[DEV-13727]File C Custom Account DuckDB downloads

usaspending_api/common/etl/spark.py

@@ -390,9 +390,7 @@ def load_es_index(
     )
 
 
-def merge_delta_table(
-    spark: SparkSession, source_df: DataFrame, delta_table_name: str, merge_column: str
-) -> None:
+def merge_delta_table(spark: SparkSession, source_df: DataFrame, delta_table_name: str, merge_column: str) -> None:
     source_df.create_or_replace_temporary_view("temp_table")
 
     spark.sql(
@@ -409,7 +407,7 @@ def diff(
     left: DataFrame,
     right: DataFrame,
     unique_key_col: str = "id",
-    compare_cols: list[str] | None = None,
+    compare_cols: list[str] = None,
     include_unchanged_rows: bool = False,
 ) -> DataFrame:
     """Compares two Spark DataFrames that share a schema and returns row-level differences in a DataFrame
@@ -563,8 +561,7 @@ def convert_array_cols_to_string(
               2. Escape any quotes inside the array element with backslash.
               - A case that involves all of this will yield CSV field value like this when viewed in a text editor,
                 assuming Spark CSV options are: quote='"', escape='"' (the default is for it to match quote)
-                ...,"{""{\""simple\"": \""elem1\"", \""other\"": \""elem1\""}"",
-                    ""{\""simple\"": \""elem2\"", \""other\"": \""elem2\""}""}",...
+                ...,"{""{\""simple\"": \""elem1\"", \""other\"": \""elem1\""}"",...
     """
     arr_open_bracket = "["
     arr_close_bracket = "]"
@@ -599,7 +596,8 @@ def convert_array_cols_to_string(
                                     # Special handling in case of data that already has either a quote " or backslash \
                                     # inside an array element
                                     # First replace any single backslash character \ with TWO \\ (an escaped backslash)
-                                    # Then replace quote " character with \" (escaped quote, inside a quoted array elem)
+                                    # Then replace any quote " character with \"
+                                    #   (escaped quote, inside a quoted array elem)
                                     # NOTE: these regexp_replace get sent down to a Java replaceAll, which will require
                                     #       FOUR backslashes to represent ONE
                                     regexp_replace(
@@ -649,7 +647,7 @@ def _generate_global_view_sql_strings(tables: list[str], jdbc_url: str) -> list[
 
 def create_ref_temp_views(  # noqa: PLR0912
     spark: SparkSession | DuckDBSparkSession, create_broker_views: bool = False
-) -> None:
+) -> None:  # noqa: PLR0912
     """Create global temporary Spark reference views that sit atop remote PostgreSQL RDS tables
     Setting create_broker_views to True will create views for all tables list in _BROKER_REF_TABLES
     Note: They will all be listed under global_temp.{table_name}
@@ -792,9 +790,8 @@ def write_csv_file(  # noqa: PLR0913
     spark: SparkSession,
     df: DataFrame,
     parts_dir: str,
-    max_records_per_file: int = EXCEL_ROW_LIMIT,
     overwrite: bool = True,
-    logger: logging.Logger | None = None,
+    logger: logging.Logger = None,
     delimiter: str = ",",
 ) -> int:
     """Write DataFrame data to CSV file parts.
@@ -804,8 +801,6 @@ def write_csv_file(  # noqa: PLR0913
         parts_dir: Path to dir that will contain the outputted parts files from partitions
         num_partitions: Indicates the number of partitions to use when writing the Dataframe
         overwrite: Whether to replace the file CSV files if they already exist by that name
-        max_records_per_file: Suggestion to Spark of how many records to put in each written CSV file part,
-            if it will end up writing multiple files.
         logger: The logger to use. If one note provided (e.g. to log to console or stdout) the underlying JVM-based
             Logger will be extracted from the ``spark`` ``SparkSession`` and used as the logger.
         delimiter: Charactor used to separate columns in the CSV
@@ -822,10 +817,10 @@ def write_csv_file(  # noqa: PLR0913
         f"Writing source data DataFrame to csv part files for file {parts_dir}..."
     )
     df_record_count = df.count()
-    num_partitions = math.ceil(df_record_count / max_records_per_file) or 1
+    num_partitions = math.ceil(df_record_count / EXCEL_ROW_LIMIT) or 1
     df.repartition(num_partitions).write.options(
         # NOTE: this is a suggestion, to be used by Spark if partitions yield multiple files
-        maxRecordsPerFile=max_records_per_file,
+        maxRecordsPerFile=EXCEL_ROW_LIMIT,
     ).csv(
         path=parts_dir,
         header=True,
@@ -848,7 +843,6 @@ def write_csv_file_duckdb(
     df: DuckDBDataFrame,
     download_file_name: str,
     temp_csv_directory_path: str = CSV_LOCAL_PATH,
-    max_records_per_file: int = EXCEL_ROW_LIMIT,
     logger: logging.Logger | None = None,
     delimiter: str = ",",
 ) -> tuple[int, list[str] | list]:
@@ -858,8 +852,6 @@ def write_csv_file_duckdb(
         download_file_name: Name of the download being generated.
         temp_csv_directory_path: Directory that will contain the individual CSV files before zipping.
             Defaults to CSV_LOCAL_PATH
-        max_records_per_file: Max number of records to put in each written CSV file.
-            Defaults to EXCEL_ROW_LIMIT
         logger: Logging instance to use.
             Defaults to None
         delimiter: Charactor used to separate columns in the CSV
@@ -870,7 +862,7 @@ def write_csv_file_duckdb(
     """
     start = time.time()
     _pandas_df = df.toPandas()
-    _pandas_df["file_number"] = (_pandas_df.index // max_records_per_file) + 1
+    _pandas_df["file_number"] = (_pandas_df.index // EXCEL_ROW_LIMIT) + 1
     df_record_count = len(_pandas_df)
     rel = duckdb.from_df(_pandas_df)
 
@@ -894,15 +886,13 @@ def write_csv_file_duckdb(
         f"{temp_csv_directory_path}{download_file_name}/{d}"
         for d in os.listdir(f"{temp_csv_directory_path}{download_file_name}")
     ]
-    for dir in _partition_dirs:
-        _old_csv_path = f"{dir}/{os.listdir(dir)[0]}"
-        _new_csv_path = (
-            f"{temp_csv_directory_path}{download_file_name}"
-            f"/{download_file_name}_{dir.split('=')[1].zfill(2)}.csv"
-        )
+    for _dir in _partition_dirs:
+        _file_number = _dir.split("=")[1].zfill(2)
+        _old_csv_path = f"{_dir}/{os.listdir(_dir)[0]}"
+        _new_csv_path = f"{temp_csv_directory_path}{download_file_name}/{download_file_name}_{_file_number}.csv"
         shutil.move(_old_csv_path, _new_csv_path)
         full_file_paths.append(_new_csv_path)
-        os.rmdir(dir)
+        os.rmdir(_dir)
 
     logger.info(
         f"{temp_csv_directory_path}{download_file_name} contains {df_record_count:,} rows of data"

usaspending_api/common/helpers/download_csv_strategies.py

@@ -7,6 +7,7 @@
 from typing import List, Optional
 
 from django.conf import settings
+from duckdb.experimental.spark.sql import DataFrame as DuckDBDataFrame
 from duckdb.experimental.spark.sql import SparkSession as DuckDBSparkSession
 from pyspark.sql import DataFrame
 
@@ -17,7 +18,6 @@
     download_s3_object,
 )
 from usaspending_api.download.filestreaming.download_generation import (
-    EXCEL_ROW_LIMIT,
     execute_psql,
     generate_export_query_temp_file,
     split_and_zip_data_files,
@@ -80,20 +80,24 @@ def __init__(self, logger: logging.Logger, *args, **kwargs):
 
     def download_to_csv(
         self,
-        source_sql,
-        destination_path,
-        destination_file_name,
-        working_dir_path,
-        download_zip_path,
-        source_df=None,
-    ):
+        source_sql: str,
+        destination_path: str,
+        destination_file_name: str,
+        working_dir_path: str,
+        download_zip_path: str,
+        source_df: DataFrame | None = None,
+    ) -> CSVDownloadMetadata:
         start_time = time.perf_counter()
         self._logger.info(f"Downloading data to {destination_path}")
-        temp_data_file_name = destination_path.parent / (destination_path.name + "_temp")
+        temp_data_file_name = destination_path.parent / (
+            destination_path.name + "_temp"
+        )
         options = FILE_FORMATS[self.file_format]["options"]
         export_query = r"\COPY ({}) TO STDOUT {}".format(source_sql, options)
         try:
-            temp_file, temp_file_path = generate_export_query_temp_file(export_query, None, working_dir_path)
+            temp_file, temp_file_path = generate_export_query_temp_file(
+                export_query, None, working_dir_path
+            )
             # Create a separate process to run the PSQL command; wait
             psql_process = multiprocessing.Process(
                 target=execute_psql, args=(temp_file_path, temp_data_file_name, None)
@@ -104,12 +108,20 @@ def download_to_csv(
             delim = FILE_FORMATS[self.file_format]["delimiter"]
 
             # Log how many rows we have
-            self._logger.info(f"Counting rows in delimited text file {temp_data_file_name}")
+            self._logger.info(
+                f"Counting rows in delimited text file {temp_data_file_name}"
+            )
             try:
-                row_count = count_rows_in_delimited_file(filename=temp_data_file_name, has_header=True, delimiter=delim)
-                self._logger.info(f"{destination_path} contains {row_count:,} rows of data")
+                row_count = count_rows_in_delimited_file(
+                    filename=temp_data_file_name, has_header=True, delimiter=delim
+                )
+                self._logger.info(
+                    f"{destination_path} contains {row_count:,} rows of data"
+                )
             except Exception:
-                self._logger.exception("Unable to obtain delimited text file line count")
+                self._logger.exception(
+                    "Unable to obtain delimited text file line count"
+                )
 
             start_time = time.perf_counter()
             zip_process = multiprocessing.Process(
@@ -136,32 +148,36 @@ def __init__(self, logger: logging.Logger, *args, **kwargs):
         super().__init__(*args, **kwargs)
         self._logger = logger
 
-    def download_to_csv(
+    def download_to_csv(  # noqa: PLR0913
         self,
-        source_sql,
-        destination_path,
-        destination_file_name,
-        working_dir_path,
-        download_zip_path,
-        source_df=None,
-        delimiter=",",
-        file_format="csv",
-    ):
-        # These imports are here for a reason.
-        #   some strategies do not require spark
-        #   we do not want to force all containers where
-        #   other strategies run to have pyspark installed when the strategy
-        #   doesn't require it.
+        source_sql: str,
+        destination_path: str,
+        destination_file_name: str,
+        working_dir_path: str,
+        download_zip_path: str,
+        source_df: DataFrame | None = None,
+        delimiter: str = ",",
+        file_format: str = "csv",
+    ) -> CSVDownloadMetadata:
+        # Some strategies do not require spark we do not want to force all containers where
+        #   other strategies run to have pyspark installed when the strategy doesn't require it.
         from usaspending_api.common.etl.spark import write_csv_file
-        from usaspending_api.common.helpers.spark_helpers import configure_spark_session, get_active_spark_session
+        from usaspending_api.common.helpers.spark_helpers import (
+            configure_spark_session,
+            get_active_spark_session,
+        )
 
         self.spark = None
-        destination_path_dir = str(destination_path).replace(f"/{destination_file_name}", "")
+        destination_path_dir = str(destination_path).replace(
+            f"/{destination_file_name}", ""
+        )
         # The place to write intermediate data files to in s3
         s3_bucket_name = settings.BULK_DOWNLOAD_S3_BUCKET_NAME
         s3_bucket_path = f"s3a://{s3_bucket_name}"
         s3_bucket_sub_path = "temp_download"
-        s3_destination_path = f"{s3_bucket_path}/{s3_bucket_sub_path}/{destination_file_name}"
+        s3_destination_path = (
+            f"{s3_bucket_path}/{s3_bucket_sub_path}/{destination_file_name}"
+        )
         try:
             extra_conf = {
                 # Config for Delta Lake tables and SQL. Need these to keep Dela table metadata in the metastore
@@ -176,7 +192,9 @@ def download_to_csv(
             self.spark_created_by_command = False
             if not self.spark:
                 self.spark_created_by_command = True
-                self.spark = configure_spark_session(**extra_conf, spark_context=self.spark)
+                self.spark = configure_spark_session(
+                    **extra_conf, spark_context=self.spark
+                )
             if source_df is not None:
                 df = source_df
             else:
@@ -185,7 +203,6 @@ def download_to_csv(
                 self.spark,
                 df,
                 parts_dir=s3_destination_path,
-                max_records_per_file=EXCEL_ROW_LIMIT,
                 logger=self._logger,
                 delimiter=delimiter,
             )
@@ -208,12 +225,19 @@ def download_to_csv(
             self._logger.exception("Exception encountered. See logs")
             raise
         finally:
-            delete_s3_objects(s3_bucket_name, key_prefix=f"{s3_bucket_sub_path}/{destination_file_name}")
+            delete_s3_objects(
+                s3_bucket_name,
+                key_prefix=f"{s3_bucket_sub_path}/{destination_file_name}",
+            )
             if self.spark_created_by_command:
                 self.spark.stop()
         append_files_to_zip_file(final_csv_data_file_locations, download_zip_path)
-        self._logger.info(f"Generated the following data csv files {final_csv_data_file_locations}")
-        return CSVDownloadMetadata(final_csv_data_file_locations, record_count, column_count)
+        self._logger.info(
+            f"Generated the following data csv files {final_csv_data_file_locations}"
+        )
+        return CSVDownloadMetadata(
+            final_csv_data_file_locations, record_count, column_count
+        )
 
     def _move_data_csv_s3_to_local(
         self,
@@ -230,7 +254,7 @@ def _move_data_csv_s3_to_local(
             s3_file_paths: A list of file paths to move from s3, name should
                 include s3a:// and bucket name
             s3_bucket_path: The bucket path, e.g. s3a:// + bucket name
-            s3_bucket_sub_path: The path to the s3 files in the bucket, exluding s3a:// + bucket name, e.g. temp_directory/files
+            s3_bucket_sub_path: The path to the s3 files in the bucket, exluding s3a:// + bucket name
             destination_path_dir: The location to move those files from s3 to, must not include the
                 file name in the path. This path should be a directory.
 
@@ -251,27 +275,31 @@ def _move_data_csv_s3_to_local(
                 final_path,
             )
             local_csv_file_paths.append(final_path)
-        self._logger.info(f"Copied data files from S3 to local machine in {(time.time() - start_time):3f}s")
+        self._logger.info(
+            f"Copied data files from S3 to local machine in {(time.time() - start_time):3f}s"
+        )
         return local_csv_file_paths
 
 
 class DuckDBToCSVStrategy(AbstractToCSVStrategy):
-    def __init__(self, logger: logging.Logger, spark: DuckDBSparkSession, *args, **kwargs):
+    def __init__(
+        self, logger: logging.Logger, spark: DuckDBSparkSession, *args, **kwargs
+    ):
         super().__init__(*args, **kwargs)
         self._logger = logger
         self.spark = spark
 
-    def download_to_csv(
+    def download_to_csv(  # noqa: PLR0913
         self,
         source_sql: str | None,
         destination_path: str,
         destination_file_name: str,
         working_dir_path: str,
         download_zip_path: str,
-        source_df=None,
-        delimiter=",",
-        file_format="csv",
-    ):
+        source_df: DuckDBDataFrame | None = None,
+        delimiter: str = ",",
+        file_format: str = "csv",
+    ) -> CSVDownloadMetadata:
         from usaspending_api.common.etl.spark import write_csv_file_duckdb
 
         try:
@@ -282,7 +310,6 @@ def download_to_csv(
             record_count, final_csv_data_file_locations = write_csv_file_duckdb(
                 df=df,
                 download_file_name=destination_file_name,
-                max_records_per_file=EXCEL_ROW_LIMIT,
                 logger=self._logger,
                 delimiter=delimiter,
             )
@@ -291,11 +318,20 @@ def download_to_csv(
             self._logger.exception("Exception encountered. See logs")
             raise
         append_files_to_zip_file(final_csv_data_file_locations, download_zip_path)
-        self._logger.info(f"Generated the following data csv files {final_csv_data_file_locations}")
-        return CSVDownloadMetadata(final_csv_data_file_locations, record_count, column_count)
+        self._logger.info(
+            f"Generated the following data csv files {final_csv_data_file_locations}"
+        )
+        return CSVDownloadMetadata(
+            final_csv_data_file_locations, record_count, column_count
+        )
 
     def _move_data_csv_s3_to_local(
-        self, bucket_name, s3_file_paths, s3_bucket_path, s3_bucket_sub_path, destination_path_dir
+        self,
+        bucket_name: str,
+        s3_file_paths: list[str] | set[str] | tuple[str],
+        s3_bucket_path: str,
+        s3_bucket_sub_path: str,
+        destination_path_dir: str,
     ) -> List[str]:
         """Moves files from s3 data csv location to a location on the local machine.
 
@@ -304,7 +340,7 @@ def _move_data_csv_s3_to_local(
             s3_file_paths: A list of file paths to move from s3, name should
                 include s3a:// and bucket name
             s3_bucket_path: The bucket path, e.g. s3a:// + bucket name
-            s3_bucket_sub_path: The path to the s3 files in the bucket, exluding s3a:// + bucket name, e.g. temp_directory/files
+            s3_bucket_sub_path: The path to the s3 files in the bucket, exluding s3a:// + bucket name
             destination_path_dir: The location to move those files from s3 to, must not include the
                 file name in the path. This path should be a diretory.
 
@@ -325,5 +361,7 @@ def _move_data_csv_s3_to_local(
                 final_path,
             )
             local_csv_file_paths.append(final_path)
-        self._logger.info(f"Copied data files from S3 to local machine in {(time.time() - start_time):3f}s")
+        self._logger.info(
+            f"Copied data files from S3 to local machine in {(time.time() - start_time):3f}s"
+        )
         return local_csv_file_paths