def _write_parquet_to_file(self,
file_path,
file_name,
data_frame,
file_type=None):
"""Write Parquet file and send to S3."""
s3_path = self._determin_s3_path(file_type)
data_frame.to_parquet(file_path,
allow_truncated_timestamps=True,
coerce_timestamps="ms",
index=False)
try:
with open(file_path, "rb") as fin:
copy_data_to_s3_bucket(self.request_id,
s3_path,
file_name,
fin,
manifest_id=self.manifest_id,
context=self.error_context)
msg = f"{file_path} sent to S3."
LOG.info(log_json(self.request_id, msg, self.error_context))
except Exception as err:
s3_key = f"{self.parquet_path_s3}/{file_path}"
msg = f"File {file_name} could not be written as parquet to S3 {s3_key}. Reason: {str(err)}"
LOG.warn(log_json(self.request_id, msg, self.error_context))
return False
finally:
self.files_to_remove.append(file_path)
return True
def _write_parquet_to_file(self, file_path, file_name, data_frame, file_type=None):
"""Write Parquet file and send to S3."""
if self._provider_type == Provider.PROVIDER_GCP:
# We need to determine the parquet file path based off
# of the start of the invoice month and usage start for GCP.
s3_path = self._determin_s3_path_for_gcp(file_type, file_name)
else:
s3_path = self._determin_s3_path(file_type)
data_frame.to_parquet(file_path, allow_truncated_timestamps=True, coerce_timestamps="ms", index=False)
try:
with open(file_path, "rb") as fin:
copy_data_to_s3_bucket(
self.tracing_id, s3_path, file_name, fin, manifest_id=self.manifest_id, context=self.error_context
)
msg = f"{file_path} sent to S3."
LOG.info(log_json(self.tracing_id, msg, self.error_context))
except Exception as err:
s3_key = f"{self.parquet_path_s3}/{file_path}"
msg = f"File {file_name} could not be written as parquet to S3 {s3_key}. Reason: {str(err)}"
LOG.warn(log_json(self.tracing_id, msg, self.error_context))
return False
finally:
self.files_to_remove.append(file_path)
return True
def test_copy_data_to_s3_bucket(self):
"""Test copy_data_to_s3_bucket."""
upload = utils.copy_data_to_s3_bucket("request_id", "path", "filename", "data", "manifest_id")
self.assertEqual(upload, None)
with patch("masu.util.aws.common.settings", ENABLE_S3_ARCHIVING=True):
with patch("masu.util.aws.common.get_s3_resource") as mock_s3:
upload = utils.copy_data_to_s3_bucket("request_id", "path", "filename", "data", "manifest_id")
self.assertIsNotNone(upload)
with patch("masu.util.aws.common.settings", ENABLE_S3_ARCHIVING=True):
with patch("masu.util.aws.common.get_s3_resource") as mock_s3:
mock_s3.side_effect = ClientError({}, "Error")
upload = utils.copy_data_to_s3_bucket("request_id", "path", "filename", "data", "manifest_id")
self.assertEqual(upload, None)
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
