OpenMetadata/openmetadata-docs/content/v1.1.0/connectors/database/sqlite/cli.md

title	slug
Run SQLite Connector using the CLI	/connectors/database/sqlite/cli

Run SQLite using the metadata CLI

{% multiTablesWrapper %}

Feature	Status
Stage	PROD
Metadata	{% icon iconName="check" /%}
Query Usage	{% icon iconName="check" /%}
Data Profiler	{% icon iconName="check" /%}
Data Quality	{% icon iconName="cross" /%}
Lineage	{% icon iconName="check" /%}
DBT	{% icon iconName="check" /%}
Supported Versions	--

Feature	Status
Lineage	Partially via Views
Table-level	{% icon iconName="check" /%}
Column-level	{% icon iconName="check" /%}

{% /multiTablesWrapper %}

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

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

• Requirements
• Metadata Ingestion
• Query Usage
• Data Profiler
• Lineage
• dbt Integration

Requirements

{%inlineCallout icon="description" bold="OpenMetadata 0.12 or later" href="/deployment"%} To deploy OpenMetadata, check the Deployment guides. {%/inlineCallout%}

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.

Python Requirements

To ingest basic metadata sqlite user must have the following priviledges:

• SELECT Privilege on sqlite_temp_master

Metadata Ingestion

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

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

1. Define the YAML Config

This is a sample config for SQLite:

{% codePreview %}

{% codeInfoContainer %}

Source Configuration - Service Connection

{% codeInfo srNumber=1 %}

username: Username to connect to SQLite. Blank for in-memory database.

{% /codeInfo %}

{% codeInfo srNumber=2 %}

password: Password to connect to SQLite. Blank for in-memory database.

{% /codeInfo %}

{% codeInfo srNumber=3 %}

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

{% /codeInfo %}

{% codeInfo srNumber=4 %}

database: The database of the data source is an optional parameter, if you would like to restrict the metadata reading to a single database. If left blank, OpenMetadata ingestion attempts to scan all the databases.

{% /codeInfo %}

{% codeInfo srNumber=5 %}

databaseMode: How to run the SQLite database. :memory: by default.

{% /codeInfo %}

Source Configuration - Source Config

{% codeInfo srNumber=6 %}

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

{% /codeInfo %}

Sink Configuration

{% codeInfo srNumber=7 %}

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

{% /codeInfo %}

{% partial file="workflow-config.md" /%}

Advanced Configuration

{% codeInfo srNumber=9 %}

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.

{% /codeInfo %}

{% codeInfo srNumber=10 %}

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"

{% /codeInfo %}

{% /codeInfoContainer %}

{% codeBlock fileName="filename.yaml" %}

source:
  type: sqlite
  serviceName: <service name>
  serviceConnection:
    config:
      type: SQLite

      username: <username>

      password: <password>

      hostPort: <warehouse>

      database: <database>

      databaseMode: <database-mode>

      # connectionOptions:
      #   key: value

      # connectionArguments:
      #   key: value

  sourceConfig:
    config:
      type: DatabaseMetadata
      markDeletedTables: true
      includeTables: true
      includeViews: true
      # includeTags: true
      # databaseFilterPattern:
      #   includes:
      #     - database1
      #     - database2
      #   excludes:
      #     - database3
      #     - database4
      # schemaFilterPattern:
      #   includes:
      #     - schema1
      #     - schema2
      #   excludes:
      #     - schema3
      #     - schema4
      # tableFilterPattern:
      #   includes:
      #     - users
      #     - type_test
      #   excludes:
      #     - table3
      #     - table4

sink:
  type: metadata-rest
  config: {}

{% partial file="workflow-config-yaml.md" /%}

{% /codeBlock %}

{% /codePreview %}

2. Run with the CLI

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

metadata ingest -c <path-to-yaml>

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.

Query Usage

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.

1. Define the YAML Config

This is a sample config for SQLite Usage:

{% codePreview %}

{% codeInfoContainer %}

{% codeInfo srNumber=15 %}

Source Configuration - Source Config

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.

{% /codeInfo %}

{% codeInfo srNumber=16 %}

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

{% /codeInfo %}

{% codeInfo srNumber=17 %}

resultLimit: Configuration to set the limit for query logs

{% /codeInfo %}

{% codeInfo srNumber=18 %}

queryLogFilePath: Configuration to set the file path for query logs

{% /codeInfo %}

{% codeInfo srNumber=19 %}

Processor, Stage and Bulk Sink Configuration

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.

{% /codeInfo %}

{% codeInfo srNumber=20 %}

Workflow Configuration

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:

{% /codeInfo %}

{% /codeInfoContainer %}

{% codeBlock fileName="filename.yaml" %}

source:
  type: sqlite-usage
  serviceName: <service name>
  sourceConfig:
    config:
      type: DatabaseUsage

      # Number of days to look back
      queryLogDuration: 7

      # This is a directory that will be DELETED after the usage runs
      stageFileLocation: <path to store the stage file>

      # resultLimit: 1000

      # If instead of getting the query logs from the database we want to pass a file with the queries
      # queryLogFilePath: path-to-file

processor:
  type: query-parser
  config: {}
stage:
  type: table-usage
  config:
    filename: /tmp/sqlite_usage
bulkSink:
  type: metadata-usage
  config:
    filename: /tmp/sqlite_usage

workflowConfig:
  # loggerLevel: DEBUG  # DEBUG, INFO, WARN or ERROR
  openMetadataServerConfig:
    hostPort: <OpenMetadata host and port>
    authProvider: <OpenMetadata auth provider>

{% /codeBlock %} {% /codePreview %}

2. Run with the CLI

There is an extra requirement to run the Usage pipelines. You will need to install:

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

metadata ingest -c <path-to-yaml>

Data Profiler

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.

1. Define the YAML Config

This is a sample config for the profiler:

{% codePreview %}

{% codeInfoContainer %}

{% codeInfo srNumber=21 %}

Source Configuration - Source Config

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

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

{% /codeInfo %}

{% codeInfo srNumber=22 %}

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

{% /codeInfo %}

{% codeInfo srNumber=23 %}

threadCount: Number of threads to use during metric computations.

{% /codeInfo %}

{% codeInfo srNumber=24 %}

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

{% /codeInfo %}

{% codeInfo srNumber=25 %}

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

{% /codeInfo %}

{% codeInfo srNumber=26 %}

timeoutSeconds: Profiler Timeout in Seconds

{% /codeInfo %}

{% codeInfo srNumber=27 %}

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

{% /codeInfo %}

{% codeInfo srNumber=28 %}

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

{% /codeInfo %}

{% codeInfo srNumber=29 %}

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

{% /codeInfo %}

{% codeInfo srNumber=30 %}

Processor Configuration

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. {% /codeInfo %}

{% codeInfo srNumber=31 %}

Sink Configuration

To send the metadata to OpenMetadata, it needs to be specified as type: metadata-rest. {% /codeInfo %}

{% codeInfo srNumber=32 %}

Workflow Configuration

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:

{% /codeInfo %}

{% /codeInfoContainer %}

{% codeBlock fileName="filename.yaml" %}

source:
  type: sqlite
  serviceName: local_sqlite
  sourceConfig:
    config:
      type: Profiler

      generateSampleData: true

      # profileSample: 85

      # threadCount: 5

      processPiiSensitive: false

      # confidence: 80

      # timeoutSeconds: 43200

      # databaseFilterPattern:
      #   includes:
      #     - database1
      #     - database2
      #   excludes:
      #     - database3
      #     - database4

      # schemaFilterPattern:
      #   includes:
      #     - schema1
      #     - schema2
      #   excludes:
      #     - schema3
      #     - schema4

      # tableFilterPattern:
      #   includes:
      #     - table1
      #     - table2
      #   excludes:
      #     - table3
      #     - table4

processor:
  type: orm-profiler
  config: {}  # Remove braces if adding properties
    # tableConfig:
    #   - fullyQualifiedName: <table fqn>
    #     profileSample: <number between 0 and 99> # default 

    #     profileSample: <number between 0 and 99> # default will be 100 if omitted
    #     profileQuery: <query to use for sampling data for the profiler>
    #     columnConfig:
    #       excludeColumns:
    #         - <column name>
    #       includeColumns:
    #         - columnName: <column name>
    #         - metrics:
    #           - MEAN
    #           - MEDIAN
    #           - ...
    #     partitionConfig:
    #       enablePartitioning: <set to true to use partitioning>
    #       partitionColumnName: <partition column name. Must be a timestamp or datetime/date field type>
    #       partitionInterval: <partition interval>
    #       partitionIntervalUnit: <YEAR, MONTH, DAY, HOUR>

sink:
  type: metadata-rest
  config: {}

workflowConfig:
  # loggerLevel: DEBUG  # DEBUG, INFO, WARN or ERROR
  openMetadataServerConfig:
    hostPort: <OpenMetadata host and port>
    authProvider: <OpenMetadata auth provider>

{% /codeBlock %}

{% /codePreview %}

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

2. Prepare the Profiler DAG

Here, we follow a similar approach as with the metadata and usage pipelines, although we will use a different Workflow class:

{% codePreview %}

{% codeInfoContainer %}

{% codeInfo srNumber=33 %}

Import necessary modules

The ProfilerWorkflow class that is being imported is a part of a metadata orm_profiler framework, which defines a process of extracting Profiler data.

Here we are also importing all the basic requirements to parse YAMLs, handle dates and build our DAG.

{% /codeInfo %}

{% codeInfo srNumber=34 %}

Default arguments for all tasks in the Airflow DAG.

• Default arguments dictionary contains default arguments for tasks in the DAG, including the owner's name, email address, number of retries, retry delay, and execution timeout.

{% /codeInfo %}

{% codeInfo srNumber=35 %}

• config: Specifies config for the profiler as we prepare above.

{% /codeInfo %}

{% codeInfo srNumber=36 %}

• metadata_ingestion_workflow(): This code defines a function metadata_ingestion_workflow() that loads a YAML configuration, creates a ProfilerWorkflow object, executes the workflow, checks its status, prints the status to the console, and stops the workflow.

{% /codeInfo %}

{% codeInfo srNumber=37 %}

• DAG: creates a DAG using the Airflow framework, and tune the DAG configurations to whatever fits with your requirements
• For more Airflow DAGs creation details visit here.

{% /codeInfo %}

{% /codeInfoContainer %}

{% codeBlock fileName="filename.py" %}

import yaml
from datetime import timedelta
from airflow import DAG
from metadata.profiler.api.workflow import ProfilerWorkflow

try:
   from airflow.operators.python import PythonOperator
except ModuleNotFoundError:
   from airflow.operators.python_operator import PythonOperator

from airflow.utils.dates import days_ago

default_args = {
   "owner": "user_name",
   "email_on_failure": False,
   "retries": 3,
   "retry_delay": timedelta(seconds=10),
   "execution_timeout": timedelta(minutes=60),
}

config = """
<your YAML configuration>
"""

def metadata_ingestion_workflow():
   workflow_config = yaml.safe_load(config)
   workflow = ProfilerWorkflow.create(workflow_config)
   workflow.execute()
   workflow.raise_from_status()
   workflow.print_status()
   workflow.stop()

with DAG(
   "profiler_example",
   default_args=default_args,
   description="An example DAG which runs a OpenMetadata ingestion workflow",
   start_date=days_ago(1),
   is_paused_upon_creation=False,
   catchup=False,
) as dag:
   ingest_task = PythonOperator(
       task_id="profile_and_test_using_recipe",
       python_callable=metadata_ingestion_workflow,
   )

{% /codeBlock %}

{% /codePreview %}

Lineage

You can learn more about how to ingest lineage here.

dbt Integration

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