如何从GCP bucket中读取Apache Beam中的多个文件

Question

我试图用Apache Beam阅读并应用一些子集在GCP中的多个文件上。我准备了两个管道，它们只适用于一个文件，但当我在多个文件上尝试它们时失败了。除此之外，如果可能的话，我会很方便地将我的管道组合成一个，或者有一种方法来编排它们，以便它们有序地工作。现在管道在本地工作，但我的最终目标是用Dataflow运行它们。

def toJson(file):
    with open(file) as f:
        return json.load(f)


 with beam.Pipeline(options=PipelineOptions()) as p:
       files = (p
        | beam.io.textio.ReadFromText("gs://my_bucket/file1.txt.gz", skip_header_lines = 0)
        | beam.io.WriteToText("/home/test",
                   file_name_suffix=".json", num_shards=1 , append_trailing_newlines = True))

 with beam.Pipeline(options=PipelineOptions()) as p:
lines = (p  
            | 'read_data' >> beam.Create(['test-00000-of-00001.json'])
            | "toJson" >> beam.Map(toJson)
            | "takeItems" >> beam.FlatMap(lambda line: line["Items"])
            | "takeSubjects" >> beam.FlatMap(lambda line: line['data']['subjects'])
            | beam.combiners.Count.PerElement()
            | beam.io.WriteToText("/home/items",
                   file_name_suffix=".txt", num_shards=1 , append_trailing_newlines = True))

Answer 1

我解决了如何使它工作于多个文件，但不能使它在一个管道内运行。我使用循环，然后波束。平坦选项。

以下是我的解决方案：

file_list = ["gs://my_bucket/file*.txt.gz"]
res_list = ["/home/subject_test_{}-00000-of-00001.json".format(i) for i in range(len(file_list))]

with beam.Pipeline(options=PipelineOptions()) as p:
    for i,file in enumerate(file_list):
       (p 
        | "Read Text {}".format(i) >> beam.io.textio.ReadFromText(file, skip_header_lines = 0)
        | "Write TExt {}".format(i) >> beam.io.WriteToText("/home/subject_test_{}".format(i),
                   file_name_suffix=".json", num_shards=1 , append_trailing_newlines = True))

pcols = []
with beam.Pipeline(options=PipelineOptions()) as p:
   for i,res in enumerate(res_list):
         pcol = (p   | 'read_data_{}'.format(i) >> beam.Create([res])
            | "toJson_{}".format(i) >> beam.Map(toJson)
            | "takeItems_{}".format(i) >> beam.FlatMap(lambda line: line["Items"])
            | "takeSubjects_{}".format(i) >> beam.FlatMap(lambda line: line['data']['subjects']))
        pcols.append(pcol)
   out = (pcols
    | beam.Flatten()
    | beam.combiners.Count.PerElement()
    | beam.io.WriteToText("/home/items",
                   file_name_suffix=".txt", num_shards=1 , append_trailing_newlines = True))