I’m having a bucket in GCS that contain list of JSON files. I came to extract the list of the file names using
def list_blobs(bucket_name):
storage_client = storage.Client()
blobs = storage_client.list_blobs(bucket_name)
json_paths = []
for blob in blobs:
json_paths.append(f"gs://{bucket_name}/{blob.name}")
return json_paths
Now I want to pass this list of filenames to apache beam to read them. I wrote this code, but it doesn’t seem a good pattern
for i,file in enumerate(list_files):
print("parsing file:", file)
concat_data = (p |'Data {}'.format(i) >> ReadFromText(file)
)
final_result.append(concat_data)
Have you faced the same issue before?
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Answer
In the end I came to use the google-cloud storage as reading API for this.
Listing all elements of the bucket
def list_blobs(bucket_name):
"""Lists all the blobs in the bucket."""
storage_client = storage.Client()
blobs = storage_client.list_blobs(bucket_name)
json_paths = []
for blob in blobs:
#json_paths.append(f"gs://{bucket_name}/{blob.name}")
json_paths.append(f"{blob.name}")
return json_paths
and I created this ParDo for reading the content
class ReadFileContent(beam.DoFn):
def setup(self):
# Called whenever the DoFn instance is deserialized on the worker.
# This means it can be called more than once per worker because multiple instances of a given DoFn subclass may be created (e.g., due to parallelization, or due to garbage collection after a period of disuse).
# This is a good place to connect to database instances, open network connections or other resources.
self.storage_client = storage.Client()
def process(self, file_name, bucket_name):
bucket = self.storage_client.get_bucket(bucket_name)
blob = bucket.get_blob(file_name)
yield blob.download_as_string()
And mu pipeline looked like this:
list_files = list_blobs(bucket_name)
with beam.Pipeline(options=pipeline_options) as p:
results = (
p | 'Create' >> beam.Create(list_files)
| 'Read each file content' >> beam.ParDo(ReadFileContent(), bucket_name)
| 'next transformation' >> ...