terragrunt VS jaeger

name: Configure on: push: branches: - main pull_request: branches: - main workflow_dispatch: inputs: destroy: description: 'Run Terragrunt destroy command' required: true default: 'false' type: choice options: - true - false jobs: apply: if: ${{ !inputs.destroy || inputs.destroy == 'false' }} runs-on: ubuntu-latest steps: - name: Checkout repository uses: actions/checkout@v2 - name: Setup SSH uses: webfactory/[email protected] with: ssh-private-key: ${{ secrets.SSH_PRIVATE_KEY }} - name: Setup Terraform uses: hashicorp/setup-terraform@v2 with: terraform_version: 1.5.5 terraform_wrapper: false - name: Setup Terragrunt run: | curl -LO "https://github.com/gruntwork-io/terragrunt/releases/download/v0.48.1/terragrunt_linux_amd64" chmod +x terragrunt_linux_amd64 sudo mv terragrunt_linux_amd64 /usr/local/bin/terragrunt terragrunt -v - name: Apply Terraform changes run: | cd dev terragrunt run-all apply -auto-approve --terragrunt-non-interactive -var AWS_ACCESS_KEY_ID=$AWS_ACCESS_KEY_ID -var AWS_SECRET_ACCESS_KEY=$AWS_SECRET_ACCESS_KEY -var AWS_REGION=$AWS_DEFAULT_REGION env: AWS_ACCESS_KEY_ID: ${{ vars.AWS_ACCESS_KEY_ID }} AWS_SECRET_ACCESS_KEY: ${{ secrets.AWS_SECRET_ACCESS_KEY }} AWS_DEFAULT_REGION: ${{ vars.AWS_DEFAULT_REGION }} destroy: if: ${{ inputs.destroy == 'true' }} runs-on: ubuntu-latest steps: - name: Checkout repository uses: actions/checkout@v2 - name: Setup SSH uses: webfactory/[email protected] with: ssh-private-key: ${{ secrets.SSH_PRIVATE_KEY }} - name: Setup Terraform uses: hashicorp/setup-terraform@v2 with: terraform_version: 1.5.5 terraform_wrapper: false - name: Setup Terragrunt run: | curl -LO "https://github.com/gruntwork-io/terragrunt/releases/download/v0.48.1/terragrunt_linux_amd64" chmod +x terragrunt_linux_amd64 sudo mv terragrunt_linux_amd64 /usr/local/bin/terragrunt terragrunt -v - name: Destroy Terraform changes run: | cd dev terragrunt run-all destroy -auto-approve --terragrunt-non-interactive -var AWS_ACCESS_KEY_ID=$AWS_ACCESS_KEY_ID -var AWS_SECRET_ACCESS_KEY=$AWS_SECRET_ACCESS_KEY -var AWS_REGION=$AWS_DEFAULT_REGION env: AWS_ACCESS_KEY_ID: ${{ vars.AWS_ACCESS_KEY_ID }} AWS_SECRET_ACCESS_KEY: ${{ secrets.AWS_SECRET_ACCESS_KEY }} AWS_DEFAULT_REGION: ${{ vars.AWS_DEFAULT_REGION }}

‍Terragrunt is a thin wrapper that provides extra tools for keeping your Terraform configurations DRY (Don't Repeat Yourself), working with multiple Terraform modules, and managing remote state. It's particularly useful in managing large-scale infrastructure deployments with Terraform.

... #************************** Terraform ************************************* ARG TERRAFORM_VERSION=1.7.3 RUN set -ex \ && curl -O https://releases.hashicorp.com/terraform/${TERRAFORM_VERSION}/terraform_${TERRAFORM_VERSION}_linux_amd64.zip && unzip terraform_${TERRAFORM_VERSION}_linux_amd64.zip -d /usr/local/bin/ RUN set -ex \ && mkdir -p $HOME/.terraform.d/plugin-cache && echo 'plugin_cache_dir = "$HOME/.terraform.d/plugin-cache"' > ~/.terraformrc #************************* Terragrunt ************************************* ARG TERRAGRUNT_VERSION=0.55.1 RUN set -ex \ && wget https://github.com/gruntwork-io/terragrunt/releases/download/v${TERRAGRUNT_VERSION}/terragrunt_linux_amd64 -q \ && mv terragrunt_linux_amd64 /usr/local/bin/terragrunt \ && chmod +x /usr/local/bin/terragrunt #*********************** Terramate **************************************** ARG TERRAMATE_VERSION=0.4.5 RUN set -ex \ && wget https://github.com/mineiros-io/terramate/releases/download/v${TERRAMATE_VERSION}/terramate_${TERRAMATE_VERSION}_linux_x86_64.tar.gz \ && tar -xzf terramate_${TERRAMATE_VERSION}_linux_x86_64.tar.gz \ && mv terramate /usr/local/bin/terramate \ && chmod +x /usr/local/bin/terramate #*********************** tfsec ******************************************** ARG TFSEC_VERSION=1.28.5 RUN set -ex \ && wget https://github.com/aquasecurity/tfsec/releases/download/v${TFSEC_VERSION}/tfsec-linux-amd64 \ && mv tfsec-linux-amd64 /usr/local/bin/tfsec \ && chmod +x /usr/local/bin/tfsec \ && terragrunt --version #**********************Terraform docs ************************************ ARG TERRRAFORM_DOCS_VERSION=0.17.0 RUN set -ex \ && curl -sSLo ./terraform-docs.tar.gz https://terraform-docs.io/dl/v${TERRRAFORM_DOCS_VERSION}/terraform-docs-v${TERRRAFORM_DOCS_VERSION}-$(uname)-amd64.tar.gz \ && tar -xzf terraform-docs.tar.gz \ && chmod +x terraform-docs \ && mv terraform-docs /usr/local/bin/terraform-docs #********************* ShellCheck ***************************************** ARG SHELLCHECK_VERSION="stable" RUN set -ex \ && wget -qO- "https://github.com/koalaman/shellcheck/releases/download/${SHELLCHECK_VERSION?}/shellcheck-${SHELLCHECK_VERSION?}.linux.x86_64.tar.xz" | tar -xJv \ && cp "shellcheck-${SHELLCHECK_VERSION}/shellcheck" /usr/bin/ \ && shellcheck --version ...

Our first attempt was to introduce other engineering teams to Terraform - the Platform team was already using it extensively with Terragrunt, and using Atlantis to automate plan and apply operations in a Git flow to ensure infrastructure was consistent. We'd written modules, with documentation, and an engineer would simply need to raise a PR to use the module and provide the right values, and Atlantis (once the PR was approved by Platform) would go ahead and set it up for them.

Terragrunt: An extension of Terraform, Terragrunt assists in managing complex infrastructure with less duplication and more efficiency. Its power lies in its ability to manage dependencies and its dry configuration approach.

Copy the link and download on your terminal using the wget command. Example: wget https://github.com/gruntwork-io/terragrunt/releases/download/v0.54.19/terragrunt_linux_amd64

name: Configure on: push: branches: - main pull_request: branches: - main jobs: terraform: runs-on: ubuntu-latest steps: - name: Checkout repository uses: actions/checkout@v2 - name: Setup SSH uses: webfactory/[email protected] with: ssh-private-key: ${{ secrets.SSH_PRIVATE_KEY }} - name: Setup Terraform uses: hashicorp/setup-terraform@v2 with: terraform_version: 1.5.5 terraform_wrapper: false - name: Setup Terragrunt run: | curl -LO "https://github.com/gruntwork-io/terragrunt/releases/download/v0.48.1/terragrunt_linux_amd64" chmod +x terragrunt_linux_amd64 sudo mv terragrunt_linux_amd64 /usr/local/bin/terragrunt terragrunt -v - name: Apply Terraform changes run: | cd dev terragrunt run-all apply -auto-approve --terragrunt-non-interactive -var AWS_ACCESS_KEY_ID=$AWS_ACCESS_KEY_ID -var AWS_SECRET_ACCESS_KEY=$AWS_SECRET_ACCESS_KEY -var AWS_REGION=$AWS_DEFAULT_REGION cd apache-server/ec2-web-server public_ip=$(terragrunt output instance_public_ip) echo "$public_ip" > public_ip.txt cat public_ip.txt env: AWS_ACCESS_KEY_ID: ${{ secrets.AWS_ACCESS_KEY_ID }} AWS_SECRET_ACCESS_KEY: ${{ secrets.AWS_SECRET_ACCESS_KEY }} AWS_DEFAULT_REGION: ${{ secrets.AWS_DEFAULT_REGION }} - name: Upload artifact uses: actions/upload-artifact@v4 with: name: ip-artifact path: dev/apache-server/ec2-web-server/public_ip.txt ansible: runs-on: ubuntu-latest needs: terraform steps: - name: Download artifact uses: actions/download-artifact@v4 with: name: ip-artifact - name: Configure Ansible run: | sudo apt update sudo pipx inject ansible-core jmespath ansible-playbook --version sudo echo "[web]" >> ansible_hosts sudo cat public_ip.txt >> ansible_hosts mv ansible_hosts $HOME sudo cat $HOME/ansible_hosts - name: Configure playbook run: | cd $HOME cat > deploy.yml < Test Page This is a test page EOF cat $HOME/deploy.yml - name: Run playbook uses: dawidd6/action-ansible-playbook@v2 with: playbook: deploy.yml directory: /home/runner key: ${{secrets.SSH_PRIVATE_KEY}} options: | --inventory ansible_hosts --verbose

Created and maintained by Gruntwork, Terragrunt is a tool designed to enhance Terraform’s capabilities. It acts as a thin wrapper around Terraform, offering additional features to streamline and optimise Terraform usage. Key functions of Terragrunt include helping users keep their Terraform configurations DRY (Don’t Repeat Yourself), efficiently managing multiple Terraform modules, and handling remote state management. By reducing repetition in Terraform code and simplifying the management of complex module dependencies and remote state, Terragrunt makes working with Terraform more efficient, especially for larger or more complex infrastructure deployments.

locals { # Automatically load region-level variables region_vars = read_terragrunt_config(find_in_parent_folders("region.hcl")) # Automatically load environment-level variables` environment_vars = read_terragrunt_config(find_in_parent_folders("env.hcl")) # Extract the variables we need for easy access account_name = local.environment_vars.locals.account_name account_id = local.environment_vars.locals.aws_account_id aws_region = local.region_vars.locals.aws_region # This is the S3 bucket where the Terraform State Files will be stored remote_state_bucket = "devops-bucket" # This is the DynamoDB table where Terraform will add the locking status dynamodb_table = "terraform-state-lock" # IAM Role for Terraform backend to assume terraform_backend_role = "arn:aws:iam::{shared-services_account_id}:role/terraform-backend-role" environment_path = replace(path_relative_to_include(), "environments/", "") # https://github.com/hashicorp/terraform/releases terraform_version = "latest" # https://github.com/gruntwork-io/terragrunt/releases terragrunt_version = "latest" } # Generate an AWS provider block generate "provider" { path = "provider.tf" if_exists = "overwrite_terragrunt" contents = <

If your team works with Terraform, you should definitely try Terragrunt (and obviously, its CLI tool!). It's an Infrastructure as Code (IaC) tool that acts as a wrapper for Terraform and simplifies dealing with multiple Terraform modules in different environments.

Jaeger maps the flow of requests and data as they traverse a distributed system. These requests may make calls to multiple services, which may introduce their own delays or errors. https://www.jaegertracing.io/

As engineers at past startups, we often had to debug slow queries, poor load times, inconsistent errors, etc... While tools like Jaegar [2] helped us inspect server-side performance, we had no way to tie user events to the traces we were inspecting. In other words, although we had an idea of what API route was slow, there wasn’t much visibility into the actual bottleneck.

This is where our performance product comes in: we’re rethinking a tracing/performance tool that focuses on bridging the gap between the client and server.

What’s unique about our approach is that we lean heavily into creating traces from the frontend. For example, if you’re using our Next.js SDK, we automatically connect browser HTTP requests with server-side code execution, all from the perspective of a user. We find this much more powerful because you can understand what part of your frontend codebase causes a given trace to occur. There’s an example here [3].

From an instrumentation perspective, we’ve built our SDKs on-top of OTel, so you can create custom spans to expand highlight-created traces in server routes that will transparently roll up into the flame graph you see in our UI. You can also send us raw OTel traces and manually set up the client-server connection if you want. [4] Here’s an example of what a trace looks like with a database integration using our Golang GORM SDK, triggered by a frontend GraphQL query [5] [6].

In terms of how it's built, we continue to rely heavily on ClickHouse as our time-series storage engine. Given that traces require that we also query based on an ID for specific groups of spans (more akin to an OLTP db), we’ve leveraged the power of CH materialized views to make these operations efficient (described here [7]).

To try it out, you can spin up the project with our self hosted docs [8] or use our cloud offering at app.highlight.io. The entire stack runs in docker via a compose file, including an OpenTelemetry collector for data ingestion. You’ll need to point your SDK to export data to it by setting the relevant OTLP endpoint configuration (ie. environment variable OTEL_EXPORTER_OTLP_LOGS_ENDPOINT [9]).

Overall, we’d really appreciate feedback on what we’re building here. We’re also all ears if anyone has opinions on what they’d like to see in a product like this!

[1] https://github.com/highlight/highlight/blob/main/LICENSE

[2] https://www.jaegertracing.io

[3] https://app.highlight.io/1383/sessions/COu90Th4Qc3PVYTXbx9Xe...

[4] https://www.highlight.io/docs/getting-started/native-opentel...

[5] https://static.highlight.io/assets/docs/gorm.png

[6] https://github.com/highlight/highlight/blob/1fc9487a676409f1...

[7] https://highlight.io/blog/clickhouse-materialized-views

[8] https://www.highlight.io/docs/getting-started/self-host/self...

[9] https://opentelemetry.io/docs/concepts/sdk-configuration/otl...

For the request following, something like jeager https://www.jaegertracing.io/, because you are talking more about tracing than necessarily logging. For just monitoring, https://github.com/prometheus-community/helm-charts/tree/main/charts/kube-prometheus-stack would be the starting point, then it depends. Nginx gives metrics out of the box, then you can pull in the dashboard like https://grafana.com/grafana/dashboards/14314-kubernetes-nginx-ingress-controller-nextgen-devops-nirvana/ , or full metal with something like service mesh monitoring which would provably fulfil most of the requirements

Have you checked out Jaeger [1]? It is lightweight enough for a personal project, but featureful enough to really help "turn on the lightbulb" with other engineers to show them the difference between logging/monitoring and tracing.

[1] https://www.jaegertracing.io/

From the perspective of the realization of GraphQL infrastructure, the interesting direction is "Finding". How to find the problem? How to find the bottleneck of the system? Distributed Tracing System (DTS) will help answer this question. Distributed tracing is a method of observing requests as they propagate through distributed environments. In our scenario, we have dozens of subgraphs, gateway, and transport layer through which the request goes. We have several tools that can be used to detect the whole lifecycle of the request through the system, e.g. Jaeger, Zipkin or solutions that provided DTS as a part of the solution NewRelic.

Jaeger is an open-source, distributed tracing system that monitors and troubleshoots the flow of requests through complex, microservices-based applications, providing a comprehensive view of system interactions.

However, ensuring fault tolerance in distributed systems is not at all easy. These systems are complex, with multiple nodes or components working together. A failure in one node can cascade across the system if not addressed timely. Moreover, the inherently distributed nature of these systems can make it challenging to pinpoint the exact location and cause of fault - that is why modern systems rely heavily on distributed tracing solutions pioneered by Google Dapper and widely available now in Jaeger and OpenTracing. But still, understanding and implementing fault tolerance becomes not just about addressing the failure but predicting and mitigating potential risks before they escalate.

In this article, we'll use HoneyComb.io as our tracing backend. While there are other tools in the market, some of which can be run on your local machine (e.g., Jaeger), I chose HoneyComb because of their complementary tools that offer improved monitoring of the service and insights into its behavior.

The best way to do this, is with the help of tracing tools such as paid tools such as Honeycomb, or your own instance of the open source Jaeger offering, or perhaps Encore's built in tracing system.

In this example, I will create 3 Node.js services (shipping, notification, and courier) using Amplication, add traces to all services, and show how to analyze trace data using Jaeger.