feat(ingestion/glue): delta schemas

metadata-ingestion/src/datahub/ingestion/source/aws/glue.py

@@ -1,3 +1,4 @@
+import json
 import logging
 from collections import defaultdict
 from dataclasses import dataclass, field as dataclass_field
@@ -98,6 +99,7 @@
     UpstreamClass,
     UpstreamLineageClass,
 )
+from datahub.utilities.delta import delta_type_to_hive_type
 from datahub.utilities.hive_schema_to_avro import get_schema_fields_for_hive_column
 
 logger = logging.getLogger(__name__)
@@ -161,6 +163,11 @@ class GlueSourceConfig(
     stateful_ingestion: Optional[StatefulStaleMetadataRemovalConfig] = Field(
         default=None, description=""
     )
+    extract_delta_schema_from_parameters: Optional[bool] = Field(
+        default=False,
+        description="If enabled, delta schemas can be alternatively fetched from table parameters "
+        "(https://github.com/delta-io/delta/pull/2310)",
+    )
 
     def is_profiling_enabled(self) -> bool:
         return self.profiling is not None and is_profiling_enabled(
@@ -204,6 +211,8 @@ class GlueSourceReport(StaleEntityRemovalSourceReport):
     num_job_script_failed_parsing: int = 0
     num_job_without_nodes: int = 0
     num_dataset_to_dataset_edges_in_job: int = 0
+    num_dataset_invalid_delta_schema: int = 0
+    num_dataset_valid_delta_schema: int = 0
 
     def report_table_scanned(self) -> None:
         self.tables_scanned += 1
@@ -1147,10 +1156,42 @@ def get_s3_tags() -> Optional[GlobalTagsClass]:
             )
             return new_tags
 
+        def _is_delta_schema(
+            provider: str, num_parts: int, columns: Optional[List[Mapping[str, Any]]]
+        ) -> bool:
+            return (
+                (self.source_config.extract_delta_schema_from_parameters is True)
+                and (provider == "delta")
+                and (num_parts > 0)
+                and (columns is not None)
+                and (len(columns) == 1)
+                and (columns[0].get("Name", "") == "col")
+                and (columns[0].get("Type", "") == "array<string>")
+            )
+
         def get_schema_metadata() -> Optional[SchemaMetadata]:
-            if not table.get("StorageDescriptor"):
+            # As soon as the hive integration with Spark is correctly providing the schema as expected in the
+            # StorageProperties, the alternative path to fetch schema from table parameters can be removed.
+            # https://github.com/datahub-project/datahub/pull/10299
+            # https://github.com/delta-io/delta/pull/2310
+            provider = table.get("Parameters", {}).get("spark.sql.sources.provider", "")
+            num_parts = int(
+                table.get("Parameters", {}).get(
+                    "spark.sql.sources.schema.numParts", "0"
+                )
+            )
+            columns = table.get("StorageDescriptor", {}).get("Columns", [{}])
+
+            if _is_delta_schema(provider, num_parts, columns):
+                return _get_delta_schema_metadata()
+
+            elif table.get("StorageDescriptor"):
+                return _get_glue_schema_metadata()
+
+            else:
                 return None
 
+        def _get_glue_schema_metadata() -> Optional[SchemaMetadata]:
             schema = table["StorageDescriptor"]["Columns"]
             fields: List[SchemaField] = []
             for field in schema:
@@ -1183,6 +1224,51 @@ def get_schema_metadata() -> Optional[SchemaMetadata]:
                 platformSchema=MySqlDDL(tableSchema=""),
             )
 
+        def _get_delta_schema_metadata() -> Optional[SchemaMetadata]:
+            assert (
+                table["Parameters"]["spark.sql.sources.provider"] == "delta"
+                and int(table["Parameters"]["spark.sql.sources.schema.numParts"]) > 0
+            )
+
+            try:
+                numParts = int(table["Parameters"]["spark.sql.sources.schema.numParts"])
+                schema_str = "".join(
+                    [
+                        table["Parameters"][f"spark.sql.sources.schema.part.{i}"]
+                        for i in range(numParts)
+                    ]
+                )
+                schema_json = json.loads(schema_str)
+                fields: List[SchemaField] = []
+                for field in schema_json["fields"]:
+                    field_type = delta_type_to_hive_type(field.get("type", "unknown"))
+                    schema_fields = get_schema_fields_for_hive_column(
+                        hive_column_name=field["name"],
+                        hive_column_type=field_type,
+                        description=field.get("description"),
+                        default_nullable=bool(field.get("nullable", True)),
+                    )
+                    assert schema_fields
+                    fields.extend(schema_fields)
+
+                self.report.num_dataset_valid_delta_schema += 1
+                return SchemaMetadata(
+                    schemaName=table_name,
+                    version=0,
+                    fields=fields,
+                    platform=f"urn:li:dataPlatform:{self.platform}",
+                    hash="",
+                    platformSchema=MySqlDDL(tableSchema=""),
+                )
+
+            except Exception as e:
+                self.report_warning(
+                    dataset_urn,
+                    f"Could not parse schema for {table_name} because of {type(e).__name__}: {e}",
+                )
+                self.report.num_dataset_invalid_delta_schema += 1
+                return None
+
         def get_data_platform_instance() -> DataPlatformInstanceClass:
             return DataPlatformInstanceClass(
                 platform=make_data_platform_urn(self.platform),

metadata-ingestion/src/datahub/ingestion/source/delta_lake/source.py

@@ -2,7 +2,7 @@
 import logging
 import os
 import time
-from typing import Any, Dict, Iterable, List
+from typing import Dict, Iterable, List
 from urllib.parse import urlparse
 
 from deltalake import DeltaTable
@@ -51,6 +51,7 @@
     SchemaFieldClass,
 )
 from datahub.telemetry import telemetry
+from datahub.utilities.delta import delta_type_to_hive_type
 from datahub.utilities.hive_schema_to_avro import get_schema_fields_for_hive_column
 
 logging.getLogger("py4j").setLevel(logging.ERROR)
@@ -126,46 +127,12 @@ def create(cls, config_dict: dict, ctx: PipelineContext) -> "Source":
         config = DeltaLakeSourceConfig.parse_obj(config_dict)
         return cls(config, ctx)
 
-    def delta_type_to_hive_type(self, field_type: Any) -> str:
-        if isinstance(field_type, str):
-            """
-            return the field type
-            """
-            return field_type
-        else:
-            if field_type.get("type") == "array":
-                """
-                if array is of complex type, recursively parse the
-                fields and create the native datatype
-                """
-                return (
-                    "array<"
-                    + self.delta_type_to_hive_type(field_type.get("elementType"))
-                    + ">"
-                )
-            elif field_type.get("type") == "struct":
-                parsed_struct = ""
-                for field in field_type.get("fields"):
-                    """
-                    if field is of complex type, recursively parse
-                    and create the native datatype
-                    """
-                    parsed_struct += (
-                        "{0}:{1}".format(
-                            field.get("name"),
-                            self.delta_type_to_hive_type(field.get("type")),
-                        )
-                        + ","
-                    )
-                return "struct<" + parsed_struct.rstrip(",") + ">"
-            return ""
-
     def _parse_datatype(self, raw_field_json_str: str) -> List[SchemaFieldClass]:
         raw_field_json = json.loads(raw_field_json_str)
 
         # get the parent field name and type
         field_name = raw_field_json.get("name")
-        field_type = self.delta_type_to_hive_type(raw_field_json.get("type"))
+        field_type = delta_type_to_hive_type(raw_field_json.get("type"))
 
         return get_schema_fields_for_hive_column(field_name, field_type)
 

metadata-ingestion/src/datahub/utilities/delta.py

@@ -0,0 +1,34 @@
+from typing import Any
+
+
+def delta_type_to_hive_type(field_type: Any) -> str:
+    if isinstance(field_type, str):
+        """
+        return the field type
+        """
+        return field_type
+    else:
+        if field_type.get("type") == "array":
+            """
+            if array is of complex type, recursively parse the
+            fields and create the native datatype
+            """
+            return (
+                "array<" + delta_type_to_hive_type(field_type.get("elementType")) + ">"
+            )
+        elif field_type.get("type") == "struct":
+            parsed_struct = ""
+            for field in field_type.get("fields"):
+                """
+                if field is of complex type, recursively parse
+                and create the native datatype
+                """
+                parsed_struct += (
+                    "{0}:{1}".format(
+                        field.get("name"),
+                        delta_type_to_hive_type(field.get("type")),
+                    )
+                    + ","
+                )
+            return "struct<" + parsed_struct.rstrip(",") + ">"
+        return ""

metadata-ingestion/src/datahub/utilities/hive_schema_to_avro.py

@@ -28,6 +28,7 @@ class HiveColumnToAvroConverter:
         "bigint": "long",
         "varchar": "string",
         "char": "string",
+        "long": "long",
         "bytes": "bytes",
     }
     _EXTRA_BIGQUERY_TYPE_TO_AVRO_TYPE = {