This document lists CRD configurations available for Pulsar sink connectors. The sink CRD configurations consist of sink connector configurations and the common CRD configurations.
This table lists sink configurations.
|The name of a sink connector.|
|The class name of a sink connector.|
|The tenant of a sink connector.|
|The Pulsar namespace of a sink connector.|
|The Pulsar cluster of a sink connector.|
|The number of instances that you want to run this sink connector. By default, the |
|The maximum number of Pulsar instances that you want to run for this sink connector. When the value of the |
|The sink connector configurations in YAML format.|
|The message timeout in milliseconds.|
|The number of redelivered messages due to negative acknowledgement.|
|Whether or not the framework acknowledges messages automatically. This field is required. You can set it to |
|How many times to process a message before giving up.|
|The processing guarantees (delivery semantics) applied to the sink connector. Available values: |
|The sink connector consumes and processes messages in order.|
|The topic where all messages that were not processed successfully are sent.|
|The subscription name of the sink connector if you want a specific subscription name for the input-topic consumer.|
|Configure whether to clean up subscriptions.|
|The subscription position.|
This section describes image options available for Pulsar sink CRDs.
The base runner is an image base for other runners. The base runner is located at
./pulsar-functions-base-runner. The base runner image contains basic tool-chains like
/pulsar/lib to ensure that the
pulsar-admin CLI tool works properly to support Apache Pulsar Packages.
Function Mesh uses runner images as images of Pulsar connectors. Each runner image only contains necessary tool-chains and libraries for specified runtime.
Pulsar connectors support using the Java runner images as their images. The Java runner is based on the base runner and contains the Java function instance to run Java functions or connectors. The
streamnative/pulsar-functions-java-runner Java runner is stored at the Docker Hub and is automatically updated to align with Apache Pulsar release.
Image pull policies
When the Function Mesh Operator creates a container, it uses the
imagePullPolicy option to determine whether the image should be pulled prior to starting the container. There are three possible values for the
|Always pull the image.|
|Never pull the image.|
|Only pull the image if the image does not already exist locally.|
The input topics of a Pulsar Function. The following table lists options available for the
|The configuration of the topic from which messages are fetched.|
|The map of input topics to SerDe class names (as a JSON string).|
|The map of input topics to Schema class names (as a JSON string).|
|The map of source specifications to consumer specifications. Consumer specifications include these options: |
When you specify a function or connector, you can optionally specify how much of each resource they need. The resources available to specify are CPU and memory (RAM).
If the node where a Pod is running has enough of a resource available, it is possible (and allowed) for a Pod to use more resources than its
request for that resource. However, a Pod is not allowed to use more than its resource
Function Mesh provides the
secretsMap field for Function, Source, and Sink in the CRD definition. You can refer to the created secrets under the same namespace and the controller can include those referred secrets. The secrets are provide by
EnvironmentBasedSecretsProvider, which can be used by
context.getSecret() in Pulsar functions and connectors.
secretsMap field is defined as a
Map struct with
String keys and
SecretReference values. The key indicates the environment value in the container, and the
SecretReference is defined as below.
|The name of the secret in the Pod's namespace to select from.|
|The key of the secret to select from. It must be a valid secret key.|
Suppose that there is a Kubernetes Secret named
credential-secret defined as below:
To use it in Pulsar Functions in a secure way, you can define the
secretsMap in the Custom Resource:
Then, in the Pulsar Functions and Connectors, you can call
context.getSecret("username") to get the secret value (
Function Mesh provides the
authSecret fields for Function, Source, and Sink in the CRD definition. You can configure TLS encryption and/or TLS authentication using the following configurations.
Allow insecure TLS connection.
Enable hostname verification.
The path of the TLS trust certificate file.
The client authentication plugin.
The client authentication parameters.
Function Mesh supports running Pulsar connectors in Java.
|Path to the JAR file for the connector.|
|It specifies the dependent directory for the JAR package.|
In Function Mesh, the Pulsar cluster is defined through a ConfigMap. Pods can consume ConfigMaps as environment variables in a volume. The Pulsar cluster ConfigMap defines the Pulsar cluster URLs.
|The Web service URL of the Pulsar cluster.|
|The broker service URL of the Pulsar cluster.|
Function Mesh supports customizing the Pod running Pulsar connectors. This table lists sub-fields available for the
|Specify labels attached to a Pod.|
|Specify a map of key-value pairs. For a Pod running on a node, the node must have each of the indicated key-value pairs as labels.|
|Specify the scheduling constraints of a Pod.|
|Specify the tolerations of a Pod.|
|Specify the annotations attached to a Pod.|
|Specify the security context for a Pod.|
|It is the amount of time that Kubernetes gives for a Pod before terminating it.|
|It is a list of volumes that can be mounted by containers belonging to a Pod.|
|It is an optional list of references to secrets in the same namespace for pulling any of the images used by a Pod.|
|Specify the name of the service account which is used to run Pulsar Functions or connectors.|
|The initialization containers belonging to a Pod. A typical use case could be using an initialization container to download a remote JAR to a local path.|
|Sidecar containers run together with the main function container in a Pod.|
|Specify the built-in autoscaling rules. |
- CPU-based autoscaling: auto-scale the number of Pods based on the CPU usage (80%, 50%, or 20%).
- Memory-based autoscaling: auto-scale the number of Pods based on the memory usage (80%, 50%, or 20%).
If you configure the
|Specify how to scale based on customized metrics defined in connectors. For details, see MetricSpec v2beta2 autoscaling.|
|Configure the scaling behavior of the target in both up and down directions (|