Removes `testenv` annotations and related code. These annotations originally let you skip x-pack snippet tests in the docs. However, that's no longer possible.
Relates to #79309, #31619
The char filter replaces the previous default of `first_non_blank_line`.
`first_non_blank_line` worked well to figure out what line had characters at all, but log lines
like the following were handled poorly:
```
--------------------------------------------------------------------------------
Alias 'foo' already exists and this prevents setting up ILM for logs
--------------------------------------------------------------------------------
```
When combined with the `ml_standard` tokenizer, the first line was used:
```
--------------------------------------------------------------------------------
```
This has no valid tokens for our standard tokenizer. Consequently, no tokens were found by `ml_standard` tokenizer.
The new filter, `first_line_with_letters`, returns the first line with any letter character (e.g. `Character#isLetter` returns true).
Given the previously poorly handled log, when combining with our `ml_standard` tokenizer, we get the following, more appropriate, tokens:
```
"tokens" : ["Alias", "foo", "already", "exists", "and", "this", "prevents", "setting", "up", "ILM", "for", "logs"]
```
Categorization jobs created once the entire cluster is upgraded to
version 7.14 or higher will default to using the new ml_standard
tokenizer rather than the previous default of the ml_classic
tokenizer, and will incorporate the new first_non_blank_line char
filter so that categorization is based purely on the first non-blank
line of each message.
The difference between the ml_classic and ml_standard tokenizers
is that ml_classic splits on slashes and colons, so creates multiple
tokens from URLs and filesystem paths, whereas ml_standard attempts
to keep URLs, email addresses and filesystem paths as single tokens.
It is still possible to config the ml_classic tokenizer if you
prefer: just provide a categorization_analyzer within your
analysis_config and whichever tokenizer you choose (which could be
ml_classic or any other Elasticsearch tokenizer) will be used.
To opt out of using first_non_blank_line as a default char filter,
you must explicitly specify a categorization_analyzer that does not
include it.
If no categorization_analyzer is specified but categorization_filters
are specified then the categorization filters are converted to char
filters applied that are applied after first_non_blank_line.
Closeselastic/ml-cpp#1724
