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jtimberlake / serverless-artillery

Combine serverless with artillery and you get serverless-artillery for instant, cheap, and easy performance testing at scale.
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Introduction

Combine serverless with artillery and you get serverless-artillery (a.k.a. slsart).

Serverless-artillery makes it easy to test your services for performance and functionality quickly, easily and without having to maintain any servers or testing infrastructure.

Use serverless-artillery if

  1. You want to know if your services (either internal or public) can handle different amount of traffic load (i.e. performance or load testing).
  2. You want to test if your services behave as you expect after you deploy new changes (i.e. acceptance testing).
  3. You want to constantly monitor your services over time to make sure the latency of your services is under control (i.e. monitoring mode).

Table of Contents

Installation

Installing on local machine

You can install serverless-artillery on your local machine as follows.

Prerequisite

1. Node JS

Before installing serverless-artillery, install Node JS from https://nodejs.org/en/download/ or with your operating system’s package manager. You can install the latest LTS version. We support any version higher than maintenance LTS (v8+).

2. Serverless Framework CLI

Before installing serverless-artillery, install Serverless Framework CLI (a.k.a. Serverless) (v1.38+). It should be either installed globally or available in the local node_modules. To install globally use the following command.

npm install -g serverless

Installing serverless-artillery

Now you can install serverless-artillery on your local machine using the following command.

npm install -g serverless-artillery

To check that the installation succeeded, run:

slsart --version

You should see serverless-artillery print its version if the installation has been successful.

Installing in Docker

If you prefer using Docker, refer to example Dockerfile for installation. Please note that, post installation causes permission issues when installing in a Docker image. To successfully install in Docker make sure to add the following to your Dockerfile before the Serverless Framework CLI (a.k.a. Serverless) and serverless-artillery install.

ENV NPM_CONFIG_PREFIX=/home/node/.npm-global
ENV PATH=$PATH:/home/node/.npm-global/bin

Uninstallation

When needed, you can uninstall serverless-artillery from you local machine using the following command.

npm uninstall -g serverless-artillery

How it works?

Technologies powering serverless-artillery

Click to expand/collapse

### Serverless Framework - The [Serverless Framework](https://serverless.com) makes managing (deploying/updating/removing) cloud assets easy. - It translates a `yaml` file to deployable assets of the target cloud provider (like AWS). - Serverless-artillery uses it to manage required assets to your cloud account. ### Artillery.io - [Artillery.io](https://artillery.io/) (built by Hassy Veldstra of shoreditch-ops) is an existing open-source node package, built for easy load testing and functional testing of a target/service/endpoint/URL. It provides a simple but powerful means of specifying how much load to create and what requests that load should comprise. - It takes in a developer-friendly JSON or YAML load script that specifies - target/URL/endpoint - load progression - and the scenarios that are important for your service to test. - It generates specified load, and measures and reports the resulting latency and return codes. - It generates the load by running on your local machine or servers. - However, if you specify more load in your script than what can be produced on your machine, artillery will throttle down the load specified in your script. While it is simple to distribute artillery across a fleet of servers, you must then manage, coordinate, and retire them. It is not a serverless solution. This is the task that serverless-artillery steps in to remove from your plate. ### Serverless-artillery - Serverless-artillery allows your script to specify an amount of load far exceeding the capacity of a single server to execute. - It breaks that script into smaller chunks (sized for a single instance of load generator Lambda function) and distribute the chunks for execution across multiple instances of load generator Lambda function. - Since this is done using a FaaS provider, the ephemeral infrastructure used to execute your load disappears as soon as your load tests are complete.

Load generator Lambda function on AWS

Click to expand/collapse

- Serverless-artillery generates the requests to run the specified tests using load generator Lambda function, which is deployed and invoked on AWS along with other assets. - Naming format is `--loadGenerator`. For example, `serverless-artillery-dev-loadGenerator` or `serverless-artillery-XnBa473psJ-dev-loadGenerator`. - It has an ephimeral architecture. It only exists as long as you need it. - It runs Artillery.io node package in AWS Lambda function. - Each lambda function can only generate a certain amount of load, and can only run for up to five minutes (five minutes was a built-in limitation of AWS Lambda. Now it has been raised to 15 minutes). - Given these limitations, it is often necessary to invoke more lambdas - both to scale horizontally (to generate higher load) as well as handing off the work to a new generation of lambdas before their run-time has expired. - Above diagram shows how Serverless Artillery solves this problem. - It first runs the Lamdba function in a **controller** mode. It examines the submitted load config JSON/YAML script (this is identical to the original “servered” [Artillery.io](https://artillery.io/) script). This script is also referred to as original script. If the load in the original script exceeds what a single lambda is configured to handle, then the load config is chopped up into workloads achievable by a single lambda. - Controller lambda then invokes as many **worker** lambdas as necessary to generate the load. Controller lambda passes a script to worker lambda that is created by chopping up the original script. - Towards the end of the Lambda runtime the controller lambda invokes a new controller lambda to produce load for the remaining duration. - The result of the load test can be reported to CloudWatch, InfluxDB or Datadog through plugins and then visualized with CloudWatch, Grafana or Datadog dashboard.

Before running serverless-artillery

Serverless-artillery needs to deploy assets like load generator Lambda function to AWS, invoke the function to run the tests and remove these assets from AWS when not needed. Hence you need an AWS account and setup credentials with which to deploy, invoke and remove the assets from AWS.

Setup for Nordstrom Technology

If you are a Nordstrom engineer, please see the page titled Serverless Artillery - Nordstrom Technology Setup in Confluence and follow the instructions there.

Setup for everyone else

In order to use serverless-artillery, depending on the AWS account environment you're working in, you may need to define AWS_PROFILE to declare the AWS credentials to use and possibly HTTP_PROXY in order to escape your corporate proxy. See the Serverless Framework docs or serverless-artillery workshop's Lesson 0 followed by Step 1 of Lesson 1 for details of how to set your local machine for successful deployment, invocation, and removal of assets from your AWS accounts.

Performance mode (performance/load testing)

You can use serverless-artillery to performance test or load test your service/target/endpoint/URL. Performance testing framework forms the basis of the other two modes of serverless-artillery, i.e. acceptance mode and monitoring mode.

Tutorial 1: Run a quick performance test

If you want to quickly test your setup or see serverless-artillery in action, do the following to quickly run a small load/performance test.

1. Setup AWS account credentials

Make sure you have setup your AWS account credentials before proceeding.

2. Command line

Go to command line for all the following steps in this tutorial. You can run the steps of this tutorial from anywhere in command line since the commands you run in this tutorial will not create any files on your local machine.

3. Deploy

The slsart deploy command deploys required assets (like load generator Lambda function) to the AWS account you selected in the previous step.

By default it uses service name serverless-artillery and stage name dev. And hence the default AWS CloudFormation Stack name becomes serverless-artillery-dev (format: <service-name default:serverless-artillery>-<stage-name default:dev>). You will see that if you go to your AWS account console > CloudFormation after running the command.

Since multiple developers could share an AWS account, we recommend creating a unique stack for your use. For that we recommend either using custom deployment assets as shown in Tutorial 3 or use the optional stage argument as shown in the following command.

slsart deploy --stage <your-unique-stage-name>

The AWS CloudFormation Stack name would be serverless-artillery-<your-unique-stage-name>.

For example,

slsart deploy --stage test1

The AWS CloudFormation Stack name in this case would be serverless-artillery-test1.

4. Invoke

The following command will invoke load generator Lambda function using the default load script (script.yml), creating small traffic against the sample endpoint specified in the default script. Note that this default load script is part of the global install of serverless-artillery and not in the local folder from where you are running the command.

slsart invoke --stage <your-unique-stage-name>

At the end of the test serverless-artillery will generate a report of the test. Please note that this report is generated only for small load. See here for details.

If you go to AWS Lambda console > find the loadGenerator Lambda corresponding to your stack > Monitoring tab > Invocations graph, you will see that the Lambda function was invoked to generate the load. You can also see the logs produced by the Lambda in CloudWatch Logs.

5. Remove

The following command will remove the AWS CloudFormation Stack deployed in step 3. If you are a Nordstrom engineer, please see the page titled Serverless Artillery - Remove Instructions in Confluence and follow the instructions there.

slsart remove --stage <your-unique-stage-name>

Tutorial 2: Performance test with custom script

Throughout this tutorial we will walk you towards performance testing the AWS website, https://aws.amazon.com/.

We would test with our custom script but would use default deployment assets.

1. Create new directory

Start by creating a new directory for this tutorial and go to that directory in command line.

2. Create script.yml

Serverless-artillery needs to know information about the performance test that user wants to run. It needs information like, the target URL of the service that user wants to test, load progression, user's interaction with the service (scenarios) etc. All these are described in a yml file. It is the same yml that Artillery.io uses.

Run the following command to create the initial script.yml file.

slsart script

3. Understanding script.yml

Open script.yml with your favorite editor to see what it contains.

Click to expand/collapse

``` # Thank you for trying serverless-artillery! # This default script is intended to get you started quickly. # There is a lot more that Artillery can do. # You can find great documentation of the possibilities at: # https://artillery.io/docs/ config: # this hostname will be used as a prefix for each URI in the flow unless a complete URI is specified target: "http://aws.amazon.com" phases: - duration: 5 arrivalRate: 2 scenarios: - flow: - get: url: "/" ```

4. Customizing script.yml

This step is optional in the tutorial. If you like you can customize script.yml as follows.

5. Setup AWS account credentials

Make sure you have setup your AWS account credentials before proceeding.

6. Deploy assets to AWS

This section is same as before. See here for details.

7. Invoke performance test

Now you are all set to invoke performance test using following command.

slsart invoke --stage <your-unique-stage-name>

At the end of the test serverless-artillery will generate a report of the test. Please note that this report is generated only for small load. See here for details.

If you go to AWS Lambda console > find the loadGenerator Lambda corresponding to your stack > Monitoring tab > Invocations graph, you will see that the Lambda function was invoked to generate the load. You can also see the logs produced by the Lambda in CloudWatch Logs.

NOTE that for performance testing, the command will take the script.yml from your local machine (and not the one deployed in AWS account) to run the performance test. Hence if you edit it on your local machine after deploying assets to AWS, you don't need to deploy again in order to run the performance test again. Also note that this is true only for performance test and acceptance test and not monitoring.

8. Remove assets from AWS

After the test is done, you can remove the assets from AWS using following command. If you are a Nordstrom engineer, please see the page titled Serverless Artillery - Remove Instructions in Confluence and follow the instructions there.

slsart remove --stage <your-unique-stage-name>

Tutorial 3: Performance test with custom deployment assets

Throughout this tutorial we will walk you towards performance testing the AWS website, https://aws.amazon.com/.

We would test with our custom script and custom deployment assets.

1. Create new directory

Start by creating a new directory for this tutorial and go to that directory in command line.

2. Create script.yml

This section is same as before. See here for details.

3. Understanding script.yml

This section is same as before. See here for details.

4. Customizing script.yml

This section is same as before. See here for details.

5. Create custom deployment assets

Create a local copy of the deployment assets for your customization and then deployment to AWS, using following command. The command generates a local copy of the load generator lambda function code (along with other assets) that can be edited and deployed with your changed settings if needed. It also runs npm install after creating local copy of the deployment assets.

slsart configure

The important files among other files created by this command are as follows.

File Description
package.json Node.js dependencies for the load generator Lambda. Add Artillery.io plugins you want to use here.
serverless.yml Serverless-artillery's service definition/configuration using Serverless Framework. Change AWS-specific settings here.
handler.js Load generator Lambda code. EDIT AT YOUR OWN RISK.

Note that everytime you make changes to these local copy of deployment assets or serverless.yml file, you need to redeploy using slsart deploy command.

Note that if you change package.json then you need to run npm install and then redeploy using slsart deploy command.

6. Understanding serverless.yml

serverless.yml contains serverless-artillery's service definition/configuration using Serverless Framework.

Open serverless.yml with your favorite editor to see what it contains.

Click to expand/collapse

``` # We're excited that this project has provided you enough value that you are looking at its code! # # This is a standard [Serverless Framework](https://www.serverless.com) project and you should # feel welcome to customize it to your needs and delight. # # If you do something super cool and would like to share the capability, please open a PR against # https://www.github.com/Nordstrom/serverless-artillery # # Thanks! # If the following value is changed, your service may be duplicated (this value is used to build the CloudFormation # Template script's name) service: serverless-artillery-XnBa473psJ provider: name: aws runtime: nodejs10.x iamRoleStatements: # This policy allows the function to invoke itself which is important if the script is larger than a single # function can produce - Effect: 'Allow' Action: - 'lambda:InvokeFunction' Resource: 'Fn::Join': - ':' - - 'arn:aws:lambda' - Ref: 'AWS::Region' - Ref: 'AWS::AccountId' - 'function' - '${self:service}-${opt:stage, self:provider.stage}-loadGenerator*' # must match function name # This policy allows the function to publish notifications to the SNS topic defined below with logical ID monitoringAlerts - Effect: 'Allow' Action: - 'sns:Publish' Resource: Ref: monitoringAlerts # must match the SNS topic's logical ID functions: loadGenerator: # !!Do not edit this name!! handler: handler.handler # the serverlessArtilleryLoadTester handler() method can be found in the handler.js source file timeout: 300 # set timeout to be 5 minutes (max for Lambda) environment: TOPIC_ARN: Ref: monitoringAlerts TOPIC_NAME: 'Fn::GetAtt': - monitoringAlerts - TopicName events: - schedule: name: '${self:service}-${opt:stage, self:provider.stage}-monitoring' # !!Do not edit this name!! description: The scheduled event for running the function in monitoring mode rate: rate(1 minute) ######################################################################################################################## ### !! BEFORE ENABLING... !!! ### 0. Change `'>>': script.yml` below to reference the script you want to use for monitoring if that is not its name. ### The script must be in this directory or a subdirectory. ### 1. Modify your `script.yml` to provide the details of invoking every important surface of your service, as per ### https://artillery.io/docs ### 2. Add a `match` clause to your requests, specifying your expectations of a successful request. This relatively ### undocumented feature is implemented at: https://github.com/shoreditch-ops/artillery/blob/82bdcdfc32ce4407bb197deff2cee13b4ecbab3b/core/lib/engine_util.js#L318 ### We would welcome the contribution of a plugin replacing this as discussed in https://github.com/Nordstrom/serverless-artillery/issues/116 ### 3. Modify the `monitoringAlerts` SNS Topic below, uncommenting `Subscription` and providing subscriptions for any ### alerts that might be raised by the monitoring function. (To help you out, we've provided commented-out examples) ### (After all, what good is monitoring if noone is listening?) ### 4. Deploy your new assets/updated service using `slsart deploy` ### 5. [As appropriate] approve the subscription verifications for the SNS topic that will be sent following its creation ######################################################################################################################## enabled: false input: '>>': script.yml mode: monitoring resources: Resources: monitoringAlerts: # !!Do not edit this name!! Type: 'AWS::SNS::Topic' Properties: DisplayName: '${self:service} Monitoring Alerts' # Subscription: # docs at https://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-sns-subscription.html # - Endpoint: http:/// # the endpoint is an URL beginning with "http://" # Protocol: http # - Endpoint: https:/// # the endpoint is a URL beginning with "https://" # Protocol: https # - Endpoint: @ # the endpoint is an email address # Protocol: email # - Endpoint: @ # the endpoint is an email address # Protocol: email-json # - Endpoint: # the endpoint is a phone number of an SMS-enabled device # Protocol: sms # - Endpoint: # the endpoint is the ARN of an Amazon SQS queue # Protocol: sqs # - Endpoint: # the endpoint is the EndpointArn of a mobile app and device. # Protocol: application # - Endpoint: # the endpoint is the ARN of an AWS Lambda function. # Protocol: lambda ```

Please refer to serverless.yml documentation for details. It defines assets needed for monitoring (turned off by default) as well which we will discuss later.

a. Service name

b. Load generator Lambda function name

The Serverless Framework automatically names the Lambda function based on the service, stage and function name as follows.

7. Customizing serverless.yml

NOTE: Except for one step for Nordstrom Engineers, all customizations are optional in the tutorial.

If you like you can customize serverless.yml as follows.

a. Customization for Nordstrom Engineers

If you are a Nordstrom engineer, please see the page titled Serverless Artillery - Nordstrom Technology Policies in Confluence and follow the instructions there.

b. Service name

c. Plugins

You can customize the serverless.yml to use required tools/plugins mentioned below.

i. CloudWatch plugin

In this tutorial you can add artillery-plugin-cloudwatch to record test results to AWS CloudWatch.

  1. To allow the Lambda code to write to CloudWatch, the correct NPM package dependency must be added. This modifies the package.json file to include the necessary dependency.

    npm install --save artillery-plugin-cloudwatch

  2. In script.yml, at the end of the config block (which already exists)

    config:

    add CloudWatch plugin as follows:

    plugins:
cloudwatch:
  namespace: "<cloud-watch-namespace>"

    For example, you can use

      namespace: "serverless-artillery-myperftestservice-loadtest"

  3. In serverless.yml, at the end of the following block (which already exists)

    provider:
iamRoleStatements:

    add the following:

    - Effect: 'Allow'
  Action:
    - 'cloudwatch:PutMetricData'
  Resource:
    - '*'
    ii. Datadog plugin

    In this tutorial you can add artillery-plugin-datadog to record test results to Datadog.

  4. To allow the Lambda code to write to Datadog, the correct NPM package dependency must be added. This modifies the package.json file to include the necessary dependency.

    npm install --save artillery-plugin-datadog

  5. Update the config portion of script.yml to add Datadog plugin as follows and customize the host, prefix and tags as per your requirement.

    config:
plugins:
datadog:
  # Custom hostname (leave blank if not desired) 
  host: ''
  # Custom metric prefix (example, to 'serverless-artillery') 
  prefix: 'serverless-artillery'
  # Additional tags for all metrics 
  tags:
    - 'mode:test'

  6. In serverless.yml, under provider section specify Datadog API key as an environment variable as follows. NOTE that you should not save sensitive information like Datadog API Key in plain text in a source control. Below is just for the tutorial.

    provider:
environment:
DATADOG_API_KEY: "<your-datadog-api-key>"

8. Setup AWS account credentials

This section is same as before. See here for details.

9. Deploy assets to AWS

This section is same as before. See here for details.

You can go to your AWS account console > CloudFormation, and see AWS stack <service-name default:serverless-artillery>-<stage-name default:dev> created there depending on the customizations explained in the steps above.

10. Invoke performance test

This section is same as before. See here for details.

If you used CloudWatch/Datadog plugins you will be able to view the metrics on the CloudWatch/Datadog dashboard. You can learn more about using CloudWatch dashboard here. Note that it can take few minutes for the data to propogate to CloudWatch/Datadog.

11. Remove assets from AWS

This section is same as before. See here for details.

Tutorial 4: Killing in-progress performance test

While running performance/load test it is sometimes necessary to kill the test before it is complete. Read more about the kill command.

1. Increase duration

If you are a Nordstrom engineer, please follow Tutorial 3 to create custom script and custom deployment assets. Make sure you do customization for Nordstrom Engineers. Other optional customizations are not necessary for this tutorial.

Others can follow Tutorial 2 to create custom script.yml.

Edit script.yml in your favorite editor and increase the duration to 60 seconds.

2. Setup AWS account credentials

This section is same as before. See here for details.

3. Deploy assets to AWS

This section is same as before. See here for details.

4. Invoke performance test

This section is same as before. See here for details.

5. Kill the in-progress performance test

Run the following command to kill the performance test. Read more about the kill command here. Note that kill command will also remove the deployed assets. Hence running slsart remove after this is not needed. 

slsart kill --stage <your-unique-stage-name> --region=<region-used-for-deploy>

You must specify a region when running this command:

6. Wait before re-deploying

Wait for ~5 minutes before re-deploying to let the Lambda invocation queue drain.

Performance test workshop

We've created a workshop detailing end-to-end usage of serverless-artillery for performance testing. Check out our conference-style workshop for step by step lessons on how to set your system up for successful deployment, invocation, and removal.

Other commands and use cases

Killing in-progress performance test

While running performance/load test it is sometimes necessary to kill the test before it is complete. For example, it might be done when the test target is not able to handle the current load and you want to stop the test before the service goes down.

You can run the following command to kill the performance test.

slsart kill --stage <your-unique-stage-name> --region=<region-used-for-deploy>

You must specify a region when running this command:

The command will do the followings:

Result:

You will want to wait approximately 5 minutes before redeploying to avoid the killed performance test from resuming. Behind the scenes, AWS creates a queue for Lambda invocations. While processing the invocation requests from the queue, if a function is not available then that message will be placed back onto the queue for further attempts. As a result, redeploying your function can allow those re-queued messages to be used to invoke your re-deployed function. In our observation based on a limited set of tests, messages will be permanently failed out of the queues after 5 minutes. That is the basis of our recommendation.

The default maximum duration of a script chunk is 2 minutes (maxChunkDurationInSeconds). As a result of this, on average, load will not be produced after 1 minute but it could continue for up to the full 2 minutes. To lower the wait times after killing, this value can be overridden in your script.yml within the _split attribute, as shown here. This value can be as low as 15 seconds and using this value causes each script chunk to run for a maximum duration of 15 seconds. Theoretically, this means that you’d only have to wait 7.5 seconds on average for tests to stop running after killing your test (in practice we have observed roughly 20 seconds lag between killing a function and termination of invocations).

Create customized script.yml

Above you used how to use slsart script to create the default script.yml (see here) and how to customize it by manually editing it (see here).

slsart script command has options to quickly do the above in one command. Run the following command to create custom script.yml with one load phase.

slsart script -e <your-target-endpoint> -d <duration-in-sec> -r <arrival-rate-in-virtual-users-arriving-per-second> -t <ramp-to-in-virtual-users-arriving-per-second>

For example, following command will create a script.yml with test target https://example.com, performance test starting with 10 requests per second, and scaling up to 25 requests per second, over a duration of 60 seconds.

slsart script -e https://example.com -d 60 -r 10 -t 25

For more details see

slsart script --help

Performance test using script file with different name/path

The slsart script command by default gives the file name script.yml. If you want to give a different name to your yml file then you can use the -o option of the slsart script command. Seeslsart script --help for more details.

slsart script -o <preferred-filename.yml>

Example,

slsart script -o myservicetests.yml

By default slsart invoke command will look for script.yml under the local folder to run performance test. You can use -p option to specify script file with different name/path as follows.

slsart invoke -p <path-to-your-script-file>

For example, following command will invoke performance test using the specified file.

slsart invoke -p /my/path/to/myotherscript.yml

For more options see,

slsart invoke --help

Reserved and unsupported flags

slsart commands support most commandline flags of the corresponding sls (Serverless Framework) commands.

Reserved flags

Following flags are reserved in slsart invoke command.

Providing a data store to view the results of your performance test

Related tools and plugins

You would need to create custom deployment assets and customize serverless.yml to use a plugin as shown in the examples here.

Plugin Description
artillery-plugin-aws-sigv4 for testing against an authenticated AWS API Gateway endpoint.
artillery-plugin-influxdb to record test results to InfluxDB.
artillery-plugin-cloudwatch to record test results to AWS CloudWatch.
artillery-plugin-datadog to record test results to DataDog.
serverless-attach-managed-policy if you have automatic IAM role modification in your corporate/shared AWS account.

Performance testing VPC hosted services

The default deployment assets (used in Tutorial 1 and Tutorial 2) of serverless-artillery are not deployed in a VPC and hence it can only successfully send requests to public endpoints. If your service is hosted in VPC (i.e. service is internal and does not have public endpoint), you would need to use custom deployment assets.

Please refer to Serverless Frameworks's doc to understand how to customize serverless.yml to deploy the customized assets to VPC.

You need to add following section to your serverless.yml and add appropriate securityGroupIds and subnetIds.

provider:
  name: aws
  vpc:
    securityGroupIds:
      - securityGroupId1
      - securityGroupId2
    subnetIds:
      - subnetId1
      - subnetId2

Using Payload/CSV files to inject data in scenarios of your script.yml

Advanced customization use cases

Deployment assets and settings customization

Test script and execution customization using Artillery.io

Script splitting customization

As mentioned here, the controller mode load generator Lambda function splits the work to generate the required load between multiple worker mode load generator Lambdas. The following controls are available to control how splitting is done. That said, the defaults are good and you generally won't need them until you have gotten deeper into implementation.
To use these, define a _split attribute within your script.yml. The values of that object will be used to alter the splitting of your script.

{
  _split: {
    maxScriptDurationInSeconds: 86400,  # Default listed.  Hard-coded max is 518400
    maxChunkDurationInSeconds: 120,     # Default listed.  Hard-coded max is 285, min is 15
    maxScriptRequestsPerSecond: 5000,   # Default listed.  Hard-coded max is 50000
    maxChunkRequestsPerSecond: 25,      # Default listed.  Hard-coded max is 500
    timeBufferInMilliseconds: 15000,    # Default listed.  Hard-coded max is 30000
  }
  ...
}

See the Splitting and Distribution Logic Customization section for an in depth discussion of how splitting is implemented and what you control with these parameters as well as the concerns involved in making decisions about them. See the comments in ~/lambda/handler.js for detailed documentation of the semantics the code has with regard to them (search for 'const constants'). By the way, you now have the source code to change those hard-coded limits and can change them at will if you so desire - we wanted to provide a margin of safety and guardrails but not restrictions.

Debugging and Tracing Behavior Customization

There are two primary tools for debugging and tracing the load generator Lambda function and how it splits and executes the task it has been given. Define the following in your script.yml:

{
  _trace: true,
  _simulation: true,
  ...
}
_trace

_trace causes the load generator Lambda function to report the actions it is taking with your script and the chunks that it breaks your script into. Expect statements such as this:

scheduling self invocation for 1234567890123 in 2345678901234 with a 3456789012345 ms delay

This would be produced by the following line in the source code:

console.log(`scheduling self invocation for ${event._genesis} in ${event._start} with a ${timeDelay} ms delay`);

Here are definitions that will help you understand these statements. In the code you will see _genesis, _start, now, and timeDelay:

_trace is very useful in identifying what the system is doing or where something is going wrong. #bugs-happen

_simulation

Setting the _simulation attribute to a truthy value will cause the load generator Lambda function to split the script without taking action on the script. Functionally, this comprises splitting the given script into pieces without invoking functions to handle the split chunks and/or execute the load described by those chunks. Concretely, when it comes time to invoke new function instances for distributing the load, it simply invokes (or schedules an invokation of) itself. Likewise, when it comes time to invoke the artillery entry point for generator load from the chunk, it instead invokes the simulation shim that reports what would have been executed and immediately completes.

This mode, in combination with _trace related behavior is very helpful in debugging script splitting behavior and identifying what the logic declares should occur.

Splitting and Distribution Logic Customization

You've got the code. Have at! Have fun and consider contributing improvements back into the tool. Thank you!

Some helpful notions used in the code and discussion of them follows.

Scripts

An artillery script is composed of a number of phases which occur one after the other. Each of these phases has its own duration and maximum load. The duration is straightforwardly how long the phase lasts. The maximum load of the phase is the maximum Requests Per Second (RPS) that are declared for the entirety of that phase (e.g. a phase declaring a ramp up from 0 to 500 RPS has a maximum load of 500 RPS). Phases are declared in serial in order to provide warming or not as appropriate for the load testing scenario that interests you.

The duration of the script is the sum of the durations of its phases. The maximum load of the script is the maximum RPS that any of its phases declares.

Splitting

The splitting of a script comprises taking "chunks" off of the script.

First, we take chunks from the script by duration. This is driven by the maximum duration of the underlying function as a service (FaaS) provider that we are using. For AWS Lambda, this at the time of original implementation was 5 minutes (now 15 minutes). However, we need to allow for cold starts and as such must provide a buffer of time before we begin the execution of any specific load job. Following the execution of a load job, the artillery framework calculates a summary and invokes custom analyzers (via the plugin capabilities it offers). As a result, a tailing buffer is also needed to ensure execution can properly complete.

The result is a script chunk that can be executed within the duration limited period the FaaS provider allows (no guarantees yet exist on whether a single function can execute the demanded load). This chunk will be called the script for referential simplicity. We also may have a remainder script that must be executed by a new function instance as the current splitting function nears its timeout.

Next, we take chunks from the script by maximum load. This is driven by the maximum requests per second that a single execution of the underlying function as a service (FaaS) provider is capable of pushing with high fidelity. For AWS Lambda (with the default 1024 MB configuration), we found 25 RPS to be a good level. This is lower than the absolute ceiling that Lambda is capable of pushing for a reason. First, each connection will be a separate opened and closed socket. Second, if we are producing too many connections, we can be in the middle of making a request when we receive the response of a separate request. Given that this is implemented in NodeJS, we have one thread and that means the timestamping of the receipt of that response is artificially and incorrectly delayed. We found that at RPS above 25 we observed an increase in the volatility of observed latencies. That written, if you do not intend to record your latencies, then you could bump this up to the limit of the FaaS service (i.e. _split.maxChunkRequestsPerSecond = 300 or so).

The result is a script chunk that is less than the limited period and also executable by a single function instance. Therefore, we invoke a single function with the chunk to execute it.

Acceptance mode

Find defects before performance testing! Acceptance mode runs each scenario/flow in your script exactly once and reports the results. For example, you can run your script in acceptance mode in your CI/CD to ensure that merges don't break the scenarios in your script.

Performance testing framework forms the basis of acceptance mode of serverless-artillery. Hence please go through performance mode section before proceeding.

match clause

Ensure that you have match clauses defined for each request in your script's flows to validate the responses. You can read about how to use match in Artillery.io docs and "official" docs. Also see, serverless-artillery issue #116.

Acceptance test command

When -a option is used in slsart invoke command, serverless-artillery invokes the load generator Lambda in acceptance mode.

slsart invoke -a

Expect a non-zero exit code if a match clause fails.

Tutorial 5: Acceptance mode

1. Customize script.yml

Follow Tutorial 2 to create custom script.yml and customize your script.yml by copy pasting the following content in it. Note the match clauses.

# Thank you for trying serverless-artillery!
# This default script is intended to get you started quickly.
# There is a lot more that Artillery can do.
# You can find great documentation of the possibilities at:
# https://artillery.io/docs/
config:
  # this hostname will be used as a prefix for each URI in the flow unless a complete URI is specified
  target: "https://postman-echo.com/headers"
  phases:
    -
      duration: 1
      arrivalRate: 1
  defaults:
    headers:
      my-sample-header: "my-sample-header-value"
scenarios:
  -
    flow:
      -
        get:
          url: "/"
          match:
            - json: "$.headers.my-sample-header"
              value: "my-sample-header-value"
              #value: "failvalue"
            - json: "$.headers.host"
              value: "postman-echo.com"
              #value: "failvalue"
            - json: "$.headers.x-forwarded-proto"
              value: "https"
              #value: "failvalue"

The script is using target https://postman-echo.com/headers which at the time of writing this document is designed to return JSON response with headers that are passed in the request.

Run the following to try that out.

curl --location --request GET "https://postman-echo.com/headers" --header "my-sample-header: my-sample-header-value"

The JSON response will be as follows.

{
  "headers": {
    "x-forwarded-proto": "https",
    "host": "postman-echo.com",
    "accept": "*/*",
    "my-sample-header": "my-sample-header-value",
    "user-agent": "curl/7.54.0",
    "x-forwarded-port": "443"
  }
}

The match clauses check if the return value is same as what is expected.

2. Setup AWS account credentials

This section is same as before. See here for details.

3. Deploy assets to AWS

This section is same as before. See here for details.

Note that you don't need to deploy the assets everytime script.yml changes.

4. Invoke acceptance test

Run following command to run acceptance test.

slsart invoke -a --stage <your-unique-stage-name>

5. Observe the results

You will see an output similar to the following.

Click to expand/collapse

``` Invoking test Lambda { "errors": 0, "reports": [ { "timestamp": "2019-04-17T21:40:59.127Z", "scenariosCreated": 1, "scenariosCompleted": 1, "requestsCompleted": 1, "latency": { "min": 26.6, "max": 26.6, "median": 26.6, "p95": 26.6, "p99": 26.6 }, "rps": { "count": 1, "mean": 2.7 }, "scenarioDuration": { "min": 177.8, "max": 177.8, "median": 177.8, "p95": 177.8, "p99": 177.8 }, "scenarioCounts": { "0": 1 }, "errors": {}, "codes": { "200": 1 }, "matches": 3, "customStats": {}, "phases": [ { "pause": 0.2430741319294641 }, { "duration": 1, "arrivalRate": 1 } ] } ], "totals": { "scenariosCreated": 1, "scenariosCompleted": 1, "requestsCompleted": 1, "codes": { "200": 1 }, "errors": {} } } Your function invocation has completed. { "errors": 0, "reports": [ { "timestamp": "2019-04-17T21:40:59.127Z", "scenariosCreated": 1, "scenariosCompleted": 1, "requestsCompleted": 1, "latency": { "min": 26.6, "max": 26.6, "median": 26.6, "p95": 26.6, "p99": 26.6 }, "rps": { "count": 1, "mean": 2.7 }, "scenarioDuration": { "min": 177.8, "max": 177.8, "median": 177.8, "p95": 177.8, "p99": 177.8 }, "scenarioCounts": { "0": 1 }, "errors": {}, "codes": { "200": 1 }, "matches": 3, "customStats": {}, "phases": [ { "pause": 0.2430741319294641 }, { "duration": 1, "arrivalRate": 1 } ] } ], "totals": { "scenariosCreated": 1, "scenariosCompleted": 1, "requestsCompleted": 1, "codes": { "200": 1 }, "errors": {} } } Results: PASSED ```

You can observe the following in the result:

Also, as this test passed, you will also observe that the process exit code is 0. On Mac you can run command echo $? immediately after that to see the process exit code 0 in this case.

6. Test failure scenario

6.1. Edit script.yml to fail match

Edit match section in script.yml to look for wrong return value to simulate failure scenario. Note that we are causing failure for two matches out of three.

          match:
            - json: "$.headers.my-sample-header"
              value: "my-sample-header-value"
              #value: "failvalue"
            - json: "$.headers.host"
              #value: "postman-echo.com"
              value: "failvalue"
            - json: "$.headers.x-forwarded-proto"
              #value: "https"
              value: "failvalue"

6.2. Invoke acceptance test

Invoke acceptance test as mentioned above.

6.3. Observe the results

You will see an output similar to the following.

Click to expand/collapse

``` Invoking test Lambda { "errors": 2, "reports": [ { "timestamp": "2019-04-17T21:56:59.527Z", "scenariosCreated": 1, "scenariosCompleted": 0, "requestsCompleted": 1, "latency": { "min": 24.7, "max": 24.7, "median": 24.7, "p95": 24.7, "p99": 24.7 }, "rps": { "count": 1, "mean": 2 }, "scenarioDuration": { "min": null, "max": null, "median": null, "p95": null, "p99": null }, "scenarioCounts": { "0": 1 }, "errors": { "Failed match: expected=failvalue got=postman-echo.com expression=$.headers.host": 1, "Failed match: expected=failvalue got=https expression=$.headers.x-forwarded-proto": 1 }, "codes": { "200": 1 }, "matches": 0, "customStats": {}, "phases": [ { "pause": 0.29122591362828076 }, { "duration": 1, "arrivalRate": 1 } ] } ], "totals": { "scenariosCreated": 1, "scenariosCompleted": 0, "requestsCompleted": 1, "codes": { "200": 1 }, "errors": { "Failed match: expected=failvalue got=postman-echo.com expression=$.headers.host": 1, "Failed match: expected=failvalue got=https expression=$.headers.x-forwarded-proto": 1 } }, "errorMessage": "acceptance failure: scenarios run: 1, total errors: 2, error budget: 0" } Your function invocation has completed. { "errors": 2, "reports": [ { "timestamp": "2019-04-17T21:56:59.527Z", "scenariosCreated": 1, "scenariosCompleted": 0, "requestsCompleted": 1, "latency": { "min": 24.7, "max": 24.7, "median": 24.7, "p95": 24.7, "p99": 24.7 }, "rps": { "count": 1, "mean": 2 }, "scenarioDuration": { "min": null, "max": null, "median": null, "p95": null, "p99": null }, "scenarioCounts": { "0": 1 }, "errors": { "Failed match: expected=failvalue got=postman-echo.com expression=$.headers.host": 1, "Failed match: expected=failvalue got=https expression=$.headers.x-forwarded-proto": 1 }, "codes": { "200": 1 }, "matches": 0, "customStats": {}, "phases": [ { "pause": 0.29122591362828076 }, { "duration": 1, "arrivalRate": 1 } ] } ], "totals": { "scenariosCreated": 1, "scenariosCompleted": 0, "requestsCompleted": 1, "codes": { "200": 1 }, "errors": { "Failed match: expected=failvalue got=postman-echo.com expression=$.headers.host": 1, "Failed match: expected=failvalue got=https expression=$.headers.x-forwarded-proto": 1 } }, "errorMessage": "acceptance failure: scenarios run: 1, total errors: 2, error budget: 0" } Results: FAILED acceptance failure: scenarios run: 1, total errors: 2, error budget: 0 ```

You can observe the following in the result.

Also, as this test failed, you will also observe that the process exit code is non-zero (number of match failures). On Mac you can run command echo $? immediately after that to see the process exit code 2 in this case.

7. Remove assets from AWS

This section is same as before. See here for details.

More about acceptance mode

Acceptance testing in CI/CD

For the purposes of facilitating the use of this tool in a CI/CD pipeline, if any of the acceptance tests fail to successfully complete, the process will exit with a non-zero exit code.

Run script.yml exclusively in acceptance mode

To hard code acceptance mode into your script add the following in your script.yml:

mode: acceptance
...

note: 'acceptance' may be abbreviated to 'acc' in the script

Use same script.yml for performance and acceptance testing and monitoring

You can use the same script.yml for performance, acceptance testing and monitoring so you don't have to maintain multiple files. The scenarios that are important for performance test would be used for acceptance testing and monitoring as well.

Since acceptance mode will run all scenarios only once (by default), the scripts that only are run in acceptance mode are not required to have a phases array in the config section of the script.

To configure acceptance behavior

You may configure sampling behavior. To control the number of samples taken, the time before taking a sample, or the number of errors constituting a failure, you may supply the following (default values listed):

sampling:
  size: 1            # The size of sample set
  averagePause: 0.2  # The average number of seconds to pause between samples
  pauseVariance: 0.1 # The maximum difference of the actual pause from the average pause (in either direction)
  errorBudget: 0     # The number of observed errors to accept before alerting

Monitoring mode

Detect outages quickly. Use serverless-artillery to generate synthetic customer activity to continously validate the expected system behavior and optionally alert you immediately if your users will be impacted.

Performance testing framework forms the basis of monitoring mode of serverless-artillery. Hence please go through performance mode section before proceeding.

Tutorial 6: Monitoring mode without serverless-artillery alert

If you don't need serverless-artillery to send an alert when monitoring detects a problem then follow the tutorial here. You can forward the test result to your data store and use alerting service there to noify you.

1. Create custom deployment assets

Follow Tutorial 3 to create custom deployment assets.

2. Setup AWS account credentials

This section is same as before. See here for details.

3. Tryout monitoring mode

3.1. Deploy assets to AWS

This section is same as before. See here for details. Note that monitoring is turned off by default in serverless.yml and hence the assets deployed in this step would not start monitoring.

3.2. Invoke monitoring once

When -m option is used in slsart invoke command, serverless-artillery invokes the load generator Lambda in monitoring mode. This is useful also during script development to avoid having to redeploy everytime you edit script.yml as mentioned below.

slsart invoke -m --stage <your-unique-stage-name>

Given default monitoring behavior configuration, each scenario/flow in your script will be executed five times only once.

4. Customize deployment assets to turn on monitoring

Open serverless.yml in your favorite editor. Under functions > loadGenerator > events > schedule > find enabled: false. Set it to true.

Notice instruction 0 and 1 under BEFORE ENABLING section if they are applicable for your use case.

5. Deploy assets to AWS to start monitoring

This section is same as before. See here for details. Note that in the previous step monitoring was turned on and hence just deploying the assets would turn on monitoring. Separate invoke is not needed.

NOTE: In performance test and acceptance test, the script.yml is passed with invoke command and hence redeployment is not needed when you edit script.yml. But monitoring mode uses the script.yml that is deployed in slsart deploy command. Also invoke command is not used in monitoring mode. Hence you need to redeploy everytime you edit script.yml. During script development you can take advantage of slsart invoke -m to try monitoring with your script and avoid having to redeploy each time it is changed.

Given default moitoring behavior configuration, each scenario/flow in your script will be executed five times every minute.

6. Pause monitoring

Monitoring mode will run 24x7 until turned off or paused. If you need to pause monitoring you can do the following.

6.1. Method 1: Using CloudWatch Rules

7. Remove assets from AWS

If you want to keep the 24x7 monitoring then you don't need to do this step.

When you want to turn off monitoring then remove the assets from AWS. See here for details.

Tutorial 7: Monitoring mode with serverless-artillery alert

Here we will look into how to setup monitoring such that serverless-artillery sends alert when it detects a problem.

1. Create custom deployment assets

Follow Tutorial 3 to create custom deployment assets.

2. Setup AWS account credentials

This section is same as before. See here for details.

3. Customize script to have match clause

Ensure that you have match clauses defined for each request in your script's flows to validate the responses. See here to learn more about match.

For the purpose of this tutorial you can copy paste the script from here.

4. Customize deployment assets to add at least one subscription

Open serverless.yml in your favorite editor.

5. Tryout monitoring mode

5.1. Deploy assets to AWS

This section is same as before. See here for details.

5.2. Invoke monitoring once

This section is same as before. See here for details.

6. Test failure scenario

We will inject failure scenario so that the match fails and monitoring mode sends us an alert.

6.1. Edit script.yml to fail match

Edit script.yml as mentioned here to cause match to fail.

6.2. Invoke monitoring once

Follow instruction here to use -m option of slsart invoke command to invoke monitoring once to try our modified script.yml without having to redeploy assets.

Given default moitoring behavior configuration, each scenario/flow in your script will be executed five times only once. If all five of them fail (we try to avoid notifying you about blips) then you should receive a notification via the configured mechanism (email in the case of this tutorial).

Below is sample email.

Click to expand/collapse

``` Alert: monitoring failure: scenarios run: 5, total errors: 10, error budget: 4 Logs: Full analysis: { "errors": 10, "reports": [ { "timestamp": "2019-04-17T23:16:17.050Z", "scenariosCreated": 5, "scenariosCompleted": 0, "requestsCompleted": 5, "latency": { "min": 12.4, "max": 25.2, "median": 24, "p95": 25.2, "p99": 25.2 }, "rps": { "count": 5, "mean": 0.93 }, "scenarioDuration": { "min": null, "max": null, "median": null, "p95": null, "p99": null }, "scenarioCounts": { "0": 5 }, "errors": { "Failed match: expected=failvalue got=postman-echo.com expression=$.headers.host": 5, "Failed match: expected=failvalue got=https expression=$.headers.x-forwarded-proto": 5 }, "codes": { "200": 5 }, "matches": 0, "customStats": {}, "phases": [ { "pause": 0.2255259122021872 }, { "duration": 1, "arrivalRate": 1 }, { "pause": 0.2968933399583734 }, { "duration": 1, "arrivalRate": 1 }, { "pause": 0.16654656047499483 }, { "duration": 1, "arrivalRate": 1 }, { "pause": 0.19488041671127268 }, { "duration": 1, "arrivalRate": 1 }, { "pause": 0.19656039636288947 }, { "duration": 1, "arrivalRate": 1 } ] } ], "totals": { "scenariosCreated": 5, "scenariosCompleted": 0, "requestsCompleted": 5, "codes": { "200": 5 }, "errors": { "Failed match: expected=failvalue got=postman-echo.com expression=$.headers.host": 5, "Failed match: expected=failvalue got=https expression=$.headers.x-forwarded-proto": 5 } }, "errorMessage": "monitoring failure: scenarios run: 5, total errors: 10, error budget: 4" } -- If you wish to stop receiving notifications from this topic, please click or visit the link below to unsubscribe: https://sns.us-east-1.amazonaws.com/unsubscribe.html?SubscriptionArn=arn:aws:sns:us-east-1:515126931066:serverless-artillery-hPDAiDvuzL-ash-monitoringAlerts-3PPB71S63RM2:e11606d1-e70d-482c-82f9-eff26a760e68&Endpoint=ashmi.s.bhanushali@nordstrom.com Please do not reply directly to this email. If you have any questions or comments regarding this email, please contact us at https://aws.amazon.com/support ```

You will also see the following output at the command line.

Click to expand/collapse

``` Invoking test Lambda { "errors": 10, "reports": [ { "timestamp": "2019-04-17T23:06:29.570Z", "scenariosCreated": 5, "scenariosCompleted": 0, "requestsCompleted": 5, "latency": { "min": 21.4, "max": 52.1, "median": 25.8, "p95": 52.1, "p99": 52.1 }, "rps": { "count": 5, "mean": 0.94 }, "scenarioDuration": { "min": null, "max": null, "median": null, "p95": null, "p99": null }, "scenarioCounts": { "0": 5 }, "errors": { "Failed match: expected=failvalue got=postman-echo.com expression=$.headers.host": 5, "Failed match: expected=failvalue got=https expression=$.headers.x-forwarded-proto": 5 }, "codes": { "200": 5 }, "matches": 0, "customStats": {}, "phases": [ { "pause": 0.19110438826323195 }, { "duration": 1, "arrivalRate": 1 }, { "pause": 0.2695130316914205 }, { "duration": 1, "arrivalRate": 1 }, { "pause": 0.10236624757585773 }, { "duration": 1, "arrivalRate": 1 }, { "pause": 0.13588464289194607 }, { "duration": 1, "arrivalRate": 1 }, { "pause": 0.2951659631896233 }, { "duration": 1, "arrivalRate": 1 } ] } ], "totals": { "scenariosCreated": 5, "scenariosCompleted": 0, "requestsCompleted": 5, "codes": { "200": 5 }, "errors": { "Failed match: expected=failvalue got=postman-echo.com expression=$.headers.host": 5, "Failed match: expected=failvalue got=https expression=$.headers.x-forwarded-proto": 5 } }, "errorMessage": "monitoring failure: scenarios run: 5, total errors: 10, error budget: 4" } Your function invocation has completed. { "errors": 10, "reports": [ { "timestamp": "2019-04-17T23:06:29.570Z", "scenariosCreated": 5, "scenariosCompleted": 0, "requestsCompleted": 5, "latency": { "min": 21.4, "max": 52.1, "median": 25.8, "p95": 52.1, "p99": 52.1 }, "rps": { "count": 5, "mean": 0.94 }, "scenarioDuration": { "min": null, "max": null, "median": null, "p95": null, "p99": null }, "scenarioCounts": { "0": 5 }, "errors": { "Failed match: expected=failvalue got=postman-echo.com expression=$.headers.host": 5, "Failed match: expected=failvalue got=https expression=$.headers.x-forwarded-proto": 5 }, "codes": { "200": 5 }, "matches": 0, "customStats": {}, "phases": [ { "pause": 0.19110438826323195 }, { "duration": 1, "arrivalRate": 1 }, { "pause": 0.2695130316914205 }, { "duration": 1, "arrivalRate": 1 }, { "pause": 0.10236624757585773 }, { "duration": 1, "arrivalRate": 1 }, { "pause": 0.13588464289194607 }, { "duration": 1, "arrivalRate": 1 }, { "pause": 0.2951659631896233 }, { "duration": 1, "arrivalRate": 1 } ] } ], "totals": { "scenariosCreated": 5, "scenariosCompleted": 0, "requestsCompleted": 5, "codes": { "200": 5 }, "errors": { "Failed match: expected=failvalue got=postman-echo.com expression=$.headers.host": 5, "Failed match: expected=failvalue got=https expression=$.headers.x-forwarded-proto": 5 } }, "errorMessage": "monitoring failure: scenarios run: 5, total errors: 10, error budget: 4" } Results: FAILED monitoring failure: scenarios run: 5, total errors: 10, error budget: 4 ```

You can observe the following in the result.

Also, as this test failed, you will also observe that the process exit code is non-zero (number of match failures). On Mac you can run command echo $? immediately after that to see the process exit code 10 in this case.

7. Customize deployment assets to turn on monitoring

This section is same as before. See here for details.

8. Deploy assets to AWS to start monitoring

This section is same as before. See here for details.

In this step, along with deployment asset we are also deploying the modified script.yml where match will fail. Hence once the assets are deployed, monitoring mode will turn on and send you an email to alert about these failures.

Given default monitoring behavior configuration, each scenario/flow in your script will be executed five times every minute. All five of the executions will cause the match statements to fail exceeding errorBudget (default 4) and hence would send the email alerting about the problem (once every minute).

9. Pause monitoring

This section is same as before. See here for details.

10. Remove assets from AWS

This section is same as before. See here for details.

More about monitoring mode

Run script.yml exclusively in monitoring mode

To hard code monitoring mode into your script add the following in your script.yml:

mode: monitoring
...

note: 'monitoring' may be abbreviated to 'mon' in the script

Use same script.yml for performance and acceptance testing and monitoring

You can use the same script.yml for performance, acceptance testing and monitoring so you don't have to maintain multiple files. The scenarios that are important for performance test would be used for acceptance testing and monitoring as well.

Since monitoring mode will run all scenarios five times (by default), the scripts that only are run in monitoring mode are not required to have a phases array in the config section of the script.

To configure monitoring behavior

You may configure sampling behavior. To control the number of samples taken, the time before taking a sample, or the number of errors constituting a failure, you may supply the following (default values listed):

sampling:
  size: 5            # The size of sample set
  averagePause: 0.2  # The average number of seconds to pause between samples
  pauseVariance: 0.1 # The maximum difference of the actual pause from the average pause (in either direction)
  errorBudget: 4     # The number of observed errors to accept before alerting

Detailed Usage

$ slsart --help

slsart <command>

Commands:
  slsart deploy     Deploy a default version of the function that will execute
                    your Artillery scripts.  See
                    https://serverless.com/framework/docs/providers/aws/cli-refe
                    rence/deploy/ for reference.
  slsart invoke     Invoke your function with your Artillery script.  Will
                    prefer a script given by `-d`, `--data`, `-p`, or `--path`
                    over a `script.[yml|json]` file in the current directory
                    over the default script.  Invocation mode will default to
                    "performance" but adding the `-a` flag will run the script
                    in "acceptance" mode.  See
                    https://serverless.com/framework/docs/providers/aws/cli-refe
                    rence/invoke/ for reference.
  slsart kill       Stop a currently running load test and remove the function.
  slsart remove     Remove the function and the associated resources created for
                    or by it.  See
                    https://serverless.com/framework/docs/providers/aws/cli-refe
                    rence/remove/ for reference.
  slsart script     Create a local Artillery script so that you can customize it
                    for your specific load requirements.  See
                    https://artillery.io for documentation.
  slsart configure  Create a local copy of the deployment assets for
                    modification and deployment.  See
                    https://serverless.com/framework/docs/ for documentation.

Options:
  --help         Show help                                             [boolean]
  --version      Show version number                                   [boolean]
  -D, --debug    Execute the command in debug mode.  It will be chatty about
                 what it is happening in the code.
  -V, --verbose  Execute the command in verbose mode.  It will be chatty about
                 what it is attempting to accomplish.

Commands

deploy

slsart deploy --help
slsart deploy

Deploy a default version of the function that will execute your Artillery
scripts.  See
https://serverless.com/framework/docs/providers/aws/cli-reference/deploy/ for
reference.

Options:
  --help         Show help                                             [boolean]
  --version      Show version number                                   [boolean]
  -D, --debug    Execute the command in debug mode.  It will be chatty about
                 what it is happening in the code.
  -V, --verbose  Execute the command in verbose mode.  It will be chatty about
                 what it is attempting to accomplish.

invoke

slsart invoke --help
slsart invoke

Invoke your function with your Artillery script.  Will prefer a script given by
`-d`, `--data`, `-p`, or `--path` over a `script.[yml|json]` file in the current
directory over the default script.  Invocation mode will default to
"performance" but adding the `-a` flag will run the script in "acceptance" mode.
See https://serverless.com/framework/docs/providers/aws/cli-reference/invoke/
for reference.

Options:
  --help            Show help                                          [boolean]
  --version         Show version number                                [boolean]
  -D, --debug       Execute the command in debug mode.  It will be chatty about
                    what it is happening in the code.
  -V, --verbose     Execute the command in verbose mode.  It will be chatty
                    about what it is attempting to accomplish.
  -a, --acceptance  Execute the script in acceptance mode.  It will execute each
                    flow once, reporting failures.
  -m, --monitoring  Execute the script in monitoring mode.  It will execute each
                    flow a multiple of times, alerting if the number of errors
                    exceeds the configured threshold.
  -d, --data        A stringified script to execute
  -p, --path        A path to the file containing the script to execute
  --si, --stdIn     Have serverless read the event to invoke the remote function
                    with from the "standard in" stream
  --jo, --jsonOnly  Only write JSON to console.log to facilitate piping the
                    invocation result into a tool such as jq

kill

slsart kill --help
slsart kill

Stop a currently running load test and remove the function.

Options:
  --help         Show help                                             [boolean]
  --version      Show version number                                   [boolean]
  -D, --debug    Execute the command in debug mode.  It will be chatty about
                 what it is happening in the code.
  -V, --verbose  Execute the command in verbose mode.  It will be chatty about
                 what it is attempting to accomplish.

remove

slsart remove --help
slsart remove

Remove the function and the associated resources created for or by it.  See
https://serverless.com/framework/docs/providers/aws/cli-reference/remove/ for
reference.

Options:
  --help         Show help                                             [boolean]
  --version      Show version number                                   [boolean]
  -D, --debug    Execute the command in debug mode.  It will be chatty about
                 what it is happening in the code.
  -V, --verbose  Execute the command in verbose mode.  It will be chatty about
                 what it is attempting to accomplish.

script

slsart script --help
slsart script

Create a local Artillery script so that you can customize it for your specific
load requirements.  See https://artillery.io for documentation.

Options:
  --help          Show help                                            [boolean]
  --version       Show version number                                  [boolean]
  -D, --debug     Execute the command in debug mode.  It will be chatty about
                  what it is happening in the code.
  -V, --verbose   Execute the command in verbose mode.  It will be chatty about
                  what it is attempting to accomplish.
  -e, --endpoint  The endpoint to load with traffic.                    [string]
  -d, --duration  The duration, in seconds, to load the given endpoint. [number]
  -r, --rate      The rate, in requests per second, at which to load the given
                  endpoint.                                             [number]
  -t, --rampTo    The rate to ramp up to from the given (starting) rate, in
                  requests per second at which to load the given endpoint.
                                                                        [number]
  -o, --out       The file to output the generated script in to.        [string]

configure

slsart configure --help
slsart configure

Create a local copy of the deployment assets for modification and deployment.
See https://docs.serverless.com for documentation.

Options:
  --help         Show help                                             [boolean]
  --version      Show version number                                   [boolean]
  -D, --debug    Execute the command in debug mode.  It will be chatty about
                 what it is happening in the code.
  -V, --verbose  Execute the command in verbose mode.  It will be chatty about
                 what it is attempting to accomplish.

Troubleshooting

Problems installing?

Error: npm ERR! code EACCES

If you are installing into a node_modules owned by root and getting error npm ERR! code EACCES, read this.

External References

  1. artillery.io for documentation about how to define your load shape, volume, targets, inputs, et cetera
  2. serverless.com for documentation about how to create a custom function configuration
  3. serverless-artillery README for documentation on the use of this tool
  4. serverless-star Next generation implementation and generalization of the arbitrarily wide work distribution capability

If you've read this far

Please let us know any feedback on this tool. We would love to hear from you. Thank you!