Real-time Analytics for AI Application Logs with AWS Elasticsearch

Question

Pulumi · Accepted Answer

In order to set up real-time analytics for AI application logs using AWS Elasticsearch, we need to build a pipeline that accomplishes the following:

1. Collects logs from your AI applications.
2. Streams those logs to a processing service (like AWS Kinesis or AWS Firehose).
3. Delivers the logs to AWS Elasticsearch for analysis and visualization.

Below is a detailed explanation of how you could set this up using Pulumi to create the necessary resources on AWS. The program will include:

- An **AWS Kinesis Stream**, which is a scalable and durable real-time data streaming service. It allows for the collection and processing of large streams of data records in real time.
  
- An **AWS Elasticsearch Domain**, which provides a fully managed Elasticsearch service where you can store, search, and analyze large volumes of data quickly and in near real-time.

- An **AWS Kinesis Firehose delivery stream**, which is the easiest way to reliably load streaming data into data lakes, data stores, and analytics services. It can capture, transform, and load data into AWS Elasticsearch for analysis.

- **IAM Roles and Policies**, which are necessary for granting proper permissions to the Kinesis streams and Firehose delivery stream to interact with Elasticsearch and other services.

Let’s break down the process into code and see how to orchestrate it using Pulumi:

```python
import pulumi
import pulumi_aws as aws

# Assume that the AWS provider and region configuration are already in place.

# Create an AWS Elasticsearch Domain to store and index logs
es_domain = aws.elasticsearch.Domain("esDomain",
    elasticsearch_version="7.4",  # Use the appropriate version
    cluster_config=aws.elasticsearch.DomainClusterConfigArgs(
        instance_type="r5.large.elasticsearch",
    ),
    ebs_options=aws.elasticsearch.DomainEbsOptionsArgs(
        ebs_enabled=True,
        volume_size=10,
        volume_type="gp2",
    ),
    snapshot_options=aws.elasticsearch.DomainSnapshotOptionsArgs(
        automated_snapshot_start_hour=23,
    ),
    tags={
        "Domain": "Logging",
    })

# Output the endpoint of Elasticsearch domain to be used by Kinesis Firehose
pulumi.export('es_domain_endpoint', es_domain.endpoint)

# Create an AWS Kinesis Stream
kinesis_stream = aws.kinesis.Stream("appLogsStream",
    shard_count=1,  # Start with one shard (scale as needed)
    retention_period=24)  # Retention period in hours

# Create IAM role for AWS Firehose
firehose_role = aws.iam.Role("firehoseRole", assume_role_policy="""{
   "Version": "2012-10-17",
   "Statement": [
     {
       "Effect": "Allow",
       "Principal": {
         "Service": "firehose.amazonaws.com"
       },
       "Action": "sts:AssumeRole"
     }
   ]
}""")

# Attach policies to the Firehose role
firehose_role_policy = aws.iam.RolePolicy("firehoseRolePolicy",
    role=firehose_role.id,
    policy=pulumi.Output.all(es_domain.arn, kinesis_stream.arn).apply(lambda args: f"""{{
      "Version": "2012-10-17",
      "Statement": [
        {{
          "Effect": "Allow",
          "Action": [
            "es:DescribeElasticsearchDomain",
            "es:DescribeElasticsearchDomains",
            "es:DescribeElasticsearchDomainConfig",
            "es:ESHttpPost",
            "es:ESHttpPut",
            "es:ESHttpGet"
          ],
          "Resource": ["{args[0]}"]
        }},
        {{
          "Effect": "Allow",
          "Action": "kinesis:Get*",
          "Resource": ["{args[1]}"]
        }}
      ]
    }}"""))

# Create a Kinesis Firehose delivery stream to send logs to AWS Elasticsearch
firehose_delivery_stream = aws.kinesis.FirehoseDeliveryStream("appLogsFirehose",
    destination="elasticsearch",
    s3_configuration=aws.kinesis.FirehoseDeliveryStreamS3ConfigurationArgs(
        role_arn=firehose_role.arn,
        bucket_arn=aws.s3.Bucket("firehoseBucket").arn,
    ),
    elasticsearch_configuration=aws.kinesis.FirehoseDeliveryStreamElasticsearchConfigurationArgs(
        domain_arn=es_domain.arn,
        role_arn=firehose_role.arn,
        index_name="app-logs",
        type_name="log",
        index_rotation_period="OneDay",
    ),
    kinesis_source_configuration=aws.kinesis.FirehoseDeliveryStreamKinesisSourceConfigurationArgs(
        kinesis_stream_arn=kinesis_stream.arn,
        role_arn=firehose_role.arn,
    ))

# Export the name of the firehose delivery stream
pulumi.export("firehose_delivery_stream_name", firehose_delivery_stream.name)
```

This Pulumi program creates the infrastructure to collect logs from AI applications and analyze them in real-time using AWS Elasticsearch. It sets up an Elasticsearch domain to store and search the logs, a Kinesis Stream to capture the real-time data, and a Kinesis Firehose delivery stream to transform and load the data into Elasticsearch.

The important aspect here is the `FirehoseDeliveryStream`, which is configured to pull data from the Kinesis Stream and then deliver the logs to the Elasticsearch domain. IAM roles and policies are created to ensure that Firehose has the necessary permissions to pull data from Kinesis and send it to Elasticsearch.

Once you apply this Pulumi program, you will have a fully operational real-time analytics pipeline for AI application logs using AWS Elasticsearch. You can then use Kibana, which comes integrated with AWS Elasticsearch, or any other visualization tool of your choice to create dashboards, alerts, and perform real-time analysis of your log data.