[COST-3617] - Fix GCP end of month crossover summary (#5080)

* [COST-3617] - Fix GCP end of month crossover summary

koku/api/utils.py

@@ -448,6 +448,14 @@ def gcp_find_invoice_months_in_date_range(self, start, end):
                 invoice_months.append(invoice_month)
         return invoice_months
 
+    def get_year_month_list_from_start_end(self, start, end):
+        if isinstance(start, datetime.date):
+            start = datetime.datetime(start.year, start.month, start.day, tzinfo=settings.UTC)
+        if isinstance(end, datetime.date):
+            end = datetime.datetime(end.year, end.month, end.day, tzinfo=settings.UTC)
+        dates = self.list_months(start, end)
+        return [{"year": date.strftime("%Y"), "month": date.strftime("%m")} for date in dates]
+
 
 def materialized_view_month_start(dh=DateHelper()):
     """Datetime of midnight on the first of the month where materialized summary starts."""

koku/masu/database/gcp_report_db_accessor.py

@@ -21,6 +21,7 @@
 
 from api.common import log_json
 from api.provider.models import Provider
+from api.utils import DateHelper
 from koku.database import SQLScriptAtomicExecutorMixin
 from masu.database import GCP_REPORT_TABLE_MAP
 from masu.database import OCP_REPORT_TABLE_MAP
@@ -107,6 +108,7 @@ def populate_line_item_daily_summary_table_trino(
             (None)
 
         """
+        date_dicts = DateHelper().get_year_month_list_from_start_end(start_date, end_date)
         last_month_end = datetime.date.today().replace(day=1) - datetime.timedelta(days=1)
         if end_date == last_month_end:
 
@@ -134,15 +136,14 @@ def populate_line_item_daily_summary_table_trino(
             "schema": self.schema,
             "table": TRINO_LINE_ITEM_TABLE,
             "source_uuid": source_uuid,
-            "year": invoice_month_date.strftime("%Y"),
-            "month": invoice_month_date.strftime("%m"),
             "markup": markup_value or 0,
             "bill_id": bill_id,
         }
-
-        self._execute_trino_raw_sql_query(
-            sql, sql_params=sql_params, log_ref="reporting_gcpcostentrylineitem_daily_summary.sql"
-        )
+        for date_dict in date_dicts:
+            sql_params = sql_params | {"year": date_dict["year"], "month": date_dict["month"]}
+            self._execute_trino_raw_sql_query(
+                sql, sql_params=sql_params, log_ref="reporting_gcpcostentrylineitem_daily_summary.sql"
+            )
 
     def populate_tags_summary_table(self, bill_ids, start_date, end_date):
         """Populate the line item aggregated totals data table."""

koku/masu/database/trino_sql/reporting_gcpcostentrylineitem_daily_summary.sql

@@ -63,7 +63,6 @@ CROSS JOIN
 WHERE source = '{{source_uuid | sqlsafe}}'
     AND year = '{{year | sqlsafe}}'
     AND month = '{{month | sqlsafe}}'
-    AND invoice_month = '{{year | sqlsafe}}{{month | sqlsafe}}'
     AND usage_start_time >= TIMESTAMP '{{start_date | sqlsafe}}'
     AND usage_start_time < date_add('day', 1, TIMESTAMP '{{end_date | sqlsafe}}')
 GROUP BY billing_account_id,

koku/masu/external/downloader/gcp/gcp_report_downloader.py

@@ -91,12 +91,14 @@ def create_daily_archives(
             data_frame = pd_read_csv(local_file_path)
             data_frame = add_label_columns(data_frame)
             # putting it in for loop handles crossover data, when we have distinct invoice_month
+            unique_usage_days = pd.to_datetime(data_frame["usage_start_time"]).dt.date.unique()
+            days = list({day.strftime("%Y-%m-%d") for day in unique_usage_days})
+            date_range = {"start": min(days), "end": max(days)}
             for invoice_month in data_frame["invoice.month"].unique():
                 invoice_filter = data_frame["invoice.month"] == invoice_month
                 invoice_month_data = data_frame[invoice_filter]
-                unique_usage_days = pd.to_datetime(invoice_month_data["usage_start_time"]).dt.date.unique()
-                days = list({day.strftime("%Y-%m-%d") for day in unique_usage_days})
-                date_range = {"start": min(days), "end": max(days), "invoice_month": str(invoice_month)}
+                # We may be able to completely remove invoice month in the future
+                date_range["invoice_month"] = str(invoice_month)
                 partition_dates = invoice_month_data.partition_date.unique()
                 for partition_date in partition_dates:
                     partition_date_filter = invoice_month_data["partition_date"] == partition_date
@@ -129,8 +131,8 @@ def create_daily_archives(
                         tracing_id, s3_csv_path, day_filepath, day_file, manifest_id, context
                     )
                     daily_file_names.append(day_filepath)
-    except Exception:
-        msg = f"unable to create daily archives from: {local_file_paths}"
+    except Exception as e:
+        msg = f"unable to create daily archives from: {local_file_paths}. reason: {e}"
         LOG.info(log_json(tracing_id, msg=msg, context=context))
         raise CreateDailyArchivesError(msg)
     return daily_file_names, date_range

koku/masu/external/downloader/gcp_local/gcp_local_report_downloader.py

@@ -54,12 +54,14 @@ def create_daily_archives(tracing_id, account, provider_uuid, filename, filepath
         LOG.error(f"File {filepath} could not be parsed. Reason: {str(error)}")
         raise GCPReportDownloaderError(error)
     # putting it in for loop handles crossover data, when we have distinct invoice_month
+    unique_usage_days = pd.to_datetime(data_frame["usage_start_time"]).dt.date.unique()
+    days = list({day.strftime("%Y-%m-%d") for day in unique_usage_days})
+    date_range = {"start": min(days), "end": max(days)}
     for invoice_month in data_frame["invoice.month"].unique():
         invoice_filter = data_frame["invoice.month"] == invoice_month
         invoice_month_data = data_frame[invoice_filter]
-        unique_usage_days = pd.to_datetime(invoice_month_data["usage_start_time"]).dt.date.unique()
-        days = list({day.strftime("%Y-%m-%d") for day in unique_usage_days})
-        date_range = {"start": min(days), "end": max(days), "invoice_month": str(invoice_month)}
+        # We may be able to completely remove invoice month in the future
+        date_range["invoice_month"] = str(invoice_month)
         partition_dates = invoice_month_data.partition_date.unique()
         for partition_date in partition_dates:
             partition_date_filter = invoice_month_data["partition_date"] == partition_date

koku/masu/processor/parquet/parquet_report_processor.py

@@ -476,6 +476,9 @@ def convert_to_parquet(self):  # noqa: C901
             daily_data_frames.extend(daily_frame)
             if self.provider_type not in (Provider.PROVIDER_AZURE):
                 self.create_daily_parquet(parquet_base_filename, daily_frame)
+            if self.provider_type in [Provider.PROVIDER_GCP, Provider.PROVIDER_GCP_LOCAL]:
+                # Sync partitions on each file to create partitions that cross month bondaries
+                self.create_parquet_table(parquet_base_filename)
             if not success:
                 msg = "failed to convert files to parquet"
                 LOG.warning(