damccorm commented on code in PR #32412:
URL: https://github.com/apache/beam/pull/32412#discussion_r1757590423
##########
website/www/site/content/en/blog/unit-testing-in-beam.md:
##########
@@ -178,20 +178,21 @@ def test_error_message_wrong_length(self, mock_get_data):
result
```
-###The following cover other testing best practices:
+## Other testing best practices:
-1) Test all error messages you raise.
-2) Cover any edge cases that might be present in your data.
-3) Notice that in example 1, we could have written the `beam.Map` step with
lambda functions:
+1) Test all error messages that you raise.
+2) Cover any edge cases that might exist in your data.
+3) Example 1 could have written the `beam.Map` step with lambda functions
instead of with `beam.Map(median_house_value_per_bedroom)`:
```
beam.Map(lambda x: x.strip().split(',')) | beam.Map(lambda x:
float(x[8])/float(x[4])
```
-, instead of `beam.Map(median_house_value_per_bedroom)`. The latter
(separating lambdas into a helper function) is the recommended approach for
more testable code, as changes to the function would be modularized.
-5) Use the `assert_that` statement to ensure that PCollection values match up
correctly, such as done above
+Separating lambdas into a helper function by using
`beam.Map(median_house_value_per_bedroom)` is the recommended approach for more
testable code, because changes to the function would be modularized.
+
+4) Use the `assert_that` statement to ensure that `PCollection` values match
correctly, as in the previous example.
-For more pointed guidance on testing on Beam/Dataflow, see the [Google Cloud
documentation](https://cloud.google.com/dataflow/docs/guides/develop-and-test-pipelines).
Additionally, see some more examples of unit testing in Beam
[here](https://github.com/apache/beam/blob/736cf50430b375d32093e793e1556567557614e9/sdks/python/apache_beam/ml/inference/base_test.py#L262).
+For more guidance about testing on Beam and Dataflow, see the [Google Cloud
documentation](https://cloud.google.com/dataflow/docs/guides/develop-and-test-pipelines).
For more examples of unit testing in Beam, see [the `base_test.py`
code](https://github.com/apache/beam/blob/736cf50430b375d32093e793e1556567557614e9/sdks/python/apache_beam/ml/inference/base_test.py#L262).
Review Comment:
```suggestion
For more guidance about testing on Beam and Dataflow, see the [Google Cloud
documentation](https://cloud.google.com/dataflow/docs/guides/develop-and-test-pipelines).
For more examples of unit testing in Beam, see [the base_test.py
code](https://github.com/apache/beam/blob/736cf50430b375d32093e793e1556567557614e9/sdks/python/apache_beam/ml/inference/base_test.py#L262).
```
This renders awkwardly with the backticks
##########
website/www/site/content/en/blog/unit-testing-in-beam.md:
##########
@@ -178,20 +178,21 @@ def test_error_message_wrong_length(self, mock_get_data):
result
```
-###The following cover other testing best practices:
+## Other testing best practices:
-1) Test all error messages you raise.
-2) Cover any edge cases that might be present in your data.
-3) Notice that in example 1, we could have written the `beam.Map` step with
lambda functions:
+1) Test all error messages that you raise.
+2) Cover any edge cases that might exist in your data.
+3) Example 1 could have written the `beam.Map` step with lambda functions
instead of with `beam.Map(median_house_value_per_bedroom)`:
```
beam.Map(lambda x: x.strip().split(',')) | beam.Map(lambda x:
float(x[8])/float(x[4])
```
-, instead of `beam.Map(median_house_value_per_bedroom)`. The latter
(separating lambdas into a helper function) is the recommended approach for
more testable code, as changes to the function would be modularized.
-5) Use the `assert_that` statement to ensure that PCollection values match up
correctly, such as done above
+Separating lambdas into a helper function by using
`beam.Map(median_house_value_per_bedroom)` is the recommended approach for more
testable code, because changes to the function would be modularized.
+
+4) Use the `assert_that` statement to ensure that `PCollection` values match
correctly, as in the previous example.
-For more pointed guidance on testing on Beam/Dataflow, see the [Google Cloud
documentation](https://cloud.google.com/dataflow/docs/guides/develop-and-test-pipelines).
Additionally, see some more examples of unit testing in Beam
[here](https://github.com/apache/beam/blob/736cf50430b375d32093e793e1556567557614e9/sdks/python/apache_beam/ml/inference/base_test.py#L262).
+For more guidance about testing on Beam and Dataflow, see the [Google Cloud
documentation](https://cloud.google.com/dataflow/docs/guides/develop-and-test-pipelines).
For more examples of unit testing in Beam, see [the `base_test.py`
code](https://github.com/apache/beam/blob/736cf50430b375d32093e793e1556567557614e9/sdks/python/apache_beam/ml/inference/base_test.py#L262).
Review Comment:
https://apache-beam-website-pull-requests.storage.googleapis.com/32412/blog/unit-testing-in-beam/index.html
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