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atlassian / localstack


A fully functional local AWS cloud stack. Develop and test your cloud apps offline!


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LocalStack - A fully functional local AWS cloud stack


Please note: The main version of this repository is, please raise PRs against that repo.

LocalStack provides an easy-to-use test/mocking framework for developing Cloud applications.

Currently, the focus is primarily on supporting the AWS cloud stack.

LocalStack spins up the following core Cloud APIs on your local machine:

Additionally, LocalStack provides a powerful set of tools to interact with the cloud services, including a fully featured KCL Kinesis client with Python binding, simple setup/teardown integration for nosetests, as well as an Environment abstraction that allows to easily switch between local and remote Cloud execution.

Why LocalStack?

LocalStack builds on existing best-of-breed mocking/testing tools, most notably kinesalite/dynalite and moto. While these tools are awesome (!), they lack functionality for certain use cases. LocalStack combines the tools, makes them interoperable, and adds important missing functionality on top of them:

  • Error injection: LocalStack allows to inject errors frequently occurring in real Cloud environments, for instance ProvisionedThroughputExceededException which is thrown by Kinesis or DynamoDB if the amount of read/write throughput is exceeded.
  • Actual HTTP REST services: All services in LocalStack allow actual HTTP connections on a TCP port. In contrast, moto uses boto client proxies that are injected into all methods annotated with @mock_sqs. These client proxies do not perform an actual REST call, but rather call a local mock service method that lives in the same process as the test code.
  • Language agnostic: Although LocalStack is written in Python, it works well with arbitrary programming languages and environments, due to the fact that we are using the actual REST APIs via HTTP.
  • Isolated processes: All services in LocalStack run in separate processes. The overhead of additional processes is negligible, and the entire stack can easily be executed on any developer machine and CI server. In moto, components are often hard-wired in RAM (e.g., when forwarding a message on an SNS topic to an SQS queue, the queue endpoint is looked up in a local hash map). In contrast, LocalStack services live in isolation (separate processes available via HTTP), which fosters true decoupling and more closely resembles the real cloud environment.
  • Pluggable services: All services in LocalStack are easily pluggable (and replaceable), due to the fact that we are using isolated processes for each service. This allows us to keep the framework up-to-date and select best-of-breed mocks for each individual service (e.g., kinesalite is much more advanced than its moto counterpart).


  • make
  • python (currently only Python 2 supported)
  • pip (python package manager)
  • npm (node.js package manager)
  • java/javac (Java runtime environment and compiler)
  • mvn (Maven, the build system for Java)


The easiest way to install LocalStack is via pip:

pip install localstack

Running in Docker

You can also spin up LocalStack without any installation requirements, using Docker:

docker run -it -p 4567-4580:4567-4580 -p 8080:8080 atlassianlabs/localstack

... or simply use the make target which runs the same command:

make docker-run


You can pass the following environment variables to LocalStack:

  • SERVICES: Comma-separated list of service names and (optional) ports they should run on. If no port is specified, a default port is used. Service names basically correspond to the service names of the AWS CLI (kinesis, lambda, sqs, etc), although LocalStack only supports a subset of them. Example value: kinesis,lambda:4569,sqs:4570 to start Kinesis on the default port, Lambda on port 4569, and SQS on port 4570.
  • DEFAULT_REGION: AWS region to use when talking to the API (defaults to us-east-1).
  • HOSTNAME: If you need to expose your services on a specific host (defaults to localhost).
  • KINESIS_ERROR_PROBABILITY: Decimal value between 0.0 (default) and 1.0 to randomly inject ProvisionedThroughputExceededException errors into Kinesis API responses.
  • DYNAMODB_ERROR_PROBABILITY: Decimal value between 0.0 (default) and 1.0 to randomly inject ProvisionedThroughputExceededException errors into DynamoDB API responses.
  • LAMBDA_EXECUTOR: Method to use for executing Lambda functions. Valid values are local (run the code in a temporary directory on the local machine) or docker (run code in a separate Docker container). In the latter case, if LocalStack itself is started inside Docker, then the docker command needs to be available inside the container (usually requires to run the container in privileged mode). Default is docker, fallback to local if Docker is not available.
  • DATA_DIR: Local directory for saving persistent data (currently only supported for these services: Kinesis, DynamoDB, Elasticsearch). Defaults to /tmp/localstack/data.


If you pull the repo in order to extend/modify LocalStack, run this command to install all the dependencies:

make install

This will install the required pip dependencies in a local Python virtualenv directory .venv (your global python packages will remain untouched), as well as some node modules in ./localstack/node_modules/. Depending on your system, some pip/npm modules may require additional native libs installed.


The project comes with a set of unit and integration tests which can be kicked off via a make target:

make test

Running the infrastructure

The Makefile contains a target to conveniently run the local infrastructure.

make infra

Then you can point your aws CLI to use the local infrastructure, for example:

aws --endpoint-url=http://localhost:4568 kinesis list-streams
    "StreamNames": []

If you are accessing the cloud APIs from within yout Python code, you can also use boto3 and use the endpoint_url parameter to connect to the respective service on localhost. See for convenience methods to connect to the local services.

Integration with nosetests

If you want to use LocalStack in your integration tests (e.g., nosetests), simply fire up the infrastructure in your test setup method and then clean up everything in your teardown method:

from localstack.mock import infra

def setup():

def teardown():

def my_app_test():
    # here goes your test logic

See the example test file tests/ for more details.

Integration with Java/JUnit

In order to use LocalStack with Java, the project ships with a simple JUnit runner. Take a look at the example JUnit test in ext/java. When you run the test, all dependencies are automatically downloaded and installed to a temporary directory in your system.

public class MyCloudAppTest {

  public void testLocalS3API() {
    AmazonS3 s3 = new AmazonS3Client(...);
    List<Bucket> buckets = s3.listBuckets();


The LocalStack JUnit test runner is published as a Maven artifact in the Bitbucket repository. Simply add the following configuration to your pom.xml file:

<project ...>



Web Dashboard

The projects also comes with a simple Web dashboard that allows to view the deployed AWS components and the relationship between them.

make install-web
make web

Change Log

  • v0.4.0: Execute Lambda functions in Docker containers; CORS headers for S3
  • v0.3.11: Add Route53, SES, CloudFormation; DynamoDB fault injection; UI tweaks; refactor config
  • v0.3.10: Add initial support for S3 bucket notifications; fix subprocess32 installation
  • v0.3.9: Make services/ports configurable via $SERVICES; add tests for Firehose+S3
  • v0.3.8: Fix Elasticsearch via local bind and proxy; refactoring; improve error logging
  • v0.3.5: Fix lambda handler name; fix host name for S3 API; install web libs on pip install
  • v0.3.4: Fix file permissions in build; fix and add UI to Docker image; add stub of ES API
  • v0.3.3: Add version tags to Docker images
  • v0.3.2: Add support for Redshift API; code refactoring
  • v0.3.1: Add Dockerfile and push image to Docker Hub
  • v0.3.0: Add simple integration for JUnit; improve process signal handling
  • v0.2.11: Refactored the AWS assume role function
  • v0.2.10: Added AWS assume role functionality.
  • v0.2.9: Kinesis error response formatting
  • v0.2.7: Throw Kinesis errors randomly
  • v0.2.6: Decouple SNS/SQS: intercept SNS calls and forward to subscribed SQS queues
  • v0.2.5: Return error response from Kinesis if flag is set
  • v0.2.4: Allow Lambdas to use file (import from file instead of exec'ing)
  • v0.2.3: Improve Kinesis/KCL auto-checkpointing (leases in DDB)
  • v0.2.0: Speed up installation time by lazy loading libraries
  • v0.1.19: Pass shard_id in records sent from KCL process
  • v0.1.16: Minor restructuring and refactoring (create separate
  • v0.1.14: Fix AWS tokens when creating Elasticsearch client
  • v0.1.11: Add startup/initialization notification for KCL process
  • v0.1.10: Bump version of amazon_kclpy to 1.4.1
  • v0.1.9: Add initial support for SQS/SNS
  • v0.1.8: Fix installation of JARs in amazon_kclpy if localstack is installed transitively
  • v0.1.7: Bump version of amazon_kclpy to 1.4.0
  • v0.1.6: Add travis-ci and coveralls configuration
  • v0.1.5: Refactor Elasticsearch utils; fix bug in method to delete all ES indexes
  • v0.1.4: Enhance logging; extend java KCL credentials provider (support STS assumed roles)
  • v0.1.2: Add configurable KCL log output
  • v0.1.0: Initial release


We welcome feedback, bug reports, and pull requests!

For pull requests, please stick to the following guidelines:

  • Add tests for any new features and bug fixes. Ideally, each PR should increase the test coverage.
  • Follow the existing code style (e.g., indents). A PEP8 code linting target is included in the Makefile.
  • Put a reasonable amount of comments into the code.
  • Separate unrelated changes into multiple pull requests.

Please note that we need to collect a signed Contributors License Agreement from each individual developer who contributes code to this repository. Please refer to the following links:


Copyright (c) 2016 Atlassian and others.

LocalStack is released under the Apache License, Version 2.0 (see LICENSE.txt).

We build on a number of third-party software tools, with the following licenses:

Third-Party software License
Python/pip modules:
airspeed BSD License
amazon_kclpy Amazon Software License
boto3 Apache License 2.0
coverage Apache License 2.0
docopt MIT License
elasticsearch Apache License 2.0
flask BSD License
flask_swagger MIT License
jsonpath-rw Apache License 2.0
moto Apache License 2.0
pep8 Expat license
requests Apache License 2.0
sh MIT License
subprocess32 PSF License
Node.js/npm modules:
dynalite MIT License
kinesalite MIT License
Other tools:
Elasticsearch Apache License 2.0