Assertions
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.
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.
Create Assertions
You can create new dataset Assertions to DataHub using the following APIs.
- GraphQL
Freshness Assertion
To create a new freshness assertion, use the upsertDatasetFreshnessAssertionMonitor GraphQL Mutation.
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:
{
"data": {
"upsertDatasetFreshnessAssertionMonitor": {
"urn": "urn:li:assertion:your-new-assertion-id"
}
},
"extensions": {}
}
For more details, see the Freshness Assertions guide.
Volume Assertions
To create a new volume assertion, use the upsertDatasetVolumeAssertionMonitor GraphQL Mutation.
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:
{
"data": {
"upsertDatasetVolumeAssertionMonitor": {
"urn": "urn:li:assertion:your-new-assertion-id"
}
},
"extensions": {}
}
For more details, see the Volume Assertions guide.
Column Assertions
To create a new column assertion, use the upsertDatasetFieldAssertionMonitor GraphQL Mutation.
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:
{
"data": {
"upsertDatasetFieldAssertionMonitor": {
"urn": "urn:li:assertion:your-new-assertion-id"
}
},
"extensions": {}
}
For more details, see the Column Assertions guide.
Custom SQL Assertions
To create a new column assertion, use the upsertDatasetSqlAssertionMonitor GraphQL Mutation.
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:
{
"data": {
"upsertDatasetSqlAssertionMonitor": {
"urn": "urn:li:assertion:your-new-assertion-id"
}
},
"extensions": {}
}
For more details, see the Custom SQL Assertions guide.
Schema Assertions
To create a new schema assertion, use the upsertDatasetSchemaAssertionMonitor GraphQL Mutation.
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:
{
"data": {
"upsertDatasetSchemaAssertionMonitor": {
"urn": "urn:li:assertion:your-new-assertion-id"
}
},
"extensions": {}
}
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
asyncparameter, which can be set totrueto run the assertion asynchronously. When set totrue, 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 theassertion(urn: String!)GraphQL query.
- GraphQL
- Python
Run Assertion
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. The value defaults to true.
If the assertion is external (not natively executed by Acryl), this API will return an error.
If running the assertion is successful, the result will be returned as follows:
{
"data": {
"runAssertion": {
"type": "SUCCESS",
"nativeResults": [
{
"key": "Value",
"value": "1382"
}
]
}
},
"extensions": {}
}
Run Group of Assertions
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. The value defaults to true.
If any of the assertion are external (not natively executed by Acryl), they will simply be omitted from the result set.
If running the assertions is successful, the results will be returned as follows:
{
"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.
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. The value defaults to true.
If any of the assertion are external (not natively executed by Acryl), they will simply be omitted from the result set.
If running the assertions is successful, the results will be returned as follows:
{
"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:
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:
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.
Run Assertion
# Inlined from /metadata-ingestion/examples/library/run_assertion.py
import logging
from datahub.ingestion.graph.client import DatahubClientConfig, DataHubGraph
log = logging.getLogger(__name__)
graph = DataHubGraph(
config=DatahubClientConfig(
server="http://localhost:8080",
)
)
assertion_urn = "urn:li:assertion:6e3f9e09-1483-40f9-b9cd-30e5f182694a"
# Run the assertion
assertion_result = graph.run_assertion(urn=assertion_urn, save_result=True)
log.info(
f'Assertion result (SUCCESS / FAILURE / ERROR): {assertion_result.get("type")}'
)
Run Group of Assertions
# Inlined from /metadata-ingestion/examples/library/run_assertions.py
import logging
from datahub.ingestion.graph.client import DatahubClientConfig, DataHubGraph
log = logging.getLogger(__name__)
graph = DataHubGraph(
config=DatahubClientConfig(
server="http://localhost:8080",
)
)
assertion_urns = [
"urn:li:assertion:6e3f9e09-1483-40f9-b9cd-30e5f182694a",
"urn:li:assertion:9e3f9e09-1483-40f9-b9cd-30e5f182694g",
]
# Run the assertions
assertion_results = graph.run_assertions(urns=assertion_urns, save_result=True).get(
"results"
)
if assertion_results is not None:
assertion_result_1 = assertion_results.get(
"urn:li:assertion:6e3f9e09-1483-40f9-b9cd-30e5f182694a"
)
assertion_result_2 = assertion_results.get(
"urn:li:assertion:9e3f9e09-1483-40f9-b9cd-30e5f182694g"
)
log.info(f"Assertion results: {assertion_results}")
log.info(
f"Assertion result 1 (SUCCESS / FAILURE / ERROR): {assertion_result_1.get('type')}"
)
log.info(
f"Assertion result 2 (SUCCESS / FAILURE / ERROR): {assertion_result_2.get('type')}"
)
Run All Assertions for Table
# Inlined from /metadata-ingestion/examples/library/run_assertions_for_asset.py
import logging
from datahub.ingestion.graph.client import DatahubClientConfig, DataHubGraph
log = logging.getLogger(__name__)
graph = DataHubGraph(
config=DatahubClientConfig(
server="http://localhost:8080",
)
)
dataset_urn = "urn:li:dataset:(urn:li:dataPlatform:snowflake,my_snowflake_table,PROD)"
# Run all native assertions for the dataset
assertion_results = graph.run_assertions_for_asset(urn=dataset_urn).get("results")
if assertion_results is not None:
assertion_result_1 = assertion_results.get(
"urn:li:assertion:6e3f9e09-1483-40f9-b9cd-30e5f182694a"
)
assertion_result_2 = assertion_results.get(
"urn:li:assertion:9e3f9e09-1483-40f9-b9cd-30e5f182694g"
)
log.info(f"Assertion results: {assertion_results}")
log.info(
f"Assertion result 1 (SUCCESS / FAILURE / ERROR): {assertion_result_1.get('type')}"
)
log.info(
f"Assertion result 2 (SUCCESS / FAILURE / ERROR): {assertion_result_2.get('type')}"
)
# Run a subset of native assertions having a specific tag
important_assertion_tag = "urn:li:tag:my-important-assertion-tag"
assertion_results = graph.run_assertions_for_asset(
urn=dataset_urn, tag_urns=[important_assertion_tag]
).get("results")
Experimental: 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:
mutation runAssertion {
runAssertion(urn: "urn:li:assertion:your-assertion-id", parameters: [{key: "parameterName", value: "parameterValue"}]) {
type
nativeResults {
key
value
}
}
}
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
- Fetch existing assertion definitions + run history
- Fetch the assertions associated with a given table + their run history.
- GraphQL
- Python
Get Assertions for Table
To retrieve all the assertions for a table, you can use the following GraphQL Query.
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.
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
}
}
}
}
}
Python support coming soon!
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.
- 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:
{
"data": {
"addTag": true
},
"extensions": {}
}
You can create new tags using the createTag mutation or via the UI.
Delete Assertions
You can use delete dataset operations to DataHub using the following APIs.
- GraphQL
- Python
mutation deleteAssertion {
deleteAssertion(urn: "urn:li:assertion:test")
}
If you see the following response, the operation was successful:
{
"data": {
"deleteAssertion": true
},
"extensions": {}
}
# Inlined from /metadata-ingestion/examples/library/delete_assertion.py
import logging
from datahub.ingestion.graph.client import DatahubClientConfig, DataHubGraph
log = logging.getLogger(__name__)
graph = DataHubGraph(
config=DatahubClientConfig(
server="http://localhost:8080",
)
)
assertion_urn = "urn:li:assertion:my-assertion"
# Delete the Assertion
graph.delete_entity(urn=assertion_urn, hard=True)
log.info(f"Deleted assertion {assertion_urn}")
(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 Acryl, you need to generate 2 important Assertion Entity aspects, and give the assertion a unique URN of the following format:
- Generate a unique URN for your assertion
urn:li:assertion:<unique-assertion-id>
Generate the AssertionInfo aspect for the assertion. You can do this using the Python SDK. Give your assertion a
typeand asourcewith typeEXTERNALto mark it as an external assertion, not run by DataHub itself.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.