In #42838 we moved the terms index of all fields off-heap except the
`_id` field because we were worried it might make indexing slower. In
general, the indexing rate is only affected if explicit IDs are used, as
otherwise Elasticsearch almost never performs lookups in the terms
dictionary for the purpose of indexing. So it's quite wasteful to
require the terms index of `_id` to be loaded on-heap for users who have
append-only workloads. Furthermore I've been conducting benchmarks when
indexing with explicit ids on the http_logs dataset that suggest that
the slowdown is low enough that it's probably not worth forcing the terms
index to be kept on-heap. Here are some numbers for the median indexing
rate in docs/s:
| Run | Master | Patch |
| --- | ------- | ------- |
| 1 | 45851.2 | 46401.4 |
| 2 | 45192.6 | 44561.0 |
| 3 | 45635.2 | 44137.0 |
| 4 | 46435.0 | 44692.8 |
| 5 | 45829.0 | 44949.0 |
And now heap usage in MB for segments:
| Run | Master | Patch |
| --- | ------- | -------- |
| 1 | 41.1720 | 0.352083 |
| 2 | 45.1545 | 0.382534 |
| 3 | 41.7746 | 0.381285 |
| 4 | 45.3673 | 0.412737 |
| 5 | 45.4616 | 0.375063 |
Indexing rate decreased by 1.8% on average, while memory usage decreased
by more than 100x.
The `http_logs` dataset contains small documents and has a simple
indexing chain. More complex indexing chains, e.g. with more fields,
ingest pipelines, etc. would see an even lower decrease of indexing rate.
f993ef80f8