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Fast cross-platform HTTP benchmarking tool written in Go
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Modern HTTP benchmarking tool
HTTP load generator, ApacheBench (ab) replacement
A constant throughput, correct latency recording variant of wrk
Write scalable load tests in plain Python 🚗💨
Quick Overview
Drill is an HTTP load testing application written in Rust. It's inspired by Ansible syntax and designed to be easy to use, providing a way to perform complex load testing scenarios with a simple YAML configuration file.
Pros
- Easy to use with a simple YAML configuration
- Supports complex load testing scenarios
- Written in Rust, offering high performance
- Provides detailed statistics and reporting
Cons
- Limited documentation compared to some other load testing tools
- Fewer features compared to more established tools like Apache JMeter
- Requires Rust knowledge for advanced customization or contribution
Getting Started
To get started with Drill:
- Install Rust and Cargo (Rust's package manager)
- Install Drill:
cargo install drill
- Create a YAML file (e.g.,
benchmark.yml
) with your test plan:--- concurrency: 4 base: 'http://localhost:9000' iterations: 10 plan: - name: Fetch users request: url: /api/users
- Run the test:
drill --benchmark benchmark.yml --stats
This will execute the defined test plan, making 10 requests to the specified endpoint with 4 concurrent users, and display statistics about the test run.
Competitor Comparisons
Fast cross-platform HTTP benchmarking tool written in Go
Pros of Bombardier
- Written in Go, potentially offering better performance and cross-platform compatibility
- Supports HTTP/1.1 and HTTP/2 protocols
- Provides more detailed statistics and percentiles in the output
Cons of Bombardier
- Less flexible in terms of request customization compared to Drill
- Lacks support for custom plugins or extensions
- May have a steeper learning curve for users familiar with YAML-based configuration
Code Comparison
Drill (YAML configuration):
plan:
- name: Search for keywords
request:
url: https://api.example.com/search
method: POST
body: '{"query": "performance testing"}'
Bombardier (Command-line usage):
bombardier -c 100 -n 10000 -m POST -H "Content-Type: application/json" \
-b '{"query": "performance testing"}' https://api.example.com/search
Both tools allow for specifying HTTP methods, headers, and request bodies, but Drill's YAML configuration may be more readable for complex scenarios, while Bombardier's command-line approach offers quick setup for simpler tests.
HTTP load testing tool and library. It's over 9000!
Pros of Vegeta
- Written in Go, offering better performance and concurrency handling
- More mature project with a larger community and ecosystem
- Supports multiple output formats (JSON, CSV, Histogram)
Cons of Vegeta
- Less intuitive syntax for complex scenarios compared to Drill's YAML
- Limited built-in support for dynamic data generation
- Steeper learning curve for users not familiar with Go
Code Comparison
Vegeta:
echo "GET http://example.com" | vegeta attack -duration=5s | vegeta report
Drill:
---
concurrency: 4
base: 'http://example.com'
iterations: 10
plan:
- name: Fetch users
request:
url: /api/users
Key Differences
- Drill uses YAML for test configuration, making it more readable for complex scenarios
- Vegeta offers a command-line interface for quick tests, while Drill requires a config file
- Drill provides built-in support for iterations and concurrency settings in its config
- Vegeta excels in raw performance and scalability for high-load testing
- Drill offers more intuitive syntax for chaining requests and handling dynamic data
Both tools are valuable for load testing, with Vegeta being more suitable for high-performance scenarios and Drill offering easier configuration for complex test cases.
Modern HTTP benchmarking tool
Pros of wrk
- Written in C, offering high performance and low resource usage
- Simple command-line interface for quick benchmarking
- Supports Lua scripting for more advanced request generation
Cons of wrk
- Limited built-in reporting capabilities
- Less focus on API testing and complex scenarios
- Steeper learning curve for advanced usage with Lua scripting
Code Comparison
wrk:
wrk.method = "POST"
wrk.body = '{"foo": "bar"}'
wrk.headers["Content-Type"] = "application/json"
drill:
- name: Create user
request:
url: /api/users
method: POST
body: '{"name": "John", "email": "john@example.com"}'
headers:
Content-Type: application/json
Key Differences
- wrk is primarily a HTTP benchmarking tool, while drill focuses on API testing and load testing
- drill uses YAML for test configuration, making it more readable and easier to maintain for complex scenarios
- wrk requires Lua scripting for advanced features, whereas drill provides built-in functionality for common testing needs
- drill offers more detailed reporting and assertion capabilities out of the box
- wrk is generally faster and more lightweight, suitable for pure performance benchmarking
HTTP load generator, ApacheBench (ab) replacement
Pros of hey
- Written in Go, which may offer better performance for some use cases
- Simpler command-line interface, easier to use for basic load testing
- Supports HTTP/2 out of the box
Cons of hey
- Less flexible than drill for complex scenarios
- Lacks support for custom scripting or advanced request chaining
- Limited reporting options compared to drill's detailed output
Code comparison
hey:
hey -n 1000 -c 100 https://example.com
drill:
---
concurrency: 4
base: 'http://example.com'
iterations: 10
plan:
- name: Fetch users
request:
url: /api/users
Summary
hey is a straightforward load testing tool written in Go, offering simplicity and HTTP/2 support. drill, on the other hand, provides more flexibility with its YAML-based configuration and advanced scripting capabilities. hey is better suited for quick, simple load tests, while drill excels in complex scenarios requiring detailed control and reporting.
A constant throughput, correct latency recording variant of wrk
Pros of wrk2
- More accurate latency measurements due to coordinated omission compensation
- Better suited for high-throughput, constant-rate load testing
- Includes built-in scripting support using Lua
Cons of wrk2
- Less flexible in terms of request customization compared to drill
- Limited to HTTP/HTTPS protocols
- Steeper learning curve for users unfamiliar with Lua scripting
Code Comparison
wrk2:
wrk.method = "POST"
wrk.body = "foo=bar&baz=quux"
wrk.headers["Content-Type"] = "application/x-www-form-urlencoded"
drill:
plan:
- name: Post data
request:
url: https://example.com/api
method: POST
body: foo=bar&baz=quux
headers:
Content-Type: application/x-www-form-urlencoded
Summary
wrk2 excels in accurate, high-throughput load testing with built-in scripting capabilities, while drill offers more flexibility in request customization and easier configuration through YAML files. wrk2 is better suited for performance-critical scenarios, whereas drill provides a more user-friendly approach for general-purpose API testing and benchmarking.
Write scalable load tests in plain Python 🚗💨
Pros of Locust
- More mature and widely adopted project with a larger community
- Supports distributed load testing across multiple machines
- Provides a web-based UI for real-time monitoring and control
Cons of Locust
- Requires Python knowledge to write test scenarios
- Can be more resource-intensive for high-concurrency tests
- Steeper learning curve for complex test scenarios
Code Comparison
Locust example:
from locust import HttpUser, task, between
class WebsiteUser(HttpUser):
wait_time = between(1, 5)
@task
def index_page(self):
self.client.get("/")
Drill example:
plan:
- name: Fetch home page
request:
url: https://example.com/
Locust uses Python for defining test scenarios, while Drill uses YAML for a more declarative approach. Locust provides more flexibility and programmability, whereas Drill offers a simpler configuration for basic load testing needs.
Both tools serve the purpose of load testing, but Locust is better suited for complex, distributed scenarios, while Drill excels in simplicity and ease of use for straightforward load testing tasks.
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Drill
Drill is a HTTP load testing application written in Rust. The main goal for this project is to build a really lightweight tool as alternative to other that require JVM and other stuff.
You can write benchmark files, in YAML format, describing all the stuff you want to test.
It was inspired by Ansible syntax because it is really easy to use and extend.
Here is an example for benchmark.yml:
---
concurrency: 4
base: 'http://localhost:9000'
iterations: 5
rampup: 2
plan:
- name: Include comments
include: comments.yml
- name: Fetch users
request:
url: /api/users.json
- name: Fetch organizations
request:
url: /api/organizations
- name: Fetch account
request:
url: /api/account
assign: foo
- name: Fetch manager user
request:
url: /api/users/{{ foo.body.manager_id }}
- name: Assert request response code
assert:
key: foo.status
value: 200
- name: Assign values
assign:
key: bar
value: "2"
- name: Assert values
assert:
key: bar
value: "2"
- name: Fetch user from assign
request:
url: /api/users/{{ bar }}
- name: Fetch some users
request:
url: /api/users/{{ item }}
with_items:
- 70
- 73
- 75
- name: Tagged user request
request:
url: /api/users/70
tags:
- tag_user
- name: Fetch some users by hash
request:
url: /api/users/{{ item.id }}
with_items:
- { id: 70 }
- { id: 73 }
- { id: 75 }
- name: Fetch some users by range, index {{ index }}
request:
url: /api/users/{{ item }}
with_items_range:
start: 70
step: 5
stop: 75
- name: Fetch some users from CSV, index {{ index }}
request:
url: /api/users/contacts/{{ item.id }}
with_items_from_csv: ./fixtures/users.csv
shuffle: true
- name: POST some crafted JSONs stored in CSV, index {{ index }}
request:
url: /api/transactions
method: POST
body: '{{ item.txn }}'
headers:
Content-Type: 'application/json'
with_items_from_csv:
file_name: ./fixtures/transactions.csv
quote_char: "\'"
- name: Fetch no relative url
request:
url: http://localhost:9000/api/users.json
- name: Interpolate environment variables
request:
url: http://localhost:9000/api/{{ EDITOR }}
- name: Support for POST method
request:
url: /api/users
method: POST
body: foo=bar&arg={{ bar }}
- name: Login user
request:
url: /login?user=example&password=3x4mpl3
- name: Fetch counter
request:
url: /counter
assign: memory
- name: Fetch counter
request:
url: /counter
assign: memory
- name: Fetch endpoint
request:
url: /?counter={{ memory.body.counter }}
- name: Reset counter
request:
method: DELETE
url: /
- name: Exec external commands
exec:
command: "echo '{{ foo.body }}' | jq .phones[0] | tr -d '\"'"
assign: baz
- name: Custom headers
request:
url: /admin
headers:
Authorization: Basic aHR0cHdhdGNoOmY=
X-Foo: Bar
X-Bar: Bar {{ memory.headers.token }}
- name: One request with a random item
request:
url: /api/users/{{ item }}
with_items:
- 70
- 73
- 75
shuffle: true
pick: 1
- name: Three requests with random items from a range
request:
url: /api/users/{{ item }}
with_items_range:
start: 1
stop: 1000
shuffle: true
pick: 3
As you can see, you can play with interpolations in different ways. This will let you specify a benchmark with different requests and dependencies between them.
If you want to know more about the benchmark file syntax, read this
Install
Right now, the easiest way to get drill
is to go to the
latest release
page and download the binary file for your platform.
Another way to install drill
, if you have Rust available in
your system, is with cargo:
cargo install drill
drill --benchmark benchmark.yml --stats
or download the source code and compile it:
git clone git@github.com:fcsonline/drill.git && cd drill
cargo build --release
./target/release/drill --benchmark benchmark.yml --stats
Dependencies
OpenSSL is needed in order to compile Drill, whether it is through cargo install
or when compiling from source with cargo build
.
Depending on your platform, the name of the dependencies may differ.
Linux
Install libssl-dev
and pkg-config
packages with your favorite package manager
(if libssl-dev
is not found, try other names like openssl
or openssl-devel
).
macOS
First, install the Homebrew package manager.
And then install openssl
with Homebrew.
Windows
First, install vcpkg.
And then run vcpkg install openssl:x64-windows-static-md
.
Demo
Features
This is the list of all features supported by the current version of drill
:
- Concurrency: run your benchmarks choosing the number of concurrent iterations.
- Multi iterations: specify the number of iterations you want to run the benchmark.
- Ramp-up: specify the amount of time, in seconds, that it will take
drill
to start all iterations. - Delay: introduce controlled delay between requests. Example: delay.yml
- Dynamic urls: execute requests with dynamic interpolations in the url, like
/api/users/{{ item }}
- Dynamic headers: execute requests with dynamic headers. Example: headers.yml
- Interpolate environment variables: set environment variables, like
/api/users/{{ EDITOR }}
- Executions: execute remote commands with test plan data.
- Assertions: assert values during the test plan. Example: iterations.yml
- Request dependencies: create dependencies between requests with
assign
and url interpolations. - Split files: organize your benchmarks in multiple files and include them.
- CSV support: read CSV files and build N requests fill dynamic interpolations with CSV data.
- HTTP methods: build request with different http methods like GET, POST, PUT, PATCH, HEAD or DELETE.
- Cookie support: create benchmarks with sessions because cookies are propagates between requests.
- Stats: get nice statistics about all the requests. Example: cookies.yml
- Thresholds: compare the current benchmark performance against a stored one session and fail if a threshold is exceeded.
- Tags: specify test plan items by tags.
Test it
Go to the example
directory and you'll find a README how
to test it in a safe environment.
Disclaimer: We really recommend not to run intensive benchmarks against production environments.
Command line interface
Full list of cli options, which is available under drill --help
drill 0.8.3
HTTP load testing application written in Rust inspired by Ansible syntax
USAGE:
drill [FLAGS] [OPTIONS] --benchmark <benchmark>
FLAGS:
-h, --help Prints help information
--list-tags List all benchmark tags
--list-tasks List benchmark tasks (executes --tags/--skip-tags filter)
-n, --nanosec Shows statistics in nanoseconds
--no-check-certificate Disables SSL certification check. (Not recommended)
-q, --quiet Disables output
--relaxed-interpolations Do not panic if an interpolation is not present. (Not recommended)
-s, --stats Shows request statistics
-V, --version Prints version information
-v, --verbose Toggle verbose output
OPTIONS:
-b, --benchmark <benchmark> Sets the benchmark file
-c, --compare <compare> Sets a compare file
-r, --report <report> Sets a report file
--skip-tags <skip-tags> Tags to exclude
--tags <tags> Tags to include
-t, --threshold <threshold> Sets a threshold value in ms amongst the compared file
-o, --timeout <timeout> Set timeout in seconds for all requests
Roadmap
- Complete and improve the interpolation engine
- Add writing to a file support
Donations
If you appreciate all the job done in this project, a small donation is always welcome:
Contribute
This project started as a side project to learn Rust, so I'm sure that is full of mistakes and areas to be improve. If you think you can tweak the code to make it better, I'll really appreciate a pull request. ;)
Top Related Projects
Fast cross-platform HTTP benchmarking tool written in Go
HTTP load testing tool and library. It's over 9000!
Modern HTTP benchmarking tool
HTTP load generator, ApacheBench (ab) replacement
A constant throughput, correct latency recording variant of wrk
Write scalable load tests in plain Python 🚗💨
Convert designs to code with AI
Introducing Visual Copilot: A new AI model to turn Figma designs to high quality code using your components.
Try Visual Copilot