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Assertions

docs/api/tutorials/assertions.md

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import Tabs from '@theme/Tabs'; import TabItem from '@theme/TabItem';

Assertions

<FeatureAvailability saasOnly />

This guide specifically covers how to use the Assertion APIs for DataHub Cloud native assertions, including:

Why Would You Use Assertions APIs?

The Assertions APIs allow you to create, schedule, run, and delete Assertions with DataHub Cloud. Additionally, you can manage subscriptions to receive notifications when assertions change state or when other entity changes occur.

Goal Of This Guide

This guide will show you how to create, schedule, run and delete Assertions for a Table.

Prerequisites

The actor making API calls must have the Edit Assertions and Edit Monitors privileges for the Tables at hand.

If you are using the Python examples in this guide, install the DataHub Cloud SDK extension:

bash
pip install acryl-datahub-cloud

Create Assertions

You can create new dataset Assertions to DataHub using the following APIs.

Freshness Assertion

<Tabs> <TabItem value="graphql" label="GraphQL" default>

To create a new freshness assertion, use the upsertDatasetFreshnessAssertionMonitor GraphQL Mutation.

graphql
mutation upsertDatasetFreshnessAssertionMonitor {
  upsertDatasetFreshnessAssertionMonitor(
    input: {
      entityUrn: "<urn of entity being monitored>"
      schedule: {
        type: FIXED_INTERVAL
        fixedInterval: { unit: HOUR, multiple: 8 }
      }
      evaluationSchedule: {
        timezone: "America/Los_Angeles"
        cron: "0 */8 * * *"
      }
      evaluationParameters: { sourceType: INFORMATION_SCHEMA }
      mode: ACTIVE
    }
  ) {
    urn
  }
}

This API will return a unique identifier (URN) for the new assertion if you were successful:

json
{
  "data": {
    "upsertDatasetFreshnessAssertionMonitor": {
      "urn": "urn:li:assertion:your-new-assertion-id"
    }
  },
  "extensions": {}
}
</TabItem> <TabItem value="python" label="Python">
python
from datahub.sdk import DataHubClient
from datahub.metadata.urns import DatasetUrn

# Initialize the client
client = DataHubClient(server="<your_server>", token="<your_token>")

# Create smart freshness assertion (AI-powered anomaly detection)
dataset_urn = DatasetUrn.from_string("urn:li:dataset:(urn:li:dataPlatform:snowflake,database.schema.table,PROD)")

smart_freshness_assertion = client.assertions.sync_smart_freshness_assertion(
    dataset_urn=dataset_urn,
    display_name="Smart Freshness Anomaly Monitor",
    # Detection mechanism - information_schema is recommended
    detection_mechanism="information_schema",
    # Smart sensitivity setting
    sensitivity="medium",  # options: "low", "medium", "high"
    # Tags for grouping
    tags=["automated", "freshness", "data_quality"],
    # Enable the assertion
    enabled=True
)

print(f"Created smart freshness assertion: {smart_freshness_assertion.urn}")

# Create traditional freshness assertion (fixed interval)
freshness_assertion = client.assertions.sync_freshness_assertion(
    dataset_urn=dataset_urn,
    display_name="Fixed Interval Freshness Check",
    # Fixed interval check - table should be updated within lookback window
    freshness_schedule_check_type="fixed_interval",
    # Lookback window - table should be updated within 8 hours
    lookback_window={"unit": "HOUR", "multiple": 8},
    # Detection mechanism
    detection_mechanism="information_schema",
    # Evaluation schedule - how often to check
    schedule="0 */2 * * *",  # Check every 2 hours
    # Tags
    tags=["automated", "freshness", "fixed_interval"],
    enabled=True
)

print(f"Created freshness assertion: {freshness_assertion.urn}")

# Create since-last-check freshness assertion
since_last_check_assertion = client.assertions.sync_freshness_assertion(
    dataset_urn=dataset_urn,
    display_name="Since Last Check Freshness",
    # Since last check - table should be updated since the last evaluation
    freshness_schedule_check_type="since_the_last_check",
    # Detection mechanism with last modified column
    detection_mechanism={
        "type": "last_modified_column",
        "column_name": "updated_at",
        "additional_filter": "status = 'active'"
    },
    # Evaluation schedule - how often to check
    schedule="0 */6 * * *",  # Check every 6 hours
    # Tags
    tags=["automated", "freshness", "since_last_check"],
    enabled=True
)

print(f"Created since last check assertion: {since_last_check_assertion.urn}")

# Create freshness assertion with high watermark column
watermark_freshness_assertion = client.assertions.sync_freshness_assertion(
    dataset_urn=dataset_urn,
    display_name="High Watermark Freshness Check",
    # Fixed interval check with specific lookback window
    freshness_schedule_check_type="fixed_interval",
    # Lookback window - check for updates in the last 24 hours
    lookback_window={"unit": "DAY", "multiple": 1},
    # Detection mechanism using high watermark column (e.g., auto-incrementing ID)
    detection_mechanism={
        "type": "high_watermark_column",
        "column_name": "id",
        "additional_filter": "status != 'deleted'"
    },
    # Evaluation schedule
    schedule="0 8 * * *",  # Check daily at 8 AM
    # Tags
    tags=["automated", "freshness", "high_watermark"],
    enabled=True
)

print(f"Created watermark freshness assertion: {watermark_freshness_assertion.urn}")
</TabItem> </Tabs>

For more details, see the Freshness Assertions guide.

Volume Assertions

<Tabs> <TabItem value="graphql" label="GraphQL" default>

To create a new volume assertion, use the upsertDatasetVolumeAssertionMonitor GraphQL Mutation.

graphql
mutation upsertDatasetVolumeAssertionMonitor {
  upsertDatasetVolumeAssertionMonitor(
    input: {
      entityUrn: "<urn of entity being monitored>"
      type: ROW_COUNT_TOTAL
      rowCountTotal: {
        operator: BETWEEN
        parameters: {
          minValue: { value: "10", type: NUMBER }
          maxValue: { value: "20", type: NUMBER }
        }
      }
      evaluationSchedule: {
        timezone: "America/Los_Angeles"
        cron: "0 */8 * * *"
      }
      evaluationParameters: { sourceType: INFORMATION_SCHEMA }
      mode: ACTIVE
    }
  ) {
    urn
  }
}

This API will return a unique identifier (URN) for the new assertion if you were successful:

json
{
  "data": {
    "upsertDatasetVolumeAssertionMonitor": {
      "urn": "urn:li:assertion:your-new-assertion-id"
    }
  },
  "extensions": {}
}
</TabItem> <!-- TODO: move the python examples to metadata-ingestion/examples/library --> <TabItem value="python" label="Python">
python
from datahub.sdk import DataHubClient
from datahub.metadata.urns import DatasetUrn

# Initialize the client
client = DataHubClient(server="<your_server>", token="<your_token>")

# Create smart volume assertion (AI-powered anomaly detection)
dataset_urn = DatasetUrn.from_string("urn:li:dataset:(urn:li:dataPlatform:snowflake,database.schema.table,PROD)")

smart_volume_assertion = client.assertions.sync_smart_volume_assertion(
    dataset_urn=dataset_urn,
    display_name="Smart Volume Check",
    # Detection mechanism options
    detection_mechanism="information_schema",
    # Smart sensitivity setting
    sensitivity="medium",  # options: "low", "medium", "high"
    # Tags for grouping
    tags=["automated", "volume", "data_quality"],
    # Optional: partition data into daily/weekly time buckets
    time_bucketing_strategy={
        "timestamp_field_path": "created_at",
        "bucket_interval": {"unit": "DAY", "multiple": 1},
        "timezone": "America/Los_Angeles",
    },
    # Optional: backfill historical data for smarter predictions
    backfill_config={"backfill_start_date_ms": 1704067200000},  # 2024-01-01
    # Enable the assertion
    enabled=True
)

print(f"Created smart volume assertion: {smart_volume_assertion.urn}")

# Create traditional volume assertion (fixed threshold range)
volume_assertion = client.assertions.sync_volume_assertion(
    dataset_urn=dataset_urn,
    display_name="Row Count Range Check",
    criteria_condition="ROW_COUNT_IS_WITHIN_A_RANGE",
    criteria_parameters=(1000, 10000),  # Between 1000 and 10000 rows
    # Detection mechanism
    detection_mechanism="information_schema",
    # Evaluation schedule
    schedule="0 */4 * * *",  # Every 4 hours
    # Tags
    tags=["automated", "volume", "threshold_check"],
    enabled=True
)

print(f"Created volume assertion: {volume_assertion.urn}")

# Example with single threshold
min_volume_assertion = client.assertions.sync_volume_assertion(
    dataset_urn=dataset_urn,
    display_name="Minimum Row Count Check",
    criteria_condition="ROW_COUNT_IS_GREATER_THAN_OR_EQUAL_TO",
    criteria_parameters=500,  # At least 500 rows
    detection_mechanism="information_schema",
    schedule="0 */2 * * *",  # Every 2 hours
    tags=["automated", "volume", "minimum_check"],
    enabled=True
)

print(f"Created minimum volume assertion: {min_volume_assertion.urn}")

# Example with growth-based assertion
growth_volume_assertion = client.assertions.sync_volume_assertion(
    dataset_urn=dataset_urn,
    display_name="Daily Growth Check",
    criteria_condition="ROW_COUNT_GROWS_BY_AT_MOST_ABSOLUTE",
    criteria_parameters=1000,  # Grows by at most 1000 rows between checks
    detection_mechanism="information_schema",
    schedule="0 6 * * *",  # Daily at 6 AM
    tags=["automated", "volume", "growth_check"],
    enabled=True
)

print(f"Created growth volume assertion: {growth_volume_assertion.urn}")
</TabItem> </Tabs>

For more details, see the Volume Assertions guide.

Column Assertions

<Tabs> <TabItem value="graphql" label="GraphQL" default>

To create a new column assertion, use the upsertDatasetFieldAssertionMonitor GraphQL Mutation.

graphql
mutation upsertDatasetFieldAssertionMonitor {
  upsertDatasetFieldAssertionMonitor(
    input: {
      entityUrn: "<urn of entity being monitored>"
      type: FIELD_VALUES
      fieldValuesAssertion: {
        field: {
          path: "<name of the column to be monitored>"
          type: "NUMBER"
          nativeType: "NUMBER(38,0)"
        }
        operator: GREATER_THAN
        parameters: { value: { type: NUMBER, value: "10" } }
        failThreshold: { type: COUNT, value: 0 }
        excludeNulls: true
      }
      evaluationSchedule: {
        timezone: "America/Los_Angeles"
        cron: "0 */8 * * *"
      }
      evaluationParameters: { sourceType: ALL_ROWS_QUERY }
      mode: ACTIVE
    }
  ) {
    urn
  }
}

This API will return a unique identifier (URN) for the new assertion if you were successful:

json
{
  "data": {
    "upsertDatasetFieldAssertionMonitor": {
      "urn": "urn:li:assertion:your-new-assertion-id"
    }
  },
  "extensions": {}
}
</TabItem> <TabItem value="python" label="Python">
python
from datahub.sdk import DataHubClient
from datahub.metadata.urns import DatasetUrn

# Initialize the client
client = DataHubClient(server="<your_server>", token="<your_token>")

# Create smart column metric assertion (AI-powered anomaly detection).
# Note: Smart Assertions currently only support the following column metrics:
# null_count, unique_count, empty_count, zero_count, negative_count.
# For other metrics (e.g. min, max, mean), use a regular column metric assertion
# with a fixed threshold (see below).
dataset_urn = DatasetUrn.from_string("urn:li:dataset:(urn:li:dataPlatform:snowflake,database.schema.table,PROD)")

smart_column_assertion = client.assertions.sync_smart_column_metric_assertion(
    dataset_urn=dataset_urn,
    column_name="user_id",
    metric_type="null_count",
    display_name="Smart Null Count Check - user_id",
    # Detection mechanism for column metrics
    detection_mechanism="all_rows_query_datahub_dataset_profile",
    # Smart sensitivity setting
    sensitivity="medium",  # options: "low", "medium", "high"
    # Optional: partition data into daily time buckets
    time_bucketing_strategy={
        "timestamp_field_path": "created_at",
        "bucket_interval": {"unit": "DAY", "multiple": 1},
        "timezone": "UTC",
    },
    # Optional: backfill historical data for smarter predictions
    backfill_config={"backfill_start_date_ms": 1704067200000},  # 2024-01-01
    # Tags
    tags=["automated", "column_quality", "null_checks"],
    enabled=True
)

print(f"Created smart column assertion: {smart_column_assertion.urn}")

# Create regular column metric assertion (fixed threshold on aggregated metric).
# Use this for metrics not supported by Smart Assertions (e.g. min, max, mean, median, stddev).
column_metric_assertion = client.assertions.sync_column_metric_assertion(
    dataset_urn=dataset_urn,
    column_name="price",
    metric_type="min",
    operator="greater_than_or_equal_to",
    criteria_parameters=0,
    display_name="Price Minimum Check",
    # Evaluation schedule
    schedule="0 */4 * * *",  # Every 4 hours
    # Tags
    tags=["automated", "column_quality", "price_validation"],
    enabled=True
)

print(f"Created column metric assertion: {column_metric_assertion.urn}")

# ----------------------------
# Column value assertions (row-level checks)
# ----------------------------

# Example 1: Simple email validation with regex pattern
# Validates that all email values match a valid email format
email_regex_assertion = client.assertions.sync_column_value_assertion(
    dataset_urn=dataset_urn,
    column_name="email",
    operator="regex_match",
    criteria_parameters=r"^[a-zA-Z0-9._%+-]+@[a-zA-Z0-9.-]+\.[a-zA-Z]{2,}$"
)

print(f"Created email regex assertion: {email_regex_assertion.urn}")

# Example 2: Detailed column value assertion with all parameters
# Validates individual row values in a column against semantic constraints
column_value_assertion = client.assertions.sync_column_value_assertion(
    dataset_urn=dataset_urn,
    column_name="quantity",
    display_name="Quantity Positive Check",
    # Operator applied to each row's value (e.g., "greater_than", "between", "regex_match", "not_null")
    operator="greater_than",
    criteria_parameters=0,  # Each quantity must be > 0
    # Optional: Apply a transform before validation (currently supports "length" for strings)
    # transform="length",
    # Fail threshold configuration
    fail_threshold_type="count",  # How to count failures: "count" (absolute number) or "percentage"
    fail_threshold_value=0,  # Assertion fails if this many rows fail (0 = zero tolerance)
    # Whether to exclude null values from validation
    exclude_nulls=True,
    # Evaluation schedule - how often to check
    schedule="0 */4 * * *",  # Every 4 hours (cron format)
    # Tags for grouping and categorization
    tags=["automated", "column_quality", "value_validation"],
    # Enable the assertion
    enabled=True
)

print(f"Created column value assertion: {column_value_assertion.urn}")
</TabItem> </Tabs>

For more details, see the Column Assertions guide.

Custom SQL Assertions

<Tabs> <TabItem value="graphql" label="GraphQL" default>

To create a new custom SQL assertion, use the upsertDatasetSqlAssertionMonitor GraphQL Mutation.

graphql
mutation upsertDatasetSqlAssertionMonitor {
  upsertDatasetSqlAssertionMonitor(
    assertionUrn: "<urn of assertion created in earlier query>"
    input: {
      entityUrn: "<urn of entity being monitored>"
      type: METRIC
      description: "<description of the custom assertion>"
      statement: "<SQL query to be evaluated>"
      operator: GREATER_THAN_OR_EQUAL_TO
      parameters: { value: { value: "100", type: NUMBER } }
      evaluationSchedule: {
        timezone: "America/Los_Angeles"
        cron: "0 */6 * * *"
      }
      mode: ACTIVE
    }
  ) {
    urn
  }
}

This API will return a unique identifier (URN) for the new assertion if you were successful:

json
{
  "data": {
    "upsertDatasetSqlAssertionMonitor": {
      "urn": "urn:li:assertion:your-new-assertion-id"
    }
  },
  "extensions": {}
}

To create a new smart SQL assertion (AI anomaly detection), use the same mutation with inferWithAI: true.

graphql
mutation upsertDatasetSqlAssertionMonitor {
  upsertDatasetSqlAssertionMonitor(
    input: {
      entityUrn: "<urn of entity being monitored>"
      type: METRIC
      description: "<description of the smart SQL assertion>"
      statement: "<SQL query to be evaluated>"
      inferWithAI: true
      inferenceSettings: { sensitivity: { level: 5 } }
      # Placeholder operator and parameters (AI will infer actual thresholds)
      operator: GREATER_THAN_OR_EQUAL_TO
      parameters: { value: { value: "0", type: NUMBER } }
      evaluationSchedule: {
        timezone: "America/Los_Angeles"
        cron: "0 */6 * * *"
      }
      mode: ACTIVE
    }
  ) {
    urn
  }
}
</TabItem> <TabItem value="python" label="Python">
python
from datahub.sdk import DataHubClient
from datahub.metadata.urns import DatasetUrn

# Initialize the client
client = DataHubClient(server="<your_server>", token="<your_token>")

# Create custom SQL assertion
dataset_urn = DatasetUrn.from_string("urn:li:dataset:(urn:li:dataPlatform:snowflake,database.schema.table,PROD)")

sql_assertion = client.assertions.sync_sql_assertion(
    dataset_urn=dataset_urn,
    display_name="Revenue Quality Check",
    statement="SELECT SUM(revenue) FROM database.schema.table WHERE date >= CURRENT_DATE - INTERVAL '1 day'",
    criteria_condition="IS_GREATER_THAN_OR_EQUAL_TO",
    criteria_parameters=1000,
    # Evaluation schedule
    schedule="0 6 * * *",  # Daily at 6 AM
    # Tags
    tags=["automated", "revenue", "data_quality"],
    enabled=True
)

print(f"Created SQL assertion: {sql_assertion.urn}")

# Example with range check
range_sql_assertion = client.assertions.sync_sql_assertion(
    dataset_urn=dataset_urn,
    display_name="Daily Order Count Range Check",
    statement="SELECT COUNT(*) FROM database.schema.orders WHERE DATE(created_at) = CURRENT_DATE",
    criteria_condition="IS_WITHIN_A_RANGE",
    criteria_parameters=(50, 500),  # Between 50 and 500 orders per day
    schedule="0 */6 * * *",  # Every 6 hours
    tags=["automated", "orders", "volume_check"],
    enabled=True
)

print(f"Created range SQL assertion: {range_sql_assertion.urn}")

# ----------------------------
# Smart SQL assertions (AI anomaly detection)
# ----------------------------

smart_sql_assertion = client.assertions.sync_smart_sql_assertion(
    dataset_urn=dataset_urn,
    display_name="Smart Revenue Monitor",
    # The SQL statement to evaluate - should return a single numeric value
    statement="SELECT SUM(revenue) FROM database.schema.table WHERE date >= CURRENT_DATE - INTERVAL '1 day'",
    # AI sensitivity setting
    sensitivity="medium",  # options: "low", "medium", "high"
    # Evaluation schedule
    schedule="0 */6 * * *",  # Every 6 hours
    # Optional: training data lookback
    training_data_lookback_days=60,
    # Tags
    tags=["automated", "revenue", "smart_sql"],
    enabled=True
)

print(f"Created smart SQL assertion: {smart_sql_assertion.urn}")
</TabItem> </Tabs>

For more details, see the Custom SQL Assertions guide.

Schema Assertions

<Tabs> <TabItem value="graphql" label="GraphQL" default>

To create a new schema assertion, use the upsertDatasetSchemaAssertionMonitor GraphQL Mutation.

graphql
mutation upsertDatasetSchemaAssertionMonitor {
  upsertDatasetSchemaAssertionMonitor(
    assertionUrn: "urn:li:assertion:existing-assertion-id"
    input: {
      entityUrn: "<urn of the table to be monitored>"
      assertion: {
        compatibility: EXACT_MATCH
        fields: [
          { path: "id", type: STRING }
          { path: "count", type: NUMBER }
          { path: "struct", type: STRUCT }
          { path: "struct.nestedBooleanField", type: BOOLEAN }
        ]
      }
      description: "<description of the schema assertion>"
      mode: ACTIVE
    }
  )
}

This API will return a unique identifier (URN) for the new assertion if you were successful:

json
{
  "data": {
    "upsertDatasetSchemaAssertionMonitor": {
      "urn": "urn:li:assertion:your-new-assertion-id"
    }
  },
  "extensions": {}
}
</TabItem> <TabItem value="python" label="Python">
python
from datahub.sdk import DataHubClient
from datahub.metadata.urns import DatasetUrn

# Initialize the client
client = DataHubClient(server="<your_server>", token="<your_token>")

# Create schema assertion with exact match compatibility
dataset_urn = DatasetUrn.from_string("urn:li:dataset:(urn:li:dataPlatform:snowflake,database.schema.table,PROD)")

schema_assertion = client.assertions.sync_schema_assertion(
    dataset_urn=dataset_urn,
    display_name="Expected Schema Check",
    # Compatibility mode - how strictly to match the schema
    compatibility="EXACT_MATCH",  # options: "EXACT_MATCH", "SUPERSET", "SUBSET"
    # Expected schema fields
    fields=[
        {"path": "id", "type": "STRING"},
        {"path": "count", "type": "NUMBER"},
        {"path": "created_at", "type": "TIME"},
        {"path": "is_active", "type": "BOOLEAN"},
    ],
    # Tags for grouping
    tags=["automated", "schema", "data_quality"],
    # Enable the assertion
    enabled=True
)

print(f"Created schema assertion: {schema_assertion.urn}")

# Create schema assertion with superset compatibility
# (actual schema must contain at least these fields, but can have more)
superset_assertion = client.assertions.sync_schema_assertion(
    dataset_urn=dataset_urn,
    display_name="Required Fields Check",
    compatibility="SUPERSET",
    fields=[
        {"path": "id", "type": "STRING"},
        {"path": "name", "type": "STRING"},
    ],
    # Evaluation schedule
    schedule="0 */6 * * *",  # Every 6 hours
    tags=["automated", "schema", "required_fields"],
    enabled=True
)

print(f"Created superset schema assertion: {superset_assertion.urn}")

# Create schema assertion with native type specification
detailed_schema_assertion = client.assertions.sync_schema_assertion(
    dataset_urn=dataset_urn,
    display_name="Detailed Schema Validation",
    compatibility="EXACT_MATCH",
    fields=[
        {"path": "id", "type": "STRING", "native_type": "VARCHAR(255)"},
        {"path": "amount", "type": "NUMBER", "native_type": "DECIMAL(10,2)"},
        {"path": "metadata", "type": "STRUCT"},
        {"path": "metadata.key", "type": "STRING"},
        {"path": "tags", "type": "ARRAY"},
    ],
    enabled=True
)

print(f"Created detailed schema assertion: {detailed_schema_assertion.urn}")
</TabItem> </Tabs>

For more details, see the Schema Assertions guide.

Run Assertions

You can use the following APIs to trigger the assertions you've created to run on-demand. This is particularly useful for running assertions on a custom schedule, for example from your production data pipelines.

Long-Running Assertions: The timeout for synchronously running an assertion is currently limited to a maximum of 30 seconds. Each of the following APIs support an async parameter, which can be set to true to run the assertion asynchronously. When set to true, the API will kick off the assertion run and return null immediately. To view the result of the assertion, simply fetching the runEvents field of the assertion(urn: String!) GraphQL query.

<Tabs> <TabItem value="graphql" label="GraphQL" default>

Run Assertion

graphql
mutation runAssertion {
  runAssertion(urn: "urn:li:assertion:your-assertion-id", saveResult: true) {
    type
    nativeResults {
      key
      value
    }
  }
}

Where type will contain the Result of the assertion run, either SUCCESS, FAILURE, or ERROR.

The saveResult argument determines whether the result of the assertion will be saved to DataHub's backend, and available to view through the DataHub UI. If this is set to false, the result will NOT be stored in DataHub's backend. Default: true (results are saved when not specified).

The async argument controls whether the assertion runs asynchronously. When set to true, the API will kick off the assertion run and return immediately. When set to false or omitted, the assertion runs synchronously with a 30-second timeout. Default: false (synchronous execution when not specified).

If the assertion is external (not natively executed by DataHub), this API will return an error.

If running the assertion is successful, the result will be returned as follows:

json
{
  "data": {
    "runAssertion": {
      "type": "SUCCESS",
      "nativeResults": [
        {
          "key": "Value",
          "value": "1382"
        }
      ]
    }
  },
  "extensions": {}
}

Run Group of Assertions

graphql
mutation runAssertions {
  runAssertions(
    urns: [
      "urn:li:assertion:your-assertion-id-1"
      "urn:li:assertion:your-assertion-id-2"
    ]
    saveResults: true
  ) {
    passingCount
    failingCount
    errorCount
    results {
      urn
      result {
        type
        nativeResults {
          key
          value
        }
      }
    }
  }
}

Where type will contain the Result of the assertion run, either SUCCESS, FAILURE, or ERROR.

The saveResults argument determines whether the result of the assertion will be saved to DataHub's backend, and available to view through the DataHub UI. If this is set to false, the result will NOT be stored in DataHub's backend. Default: true (results are saved when not specified).

The async argument controls whether the assertions run asynchronously. When set to true, the API will kick off the assertion runs and return immediately. When set to false or omitted, the assertions run synchronously with a 30-second timeout per assertion. Default: false (synchronous execution when not specified).

If any of the assertion are external (not natively executed by DataHub), they will simply be omitted from the result set.

If running the assertions is successful, the results will be returned as follows:

json
{
  "data": {
    "runAssertions": {
      "passingCount": 2,
      "failingCount": 0,
      "errorCount": 0,
      "results": [
        {
          "urn": "urn:li:assertion:your-assertion-id-1",
          "result": {
            "type": "SUCCESS",
            "nativeResults": [
              {
                "key": "Value",
                "value": "1382"
              }
            ]
          }
        },
        {
          "urn": "urn:li:assertion:your-assertion-id-2",
          "result": {
            "type": "FAILURE",
            "nativeResults": [
              {
                "key": "Value",
                "value": "12323"
              }
            ]
          }
        }
      ]
    }
  },
  "extensions": {}
}

Where you should see one result object for each assertion.

Run All Assertions for Table

You can also run all assertions for a specific data asset using the runAssertionsForAsset mutation.

graphql
mutation runAssertionsForAsset {
  runAssertionsForAsset(
    urn: "urn:li:dataset:(urn:li:dataPlatform:snowflake,purchase_events,PROD)"
    saveResults: true
  ) {
    passingCount
    failingCount
    errorCount
    results {
      urn
      result {
        type
        nativeResults {
          key
          value
        }
      }
    }
  }
}

Where type will contain the Result of the assertion run, either SUCCESS, FAILURE, or ERROR.

The saveResults argument determines whether the result of the assertion will be saved to DataHub's backend, and available to view through the DataHub UI. If this is set to false, the result will NOT be stored in DataHub's backend. Default: true (results are saved when not specified).

The async argument controls whether the assertions run asynchronously. When set to true, the API will kick off the assertion runs and return immediately. When set to false or omitted, the assertions run synchronously with a 30-second timeout per assertion. Default: false (synchronous execution when not specified).

If any of the assertion are external (not natively executed by DataHub), they will simply be omitted from the result set.

If running the assertions is successful, the results will be returned as follows:

json
{
  "data": {
    "runAssertionsForAsset": {
      "passingCount": 2,
      "failingCount": 0,
      "errorCount": 0,
      "results": [
        {
          "urn": "urn:li:assertion:your-assertion-id-1",
          "result": {
            "type": "SUCCESS",
            "nativeResults": [
              {
                "key": "Value",
                "value": "1382"
              }
            ]
          }
        },
        {
          "urn": "urn:li:assertion:your-assertion-id-2",
          "result": {
            "type": "FAILURE",
            "nativeResults": [
              {
                "key": "Value",
                "value": "12323"
              }
            ]
          }
        }
      ]
    }
  },
  "extensions": {}
}

Where you should see one result object for each assertion.

Run Group of Assertions for Table

If you don't always want to run all assertions for a given table, you can also opt to run a subset of the table's assertions using Assertion Tags. First, you'll add tags to your assertions to group and categorize them, then you'll call the runAssertionsForAsset mutation with the tagUrns argument to filter for assertions having those tags.

Step 1: Adding Tag to an Assertion

Currently, you can add tags to an assertion only via the DataHub GraphQL API. You can do this using the following mutation:

graphql
mutation addTags {
  addTag(
    input: {
      resourceUrn: "urn:li:assertion:your-assertion"
      tagUrn: "urn:li:tag:my-important-tag"
    }
  )
}

Step 2: Run All Assertions for a Table with Tags

Now, you can run all assertions for a table with a specific tag(s) using the runAssertionsForAsset mutation with the tagUrns input parameter:

graphql
mutation runAssertionsForAsset {
  runAssertionsForAsset(
    urn: "urn:li:dataset:(urn:li:dataPlatform:snowflake,purchase_events,PROD)"
    tagUrns: ["urn:li:tag:my-important-tag"]
  ) {
    passingCount
    failingCount
    errorCount
    results {
      urn
      result {
        type
        nativeResults {
          key
          value
        }
      }
    }
  }
}

Coming Soon: Support for adding tags to assertions through the DataHub UI.

</TabItem> <TabItem value="python" label="Python">

Run Assertion

python
{{ inline /metadata-ingestion/examples/library/run_assertion.py show_path_as_comment }}

Run Group of Assertions

python
{{ inline /metadata-ingestion/examples/library/run_assertions.py show_path_as_comment }}

Run All Assertions for Table

python
{{ inline /metadata-ingestion/examples/library/run_assertions_for_asset.py show_path_as_comment }}
</TabItem> </Tabs>

Providing Dynamic Parameters to Assertions

You can provide dynamic parameters to your assertions to customize their behavior. This is particularly useful for assertions that require dynamic parameters, such as a threshold value that changes based on the time of day.

Dynamic parameters can be injected into the SQL fragment portion of any Assertion. For example, it can appear in any part of the SQL statement in a Custom SQL Assertion, or it can appear in the Advanced > Filter section of a Column, Volume, or Freshness Assertion.

To do so, you'll first need to edit the SQL fragment to include the dynamic parameter. Dynamic parameters appear as ${parameterName} in the SQL fragment.

Next, you'll call the runAssertion, runAssertions, or runAssertionsForAsset mutations with the parameters input argument. This argument is a list of key-value tuples, where the key is the parameter name and the value is the parameter value:

<Tabs> <TabItem value="graphql" label="GraphQL" default>
graphql
mutation runAssertion {
  runAssertion(
    urn: "urn:li:assertion:your-assertion-id"
    parameters: [{ key: "parameterName", value: "parameterValue" }]
  ) {
    type
    nativeResults {
      key
      value
    }
  }
}
</TabItem> <TabItem value="python" label="Python">
python
{{ inline /metadata-ingestion/examples/library/run_assertion_with_parameters.py show_path_as_comment }}
</TabItem> </Tabs>

At runtime, the ${parameterName} placeholder in the SQL fragment will be replaced with the provided parameterValue before the query is sent to the database for execution.

Get Assertion Details

You can use the following APIs to

  1. Fetch existing assertion definitions + run history
  2. Fetch the assertions associated with a given table + their run history.
<Tabs> <TabItem value="graphql" label="GraphQL" default>

Get Assertions for Table

To retrieve all the assertions for a table, you can use the following GraphQL Query.

graphql
query dataset {
  dataset(
    urn: "urn:li:dataset:(urn:li:dataPlatform:snowflake,purchases,PROD)"
  ) {
    assertions(start: 0, count: 1000) {
      start
      count
      total
      assertions {
        urn
        # Fetch the last run of each associated assertion.
        runEvents(status: COMPLETE, limit: 1) {
          total
          failed
          succeeded
          runEvents {
            timestampMillis
            status
            result {
              type
              nativeResults {
                key
                value
              }
            }
          }
        }
        info {
          type
          description
          lastUpdated {
            time
            actor
          }
          datasetAssertion {
            datasetUrn
            scope
            aggregation
            operator
            parameters {
              value {
                value
                type
              }
              minValue {
                value
                type
              }
              maxValue {
                value
                type
              }
            }
            fields {
              urn
              path
            }
            nativeType
            nativeParameters {
              key
              value
            }
            logic
          }
          freshnessAssertion {
            type
            entityUrn
            schedule {
              type
              cron {
                cron
                timezone
              }
              fixedInterval {
                unit
                multiple
              }
            }
            filter {
              type
              sql
            }
          }
          sqlAssertion {
            type
            entityUrn
            statement
            changeType
            operator
            parameters {
              value {
                value
                type
              }
              minValue {
                value
                type
              }
              maxValue {
                value
                type
              }
            }
          }
          fieldAssertion {
            type
            entityUrn
            filter {
              type
              sql
            }
            fieldValuesAssertion {
              field {
                path
                type
                nativeType
              }
              transform {
                type
              }
              operator
              parameters {
                value {
                  value
                  type
                }
                minValue {
                  value
                  type
                }
                maxValue {
                  value
                  type
                }
              }
              failThreshold {
                type
                value
              }
              excludeNulls
            }
            fieldMetricAssertion {
              field {
                path
                type
                nativeType
              }
              metric
              operator
              parameters {
                value {
                  value
                  type
                }
                minValue {
                  value
                  type
                }
                maxValue {
                  value
                  type
                }
              }
            }
          }
          volumeAssertion {
            type
            entityUrn
            filter {
              type
              sql
            }
            rowCountTotal {
              operator
              parameters {
                value {
                  value
                  type
                }
                minValue {
                  value
                  type
                }
                maxValue {
                  value
                  type
                }
              }
            }
            rowCountChange {
              type
              operator
              parameters {
                value {
                  value
                  type
                }
                minValue {
                  value
                  type
                }
                maxValue {
                  value
                  type
                }
              }
            }
          }
          schemaAssertion {
            entityUrn
            compatibility
            fields {
              path
              type
              nativeType
            }
            schema {
              fields {
                fieldPath
                type
                nativeDataType
              }
            }
          }
          source {
            type
            created {
              time
              actor
            }
          }
        }
      }
    }
  }
}

Get Assertion Details

You can use the following GraphQL query to fetch the details for an assertion along with its evaluation history by URN.

graphql
query getAssertion {
  assertion(urn: "urn:li:assertion:assertion-id") {
    urn
    # Fetch the last 10 runs for the assertion.
    runEvents(status: COMPLETE, limit: 10) {
      total
      failed
      succeeded
      runEvents {
        timestampMillis
        status
        result {
          type
          nativeResults {
            key
            value
          }
        }
      }
    }
    info {
      type
      description
      lastUpdated {
        time
        actor
      }
      datasetAssertion {
        datasetUrn
        scope
        aggregation
        operator
        parameters {
          value {
            value
            type
          }
          minValue {
            value
            type
          }
          maxValue {
            value
            type
          }
        }
        fields {
          urn
          path
        }
        nativeType
        nativeParameters {
          key
          value
        }
        logic
      }
      freshnessAssertion {
        type
        entityUrn
        schedule {
          type
          cron {
            cron
            timezone
          }
          fixedInterval {
            unit
            multiple
          }
        }
        filter {
          type
          sql
        }
      }
      sqlAssertion {
        type
        entityUrn
        statement
        changeType
        operator
        parameters {
          value {
            value
            type
          }
          minValue {
            value
            type
          }
          maxValue {
            value
            type
          }
        }
      }
      fieldAssertion {
        type
        entityUrn
        filter {
          type
          sql
        }
        fieldValuesAssertion {
          field {
            path
            type
            nativeType
          }
          transform {
            type
          }
          operator
          parameters {
            value {
              value
              type
            }
            minValue {
              value
              type
            }
            maxValue {
              value
              type
            }
          }
          failThreshold {
            type
            value
          }
          excludeNulls
        }
        fieldMetricAssertion {
          field {
            path
            type
            nativeType
          }
          metric
          operator
          parameters {
            value {
              value
              type
            }
            minValue {
              value
              type
            }
            maxValue {
              value
              type
            }
          }
        }
      }
      volumeAssertion {
        type
        entityUrn
        filter {
          type
          sql
        }
        rowCountTotal {
          operator
          parameters {
            value {
              value
              type
            }
            minValue {
              value
              type
            }
            maxValue {
              value
              type
            }
          }
        }
        rowCountChange {
          type
          operator
          parameters {
            value {
              value
              type
            }
            minValue {
              value
              type
            }
            maxValue {
              value
              type
            }
          }
        }
      }
      schemaAssertion {
        entityUrn
        compatibility
        fields {
          path
          type
          nativeType
        }
        schema {
          fields {
            fieldPath
            type
            nativeDataType
          }
        }
      }
      source {
        type
        created {
          time
          actor
        }
      }
    }
  }
}
</TabItem> <TabItem value="python" label="Python">
python
Python support coming soon!
</TabItem> </Tabs>

Add Tag to Assertion

You can add tags to individual assertions to group and categorize them, for example by its priority or severity. Note that the tag should already exist in DataHub, or the operation will fail.

<Tabs> <TabItem value="graphql" label="GraphQL" default>
graphql
mutation addTags {
  addTag(
    input: {
      resourceUrn: "urn:li:assertion:your-assertion"
      tagUrn: "urn:li:tag:my-important-tag"
    }
  )
}

If you see the following response, the operation was successful:

json
{
  "data": {
    "addTag": true
  },
  "extensions": {}
}

You can create new tags using the createTag mutation or via the UI.

</TabItem> </Tabs>

Delete Assertions

You can use delete dataset operations to DataHub using the following APIs.

<Tabs> <TabItem value="graphql" label="GraphQL" default>
graphql
mutation deleteAssertion {
  deleteAssertion(urn: "urn:li:assertion:test")
}

If you see the following response, the operation was successful:

json
{
  "data": {
    "deleteAssertion": true
  },
  "extensions": {}
}
</TabItem> <TabItem value="python" label="Python">
python
{{ inline /metadata-ingestion/examples/library/assertion_delete.py show_path_as_comment }}
</TabItem> </Tabs>

(Advanced) Create and Report Results for Custom Assertions

If you'd like to create and report results for your own custom assertions, e.g. those which are run and evaluated outside of DataHub Cloud, you need to generate 2 important Assertion Entity aspects, and give the assertion a unique URN of the following format:

  1. Generate a unique URN for your assertion
plaintext
urn:li:assertion:<unique-assertion-id>

  1. Generate the AssertionInfo aspect for the assertion. You can do this using the Python SDK. Give your assertion a type and a source with type EXTERNAL to mark it as an external assertion, not run by DataHub itself.

  2. Generate the AssertionRunEvent timeseries aspect using the Python SDK. This aspect should contain the result of the assertion run at a given timestamp and will be shown on the results graph in DataHub's UI.

Create and Remove Subscriptions

Reference the Subscriptions SDK for more information on how to create and remove subscriptions on Datasets or Assertions.