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karapace Your Kafka essentials in one tool


  • Schema Registry and Rest Proxy that are 1:1 Compatible with the pre-existing proprietary Confluent Schema Registry and Kafka Rest Proxy
  • Drop in replacement both on pre-existing Schema Registry / Kafka Rest Proxy client and server-sides
  • Moderate memory consumption
  • Asynchronous architecture based on aiohttp


Karapace supports the storing of schemas in a central repository, which clients can access to serialize and deserialize messages. The schemas also maintain their own version histories and can be checked for compatibility between their different respective versions.

Karapace rest provides a RESTful interface to your Kafka cluster, allowing you to perform tasks such as producing and consuming messages and perform administrative cluster work, all the while using the language of the WEB.


Karapace is a Python project, and requires Kafka for its backend storage. There is also a Docker setup for development.


Karapace requires Python 3.6 or later and some additional components in order to operate:

  • aiohttp for serving schemas over HTTP in an asynchronous fashion
  • avro-python3 for Avro serialization
  • kafka-python to read, write and coordinate Karapace's persistence in Kafka
  • raven-python (optional) to report exceptions to sentry
  • aiokafka for some components of the rest proxy

Developing and testing Karapace also requires the following utilities: requests, flake8, pylint and pytest.

Karapace has been developed and tested on modern Linux x86-64 systems, but should work on other platforms that provide the required modules.


To build an installation package for your distribution, go to the root directory of a Karapace Git checkout and run:

python3 bdist_egg

This will produce an egg file into a dist directory within the same folder.



easy_install dist/karapace-0.1.0-py3.6.egg

On Linux systems it is recommended to simply run karapace under systemd:

systemctl enable karapace.service

and eventually after the setup section, you can just run:

systemctl start karapace.service


After this you need to create a suitable JSON configuration file for your installation. Keys to take special care are the ones needed to configure Kafka and advertised_hostname.

Each configuration key can be overridden with an environment variable prefixed with KARAPACE_, exception being configuration keys that actually start with the karapace string. For example, to override the bootstrap_uri config value, one would use the environment variable KARAPACE_BOOTSTRAP_URI

To see descriptions of configuration keys see section config. Here's an example configuration file to give you an idea what you need to change:

    "advertised_hostname": "localhost",
    "bootstrap_uri": "",
    "client_id": "sr-1",
    "compatibility": "FULL",
    "group_id": "schema-registry",
    "host": "",
    "log_level": "DEBUG",
    "port": 8081,
    "master_eligibility": true,
    "replication_factor": 1,
    "security_protocol": "PLAINTEXT",
    "ssl_cafile": null,
    "ssl_certfile": null,
    "ssl_keyfile": null,
    "topic_name": "_schemas"

Local Development

Currently Karapace runs on the Python major versions 3.7, 3.8 and 3.9. You can use any of these for development. Naturally, independently of Python version you use, the code needs to run on all the supported versions. The CI pipeline in GitHub actions will run the tests on all these Python versions to ensure this.

To run Karapace locally, or develop it, first install the dependencies. If you only need the runtime, i.e. you're not running tests or committing to Git, it's enough to install the runtime dependencies:

# Runtime dependencies
python3 -m pip install -r requirements.txt

If you are developing and e.g. running tests, install the development dependencies. This will install also the runtime dependencies:

# Development dependencies, contains runtime dependencies
python3 -m pip install -r requirements-dev.txt

To run the local/current version of the code, set up the configuration file in karapace.config.json to include connection details for Kafka and any other config you want to change, then run:

python3 -m karapace.karapace_all karapace.config.json

There are two flavors of tests, unit tests and integration tests. The unit tests are standalone, i.e. can be run without anything outside of the test running. The integration tests in turn need a running ZooKeeper and Kafka, but take internally care of starting and stopping them.

The tests can be run from the command line using make:

# Running unit tests
make unittest

# Running integration tests
make integrationtest

To run the tests in an IDE, you need once download and untar Kafka by make fetch-kafka. Additionally ensure that the working directory when running tests, is set to Git root, e.g. in PyCharm you can create a configuration template with the correct working directory.

The integration tests are run in parallel e.g. in the CI-pipeline. The tests need to be engineered taking this in mind.

There are several coding style checks in GitHub Actions. Your code changes need to pass these tests. To run the checks locally, you can run them manually:

# Runs all coding style checks
make pre-commit

Alternatively,you can use pre-commit to automatically run the checks on commit time:

pre-commit install

Docker setup for development

To get you up and running with a development copy of Karapace, a docker setup is available. You can find everything you need for this in the container/ folder.

Get the containers running:

docker-compose up

Then you should be able to reach two sets of endpoints:


To Register the first version of a schema under the subject "test":

$ curl -X POST -H "Content-Type: application/vnd.schemaregistry.v1+json" \
--data '{"schema": "{\"type\": \"record\", \"name\": \"Obj\", \"fields\":[{\"name\": \"age\", \"type\": \"int\"}]}"}' \

To list all subjects (including the one created just above):

$ curl -X GET http://localhost:8081/subjects

To list all the versions of a given schema (including the one just created above):

$ curl -X GET http://localhost:8081/subjects/test-key/versions

To fetch back the schema whose global id is 1 (i.e. the one registered above):

$ curl -X GET http://localhost:8081/schemas/ids/1

To get the specific version 1 of the schema just registered run:

$ curl -X GET http://localhost:8081/subjects/test-key/versions/1

To get the latest version of the schema under subject test-key run:

$ curl -X GET http://localhost:8081/subjects/Kafka-value/versions/latest

In order to delete version 10 of the schema registered under subject "test-key" (if it exists):

$ curl -X DELETE http://localhost:8081/subjects/test-key/versions/10

To Delete all versions of the schema registered under subject "test-key":

$ curl -X DELETE http://localhost:8081/subjects/test-key

Test the compatibility of a schema with the latest schema under subject "test-key":

$ curl -X POST -H "Content-Type: application/vnd.schemaregistry.v1+json" \
    --data '{"schema": "{\"type\": \"int\"}"}' \

Get current global backwards compatibility setting value:

$ curl -X GET http://localhost:8081/config

Change compatibility requirements for all subjects where it's not specifically defined otherwise:

$ curl -X PUT -H "Content-Type: application/vnd.schemaregistry.v1+json" \
  --data '{"compatibility": "NONE"}' http://localhost:8081/config

Change compatibility requirement to FULL for the test-key subject:

$ curl -X PUT -H "Content-Type: application/vnd.schemaregistry.v1+json" \
    --data '{"compatibility": "FULL"}' http://localhost:8081/config/test-key

List topics:

$ curl "http://localhost:8081/topics"

Get info for one particular topic:

$ curl "http://localhost:8081/topics/my_topic"

Produce a message backed up by schema registry:

$ curl -H "Content-Type: application/vnd.kafka.avro.v2+json" -X POST -d \
'{"value_schema": "{\"namespace\": \"example.avro\", \"type\": \"record\", \"name\": \"simple\", \"fields\": \
[{\"name\": \"name\", \"type\": \"string\"}]}", "records": [{"value": {"name": "name0"}}]}' http://localhost:8081/topics/my_topic

Create a consumer:

$ curl -X POST -H "Content-Type: application/vnd.kafka.v2+json" -H "Accept: application/vnd.kafka.v2+json" \
  --data '{"name": "my_consumer", "format": "avro", "auto.offset.reset": "earliest"}' \

Subscribe to the topic we previously published to:

$ curl -X POST -H "Content-Type: application/vnd.kafka.v2+json" --data '{"topics":["my_topic"]}' \

Consume previously published message:

$ curl -X GET -H "Accept: application/vnd.kafka.avro.v2+json" \

Commit offsets for a particular topic partition:

$ curl -X POST -H "Content-Type: application/vnd.kafka.v2+json" --data '{}'

Delete consumer:

$ curl -X DELETE -H "Accept: application/vnd.kafka.v2+json" \

Backing up your Karapace

Karapace natively stores its data in a Kafka topic the name of which you can configure freely but which by default is called _schemas.

Karapace includes a tool to backing up and restoring data. To back up, run:

karapace_schema_backup get --config karapace.config.json --location schemas.log

You can also back up the data simply by using Kafka's Java console consumer:

./ --bootstrap-server brokerhostname:9092 --topic _schemas --from-beginning --property print.key=true --timeout-ms 1000 1> schemas.log

Restoring Karapace from backup

Your backup can be restored with Karapace by running:

karapace_schema_backup restore --config karapace.config.json --location schemas.log

Or Kafka's Java console producer can be used to restore the data to a new Kafka cluster.

You can restore the data from the previous step by running:

./ --broker-list brokerhostname:9092 --topic _schemas --property parse.key=true < schemas.log

Performance comparison to Confluent stack


  • 50 concurrent connections, 50.000 requests
Format Karapace Confluent
Avro 80.95 7.22
Binary 66.32 46.99
Json 60.36 53.7
  • 15 concurrent connections, 50.000 requests
Format Karapace Confluent
Avro 25.05 18.14
Binary 21.35 15.85
Json 21.38 14.83
  • 4 concurrent connections, 50.000 requests
Format Karapace Confluent
Avro 6.54 5.67
Binary 6.51 4.56
Json 6.86 5.32

Also, it appears there is quite a bit of variation on subsequent runs, especially for the lower numbers, so once more exact measurements are required, it's advised we increase the total req count to something like 500K

We'll focus on avro serialization only after this round, as it's the more expensive one, plus it tests the entire stack

Consuming RAM

A basic push pull test , with 12 connections on the publisher process and 3 connections on the subscriber process, with a 10 minute duration. The publisher has the 100 ms timeout and 100 max_bytes parameters set on each request so both processes have work to do Heap size limit is set to 256M on Rest proxy

Ram consumption, different consumer count, over 300s

Consumers Karapace combined Confluent rest
1 47 200
10 55 400
20 83 530


Once installed, the karapace program should be in your path. It is the main daemon process that should be run under a service manager such as systemd to serve clients.

Configuration keys

advertised_hostname (default socket.gethostname())

The hostname being advertised to other instances of Karapace that are attached to the same Kafka group. All nodes within the cluster need to have their advertised_hostname's set so that they can all reach each other.

bootstrap_uri (default localhost:9092)

The URI to the Kafka service where to store the schemas and to run coordination among the Karapace instances.

client_id (default sr-1)

The client_id name by which the Karapace will use when coordinating with other Karapaces who is master. The one with the name that sorts as the first alphabetically is chosen as master from among the services with master_eligibility set to true.

consumer_enable_autocommit (default True)

Enable auto commit on rest proxy consumers

consumer_request_timeout_ms (default 11000)

Rest proxy consumers timeout for reads that do not limit the max bytes or provide their own timeout

consumer_request_max_bytes (default 67108864)

Rest proxy consumers maximum bytes to be fetched per request

fetch_min_bytes (default -1)

Rest proxy consumers minimum bytes to be fetched per request. -1 means no limit

group_id (default schema-registry)

The Kafka group name used for selecting a master service to coordinate the storing of Schemas.

master_eligibility (true)

Should the service instance be considered for promotion to be the master service. Reason to turn this off would be to have an instances of Karapace running somewhere else for HA purposes but which you wouldn't want to automatically promote to master if the primary instances were to become unavailable.

producer_compression_type (default None)

Type of compression to be used by rest proxy producers

producer_acks (default 1)

Level of consistency desired by each producer message sent on the rest proxy More on

producer_linger_ms (default 0)

Time to wait for grouping together requests More on

security_protocol (default PLAINTEXT)

Default Kafka security protocol needed to communicate with the Kafka cluster. Other options is to use SSL for SSL client certificate authentication.

sentry (default None)

Used to configure parameters for sentry integration (dsn, tags, ...). Setting the environment variable SENTRY_DSN will also enable sentry integration.

ssl_cafile (default Path to CA certificate)

Used when security_protocol is set to SSL, the path to the SSL CA certificate.

ssl_certfile (default /path/to/certfile)

Used when security_protocol is set to SSL, the path to the SSL certfile.

ssl_keyfile (default /path/to/keyfile)

Used when security_protocol is set to SSL, the path to the SSL keyfile.

topic_name (default _schemas)

The name of the Kafka topic where to store the schemas.

replication_factor (default 1)

The replication factor to be used with the schema topic.

host (default "")

Address to bind the Karapace HTTP server to. Set to an empty string to listen to all available addresses.

registry_host (default "")

Kafka Registry host, used by Kafka Rest for avro related requests. If running both in the same process, it should be left to its default value

port (default 8081)

HTTP webserver port to bind the Karapace to.

registry_port (default 8081)

Kafka Registry port, used by Kafka Rest for avro related requests. If running both in the same process, it should be left to its default value

metadata_max_age_ms (default 60000)

Preiod of time in milliseconds after Kafka metadata is force refreshed.

karapace_rest (default true)

If the rest part of the app should be included in the starting process At least one of this and karapace_registry options need to be enabled in order for the service to start

karapace_registry (default true)

If the registry part of the app should be included in the starting process At least one of this and karapace_registry options need to be enabled in order for the service to start

name_strategy (default subject_name)

Name strategy to use when storing schemas from the kafka rest proxy service

master_election_strategy (default lowest)

Decides on what basis the karapace cluster master is chosen (only relevant in a multi node setup)


Karapace is licensed under the Apache license, version 2.0. Full license text is available in the LICENSE file.

Please note that the project explicitly does not require a CLA (Contributor License Agreement) from its contributors.


Bug reports and patches are very welcome, please post them as GitHub issues and pull requests at . Any possible vulnerabilities or other serious issues should be reported directly to the maintainers <>.


Karapace was created by, and is maintained by, Aiven cloud data hub developers.

The schema storing part of Karapace loans heavily from the ideas of the earlier Schema Registry implementation by Confluent and thanks are in order to them for pioneering the concept.

Recent contributors are listed on the GitHub project page,

Copyright ⓒ 2019 Aiven Ltd.