mongodbatlas import pulumi_harness as harness # MongoDB Atlas requirements # You will need a project ID and credentials from MongoDB Atlas. # The credentials, such as an API key or username/password, should be secured and managed outside of version control. mongo_project_id = "your_project_id_here" # Replace with your MongoDB Atlas project ID mongo_cluster_name = "your_cluster_name_here" # Name for the MongoDB Cluster # Create a MongoDB Atlas Cluster mongo_cluster = mongodbatlas.Cluster("mongoCluster", projectId=mongo_project_id, name=mongo_cluster_name, providerBackupEnabled=True, clusterType="REPLICASET", replicationSpecs=[mongodbatlas.ClusterReplicationSpecArgs( numShards=1, regionsConfigs=[mongodbatlas.ClusterReplicationSpecRegionsConfigArgs( regionName="AWS_REGION_HERE", # Replace with your desired region electableNodes=3, priority=7, readPreference="secondaryPreferred", )] )], providerInstanceSizeName="M10", providerName="AWS", mongoDbMajorVersion="4.4" ) # Configure Splunk connector settings # You will need your Splunk instance URL and credentials. # As with MongoDB credentials, store these credentials securely. splunk_connector = harness.platform.SplunkConnector("splunkConnector", url="your_splunk_instance_url_here", # Replace with your Splunk instance URL name="MongoDBAtlasSplunkConnector", # Set a name for your Splunk connector username="your_username_here", # Replace with your Splunk username passwordRef="your_password_reference_here", # Reference to your Splunk password in a secure store accountId="your_harness_account_id_here", # Replace with your Harness account ID identifier="your_identifier_here", # Replace with your identifier ) pulumi.export('mongo_cluster_id', mongo_cluster.id) pulumi.export('splunk_connector_id', splunk_connector.id)

    In the above program, we first import the necessary Pulumi providers for MongoDB Atlas and Harness. Then we define a MongoDB Atlas cluster with basic configurations including the backing provider (AWS in this example), instance size, region, and MongoDB version. Replace the placeholder variables with your specific configuration values.

    The Splunk connector is also defined, although the Harness platform is being used as a stand-in for direct Splunk integration. To complete this setup, you would need to secure your MongoDB Atlas logs and metrics to Splunk outside of this Pulumi configuration, which this program doesn't cover due to the lack of a direct Pulumi Splunk provider resource.

    Keep in mind that additional setup for both MongoDB Atlas monitoring and Splunk logging configuration might be needed beyond what is demonstrated in this program.

    This program will give you the Pulumi resource IDs upon successful completion, which you can use to reference these resources in your infrastructure.

    The documentation links for MongoDB Atlas Cluster and Splunk Connector from Harness are as follows:

    This program can only be considered an initial step towards a fully functional logging and monitoring pipeline. Production setups would need to consider data transportation mechanisms, security, data privacy, and actual integration steps to ensure that MongoDB Atlas logs and metrics are consumed properly by Splunk for analysis.