Fixes bugs in pyspark @with_columns

1. Configuration linkage was not exposed. So adding a parameter
for people to specify what configuration is required.
2. There was a bug in the UDF logic, because `all(empty)` evaluates
to True. So fixed that.
3. The `select` in append mode appended all possible columns. It
didn't only append what was in the `select`. This changes that behavior
by dropping "outputs" from the subdag that aren't in the select. We
do it this way because we don't know what's in the original dataframe.

examples/LLM_Workflows/pdf_summarizer/run_on_spark/README.md

@@ -1,17 +1,29 @@
 # PDF Summarizer on Spark
 
 Here we show how you can run the same Hamilton dataflow, that we defined in the backend
-folder, on Spark. This is useful if you want to run the same dataflow on a larger dataset,
-or have to run it on a cluster. Importantly this means you don't have to rewrite your
-code, or have to change where/how you develop!
+folder, on Spark (using two approaches). This is useful if you want to run the same dataflow
+on a larger dataset, or have to run it on a cluster. Importantly this means you don't have
+to rewrite your code, or have to change where/how you develop!
 
 ![Summarization dataflow](spark_summarization.png)
 
 # File organization
  - `summarization.py` this should be a carbon copy of the one in the backend folder.
- - `run.py` this contains the code to create a spark job and run the summarization code.
+ - `run_with_columns.py` this contains the code to create a spark job and run the summarization code, but
+using the `@with_columns` syntax. This is a more ergonomic way to write a full spark job with Hamilton.
+ - `run.py` this contains the code to create a spark job and run the summarization code as UDFs.
 
-# How this works
+# How `run_with_columns.py` works
+We take the dataflow defined by `summarization.py` and execute it as a bunch
+of row based UDFs on spark. The magic to do this happens in the `@with_columns` decorator.
+
+This approach allows you to put your entire pyspark workflow into Hamilton in an ergonomic way.
+You can request any intermediate outputs of summarization.py as columns in the dataframe.
+This is great for debugging and understanding your dataflow.
+
+![with_columns](spark_with_columns_summarization.png)
+
+# How `run.py` works
 We take the dataflow defined by `summarization.py` and execute it as a bunch
 of row based UDFs on spark. The magic to do this happens in the Hamilton PySparkUDFGraphAdapter.
 
@@ -20,7 +32,18 @@ that contains a column that maps to the required input. Which in this example
 is `pdf_source`. You can request whatever intermediate outputs as columns, which
 in this example we do with `["raw_text", "chunked_text", "summarized_text"]`.
 
-## Running the code
+![udfs](spark_with_columns_summarization.png)
+
+## Running `run_with_columns.py`
+
+1. Make sure you have the right dependencies installed. You can do this by running
+`pip install -r requirements.txt`.
+2. You can then run the code with the single PDF that is provided, else add more paths to
+PDF files in `spark_pdf_pipeline.py`.
+3. Then you can run the code with `python run_with_columns.py`. Be sure to have your OPENAI_API_KEY in the
+environment.
+
+## Running `run.py`
 
 1. Make sure you have the right dependencies installed. You can do this by running
 `pip install -r requirements.txt`.

examples/LLM_Workflows/pdf_summarizer/run_on_spark/run_with_columns.py

@@ -0,0 +1,59 @@
+"""Spark driver and Hamilton driver code."""
+
+import spark_pdf_pipeline
+from pyspark.sql import SparkSession
+
+from hamilton import base, driver, log_setup
+
+
+def my_spark_job(spark: SparkSession, openai_gpt_model: str, content_type: str, user_query: str):
+ """Template for a Spark job that uses Hamilton for their featuring engineering, i.e. any map, operations.
+
+ :param spark: the SparkSession
+ :param openai_gpt_model: the model to use for summarization
+ :param content_type: the content type of the document to summarize
+ :param user_query: the user query to use for summarization
+ """
+ dr = (
+ driver.Builder()
+ .with_config({"file_type": "pdf"})
+ .with_modules(spark_pdf_pipeline)
+ .with_adapter(base.DefaultAdapter())
+ .build()
+ )
+ # create inputs to the UDFs - this needs to be column_name -> spark dataframe.
+ execute_inputs = {
+ "spark_session": spark,
+ "save_path": "summarized_pdf_df.parquet",
+ "openai_gpt_model": openai_gpt_model,
+ "content_type": content_type,
+ "user_query": user_query,
+ }
+ output = ["saved_summarized_pdf_df"]
+ # visualize execution of what is going to be appended
+ dr.visualize_execution(
+ output,
+ "./spark_with_columns_summarization.png",
+ inputs=execute_inputs,
+ deduplicate_inputs=True,
+ )
+ # tell Hamilton to tell Spark what to do
+ dict_result = dr.execute(output, inputs=execute_inputs)
+ return dict_result["saved_summarized_pdf_df"]
+
+
+if __name__ == "__main__":
+ import os
+
+ openai_api_key = os.environ.get("OPENAI_API_KEY")
+ log_setup.setup_logging(log_level=log_setup.LOG_LEVELS["INFO"])
+ # create the SparkSession -- note in real life, you'd adjust the number of executors to control parallelism.
+ spark = SparkSession.builder.config(
+ "spark.executorEnv.OPENAI_API_KEY", openai_api_key
+ ).getOrCreate()
+ spark.sparkContext.setLogLevel("info")
+ # run the job
+ _df = my_spark_job(spark, "gpt-3.5-turbo-0613", "Scientific article", "Can you ELI5 the paper?")
+ # show the dataframe & thus make spark compute something
+ _df.show()
+ spark.stop()

examples/LLM_Workflows/pdf_summarizer/run_on_spark/spark_pdf_pipeline.py

@@ -0,0 +1,34 @@
+import pandas as pd
+import pyspark.sql as ps
+import summarization
+
+from hamilton.plugins.h_spark import with_columns
+
+
+def pdf_df(spark_session: ps.SparkSession) -> ps.DataFrame:
+ pandas_df = pd.DataFrame(
+ # TODO: update this to point to a PDF or two.
+ {"pdf_source": ["CDMS_HAMILTON_PAPER.pdf"]}
+ )
+ df = spark_session.createDataFrame(pandas_df)
+ return df
+
+
+@with_columns(
+ summarization,
+ select=["summarized_chunks", "summarized_text"],
+ columns_to_pass=["pdf_source"],
+ config_required=["file_type"],
+)
+def summarized_pdf_df(pdf_df: ps.DataFrame) -> ps.DataFrame:
+ return pdf_df
+
+
+def saved_summarized_pdf_df(
+ summarized_pdf_df: ps.DataFrame, save_path: str, persist_before_save: bool = True
+) -> ps.DataFrame:
+ """Save the summarized PDF dataframe to a parquet file."""
+ if persist_before_save:
+ summarized_pdf_df.persist()
+ summarized_pdf_df.write.parquet(save_path, mode="overwrite")
+ return summarized_pdf_df

hamilton/plugins/h_spark.py

@@ -396,7 +396,7 @@ def _lambda_udf(df: DataFrame, node_: node.Node, actual_kwargs: Dict[str, Any])
  raise ValueError(
  f"Currently unsupported function for {node_.name} with function signature:\n{node_.input_types}."
  )
- elif all(pandas_annotation.values()):
+ elif all(pandas_annotation.values()) and len(pandas_annotation) > 1:
  hamilton_udf = _fabricate_spark_function(node_, params_to_bind, params_from_df, True)
  # pull from annotation here instead of tag.
  base_type, type_args = htypes.get_type_information(node_.type)
@@ -944,6 +944,7 @@ def __init__(
  select: List[str] = None,
  namespace: str = None,
  mode: str = "append",
+ config_required: List[str] = None,
  ):
  """Initializes a with_columns decorator for spark. This allows you to efficiently run
  groups of map operations on a dataframe, represented as pandas/primitives UDFs. This
@@ -1002,7 +1003,7 @@ def final_df(initial_df: ps.DataFrame) -> ps.DataFrame:
 
  :param load_from: The functions that will be used to generate the group of map operations.
  :param select: Columns to select from the transformation. If this is left blank it will
- keep all columns in the subdag.
+ add all possible columns from the subdag to the dataframe.
  :param columns_to_pass: The initial schema of the dataframe. This is used to determine which
  upstream inputs should be taken from the dataframe, and which shouldn't. Note that, if this is
  left empty (and external_inputs is as well), we will assume that all dependencies come
@@ -1014,11 +1015,12 @@ def final_df(initial_df: ps.DataFrame) -> ps.DataFrame:
  and so this can be reused. If its left out, there will be no namespace (in which case you'll want
  to be careful about repeating it/reusing the nodes in other parts of the DAG.)
  :param mode: The mode of the operation. This can be either "append" or "select".
- If it is "append", it will keep all columns in the dataframe. If it is "select",
+ If it is "append", it will keep all original columns in the dataframe. If it is "select",
  it will only keep the columns in the dataframe from the `select` parameter. Note that,
- if the `select` parameter is left blank, it will keep all columns in the dataframe
- that are in the subdag (as that is the behavior of the `select` parameter. This
- defaults to `append`
+ if the `select` parameter is left blank, it will add all columns in the dataframe
+ that are in the subdag. This defaults to `append`.
+ :param config_required: the list of config keys that are required to resolve any functions. Pass in None\
+ if you want the functions/modules to have access to all possible config.
  """
  self.subdag_functions = subdag.collect_functions(load_from)
  self.select = select
@@ -1043,6 +1045,7 @@ def final_df(initial_df: ps.DataFrame) -> ps.DataFrame:
  self.namespace = namespace
  self.upstream_dependency = dataframe
  self.mode = mode
+ self.config_required = config_required
 
  @staticmethod
  def _prep_nodes(initial_nodes: List[node.Node]) -> List[node.Node]:
@@ -1068,11 +1071,10 @@ def create_selector_node(
  upstream_name: str, columns: List[str], node_name: str = "select"
  ) -> node.Node:
  """Creates a selector node. The sole job of this is to select just the specified columns.
- Note this is a utility function that's only called
+ Note this is a utility function that's only called here.
 
  :param upstream_name: Name of the upstream dataframe node
  :param columns: Columns to select
- :param node_namespace: Namespace of the node
  :param node_name: Name of the node to create
  :return:
  """
@@ -1087,6 +1089,29 @@ def new_callable(**kwargs) -> DataFrame:
  input_types={upstream_name: DataFrame},
  )
 
+ @staticmethod
+ def create_drop_node(
+ upstream_name: str, columns: List[str], node_name: str = "select"
+ ) -> node.Node:
+ """Creates a drop node. The sole job of this is to drop just the specified columns.
+ Note this is a utility function that's only called here.
+
+ :param upstream_name: Name of the upstream dataframe node
+ :param columns: Columns to drop
+ :param node_name: Name of the node to create
+ :return:
+ """
+
+ def new_callable(**kwargs) -> DataFrame:
+ return kwargs[upstream_name].drop(*columns)
+
+ return node.Node(
+ name=node_name,
+ typ=DataFrame,
+ callabl=new_callable,
+ input_types={upstream_name: DataFrame},
+ )
+
  def _validate_dataframe_subdag_parameter(self, nodes: List[node.Node], fn_name: str):
  all_upstream_dataframe_nodes = _identify_upstream_dataframe_nodes(nodes)
  initial_schema = set(self.initial_schema) if self.initial_schema is not None else set()
@@ -1121,6 +1146,9 @@ def _validate_dataframe_subdag_parameter(self, nodes: List[node.Node], fn_name:
  f"Instead, we found: {upstream_dependency}."
  )
 
+ def required_config(self) -> List[str]:
+ return self.config_required
+
  def generate_nodes(self, fn: Callable, config: Dict[str, Any]) -> List[node.Node]:
  """Generates nodes in the with_columns groups. This does the following:
 
@@ -1154,6 +1182,7 @@ def generate_nodes(self, fn: Callable, config: Dict[str, Any]) -> List[node.Node
  columns_passed_in_from_dataframe = (
  set(self.initial_schema) if self.initial_schema is not None else []
  )
+ drop_list = []
  # Or from the dataframe passed in...
  for node_ in sorted_initial_nodes:
  # dependent columns are broken into two sets:
@@ -1185,11 +1214,16 @@ def generate_nodes(self, fn: Callable, config: Dict[str, Any]) -> List[node.Node
  dependent_columns_in_mapgroup,
  dependent_columns_in_dataframe,
  )
+ if self.select is not None and sparkified.name not in self.select:
+ # we need to create a drop list because we don't want to drop
+ # original columns from the DF by accident.
+ drop_list.append(sparkified.name)
  output_nodes.append(sparkified)
  current_dataframe_node = sparkified.name
  # We get the final node, which is the function we're using
  # and reassign inputs to be the dataframe
  if self.mode == "select":
+ # this selects over the original DF and the additions
  select_columns = (
  self.select if self.select is not None else [item.name for item in output_nodes]
  )
@@ -1200,6 +1234,17 @@ def generate_nodes(self, fn: Callable, config: Dict[str, Any]) -> List[node.Node
  )
  output_nodes.append(select_node)
  current_dataframe_node = select_node.name
+ elif self.select is not None and len(drop_list) > 0:
+ # since it's in append mode, we only want to append what's in the select
+ # but we don't know what the original schema is, so we instead drop
+ # things from the DF to achieve the same result
+ select_node = with_columns.create_drop_node(
+ upstream_name=current_dataframe_node,
+ columns=drop_list,
+ node_name="_select",
+ )
+ output_nodes.append(select_node)
+ current_dataframe_node = select_node.name
  output_nodes = subdag.add_namespace(output_nodes, namespace)
  final_node = node.Node.from_fn(fn).reassign_inputs(
  {inject_parameter: assign_namespace(current_dataframe_node, namespace)}