Add Databricks config to Spark Job

.github/workflows/pythonbuild.yml

@@ -80,7 +80,7 @@ jobs:
           - flytekit-modin
           - flytekit-onnx-pytorch
           - flytekit-onnx-scikitlearn
-          # onnxx-tensorflow needs a version of tensorflow that does not work with protobuf>4.
+          # onnx-tensorflow needs a version of tensorflow that does not work with protobuf>4.
           # The issue is being tracked on the tensorflow side in https://github.com/tensorflow/tensorflow/issues/53234#issuecomment-1330111693
           # flytekit-onnx-tensorflow
           - flytekit-pandera

dev-requirements.txt

@@ -130,7 +130,7 @@ filelock==3.8.2
     # via virtualenv
 flatbuffers==22.12.6
     # via tensorflow
-flyteidl==1.3.0
+flyteidl==1.3.1
     # via
     #   -c requirements.txt
     #   flytekit

doc-requirements.txt

@@ -182,7 +182,7 @@ filelock==3.8.2
     #   virtualenv
 flatbuffers==22.12.6
     # via tensorflow
-flyteidl==1.3.0
+flyteidl==1.3.1
     # via flytekit
 fonttools==4.38.0
     # via matplotlib

plugins/flytekit-spark/flytekitplugins/spark/models.py

@@ -1,7 +1,9 @@
 import enum
-import typing
+from typing import Dict, Optional
 
 from flyteidl.plugins import spark_pb2 as _spark_task
+from google.protobuf import json_format
+from google.protobuf.struct_pb2 import Struct
 
 from flytekit.exceptions import user as _user_exceptions
 from flytekit.models import common as _common
@@ -17,42 +19,60 @@ class SparkType(enum.Enum):
 class SparkJob(_common.FlyteIdlEntity):
     def __init__(
         self,
-        spark_type,
-        application_file,
-        main_class,
-        spark_conf,
-        hadoop_conf,
-        executor_path,
+        spark_type: SparkType,
+        application_file: str,
+        main_class: str,
+        spark_conf: Dict[str, str],
+        hadoop_conf: Dict[str, str],
+        executor_path: str,
+        databricks_conf: Dict[str, Dict[str, Dict]] = {},
+        databricks_token: Optional[str] = None,
+        databricks_instance: Optional[str] = None,
     ):
         """
         This defines a SparkJob target.  It will execute the appropriate SparkJob.
 
         :param application_file: The main application file to execute.
         :param dict[Text, Text] spark_conf: A definition of key-value pairs for spark config for the job.
         :param dict[Text, Text] hadoop_conf: A definition of key-value pairs for hadoop config for the job.
+        :param Optional[dict[Text, dict]] databricks_conf: A definition of key-value pairs for databricks config for the job. Refer to https://docs.databricks.com/dev-tools/api/latest/jobs.html#operation/JobsRunsSubmit.
+        :param Optional[str] databricks_token: databricks access token.
+        :param Optional[str] databricks_instance: Domain name of your deployment. Use the form <account>.cloud.databricks.com.
         """
         self._application_file = application_file
         self._spark_type = spark_type
         self._main_class = main_class
         self._executor_path = executor_path
         self._spark_conf = spark_conf
         self._hadoop_conf = hadoop_conf
+        self._databricks_conf = databricks_conf
+        self._databricks_token = databricks_token
+        self._databricks_instance = databricks_instance
 
     def with_overrides(
-        self, new_spark_conf: typing.Dict[str, str] = None, new_hadoop_conf: typing.Dict[str, str] = None
+        self,
+        new_spark_conf: Optional[Dict[str, str]] = None,
+        new_hadoop_conf: Optional[Dict[str, str]] = None,
+        new_databricks_conf: Optional[Dict[str, Dict]] = None,
     ) -> "SparkJob":
         if not new_spark_conf:
             new_spark_conf = self.spark_conf
 
         if not new_hadoop_conf:
             new_hadoop_conf = self.hadoop_conf
 
+        if not new_databricks_conf:
+            new_databricks_conf = self.databricks_conf
+
         return SparkJob(
             spark_type=self.spark_type,
             application_file=self.application_file,
             main_class=self.main_class,
             spark_conf=new_spark_conf,
             hadoop_conf=new_hadoop_conf,
+            databricks_conf=new_databricks_conf,
+            databricks_token=self.databricks_token,
+            databricks_instance=self.databricks_instance,
             executor_path=self.executor_path,
         )
 
@@ -104,6 +124,31 @@ def hadoop_conf(self):
         """
         return self._hadoop_conf
 
+    @property
+    def databricks_conf(self) -> Dict[str, Dict]:
+        """
+        databricks_conf: Databricks job configuration.
+        Config structure can be found here. https://docs.databricks.com/dev-tools/api/2.0/jobs.html#request-structure
+        :rtype: dict[Text, dict[Text, Text]]
+        """
+        return self._databricks_conf
+
+    @property
+    def databricks_token(self) -> str:
+        """
+        Databricks access token
+        :rtype: str
+        """
+        return self._databricks_token
+
+    @property
+    def databricks_instance(self) -> str:
+        """
+        Domain name of your deployment. Use the form <account>.cloud.databricks.com.
+        :rtype: str
+        """
+        return self._databricks_instance
+
     def to_flyte_idl(self):
         """
         :rtype: flyteidl.plugins.spark_pb2.SparkJob
@@ -120,13 +165,19 @@ def to_flyte_idl(self):
         else:
             raise _user_exceptions.FlyteValidationException("Invalid Spark Application Type Specified")
 
+        databricks_conf = Struct()
+        databricks_conf.update(self.databricks_conf)
+
         return _spark_task.SparkJob(
             applicationType=application_type,
             mainApplicationFile=self.application_file,
             mainClass=self.main_class,
             executorPath=self.executor_path,
             sparkConf=self.spark_conf,
             hadoopConf=self.hadoop_conf,
+            databricksConf=databricks_conf,
+            databricksToken=self.databricks_token,
+            databricksInstance=self.databricks_instance,
         )
 
     @classmethod
@@ -151,4 +202,7 @@ def from_flyte_idl(cls, pb2_object):
             main_class=pb2_object.mainClass,
             hadoop_conf=pb2_object.hadoopConf,
             executor_path=pb2_object.executorPath,
+            databricks_conf=json_format.MessageToDict(pb2_object.databricksConf),
+            databricks_token=pb2_object.databricksToken,
+            databricks_instance=pb2_object.databricksInstance,
         )

plugins/flytekit-spark/flytekitplugins/spark/task.py

@@ -36,6 +36,23 @@ def __post_init__(self):
             self.hadoop_conf = {}
 
 
+@dataclass
+class Databricks(Spark):
+    """
+    Use this to configure a Databricks task. Task's marked with this will automatically execute
+    natively onto databricks platform as a distributed execution of spark
+
+    Args:
+        databricks_conf: Databricks job configuration. Config structure can be found here. https://docs.databricks.com/dev-tools/api/2.0/jobs.html#request-structure
+        databricks_token: Databricks access token. https://docs.databricks.com/dev-tools/api/latest/authentication.html.
+        databricks_instance: Domain name of your deployment. Use the form <account>.cloud.databricks.com.
+    """
+
+    databricks_conf: typing.Optional[Dict[str, typing.Union[str, dict]]] = None
+    databricks_token: Optional[str] = None
+    databricks_instance: Optional[str] = None
+
+
 # This method does not reset the SparkSession since it's a bit hard to handle multiple
 # Spark sessions in a single application as it's described in:
 # https://stackoverflow.com/questions/41491972/how-can-i-tear-down-a-sparksession-and-create-a-new-one-within-one-application.
@@ -100,6 +117,12 @@ def get_custom(self, settings: SerializationSettings) -> Dict[str, Any]:
             main_class="",
             spark_type=SparkType.PYTHON,
         )
+        if isinstance(self.task_config, Databricks):
+            cfg = typing.cast(self.task_config, Databricks)
+            job._databricks_conf = cfg.databricks_conf
+            job._databricks_token = cfg.databricks_token
+            job._databricks_instance = cfg.databricks_instance
+
         return MessageToDict(job.to_flyte_idl())
 
     def pre_execute(self, user_params: ExecutionParameters) -> ExecutionParameters:

plugins/flytekit-spark/requirements.txt

@@ -46,7 +46,7 @@ docker-image-py==0.1.12
     # via flytekit
 docstring-parser==0.15
     # via flytekit
-flyteidl==1.3.0
+flyteidl==1.3.1
     # via flytekit
 flytekit==1.3.0b2
     # via flytekitplugins-spark

plugins/flytekit-spark/tests/test_spark_task.py

@@ -2,7 +2,7 @@
 import pyspark
 import pytest
 from flytekitplugins.spark import Spark
-from flytekitplugins.spark.task import new_spark_session
+from flytekitplugins.spark.task import Databricks, new_spark_session
 from pyspark.sql import SparkSession
 
 import flytekit
@@ -19,6 +19,23 @@ def reset_spark_session() -> None:
 
 
 def test_spark_task(reset_spark_session):
+    databricks_conf = {
+        "name": "flytekit databricks plugin example",
+        "new_cluster": {
+            "spark_version": "11.0.x-scala2.12",
+            "node_type_id": "r3.xlarge",
+            "aws_attributes": {"availability": "ON_DEMAND"},
+            "num_workers": 4,
+            "docker_image": {"url": "pingsutw/databricks:latest"},
+        },
+        "timeout_seconds": 3600,
+        "max_retries": 1,
+        "spark_python_task": {
+            "python_file": "dbfs:///FileStore/tables/entrypoint-1.py",
+            "parameters": "ls",
+        },
+    }
+
     @task(task_config=Spark(spark_conf={"spark": "1"}))
     def my_spark(a: str) -> int:
         session = flytekit.current_context().spark_session
@@ -53,6 +70,28 @@ def my_spark(a: str) -> int:
     assert ("spark", "1") in configs
     assert ("spark.app.name", "FlyteSpark: ex:local:local:local") in configs
 
+    databricks_token = "token"
+    databricks_instance = "account.cloud.databricks.com"
+
+    @task(
+        task_config=Databricks(
+            spark_conf={"spark": "2"},
+            databricks_conf=databricks_conf,
+            databricks_instance="account.cloud.databricks.com",
+            databricks_token="token",
+        )
+    )
+    def my_databricks(a: str) -> int:
+        session = flytekit.current_context().spark_session
+        assert session.sparkContext.appName == "FlyteSpark: ex:local:local:local"
+        return 10
+
+    assert my_databricks.task_config is not None
+    assert my_databricks.task_config.spark_conf == {"spark": "2"}
+    assert my_databricks.task_config.databricks_conf == databricks_conf
+    assert my_databricks.task_config.databricks_instance == databricks_instance
+    assert my_databricks.task_config.databricks_token == databricks_token
+
 
 def test_new_spark_session():
     name = "SessionName"

requirements-spark2.txt

@@ -52,7 +52,7 @@ docker-image-py==0.1.12
     # via flytekit
 docstring-parser==0.15
     # via flytekit
-flyteidl==1.3.0
+flyteidl==1.3.1
     # via flytekit
 googleapis-common-protos==1.57.0
     # via

requirements.txt

@@ -50,7 +50,7 @@ docker-image-py==0.1.12
     # via flytekit
 docstring-parser==0.15
     # via flytekit
-flyteidl==1.3.0
+flyteidl==1.3.1
     # via flytekit
 googleapis-common-protos==1.57.0
     # via