In Python, appending strings is not guaranteed to be constant-time,
since they are documented to be immutable. In some corner cases,
CPython is able to make these operations constant-time, but reaching
into ETree objects is not such a case.
This leads to parse times being quadratic in the size of the text in
the input in pathological cases where parsing outputs a large number
of adjacent text nodes which must be combined (e.g. HTML-escaped
values). Specifically, we expect doubling the size of the input to
result in approximately doubling the time to parse; instead, we
observe quadratic behavior:
```
In [1]: import html5lib
In [2]: %timeit -n1 -r5 html5lib.parse("<" * 200000)
2.99 s ± 269 ms per loop (mean ± std. dev. of 5 runs, 1 loop each)
In [3]: %timeit -n1 -r5 html5lib.parse("<" * 400000)
6.7 s ± 242 ms per loop (mean ± std. dev. of 5 runs, 1 loop each)
In [4]: %timeit -n1 -r5 html5lib.parse("<" * 800000)
19.5 s ± 1.48 s per loop (mean ± std. dev. of 5 runs, 1 loop each)
```
Switch from appending to the internal `str`, to appending text to an
array of text chunks, as appends can be done in constant time. Using
`bytearray` is a similar solution, but benchmarks slightly worse
because the strings must be encoded before being appended.
This improves parsing of text documents noticeably:
```
In [1]: import html5lib
In [2]: %timeit -n1 -r5 html5lib.parse("<" * 200000)
2.3 s ± 373 ms per loop (mean ± std. dev. of 5 runs, 1 loop each)
In [3]: %timeit -n1 -r5 html5lib.parse("<" * 400000)
3.85 s ± 29.7 ms per loop (mean ± std. dev. of 5 runs, 1 loop each)
In [4]: %timeit -n1 -r5 html5lib.parse("<" * 800000)
8.04 s ± 317 ms per loop (mean ± std. dev. of 5 runs, 1 loop each)
```