Roll back all the changes I made on the Tascomi snapshot job.

Roll back all the changes:

The below is the code of previous verison  (Aug 10, 2023) before I made changed
https://github.com/LBHackney-IT/Data-Platform/blob/04263eef9512467d9d38b11c46cd2ad48f0c280c/scripts/jobs/planning/tascomi_create_daily_snapshot.py

Author: Tian-2017

scripts/jobs/planning/tascomi_create_daily_snapshot.py

@@ -1,7 +1,7 @@
 import sys
-from datetime import datetime, timedelta
-from typing import Optional
+from datetime import datetime
 
+import pydeequ
 import pyspark.sql.functions as F
 from awsglue.context import GlueContext
 from awsglue.dynamicframe import DynamicFrame
@@ -11,7 +11,7 @@
 from pydeequ.repository import FileSystemMetricsRepository, ResultKey
 from pydeequ.verification import VerificationResult, VerificationSuite
 from pyspark import SparkContext
-from pyspark.sql import DataFrame, SparkSession, Window
+from pyspark.sql import SparkSession, Window
 from pyspark.sql.functions import col, max
 
 from scripts.helpers.data_quality_testing import (
@@ -27,15 +27,9 @@
 )
 
 
-def get_latest_snapshot(dfa: DataFrame) -> DataFrame:
-    if "snapshot_date" not in dfa.columns:
-        logger.warn(
-            "No snapshot_date column found in the dataframe, adding snapshot date columns"
-        )
-        dfa = add_snapshot_date_columns(dfa)
-    max_date = dfa.select(max("snapshot_date")).first()[0]
-    df_latest_snapshot = dfa.where(col("snapshot_date") == max_date)
-    return df_latest_snapshot
+def get_latest_snapshot(dfa):
+    dfa = dfa.where(col("snapshot_date") == dfa.select(max("snapshot_date")).first()[0])
+    return dfa
 
 
 def add_snapshot_date_columns(data_frame):
@@ -78,22 +72,11 @@ def apply_increments(snapshot_df, increment_df):
     return snapshot_df
 
 
-def loadIncrementsSinceDate(
-    increment_table_name: str, name_space: str, date: Optional[str] = None
-) -> DataFrame:
-    """
-    Loads increments from the specified catalog table starting from a given date.
-    If the provided date is None, it defaults to 60 days ago.
-
-    Returns:
-        DataFrame: A Spark DataFrame containing the loaded increments.
-    """
-    if date is None:
-        date = (datetime.now() - timedelta(days=60)).strftime("%Y%m%d")  # default date
+def loadIncrementsSinceDate(increment_table_name, name_space, date):
     increment_ddf = glueContext.create_dynamic_frame.from_catalog(
         name_space=name_space,
         table_name=increment_table_name,
-        push_down_predicate=f"import_date>='{date}'",
+        push_down_predicate=f"import_date>={date}",
         transformation_ctx=f"datasource_{increment_table_name}",
     )
     increment_df = increment_ddf.toDF()
@@ -149,32 +132,6 @@ def loadIncrementsSinceDate(
     "enforcement_breach_details": {"unique": ["id"]},
 }
 
-
-def purge_today_partition(
-    glueContext: GlueContext, target_destination: str, retentionPeriod: int = 0
-) -> None:
-    """
-    Purges (delete) only today's partition under the given target destination.
-
-    Parameters:
-      glueContext: GlueContext instance.
-      target_destination: Base S3 path (e.g., "s3://your-bucket/path").
-      retentionPeriod: Retention period in hours (default 0, meaning delete all files immediately).
-
-    Returns:
-      partition_path: The S3 partition path that was purged.
-    """
-    now = datetime.now()
-    snapshot_year = str(now.year)
-    snapshot_month = str(now.month).zfill(2)
-    snapshot_day = str(now.day).zfill(2)
-    snapshot_date = snapshot_year + snapshot_month + snapshot_day
-
-    partition_path = f"{target_destination}/snapshot_year={snapshot_year}/snapshot_month={snapshot_month}/snapshot_day={snapshot_day}/snapshot_date={snapshot_date}"
-
-    glueContext.purge_s3_path(partition_path, {"retentionPeriod": retentionPeriod})
-
-
 if __name__ == "__main__":
     args = getResolvedOptions(sys.argv, ["JOB_NAME"])
 
@@ -216,11 +173,10 @@ def purge_today_partition(
                         f"No snapshot and no increment for {increment_table_name}, going to the next table"
                     )
                     continue
-
-                # Load increments from default date
                 increment_df = loadIncrementsSinceDate(
                     increment_table_name=increment_table_name,
                     name_space=source_catalog_database,
+                    date="20210101",
                 )
                 if increment_df.rdd.isEmpty():
                     logger.info(
@@ -234,18 +190,14 @@ def purge_today_partition(
             # snapshot table in glue catalogue
             else:
                 pushDownPredicate = create_pushdown_predicate(
-                    partitionDateColumn="snapshot_date", daysBuffer=60
+                    partitionDateColumn="snapshot_date", daysBuffer=3
                 )
                 #   load latest snpashot
                 snapshot_ddf = glueContext.create_dynamic_frame.from_catalog(
                     name_space=source_catalog_database,
                     table_name=snapshot_table_name,
                     push_down_predicate=pushDownPredicate,
                 )
-                if snapshot_ddf.count() == 0:
-                    logger.error(
-                        f"No data returned for table {snapshot_table_name} using push_down_predicate: {pushDownPredicate}. "
-                    )
                 snapshot_df = snapshot_ddf.toDF()
                 snapshot_df = get_latest_snapshot(snapshot_df)
                 last_snapshot_date = snapshot_df.select(max("snapshot_date")).first()[0]
@@ -278,7 +230,6 @@ def purge_today_partition(
                         # apply COU
                         logger.info(f"Applying increment {increment_table_name}")
                         snapshot_df = apply_increments(snapshot_df, increment_df)
-
                 else:
                     logger.info(
                         f"Couldn't find table {increment_table_name} in database {source_catalog_database}, saving same snapshot as yesterday"
@@ -347,7 +298,7 @@ def purge_today_partition(
 
             except Exception as verificationError:
                 logger.info(
-                    "Job cancelled due to data quality test failure, continuing to next table."
+                    f"Job cancelled due to data quality test failure, continuing to next table."
                 )
                 message = verificationError.args
                 logger.info(f"{message[0]}")
@@ -358,7 +309,7 @@ def purge_today_partition(
 
             else:
                 logger.info(
-                    "Data quality tests passed, appending data quality results to JSON and moving on to writing data"
+                    f"Data quality tests passed, appending data quality results to JSON and moving on to writing data"
                 )
                 verificationSuite.saveOrAppendResult(resultKey).run()
 
@@ -368,9 +319,6 @@ def purge_today_partition(
                     snapshot_df, glueContext, "resultDataFrame"
                 )
                 target_destination = s3_bucket_target + table_name
-
-                # Clean up today's partition before writing
-                purge_today_partition(glueContext, target_destination)
                 parquetData = glueContext.write_dynamic_frame.from_options(
                     frame=resultDataFrame,
                     connection_type="s3",
@@ -380,7 +328,6 @@ def purge_today_partition(
                         "partitionKeys": PARTITION_KEYS,
                     },
                 )
-
         job.commit()
     finally:
         if len(dq_errors) > 0: