Merge remote-tracking branch 'origin/master+iceberg-release' into ice…

…berg-rest-api

datahub-graphql-core/src/main/java/com/linkedin/datahub/graphql/resolvers/search/SearchUtils.java

@@ -91,7 +91,8 @@ private SearchUtils() {}
           EntityType.DATA_PRODUCT,
           EntityType.NOTEBOOK,
           EntityType.BUSINESS_ATTRIBUTE,
-          EntityType.SCHEMA_FIELD);
+          EntityType.SCHEMA_FIELD,
+          EntityType.DATA_PLATFORM_INSTANCE);
 
   /** Entities that are part of autocomplete by default in Auto Complete Across Entities */
   public static final List<EntityType> AUTO_COMPLETE_ENTITY_TYPES =

datahub-web-react/src/graphql/search.graphql

@@ -482,6 +482,9 @@ fragment searchResultsWithoutSchemaField on Entity {
             }
         }
     }
+    ... on DataPlatformInstance {
+        ...dataPlatformInstanceFields
+    }
     ... on Role {
         id
         properties {

docs/advanced/mcp-mcl.md

@@ -210,6 +210,7 @@ A writer can specify that the aspect must NOT have been modified after a specifi
 `If-Modified-Since`
 A writer can specify that the aspect must have been modified after a specific time, following [If-Modified-Since](https://developer.mozilla.org/en-US/docs/Web/HTTP/Headers/If-Modified-Since) http headers.
 
+
 #### Change Types: [`CREATE`, `CREATE_ENTITY`]
 
 Another form of conditional writes which considers the existence of an aspect or entity uses the following Change Types.
@@ -221,3 +222,8 @@ Another form of conditional writes which considers the existence of an aspect or
 By default, a validation exception is thrown if the `CREATE`/`CREATE_ENTITY` constraint is violated. If the write operation
 should be dropped without considering it an exception, then add the following header: `If-None-Match: *` to the MCP.
 
+### Synchronous ElasticSearch Updates
+
+The writes to the elasticsearch are asynchronous by default. A writer can add a custom header
+`X-DataHub-Sync-Index-Update` to the MCP `headers` with value set to `true` to enable a synchronous update of
+elasticsearch for specific MCPs that may benefit from it.

li-utils/src/main/java/com/linkedin/metadata/Constants.java

@@ -10,6 +10,9 @@ public class Constants {
   public static final String INTERNAL_DELEGATED_FOR_ACTOR_HEADER_NAME = "X-DataHub-Delegated-For";
   public static final String INTERNAL_DELEGATED_FOR_ACTOR_TYPE = "X-DataHub-Delegated-For-";
 
+  // Use on specific MCP to request an synchronous index update avoid the kafka lag.
+  public static final String SYNC_INDEX_UPDATE_HEADER_NAME = "X-DataHub-Sync-Index-Update";
+
   public static final String URN_LI_PREFIX = "urn:li:";
   public static final String DATAHUB_ACTOR = "urn:li:corpuser:datahub"; // Super user.
   public static final String SYSTEM_ACTOR =
@@ -103,6 +106,7 @@ public class Constants {
   public static final String FORM_ENTITY_NAME = "form";
   public static final String RESTRICTED_ENTITY_NAME = "restricted";
   public static final String BUSINESS_ATTRIBUTE_ENTITY_NAME = "businessAttribute";
+  public static final String PLATFORM_RESOURCE_ENTITY_NAME = "platformResource";
 
   /** Aspects */
   // Common
@@ -211,6 +215,9 @@ public class Constants {
   public static final String DATA_PLATFORM_INSTANCE_PROPERTIES_ASPECT_NAME =
       "dataPlatformInstanceProperties";
 
+  // PlatformResource
+  public static final String PLATFORM_RESOURCE_INFO_ASPECT_NAME = "platformResourceInfo";
+
   // ML Feature
   public static final String ML_FEATURE_KEY_ASPECT_NAME = "mlFeatureKey";
   public static final String ML_FEATURE_PROPERTIES_ASPECT_NAME = "mlFeatureProperties";

metadata-auth/auth-api/src/main/java/com/datahub/authorization/AuthUtil.java

@@ -561,8 +561,7 @@ public static DisjunctivePrivilegeGroup buildDisjunctivePrivilegeGroup(
     return buildDisjunctivePrivilegeGroup(lookupAPIPrivilege(apiGroup, apiOperation, entityType));
   }
 
-  @VisibleForTesting
-  static DisjunctivePrivilegeGroup buildDisjunctivePrivilegeGroup(
+  public static DisjunctivePrivilegeGroup buildDisjunctivePrivilegeGroup(
       final Disjunctive<Conjunctive<PoliciesConfig.Privilege>> privileges) {
     return new DisjunctivePrivilegeGroup(
         privileges.stream()

metadata-ingestion/examples/iceberg/constants.py

@@ -0,0 +1,3 @@
+warehouse = "arctic_warehouse"
+namespace = "alpine_db"
+table_name = "resort_metrics"

metadata-ingestion/examples/iceberg/create_table.py

@@ -0,0 +1,108 @@
+from datetime import datetime
+
+import pyarrow as pa
+import pyiceberg
+from constants import namespace, table_name, warehouse
+from pyiceberg.catalog import load_catalog
+from pyiceberg.schema import Schema
+from pyiceberg.types import LongType, NestedField, StringType, TimestampType
+
+from datahub.ingestion.graph.client import get_default_graph
+
+# Define a more comprehensive schema for ski resort data
+schema = Schema(
+    NestedField(
+        field_id=1,
+        name="resort_id",
+        field_type=LongType(),
+        required=True,
+        doc="Unique identifier for each ski resort",
+        initial_default=None,
+    ),
+    NestedField(
+        field_id=2,
+        name="resort_name",
+        field_type=StringType(),
+        required=True,
+        doc="Official name of the ski resort",
+        initial_default=None,
+    ),
+    NestedField(
+        field_id=3,
+        name="daily_snowfall",
+        field_type=LongType(),
+        required=False,
+        doc="Amount of new snow in inches during the last 24 hours. Null if no measurement available",
+        initial_default=0,
+    ),
+    NestedField(
+        field_id=4,
+        name="conditions",
+        field_type=StringType(),
+        required=False,
+        doc="Current snow conditions description (e.g., 'Powder', 'Packed Powder', 'Groomed'). Null if not reported",
+        initial_default=None,
+    ),
+    NestedField(
+        field_id=5,
+        name="last_updated",
+        field_type=TimestampType(),
+        required=False,
+        doc="Timestamp of when the snow report was last updated",
+        initial_default=None,
+    ),
+)
+
+# Load the catalog with new warehouse name
+graph = get_default_graph()
+catalog = load_catalog("local_datahub", warehouse=warehouse, token=graph.config.token)
+
+# Create namespace (database)
+try:
+    catalog.create_namespace(namespace)
+except Exception as e:
+    print(f"Namespace creation error (might already exist): {e}")
+
+full_table_name = f"{namespace}.{table_name}"
+try:
+    catalog.create_table(full_table_name, schema)
+except pyiceberg.exceptions.TableAlreadyExistsError:
+    print(f"Table {full_table_name} already exists")
+
+# Create sample data with explicit PyArrow schema to match required fields
+pa_schema = pa.schema(
+    [
+        ("resort_id", pa.int64(), False),  # False means not nullable
+        ("resort_name", pa.string(), False),  # False means not nullable
+        ("daily_snowfall", pa.int64(), True),
+        ("conditions", pa.string(), True),
+        ("last_updated", pa.timestamp("us"), True),
+    ]
+)
+# Create sample data
+sample_data = pa.Table.from_pydict(
+    {
+        "resort_id": [1, 2, 3],
+        "resort_name": ["Snowpeak Resort", "Alpine Valley", "Glacier Heights"],
+        "daily_snowfall": [12, 8, 15],
+        "conditions": ["Powder", "Packed", "Fresh Powder"],
+        "last_updated": [
+            pa.scalar(datetime.now()),
+            pa.scalar(datetime.now()),
+            pa.scalar(datetime.now()),
+        ],
+    },
+    schema=pa_schema,
+)
+
+# Write data to table
+table = catalog.load_table(full_table_name)
+table.overwrite(sample_data)
+
+table.refresh()
+# Read and verify data
+con = table.scan().to_duckdb(table_name=f"{table_name}")
+print("\nResort Metrics Data:")
+print("-" * 50)
+for row in con.execute(f"SELECT * FROM {table_name}").fetchall():
+    print(row)

metadata-ingestion/examples/iceberg/drop_table.py

@@ -0,0 +1,10 @@
+from constants import namespace, table_name, warehouse
+from pyiceberg.catalog import load_catalog
+
+# Load the catalog
+from datahub.ingestion.graph.client import get_default_graph
+
+graph = get_default_graph()
+catalog = load_catalog("local_datahub", warehouse=warehouse, token=graph.config.token)
+# Append the data to the Iceberg table
+catalog.drop_table(f"{namespace}.{table_name}")