mirror of
https://github.com/datahub-project/datahub.git
synced 2025-12-02 13:44:39 +00:00
fix(git-ignore): Git ignore generated python and avro artifacts (#3320)
This commit is contained in:
Dexter Lee
GitHub
parent
cfc97107e8
commit
4c038d7cfe
3
.github/workflows/build-and-test.yml
vendored
3
.github/workflows/build-and-test.yml
vendored
@ -63,6 +63,7 @@ jobs:
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run: ./metadata-ingestion/scripts/install_deps.sh
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- name: Run metadata-ingestion tests
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run: ./gradlew :metadata-ingestion:build :metadata-ingestion:check
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metadata-ingestion-by-version:
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runs-on: ubuntu-latest
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strategy:
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@ -75,6 +76,8 @@ jobs:
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python-version: ${{ matrix.python-version }}
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- name: Install dependencies
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run: ./metadata-ingestion/scripts/install_deps.sh && python -m pip install --upgrade pip && pip install tox tox-gh-actions
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- name: Codegen
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run: ./gradlew :metadata-ingestion:codegen
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- name: Run tox tests
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run: cd metadata-ingestion && tox
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1
.gitignore
vendored
1
.gitignore
vendored
@ -15,6 +15,7 @@
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**/src/mainGenerated*
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**/src/testGenerated*
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metadata-events/mxe-registration/src/main/resources/**/*.avsc
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metadata-ingestion/src/datahub/metadata
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# Java
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.java-version
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@ -18,7 +18,7 @@ The reporter interface enables the source to report statistics, warnings, failur
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The core for the source is the `get_workunits` method, which produces a stream of MCE objects. The [file source](./src/datahub/ingestion/source/file.py) is a good and simple example.
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The MetadataChangeEventClass is defined in the [metadata models](./src/datahub/metadata/schema_classes.py). There are also some [convenience methods](./src/datahub/emitter/mce_builder.py) for commonly used operations.
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The MetadataChangeEventClass is defined in the metadata models which are generated under `metadata-ingestion/src/datahub/metadata/schema_classes.py`. There are also some [convenience methods](./src/datahub/emitter/mce_builder.py) for commonly used operations.
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### 4. Set up the dependencies
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@ -1,7 +0,0 @@
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# flake8: noqa
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# This file is autogenerated by /metadata-ingestion/scripts/avro_codegen.py
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# Do not modify manually!
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# fmt: off
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# fmt: on
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@ -1,7 +0,0 @@
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# flake8: noqa
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# This file is autogenerated by /metadata-ingestion/scripts/avro_codegen.py
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# Do not modify manually!
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# fmt: off
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# fmt: on
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@ -1,7 +0,0 @@
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# flake8: noqa
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# This file is autogenerated by /metadata-ingestion/scripts/avro_codegen.py
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# Do not modify manually!
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# fmt: off
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# fmt: on
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@ -1,11 +0,0 @@
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# flake8: noqa
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# This file is autogenerated by /metadata-ingestion/scripts/avro_codegen.py
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# Do not modify manually!
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# fmt: off
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from ....schema_classes import KafkaAuditHeaderClass
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KafkaAuditHeader = KafkaAuditHeaderClass
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# fmt: on
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@ -1,7 +0,0 @@
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# flake8: noqa
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# This file is autogenerated by /metadata-ingestion/scripts/avro_codegen.py
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# Do not modify manually!
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# fmt: off
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# fmt: on
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@ -1,19 +0,0 @@
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# flake8: noqa
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# This file is autogenerated by /metadata-ingestion/scripts/avro_codegen.py
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# Do not modify manually!
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# fmt: off
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from .....schema_classes import ChartInfoClass
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from .....schema_classes import ChartQueryClass
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from .....schema_classes import ChartQueryTypeClass
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from .....schema_classes import ChartTypeClass
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from .....schema_classes import EditableChartPropertiesClass
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ChartInfo = ChartInfoClass
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ChartQuery = ChartQueryClass
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ChartQueryType = ChartQueryTypeClass
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ChartType = ChartTypeClass
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EditableChartProperties = EditableChartPropertiesClass
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# fmt: on
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@ -1,59 +0,0 @@
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# flake8: noqa
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# This file is autogenerated by /metadata-ingestion/scripts/avro_codegen.py
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# Do not modify manually!
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# fmt: off
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from .....schema_classes import AccessLevelClass
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from .....schema_classes import AuditStampClass
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from .....schema_classes import BrowsePathsClass
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from .....schema_classes import ChangeAuditStampsClass
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from .....schema_classes import CostClass
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from .....schema_classes import CostCostClass
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from .....schema_classes import CostCostDiscriminatorClass
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from .....schema_classes import CostTypeClass
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from .....schema_classes import DeprecationClass
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from .....schema_classes import FabricTypeClass
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from .....schema_classes import GlobalTagsClass
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from .....schema_classes import GlossaryTermAssociationClass
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from .....schema_classes import GlossaryTermsClass
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from .....schema_classes import InstitutionalMemoryClass
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from .....schema_classes import InstitutionalMemoryMetadataClass
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from .....schema_classes import MLFeatureDataTypeClass
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from .....schema_classes import OwnerClass
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from .....schema_classes import OwnershipClass
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from .....schema_classes import OwnershipSourceClass
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from .....schema_classes import OwnershipSourceTypeClass
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from .....schema_classes import OwnershipTypeClass
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from .....schema_classes import StatusClass
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from .....schema_classes import TagAssociationClass
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from .....schema_classes import VersionTagClass
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from .....schema_classes import WindowDurationClass
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AccessLevel = AccessLevelClass
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AuditStamp = AuditStampClass
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BrowsePaths = BrowsePathsClass
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ChangeAuditStamps = ChangeAuditStampsClass
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Cost = CostClass
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CostCost = CostCostClass
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CostCostDiscriminator = CostCostDiscriminatorClass
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CostType = CostTypeClass
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Deprecation = DeprecationClass
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FabricType = FabricTypeClass
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GlobalTags = GlobalTagsClass
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GlossaryTermAssociation = GlossaryTermAssociationClass
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GlossaryTerms = GlossaryTermsClass
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InstitutionalMemory = InstitutionalMemoryClass
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InstitutionalMemoryMetadata = InstitutionalMemoryMetadataClass
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MLFeatureDataType = MLFeatureDataTypeClass
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Owner = OwnerClass
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Ownership = OwnershipClass
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OwnershipSource = OwnershipSourceClass
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OwnershipSourceType = OwnershipSourceTypeClass
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OwnershipType = OwnershipTypeClass
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Status = StatusClass
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TagAssociation = TagAssociationClass
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VersionTag = VersionTagClass
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WindowDuration = WindowDurationClass
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# fmt: on
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@ -1,13 +0,0 @@
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# flake8: noqa
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# This file is autogenerated by /metadata-ingestion/scripts/avro_codegen.py
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# Do not modify manually!
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# fmt: off
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from ......schema_classes import TransformationTypeClass
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from ......schema_classes import UDFTransformerClass
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TransformationType = TransformationTypeClass
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UDFTransformer = UDFTransformerClass
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# fmt: on
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@ -1,13 +0,0 @@
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# flake8: noqa
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# This file is autogenerated by /metadata-ingestion/scripts/avro_codegen.py
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# Do not modify manually!
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# fmt: off
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from .....schema_classes import DashboardInfoClass
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from .....schema_classes import EditableDashboardPropertiesClass
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DashboardInfo = DashboardInfoClass
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EditableDashboardProperties = EditableDashboardPropertiesClass
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# fmt: on
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@ -1,21 +0,0 @@
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# flake8: noqa
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# This file is autogenerated by /metadata-ingestion/scripts/avro_codegen.py
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# Do not modify manually!
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# fmt: off
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from .....schema_classes import DataFlowInfoClass
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from .....schema_classes import DataJobInfoClass
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from .....schema_classes import DataJobInputOutputClass
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from .....schema_classes import EditableDataFlowPropertiesClass
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from .....schema_classes import EditableDataJobPropertiesClass
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from .....schema_classes import JobStatusClass
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DataFlowInfo = DataFlowInfoClass
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DataJobInfo = DataJobInfoClass
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DataJobInputOutput = DataJobInputOutputClass
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EditableDataFlowProperties = EditableDataFlowPropertiesClass
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EditableDataJobProperties = EditableDataJobPropertiesClass
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JobStatus = JobStatusClass
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# fmt: on
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@ -1,11 +0,0 @@
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# flake8: noqa
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# This file is autogenerated by /metadata-ingestion/scripts/avro_codegen.py
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# Do not modify manually!
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# fmt: off
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from ......schema_classes import AzkabanJobTypeClass
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AzkabanJobType = AzkabanJobTypeClass
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# fmt: on
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@ -1,13 +0,0 @@
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# flake8: noqa
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# This file is autogenerated by /metadata-ingestion/scripts/avro_codegen.py
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# Do not modify manually!
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# fmt: off
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from .....schema_classes import DataPlatformInfoClass
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from .....schema_classes import PlatformTypeClass
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DataPlatformInfo = DataPlatformInfoClass
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PlatformType = PlatformTypeClass
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# fmt: on
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@ -1,11 +0,0 @@
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# flake8: noqa
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# This file is autogenerated by /metadata-ingestion/scripts/avro_codegen.py
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# Do not modify manually!
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# fmt: off
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from .....schema_classes import DataProcessInfoClass
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DataProcessInfo = DataProcessInfoClass
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# fmt: on
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@ -1,41 +0,0 @@
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# flake8: noqa
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# This file is autogenerated by /metadata-ingestion/scripts/avro_codegen.py
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# Do not modify manually!
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# fmt: off
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from .....schema_classes import DatasetDeprecationClass
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from .....schema_classes import DatasetFieldMappingClass
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from .....schema_classes import DatasetFieldProfileClass
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from .....schema_classes import DatasetFieldUsageCountsClass
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from .....schema_classes import DatasetLineageTypeClass
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from .....schema_classes import DatasetProfileClass
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from .....schema_classes import DatasetPropertiesClass
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from .....schema_classes import DatasetUpstreamLineageClass
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from .....schema_classes import DatasetUsageStatisticsClass
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from .....schema_classes import DatasetUserUsageCountsClass
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from .....schema_classes import EditableDatasetPropertiesClass
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from .....schema_classes import HistogramClass
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from .....schema_classes import QuantileClass
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from .....schema_classes import UpstreamClass
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from .....schema_classes import UpstreamLineageClass
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from .....schema_classes import ValueFrequencyClass
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DatasetDeprecation = DatasetDeprecationClass
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DatasetFieldMapping = DatasetFieldMappingClass
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DatasetFieldProfile = DatasetFieldProfileClass
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DatasetFieldUsageCounts = DatasetFieldUsageCountsClass
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DatasetLineageType = DatasetLineageTypeClass
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DatasetProfile = DatasetProfileClass
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DatasetProperties = DatasetPropertiesClass
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DatasetUpstreamLineage = DatasetUpstreamLineageClass
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DatasetUsageStatistics = DatasetUsageStatisticsClass
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DatasetUserUsageCounts = DatasetUserUsageCountsClass
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EditableDatasetProperties = EditableDatasetPropertiesClass
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Histogram = HistogramClass
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Quantile = QuantileClass
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Upstream = UpstreamClass
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UpstreamLineage = UpstreamLineageClass
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ValueFrequency = ValueFrequencyClass
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# fmt: on
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@ -1,7 +0,0 @@
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# flake8: noqa
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# This file is autogenerated by /metadata-ingestion/scripts/avro_codegen.py
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# Do not modify manually!
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# fmt: off
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# fmt: on
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@ -1,11 +0,0 @@
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# flake8: noqa
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# This file is autogenerated by /metadata-ingestion/scripts/avro_codegen.py
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# Do not modify manually!
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# fmt: off
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from ......schema_classes import ChangeTypeClass
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ChangeType = ChangeTypeClass
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# fmt: on
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@ -1,15 +0,0 @@
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# flake8: noqa
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# This file is autogenerated by /metadata-ingestion/scripts/avro_codegen.py
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# Do not modify manually!
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# fmt: off
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from .....schema_classes import GlossaryNodeInfoClass
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from .....schema_classes import GlossaryRelatedTermsClass
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from .....schema_classes import GlossaryTermInfoClass
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GlossaryNodeInfo = GlossaryNodeInfoClass
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GlossaryRelatedTerms = GlossaryRelatedTermsClass
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GlossaryTermInfo = GlossaryTermInfoClass
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# fmt: on
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@ -1,17 +0,0 @@
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# flake8: noqa
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# This file is autogenerated by /metadata-ingestion/scripts/avro_codegen.py
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# Do not modify manually!
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# fmt: off
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from .....schema_classes import CorpGroupInfoClass
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from .....schema_classes import CorpUserEditableInfoClass
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from .....schema_classes import CorpUserInfoClass
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from .....schema_classes import GroupMembershipClass
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CorpGroupInfo = CorpGroupInfoClass
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CorpUserEditableInfo = CorpUserEditableInfoClass
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CorpUserInfo = CorpUserInfoClass
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GroupMembership = GroupMembershipClass
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# fmt: on
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@ -1,7 +0,0 @@
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# flake8: noqa
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# This file is autogenerated by /metadata-ingestion/scripts/avro_codegen.py
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# Do not modify manually!
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# fmt: off
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# fmt: on
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@ -1,49 +0,0 @@
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# flake8: noqa
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# This file is autogenerated by /metadata-ingestion/scripts/avro_codegen.py
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# Do not modify manually!
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# fmt: off
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from ......schema_classes import ChartKeyClass
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from ......schema_classes import CorpGroupKeyClass
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from ......schema_classes import CorpUserKeyClass
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from ......schema_classes import DashboardKeyClass
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from ......schema_classes import DataFlowKeyClass
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from ......schema_classes import DataHubPolicyKeyClass
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from ......schema_classes import DataJobKeyClass
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from ......schema_classes import DataPlatformKeyClass
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from ......schema_classes import DataProcessKeyClass
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from ......schema_classes import DatasetKeyClass
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from ......schema_classes import GlossaryNodeKeyClass
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from ......schema_classes import GlossaryTermKeyClass
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from ......schema_classes import MLFeatureKeyClass
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from ......schema_classes import MLFeatureTableKeyClass
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from ......schema_classes import MLModelDeploymentKeyClass
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from ......schema_classes import MLModelGroupKeyClass
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from ......schema_classes import MLModelKeyClass
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from ......schema_classes import MLPrimaryKeyKeyClass
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from ......schema_classes import SchemaFieldKeyClass
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from ......schema_classes import TagKeyClass
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ChartKey = ChartKeyClass
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CorpGroupKey = CorpGroupKeyClass
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CorpUserKey = CorpUserKeyClass
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DashboardKey = DashboardKeyClass
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DataFlowKey = DataFlowKeyClass
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DataHubPolicyKey = DataHubPolicyKeyClass
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DataJobKey = DataJobKeyClass
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DataPlatformKey = DataPlatformKeyClass
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DataProcessKey = DataProcessKeyClass
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DatasetKey = DatasetKeyClass
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GlossaryNodeKey = GlossaryNodeKeyClass
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GlossaryTermKey = GlossaryTermKeyClass
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MLFeatureKey = MLFeatureKeyClass
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MLFeatureTableKey = MLFeatureTableKeyClass
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MLModelDeploymentKey = MLModelDeploymentKeyClass
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MLModelGroupKey = MLModelGroupKeyClass
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MLModelKey = MLModelKeyClass
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MLPrimaryKeyKey = MLPrimaryKeyKeyClass
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SchemaFieldKey = SchemaFieldKeyClass
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TagKey = TagKeyClass
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# fmt: on
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@ -1,49 +0,0 @@
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# flake8: noqa
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# This file is autogenerated by /metadata-ingestion/scripts/avro_codegen.py
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# Do not modify manually!
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# fmt: off
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from ......schema_classes import ChartSnapshotClass
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from ......schema_classes import CorpGroupSnapshotClass
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from ......schema_classes import CorpUserSnapshotClass
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from ......schema_classes import DashboardSnapshotClass
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from ......schema_classes import DataFlowSnapshotClass
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from ......schema_classes import DataHubPolicySnapshotClass
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from ......schema_classes import DataJobSnapshotClass
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from ......schema_classes import DataPlatformSnapshotClass
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from ......schema_classes import DataProcessSnapshotClass
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from ......schema_classes import DatasetSnapshotClass
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from ......schema_classes import GlossaryNodeSnapshotClass
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from ......schema_classes import GlossaryTermSnapshotClass
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from ......schema_classes import MLFeatureSnapshotClass
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from ......schema_classes import MLFeatureTableSnapshotClass
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from ......schema_classes import MLModelDeploymentSnapshotClass
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from ......schema_classes import MLModelGroupSnapshotClass
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from ......schema_classes import MLModelSnapshotClass
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from ......schema_classes import MLPrimaryKeySnapshotClass
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from ......schema_classes import SchemaFieldSnapshotClass
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from ......schema_classes import TagSnapshotClass
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ChartSnapshot = ChartSnapshotClass
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CorpGroupSnapshot = CorpGroupSnapshotClass
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CorpUserSnapshot = CorpUserSnapshotClass
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DashboardSnapshot = DashboardSnapshotClass
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DataFlowSnapshot = DataFlowSnapshotClass
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DataHubPolicySnapshot = DataHubPolicySnapshotClass
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DataJobSnapshot = DataJobSnapshotClass
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DataPlatformSnapshot = DataPlatformSnapshotClass
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DataProcessSnapshot = DataProcessSnapshotClass
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DatasetSnapshot = DatasetSnapshotClass
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GlossaryNodeSnapshot = GlossaryNodeSnapshotClass
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GlossaryTermSnapshot = GlossaryTermSnapshotClass
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MLFeatureSnapshot = MLFeatureSnapshotClass
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MLFeatureTableSnapshot = MLFeatureTableSnapshotClass
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MLModelDeploymentSnapshot = MLModelDeploymentSnapshotClass
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MLModelGroupSnapshot = MLModelGroupSnapshotClass
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MLModelSnapshot = MLModelSnapshotClass
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MLPrimaryKeySnapshot = MLPrimaryKeySnapshotClass
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SchemaFieldSnapshot = SchemaFieldSnapshotClass
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TagSnapshot = TagSnapshotClass
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# fmt: on
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@ -1,7 +0,0 @@
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# flake8: noqa
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# This file is autogenerated by /metadata-ingestion/scripts/avro_codegen.py
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# Do not modify manually!
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# fmt: off
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# fmt: on
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@ -1,57 +0,0 @@
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# flake8: noqa
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# This file is autogenerated by /metadata-ingestion/scripts/avro_codegen.py
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# Do not modify manually!
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# fmt: off
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from ......schema_classes import BaseDataClass
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from ......schema_classes import CaveatDetailsClass
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from ......schema_classes import CaveatsAndRecommendationsClass
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from ......schema_classes import DeploymentStatusClass
|
||||
from ......schema_classes import EthicalConsiderationsClass
|
||||
from ......schema_classes import EvaluationDataClass
|
||||
from ......schema_classes import IntendedUseClass
|
||||
from ......schema_classes import IntendedUserTypeClass
|
||||
from ......schema_classes import MLFeaturePropertiesClass
|
||||
from ......schema_classes import MLFeatureTablePropertiesClass
|
||||
from ......schema_classes import MLHyperParamClass
|
||||
from ......schema_classes import MLMetricClass
|
||||
from ......schema_classes import MLModelDeploymentPropertiesClass
|
||||
from ......schema_classes import MLModelFactorPromptsClass
|
||||
from ......schema_classes import MLModelFactorsClass
|
||||
from ......schema_classes import MLModelGroupPropertiesClass
|
||||
from ......schema_classes import MLModelPropertiesClass
|
||||
from ......schema_classes import MLPrimaryKeyPropertiesClass
|
||||
from ......schema_classes import MetricsClass
|
||||
from ......schema_classes import QuantitativeAnalysesClass
|
||||
from ......schema_classes import SourceCodeClass
|
||||
from ......schema_classes import SourceCodeUrlClass
|
||||
from ......schema_classes import SourceCodeUrlTypeClass
|
||||
from ......schema_classes import TrainingDataClass
|
||||
|
||||
|
||||
BaseData = BaseDataClass
|
||||
CaveatDetails = CaveatDetailsClass
|
||||
CaveatsAndRecommendations = CaveatsAndRecommendationsClass
|
||||
DeploymentStatus = DeploymentStatusClass
|
||||
EthicalConsiderations = EthicalConsiderationsClass
|
||||
EvaluationData = EvaluationDataClass
|
||||
IntendedUse = IntendedUseClass
|
||||
IntendedUserType = IntendedUserTypeClass
|
||||
MLFeatureProperties = MLFeaturePropertiesClass
|
||||
MLFeatureTableProperties = MLFeatureTablePropertiesClass
|
||||
MLHyperParam = MLHyperParamClass
|
||||
MLMetric = MLMetricClass
|
||||
MLModelDeploymentProperties = MLModelDeploymentPropertiesClass
|
||||
MLModelFactorPrompts = MLModelFactorPromptsClass
|
||||
MLModelFactors = MLModelFactorsClass
|
||||
MLModelGroupProperties = MLModelGroupPropertiesClass
|
||||
MLModelProperties = MLModelPropertiesClass
|
||||
MLPrimaryKeyProperties = MLPrimaryKeyPropertiesClass
|
||||
Metrics = MetricsClass
|
||||
QuantitativeAnalyses = QuantitativeAnalysesClass
|
||||
SourceCode = SourceCodeClass
|
||||
SourceCodeUrl = SourceCodeUrlClass
|
||||
SourceCodeUrlType = SourceCodeUrlTypeClass
|
||||
TrainingData = TrainingDataClass
|
||||
# fmt: on
|
||||
@ -1,17 +0,0 @@
|
||||
# flake8: noqa
|
||||
|
||||
# This file is autogenerated by /metadata-ingestion/scripts/avro_codegen.py
|
||||
# Do not modify manually!
|
||||
|
||||
# fmt: off
|
||||
from .....schema_classes import GenericAspectClass
|
||||
from .....schema_classes import MetadataChangeEventClass
|
||||
from .....schema_classes import MetadataChangeProposalClass
|
||||
from .....schema_classes import SystemMetadataClass
|
||||
|
||||
|
||||
GenericAspect = GenericAspectClass
|
||||
MetadataChangeEvent = MetadataChangeEventClass
|
||||
MetadataChangeProposal = MetadataChangeProposalClass
|
||||
SystemMetadata = SystemMetadataClass
|
||||
# fmt: on
|
||||
@ -1,15 +0,0 @@
|
||||
# flake8: noqa
|
||||
|
||||
# This file is autogenerated by /metadata-ingestion/scripts/avro_codegen.py
|
||||
# Do not modify manually!
|
||||
|
||||
# fmt: off
|
||||
from .....schema_classes import DataHubActorFilterClass
|
||||
from .....schema_classes import DataHubPolicyInfoClass
|
||||
from .....schema_classes import DataHubResourceFilterClass
|
||||
|
||||
|
||||
DataHubActorFilter = DataHubActorFilterClass
|
||||
DataHubPolicyInfo = DataHubPolicyInfoClass
|
||||
DataHubResourceFilter = DataHubResourceFilterClass
|
||||
# fmt: on
|
||||
@ -1,73 +0,0 @@
|
||||
# flake8: noqa
|
||||
|
||||
# This file is autogenerated by /metadata-ingestion/scripts/avro_codegen.py
|
||||
# Do not modify manually!
|
||||
|
||||
# fmt: off
|
||||
from .....schema_classes import ArrayTypeClass
|
||||
from .....schema_classes import BinaryJsonSchemaClass
|
||||
from .....schema_classes import BooleanTypeClass
|
||||
from .....schema_classes import BytesTypeClass
|
||||
from .....schema_classes import DatasetFieldForeignKeyClass
|
||||
from .....schema_classes import DateTypeClass
|
||||
from .....schema_classes import EditableSchemaFieldInfoClass
|
||||
from .....schema_classes import EditableSchemaMetadataClass
|
||||
from .....schema_classes import EnumTypeClass
|
||||
from .....schema_classes import EspressoSchemaClass
|
||||
from .....schema_classes import FixedTypeClass
|
||||
from .....schema_classes import ForeignKeyConstraintClass
|
||||
from .....schema_classes import ForeignKeySpecClass
|
||||
from .....schema_classes import KafkaSchemaClass
|
||||
from .....schema_classes import KeyValueSchemaClass
|
||||
from .....schema_classes import MapTypeClass
|
||||
from .....schema_classes import MySqlDDLClass
|
||||
from .....schema_classes import NullTypeClass
|
||||
from .....schema_classes import NumberTypeClass
|
||||
from .....schema_classes import OracleDDLClass
|
||||
from .....schema_classes import OrcSchemaClass
|
||||
from .....schema_classes import OtherSchemaClass
|
||||
from .....schema_classes import PrestoDDLClass
|
||||
from .....schema_classes import RecordTypeClass
|
||||
from .....schema_classes import SchemaFieldClass
|
||||
from .....schema_classes import SchemaFieldDataTypeClass
|
||||
from .....schema_classes import SchemaMetadataClass
|
||||
from .....schema_classes import SchemalessClass
|
||||
from .....schema_classes import StringTypeClass
|
||||
from .....schema_classes import TimeTypeClass
|
||||
from .....schema_classes import UnionTypeClass
|
||||
from .....schema_classes import UrnForeignKeyClass
|
||||
|
||||
|
||||
ArrayType = ArrayTypeClass
|
||||
BinaryJsonSchema = BinaryJsonSchemaClass
|
||||
BooleanType = BooleanTypeClass
|
||||
BytesType = BytesTypeClass
|
||||
DatasetFieldForeignKey = DatasetFieldForeignKeyClass
|
||||
DateType = DateTypeClass
|
||||
EditableSchemaFieldInfo = EditableSchemaFieldInfoClass
|
||||
EditableSchemaMetadata = EditableSchemaMetadataClass
|
||||
EnumType = EnumTypeClass
|
||||
EspressoSchema = EspressoSchemaClass
|
||||
FixedType = FixedTypeClass
|
||||
ForeignKeyConstraint = ForeignKeyConstraintClass
|
||||
ForeignKeySpec = ForeignKeySpecClass
|
||||
KafkaSchema = KafkaSchemaClass
|
||||
KeyValueSchema = KeyValueSchemaClass
|
||||
MapType = MapTypeClass
|
||||
MySqlDDL = MySqlDDLClass
|
||||
NullType = NullTypeClass
|
||||
NumberType = NumberTypeClass
|
||||
OracleDDL = OracleDDLClass
|
||||
OrcSchema = OrcSchemaClass
|
||||
OtherSchema = OtherSchemaClass
|
||||
PrestoDDL = PrestoDDLClass
|
||||
RecordType = RecordTypeClass
|
||||
SchemaField = SchemaFieldClass
|
||||
SchemaFieldDataType = SchemaFieldDataTypeClass
|
||||
SchemaMetadata = SchemaMetadataClass
|
||||
Schemaless = SchemalessClass
|
||||
StringType = StringTypeClass
|
||||
TimeType = TimeTypeClass
|
||||
UnionType = UnionTypeClass
|
||||
UrnForeignKey = UrnForeignKeyClass
|
||||
# fmt: on
|
||||
@ -1,11 +0,0 @@
|
||||
# flake8: noqa
|
||||
|
||||
# This file is autogenerated by /metadata-ingestion/scripts/avro_codegen.py
|
||||
# Do not modify manually!
|
||||
|
||||
# fmt: off
|
||||
from .....schema_classes import TagPropertiesClass
|
||||
|
||||
|
||||
TagProperties = TagPropertiesClass
|
||||
# fmt: on
|
||||
@ -1,17 +0,0 @@
|
||||
# flake8: noqa
|
||||
|
||||
# This file is autogenerated by /metadata-ingestion/scripts/avro_codegen.py
|
||||
# Do not modify manually!
|
||||
|
||||
# fmt: off
|
||||
from .....schema_classes import CalendarIntervalClass
|
||||
from .....schema_classes import PartitionSpecClass
|
||||
from .....schema_classes import TimeWindowClass
|
||||
from .....schema_classes import TimeWindowSizeClass
|
||||
|
||||
|
||||
CalendarInterval = CalendarIntervalClass
|
||||
PartitionSpec = PartitionSpecClass
|
||||
TimeWindow = TimeWindowClass
|
||||
TimeWindowSize = TimeWindowSizeClass
|
||||
# fmt: on
|
||||
@ -1,17 +0,0 @@
|
||||
# flake8: noqa
|
||||
|
||||
# This file is autogenerated by /metadata-ingestion/scripts/avro_codegen.py
|
||||
# Do not modify manually!
|
||||
|
||||
# fmt: off
|
||||
from .....schema_classes import FieldUsageCountsClass
|
||||
from .....schema_classes import UsageAggregationClass
|
||||
from .....schema_classes import UsageAggregationMetricsClass
|
||||
from .....schema_classes import UserUsageCountsClass
|
||||
|
||||
|
||||
FieldUsageCounts = FieldUsageCountsClass
|
||||
UsageAggregation = UsageAggregationClass
|
||||
UsageAggregationMetrics = UsageAggregationMetricsClass
|
||||
UserUsageCounts = UserUsageCountsClass
|
||||
# fmt: on
|
||||
File diff suppressed because it is too large
Load Diff
File diff suppressed because it is too large
Load Diff
@ -1,297 +0,0 @@
|
||||
{
|
||||
"type": "record",
|
||||
"name": "DatasetProfile",
|
||||
"namespace": "com.linkedin.pegasus2avro.dataset",
|
||||
"doc": "Stats corresponding to datasets",
|
||||
"fields": [
|
||||
{
|
||||
"name": "timestampMillis",
|
||||
"type": "long",
|
||||
"doc": "The event timestamp field as epoch at UTC in milli seconds."
|
||||
},
|
||||
{
|
||||
"name": "eventGranularity",
|
||||
"type": [
|
||||
"null",
|
||||
{
|
||||
"type": "record",
|
||||
"name": "TimeWindowSize",
|
||||
"namespace": "com.linkedin.pegasus2avro.timeseries",
|
||||
"doc": "Defines the size of a time window.",
|
||||
"fields": [
|
||||
{
|
||||
"name": "unit",
|
||||
"type": {
|
||||
"type": "enum",
|
||||
"name": "CalendarInterval",
|
||||
"symbols": [
|
||||
"SECOND",
|
||||
"MINUTE",
|
||||
"HOUR",
|
||||
"DAY",
|
||||
"WEEK",
|
||||
"MONTH",
|
||||
"QUARTER",
|
||||
"YEAR"
|
||||
]
|
||||
},
|
||||
"doc": "Interval unit such as minute/hour/day etc."
|
||||
},
|
||||
{
|
||||
"name": "multiple",
|
||||
"type": "int",
|
||||
"doc": "How many units. Defaults to 1.",
|
||||
"default": 1
|
||||
}
|
||||
]
|
||||
}
|
||||
],
|
||||
"doc": "Granularity of the event if applicable",
|
||||
"default": null
|
||||
},
|
||||
{
|
||||
"name": "partitionSpec",
|
||||
"type": [
|
||||
"null",
|
||||
{
|
||||
"type": "record",
|
||||
"name": "PartitionSpec",
|
||||
"namespace": "com.linkedin.pegasus2avro.timeseries",
|
||||
"doc": "Defines how the data is partitioned",
|
||||
"fields": [
|
||||
{
|
||||
"name": "partition",
|
||||
"type": "string",
|
||||
"doc": "String representation of the partition"
|
||||
},
|
||||
{
|
||||
"name": "timePartition",
|
||||
"type": [
|
||||
"null",
|
||||
{
|
||||
"type": "record",
|
||||
"name": "TimeWindow",
|
||||
"fields": [
|
||||
{
|
||||
"name": "startTimeMillis",
|
||||
"type": "long",
|
||||
"doc": "Start time as epoch at UTC."
|
||||
},
|
||||
{
|
||||
"name": "length",
|
||||
"type": "TimeWindowSize",
|
||||
"doc": "The length of the window."
|
||||
}
|
||||
]
|
||||
}
|
||||
],
|
||||
"doc": "Time window of the partition if applicable",
|
||||
"default": null
|
||||
}
|
||||
]
|
||||
}
|
||||
],
|
||||
"doc": "The optional partition specification.",
|
||||
"default": null
|
||||
},
|
||||
{
|
||||
"name": "rowCount",
|
||||
"type": [
|
||||
"null",
|
||||
"long"
|
||||
],
|
||||
"default": null
|
||||
},
|
||||
{
|
||||
"name": "columnCount",
|
||||
"type": [
|
||||
"null",
|
||||
"long"
|
||||
],
|
||||
"default": null
|
||||
},
|
||||
{
|
||||
"name": "fieldProfiles",
|
||||
"type": [
|
||||
"null",
|
||||
{
|
||||
"type": "array",
|
||||
"items": {
|
||||
"type": "record",
|
||||
"name": "DatasetFieldProfile",
|
||||
"doc": "Stats corresponding to fields in a dataset",
|
||||
"fields": [
|
||||
{
|
||||
"name": "fieldPath",
|
||||
"type": "string"
|
||||
},
|
||||
{
|
||||
"name": "uniqueCount",
|
||||
"type": [
|
||||
"null",
|
||||
"long"
|
||||
],
|
||||
"default": null
|
||||
},
|
||||
{
|
||||
"name": "uniqueProportion",
|
||||
"type": [
|
||||
"null",
|
||||
"float"
|
||||
],
|
||||
"default": null
|
||||
},
|
||||
{
|
||||
"name": "nullCount",
|
||||
"type": [
|
||||
"null",
|
||||
"long"
|
||||
],
|
||||
"default": null
|
||||
},
|
||||
{
|
||||
"name": "nullProportion",
|
||||
"type": [
|
||||
"null",
|
||||
"float"
|
||||
],
|
||||
"default": null
|
||||
},
|
||||
{
|
||||
"name": "min",
|
||||
"type": [
|
||||
"null",
|
||||
"string"
|
||||
],
|
||||
"default": null
|
||||
},
|
||||
{
|
||||
"name": "max",
|
||||
"type": [
|
||||
"null",
|
||||
"string"
|
||||
],
|
||||
"default": null
|
||||
},
|
||||
{
|
||||
"name": "mean",
|
||||
"type": [
|
||||
"null",
|
||||
"string"
|
||||
],
|
||||
"default": null
|
||||
},
|
||||
{
|
||||
"name": "median",
|
||||
"type": [
|
||||
"null",
|
||||
"string"
|
||||
],
|
||||
"default": null
|
||||
},
|
||||
{
|
||||
"name": "stdev",
|
||||
"type": [
|
||||
"null",
|
||||
"string"
|
||||
],
|
||||
"default": null
|
||||
},
|
||||
{
|
||||
"name": "quantiles",
|
||||
"type": [
|
||||
"null",
|
||||
{
|
||||
"type": "array",
|
||||
"items": {
|
||||
"type": "record",
|
||||
"name": "Quantile",
|
||||
"fields": [
|
||||
{
|
||||
"name": "quantile",
|
||||
"type": "string"
|
||||
},
|
||||
{
|
||||
"name": "value",
|
||||
"type": "string"
|
||||
}
|
||||
]
|
||||
}
|
||||
}
|
||||
],
|
||||
"default": null
|
||||
},
|
||||
{
|
||||
"name": "distinctValueFrequencies",
|
||||
"type": [
|
||||
"null",
|
||||
{
|
||||
"type": "array",
|
||||
"items": {
|
||||
"type": "record",
|
||||
"name": "ValueFrequency",
|
||||
"fields": [
|
||||
{
|
||||
"name": "value",
|
||||
"type": "string"
|
||||
},
|
||||
{
|
||||
"name": "frequency",
|
||||
"type": "long"
|
||||
}
|
||||
]
|
||||
}
|
||||
}
|
||||
],
|
||||
"default": null
|
||||
},
|
||||
{
|
||||
"name": "histogram",
|
||||
"type": [
|
||||
"null",
|
||||
{
|
||||
"type": "record",
|
||||
"name": "Histogram",
|
||||
"fields": [
|
||||
{
|
||||
"name": "boundaries",
|
||||
"type": {
|
||||
"type": "array",
|
||||
"items": "string"
|
||||
}
|
||||
},
|
||||
{
|
||||
"name": "heights",
|
||||
"type": {
|
||||
"type": "array",
|
||||
"items": "float"
|
||||
}
|
||||
}
|
||||
]
|
||||
}
|
||||
],
|
||||
"default": null
|
||||
},
|
||||
{
|
||||
"name": "sampleValues",
|
||||
"type": [
|
||||
"null",
|
||||
{
|
||||
"type": "array",
|
||||
"items": "string"
|
||||
}
|
||||
],
|
||||
"default": null
|
||||
}
|
||||
]
|
||||
}
|
||||
}
|
||||
],
|
||||
"default": null
|
||||
}
|
||||
],
|
||||
"Aspect": {
|
||||
"name": "datasetProfile",
|
||||
"type": "timeseries"
|
||||
}
|
||||
}
|
||||
@ -1,212 +0,0 @@
|
||||
{
|
||||
"type": "record",
|
||||
"name": "DatasetUsageStatistics",
|
||||
"namespace": "com.linkedin.pegasus2avro.dataset",
|
||||
"doc": "Stats corresponding to dataset's usage.",
|
||||
"fields": [
|
||||
{
|
||||
"name": "timestampMillis",
|
||||
"type": "long",
|
||||
"doc": "The event timestamp field as epoch at UTC in milli seconds."
|
||||
},
|
||||
{
|
||||
"name": "eventGranularity",
|
||||
"type": [
|
||||
"null",
|
||||
{
|
||||
"type": "record",
|
||||
"name": "TimeWindowSize",
|
||||
"namespace": "com.linkedin.pegasus2avro.timeseries",
|
||||
"doc": "Defines the size of a time window.",
|
||||
"fields": [
|
||||
{
|
||||
"name": "unit",
|
||||
"type": {
|
||||
"type": "enum",
|
||||
"name": "CalendarInterval",
|
||||
"symbols": [
|
||||
"SECOND",
|
||||
"MINUTE",
|
||||
"HOUR",
|
||||
"DAY",
|
||||
"WEEK",
|
||||
"MONTH",
|
||||
"QUARTER",
|
||||
"YEAR"
|
||||
]
|
||||
},
|
||||
"doc": "Interval unit such as minute/hour/day etc."
|
||||
},
|
||||
{
|
||||
"name": "multiple",
|
||||
"type": "int",
|
||||
"doc": "How many units. Defaults to 1.",
|
||||
"default": 1
|
||||
}
|
||||
]
|
||||
}
|
||||
],
|
||||
"doc": "Granularity of the event if applicable",
|
||||
"default": null
|
||||
},
|
||||
{
|
||||
"name": "partitionSpec",
|
||||
"type": [
|
||||
"null",
|
||||
{
|
||||
"type": "record",
|
||||
"name": "PartitionSpec",
|
||||
"namespace": "com.linkedin.pegasus2avro.timeseries",
|
||||
"doc": "Defines how the data is partitioned",
|
||||
"fields": [
|
||||
{
|
||||
"name": "partition",
|
||||
"type": "string",
|
||||
"doc": "String representation of the partition"
|
||||
},
|
||||
{
|
||||
"name": "timePartition",
|
||||
"type": [
|
||||
"null",
|
||||
{
|
||||
"type": "record",
|
||||
"name": "TimeWindow",
|
||||
"fields": [
|
||||
{
|
||||
"name": "startTimeMillis",
|
||||
"type": "long",
|
||||
"doc": "Start time as epoch at UTC."
|
||||
},
|
||||
{
|
||||
"name": "length",
|
||||
"type": "TimeWindowSize",
|
||||
"doc": "The length of the window."
|
||||
}
|
||||
]
|
||||
}
|
||||
],
|
||||
"doc": "Time window of the partition if applicable",
|
||||
"default": null
|
||||
}
|
||||
]
|
||||
}
|
||||
],
|
||||
"doc": "The optional partition specification.",
|
||||
"default": null
|
||||
},
|
||||
{
|
||||
"name": "uniqueUserCount",
|
||||
"type": [
|
||||
"null",
|
||||
"int"
|
||||
],
|
||||
"doc": "Unique user count",
|
||||
"default": null,
|
||||
"TimeseriesField": {}
|
||||
},
|
||||
{
|
||||
"name": "totalSqlQueries",
|
||||
"type": [
|
||||
"null",
|
||||
"int"
|
||||
],
|
||||
"doc": "Total SQL query count",
|
||||
"default": null,
|
||||
"TimeseriesField": {}
|
||||
},
|
||||
{
|
||||
"name": "topSqlQueries",
|
||||
"type": [
|
||||
"null",
|
||||
{
|
||||
"type": "array",
|
||||
"items": "string"
|
||||
}
|
||||
],
|
||||
"doc": "Frequent SQL queries; mostly makes sense for datasets in SQL databases",
|
||||
"default": null,
|
||||
"TimeseriesField": {}
|
||||
},
|
||||
{
|
||||
"name": "userCounts",
|
||||
"type": [
|
||||
"null",
|
||||
{
|
||||
"type": "array",
|
||||
"items": {
|
||||
"type": "record",
|
||||
"name": "DatasetUserUsageCounts",
|
||||
"doc": "Records a single user's usage counts for a given resource",
|
||||
"fields": [
|
||||
{
|
||||
"name": "user",
|
||||
"type": "string",
|
||||
"doc": "The unique id of the user.",
|
||||
"java": {
|
||||
"class": "com.linkedin.pegasus2avro.common.urn.Urn"
|
||||
}
|
||||
},
|
||||
{
|
||||
"name": "count",
|
||||
"type": "int",
|
||||
"doc": "Number of times the dataset has been used by the user.",
|
||||
"TimeseriesField": {}
|
||||
},
|
||||
{
|
||||
"name": "userEmail",
|
||||
"type": [
|
||||
"null",
|
||||
"string"
|
||||
],
|
||||
"doc": "If user_email is set, we attempt to resolve the user's urn upon ingest",
|
||||
"default": null,
|
||||
"TimeseriesField": {}
|
||||
}
|
||||
]
|
||||
}
|
||||
}
|
||||
],
|
||||
"doc": "Users within this bucket, with frequency counts",
|
||||
"default": null,
|
||||
"TimeseriesFieldCollection": {
|
||||
"key": "user"
|
||||
}
|
||||
},
|
||||
{
|
||||
"name": "fieldCounts",
|
||||
"type": [
|
||||
"null",
|
||||
{
|
||||
"type": "array",
|
||||
"items": {
|
||||
"type": "record",
|
||||
"name": "DatasetFieldUsageCounts",
|
||||
"doc": "Records field-level usage counts for a given dataset",
|
||||
"fields": [
|
||||
{
|
||||
"name": "fieldPath",
|
||||
"type": "string",
|
||||
"doc": "The name of the field."
|
||||
},
|
||||
{
|
||||
"name": "count",
|
||||
"type": "int",
|
||||
"doc": "Number of times the field has been used.",
|
||||
"TimeseriesField": {}
|
||||
}
|
||||
]
|
||||
}
|
||||
}
|
||||
],
|
||||
"doc": "Field-level usage stats",
|
||||
"default": null,
|
||||
"TimeseriesFieldCollection": {
|
||||
"key": "fieldPath"
|
||||
}
|
||||
}
|
||||
],
|
||||
"Aspect": {
|
||||
"name": "datasetUsageStatistics",
|
||||
"type": "timeseries"
|
||||
}
|
||||
}
|
||||
File diff suppressed because it is too large
Load Diff
@ -1,222 +0,0 @@
|
||||
{
|
||||
"type": "record",
|
||||
"name": "MetadataChangeProposal",
|
||||
"namespace": "com.linkedin.pegasus2avro.mxe",
|
||||
"doc": "Kafka event for proposing a metadata change for an entity. A corresponding MetadataChangeLog is emitted when the change is accepted and committed, otherwise a FailedMetadataChangeProposal will be emitted instead.",
|
||||
"fields": [
|
||||
{
|
||||
"name": "auditHeader",
|
||||
"type": [
|
||||
"null",
|
||||
{
|
||||
"type": "record",
|
||||
"name": "KafkaAuditHeader",
|
||||
"namespace": "com.linkedin.events",
|
||||
"doc": "This header records information about the context of an event as it is emitted into kafka and is intended to be used by the kafka audit application. For more information see go/kafkaauditheader",
|
||||
"fields": [
|
||||
{
|
||||
"name": "time",
|
||||
"type": "long",
|
||||
"doc": "The time at which the event was emitted into kafka.",
|
||||
"compliance": [
|
||||
{
|
||||
"policy": "EVENT_TIME"
|
||||
}
|
||||
]
|
||||
},
|
||||
{
|
||||
"name": "server",
|
||||
"type": "string",
|
||||
"doc": "The fully qualified name of the host from which the event is being emitted.",
|
||||
"compliance": "NONE"
|
||||
},
|
||||
{
|
||||
"name": "instance",
|
||||
"type": [
|
||||
"null",
|
||||
"string"
|
||||
],
|
||||
"doc": "The instance on the server from which the event is being emitted. e.g. i001",
|
||||
"default": null,
|
||||
"compliance": "NONE"
|
||||
},
|
||||
{
|
||||
"name": "appName",
|
||||
"type": "string",
|
||||
"doc": "The name of the application from which the event is being emitted. see go/appname",
|
||||
"compliance": "NONE"
|
||||
},
|
||||
{
|
||||
"name": "messageId",
|
||||
"type": {
|
||||
"type": "fixed",
|
||||
"name": "UUID",
|
||||
"size": 16
|
||||
},
|
||||
"doc": "A unique identifier for the message",
|
||||
"compliance": "NONE"
|
||||
},
|
||||
{
|
||||
"name": "auditVersion",
|
||||
"type": [
|
||||
"null",
|
||||
"int"
|
||||
],
|
||||
"doc": "The version that is being used for auditing. In version 0, the audit trail buckets events into 10 minute audit windows based on the EventHeader timestamp. In version 1, the audit trail buckets events as follows: if the schema has an outer KafkaAuditHeader, use the outer audit header timestamp for bucketing; else if the EventHeader has an inner KafkaAuditHeader use that inner audit header's timestamp for bucketing",
|
||||
"default": null,
|
||||
"compliance": "NONE"
|
||||
},
|
||||
{
|
||||
"name": "fabricUrn",
|
||||
"type": [
|
||||
"null",
|
||||
"string"
|
||||
],
|
||||
"doc": "The fabricUrn of the host from which the event is being emitted. Fabric Urn in the format of urn:li:fabric:{fabric_name}. See go/fabric.",
|
||||
"default": null,
|
||||
"compliance": "NONE"
|
||||
},
|
||||
{
|
||||
"name": "clusterConnectionString",
|
||||
"type": [
|
||||
"null",
|
||||
"string"
|
||||
],
|
||||
"doc": "This is a String that the client uses to establish some kind of connection with the Kafka cluster. The exact format of it depends on specific versions of clients and brokers. This information could potentially identify the fabric and cluster with which the client is producing to or consuming from.",
|
||||
"default": null,
|
||||
"compliance": "NONE"
|
||||
}
|
||||
]
|
||||
}
|
||||
],
|
||||
"doc": "Kafka audit header. See go/kafkaauditheader for more info.",
|
||||
"default": null
|
||||
},
|
||||
{
|
||||
"name": "entityType",
|
||||
"type": "string",
|
||||
"doc": "Type of the entity being written to"
|
||||
},
|
||||
{
|
||||
"name": "entityUrn",
|
||||
"type": [
|
||||
"null",
|
||||
"string"
|
||||
],
|
||||
"doc": "Urn of the entity being written\n",
|
||||
"default": null,
|
||||
"java": {
|
||||
"class": "com.linkedin.pegasus2avro.common.urn.Urn"
|
||||
}
|
||||
},
|
||||
{
|
||||
"name": "entityKeyAspect",
|
||||
"type": [
|
||||
"null",
|
||||
{
|
||||
"type": "record",
|
||||
"name": "GenericAspect",
|
||||
"doc": "Generic record structure for serializing an Aspect\n",
|
||||
"fields": [
|
||||
{
|
||||
"name": "value",
|
||||
"type": "bytes"
|
||||
},
|
||||
{
|
||||
"name": "contentType",
|
||||
"type": "string"
|
||||
}
|
||||
]
|
||||
}
|
||||
],
|
||||
"doc": "Key aspect of the entity being written",
|
||||
"default": null
|
||||
},
|
||||
{
|
||||
"name": "changeType",
|
||||
"type": {
|
||||
"type": "enum",
|
||||
"name": "ChangeType",
|
||||
"namespace": "com.linkedin.pegasus2avro.events.metadata",
|
||||
"doc": "Descriptor for a change action",
|
||||
"symbols": [
|
||||
"UPSERT",
|
||||
"CREATE",
|
||||
"UPDATE",
|
||||
"DELETE",
|
||||
"PATCH"
|
||||
],
|
||||
"symbolDocs": {
|
||||
"CREATE": "NOT SUPPORTED YET\ninsert if not exists. otherwise fail",
|
||||
"DELETE": "NOT SUPPORTED YET\ndelete action",
|
||||
"PATCH": "NOT SUPPORTED YET\npatch the changes instead of full replace",
|
||||
"UPDATE": "NOT SUPPORTED YET\nupdate if exists. otherwise fail",
|
||||
"UPSERT": "insert if not exists. otherwise update"
|
||||
}
|
||||
},
|
||||
"doc": "Type of change being proposed"
|
||||
},
|
||||
{
|
||||
"name": "aspectName",
|
||||
"type": [
|
||||
"null",
|
||||
"string"
|
||||
],
|
||||
"doc": "Aspect of the entity being written to\nNot filling this out implies that the writer wants to affect the entire entity\nNote: This is only valid for CREATE and DELETE operations.\n",
|
||||
"default": null
|
||||
},
|
||||
{
|
||||
"name": "aspect",
|
||||
"type": [
|
||||
"null",
|
||||
"GenericAspect"
|
||||
],
|
||||
"default": null
|
||||
},
|
||||
{
|
||||
"name": "systemMetadata",
|
||||
"type": [
|
||||
"null",
|
||||
{
|
||||
"type": "record",
|
||||
"name": "SystemMetadata",
|
||||
"doc": "Kafka event for proposing a metadata change for an entity. A corresponding MetadataAuditEvent is emitted when the change is accepted and committed, otherwise a FailedMetadataChangeEvent will be emitted instead.",
|
||||
"fields": [
|
||||
{
|
||||
"name": "lastObserved",
|
||||
"type": [
|
||||
"long",
|
||||
"null"
|
||||
],
|
||||
"doc": "The timestamp the metadata was observed at",
|
||||
"default": 0
|
||||
},
|
||||
{
|
||||
"name": "runId",
|
||||
"type": [
|
||||
"string",
|
||||
"null"
|
||||
],
|
||||
"doc": "The run id that produced the metadata",
|
||||
"default": "no-run-id-provided"
|
||||
},
|
||||
{
|
||||
"name": "properties",
|
||||
"type": [
|
||||
"null",
|
||||
{
|
||||
"type": "map",
|
||||
"values": "string"
|
||||
}
|
||||
],
|
||||
"doc": "Additional properties",
|
||||
"default": null
|
||||
}
|
||||
]
|
||||
}
|
||||
],
|
||||
"doc": "A string->string map of custom properties that one might want to attach to an event\n",
|
||||
"default": null
|
||||
}
|
||||
]
|
||||
}
|
||||
@ -1,147 +0,0 @@
|
||||
{
|
||||
"type": "record",
|
||||
"name": "UsageAggregation",
|
||||
"namespace": "com.linkedin.pegasus2avro.usage",
|
||||
"doc": "Usage data for a given resource, rolled up into a bucket.",
|
||||
"fields": [
|
||||
{
|
||||
"name": "bucket",
|
||||
"type": "long",
|
||||
"doc": " Bucket start time in milliseconds "
|
||||
},
|
||||
{
|
||||
"name": "duration",
|
||||
"type": {
|
||||
"type": "enum",
|
||||
"name": "WindowDuration",
|
||||
"namespace": "com.linkedin.pegasus2avro.common",
|
||||
"doc": "Enum to define the length of a bucket when doing aggregations",
|
||||
"symbols": [
|
||||
"YEAR",
|
||||
"MONTH",
|
||||
"WEEK",
|
||||
"DAY",
|
||||
"HOUR"
|
||||
]
|
||||
},
|
||||
"doc": " Bucket duration "
|
||||
},
|
||||
{
|
||||
"name": "resource",
|
||||
"type": "string",
|
||||
"doc": " Resource associated with these usage stats ",
|
||||
"java": {
|
||||
"class": "com.linkedin.pegasus2avro.common.urn.Urn"
|
||||
}
|
||||
},
|
||||
{
|
||||
"name": "metrics",
|
||||
"type": {
|
||||
"type": "record",
|
||||
"name": "UsageAggregationMetrics",
|
||||
"doc": "Metrics for usage data for a given resource and bucket. Not all fields\nmake sense for all buckets, so every field is optional.",
|
||||
"fields": [
|
||||
{
|
||||
"name": "uniqueUserCount",
|
||||
"type": [
|
||||
"null",
|
||||
"int"
|
||||
],
|
||||
"doc": " Unique user count ",
|
||||
"default": null
|
||||
},
|
||||
{
|
||||
"name": "users",
|
||||
"type": [
|
||||
"null",
|
||||
{
|
||||
"type": "array",
|
||||
"items": {
|
||||
"type": "record",
|
||||
"name": "UserUsageCounts",
|
||||
"doc": " Records a single user's usage counts for a given resource ",
|
||||
"fields": [
|
||||
{
|
||||
"name": "user",
|
||||
"type": [
|
||||
"null",
|
||||
"string"
|
||||
],
|
||||
"default": null,
|
||||
"java": {
|
||||
"class": "com.linkedin.pegasus2avro.common.urn.Urn"
|
||||
}
|
||||
},
|
||||
{
|
||||
"name": "count",
|
||||
"type": "int"
|
||||
},
|
||||
{
|
||||
"name": "userEmail",
|
||||
"type": [
|
||||
"null",
|
||||
"string"
|
||||
],
|
||||
"doc": " If user_email is set, we attempt to resolve the user's urn upon ingest ",
|
||||
"default": null
|
||||
}
|
||||
]
|
||||
}
|
||||
}
|
||||
],
|
||||
"doc": " Users within this bucket, with frequency counts ",
|
||||
"default": null
|
||||
},
|
||||
{
|
||||
"name": "totalSqlQueries",
|
||||
"type": [
|
||||
"null",
|
||||
"int"
|
||||
],
|
||||
"doc": " Total SQL query count ",
|
||||
"default": null
|
||||
},
|
||||
{
|
||||
"name": "topSqlQueries",
|
||||
"type": [
|
||||
"null",
|
||||
{
|
||||
"type": "array",
|
||||
"items": "string"
|
||||
}
|
||||
],
|
||||
"doc": " Frequent SQL queries; mostly makes sense for datasets in SQL databases ",
|
||||
"default": null
|
||||
},
|
||||
{
|
||||
"name": "fields",
|
||||
"type": [
|
||||
"null",
|
||||
{
|
||||
"type": "array",
|
||||
"items": {
|
||||
"type": "record",
|
||||
"name": "FieldUsageCounts",
|
||||
"doc": " Records field-level usage counts for a given resource ",
|
||||
"fields": [
|
||||
{
|
||||
"name": "fieldName",
|
||||
"type": "string"
|
||||
},
|
||||
{
|
||||
"name": "count",
|
||||
"type": "int"
|
||||
}
|
||||
]
|
||||
}
|
||||
}
|
||||
],
|
||||
"doc": " Field-level usage stats ",
|
||||
"default": null
|
||||
}
|
||||
]
|
||||
},
|
||||
"doc": " Metrics associated with this bucket "
|
||||
}
|
||||
]
|
||||
}
|
||||
@ -1,325 +0,0 @@
|
||||
# flake8: noqa
|
||||
|
||||
# This file is autogenerated by /metadata-ingestion/scripts/avro_codegen.py
|
||||
# Do not modify manually!
|
||||
|
||||
# fmt: off
|
||||
|
||||
import functools
|
||||
import pathlib
|
||||
|
||||
def _load_schema(schema_name: str) -> str:
|
||||
return (pathlib.Path(__file__).parent / f"{schema_name}.avsc").read_text()
|
||||
|
||||
@functools.lru_cache(maxsize=None)
|
||||
def getMetadataChangeEventSchema() -> str:
|
||||
return _load_schema("MetadataChangeEvent")
|
||||
|
||||
@functools.lru_cache(maxsize=None)
|
||||
def getMetadataChangeProposalSchema() -> str:
|
||||
return _load_schema("MetadataChangeProposal")
|
||||
|
||||
@functools.lru_cache(maxsize=None)
|
||||
def getUsageAggregationSchema() -> str:
|
||||
return _load_schema("UsageAggregation")
|
||||
|
||||
@functools.lru_cache(maxsize=None)
|
||||
def getChartInfoSchema() -> str:
|
||||
return _load_schema("ChartInfo")
|
||||
|
||||
@functools.lru_cache(maxsize=None)
|
||||
def getChartQuerySchema() -> str:
|
||||
return _load_schema("ChartQuery")
|
||||
|
||||
@functools.lru_cache(maxsize=None)
|
||||
def getEditableChartPropertiesSchema() -> str:
|
||||
return _load_schema("EditableChartProperties")
|
||||
|
||||
@functools.lru_cache(maxsize=None)
|
||||
def getBrowsePathsSchema() -> str:
|
||||
return _load_schema("BrowsePaths")
|
||||
|
||||
@functools.lru_cache(maxsize=None)
|
||||
def getCostSchema() -> str:
|
||||
return _load_schema("Cost")
|
||||
|
||||
@functools.lru_cache(maxsize=None)
|
||||
def getDeprecationSchema() -> str:
|
||||
return _load_schema("Deprecation")
|
||||
|
||||
@functools.lru_cache(maxsize=None)
|
||||
def getGlobalTagsSchema() -> str:
|
||||
return _load_schema("GlobalTags")
|
||||
|
||||
@functools.lru_cache(maxsize=None)
|
||||
def getGlossaryTermsSchema() -> str:
|
||||
return _load_schema("GlossaryTerms")
|
||||
|
||||
@functools.lru_cache(maxsize=None)
|
||||
def getInstitutionalMemorySchema() -> str:
|
||||
return _load_schema("InstitutionalMemory")
|
||||
|
||||
@functools.lru_cache(maxsize=None)
|
||||
def getOwnershipSchema() -> str:
|
||||
return _load_schema("Ownership")
|
||||
|
||||
@functools.lru_cache(maxsize=None)
|
||||
def getStatusSchema() -> str:
|
||||
return _load_schema("Status")
|
||||
|
||||
@functools.lru_cache(maxsize=None)
|
||||
def getDashboardInfoSchema() -> str:
|
||||
return _load_schema("DashboardInfo")
|
||||
|
||||
@functools.lru_cache(maxsize=None)
|
||||
def getEditableDashboardPropertiesSchema() -> str:
|
||||
return _load_schema("EditableDashboardProperties")
|
||||
|
||||
@functools.lru_cache(maxsize=None)
|
||||
def getDataFlowInfoSchema() -> str:
|
||||
return _load_schema("DataFlowInfo")
|
||||
|
||||
@functools.lru_cache(maxsize=None)
|
||||
def getDataJobInfoSchema() -> str:
|
||||
return _load_schema("DataJobInfo")
|
||||
|
||||
@functools.lru_cache(maxsize=None)
|
||||
def getDataJobInputOutputSchema() -> str:
|
||||
return _load_schema("DataJobInputOutput")
|
||||
|
||||
@functools.lru_cache(maxsize=None)
|
||||
def getEditableDataFlowPropertiesSchema() -> str:
|
||||
return _load_schema("EditableDataFlowProperties")
|
||||
|
||||
@functools.lru_cache(maxsize=None)
|
||||
def getEditableDataJobPropertiesSchema() -> str:
|
||||
return _load_schema("EditableDataJobProperties")
|
||||
|
||||
@functools.lru_cache(maxsize=None)
|
||||
def getDataPlatformInfoSchema() -> str:
|
||||
return _load_schema("DataPlatformInfo")
|
||||
|
||||
@functools.lru_cache(maxsize=None)
|
||||
def getDataProcessInfoSchema() -> str:
|
||||
return _load_schema("DataProcessInfo")
|
||||
|
||||
@functools.lru_cache(maxsize=None)
|
||||
def getDatasetDeprecationSchema() -> str:
|
||||
return _load_schema("DatasetDeprecation")
|
||||
|
||||
@functools.lru_cache(maxsize=None)
|
||||
def getDatasetProfileSchema() -> str:
|
||||
return _load_schema("DatasetProfile")
|
||||
|
||||
@functools.lru_cache(maxsize=None)
|
||||
def getDatasetPropertiesSchema() -> str:
|
||||
return _load_schema("DatasetProperties")
|
||||
|
||||
@functools.lru_cache(maxsize=None)
|
||||
def getDatasetUpstreamLineageSchema() -> str:
|
||||
return _load_schema("DatasetUpstreamLineage")
|
||||
|
||||
@functools.lru_cache(maxsize=None)
|
||||
def getDatasetUsageStatisticsSchema() -> str:
|
||||
return _load_schema("DatasetUsageStatistics")
|
||||
|
||||
@functools.lru_cache(maxsize=None)
|
||||
def getEditableDatasetPropertiesSchema() -> str:
|
||||
return _load_schema("EditableDatasetProperties")
|
||||
|
||||
@functools.lru_cache(maxsize=None)
|
||||
def getUpstreamLineageSchema() -> str:
|
||||
return _load_schema("UpstreamLineage")
|
||||
|
||||
@functools.lru_cache(maxsize=None)
|
||||
def getGlossaryNodeInfoSchema() -> str:
|
||||
return _load_schema("GlossaryNodeInfo")
|
||||
|
||||
@functools.lru_cache(maxsize=None)
|
||||
def getGlossaryRelatedTermsSchema() -> str:
|
||||
return _load_schema("GlossaryRelatedTerms")
|
||||
|
||||
@functools.lru_cache(maxsize=None)
|
||||
def getGlossaryTermInfoSchema() -> str:
|
||||
return _load_schema("GlossaryTermInfo")
|
||||
|
||||
@functools.lru_cache(maxsize=None)
|
||||
def getCorpGroupInfoSchema() -> str:
|
||||
return _load_schema("CorpGroupInfo")
|
||||
|
||||
@functools.lru_cache(maxsize=None)
|
||||
def getCorpUserEditableInfoSchema() -> str:
|
||||
return _load_schema("CorpUserEditableInfo")
|
||||
|
||||
@functools.lru_cache(maxsize=None)
|
||||
def getCorpUserInfoSchema() -> str:
|
||||
return _load_schema("CorpUserInfo")
|
||||
|
||||
@functools.lru_cache(maxsize=None)
|
||||
def getGroupMembershipSchema() -> str:
|
||||
return _load_schema("GroupMembership")
|
||||
|
||||
@functools.lru_cache(maxsize=None)
|
||||
def getChartKeySchema() -> str:
|
||||
return _load_schema("ChartKey")
|
||||
|
||||
@functools.lru_cache(maxsize=None)
|
||||
def getCorpGroupKeySchema() -> str:
|
||||
return _load_schema("CorpGroupKey")
|
||||
|
||||
@functools.lru_cache(maxsize=None)
|
||||
def getCorpUserKeySchema() -> str:
|
||||
return _load_schema("CorpUserKey")
|
||||
|
||||
@functools.lru_cache(maxsize=None)
|
||||
def getDashboardKeySchema() -> str:
|
||||
return _load_schema("DashboardKey")
|
||||
|
||||
@functools.lru_cache(maxsize=None)
|
||||
def getDataFlowKeySchema() -> str:
|
||||
return _load_schema("DataFlowKey")
|
||||
|
||||
@functools.lru_cache(maxsize=None)
|
||||
def getDataHubPolicyKeySchema() -> str:
|
||||
return _load_schema("DataHubPolicyKey")
|
||||
|
||||
@functools.lru_cache(maxsize=None)
|
||||
def getDataJobKeySchema() -> str:
|
||||
return _load_schema("DataJobKey")
|
||||
|
||||
@functools.lru_cache(maxsize=None)
|
||||
def getDataPlatformKeySchema() -> str:
|
||||
return _load_schema("DataPlatformKey")
|
||||
|
||||
@functools.lru_cache(maxsize=None)
|
||||
def getDataProcessKeySchema() -> str:
|
||||
return _load_schema("DataProcessKey")
|
||||
|
||||
@functools.lru_cache(maxsize=None)
|
||||
def getDatasetKeySchema() -> str:
|
||||
return _load_schema("DatasetKey")
|
||||
|
||||
@functools.lru_cache(maxsize=None)
|
||||
def getGlossaryNodeKeySchema() -> str:
|
||||
return _load_schema("GlossaryNodeKey")
|
||||
|
||||
@functools.lru_cache(maxsize=None)
|
||||
def getGlossaryTermKeySchema() -> str:
|
||||
return _load_schema("GlossaryTermKey")
|
||||
|
||||
@functools.lru_cache(maxsize=None)
|
||||
def getMLFeatureKeySchema() -> str:
|
||||
return _load_schema("MLFeatureKey")
|
||||
|
||||
@functools.lru_cache(maxsize=None)
|
||||
def getMLFeatureTableKeySchema() -> str:
|
||||
return _load_schema("MLFeatureTableKey")
|
||||
|
||||
@functools.lru_cache(maxsize=None)
|
||||
def getMLModelDeploymentKeySchema() -> str:
|
||||
return _load_schema("MLModelDeploymentKey")
|
||||
|
||||
@functools.lru_cache(maxsize=None)
|
||||
def getMLModelGroupKeySchema() -> str:
|
||||
return _load_schema("MLModelGroupKey")
|
||||
|
||||
@functools.lru_cache(maxsize=None)
|
||||
def getMLModelKeySchema() -> str:
|
||||
return _load_schema("MLModelKey")
|
||||
|
||||
@functools.lru_cache(maxsize=None)
|
||||
def getMLPrimaryKeyKeySchema() -> str:
|
||||
return _load_schema("MLPrimaryKeyKey")
|
||||
|
||||
@functools.lru_cache(maxsize=None)
|
||||
def getSchemaFieldKeySchema() -> str:
|
||||
return _load_schema("SchemaFieldKey")
|
||||
|
||||
@functools.lru_cache(maxsize=None)
|
||||
def getTagKeySchema() -> str:
|
||||
return _load_schema("TagKey")
|
||||
|
||||
@functools.lru_cache(maxsize=None)
|
||||
def getCaveatsAndRecommendationsSchema() -> str:
|
||||
return _load_schema("CaveatsAndRecommendations")
|
||||
|
||||
@functools.lru_cache(maxsize=None)
|
||||
def getEthicalConsiderationsSchema() -> str:
|
||||
return _load_schema("EthicalConsiderations")
|
||||
|
||||
@functools.lru_cache(maxsize=None)
|
||||
def getEvaluationDataSchema() -> str:
|
||||
return _load_schema("EvaluationData")
|
||||
|
||||
@functools.lru_cache(maxsize=None)
|
||||
def getIntendedUseSchema() -> str:
|
||||
return _load_schema("IntendedUse")
|
||||
|
||||
@functools.lru_cache(maxsize=None)
|
||||
def getMLFeaturePropertiesSchema() -> str:
|
||||
return _load_schema("MLFeatureProperties")
|
||||
|
||||
@functools.lru_cache(maxsize=None)
|
||||
def getMLFeatureTablePropertiesSchema() -> str:
|
||||
return _load_schema("MLFeatureTableProperties")
|
||||
|
||||
@functools.lru_cache(maxsize=None)
|
||||
def getMLHyperParamSchema() -> str:
|
||||
return _load_schema("MLHyperParam")
|
||||
|
||||
@functools.lru_cache(maxsize=None)
|
||||
def getMLMetricSchema() -> str:
|
||||
return _load_schema("MLMetric")
|
||||
|
||||
@functools.lru_cache(maxsize=None)
|
||||
def getMLModelDeploymentPropertiesSchema() -> str:
|
||||
return _load_schema("MLModelDeploymentProperties")
|
||||
|
||||
@functools.lru_cache(maxsize=None)
|
||||
def getMLModelFactorPromptsSchema() -> str:
|
||||
return _load_schema("MLModelFactorPrompts")
|
||||
|
||||
@functools.lru_cache(maxsize=None)
|
||||
def getMLModelGroupPropertiesSchema() -> str:
|
||||
return _load_schema("MLModelGroupProperties")
|
||||
|
||||
@functools.lru_cache(maxsize=None)
|
||||
def getMLModelPropertiesSchema() -> str:
|
||||
return _load_schema("MLModelProperties")
|
||||
|
||||
@functools.lru_cache(maxsize=None)
|
||||
def getMLPrimaryKeyPropertiesSchema() -> str:
|
||||
return _load_schema("MLPrimaryKeyProperties")
|
||||
|
||||
@functools.lru_cache(maxsize=None)
|
||||
def getMetricsSchema() -> str:
|
||||
return _load_schema("Metrics")
|
||||
|
||||
@functools.lru_cache(maxsize=None)
|
||||
def getQuantitativeAnalysesSchema() -> str:
|
||||
return _load_schema("QuantitativeAnalyses")
|
||||
|
||||
@functools.lru_cache(maxsize=None)
|
||||
def getSourceCodeSchema() -> str:
|
||||
return _load_schema("SourceCode")
|
||||
|
||||
@functools.lru_cache(maxsize=None)
|
||||
def getTrainingDataSchema() -> str:
|
||||
return _load_schema("TrainingData")
|
||||
|
||||
@functools.lru_cache(maxsize=None)
|
||||
def getDataHubPolicyInfoSchema() -> str:
|
||||
return _load_schema("DataHubPolicyInfo")
|
||||
|
||||
@functools.lru_cache(maxsize=None)
|
||||
def getEditableSchemaMetadataSchema() -> str:
|
||||
return _load_schema("EditableSchemaMetadata")
|
||||
|
||||
@functools.lru_cache(maxsize=None)
|
||||
def getSchemaMetadataSchema() -> str:
|
||||
return _load_schema("SchemaMetadata")
|
||||
|
||||
@functools.lru_cache(maxsize=None)
|
||||
def getTagPropertiesSchema() -> str:
|
||||
return _load_schema("TagProperties")
|
||||
# fmt: on
|
||||
Loading…
x
Reference in New Issue
Block a user