Kroxylicious Proxy v0.3.0

This documentation is for a stable release version of Kroxylicious. For the documentation from the latest development version of Kroxylicious, click here.

This documentation is for an older release of Kroxylicious, and may not reflect current functionality.

Overview

What is Kroxylicious Proxy?

Kroxylicious Proxy provides a pluggable, protocol-aware ("Layer 7") proxy for Apache Kafka® brokers and clusters, together with an API for conveniently implementing custom logic within such a proxy.

Why?

Proxies are a powerful and flexible architectural pattern. For Kafka, they can be used to add functionality to Kafka clusters which is not available out-of-the-box with Apache Kafka. In an ideal world, such functionality would be implemented directly in Apache Kafka. But there are numerous practical reasons that can prevent this, for example:

Organizations having very niche requirements which are unsuitable for implementation directly in Apache Kafka.
Functionality which requires changes to Kafka’s public API and which the Apache Kafka project is unwilling to implement. This is the case for broker interceptors, for example.
Experimental functionality which might end up being implemented in Apache Kafka eventually. For example using Kroxylicious proxy it’s easier to experiment with alternative transport protocols, such as Quic, or operating system APIs, such as io_uring, because there is already support for this in Netty, the networking framework on which Kroxylicious is built.

How it works

First let’s define the concepts in the landscape surrounding Kroxylicious.

Kafka Client, or Client refers to any client application using a Kafka Client library to talk to a Kafka Cluster.
Kafka Cluster or Cluster refers to a cluster comprising one or more Kafka Brokers.
Downstream refers to the area between Kafka Client and Kroxylicious.
Upstream refers to the area between Kroxylicious and a Kafka Cluster.

Figure 1. Kroxylious landscape

Now let’s define some concepts used within Kroxylicious itself.

Virtual Cluster

The Virtual Cluster is the downstream representation of a Kafka Cluster. At the conceptual level, a Kafka Client connects to a Virtual Cluster. Kroxylicious proxies all communications made to the Virtual Cluster through to a (physical) Kafka Cluster, passing it through the Filter Chain.

So far, this explanation has elided the detail of Kafka Brokers. Let’s talk about that now.

The Virtual Cluster automatically exposes a bootstrap endpoint for the Virtual Cluster. This is what the Kafka Client must specify as the bootstrap.servers property in the client configuration.

In addition to the bootstrap endpoint, Kroxylicious automatically exposes broker endpoints. There is one broker endpoint for each broker of the physical cluster. When the Client connects to a broker endpoint, Kroxylicious proxies all communications to the corresponding broker of the (physical) Kafka Cluster.

Kroxylicious automatically intercepts all the Kafka RPC responses that contain a broker address. It rewrites the address so that it refers to the corresponding broker endpoint of the Virtual Cluster. This means when the Kafka Client goes to connect to, say broker 0, it does so through the Virtual Cluster.

Target Cluster

The Target Cluster is the definition of physical Kafka Cluster within the Kroxylicious itself.

A Virtual Cluster has exactly one Target Cluster.

There can be a one-to-one relationship between Virtual Clusters and Target Clusters. The other possibility is many-to-one, where many Virtual Clusters point to the same Target Cluster. The many-to-one pattern is exploited by filters such as the Multi-tenancy filter.

Figure 2. One-to-One relationship between Virtual Cluster and Target Cluster

Figure 3. Many-to-one between Virtual Cluster and Target Cluster

A one-to-many pattern, where one Virtual Cluster points to many Target Clusters (providing amalgamation), is not a supported use-case.

Filter Chain

A Filter Chain consists of an ordered list of pluggable protocol filters.

A protocol filter implements some logic for intercepting, inspecting and/or manipulating Kafka protocol messages. Kafka protocol requests (such as Produce requests) pass sequentially through each of the protocol filters in the chain, beginning with the 1st filter in the chain and then following with the subsequent filters, before being forwarded to the broker.

When the broker returns a response (such as a Produce response) the protocol filters in the chain are invoked in the reverse order (that is, beginning with the nth filter in the chain, then the n-1th and so on) with each having the opportunity to inspect and/or manipulating the response. Eventually a response is returned to the client.

The description above describes only the basic capabilities of the protocol filter. Richer features of filters are described later.

Figure 4. Illustration of a request and response being manipulated by filters in a chain

As mentioned above, Kroxylicious takes the responsibility to rewrite the Kafka RPC responses that carry broker address information so that they reflect the broker addresses exposed by the Virtual Cluster. These are the Metadata, DescribeCluster and FindCoordinator responses. This processing is entirely transparent to the work of the protocol filters. Filter authors are free to write their own filters that intercept these responses too.

Filter composition

An important principal for the protocol filter API is that filters should compose nicely. That means that filters generally don’t know what other filters might be present in the chain, and what they might be doing to messages. When a filter forwards a request or response it doesn’t know whether the message is being sent to the next filter in the chain, or straight back to the client.

Such composition is important because it means a proxy user can configure multiple filters (possibly written by several filter authors) and expect to get the combined effect of all of them.

It’s never quite that simple, of course. In practice they will often need to understand what each filter does in some detail in order to be able to operate their proxy properly, for example by understanding whatever metrics each filter is emitting.

Implementation

The proxy is written in Java, on top of Netty. The usual ChannelHandlers provided by the Netty project are used where appropriate (e.g. SSL support uses SslHandler), and Kroxylicious provides Kafka-specific handlers of its own.

The Kafka-aware parts use the Apache Kafka project’s own classes for serialization and deserialization.

Protocol filters get executed using a handler-per-filter model.

Deployment topologies

The proxy supports a range of possible deployment topologies. Which style is used depends on what the proxy is meant to achieve, architecturally speaking. Broadly speaking a proxy instance can be deployed:

As a forward proxy: Proxying the access of one or more clients to a particular cluster/broker that might also accessible (to other clients) directly.

Topic-level encryption provides one example use case for a forward proxy-style deployment. This might be applicable when using clients that don’t support interceptors, or if an organisation wants to apply the same encryption policy in a single place, securing access to the keys within their network.
As a reverse proxy: Proxying access for all clients trying to reach a particular cluster/broker.

Transparent multi-tenancy provides an example use case for a reverse proxy. While Apache Kafka itself has some features that enable multi-tenancy, they rely on topic name prefixing as the primary mechanism for ensuring namespace isolation. Tenants have to adhere to the naming policy and know they’re a tenant of a larger shared cluster.

Transparent multi-tenancy means each tenant has the illusion of having their own cluster, with almost complete freedom over topic and group naming, while still actually sharing a cluster.

We can further classify deployment topologies in how many proxy instances are used. For example:

Single proxy instance
Proxy pool

More about filters

Filters

Built-in filters

Only a few built in filters are provided.

Schema-validation

Multi-tenancy

Custom Filters

Custom filters can be written in the Java programming language. Knowledge of the Kafka protocol is generally required to write a protocol filter.

There is currently one class of Custom Filters users can implement:

Protocol filters: Allow customisation of how protocol messages are handled on their way to, or from, the Cluster.

The following sections explain in more detail how to write your own filters.

Sample Custom Filter Project

A collection of sample filters is available within the Kroxylicious repository for you to download, try out, and customise. You can find them here for a hands-on introduction to creating your own custom filters.

API docs

Custom Protocol Filters are built by implementing interfaces supplied by the kroxylicious-api module (io.kroxylicious:kroxylicious-api on maven central). You can view the javadoc here.

Dependencies

How filter classes are loaded is not currently defined by the filter contract. In other words, filters might be loaded using a classloader-per-filter model, or using a single class loader. This doesn’t really make a difference to filter authors except where they want to make use of libraries as dependencies. Because those dependencies might be loaded by the same classloader as the dependencies of other filters there is the possibility of collision. Filter A and Filter B might both want to use Library C, and they might want to use different versions of Library C.

For common things like logging and metric facade APIs it is recommended to use the facade APIs which are also used by the proxy core.

Protocol filters

A protocol filter is a public top-level, concrete class with a particular public constructor and which implements one or more protocol filter interfaces. You can implement two distinct types of Custom Protocol Filter:

Specific Message Protocol Filters
Request/Response Protocol Filters

Note that these types are mutually exclusive, for example a Filter is not allowed to implement both RequestFilter and MetadataRequestFilter. This is to prevent ambiguity. If we received a MetadataRequest, would it be dispatched to the onMetadataRequest(..) method of MetadataRequestFilter or the onRequest method of RequestFilter, or both? Instead, we disallow these combinations, throwing an exception at runtime if your Filter implements incompatible interfaces.

You can also implement a convenience interface to deliver multiple Protocol Filters to the filter chain using Composite Filters.

Specific Message Protocol Filters

A filter may wish to intercept specific types of Kafka messages. For example, intercept all Produce Requests, or intercept all Fetch Responses. To support this case Kroxylicious provides an interfaces for all request types and response types supported by Kafka (at the version of Kafka Kroxylicious depends on). A filter implementation can implement any combination of these interfaces.

There is no requirement that a Filter handles both the request and response halves of an RPC. A Filter can choose to intercept only the request, or only the response, or both the request and response.

Examples

To intercept all Fetch Requests your class would implement FetchRequestFilter:

public class FetchRequestClientIdFilter implements FetchRequestFilter {

    @Override
    public CompletionStage<RequestFilterResult> onFetchRequest(short apiVersion,
                                                               RequestHeaderData header,
                                                               FetchRequestData request,
                                                               FilterContext context) {
        header.setClientId("fetch-client!");
        return context.forwardRequest(header, request);
    }
}

To intercept all Fetch Responses your class would implement FetchResponseFilter:

public class FetchRequestClientIdFilter implements FetchResponseFilter {

    @Override
    public CompletionStage<ResponseFilterResult> onFetchResponse(short apiVersion,
                                                                 ResponseHeaderData header,
                                                                 FetchResponseData response,
                                                                 FilterContext context) {
        mutateResponse(response);
        return context.forwardResponse(header, response);
    }
}

To intercept all Fetch Requests and all Fetch Responses your class would implement FetchRequestFilter and FetchResponseFilter:

public class FetchRequestClientIdFilter implements FetchRequestFilter, FetchResponseFilter {

    @Override
    public CompletionStage<RequestFilterResult> onFetchRequest(short apiVersion,
                                                               RequestHeaderData header,
                                                               FetchRequestData request,
                                                               FilterContext context) {
        header.setClientId("fetch-client!");
        return context.forwardRequest(header, request);
    }

    @Override
    public CompletionStage<ResponseFilterResult> onFetchResponse(short apiVersion,
                                                                 ResponseHeaderData header,
                                                                 FetchResponseData response,
                                                                 FilterContext context) {
        mutateResponse(response);
        return context.forwardResponse(header, response);
    }
}

Specific Message Filter interfaces are mutually exclusive with Request/Response and CompositeFilter interfaces. Kroxylicious will reject invalid combinations of interfaces.

Request/Response Protocol Filters

A filter may wish to intercept every message being sent from the Client to the Cluster or from the Cluster to the Client. To do this your custom filter will implement:

RequestFilter to intercept all requests.
ResponseFilter to intercept all responses.

Custom filters are free to implement either interface or both interfaces to intercept all messages.

For example:

public class FixedClientIdFilter implements RequestFilter {

    @Override
    public CompletionStage<RequestFilterResult> onRequest(ApiKeys apiKey,
                                                          RequestHeaderData header,
                                                          ApiMessage body,
                                                          FilterContext filterContext) {
        header.setClientId("example!");
        return filterContext.forwardRequest(header, body);
    }

}

Request/Response Filter interfaces are mutually exclusive with Specific Message and CompositeFilter interfaces. Kroxylicious will reject invalid combinations of interfaces.

Composite Filters

Sometimes we want to present a chain of multiple Filters as a single cohesive unit with just one entry in the filters configuration of Kroxylicious. The CompositeFilter interface enables you to do this.

An example might look like this:

class ExampleCompositeFilter implements CompositeFilter {

        private final ExampleConfiguration configuration;

        public ExampleCompositeFilter(ExampleConfiguration configuration) {
            this.configuration = configuration;
        }

        @Override
        public List<Filter> getFilters() {
            return List.of(
                new OverrideAllClientIdHeadersFilter(configuration.clientId()),
                new PrefixProduceRequestFilter(configuration.prefix())
            );
        }
}

Which could have corresponding configuration:

filters:
- type: Example
  config:
    clientId: fixed-id
    prefix: abcde

This enables you to break a complex behaviour into logical chunks, implemented with multiple Filters, but they can be installed with a single block of configuration in the Kroxylicious configuration. For example if you wanted to intercept some specific RPCs but also change the clientId header of all requests, instead of requiring the user to configure two filters you could provide a CompositeFilter that provides both Filters.

The CompositeFilter interface is mutually exclusive with Specific Message and Request/Response interfaces. Kroxylicious will reject invalid combinations of interfaces.

The Filter Result

As seen above, filter methods (onXyz[Request|Response]) must return a CompletionStage<FilterResult> object. It is the job of FilterResult to convey what message is to forwarded to the next filter in the chain (or broker /client if at the chain’s beginning or end). It is also used to carry instructions such as indicating that the connection must be closed, or a message dropped.

If the filter returns a CompletionStage that is already completed normally, Kroxylicious will immediately perform the action described by the FilterResult.

The filter may return a CompletionStage that is not yet completed. When this happens, Kroxylicious will pause reading from the downstream (the Client writes will eventually block), and it begins to queue up in-flight requests/responses arriving at the filter. This is done so that message order is maintained. Once the CompletionStage completes, the action described by the FilterResult is performed, reading from the downstream resumes and any queued up requests/responses are processed.

The pausing of reads from the downstream is a relatively costly operation. To maintain optimal performance filter implementations should minimise the occasions on which an incomplete CompletionStage is returned.

If the CompletionStage completes exceptionally, the connection is closed. This also applies if the CompletionStage does not complete within a timeout (20000 milliseconds).

Creating a Filter Result

The FilterContext is the factory for the FilterResult objects.

There are two convenience methods^[1] that simply allow a filter to forward a result to the next filter. We’ve already seen these in action above.

context.forwardRequest(header, request) used by result filter to forward a request.
context.forwardResponse(header, response) used by result filter to forward a request.

To access richer features, use the filter result builders context.requestFilterResultBuilder() and responseFilterResultBuilder().

Filter result builders allow you to:

forward a request/response: .forward(header, request).
signal that a connection is to be closed: .withCloseConnection().
signal that a message is to be dropped (i.e. not forwarded): .drop().
for requests only, send a short-circuit response: .shortCircuitResponse(header, response)

The builder lets you combine legal behaviours together. For instance, to close the connection after forwarding a response to a client, a response filter could use:

return context.responseFilterResultBuilder()
        .forward(header, response)
        .withCloseConnection()
        .complete();

The builders yield either a completed CompletionStage<FilterResult> which can be returned directly from the filter method, or bare FilterResult. The latter exists to support asynchronous programming styles allowing you to use your own Futures.

The drop behaviour can be legally used in very specific circumstances. The Kafka Protocol is, for the most part, strictly request/response with responses expected in the order the request were sent. The client will fail if the contract isn’t upheld. The exception is Produce where acks=0. Filters may drop these requests without introducing a protocol error.

The protocol filter lifecycle

Instances of the filter class are created on demand when a protocol message is first sent by a client. Instances are specific to the channel between a single client and a single broker.

It exists while the client remains connected.

Handling state

The simplest way of managing per-client state is to use member fields. The proxy guarantees that all methods of a given filter instance will always be invoked on the same thread (also true of the CompletionStage completion in the case of Sending asynchronous requests to the Cluster). Therefore, there is no need to use synchronization when accessing such fields.

See the io.kroxylicious.proxy.filter package javadoc for more information on thread-safety.

Filter Patterns

Kroxylicious Protocol Filters support several patterns:

Intercepting Requests and Responses
Sending Response messages from a Request Filter towards the Client (Short-circuit responses)
Sending asynchronous requests to the Cluster
Filtering specific API Versions

Intercepting Requests and Responses

This is a common pattern, we want to inspect or modify a message. For example:

public class SampleFetchResponseFilter implements FetchResponseFilter {
    @Override
    public CompletionStage<ResponseFilterResult> onFetchResponse(short apiVersion,
                                                                 ResponseHeaderData header,
                                                                 FetchResponseData response,
                                                                 FilterContext context) {
        mutateResponse(response, context); (1)
        return context.forwardResponse(header, response); (2)
    }
}

1	We mutate the response object. For example, you could alter the records that have been fetched.
2	We forward the response, sending it towards the client, invoking Filters downstream of this one.

We can only forward the response and header objects passed into the onFetchResponse. New instances are not supported.

Sending Response messages from a Request Filter towards the Client (Short-circuit responses)

In some cases we may wish to not forward a request from the client to the Cluster. Instead, we want to intercept that request and generate a response message in a Kroxylicious Protocol Filter and send it towards the client. This is called a short-circuit response.

Figure 5. Illustration of responding without proxying

For example:

public class CreateTopicRejectFilter implements CreateTopicsRequestFilter {

    public CompletionStage<RequestFilterResult> onCreateTopicsRequest(short apiVersion, RequestHeaderData header, CreateTopicsRequestData request,
                                                                      FilterContext context) {
        CreateTopicsResponseData response = new CreateTopicsResponseData();
        CreateTopicsResponseData.CreatableTopicResultCollection topics = new CreateTopicsResponseData.CreatableTopicResultCollection(); (1)
        request.topics().forEach(creatableTopic -> {
            CreateTopicsResponseData.CreatableTopicResult result = new CreateTopicsResponseData.CreatableTopicResult();
            result.setErrorCode(Errors.INVALID_TOPIC_EXCEPTION.code()).setErrorMessage(ERROR_MESSAGE);
            result.setName(creatableTopic.name());
            topics.add(result);
        });
        response.setTopics(topics);
        return context.requestFilterResultBuilder().shortCircuitResponse(response).completed(); (2)
    }
}

1	Create a new instance of the corresponding response data and populate it. Note you may need to use the `apiVersion` to check which fields can be set at this request’s API version.
2	We generate a short-circuit response that will send it towards the client, invoking Filters downstream of this one.

This will respond to all Create Topic requests with an error response without forwarding any of those requests to the Cluster.

Closing the connections

There is a useful variation on the pattern above, where the filter needs, in addition to sending an error response, also to cause the connection to close. This is useful in use-cases where the filter wishes to disallow certain client behaviours.

public class DisallowAlterConfigs implements AlterConfigsRequestFilter {

    @Override
    public CompletionStage<RequestFilterResult> onAlterConfigsRequest(short apiVersion, RequestHeaderData header, AlterConfigsRequestData request,
                                                                      FilterContext context) {
        var response = new AlterConfigsResponseData();
        response.setResponses(request.resources().stream()
                .map(a -> new AlterConfigsResourceResponse()
                        .setErrorCode(Errors.INVALID_CONFIG.code())
                        .setErrorMessage("This service does not allow this operation - closing connection"))
                .toList());
        return context.requestFilterResultBuilder()
                         .shortCircuitResponse(response)
                         .withCloseConnection() (1)
                         .completed();
    }
}

1	We enable the close connection option on the builder. This will cause Kroxylicious to close the connection after the response is sent to the client.

Sending asynchronous requests to the Cluster

Filters can make additional asynchronous requests to the Cluster. This is useful if the Filter needs additional information from the Cluster in order to know how to mutate the filtered request/response.

The Filter can make use of CompletionStage chaining features ([#thenApply() etc.) to organise for actions to be done once the asynchronous request completes. For example, it could chain an action that mutates the filtered request/response using the asynchronous response, and finally, chain an action to forward the request/response to the next filter.

The asynchronous request/response will be intercepted by Filters upstream of this Filter. Filters downstream of this Filter (and the Client) do not see the asynchronous response.

Let’s take a look at an example. We’ll send an asynchronous request towards the Cluster for topic metadata while handling a FetchRequest and use the response to mutate the FetchRequest before passing it to the next filter in the chain.

public class FetchFilter implements FetchRequestFilter {
    public static final short METADATA_VERSION_SUPPORTING_TOPIC_IDS = (short) 12;

    @Override
    public CompletionStage<RequestFilterResult> onFetchRequest(ApiKeys apiKey,
                                                               RequestHeaderData header,
                                                               FetchRequestData request,
                                                               FilterContext context) {
        var metadataRequestHeader = new RequestHeaderData().setRequestApiVersion(METADATA_VERSION_SUPPORTING_TOPIC_IDS); (1)
        var metadataRequest = new MetadataRequestData(); (2)
        var topic = new MetadataRequestData.MetadataRequestTopic();
        topic.setTopicId(Uuid.randomUuid());
        metadataRequest.topics().add(topic);
        var stage = context.sendRequest(metadataRequestHeader, metadataRequest); (3)
        return stage.thenApply(metadataResponse -> mutateFetchRequest(metadataResponse, request)) (4)
                    .thenCompose(mutatedFetchRequest -> context.forwardRequest(header, mutatedFetchRequest)); (5)
    }
}

1	We construct a header object for the asynchronous request. It is important to specify the API version of the request that is to be used. The version chosen must be a version known to the Kafka Client used by Kroxylicious and must be an API version supported by the Target Cluster.
2	We construct a new request object. When constructing the request object, care needs to be taken to ensure the request is populated with the structure which matches the API version you have chosen. Refer to the Kafka Protocol Guide for more details.
3	We asynchronously send the request towards the Cluster and obtain a CompletionStage which will contain the response.
4	We use a computation stage to mutate the filtered fetch request using the response from the request sent at <3>.
5	We use another computation stage to forward the mutated request.

As you have read above, we need to know the API version we want our request to be encoded at. Your filter can discover what versions of an API the Kafka Cluster supports. To do this use the ApiVersionsService available from the FilterContext to determine programmatically what versions of an API are support and then write code to make a suitable request object.

Kroxylicious provides the guarantee that computation stages chained using the default execution methods are executed on the same thread as the rest of the Filter work, so we can safely mutate Filter members without synchronising. See the io.kroxylicious.proxy.filter package javadoc for more information on thread-safety.

Filtering specific API Versions

Kafka has a "bidirectional" client compatibility policy. In other words, new clients can talk to old servers, and old clients can talk to new servers. This allows users to upgrade either clients or servers without experiencing any downtime.

Since the Kafka protocol has changed over time, clients and servers need to agree on the schema of the message that they are sending over the wire. This is done through API versioning.

Before each request is sent, the client sends the API key and the API version. These two 16-bit numbers, when taken together, uniquely identify the schema of the message to follow.

— https://kafka.apache.org/protocol.html#protocol_compatibility

You may wish to restrict your Filter to only apply to specific versions of an API. For example, "intercept all FetchRequest messages greater than api version 7". To do this you can override a method named shouldHandleXyz[Request|Response] on your filter like:

public class FetchFilter implements FetchRequestFilter {

    @Override
    public boolean shouldHandleFetchRequest(short apiVersion) {
        return apiVersion > 7;
    }

    @Override
    @Override
    public CompletionStage<RequestFilterResult> onRequest(ApiKeys apiKey,
                                                          RequestHeaderData header,
                                                          ApiMessage body,
                                                          FilterContext filterContext) {
        return context.forwardRequest(header, request);
    }
}

Filter Construction and Configuration

For Kroxylicious to instantiate and configure your custom filter we use Java’s ServiceLoader API. Each Custom Filter should provide a corresponding FilterFactory implementation that can create an instance of your custom Filter. The factory can optionally declare a configuration class that Kroxylicious will populate (using Jackson) when loading your custom Filter. The module must package a META-INF/services/io.kroxylicious.proxy.filter.FilterFactory file containing the classnames of each filter factory implementation into the JAR file.

For example in the kroxylicious-samples we have the SampleFilterConfig class. This is used in the SampleFetchResponseFilter). The configuration is routed to the Filter instance via the SampleFetchResponseFilterFactory.

Then, when we configure a filter in Kroxylicious configuration like:

filters:
- type: SampleFetchResponseFilter
  config:
    findValue: a
    replacementValue: b

Kroxylicious will deserialize the config object into a SampleFilterConfig and use it to construct a SampleFetchResponseFilter passing the SampleFilterConfig instance as a constructor argument.

Note that we have the CompositeFilter interface available if you wish to use a single configuration block in YAML to install multiple Filters into the Filter chain.

Packaging filters

Filters are packaged as standard .jar files. A typical Custom Filter jar contains:

Filter implementation classes
A FilterFactory implementation per Filter and service metadata (see Filter Construction and Configuration)

Deploying proxies

Topologies etc

Selecting plugins

Put the filter jars in some directory

Providing implementations for facades

Configuring virtual clusters

As described earlier, the Virtual Cluster is the downstream representation of a Kafka Cluster. Kafka Client connect to the Virtual Cluster.

You must define at least one Virtual Cluster.

Let’s look at how that is done by considering first a simple example. After we will look at more advanced options including TLS.

virtualClusters:
  demo:                                         (1)
    targetCluster:
      bootstrap_servers: myprivatecluster:9092  (2)
    clusterNetworkAddressConfigProvider:
      type: PortPerBrokerClusterNetworkAddressConfigProvider                       (3)
      config:
        bootstrapAddress: mypublickroxylicious:9192    (4)

1	The name of the virtual cluster.
2	The bootstrap of the (physical) Kafka Cluster. This is the Kafka Cluster being proxied.
3	The name of a cluster network address config provider. The built-in types are `PortPerBrokerClusterNetworkAddressConfigProvider` and `SniRoutingClusterNetworkAddressConfigProvider`.
4	The hostname and port of the bootstrap that will be used by the Kafka Clients. The hostname must be resolved by the clients.

This configuration declares a virtual cluster called demo. The physical Kafka Cluster being proxied is the defined by the targetCluster element. In this example, the PortPerBroker scheme is used by Kroxlicious to present the virtual cluster to the clients. Under this schema, Kroxylicious will open a port for each broker of the target cluster with port numbers beginning at 9192 +1. So, if the target cluster has three brokers, Kroxylicious will bind 9192 for bootstrap and 9193-9195 inclusive to allow the clients to connect to each broker.

Cluster Network Address Config Providers

The Cluster Network Address Config Provider controls how the virtual cluster is presented to the network. Two alternatives are supported: PortPerBroker and SniRouting which have different characteristics which make each suitable for different use-cases. They are described next.

PortPerBroker scheme

In the PortPerBroker scheme, Kroxylicious automatically opens a port for each virtual cluster’s bootstrap and one port per broker of each target cluster. So, if you have two virtual clusters, each targeting a Kafka Cluster of three brokers, Kroxylicious will bind eight ports in total.

Kroxylicious monitors the broker topology of the target cluster at runtime. It will adjust the number of open ports dynamically. If another broker is added to the Kafka Cluster, Kroxylicious will automatically open a port for it. Similarly, if a broker is removed from the Kafka Cluster, Kroxylicious will automatically close the port that was assigned to it.

The PortPerBroker scheme can be used with either clear text or TLS downstream connections.

clusterNetworkAddressConfigProvider:
  type: PortPerBrokerClusterNetworkAddressConfigProvider
  config:
    bootstrapAddress: mycluster.kafka.com:9192                   (1)
    brokerAddressPattern: mybroker-$(nodeId).mycluster.kafka.com (2)
    brokerStartPort: 9193                                        (3)
    numberOfBrokerPorts: 3                                       (4)
    bindAddress: 192.168.0.1                                     (5)

1	The hostname and port of the bootstrap that will be used by the Kafka Clients.
2	(Optional) The broker address pattern used to form the broker addresses. If not defined, it defaults to the hostname part of the `bootstrapAddress` and the port number allocated to the broker.
3	(Optional) The starting number for broker port range. Defaults to the port of the `bootstrapAddress` plus 1.
4	(Optional) The maximum number of brokers of ports that are permitted. Defaults to 3.
5	(Optional) The bind address used when binding the ports. If undefined, all network interfaces will be bound.

The brokerAddressPattern configuration parameter understands the replacement token $(nodeId). It is optional. If present, it will be replaced by the node.id (or broker.id) assigned to the broker of the target cluster.

For example if your configuration looks like the above and your cluster has three brokers, your Kafka Client will receive broker address information like this:

mybroker-0.mycluster.kafka.com:9193
mybroker-1.mycluster.kafka.com:9194
mybroker-2.mycluster.kafka.com:9194

It is a responsibility for the deployer of Kroxylicious to ensure that the bootstrap and generated broker DNS names are resolvable and routable by the Kafka Client.

The numberOfBrokerPorts imposes a maximum on the number of brokers that a Kafka Cluster can have. Set this value to be as high as the maximum number of brokers that your operational rules allow for a Kafka Cluster.

SniRouting scheme

In the SniRouting scheme, Kroxylicious uses SNI information to route traffic to either the boostrap or individual brokers. As this relies on SNI (Server Name Indication), the use of Downstream TLS is required.

With this scheme, you have the choice to share a single port across all virtual clusters, or you can assign a different port to each. Single port operation may have cost advantages when using load balancers of public clouds, as it allows a single cloud provider load balancer to be shared across all virtual clusters.

clusterNetworkAddressConfigProvider:
  type: SniRoutingClusterNetworkAddressConfigProvider
  config:
    bootstrapAddress: mycluster.kafka.com:9192                    (1)
    brokerAddressPattern: mybroker-$(nodeId).mycluster.kafka.com  (2)
    bindAddress: 192.168.0.1                                      (3)

1	The hostname and port of the bootstrap that will be used by the Kafka Clients.
2	The broker address pattern used to form the broker addresses. The `$(nodeId)` token must be present.
3	(Optional) The bind address used when binding the port. If undefined, all network interfaces will be bound.

The brokerAddressPattern configuration parameters requires that the $(nodeId) token is present within it. This is replaced by the node.id (or `broker.id) assigned to the broker of the target cluster. This allows Kroxylicious to generate separate routes for each broker.

It is a responsibility for the deployer of Kroxylicious to ensure that the bootstrap and generated broker DNS names are resolvable and routable by the Kafka Client.

Transport Layer Security (TLS)

In this section we look at how to enable TLS for either the downstream and/or upstream. Note, there is no interdependency; it is supported to have TLS configured for the downstream and use clear text communications for the upstream, or vice-versa.

TLS is recommended for both upstream and downstream for production configurations.

Downstream TLS

Here’s how to enable TLS for the downstream side. This means the Kafka Client will connect to the virtual cluster over TLS rather than clear text. For this, you will need to obtain a TLS certificate for the virtual cluster from your Certificate Authority.

When requesting the certificate ensure that the certificate will match the names of the virtual cluster’s bootstrap and broker addresses. This may mean making use of wildcard certificates and/or Subject Alternative Names (SANs).

Kroxylicious accepts key material in PKCS12 or JKS keystore format, or PEM formatted file(s). The following configuration illustrates configuration with PKCS12 keystore.

virtualClusters:
  demo:
    tls:
        key:
          storeFile: /opt/cert/server.p12               (1)
          storePassword:
            passwordFile: /opt/cert/store.password      (2)
          keyPassword:
            passwordFile: /opt/cert/key.password        (3)
          storeType: PKCS12                             (4)
    clusterNetworkAddressConfigProvider:
      ...

1	File system location of a keystore (or in the case of `PEM` format a text file containing the concatenation of the private key, certificate, and intermediates).
2	File system location of a file containing the key store’s password.
3	(Optional) File system location of a file containing the key’s password. If omitted the key store’s password is used to decrypt the key too.
4	(Optional) Store type. Supported types are: `PKCS12`, `JKS` and `PEM`. Defaults to Java default key store type which is usually `PKCS12`.

Alternatively, if your key material is in separate PEM files (private key, and certificate/intermediates), the following configuration may be used:

virtualClusters:
  demo:
    tls:
        key:
          privateKeyFile: /opt/cert/server.key          (1)
          certificateFile: /opt/cert/server.crt         (2)
          keyPassword:
            passwordFile: /opt/cert/key.password        (3)
    clusterNetworkAddressConfigProvider:
      ...

1	File system location of the server private key.
2	File system location of the server certificate and intermediate(s).
3	(Optional) File system location of a file containing the key’s password.

For the private-key, PKCS-8 keys are supported by default. For PKCS-1 keys, Bouncycastle libraries must be added to the Kroxylicious classpath. See https://github.com/netty/netty/issues/7323 for more details.

Upstream TLS

Here’s how to enable TLS for the upstream side. This means that Kroxylicious connects to the (physical) Kafka Cluster) over TLS. For this, your Kafka Cluster must have already been configured to use TLS.

By default, Kroxylicious inherits what it trusts from the platform it is running on and uses this to determine whether the Kafka Cluster is trusted or not.

To support cases where trust must be overridden (such as use-cases involving the use of private CAs or self-signed certificates), Kroxylicious accepts override trust material in PKCS12 or JKS keystore format, or PEM formatted certificates.

The following illustrates enabling TLS, inheriting platform trust:

virtualClusters:
  demo:
    targetCluster:
      bootstrap_servers: myprivatecluster:9092
      tls: {}                                         (1)
      #...

1	Use an empty object to enable TLS inheriting trust from the platform.

The following illustrates enabling TLS but with trust coming from a PKCS12 trust store instead of the platform:

virtualClusters:
  demo:
    targetCluster:
      bootstrap_servers: myprivatecluster:9092
      tls:
        trust:
          storeFile: /opt/cert/trust.p12                (1)
          storePassword:
            passwordFile: /opt/cert/store.password      (2)
          storeType: PKCS12                             (3)
      #...

1	File system location of a truststore (or in the case of `PEM` format a text file containing the certificates).
2	File system location of a file containing the trust store’s password.
3	(Optional) Trust store type. Supported types are: `PKCS12`, `JKS` and `PEM`. Defaults to Java default key store type (PKCS12).

It is also possible to disable trust so that Kroxylicious will connect to any Kafka Cluster regardless of its certificate validity.

This option is not recommended for production use.

virtualClusters:
  demo:
    targetCluster:
      bootstrap_servers: myprivatecluster:9092
      tls:
        trust:
          insecure: true                                (1)
      #...

1	Enables insecure TLS.

YAML Proxy level configuration

Configuring proxy plugins

Filter level configuration

Operating proxies

Logging

Configuring TLS

Reconfiguration

Monitoring and observability

Kroxylicious uses micrometer as a facade for gathering metrics. A Prometheus backend is the only supported implementation so far.

Admin HTTP Endpoint

Kroxylicious offers a configurable, insecure, HTTP endpoint for adminstration purposes. It can offer:

Prometheus scrape endpoint at /metrics

minimal configuration example

adminHttp:
  endpoints:
    prometheus: {}

Defaults to binding to 0.0.0.0:9190. If no endpoints are configured the admin http server is not created.

complete configuration example

adminHttp:
  host: localhost #1
  port: 9999 #1
  endpoints:
    prometheus: {} #2

offers an insecure admin HTTP endpoint listening on localhost:9999
offers a prometheus scrape endpoint at /metrics on the admin endpoint

Micrometer Metrics

Kroxylicious integrates with micrometer.

Micrometer provides a simple facade over the instrumentation clients for the most popular observability systems, allowing you to instrument your JVM-based application code without vendor lock-in.

complete configuration example

adminHttp:
  endpoints:
    prometheus: {}
micrometer:
  - type: "CommonTagsHook"
    config:
      commonTags:
        zone: "euc-1a" #1
  - type: "StandardBindersHook"
    config:
      binderNames:
      - "JvmGcMetrics" #2

This configuration:

configures a common tag on the global micrometer registry of zone: euc-1a to add to all metrics (becomes a label in prometheus)
registers a JvmGcMetrics binder with the global registry (shipped with micrometer)

Prometheus is connected to the micrometer global registry so Filters can record metrics against it, and they will be available as part of the Prometheus scrape data.

If you executed curl localhost:9999/metrics you should see metrics like:

jvm_gc_memory_allocated_bytes_total{zone="euc-1a",} 0.0

Common Tags

We can add common tags that will be added to all metrics. These will be available as labels from the Prometheus scrape.

To configure common tags use configuration:

  - type: "CommonTagsHook"
    config:
      commonTags:
        zone: "euc-1a"
        owner: "becky"

Standard Binders

Micrometer has a concept of MeterBinder:

Binders register one or more metrics to provide information about the state of some aspect of the application or its container.

By registering some standard binders shipped with micrometer you can expose metrics about the JVM and system which can observe JVM memory usage, garbage collection and other behaviour.

To configure multiple binders you can use configuration like:

micrometer:
  - type: "StandardBindersHook"
    config:
      binderNames:
      - "JvmGcMetrics"
      - "JvmHeapPressureMetrics"

And those named binders will be bound to the global meter registry

Table 1. Available Binders
name	micrometer class
ClassLoaderMetrics	io.micrometer.core.instrument.binder.jvm.ClassLoaderMetrics
JvmCompilationMetrics	io.micrometer.core.instrument.binder.jvm.JvmCompilationMetrics
JvmGcMetrics	io.micrometer.core.instrument.binder.jvm.JvmGcMetrics
JvmHeapPressureMetrics	io.micrometer.core.instrument.binder.jvm.JvmHeapPressureMetrics
JvmInfoMetrics	io.micrometer.core.instrument.binder.jvm.JvmInfoMetrics
JvmMemoryMetrics	io.micrometer.core.instrument.binder.jvm.JvmMemoryMetrics
JvmThreadMetrics	io.micrometer.core.instrument.binder.jvm.JvmThreadMetrics
FileDescriptorMetrics	io.micrometer.core.instrument.binder.system.FileDescriptorMetrics
ProcessorMetrics	io.micrometer.core.instrument.binder.system.ProcessorMetrics
UptimeMetrics	io.micrometer.core.instrument.binder.system.UptimeMetrics

Micrometer Usage from Filters

Filters can use the static methods of Metrics to register metrics with the global registry. Or use Metrics.globalRegistry to get a reference to the global registry. Metrics registered this way will be automatically available through the prometheus scrape endpoint.

1. The context.forward*() methods behave exactly as the builder form .forward(header, message).complete()