Skip to main content
Version: 0.9.0

Source CRD configurations

This document lists CRD configurations available for Pulsar source connectors. The source CRD configurations consist of source connector configurations and the common CRD configurations.

Source configurations

This table lists source configurations.

FieldDescription
nameThe connector name is a string of up to 43 characters.
classnameThe class name of a source connector.
tenantThe tenant of a source connector.
namespaceThe Pulsar namespace of a source connector.
clusterNameThe Pulsar cluster of a source connector.
replicasThe number of instances that you want to run for this source connector. If no value is set, the system will set it to 1.
minReplicasThe minimum number of instances that you want to run for this source connector. If no value is set, the system will set it to 1. When HPA auto-scaling is enabled, the HPA controller scales the Pods up / down based on the values of the minReplicas and maxReplicas options. The number of the Pods should be greater than the value of the minReplicas and be smaller than the value of the maxReplicas.
downloaderImageThe image for installing the init container that is used to download packages or functions from Pulsar if the download path is specified.
maxReplicasThe maximum number of instances that you want to run for this source connector. When the value of the maxReplicas parameter is greater than the value of replicas, it indicates that the source controller automatically scales the source connector based on the CPU usage. By default, maxReplicas is set to 0, which indicates that auto-scaling is disabled.
sourceConfigThe source connector configurations in YAML format.
processingGuaranteeThe processing guarantees (delivery semantics) applied to the source connector. Available values: atleast_once, atmost_once, effectively_once.
forwardSourceMessagePropertyConfigure whether to pass message properties to a target topic.
batchSourceConfigThe batch source configurations in YAML format. You can configure the following properties.
- discoveryTriggererClassName: the class that is used for triggering the discovery process.
- discoveryTriggererConfig: the configurations that are required for initiating the discovery Triggerer.
pulsarThe configurations of the Pulsar cluster. For details, see messaging.

Annotations

In Kubernetes, an annotation defines an unstructured Key Value Map (KVM) that can be set by external tools to store and retrieve metadata. annotations must be a map of string keys and string values. Annotation values must pass Kubernetes annotations validation. For details, see Kubernetes documentation on Annotations.

This example shows how to use an annotation to make an object unmanaged. Therefore, the Controller will skip reconciling unmanaged objects in reconciliation loop.

apiVersion: compute.functionmesh.io/v1alpha1
kind: Source
metadata:
annotations:
compute.functionmesh.io/managed: "false"

Images

This section describes image options available for Pulsar source CRDs.

Base runner

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/bin, /pulsar/conf and /pulsar/lib to ensure that the pulsar-admin CLI tool works properly to support Apache Pulsar Packages.

Runner images

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 imagePullPolicy option:

FieldDescription
AlwaysAlways pull the image.
NeverNever pull the image.
IfNotPresentOnly pull the image if the image does not already exist locally.

Messaging

Function Mesh provides Pulsar cluster configurations in the Function, Source, and Sink CRDs. You can configure TLS encryption, TLS authentication, and OAuth2 authentication using the following configurations.

Note

The tlsConfig and tlsSecret are exclusive. If you configure TLS configurations, the TLS Secret will not take effect.

FieldDescription
authConfigThe authentication configurations of the Pulsar cluster. Currently, you can only configure OAuth2 authentication through this field. For other authentication methods, you can configure them using the authSecret field.
  • audience: specify the OAuth2 resource server identifier for the Pulsar cluster.
  • issuerUrl: specify the URL of the OAuth2 identity provider that allows a Pulsar client to obtain an access token.
  • scope: specify the scope of an access request. For more information, see access token scope.
  • keySecretName: specify the name of the Kubernetes Secret.
  • keySecretKey: specify the key of the Kubernetes Secret that contains the content of the OAuth2 private key.
authSecretThe name of the authentication ConfigMap that stores authentication configurations of the Pulsar cluster.
  • clientAuthenticationPlugin: specify the client authentication plugin.
  • clientAuthenticationParameters: specify the client authentication parameters.
pulsarConfigThe name of the ConfigMap that stores Pulsar cluster configurations.
  • webServiceURL: specify the web service URL for managing the Pulsar cluster. This URL should be a standard DNS name.
  • brokerServiceURL: specify the Pulsar protocol URL for interaction with the brokers in the Pulsar cluster. This URL should not use the same DNS name as the web service URL but should use the pulsar scheme.
tlsConfigThe TLS configurations of the Pulsar cluster.
  • allowInsecure: allow insecure TLS connection.
  • certSecretKey: specify the TLS Secret key.
  • certSecretName: specify the TLS Secret name.
  • enabled: enable TLS configurations.
  • hostnameVerification: enable hostname verification.
tlsSecretThe name of the TLS ConfigMap that stores TLS configurations of the Pulsar cluster.
  • tlsAllowInsecureConnection: allow insecure TLS connection. By default, it is set to false.
  • tlsHostnameVerificationEnable: enable hostname verification. By default, it is set to true.
  • tlsTrustCertsFilePath: specify the path of the TLS trust certificate file.

State storage

Function Mesh provides the following fields for stateful configurations in the CRD definition.

FieldDescription
statefulConfigThe state storage configuration for the source connector.
statefulConfig.pulsar.serviceUrlThe service URL that points to the state storage service. By default, the state storage service is the BookKeeper table service.
statefulConfig.pulsar.javaProvider(Optional) If you want to overwrite the default configuration, you can use the state storage configuration for the Java runtime. For example, you can change it to other backend services other than the BookKeeper table service.
statefulConfig.pulsar.javaProvider.classNameThe Java class name of the state storage provider implementation. The class must implement the org.apache.pulsar.functions.instance.state.StateStoreProvider interface. If not, org.apache.pulsar.functions.instance.state.BKStateStoreProviderImpl will be used.
statefulConfig.pulsar.javaProvider.configThe configurations that are passed to the state storage provider.

Output

The output topics of a Pulsar Function. This table lists options available for the Output.

NameDescription
topicsThe output topic of a Pulsar Function (If none is specified, no output is written).
sinkSerdeClassNameThe map of output topics to SerDe class names (as a JSON string).
sinkSchemaTypeThe built-in schema type or custom schema class name to be used for messages sent by the function.
producerConfThe producer specifications. Available options:
- maxPendingMessages: the maximum number of pending messages.
- maxPendingMessagesAcrossPartitions: the maximum number of pending messages across partitions.
- useThreadLocalProducers: configure whether the producer uses a thread.
- cryptoConfig: cryptography configurations of the producer.
- batchBuilder: support key-based batcher.
customSchemaSinksThe map of output topics to Schema class names (as a JSON string).

Resources

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's 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 limit.

Secrets

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.

The 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.

FieldDescription
pathThe name of the secret in the Pod's namespace to select from.
keyThe 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:

apiVersion: v1
data:
username: foo
password: bar
kind: Secret
metadata:
name: credential-secret
type: Opaque

To use it in Pulsar Functions in a secure way, you can define the secretsMap in the Custom Resource:

secretsMap:
username:
path: credential-secret
key: username
password:
path: credential-secret
key: password

Then, in the Pulsar Functions and Connectors, you can call context.getSecret("username") to get the secret value (foo).

Packages

Function Mesh supports running Pulsar connectors in Java.

FieldDescription
jarLocationThe path to the JAR file for the connector.
javaOptsIt specifies JVM options to better configure JVM behaviors, including exitOnOOMError, Garbage Collection logs, Garbage Collection tuning, and so on.
extraDependenciesDirIt specifies the dependent directory for the JAR package.

Cluster location

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.

FieldDescription
webServiceURLThe Web service URL of the Pulsar cluster.
brokerServiceURLThe broker service URL of the Pulsar cluster.

Health checks

Note

To enable health checks, you need to create a PVC and a PV, and bind the PVC to the PV. Then, you can use the --set controllerManager.grpcurlPersistentVolumeClaim=<your_pvc_name> option to specify the PVC when installing the Function Mesh Operator.

With the Kubernetes liveness probe, Function Mesh supports monitoring and acting on the state of Pods (Containers) to ensure that only healthy Pods serve traffic. Implementing health checks using probes provides Function Mesh a solid foundation, better reliability, and higher uptime.

apiVersion: compute.functionmesh.io/v1alpha1
kind: Function
metadata:
name: health-check-sample
namespace: default
spec:
image: streamnative/pulsar-functions-java-sample:2.9.2.23
className: org.apache.pulsar.functions.api.examples.ExclamationFunction
forwardSourceMessageProperty: true
maxPendingAsyncRequests: 1000
replicas: 1
maxReplicas: 5
liveness:
initialDelaySeconds: 10 # --- [1]
periodSeconds: 10 # --- [2]
logTopic: persistent://public/default/logging-function-logs
...
# Other configs
  • initialDelaySeconds: specify the time that should wait before performing the first liveness probe.
  • periodSeconds: specify the frequency to perform a liveness probe.

For more information about probe types, probe check mechanisms, and probe parameters, see Kubernetes documentation on Pod lifecycle and configure probes.

Pod specifications

Function Mesh supports customizing the Pod running connectors. This table lists sub-fields available for the pod field.

FieldDescription
labelsSpecify labels attached to a Pod.
livenessSpecify the liveness probe properties for a Pod.
  • initialDelaySecond: specify the time that should wait before performing the first liveness probe.
  • periodSeconds: specify the frequency to perform a liveness probe.

For details, see health checks.

nodeSelectorSpecify 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.
affinitySpecify the scheduling constraints of a Pod.
tolerationsSpecify the tolerations of a Pod.
annotationsSpecify the annotations attached to a Pod.
securityContextSpecify the security context for a Pod.
terminationGracePeriodSecondsThe amount of time that Kubernetes gives for a Pod before terminating it.
volumesA list of volumes that can be mounted by containers belonging to a Pod.
imagePullSecretsAn optional list of references to secrets in the same namespace for pulling any of the images used by a Pod.
serviceAccountNameSpecify the name of the service account that is used to run Pulsar Functions or connectors in the Function Mesh Worker service.
initContainersThe 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.
sidecarsSidecar containers run together with the main function container in a Pod.
builtinAutoscalerSpecify 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 builtinAutoscaler field, you do not need to configure the autoScalingMetrics and autoScalingBehavior options and vice versa.

autoScalingMetricsSpecify how to scale based on customized metrics defined in connectors. For details, see MetricSpec v2beta2 autoscaling.
autoScalingBehaviorConfigure the scaling behavior of the target in both up and down directions (scaleUp and scaleDown fields respectively). If not specified, the default Kubernetes scaling behaviors are adopted. For details, see HorizontalPodAutoscalerBehavior v2beta2 autoscaling.
envSpecify the environment variables to expose on the containers. It is a key/value map. You can either use the value option to specify a particular value for the environment variable or use the valueFrom option to specify the source for the environment variable's value, as shown below.
```yaml
env:
- name: example1
value: simpleValue
- name: example2
valueFrom:
secretKeyRef:
name: secret-name
key: akey
```