Accessing information (Metadata) in the file name

Question

My filename contains information that I need in my pipeline, for example the identifier for my data points is part of the filename and not a field in the data. e.g Every wind turbine generates a file turbine-loc-001-007.csv. e.g And I need the loc data within the pipeline.

Answer 1

Java (sdk 2.9.0):

Beams TextIO readers do not give access to the filename itself, for these use cases we need to make use of FileIO to match the files and gain access to the information stored in the file name. Unlike TextIO, the reading of the file needs to be taken care of by the user in transforms downstream of the FileIO read. The results of a FileIO read is a PCollection the ReadableFile class contains the file name as metadata which can be used along with the contents of the file.

FileIO does have a convenience method readFullyAsUTF8String() which will read the entire file into a String object, this will read the whole file into memory first. If memory is a concern you can work directly with the file with utility classes like FileSystems.

From: Document Link

PCollection<KV<String, String>> filesAndContents = p
     .apply(FileIO.match().filepattern("hdfs://path/to/*.gz"))
     // withCompression can be omitted - by default compression is detected from the filename.
     .apply(FileIO.readMatches().withCompression(GZIP))
     .apply(MapElements
         // uses imports from TypeDescriptors
         .into(KVs(strings(), strings()))
         .via((ReadableFile f) -> KV.of(
             f.getMetadata().resourceId().toString(), f.readFullyAsUTF8String())));

Python (sdk 2.9.0):

For 2.9.0 for python you will need to collect the list of URI from outside of the Dataflow pipeline and feed it in as a parameter to the pipeline. For example making use of FileSystems to read in the list of files via a Glob pattern and then passing that to a PCollection for processing.

Once fileio see PR https://github.com/apache/beam/pull/7791/ is available, the following code would also be an option for python.

import apache_beam as beam
from apache_beam.io import fileio

with beam.Pipeline() as p:
  readable_files = (p 
                    | fileio.MatchFiles(‘hdfs://path/to/*.txt’)
                    | fileio.ReadMatches()
                    | beam.Reshuffle())
  files_and_contents = (readable_files 
                        | beam.Map(lambda x: (x.metadata.path, 
                                              x.read_utf8()))