Add Databricks config to Spark Job

.github/workflows/pythonbuild.yml

@@ -46,6 +46,7 @@ jobs:
       - name: Install dependencies
         run: |
           make setup${{ matrix.spark-version-suffix }}
+          pip install "git+https://github.com/flyteorg/flyteidl@databricks"
           pip freeze
       - name: Test with coverage
         run: |
@@ -131,6 +132,7 @@ jobs:
           pip install -r requirements.txt
           if [ -f dev-requirements.txt ]; then pip install -r dev-requirements.txt; fi
           pip install --no-deps -U https://github.com/flyteorg/flytekit/archive/${{ github.sha }}.zip#egg=flytekit
+          pip install "git+https://github.com/flyteorg/flyteidl@databricks"
           pip freeze
       - name: Test with coverage
         run: |
@@ -155,6 +157,7 @@ jobs:
         run: |
           python -m pip install --upgrade pip==21.2.4
           pip install -r dev-requirements.txt
+          pip install "git+https://github.com/flyteorg/flyteidl@databricks"
       - name: Lint
         run: |
           make lint
@@ -176,5 +179,6 @@ jobs:
         run: |
           python -m pip install --upgrade pip==21.2.4 setuptools wheel
           pip install -r doc-requirements.txt
+          pip install "git+https://github.com/flyteorg/flyteidl@databricks"
       - name: Build the documentation
         run: make -C docs html

flytekit/bin/entrypoint.py

@@ -445,14 +445,14 @@ def _pass_through():
 def execute_task_cmd(
     inputs,
     output_prefix,
-    raw_output_data_prefix,
-    test,
-    prev_checkpoint,
-    checkpoint_path,
-    dynamic_addl_distro,
-    dynamic_dest_dir,
-    resolver,
-    resolver_args,
+    raw_output_data_prefix=None,
+    test=False,
+    prev_checkpoint=None,
+    checkpoint_path=None,
+    dynamic_addl_distro=None,
+    dynamic_dest_dir=None,
+    resolver=None,
+    resolver_args=None,
 ):
     logger.info(get_version_message())
     # We get weird errors if there are no click echo messages at all, so emit an empty string so that unit tests pass.

flytekit/core/python_auto_container.py

@@ -2,6 +2,7 @@
 
 import importlib
 import re
+import sys
 from abc import ABC
 from types import ModuleType
 from typing import Callable, Dict, List, Optional, TypeVar, Union

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
@@ -22,6 +24,9 @@ def __init__(
         main_class,
         spark_conf,
         hadoop_conf,
+        databricks_conf,
+        databricks_token,
+        databricks_instance,
         executor_path,
     ):
         """
@@ -30,29 +35,44 @@ def __init__(
         :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 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 str databricks_token: databricks access token.
+        :param 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

@@ -1,3 +1,5 @@
+import base64
+import json
 import os
 import typing
 from dataclasses import dataclass
@@ -23,10 +25,16 @@ class Spark(object):
     Args:
         spark_conf: Dictionary of spark config. The variables should match what spark expects
         hadoop_conf: Dictionary of hadoop conf. The variables should match a typical hadoop configuration for spark
+        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.
     """
 
     spark_conf: Optional[Dict[str, str]] = None
     hadoop_conf: Optional[Dict[str, str]] = None
+    databricks_conf: typing.Optional[Dict[str, typing.Union[str, dict]]] = None
+    databricks_token: Optional[str] = None
+    databricks_instance: Optional[str] = None
 
     def __post_init__(self):
         if self.spark_conf is None:
@@ -35,6 +43,9 @@ def __post_init__(self):
         if self.hadoop_conf is None:
             self.hadoop_conf = {}
 
+        if self.databricks_conf is None:
+            self.databricks_conf = {}
+
 
 # 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:
@@ -95,6 +106,9 @@ def get_custom(self, settings: SerializationSettings) -> Dict[str, Any]:
         job = SparkJob(
             spark_conf=self.task_config.spark_conf,
             hadoop_conf=self.task_config.hadoop_conf,
+            databricks_conf=self.task_config.databricks_conf,
+            databricks_token=self.task_config.databricks_token,
+            databricks_instance=self.task_config.databricks_instance,
             application_file="local://" + settings.entrypoint_settings.path,
             executor_path=settings.python_interpreter,
             main_class="",

plugins/flytekit-spark/tests/test_spark_task.py

@@ -19,14 +19,32 @@ def reset_spark_session() -> None:
 
 
 def test_spark_task(reset_spark_session):
-    @task(task_config=Spark(spark_conf={"spark": "1"}))
+    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"}, databricks_conf=databricks_conf))
     def my_spark(a: str) -> int:
         session = flytekit.current_context().spark_session
         assert session.sparkContext.appName == "FlyteSpark: ex:local:local:local"
         return 10
 
     assert my_spark.task_config is not None
     assert my_spark.task_config.spark_conf == {"spark": "1"}
+    assert my_spark.task_config.databricks_conf == databricks_conf
 
     default_img = Image(name="default", fqn="test", tag="tag")
     settings = SerializationSettings(