def _set_report_processor(self, parquet_file, daily=False):
"""Return the correct ReportParquetProcessor."""
s3_hive_table_path = get_hive_table_path(
self.account, self.provider_type, report_type=self.report_type, daily=daily
)
processor = None
if self.provider_type in (Provider.PROVIDER_AWS, Provider.PROVIDER_AWS_LOCAL):
processor = AWSReportParquetProcessor(
self.manifest_id, self.account, s3_hive_table_path, self.provider_uuid, parquet_file
)
elif self.provider_type in (Provider.PROVIDER_OCP,):
processor = OCPReportParquetProcessor(
self.manifest_id, self.account, s3_hive_table_path, self.provider_uuid, parquet_file, self.report_type
)
elif self.provider_type in (Provider.PROVIDER_AZURE, Provider.PROVIDER_AZURE_LOCAL):
processor = AzureReportParquetProcessor(
self.manifest_id, self.account, s3_hive_table_path, self.provider_uuid, parquet_file
)
elif self.provider_type in (Provider.PROVIDER_GCP, Provider.PROVIDER_GCP_LOCAL):
processor = GCPReportParquetProcessor(
self.manifest_id, self.account, s3_hive_table_path, self.provider_uuid, parquet_file
)
if processor is None:
msg = f"There is no ReportParquetProcessor for provider type {self.provider_type}"
raise ParquetReportProcessorError(msg)
return processor
def test_get_hive_table_path(self):
"""Test that we resolve the path for a Hive table."""
account = "10001"
provider_type = Provider.PROVIDER_AWS
expected_path_prefix = f"{Config.WAREHOUSE_PATH}/{Config.PARQUET_DATA_TYPE}"
expected_path = f"{expected_path_prefix}/{account}/{provider_type}"
path = common_utils.get_hive_table_path(account, provider_type)
self.assertEqual(path, expected_path)
# Test with report_type
report_type = "pod_report"
expected_path = f"{expected_path_prefix}/{account}/{provider_type}/{report_type}"
path = common_utils.get_hive_table_path(account, provider_type, report_type=report_type)
self.assertEqual(path, expected_path)
def convert_csv_to_parquet( # noqa: C901
self,
request_id,
s3_csv_path,
s3_parquet_path,
local_path,
manifest_id,
csv_filename,
converters={},
post_processor=None,
context={},
report_type=None,
):
"""
Convert CSV files to parquet on S3.
"""
if s3_csv_path is None or s3_parquet_path is None or local_path is None:
msg = (
f"Invalid paths provided to convert_csv_to_parquet."
f"CSV path={s3_csv_path}, Parquet path={s3_parquet_path}, and local_path={local_path}."
)
LOG.error(log_json(request_id, msg, context))
return False
msg = f"Running convert_csv_to_parquet on file {csv_filename} in S3 path {s3_csv_path}."
LOG.info(log_json(request_id, msg, context))
kwargs = {}
parquet_file = None
csv_file = f"{s3_csv_path}/{csv_filename}"
if csv_filename.lower().endswith(CSV_EXT):
ext = -len(CSV_EXT)
parquet_file = f"{csv_filename[:ext]}.parquet"
elif csv_filename.lower().endswith(CSV_GZIP_EXT):
ext = -len(CSV_GZIP_EXT)
parquet_file = f"{csv_filename[:ext]}.parquet"
kwargs = {"compression": "gzip"}
else:
msg = f"File {csv_filename} is not valid CSV. Conversion to parquet skipped."
LOG.warn(log_json(request_id, msg, context))
return False
Path(local_path).mkdir(parents=True, exist_ok=True)
tmpfile = f"{local_path}/{csv_filename}"
try:
s3_resource = get_s3_resource()
csv_obj = s3_resource.Object(bucket_name=settings.S3_BUCKET_NAME,
key=csv_file)
csv_obj.download_file(tmpfile)
except Exception as err:
shutil.rmtree(local_path, ignore_errors=True)
msg = f"File {csv_filename} could not obtained for parquet conversion. Reason: {str(err)}"
LOG.warn(log_json(request_id, msg, context))
return False
output_file = f"{local_path}/{parquet_file}"
try:
col_names = pd.read_csv(tmpfile, nrows=0, **kwargs).columns
converters.update(
{col: str
for col in col_names if col not in converters})
data_frame = pd.read_csv(tmpfile, converters=converters, **kwargs)
if post_processor:
data_frame = post_processor(data_frame)
data_frame.to_parquet(output_file,
allow_truncated_timestamps=True,
coerce_timestamps="ms")
except Exception as err:
shutil.rmtree(local_path, ignore_errors=True)
msg = f"File {csv_filename} could not be written as parquet to temp file {output_file}. Reason: {str(err)}"
LOG.warn(log_json(request_id, msg, context))
return False
try:
with open(output_file, "rb") as fin:
data = BytesIO(fin.read())
copy_data_to_s3_bucket(request_id,
s3_parquet_path,
parquet_file,
data,
manifest_id=manifest_id,
context=context)
except Exception as err:
shutil.rmtree(local_path, ignore_errors=True)
s3_key = f"{s3_parquet_path}/{parquet_file}"
msg = f"File {csv_filename} could not be written as parquet to S3 {s3_key}. Reason: {str(err)}"
LOG.warn(log_json(request_id, msg, context))
return False
s3_hive_table_path = get_hive_table_path(context.get("account"),
self._provider_type,
report_type=report_type)
if not self.presto_table_exists.get(report_type):
self.create_parquet_table(
context.get("account"),
context.get("provider_uuid"),
manifest_id,
s3_hive_table_path,
output_file,
report_type,
)
shutil.rmtree(local_path, ignore_errors=True)
return True
def convert_csv_to_parquet( # noqa: C901
self,
request_id,
s3_csv_path,
s3_parquet_path,
local_path,
manifest_id,
csv_filename,
converters={},
post_processor=None,
context={},
report_type=None,
):
"""
Convert CSV files to parquet on S3.
"""
csv_path, csv_name = os.path.split(csv_filename)
if s3_csv_path is None or s3_parquet_path is None or local_path is None:
msg = (
f"Invalid paths provided to convert_csv_to_parquet."
f"CSV path={s3_csv_path}, Parquet path={s3_parquet_path}, and local_path={local_path}."
)
LOG.error(log_json(request_id, msg, context))
return False
msg = f"Running convert_csv_to_parquet on file {csv_filename} in S3 path {s3_csv_path}."
LOG.info(log_json(request_id, msg, context))
kwargs = {}
parquet_file = None
if csv_name.lower().endswith(CSV_EXT):
ext = -len(CSV_EXT)
parquet_base_filename = f"{csv_name[:ext]}"
elif csv_name.lower().endswith(CSV_GZIP_EXT):
ext = -len(CSV_GZIP_EXT)
parquet_base_filename = f"{csv_name[:ext]}"
kwargs = {"compression": "gzip"}
else:
msg = f"File {csv_name} is not valid CSV. Conversion to parquet skipped."
LOG.warn(log_json(request_id, msg, context))
return False
Path(local_path).mkdir(parents=True, exist_ok=True)
try:
col_names = pd.read_csv(csv_filename, nrows=0, **kwargs).columns
converters.update(
{col: str
for col in col_names if col not in converters})
data_frame = pd.read_csv(
csv_filename,
converters=converters,
chunksize=settings.PARQUET_PROCESSING_BATCH_SIZE,
**kwargs)
with pd.read_csv(csv_filename,
converters=converters,
chunksize=settings.PARQUET_PROCESSING_BATCH_SIZE,
**kwargs) as reader:
for i, data_frame in enumerate(reader):
parquet_filename = f"{parquet_base_filename}_{i}{PARQUET_EXT}"
parquet_file = f"{local_path}/{parquet_filename}"
if post_processor:
data_frame = post_processor(data_frame)
data_frame.to_parquet(parquet_file,
allow_truncated_timestamps=True,
coerce_timestamps="ms")
try:
with open(parquet_file, "rb") as fin:
data = BytesIO(fin.read())
copy_data_to_s3_bucket(
request_id,
s3_parquet_path,
parquet_filename,
data,
manifest_id=manifest_id,
context=context,
)
msg = f"{parquet_file} sent to S3."
LOG.info(msg)
except Exception as err:
shutil.rmtree(local_path, ignore_errors=True)
s3_key = f"{s3_parquet_path}/{parquet_file}"
msg = f"File {csv_filename} could not be written as parquet to S3 {s3_key}. Reason: {str(err)}"
LOG.warn(log_json(request_id, msg, context))
return False
except Exception as err:
shutil.rmtree(local_path, ignore_errors=True)
msg = (
f"File {csv_filename} could not be written as parquet to temp file {parquet_file}. Reason: {str(err)}"
)
LOG.warn(log_json(request_id, msg, context))
return False
s3_hive_table_path = get_hive_table_path(context.get("account"),
self._provider_type,
report_type=report_type)
if not self.presto_table_exists.get(report_type):
self.create_parquet_table(
context.get("account"),
context.get("provider_uuid"),
manifest_id,
s3_hive_table_path,
parquet_file,
report_type,
)
# Delete the local parquet files
shutil.rmtree(local_path, ignore_errors=True)
# Now we can delete the local CSV
if os.path.exists(csv_filename):
os.remove(csv_filename)
return True
