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Merged
annajgibson merged 2 commits into from
Sep 9, 2025
Merged

Update GX DQ script and inputs to include more tables and data loads #2436

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a3dc7bd
Update GX DQ script and inputs to include more tables and data loads
annajgibson Sep 8, 2025
9a744f7
Fix formatting
annajgibson Sep 8, 2025
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31 changes: 13 additions & 18 deletions scripts/helpers/housing_nec_migration_gx_dq_inputs.py
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Original file line number Diff line number Diff line change
@@ -1,22 +1,17 @@
sql_config = {
"properties_1a": {
"sql": """ SELECT *
FROM "housing_nec_migration"."properties_1a" """,
"id_field": "LPRO_PROPREF",
},
"properties_1b": {
"sql": """ SELECT *
FROM "housing_nec_migration"."properties_1b" """,
"id_field": "LPRO_PROPREF",
},
"properties_1c": {
"sql": """ SELECT *
FROM "housing_nec_migration"."properties_1c" """,
"id_field": "LPRO_PROPREF",
},
}
sql_config = {"properties": {"id_field": "LPRO_PROPREF"}}

data_load_list = ["properties"]

table_list = ["properties_1a", "properties_1b", "properties_1c"]
table_list = {
"properties": [
"properties_1a",
"properties_1b",
"properties_1c",
"properties_1d",
"properties_1e",
"properties_4a",
"properties_4c",
]
}

partition_keys = ["import_date"]
189 changes: 119 additions & 70 deletions scripts/jobs/housing/housing_nec_migration_apply_gx_dq_tests.py
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Original file line number Diff line number Diff line change
Expand Up @@ -10,18 +10,34 @@
import great_expectations as gx
import pandas as pd
from pyathena import connect
from scripts.helpers.housing_nec_migration_gx_dq_inputs import sql_config, table_list
from scripts.helpers.housing_nec_migration_gx_dq_inputs import (
sql_config,
data_load_list,
table_list,
)
import scripts.jobs.housing.housing_nec_migration_properties_data_load_gx_suite

logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)

arg_keys = ['region_name', 's3_endpoint', 's3_target_location', 's3_staging_location', 'target_database',
'target_table']
arg_keys = [
"region_name",
"s3_endpoint",
"s3_target_location",
"s3_staging_location",
"target_database",
"target_table",
]
args = getResolvedOptions(sys.argv, arg_keys)
locals().update(args)


def get_sql_query(sql_config, data_load, table):
query = f"SELECT * FROM housing_nec_migration.{table}"
id_field = sql_config.get(data_load).get("id_field")
return query, id_field


def json_serial(obj):
"""JSON serializer for objects not serializable by default."""
if isinstance(obj, (datetime, date)):
Expand All @@ -35,88 +51,119 @@ def main():

table_results_df_list = []

for table in table_list:
logger.info(f'{table} loading...')

sql_query = sql_config.get(table).get('sql')
for data_load in data_load_list:
logger.info(f"{data_load} loading...")

conn = connect(s3_staging_dir=s3_staging_location,
region_name=region_name)
for table in table_list.get(data_load):
logger.info(f"{table} loading...")

df = pd.read_sql_query(sql_query, conn)

# set up batch
data_source = context.data_sources.add_pandas(f'{table}_pandas')
data_asset = data_source.add_dataframe_asset(name=f'{table}_df_asset')
batch_definition = data_asset.add_batch_definition_whole_dataframe("Athena batch definition")
batch_parameters = {"dataframe": df}
sql_query, id_field = get_sql_query(
sql_config=sql_config, data_load=data_load, table=table
)

# get expectation suite for dataset
suite = context.suites.get(name='properties_data_load_suite')
conn = connect(s3_staging_dir=s3_staging_location, region_name=region_name)

validation_definition = gx.ValidationDefinition(
data=batch_definition,
suite=suite,
name=f'validation_definition_{table}')
validation_definition = context.validation_definitions.add(validation_definition)
df = pd.read_sql_query(sql_query, conn)

# create and start checking data with checkpoints
checkpoint = context.checkpoints.add(
gx.checkpoint.checkpoint.Checkpoint(
name=f'{table}_checkpoint',
validation_definitions=[validation_definition],
result_format={"result_format": "COMPLETE",
"return_unexpected_index_query": False,
"partial_unexpected_count": 0}
# set up batch
data_source = context.data_sources.add_pandas(f"{table}_pandas")
data_asset = data_source.add_dataframe_asset(name=f"{table}_df_asset")
batch_definition = data_asset.add_batch_definition_whole_dataframe(
"Athena batch definition"
)
)
batch_parameters = {"dataframe": df}

checkpoint_result = checkpoint.run(batch_parameters=batch_parameters)
results_dict = list(checkpoint_result.run_results.values())[0].to_json_dict()
table_results_df = pd.json_normalize(results_dict['results'])
cols_not_needed = ['result.unexpected_list', 'result.observed_value']
cols_to_drop = [c for c in table_results_df.columns if c.startswith('exception_info') or c in cols_not_needed]
# get expectation suite for dataset
suite = context.suites.get(name=f"{data_load}_data_load_suite")

table_results_df = table_results_df.drop(columns=cols_to_drop)
table_results_df_list.append(table_results_df)
validation_definition = gx.ValidationDefinition(
data=batch_definition,
suite=suite,
name=f"validation_definition_{table}",
)
validation_definition = context.validation_definitions.add(
validation_definition
)

# generate id lists for each unexpected result set
query_df = table_results_df.loc[(~table_results_df['result.unexpected_index_list'].isna()) & (
table_results_df['result.unexpected_index_list'].values != '[]')]
# create and start checking data with checkpoints
checkpoint = context.checkpoints.add(
gx.checkpoint.checkpoint.Checkpoint(
name=f"{table}_checkpoint",
validation_definitions=[validation_definition],
result_format={
"result_format": "COMPLETE",
"return_unexpected_index_query": False,
"partial_unexpected_count": 0,
},
)
)

table_results_df['unexpected_id_list'] = pd.Series(dtype='object')
for i, row in query_df.iterrows():
table_results_df.loc[i, 'unexpected_id_list'] = str(
list(df[sql_config.get(table).get('id_field')].iloc[row['result.unexpected_index_list']]))
checkpoint_result = checkpoint.run(batch_parameters=batch_parameters)
results_dict = list(checkpoint_result.run_results.values())[
0
].to_json_dict()
table_results_df = pd.json_normalize(results_dict["results"])
cols_not_needed = ["result.unexpected_list", "result.observed_value"]
cols_to_drop = [
c
for c in table_results_df.columns
if c.startswith("exception_info") or c in cols_not_needed
]

table_results_df = table_results_df.drop(columns=cols_to_drop)
table_results_df_list.append(table_results_df)

# generate id lists for each unexpected result set
query_df = table_results_df.loc[
(~table_results_df["result.unexpected_index_list"].isna())
& (table_results_df["result.unexpected_index_list"].values != "[]")
]

table_results_df["unexpected_id_list"] = pd.Series(dtype="object")
for i, row in query_df.iterrows():
table_results_df.loc[i, "unexpected_id_list"] = str(
list(df[id_field].iloc[row["result.unexpected_index_list"]])
)

results_df = pd.concat(table_results_df_list)

# add clean dataset name
results_df['dataset_name'] = results_df['expectation_config.kwargs.batch_id'].map(
lambda x: x.removeprefix('pandas-').removesuffix('_df_asset'))
results_df["dataset_name"] = results_df["expectation_config.kwargs.batch_id"].map(
lambda x: x.removeprefix("pandas-").removesuffix("_df_asset")
)

# add composite key for each specific test (so can be tracked over time)
results_df.insert(loc=0, column='expectation_key',
value=results_df.set_index(['expectation_config.type', 'dataset_name']).index.factorize()[0] + 1)
results_df['expectation_id'] = results_df['expectation_config.type'] + "_" + results_df['dataset_name']
results_df['import_date'] = datetime.today().strftime('%Y%m%d')
results_df.insert(
loc=0,
column="expectation_key",
value=results_df.set_index(
["expectation_config.type", "dataset_name"]
).index.factorize()[0]
+ 1,
)
results_df["expectation_id"] = (
results_df["expectation_config.type"] + "_" + results_df["dataset_name"]
)
results_df["import_date"] = datetime.today().strftime("%Y%m%d")

# set dtypes for Athena
dtype_dict = {'expectation_config.type': 'string',
'expectation_config.kwargs.batch_id': 'string',
'expectation_config.kwargs.column': 'string',
'expectation_config.kwargs.min_value': 'string',
'expectation_config.kwargs.max_value': 'string',
'result.element_count': 'bigint',
'result.unexpected_count': 'bigint',
'result.missing_count': 'bigint',
'result.partial_unexpected_list': 'array<string>',
'result.unexpected_index_list': 'array<bigint>',
'result.unexpected_index_query': 'string',
'expectation_config.kwargs.regex': 'string',
'expectation_config.kwargs.value_set': 'string',
'expectation_config.kwargs.column_list': 'string',
'import_date': 'string'}
dtype_dict = {
"expectation_config.type": "string",
"expectation_config.kwargs.batch_id": "string",
"expectation_config.kwargs.column": "string",
"expectation_config.kwargs.min_value": "string",
"expectation_config.kwargs.max_value": "string",
"result.element_count": "bigint",
"result.unexpected_count": "bigint",
"result.missing_count": "bigint",
"result.partial_unexpected_list": "array<string>",
"result.unexpected_index_list": "array<bigint>",
"result.unexpected_index_query": "string",
"expectation_config.kwargs.regex": "string",
"expectation_config.kwargs.value_set": "string",
"expectation_config.kwargs.column_list": "string",
"import_date": "string",
}

# write to s3
wr.s3.to_parquet(
Expand All @@ -127,11 +174,13 @@ def main():
table=target_table,
mode="overwrite",
dtype=dtype_dict,
schema_evolution=True
schema_evolution=True,
)

logger.info(f'Data Quality test results for NEC data loads written to {s3_target_location}')
logger.info(
f"Data Quality test results for NEC data loads written to {s3_target_location}"
)


if __name__ == '__main__':
if __name__ == "__main__":
main()