From commits-return-114498-archive-asf-public=cust-asf.ponee.io@beam.apache.org Tue Jul 27 18:02:16 2021
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Read transforms read data from an external source and return a PCollection
representation of the data for use by your pipeline. You can use a read
transform at any point while constructing your pipeline to create a new
-PCollection
, though it will be most common at the start of your pipeline.
PCollection<String> PCollection
, though it will be most common at the start of your pipeline.PCollection<String> PCollection
's data at any point in your pipeline.output.apply(TextIOPCollection
's data at any point in your pipeline.lines := textio.Flatten transform to create a single
PCollection
.5.3.2. Writing to multiple output files
For file-based output data, write transforms write to multiple output files by
default. When you pass an output file name to a write transform, the file name
@@ -1863,7 +1863,9 @@ location. Each file has the prefix “numbers”, a numeric tag, and the
“.csv”.
5.4. Beam-provided I/O transforms
See the Beam-provided I/O Transforms
+ '/path/to/numbers', file_name_suffix='.csv')
5.4. Beam-provided I/O transforms
See the Beam-provided I/O Transforms
page for a list of the currently available I/O transforms.
6. Schemas
Often, the types of the records being processed have an obvious structure. Common Beam sources produce
JSON, Avro, Protocol Buffer, or database row objects; all of these types have well defined structures,
structures that can often be determined by examining the type. Even within a SDK pipeline, Simple Java POJOs
@@ -3919,7 +3921,7 @@ kafka_records = (
ImplicitSchemaPayloadBuilder({'data': u'0'}),
<Address of expansion service>))
assert_that(res, equal_to(['0a', '0b']))
-
After the job has been submitted to the Beam runner, shutdown the expansion service by terminating the expansion service process.
13.3. Runner Support
Currently, portable runners such as Flink, Spark, and the Direct runner can be used with multi-language pipelines.
Google Cloud Dataflow supports multi-language pipelines through the Dataflow Runner v2 backend architecture.
After the job has been submitted to the Beam runner, shutdown the expansion service by terminating the expansion service process.
13.3. Runner Support
Currently, portable runners such as Flink, Spark, and the Direct runner can be used with multi-language pipelines.
Google Cloud Dataflow supports multi-language pipelines through the Dataflow Runner v2 backend architecture.
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