k6 extension to load test Apache Kafka with support for various serialization formats, SASL, TLS, compression, Schema Registry client and beyond
The xk6-kafka project is a k6 extension that enables k6 users to load test Apache Kafka using a producer and possibly a consumer for debugging.
The real purpose of this extension is to test the system you meticulously designed to use Apache Kafka. So, you can test your consumers, hence your system, by auto-generating messages and sending them to your system via Apache Kafka.
You can send many messages with each connection to Kafka. These messages are arrays of objects containing a key and a value in various serialization formats, passed via configuration objects. Various serialization formats are supported, including strings, JSON, binary, Avro, and JSON Schema. Avro and JSON Schema can either be fetched from Schema Registry or hard-code directly in the script. SASL PLAIN/SCRAM authentication and message compression are also supported.
For debugging and testing purposes, a consumer is available to make sure you send the correct data to Kafka.
If you want to learn more about the extension, read the article (outdated) explaining how to load test your Kafka producers and consumers using k6 on the k6 blog. You can also watch this recording of the k6 Office Hours about this extension.
The official Docker image is available on Docker Hub. Before running your script, make the script available to the container by mounting a volume (a directory) or passing it via stdin.
docker run --rm -i mostafamoradian/xk6-kafka:latest run - <scripts/test_json.js
The binaries are generated by the build process and published on the releases page. Currently, binaries for the GNU/Linux, macOS, and Windows on amd64
(x86_64
) machines are available.
Note: If you want to see an official build for your machine, please build and test xk6-kafka from source and then create an issue with details. I'll add the specific binary to the build pipeline and publish them on the next release.
You can build the k6 binary on various platforms, each with its requirements. The following shows how to build k6 binary with this extension on GNU/Linux distributions.
You must have the latest Go version installed to build the k6 binary. The latest version should match k6 and xk6. I recommend gvm because it eases version management.
Feel free to skip the first two steps if you already have Go installed.
Install gvm by following its installation guide.
Install the latest version of Go using gvm. You need Go 1.4 installed for bootstrapping into higher Go versions, as explained here.
Install xk6
:
go install go.k6.io/xk6/cmd/xk6@latest
Build the binary:
xk6 build --with github.com/mostafa/xk6-kafka@latest
Note You can always use the latest version of k6 to build the extension, but the earliest version of k6 that supports extensions via xk6 is v0.32.0. The xk6 is constantly evolving, so some APIs may not be backward compatible.
If you want to add a feature or make a fix, clone the project and build it using the following commands. The xk6 will force the build to use the local clone instead of fetching the latest version from the repository. This process enables you to update the code and test it locally.
git clone [email protected]:mostafa/xk6-kafka.git && cd xk6-kafka
xk6 build --with github.com/mostafa/xk6-kafka@latest=.
There are many examples in the script directory that show how to use various features of the extension.
You can start testing your setup immediately, but it takes some time to develop the script, so it would be better to test your script against a development environment and then start testing your environment.
I recommend the fast-data-dev Docker image by Lenses.io, a Kafka setup for development that includes Kafka, Zookeeper, Schema Registry, Kafka-Connect, Landoop Tools, 20+ connectors. It is relatively easy to set up if you have Docker installed. Just monitor Docker logs to have a working setup before attempting to test because the initial setup, leader election, and test data ingestion take time.
Run the Kafka environment and expose the ports:
sudo docker run \
--detach --rm \
--name lensesio \
-p 2181:2181 \
-p 3030:3030 \
-p 8081-8083:8081-8083 \
-p 9581-9585:9581-9585 \
-p 9092:9092 \
-e ADV_HOST=127.0.0.1 \
-e RUN_TESTS=0 \
lensesio/fast-data-dev:latest
After running the command, visit localhost:3030 to get into the fast-data-dev environment.
You can run the command to see the container logs:
sudo docker logs -f -t lensesio
Note: If you have errors running the Kafka development environment, refer to the fast-data-dev documentation.
All the exported functions are available by importing the module object from k6/x/kafka
. The exported objects, constants and other data structures are available in the index.d.ts
file, and they always reflect the latest changes on the main
branch. You can access the generated documentation at api-docs/docs/README.md
.
⚠️ Warning: The Javascript API is subject to change in future versions unless a new major version is released.
The example scripts are available as test_<format/feature>.js
with more code and commented sections in the scripts directory. Since this project extends the functionality of k6, it has four stages in the test life cycle.
To use the extension, you need to import it in your script, like any other JS module:
// Either import the module object
import * as kafka from "k6/x/kafka";
// Or individual classes and constants
import {
Writer,
Reader,
Connection,
SchemaRegistry,
SCHEMA_TYPE_STRING,
} from "k6/x/kafka";
You need to instantiate the classes in the init
context. All the k6 options are also configured here:
// Creates a new Writer object to produce messages to Kafka
const writer = new Writer({
// WriterConfig object
brokers: ["localhost:9092"],
topic: "my-topic",
});
const reader = new Reader({
// ReaderConfig object
brokers: ["localhost:9092"],
topic: "my-topic",
});
const connection = new Connection({
// ConnectionConfig object
address: "localhost:9092",
});
const schemaRegistry = new SchemaRegistry();
// Can accept a SchemaRegistryConfig object
if (__VU == 0) {
// Create a topic on initialization (before producing messages)
connection.createTopic({
// TopicConfig object
topic: "my-topic",
});
}
In the VU code, you can produce messages to Kafka or consume messages from it:
export default function () {
// Fetch the list of all topics
const topics = connection.listTopics();
console.log(topics); // list of topics
// Produces message to Kafka
writer.produce({
// ProduceConfig object
messages: [
// Message object(s)
{
key: schemaRegistry.serialize({
data: "my-key",
schemaType: SCHEMA_TYPE_STRING,
}),
value: schemaRegistry.serialize({
data: "my-value",
schemaType: SCHEMA_TYPE_STRING,
}),
},
],
});
// Consume messages from Kafka
let messages = reader.consume({
// ConsumeConfig object
limit: 10,
});
// your messages
console.log(messages);
// You can use checks to verify the contents,
// length and other properties of the message(s)
// To serialize the data back into a string, you should use
// the deserialize method of the Schema Registry client. You
// can use it inside a check, as shown in the example scripts.
let deserializedValue = schemaRegistry.deserialize({
data: messages[0].value,
schemaType: SCHEMA_TYPE_STRING,
});
}
In the teardown
function, close all the connections and possibly delete the topic:
export function teardown(data) {
// Delete the topic
connection.deleteTopic("my-topic");
// Close all connections
writer.close();
reader.close();
connection.close();
}
You can now run k6 with the extension using the following command:
./k6 run --vus 50 --duration 60s scripts/test_json.js
And here's the test result output:
/\ |‾‾| /‾‾/ /‾‾/
/\ / \ | |/ / / /
/ \/ \ | ( / ‾‾\
/ \ | |\ \ | (‾) |
/ __________ \ |__| \__\ \_____/ .io
execution: local
script: scripts/test_json.js
output: -
scenarios: (100.00%) 1 scenario, 50 max VUs, 1m30s max duration (incl. graceful stop):
* default: 50 looping VUs for 1m0s (gracefulStop: 30s)
running (1m04.4s), 00/50 VUs, 20170 complete and 0 interrupted iterations
default ✓ [======================================] 50 VUs 1m0s
✓ 10 messages are received
✓ Topic equals to xk6_kafka_json_topic
✓ Key contains key/value and is JSON
✓ Value contains key/value and is JSON
✓ Header equals {'mykey': 'myvalue'}
✓ Time is past
✓ Partition is zero
✓ Offset is gte zero
✓ High watermark is gte zero
█ teardown
checks.........................: 100.00% ✓ 181530 ✗ 0
data_received..................: 0 B 0 B/s
data_sent......................: 0 B 0 B/s
iteration_duration.............: avg=153.45ms min=6.01ms med=26.8ms max=8.14s p(90)=156.3ms p(95)=206.4ms
iterations.....................: 20170 313.068545/s
kafka_reader_dial_count........: 50 0.776075/s
kafka_reader_dial_seconds......: avg=171.22µs min=0s med=0s max=1.09s p(90)=0s p(95)=0s
✓ kafka_reader_error_count.......: 0 0/s
kafka_reader_fetch_bytes_max...: 1000000 min=1000000 max=1000000
kafka_reader_fetch_bytes_min...: 1 min=1 max=1
kafka_reader_fetch_wait_max....: 200ms min=200ms max=200ms
kafka_reader_fetch_bytes.......: 58 MB 897 kB/s
kafka_reader_fetch_size........: 147167 2284.25179/s
kafka_reader_fetches_count.....: 107 1.6608/s
kafka_reader_lag...............: 1519055 min=0 max=2436190
kafka_reader_message_bytes.....: 40 MB 615 kB/s
kafka_reader_message_count.....: 201749 3131.446006/s
kafka_reader_offset............: 4130 min=11 max=5130
kafka_reader_queue_capacity....: 1 min=1 max=1
kafka_reader_queue_length......: 1 min=0 max=1
kafka_reader_read_seconds......: avg=96.5ms min=0s med=0s max=59.37s p(90)=0s p(95)=0s
kafka_reader_rebalance_count...: 0 0/s
kafka_reader_timeouts_count....: 57 0.884725/s
kafka_reader_wait_seconds......: avg=102.71µs min=0s med=0s max=85.71ms p(90)=0s p(95)=0s
kafka_writer_acks_required.....: 0 min=0 max=0
kafka_writer_async.............: 0.00% ✓ 0 ✗ 2017000
kafka_writer_attempts_max......: 0 min=0 max=0
kafka_writer_batch_bytes.......: 441 MB 6.8 MB/s
kafka_writer_batch_max.........: 1 min=1 max=1
kafka_writer_batch_size........: 2017000 31306.854525/s
kafka_writer_batch_timeout.....: 0s min=0s max=0s
✓ kafka_writer_error_count.......: 0 0/s
kafka_writer_message_bytes.....: 883 MB 14 MB/s
kafka_writer_message_count.....: 4034000 62613.709051/s
kafka_writer_read_timeout......: 0s min=0s max=0s
kafka_writer_retries_count.....: 0 0/s
kafka_writer_wait_seconds......: avg=0s min=0s med=0s max=0s p(90)=0s p(95)=0s
kafka_writer_write_count.......: 4034000 62613.709051/s
kafka_writer_write_seconds.....: avg=523.21µs min=4.84µs med=14.48µs max=4.05s p(90)=33.85µs p(95)=42.68µs
kafka_writer_write_timeout.....: 0s min=0s max=0s
vus............................: 7 min=7 max=50
vus_max........................: 50 min=50 max=50
Metric | Type | Description |
---|---|---|
kafka_reader_dial_count | Counter | Total number of times the reader tries to connect. |
kafka_reader_fetches_count | Counter | Total number of times the reader fetches batches of messages. |
kafka_reader_message_count | Counter | Total number of messages consumed. |
kafka_reader_message_bytes | Counter | Total bytes consumed. |
kafka_reader_rebalance_count | Counter | Total number of rebalances of a topic in a consumer group (deprecated). |
kafka_reader_timeouts_count | Counter | Total number of timeouts occurred when reading. |
kafka_reader_error_count | Counter | Total number of errors occurred when reading. |
kafka_reader_dial_seconds | Trend | The time it takes to connect to the leader in a Kafka cluster. |
kafka_reader_read_seconds | Trend | The time it takes to read a batch of message. |
kafka_reader_wait_seconds | Trend | Waiting time before read a batch of messages. |
kafka_reader_fetch_size | Counter | Total messages fetched. |
kafka_reader_fetch_bytes | Counter | Total bytes fetched. |
kafka_reader_offset | Gauge | Number of messages read after the given offset in a batch. |
kafka_reader_lag | Gauge | The lag between the last message offset and the current read offset. |
kafka_reader_fetch_bytes_min | Gauge | Minimum number of bytes fetched. |
kafka_reader_fetch_bytes_max | Gauge | Maximum number of bytes fetched. |
kafka_reader_fetch_wait_max | Gauge | The maximum time it takes to fetch a batch of messages. |
kafka_reader_queue_length | Gauge | The queue length while reading batch of messages. |
kafka_reader_queue_capacity | Gauge | The queue capacity while reading batch of messages. |
kafka_writer_write_count | Counter | Total number of times the writer writes batches of messages. |
kafka_writer_message_count | Counter | Total number of messages produced. |
kafka_writer_message_bytes | Counter | Total bytes produced. |
kafka_writer_error_count | Counter | Total number of errors occurred when writing. |
kafka_writer_batch_seconds | Trend | The time it takes to write a batch of messages. |
kafka_writer_batch_queue_seconds | Trend | The time it takes to queue a batch of messages. |
kafka_writer_write_seconds | Trend | The time it takes writing messages. |
kafka_writer_wait_seconds | Trend | Waiting time before writing messages. |
kafka_writer_retries_count | Counter | Total number of attempts at writing messages. |
kafka_writer_batch_size | Counter | Total batch size. |
kafka_writer_batch_bytes | Counter | Total number of bytes in a batch of messages. |
kafka_writer_attempts_max | Gauge | Maximum number of attempts at writing messages. |
kafka_writer_batch_max | Gauge | Maximum batch size. |
kafka_writer_batch_timeout | Gauge | Batch timeout. |
kafka_writer_read_timeout | Gauge | Batch read timeout. |
kafka_writer_write_timeout | Gauge | Batch write timeout. |
kafka_writer_acks_required | Gauge | Required Acks. |
kafka_writer_async | Rate | Async writer. |
Why do I receive Error writing messages
?
There are a few reasons why this might happen. The most prominent one is that the topic might not exist, which causes the producer to fail to send messages to a non-existent topic. You can use Connection.createTopic
method to create the topic in Kafka, as shown in scripts/test_topics.js
. You can also set the autoCreateTopic
on the WriterConfig
. You can also create a topic using the kafka-topics
command:
$ docker exec -it lensesio bash
(inside container)$ kafka-topics --create --topic xk6_kafka_avro_topic --bootstrap-server localhost:9092
(inside container)$ kafka-topics --create --topic xk6_kafka_json_topic --bootstrap-server localhost:9092
Why does the reader.consume
keep hanging?
If the reader.consume
keeps hanging, it might be because the topic doesn't exist or is empty.
I want to test SASL authentication. How should I do that?
If you want to test SASL authentication, look at this commit message, in which I describe how to run a test environment to test SASL authentication.
Why doesn't the consumer group consume messages from the topic?
As explained in issue #37, multiple inits by k6 cause multiple consumer group instances to be created in the init context, which sometimes causes the random partitions to be selected by each instance. This, in turn, causes confusion when consuming messages from different partitions. This can be solved by using a UUID when naming the consumer group, thereby guaranteeing that the consumer group object was assigned to all partitions in a topic.
Why do I receive a MessageTooLargeError
when I produce messages bigger than 1 MB?
Kafka has a maximum message size of 1 MB by default, which is set by message.max.bytes
, and this limit is also applied to the Writer
object.
There are two ways to produce larger messages: 1) Change the default value of your Kafka instance to a larger number. 2) Use compression.
Remember that the Writer
object will reject messages larger than the default Kafka message size limit (1 MB). Hence you need to set batchBytes
to a larger value, for example, 1024 * 1024 * 2
(2 MB). The batchBytes
refers to the raw uncompressed size of all the keys and values (data) in your array of messages you pass to the Writer
object. You can calculate the raw data size of your messages using this example script.
Can I consume messages from a consumer group in a topic with multiple partitions?
Yes, you can. Just pass the groupID
to your Reader
object. You must not specify the partition anymore. Visit this documentation article to learn more about Kafka consumer groups.
Remember that you must set sessionTimeout
on your Reader
object if the consume function terminates abruptly, thus failing to consume messages.
Why does the Reader.consume
produces an unable to read message
error?
For performance testing reasons, the maxWait
of the Reader
is set to 200ms. If you keep receiving this error, consider increasing it to a larger value.
How can I consume from multiple partitions on a single topic?
You can configure your reader to consume from a (list of) topic(s) and its partitions using a consumer group. This can be achieve by setting groupTopics
, groupID
and a few other options for timeouts, intervals and lags. Have a look at the test_consumer_group.js
example script.
How can I use autocompletion in IDEs?
Copy api-docs/index.d.ts
into your project directory and reference it at the top of your JavaScript file:
/// <reference path="index.d.ts" />
...
Why timeouts give up sooner than expected?
There are many ways to configure timeout for the Reader
and Writer
objects. They follow Go's time conventions, which means that one second is equal to 1000000000 (one billion). For ease of use, I added the constants that can be imported from the module.
import { SECOND } from "k6/x/kafka";
console.log(2 * SECOND); // 2000000000
console.log(typeof SECOND); // number
Can I catch errors returned by the consume function?
Yes. You can catch errors by using a try-catch block. The consume function returns an error object. If the consume function raises, the error object will be populated with the error message.
try {
let messages = reader.consume({
limit: 10,
});
} catch (error) {
console.error(error);
}
I am using a nested Avro schema and getting unknown errors. How can I debug them?
If you have a nested Avro schema and you want to test it against your data, I created a small tool for it, called nested-avro-schema. This tool will help you to find discrepancies and errors in your schema data, so that you can fix them before you run xk6-kafka tests. Refer to this comment for more information.
I'd be thrilled to receive contributions and feedback on this project. You're always welcome to create an issue if you find one (or many). I would do my best to address the issues. Also, feel free to contribute by opening a PR with changes, and I'll do my best to review and merge it as soon as I can.
If you want to keep up to date with the latest changes, please follow the project board. Also, since v0.9.0, the main
branch is the development branch and usually has the latest changes and might be unstable. If you want to use the latest features, you might need to build your binary by following the build from source instructions. In turn, the tagged releases and the Docker images are more stable.
I make no guarantee to keep the API stable, as this project is in active development unless I release a major version. The best way to keep up with the changes is to follow the xk6-kafka API and look at the scripts directory.
The main
branch is the development branch, and the pull requests will be squashed and merged into the main
branch. When a commit is tagged with a version, for example, v0.10.0
, the build pipeline will build the main
branch on that commit. The build process creates the binaries and the Docker image. If you want to test the latest unreleased features, you can clone the main
branch and instruct the xk6
to use the locally cloned repository instead of using the @latest
, which refers to the latest tagged version, as explained in the build for development section.
CycloneDX SBOMs in JSON format are generated for go.mod (as of v0.9.0) and the Docker image (as of v0.14.0) and they can be accessed from the the release assets.
This project was a proof of concept but seems to be used by some companies nowadays. However, it isn't supported by the k6 team, but rather by me personally, and the APIs may change in the future. USE AT YOUR OWN RISK!
This project was AGPL3-licensed up until 7 October 2021, and then we relicensed it under the Apache License 2.0.