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Documentation Index

Fetch the complete documentation index at: https://docs.open-metadata.org/llms.txt

Use this file to discover all available pages before exploring further.

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AKS Deployment: Airflow Orchestrator & Troubleshooting

This page covers the Airflow-based orchestrator setup for AKS. For the simpler recommended Kubernetes-native orchestrator, see AKS Deployment: Prerequisites & Kubernetes Orchestrator.

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Using Airflow Orchestrator (Alternative)

If you prefer to use Apache Airflow as the orchestrator (e.g., for existing Airflow investments or complex DAG requirements), follow the configuration below.
Using Airflow requires additional infrastructure: persistent volumes with ReadWriteMany access, the openmetadata-dependencies Helm chart, and more complex configuration.

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Create Persistent Volumes

OpenMetadata helm chart depends on Airflow and Airflow expects a persistent disk that support ReadWriteMany (the volume can be mounted as read-write by many nodes). The Azure CSI storage drivers we enabled earlier support the provisioning of the disks in ReadWriteMany mode. 
# logs_dags_pvc.yaml
kind: PersistentVolumeClaim
apiVersion: v1
metadata:
  name: openmetadata-dependencies-dags-pvc
  namespace: openmetadata
spec:
  accessModes:
    - ReadWriteMany
  resources:
    requests:
      storage: 10Gi
  storageClassName: azurefile-csi
---
kind: PersistentVolumeClaim
apiVersion: v1
metadata:
  name: openmetadata-dependencies-logs-pvc
  namespace: openmetadata
spec:
  accessModes:
    - ReadWriteMany
  resources:
    requests:
      storage: 5Gi
  storageClassName: azurefile-csi
Create the volume claims by applying the manifest: 
kubectl apply -f logs_dags_pvc.yaml

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Change Owner and Update Permission for Persistent Volumes

Airflow pods run as non-root user and lack write access to our persistent volumes. To fix this we create a job permissions_pod.yaml that runs a pod that mounts volumes into the persistent volume claim and updates the owner of the mounted folders /airflow-dags and /airflow-logs to user id 50000, which is the default linux user id of Airflow pods. 
# permissions_pod.yaml
apiVersion: batch/v1
kind: Job
metadata:
  labels:
    run: my-permission-pod
  name: my-permission-pod
  namespace: openmetadata
spec:
  template:
    spec:
      containers:
      - image: busybox
        name: my-permission-pod
        volumeMounts:
        - name: airflow-dags
          mountPath: /airflow-dags
        - name: airflow-logs
          mountPath: /airflow-logs
        command: ["/bin/sh", "-c", "chown -R 50000 /airflow-dags /airflow-logs", "chmod -R a+rwx /airflow-dags"]
      restartPolicy: Never
      volumes:
      - name: airflow-logs
        persistentVolumeClaim:
          claimName: openmetadata-dependencies-logs-pvc
      - name: airflow-dags
        persistentVolumeClaim:
          claimName: openmetadata-dependencies-dags-pvc
Start the job by applying the manifest: 
kubectl apply -f permissions_pod.yaml

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Create Airflow Secrets

kubectl create secret generic airflow-secrets \
  --namespace openmetadata \
  --from-literal=openmetadata-airflow-password=<AdminPassword>
For production deployments connecting external postgresql database: 
kubectl create secret generic postgresql-secret \
  --namespace openmetadata \
  --from-literal=postgresql-password=<MyPGDBPassword>

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Install OpenMetadata Dependencies

Create values-dependencies.yaml to configure Airflow with persistent volumes: 
# values-dependencies.yaml
airflow:
  airflow:
    extraVolumeMounts:
      - mountPath: /airflow-logs
        name: aks-airflow-logs
      - mountPath: /airflow-dags/dags
        name: aks-airflow-dags
    extraVolumes:
      - name: aks-airflow-logs
        persistentVolumeClaim:
          claimName: openmetadata-dependencies-logs-pvc
      - name: aks-airflow-dags
        persistentVolumeClaim:
          claimName: openmetadata-dependencies-dags-pvc
    config:
      AIRFLOW__OPENMETADATA_AIRFLOW_APIS__DAG_GENERATED_CONFIGS: "/airflow-dags/dags"
  dags:
    path: /airflow-dags/dags
    persistence:
      enabled: false
  logs:
    path: /airflow-logs
    persistence:
      enabled: false
  externalDatabase:
    type: postgres # default mysql
    host: Host_db_address
    database: Airflow_metastore_dbname
    user: db_userName
    port: 5432
    dbUseSSL: true
    passwordSecret: postgresql-secret
    passwordSecretKey: postgresql-password
Install the dependencies: 
helm install openmetadata-dependencies open-metadata/openmetadata-dependencies \
  --values values-dependencies.yaml \
  --namespace openmetadata \
  --set mysql.enabled=false
It takes a few minutes for all the pods to be correctly set-up and running: 
kubectl get pods -n openmetadata

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Install OpenMetadata with Airflow

Create openmetadata-values.yaml for Airflow-based deployment: 
# openmetadata-values.yaml
global:
  pipelineServiceClientConfig:
    apiEndpoint: http://openmetadata-dependencies-web.openmetadata.svc.cluster.local:8080
    metadataApiEndpoint: http://openmetadata.openmetadata.svc.cluster.local:8585/api

openmetadata:
  config:
    elasticsearch:
      host: <ELASTIC_CLOUD_ENDPOINT_WITHOUT_HTTPS>
      searchType: elasticsearch
      port: 443
      scheme: https
      auth:
        enabled: true
        username: <ELASTIC_CLOUD_USERNAME>
        password:
          secretRef: elasticsearch-secrets
          secretKey: openmetadata-elasticsearch-password
    database:
      host: <AZURE_SQL_ENDPOINT>
      port: 5432
      driverClass: org.postgresql.Driver
      dbScheme: postgresql
      databaseName: openmetadata_db
      auth:
        username: <DB_USERNAME>
        password:
          secretRef: postgresql-secret
          secretKey: postgresql-password

image:
  tag: "1.12.0"

helm install openmetadata open-metadata/openmetadata \
  --values openmetadata-values.yaml \
  --namespace openmetadata

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Troubleshooting

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Troubleshooting Airflow

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JSONDecodeError: Unterminated string starting

If you are using Airflow with Azure Blob Storage as PersistentVolume as explained in Storage class using blobfuse, you may encounter the following error after a few days: 
{dagbag.py:346} ERROR - Failed to import: /airflow-dags/dags/...py
json.decoder.JSONDecodeError: Unterminated string starting at: line 1 column 3552
Moreover, the Executor pods would actually be using old files. This behaviour is caused by the recommended config by the mentioned documentation: 
  - -o allow_other
  - --file-cache-timeout-in-seconds=120
  - --use-attr-cache=true
  - --cancel-list-on-mount-seconds=10  # prevent billing charges on mounting
  - -o attr_timeout=120
  - -o entry_timeout=120
  - -o negative_timeout=120
  - --log-level=LOG_WARNING  # LOG_WARNING, LOG_INFO, LOG_DEBUG
  - --cache-size-mb=1000  # Default will be 80% of available memory, eviction will happen beyond that.
Disabling the cache will help here. In this case it won’t have any negative impact, since the .py and .json files are small enough and not heavily used. The same configuration without cache: 
  - --o direct_io
  - --file-cache-timeout-in-seconds=0
  - --use-attr-cache=false
  - --cancel-list-on-mount-seconds=10
  - --o attr_timeout=0
  - --o entry_timeout=0
  - --o negative_timeout=0
  - --log-level=LOG_WARNING
  - --cache-size-mb=0
You can find more information about this error here, and similar discussions here and here.

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FAQs

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Java Memory Heap Issue

If your openmetadata pods are not in ready state at any point in time and the openmetadata pod logs speaks about the below issue - 
Exception: java.lang.OutOfMemoryError thrown from the UncaughtExceptionHandler in thread "AsyncAppender-Worker-async-file-appender"
Exception in thread "pool-5-thread-1" java.lang.OutOfMemoryError: Java heap space
Exception in thread "AsyncAppender-Worker-async-file-appender" java.lang.OutOfMemoryError: Java heap space
Exception in thread "dw-46" java.lang.OutOfMemoryError: Java heap space
Exception in thread "AsyncAppender-Worker-async-console-appender" java.lang.OutOfMemoryError: Java heap space
This is due to the default JVM Heap Space configuration (1 GiB) being not enough for your workloads. In order to resolve this issue, head over to your custom openmetadata helm values and append the below environment variable 
extraEnvs:
- name: OPENMETADATA_HEAP_OPTS
  value: "-Xmx2G -Xms2G"
The flag Xmx specifies the maximum memory allocation pool for a Java virtual machine (JVM), while Xms specifies the initial memory allocation pool. Upgrade the helm charts with the above changes using the following command helm upgrade --install openmetadata open-metadata/openmetadata --values <values.yml> --namespace <namespaceName>. Update this command your values.yml filename and namespaceName where you have deployed OpenMetadata in Kubernetes.

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PostgreSQL Issue permission denied to create extension “pgcrypto”

If you are facing the below issue with PostgreSQL as Database Backend for OpenMetadata Application, 
Message: ERROR: permission denied to create extension "pgcrypto"
Hint: Must be superuser to create this extension.
It seems the Database User does not have sufficient privileges. In order to resolve the above issue, grant usage permissions to the PSQL User. 
GRANT USAGE ON SCHEMA schema_name TO <openmetadata_psql_user>;
GRANT CREATE ON EXTENSION pgcrypto TO <openmetadata_psql_user>;
In the above command, replace <openmetadata_psql_user> with the sql user used by OpenMetadata Application to connect to PostgreSQL Database.

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How to extend and use custom docker images with OpenMetadata Helm Charts ?

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Extending OpenMetadata Server Docker Image

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1. Create a Dockerfile based on docker.open-metadata.org/openmetadata/server

OpenMetadata helm charts uses official published docker images from DockerHub. A typical scenario will be to install organization certificates for connecting with inhouse systems. For Example - 
FROM docker.open-metadata.org/openmetadata/server:x.y.z
WORKDIR /home/
COPY <my-organization-certs> .
RUN update-ca-certificates
where docker.open-metadata.org/openmetadata/server:x.y.z needs to point to the same version of the OpenMetadata server, for example docker.open-metadata.org/openmetadata/server:1.3.1. This image needs to be built and published to the container registry of your choice.

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2. Update your openmetadata helm values yaml

The OpenMetadata Application gets installed as part of openmetadata helm chart. In this step, update the custom helm values using YAML file to point the image created in the previous step. For example, create a helm values file named values.yaml with the following contents - 
...
image:
  repository: <your repository>
  # Overrides the image tag whose default is the chart appVersion.
  tag: <your tag>
...

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3. Install / Upgrade your helm release

Upgrade/Install your openmetadata helm charts with the below single command: 
helm upgrade --install openmetadata open-metadata/openmetadata--values values.yaml

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Extending OpenMetadata Ingestion Docker Image

One possible use case where you would need to use a custom image for the ingestion is because you have developed your own custom connectors. You can find a complete working example of this here. After you have your code ready, the steps would be the following:

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1. Create a Dockerfile based on docker.open-metadata.org/openmetadata/ingestion:

For example - 
FROM docker.open-metadata.org/openmetadata/ingestion:x.y.z

USER airflow
# Let's use the home directory of airflow user
WORKDIR /home/airflow

# Install our custom connector
COPY <your_package> <your_package>
COPY setup.py .
RUN pip install --no-deps .
where docker.open-metadata.org/openmetadata/ingestion:x.y.z needs to point to the same version of the OpenMetadata server, for example docker.open-metadata.org/openmetadata/ingestion:1.3.1. This image needs to be built and published to the container registry of your choice.

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2. Update the airflow in openmetadata dependencies values YAML

The ingestion containers (which is the one shipping Airflow) gets installed in the openmetadata-dependencies helm chart. In this step, we use our own custom values YAML file to point to the image we just created on the previous step. You can create a file named values.deps.yaml with the following contents: 
airflow:
  airflow:
    image:
      repository: <your repository>  # by default, openmetadata/ingestion
      tag: <your tag>  # by default, the version you are deploying, e.g., 1.1.0
      pullPolicy: "IfNotPresent"

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3. Install / Upgrade helm release

Upgrade/Install your openmetadata-dependencies helm charts with the below single command: 
helm upgrade --install openmetadata-dependencies open-metadata/openmetadata-dependencies --values values.deps.yaml

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How to disable MySQL and ElasticSearch from OpenMetadata Dependencies Helm Charts ?

If you are using MySQL and ElasticSearch externally, you would want to disable the local installation of mysql and elasticsearch while installing OpenMetadata Dependencies Helm Chart. You can disable the MySQL and ElasticSearch Helm Dependencies by setting enabled: false value for each dependency. Below is the command to set helm values from Helm CLI - 
helm upgrade --install openmetadata-dependencies open-metadata/openmetadata-dependencies --set mysql.enabled=false --set elasticsearch.enabled=false
Alternatively, you can create a custom YAML file named values.deps.yaml to disable installation of MySQL and Elasticsearch . 
mysql:
    enabled: false
    ...
elasticsearch:
    enabled: false
    ...
...

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How to configure external database like PostgreSQL with OpenMetadata Helm Charts ?

OpenMetadata Supports PostgreSQL as one of the Database Dependencies. OpenMetadata Helm Charts by default does not include PostgreSQL as Database Dependencies. In order to configure Helm Charts with External Database like PostgreSQL, follow the below guide to make the helm values change and upgrade / install OpenMetadata helm charts with the same.

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Upgrade Airflow Helm Dependencies Helm Charts to connect to External Database like PostgreSQL

We ship airflow-helm as one of OpenMetadata Dependencies with default values to connect to MySQL Database as part of externalDatabase configurations. You can find more information on setting the externalDatabase as part of helm values here. With OpenMetadata Dependencies Helm Charts, your helm values would look something like below - 
...
airflow:
  externalDatabase:
    type: postgresql
    host: <postgresql_endpoint>
    port: 5432
    database: <airflow_database_name>
    user: <airflow_database_login_user>
    passwordSecret: airflow-postgresql-secrets
    passwordSecretKey: airflow-postgresql-password
...
For the above code, it is assumed you are creating a kubernetes secret for storing Airflow Database login Credentials. A sample command to create the secret will be kubectl create secret generic airflow-postgresql-secrets --from-literal=airflow-postgresql-password=<password>.

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Upgrade OpenMetadata Helm Charts to connect to External Database like PostgreSQL

Update the openmetadata.config.database.* helm values for OpenMetadata Application to connect to External Database like PostgreSQL. With OpenMetadata Helm Charts, your helm values would look something like below - 
openmetadata:
  config:
    ...
    database:
      host: <postgresql_endpoint>
      port: 5432
      driverClass: org.postgresql.Driver
      dbScheme: postgresql
      dbUseSSL: true
      databaseName: <openmetadata_database_name>
      auth:
        username: <database_login_user>
        password:
          secretRef: openmetadata-postgresql-secrets
          secretKey: openmetadata-postgresql-password
For the above code, it is assumed you are creating a kubernetes secret for storing OpenMetadata Database login Credentials. A sample command to create the secret will be kubectl create secret generic openmetadata-postgresql-secrets --from-literal=openmetadata-postgresql-password=<password>. Once you make the above changes to your helm values, run the below command to install/upgrade helm charts - 
helm upgrade --install openmetadata-dependencies open-metadata/openmetadata-dependencies --values <<path-to-values-file>> --namespace <kubernetes_namespace>
helm upgrade --install openmetadata open-metadata/openmetadata --values <<path-to-values-file>> --namespace <kubernetes_namespace>

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How to customize OpenMetadata Dependencies Helm Chart with custom helm values

Our OpenMetadata Dependencies Helm Charts are internally depends on three sub-charts - If you are looking to customize the deployments of any of the above dependencies, please refer to the above links for customizations of helm values for further references. By default, OpenMetadata Dependencies helm chart provides initial generic customization of these helm values in order to get you started quickly. You can refer to the openmetadata-dependencies helm charts default values here.