Auto-Instrumentation

In order to deploy the resources defined in the main.tf file, you first need to make sure that Terraform is initialized in the same folder as that file.

• Change to the auto directory:

cd ~/workshop/lambda/auto

• Run the following command to initialize Terraform in this directory

terraform init

• This command will create a number of elements in the same folder:
  • .terraform.lock.hcl file: to record the providers it will use to provide resources
    • .terraform directory: to store the provider configurations
  • In addition to the above files, when terraform is run using the apply subcommand, the terraform.tfstate file will be created to track the state of your deployed resources.
  • These enable Terraform to manage the creation, state and destruction of resources, as defined within the main.tf file of the auto directory

Once we’ve initialized Terraform in this directory, we can go ahead and deploy our resources.

• First, run the terraform plan command to ensure that Terraform will be able to create your resources without encountering any issues.

terraform plan

• This will result in a plan to deploy resources and output some data, which you can review to ensure everything will work as intended.

  • Do note that a number of the values shown in the plan will be known post-creation, or are masked for security purposes.

• Next, run the terraform apply command to deploy the Lambda functions and other supporting resources from the main.tf file:

terraform apply

• Respond yes when you see the Enter a value: prompt

• This will result in the following outputs:

Outputs:

base_url = "https://______.amazonaws.com/serverless_stage/producer"
consumer_function_name = "_____-consumer"
consumer_log_group_arn = "arn:aws:logs:us-east-1:############:log-group:/aws/lambda/______-consumer"
consumer_log_group_name = "/aws/lambda/______-consumer"
environment = "______-lambda-shop"
lambda_bucket_name = "lambda-shop-______-______"
producer_function_name = "______-producer"
producer_log_group_arn = "arn:aws:logs:us-east-1:############:log-group:/aws/lambda/______-producer"
producer_log_group_name = "/aws/lambda/______-producer"

• Terraform outputs are defined in the outputs.tf file.
• These outputs will be used programmatically in other parts of our workshop, as well.

To start getting some traces from our deployed Lambda functions, we would need to generate some traffic. We will send a message to our producer-lambda function’s endpoint, which should be put as a record into our Kinesis Stream, and then pulled from the Stream by the consumer-lambda function.

• Change to the auto directory:

cd ~/workshop/lambda/auto

The send_message.py script is a Python script that will take input at the command line, add it to a JSON dictionary, and send it to your producer-lambda function’s endpoint repeatedly, as part of a while loop.

• Run the send_message.py script as a background process
  • It requires the --name and --superpower arguments

nohup ./send_message.py --name CHANGEME --superpower CHANGEME &

• You should see an output similar to the following if your message is successful

[1] 179789
nohup: ignoring input and appending output to 'nohup.out'

• The two most import bits of information here are:

  • The process ID on the first line (79829 in the case of my example), and
  • The appending output to nohup.out message
    • The nohup command ensures the script will not hang up when sent to the background. It also captures the curl output from our command in a nohup.out file in the same folder as the one you’re currently in.
    • The & tells our shell process to run this process in the background, thus freeing our shell to run other commands.

• Next, check the contents of the response.logs file, to ensure your output confirms your requests to your producer-lambda endpoint are successful:

cat response.logs

• You should see the following output among the lines printed to your screen if your message is successful:

{"message": "Message placed in the Event Stream: {prefix}-lambda_stream"}

• If unsuccessful, you will see:

{"message": "Internal server error"}

Next, let’s take a look at the logs for our Lambda functions.

• To view your producer-lambda logs, check the producer.logs file:

cat producer.logs

• To view your consumer-lambda logs, check the consumer.logs file:

cat consumer.logs

Examine the logs carefully.