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docs: improve lineage docs (#10396)
This commit is contained in:
Hyejin Yoon
GitHub
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@ -15,6 +15,7 @@ This guide will show you how to
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- Add lineage between datasets.
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- Add column-level lineage between datasets.
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- Read lineage.
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## Prerequisites
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@ -109,7 +110,7 @@ Expected Response:
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</TabItem>
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</Tabs>
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### Expected Outcomes of Adding Lineage
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### Expected Outcome
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You can now see the lineage between `fct_users_deleted` and `logging_events`.
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@ -117,6 +118,7 @@ You can now see the lineage between `fct_users_deleted` and `logging_events`.
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<img width="70%" src="https://raw.githubusercontent.com/datahub-project/static-assets/main/imgs/apis/tutorials/lineage-added.png"/>
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</p>
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## Add Column-level Lineage
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<Tabs>
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@ -129,7 +131,7 @@ You can now see the lineage between `fct_users_deleted` and `logging_events`.
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</TabItem>
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</Tabs>
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### Expected Outcome of Adding Column Level Lineage
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### Expected Outcome
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You can now see the column-level lineage between datasets. Note that you have to enable `Show Columns` to be able to see the column-level lineage.
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@ -137,18 +139,30 @@ You can now see the column-level lineage between datasets. Note that you have to
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<img width="70%" src="https://raw.githubusercontent.com/datahub-project/static-assets/main/imgs/apis/tutorials/column-level-lineage-added.png"/>
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</p>
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## Read Table Lineage
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## Add Lineage to Non-Dataset Entities
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You can also add lineage to non-dataset entities, such as DataJobs, Charts, and Dashboards.
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Please refer to the following examples.
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| Connection | Examples | A.K.A |
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|---------------------|-------------------|-----------------|
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| DataJob to DataFlow | - [lineage_job_dataflow.py](../../../metadata-ingestion/examples/library/lineage_job_dataflow.py) | |
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| DataJob to Dataset | - [lineage_dataset_job_dataset.py](../../../metadata-ingestion/examples/library/lineage_dataset_job_dataset.py) <br /> | Pipeline Lineage |
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| Chart to Dashboard | - [lineage_chart_dashboard.py](../../../metadata-ingestion/examples/library/lineage_chart_dashboard.py) | |
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| Chart to Dataset | - [lineage_dataset_chart.py](../../../metadata-ingestion/examples/library/lineage_dataset_chart.py) | |
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## Read Lineage (Lineage Impact Analysis)
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<Tabs>
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<TabItem value="graphql" label="GraphQL" default>
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```graphql
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query searchAcrossLineage {
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searchAcrossLineage(
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query scrollAcrossLineage {
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scrollAcrossLineage(
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input: {
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query: "*"
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urn: "urn:li:dataset:(urn:li:dataPlatform:dbt,long_tail_companions.adoption.human_profiles,PROD)"
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start: 0
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urn: "urn:li:dataset:(urn:li:dataPlatform:hive,logging_events,PROD)"
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count: 10
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direction: DOWNSTREAM
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orFilters: [
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@ -175,8 +189,13 @@ query searchAcrossLineage {
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}
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}
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```
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:::info Degree
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Note that `degree` means the number of hops in the lineage. For example, `degree: 1` means the immediate downstream entities, `degree: 2` means the entities that are two hops away, and so on.
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:::
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The GraphQL example shows using lineage degrees as a filter, but additional search filters can be included here as well.
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This will perform a multi-hop lineage search on the urn specified. For more information about the `scrollAcrossLineage` mutation, please refer to [scrollAcrossLineage](https://datahubproject.io/docs/graphql/queries/#scrollacrosslineage).
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This example shows using lineage degrees as a filter, but additional search filters can be included here as well.
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</TabItem>
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<TabItem value="curl" label="Curl">
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@ -184,7 +203,7 @@ This example shows using lineage degrees as a filter, but additional search filt
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```shell
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curl --location --request POST 'http://localhost:8080/api/graphql' \
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--header 'Authorization: Bearer <my-access-token>' \
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--header 'Content-Type: application/json' --data-raw '{ { "query": "query searchAcrossLineage { searchAcrossLineage( input: { query: \"*\" urn: \"urn:li:dataset:(urn:li:dataPlatform:dbt,long_tail_companions.adoption.human_profiles,PROD)\" start: 0 count: 10 direction: DOWNSTREAM orFilters: [ { and: [ { condition: EQUAL negated: false field: \"degree\" values: [\"1\", \"2\", \"3+\"] } ] } ] } ) { searchResults { degree entity { urn type } } }}"
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--header 'Content-Type: application/json' --data-raw '{ { "query": "query scrollAcrossLineage { scrollAcrossLineage( input: { query: \"*\" urn: \"urn:li:dataset:(urn:li:dataPlatform:hive,logging_events,PROD)\" count: 10 direction: DOWNSTREAM orFilters: [ { and: [ { condition: EQUAL negated: false field: \"degree\" values: [\"1\", \"2\", \"3+\"] } ] } ] } ) { searchResults { degree entity { urn type } } }}"
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}}'
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```
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@ -192,67 +211,116 @@ curl --location --request POST 'http://localhost:8080/api/graphql' \
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<TabItem value="python" label="Python">
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```python
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{{ inline /metadata-ingestion/examples/library/read_lineage_rest.py show_path_as_comment }}
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{{ inline /metadata-ingestion/examples/library/read_lineage_execute_graphql.py show_path_as_comment }}
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```
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The Python SDK example shows how to read lineage of a dataset. Please note that the `aspect_type` parameter can vary depending on the entity type.
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Below is a few examples of `aspect_type` for different entities.
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|Entity|Aspect_type| Reference |
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|-------|------------|--------------------------------------------------------------------------|
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|Dataset|`UpstreamLineageClass`| [Link](/docs/generated/metamodel/entities/dataset.md#upstreamlineage) |
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|Datajob|`DataJobInputOutputClass`| [Link](/docs/generated/metamodel/entities/dataJob.md#datajobinputoutput) |
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|Dashboard|`DashboardInfoClass`| [Link](/docs/generated/metamodel/entities/dashboard.md#dashboardinfo) |
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|DataFlow|`DataFlowInfoClass`| [Link](/docs/generated/metamodel/entities/dataFlow.md#dataflowinfo) |
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Learn more about lineages of different entities in the [Add Lineage to Non-Dataset Entities](#add-lineage-to-non-dataset-entities) Section.
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</TabItem>
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</Tabs>
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### Expected Outcome
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As an outcome, you should see the downstream entities of `logging_events`.
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```graphql
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{
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"data": {
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"scrollAcrossLineage": {
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"searchResults": [
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{
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"degree": 1,
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"entity": {
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"urn": "urn:li:dataJob:(urn:li:dataFlow:(airflow,dag_abc,PROD),task_123)",
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"type": "DATA_JOB"
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}
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},
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...
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{
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"degree": 2,
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"entity": {
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"urn": "urn:li:mlPrimaryKey:(user_analytics,user_name)",
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"type": "MLPRIMARY_KEY"
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}
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}
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]
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}
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},
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"extensions": {}
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}
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```
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## Read Column-level Lineage
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You can also read column-level lineage via Python SDK.
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<Tabs>
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<TabItem value="python" label="Python">
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```python
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{{ inline /metadata-ingestion/examples/library/read_lineage_dataset_rest.py show_path_as_comment }}
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```
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</TabItem>
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</Tabs>
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This will perform a multi-hop lineage search on the urn specified. For more information about the `searchAcrossLineage` mutation, please refer to [searchAcrossLineage](https://datahubproject.io/docs/graphql/queries/#searchacrosslineage).
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### Expected Outcome
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## Read Column Lineage
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<Tabs>
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<TabItem value="graphql" label="GraphQL" default>
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As a response, you will get the full lineage information like this.
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```graphql
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query searchAcrossLineage {
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searchAcrossLineage(
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input: {
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query: "*"
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urn: "urn:li:schemaField(urn:li:dataset:(urn:li:dataPlatform:dbt,long_tail_companions.adoption.human_profiles,PROD),profile_id)"
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start: 0
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count: 10
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direction: DOWNSTREAM
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orFilters: [
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{
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and: [
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{
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condition: EQUAL
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negated: false
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field: "degree"
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values: ["1", "2", "3+"]
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{
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"UpstreamLineageClass": {
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"upstreams": [
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{
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"UpstreamClass": {
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"auditStamp": {
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"AuditStampClass": {
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"time": 0,
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"actor": "urn:li:corpuser:unknown",
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"impersonator": null,
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"message": null
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}
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]
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},
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"created": null,
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"dataset": "urn:li:dataset:(urn:li:dataPlatform:hive,fct_users_deleted,PROD)",
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"type": "TRANSFORMED",
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"properties": null,
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"query": null
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}
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]
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}
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) {
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searchResults {
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degree
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entity {
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urn
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type
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}
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}
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],
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"fineGrainedLineages": [
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{
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"FineGrainedLineageClass": {
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"upstreamType": "FIELD_SET",
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"upstreams": [
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"urn:li:schemaField:(urn:li:dataset:(urn:li:dataPlatform:hive,fct_users_deleted,PROD),browser_id)",
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"urn:li:schemaField:(urn:li:dataset:(urn:li:dataPlatform:hive,fct_users_created,PROD),user_id)"
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],
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"downstreamType": "FIELD",
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"downstreams": [
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"urn:li:schemaField:(urn:li:dataset:(urn:li:dataPlatform:hive,logging_events,PROD),browser)"
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],
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"transformOperation": null,
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"confidenceScore": 1.0,
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"query": null
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}
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}
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]
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}
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}
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```
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This example shows using lineage degrees as a filter, but additional search filters can be included here as well.
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</TabItem>
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<TabItem value="curl" label="Curl">
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```shell
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curl --location --request POST 'http://localhost:8080/api/graphql' \
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--header 'Authorization: Bearer <my-access-token>' \
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--header 'Content-Type: application/json' --data-raw '{ { "query": "query searchAcrossLineage { searchAcrossLineage( input: { query: \"*\" urn: \"urn:li:schemaField(urn:li:dataset:(urn:li:dataPlatform:dbt,long_tail_companions.adoption.human_profiles,PROD),profile_id)\" start: 0 count: 10 direction: DOWNSTREAM orFilters: [ { and: [ { condition: EQUAL negated: false field: \"degree\" values: [\"1\", \"2\", \"3+\"] } ] } ] } ) { searchResults { degree entity { urn type } } }}"
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}}'
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```
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</TabItem>
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</Tabs>
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This will perform a multi-hop lineage search on the urn specified. You can see schemaField URNs are made up of two parts: first the table they are a column of, and second the path of the column. For more information about the `searchAcrossLineage` mutation, please refer to [searchAcrossLineage](https://datahubproject.io/docs/graphql/queries/#searchacrosslineage).
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@ -0,0 +1,13 @@
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from datahub.ingestion.graph.client import DatahubClientConfig, DataHubGraph
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# Imports for metadata model classes
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from datahub.metadata.schema_classes import DataJobInputOutputClass
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# Get the current lineage for a datajob
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gms_endpoint = "http://localhost:8080"
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graph = DataHubGraph(DatahubClientConfig(server=gms_endpoint))
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urn = "urn:li:dataset:(urn:li:dataPlatform:hive,logging_events,PROD)"
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result = graph.get_aspect(entity_urn=urn, aspect_type=DataJobInputOutputClass)
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print(result)
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@ -0,0 +1,13 @@
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from datahub.ingestion.graph.client import DatahubClientConfig, DataHubGraph
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# Imports for metadata model classes
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from datahub.metadata.schema_classes import UpstreamLineageClass
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# Get the current lineage for a dataset
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gms_endpoint = "http://localhost:8080"
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graph = DataHubGraph(DatahubClientConfig(server=gms_endpoint))
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urn = "urn:li:dataset:(urn:li:dataPlatform:hive,logging_events,PROD)"
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result = graph.get_aspect(entity_urn=urn, aspect_type=UpstreamLineageClass)
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print(result)
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@ -0,0 +1,44 @@
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# read-modify-write requires access to the DataHubGraph (RestEmitter is not enough)
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from datahub.ingestion.graph.client import DatahubClientConfig, DataHubGraph
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gms_endpoint = "http://localhost:8080"
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graph = DataHubGraph(DatahubClientConfig(server=gms_endpoint))
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# Query multiple aspects from entity
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query = """
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query scrollAcrossLineage($input: ScrollAcrossLineageInput!) {
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scrollAcrossLineage(input: $input) {
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searchResults {
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degree
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entity {
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urn
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type
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}
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}
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}
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}
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"""
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variables = {
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"input": {
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"query": "*",
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"urn": "urn:li:dataset:(urn:li:dataPlatform:hive,logging_events,PROD)",
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"count": 10,
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"direction": "DOWNSTREAM",
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"orFilters": [
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{
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"and": [
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{
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"condition": "EQUAL",
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"negated": "false",
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"field": "degree",
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"values": ["1", "2", "3+"],
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}
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]
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}
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],
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}
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}
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result = graph.execute_graphql(query=query, variables=variables)
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print(result)
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@ -1,43 +0,0 @@
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# read-modify-write requires access to the DataHubGraph (RestEmitter is not enough)
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from datahub.ingestion.graph.client import DatahubClientConfig, DataHubGraph
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gms_endpoint = "http://localhost:8080"
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graph = DataHubGraph(DatahubClientConfig(server=gms_endpoint))
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# Query multiple aspects from entity
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query = """
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query searchAcrossLineage {
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searchAcrossLineage(
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input: {
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query: "*"
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urn: "urn:li:dataset:(urn:li:dataPlatform:dbt,long_tail_companions.adoption.human_profiles,PROD)"
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start: 0
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count: 10
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direction: DOWNSTREAM
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orFilters: [
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{
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and: [
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{
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condition: EQUAL
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negated: false
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field: "degree"
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values: ["1", "2", "3+"]
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}
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] # Additional search filters can be included here as well
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}
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]
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}
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) {
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searchResults {
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degree
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entity {
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urn
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type
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}
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}
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}
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}
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"""
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result = graph.execute_graphql(query=query)
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print(result)
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