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In this section, we provide guides and references to use the Clickhouse connector.

Configure and schedule Clickhouse metadata and profiler workflows from the OpenMetadata UI:

To run the Ingestion via the UI you'll need to use the OpenMetadata Ingestion Container, which comes shipped with custom Airflow plugins to handle the workflow deployment.

If, instead, you want to manage your workflows externally on your preferred orchestrator, you can check the following docs to run the Ingestion Framework anywhere.

Clickhouse user must grant SELECT privilege on system.* and schema/tables to fetch the metadata of tables and views.

  • Create a new user
  • More details https://clickhouse.com/docs/en/sql-reference/statements/create/user
  • Grant Permissions
  • More details on permissions can be found here at https://clickhouse.com/docs/en/sql-reference/statements/grant

Executing the profiler workflow or data quality tests, will require the user to have SELECT permission on the tables/schemas where the profiler/tests will be executed. More information on the profiler workflow setup can be found here and data quality tests here.

For the usage and lineage workflow, the user will need SELECT privilege. You can find more information on the usage workflow here and the lineage workflow here.

To run the Clickhouse ingestion, you will need to install:

If you want to run the Usage Connector, you'll also need to install:

All connectors are defined as JSON Schemas. Here you can find the structure to create a connection to Clickhouse.

In order to create and run a Metadata Ingestion workflow, we will follow the steps to create a YAML configuration able to connect to the source, process the Entities if needed, and reach the OpenMetadata server.

The workflow is modeled around the following JSON Schema

This is a sample config for Clickhouse:

username: Specify the User to connect to Clickhouse. It should have enough privileges to read all the metadata.

password: Password to connect to Clickhouse.

hostPort: Enter the fully qualified hostname and port number for your Clickhouse deployment in the Host and Port field.

databaseSchema: databaseSchema of the data source. This is optional parameter, if you would like to restrict the metadata reading to a single databaseSchema. When left blank, OpenMetadata Ingestion attempts to scan all the databaseSchema.

duration: The duration of a SQL connection in ClickHouse depends on the configuration of the connection and the workload being processed. Connections are kept open for as long as needed to complete a query, but they can also be closed based on duration set.

scheme: There are 2 types of schemes that the user can choose from.

  • clickhouse+http: Uses ClickHouse's HTTP interface for communication. Widely supported, but slower than native.
  • clickhouse+native: Uses the native ClickHouse TCP protocol for communication. Faster than http, but may require additional server-side configuration. Recommended for performance-critical applications.

https: Enable this flag when the when the Clickhouse instance is hosted via HTTPS protocol. This flag is useful when you are using clickhouse+http connection scheme.

secure: Establish secure connection with ClickHouse. ClickHouse supports secure communication over SSL/TLS to protect data in transit, by checking this option, it establishes secure connection with ClickHouse. This flag is useful when you are using clickhouse+native connection scheme.

keyfile: The key file path is the location when ClickHouse looks for a file containing the private key needed for secure communication over SSL/TLS. By default, ClickHouse will look for the key file in the /etc/clickhouse-server directory, with the file name server.key. However, this can be customized in the ClickHouse configuration file (config.xml). This flag is useful when you are using clickhouse+native connection scheme and the secure connection flag is enabled.

The sourceConfig is defined here:

markDeletedTables: To flag tables as soft-deleted if they are not present anymore in the source system.

includeTables: true or false, to ingest table data. Default is true.

includeViews: true or false, to ingest views definitions.

databaseFilterPattern, schemaFilterPattern, tableFilterPattern: Note that the filter supports regex as include or exclude. You can find examples here

To send the metadata to OpenMetadata, it needs to be specified as type: metadata-rest.

The main property here is the openMetadataServerConfig, where you can define the host and security provider of your OpenMetadata installation.

Logger Level

You can specify the loggerLevel depending on your needs. If you are trying to troubleshoot an ingestion, running with DEBUG will give you far more traces for identifying issues.

JWT Token

JWT tokens will allow your clients to authenticate against the OpenMetadata server. To enable JWT Tokens, you will get more details here.

You can refer to the JWT Troubleshooting section link for any issues in your JWT configuration.

Store Service Connection

If set to true (default), we will store the sensitive information either encrypted via the Fernet Key in the database or externally, if you have configured any Secrets Manager.

If set to false, the service will be created, but the service connection information will only be used by the Ingestion Framework at runtime, and won't be sent to the OpenMetadata server.

Store Service Connection

If set to true (default), we will store the sensitive information either encrypted via the Fernet Key in the database or externally, if you have configured any Secrets Manager.

If set to false, the service will be created, but the service connection information will only be used by the Ingestion Framework at runtime, and won't be sent to the OpenMetadata server.

SSL Configuration

If you have added SSL to the OpenMetadata server, then you will need to handle the certificates when running the ingestion too. You can either set verifySSL to ignore, or have it as validate, which will require you to set the sslConfig.certificatePath with a local path where your ingestion runs that points to the server certificate file.

Find more information on how to troubleshoot SSL issues here.

Connection Options (Optional): Enter the details for any additional connection options that can be sent to Athena during the connection. These details must be added as Key-Value pairs.

Connection Arguments (Optional): Enter the details for any additional connection arguments such as security or protocol configs that can be sent to Athena during the connection. These details must be added as Key-Value pairs.

  • In case you are using Single-Sign-On (SSO) for authentication, add the authenticator details in the Connection Arguments as a Key-Value pair as follows: "authenticator" : "sso_login_url"
filename.yaml

First, we will need to save the YAML file. Afterward, and with all requirements installed, we can run:

Note that from connector to connector, this recipe will always be the same. By updating the YAML configuration, you will be able to extract metadata from different sources.

The Query Usage workflow will be using the query-parser processor.

After running a Metadata Ingestion workflow, we can run Query Usage workflow. While the serviceName will be the same to that was used in Metadata Ingestion, so the ingestion bot can get the serviceConnection details from the server.

This is a sample config for BigQuery Usage:

You can find all the definitions and types for the sourceConfig here.

queryLogDuration: Configuration to tune how far we want to look back in query logs to process usage data.

stageFileLocation: Temporary file name to store the query logs before processing. Absolute file path required.

resultLimit: Configuration to set the limit for query logs

queryLogFilePath: Configuration to set the file path for query logs

To specify where the staging files will be located.

Note that the location is a directory that will be cleaned at the end of the ingestion.

The main property here is the openMetadataServerConfig, where you can define the host and security provider of your OpenMetadata installation.

Logger Level

You can specify the loggerLevel depending on your needs. If you are trying to troubleshoot an ingestion, running with DEBUG will give you far more traces for identifying issues.

JWT Token

JWT tokens will allow your clients to authenticate against the OpenMetadata server. To enable JWT Tokens, you will get more details here.

You can refer to the JWT Troubleshooting section link for any issues in your JWT configuration.

SSL Configuration

If you have added SSL to the OpenMetadata server, then you will need to handle the certificates when running the ingestion too. You can either set verifySSL to ignore, or have it as validate, which will require you to set the sslConfig.certificatePath with a local path where your ingestion runs that points to the server certificate file.

Find more information on how to troubleshoot SSL issues here.

filename.yaml

After saving the YAML config, we will run the command the same way we did for the metadata ingestion:

After running a Metadata Ingestion workflow, we can run Lineage workflow. While the serviceName will be the same to that was used in Metadata Ingestion, so the ingestion bot can get the serviceConnection details from the server.

This is a sample config for BigQuery Lineage:

You can find all the definitions and types for the sourceConfig here.

queryLogDuration: Configuration to tune how far we want to look back in query logs to process lineage data in days.

parsingTimeoutLimit: Configuration to set the timeout for parsing the query in seconds.

filterCondition: Condition to filter the query history.

resultLimit: Configuration to set the limit for query logs.

queryLogFilePath: Configuration to set the file path for query logs.

databaseFilterPattern: Regex to only fetch databases that matches the pattern.

schemaFilterPattern: Regex to only fetch tables or databases that matches the pattern.

tableFilterPattern: Regex to only fetch tables or databases that matches the pattern.

To send the metadata to OpenMetadata, it needs to be specified as type: metadata-rest.

The main property here is the openMetadataServerConfig, where you can define the host and security provider of your OpenMetadata installation.

For a simple, local installation using our docker containers, this looks like:

filename.yaml
  • You can learn more about how to configure and run the Lineage Workflow to extract Lineage data from here

After saving the YAML config, we will run the command the same way we did for the metadata ingestion:

The Data Profiler workflow will be using the orm-profiler processor.

After running a Metadata Ingestion workflow, we can run Data Profiler workflow. While the serviceName will be the same to that was used in Metadata Ingestion, so the ingestion bot can get the serviceConnection details from the server.

This is a sample config for the profiler:

You can find all the definitions and types for the sourceConfig here.

generateSampleData: Option to turn on/off generating sample data.

profileSample: Percentage of data or no. of rows we want to execute the profiler and tests on.

threadCount: Number of threads to use during metric computations.

processPiiSensitive: Optional configuration to automatically tag columns that might contain sensitive information.

confidence: Set the Confidence value for which you want the column to be marked

timeoutSeconds: Profiler Timeout in Seconds

databaseFilterPattern: Regex to only fetch databases that matches the pattern.

schemaFilterPattern: Regex to only fetch tables or databases that matches the pattern.

tableFilterPattern: Regex to only fetch tables or databases that matches the pattern.

Choose the orm-profiler. Its config can also be updated to define tests from the YAML itself instead of the UI:

tableConfig: tableConfig allows you to set up some configuration at the table level.

To send the metadata to OpenMetadata, it needs to be specified as type: metadata-rest.

The main property here is the openMetadataServerConfig, where you can define the host and security provider of your OpenMetadata installation.

Logger Level

You can specify the loggerLevel depending on your needs. If you are trying to troubleshoot an ingestion, running with DEBUG will give you far more traces for identifying issues.

JWT Token

JWT tokens will allow your clients to authenticate against the OpenMetadata server. To enable JWT Tokens, you will get more details here.

You can refer to the JWT Troubleshooting section link for any issues in your JWT configuration.

SSL Configuration

If you have added SSL to the OpenMetadata server, then you will need to handle the certificates when running the ingestion too. You can either set verifySSL to ignore, or have it as validate, which will require you to set the sslConfig.certificatePath with a local path where your ingestion runs that points to the server certificate file.

Find more information on how to troubleshoot SSL issues here.

filename.yaml
  • You can learn more about how to configure and run the Profiler Workflow to extract Profiler data and execute the Data Quality from here

After saving the YAML config, we will run the command the same way we did for the metadata ingestion:

Note now instead of running ingest, we are using the profile command to select the Profiler workflow.

When creating a JSON config for a test workflow the source configuration is very simple.

The only sections you need to modify here are the serviceName (this name needs to be unique) and entityFullyQualifiedName (the entity for which we'll be executing tests against) keys.

Once you have defined your source configuration you'll need to define te processor configuration.

The processor type should be set to "orm-test-runner". For accepted test definition names and parameter value names refer to the tests page.

Note that while you can define tests directly in this YAML configuration, running the workflow will execute ALL THE TESTS present in the table, regardless of what you are defining in the YAML.

This makes it easy for any user to contribute tests via the UI, while maintaining the test execution external.

You can keep your YAML config as simple as follows if the table already has tests.

  • forceUpdate: if the test case exists (base on the test case name) for the entity, implements the strategy to follow when running the test (i.e. whether or not to update parameters)
  • testCases: list of test cases to add to the entity referenced. Note that we will execute all the tests present in the Table.
  • name: test case name
  • testDefinitionName: test definition
  • columnName: only applies to column test. The name of the column to run the test against
  • parameterValues: parameter values of the test

The sink and workflowConfig will have the same settings as the ingestion and profiler workflow.

To run the tests from the CLI execute the following command

You can learn more about how to ingest dbt models' definitions and their lineage here.