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µ/log hidden superpower

TL;DR: How we used μ/log to automatically generate AWS policy for Terraform.

Originally posted at:


Since I developed μ/log back in 2019, I have consistently used it in every project I have worked on. My colleagues have also embraced the idea and adopted it. We have integrated μ/log into every microservice, lambda function, data-processing job, and larger service we have developed. We publish the metrics to various systems such as Elasticsearch, AWS CloudWatch Logs/Insights, and Jaeger Tracing for later analysis.

μ/log is a data logging system that goes beyond traditional log systems like log4j, logback, and Java Logging. While those systems are designed to log simple sentences like INFO: Server started", "INFO: Order received" or "ERROR: Payment failed", μ/log is designed to capture data with a rich context. It provides detailed information such as which server started, on which machine, and which version; which order was received from which client, and if the payment failed, the reason, amount, and associated order. It even tracks the processing time for each operation and more.

While traditional loggers like log4j primarily target human consumers, μ/log events are designed to be consumed by machines. In modern systems composed of hundreds or thousands of microservices, it is impossible for humans to make sense of the huge amount of text-based messages alone. Software is necessary to sift through hundreds of millions or billions of events to find the desired information.

The surprising outcome of our use of μ/log was that, for the first time in my over 30 years of experience of developing and running systems, non-technical individuals within our project were able to identify issues in test and production systems without requiring developer intervention. This realization highlighted the effectiveness of our observability approach.

From time to time, I come across new and amazing ways to utilize μ/log. Today, I want to share another use-case made possible by μ/log’s data capture capabilities.

Most of our work at Redefine is based on AWS cloud. We firmly believe in adhering to cloud best practices such as the Principle of least Privilege for our services. Consequently, we strive to avoid using wildcards in our IAM Policies. While it is not difficult to manually craft a policy for a tiny AWS Lambda or a small microservice that uses 2-3 services, it becomes a non-trivial task when deploying larger microservices with numerous components.

This post illustrates how we leveraged μ/log to automatically generate AWS policy documents for our services.

μ/log Instrumentation

At Redefine, I have instrumented our core AWS library with μ/log to track various metrics for our AWS requests. Our core AWS library wraps the excellent Cognitect’s aws-api and incorporates internal features and patterns used throughout our platform and services.

The general usage is similar to the Cognitect’s aws-api library:

General use

Our internal invoke function looks like this:

AWS invoke function

Which produces the following event:

AWS request sample event

  • Line 1: Indicates that this is an :aws/request event.
  • Line 2: Provides the timestamp when the request was initiated.
  • Line 5: Indicates the duration of the request in nanoseconds.
  • Line 7: Specifies whether the request was successful or if it failed. In case of an error, additional details are available in :exception.
  • Line 8: Specifies the AWS service that was used.
  • Line 9: Identifies the operation that was performed.
  • Lines 10-11: Provide the details of the actual request made, including the AWS resource used.

It is remarkable how much information can be obtained from a single μ/log instrumentation. With this setup, we gain a wealth of information for each request performed on every service.

While our actual implementation is more involved, as it allows tracing activation at the service/operation/request level and collects additional parameters, the overall idea remains the same.

Extracting the AWS Information

To generate a policy for one of our services, we simply run our battery of automated tests and instruct μ/log to write all the events to a file.

We start μ/log’s publisher and configure it to write the events to a file on our disk:

***μ/log*** configuration

The next step is to run the tests for the service from which we want to extract the policy. The tests can be executed against LocalStack or an AWS test account; it doesn’t matter. Once the test execution is complete, we have all the data required to automatically generate our policy.

Extracting the Operations

The first step is to parse the EDN file and load the events:

Parsing events

The expression (where :mulog/event-name :is? :aws/request) returns a predicate function that evaluates to true if the event is a :aws/request. I developed a small library called where to make predicate functions easier to be read and understood by humans.

When we execute parse-file, we obtain the events as Clojure maps:

Parsed events

To generate an AWS Policy, we need to extract the following fields:

  • The AWS service used, indicated by the :api key in our event.
  • The operation performed, indicated by the :operation key.
  • Finally, we need to determine the targeted resource.

While the first two steps are straightforward, determining the resource requires a little more thought. The location of the resource in use differs for each operation type.

For each event, we generate the following result:

{:api :s3, :action :GetObject :resource "redefine-test"}

So we encode such information in a nested map and extract the opertaions:

Extract operations

Some operations, like DynamoDB’s TransactWriteItems, bundle multiple other operations that need to be included individually in the policy. Other operations, such as DynamoDB’s BatchGetItem, can target multiple resources. Thus, in the resource map, instead of providing a keyword to extract the resource, we can provide a Clojure function to extract the required actions.

In just a few lines of code, we obtain the list of operations performed by our microservice along with the resource information:


Pretty cool 😎!

AWS policies require the resource to be expressed in ARN formatwhile in most requests, the resource is specified by a simple name. Therefore, we need to expand short names to AWS ARNs:

resource ARN

Now we have the full resource name in ARN format:

full operations

That’s all we need to generate a policy.

Generating the policy

The final step is to generate a valid Policy document. A policy groups permissions by resource. We add the following function to our pipeline:

extract policy

At Redefine we use Terraform to manage our infrastructure using Infrastructure As Code principles.

To produce the Terraform document we need a templating library, for this project we use clostache is a very simple library based on Mustache format.

Here is the template we need:


Now we can render the policy we extracted in our previous steps:


And the final result is a Terraform policy 😎:



The integration of μ/log into our AWS services has proven to be an invaluable asset. By capturing rich contextual data and generating detailed event logs, μ/log has empowered both technical and non-technical team members to gain deep insights into the behavior of our systems. With μ/log, we have achieved a level of observability that enables quick issue identification and resolution, even for individuals without deep technical expertise.

Furthermore, we have showcased how μ/log can be utilized to automatically generate AWS IAM Policies for our services. By leveraging the captured data, we extract the necessary information about AWS services, operations, and resources, and transform it into a well-defined policy document. This automation not only saves us time and effort but also ensures that our policies adhere to the Principle of Least Privilege, bolstering the security of our infrastructure.

Overall, μ/log has revolutionized the way we approach observability and policy generation in our AWS environment. Its ability to capture comprehensive data and facilitate automated processes has significantly improved our efficiency and reliability. We look forward to continuing our journey with μ/log and exploring more innovative ways to leverage its capabilities in our microservices architecture.

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