Docs: remove notes on sparsity. (#30905)

Sparsity is less of a concern since 6.0.

Closes #30833

docs/reference/how-to/general.asciidoc

@@ -40,94 +40,3 @@ better. For instance if a user searches for two words `foo` and `bar`, a match
 across different chapters is probably very poor, while a match within the same
 paragraph is likely good.
 
-[float]
-[[sparsity]]
-=== Avoid sparsity
-
-The data-structures behind Lucene, which Elasticsearch relies on in order to
-index and store data, work best with dense data, ie. when all documents have the
-same fields. This is especially true for fields that have norms enabled (which
-is the case for `text` fields by default) or doc values enabled (which is the
-case for numerics, `date`, `ip` and `keyword` by default).
-
-The reason is that Lucene internally identifies documents with so-called doc
-ids, which are integers between 0 and the total number of documents in the
-index. These doc ids are used for communication between the internal APIs of
-Lucene: for instance searching on a term with a `match` query produces an
-iterator of doc ids, and these doc ids are then used to retrieve the value of
-the `norm` in order to compute a score for these documents. The way this `norm`
-lookup is implemented currently is by reserving one byte for each document.
-The `norm` value for a given doc id can then be retrieved by reading the
-byte at index `doc_id`. While this is very efficient and helps Lucene quickly
-have access to the `norm` values of every document, this has the drawback that
-documents that do not have a value will also require one byte of storage.
-
-In practice, this means that if an index has `M` documents, norms will require
-`M` bytes of storage *per field*, even for fields that only appear in a small
-fraction of the documents of the index. Although slightly more complex with doc
-values due to the fact that doc values have multiple ways that they can be
-encoded depending on the type of field and on the actual data that the field
-stores, the problem is very similar. In case you wonder: `fielddata`, which was
-used in Elasticsearch pre-2.0 before being replaced with doc values, also
-suffered from this issue, except that the impact was only on the memory
-footprint since `fielddata` was not explicitly materialized on disk.
-
-Note that even though the most notable impact of sparsity is on storage
-requirements, it also has an impact on indexing speed and search speed since
-these bytes for documents that do not have a field still need to be written
-at index time and skipped over at search time.
-
-It is totally fine to have a minority of sparse fields in an index. But beware
-that if sparsity becomes the rule rather than the exception, then the index
-will not be as efficient as it could be.
-
-This section mostly focused on `norms` and `doc values` because those are the
-two features that are most affected by sparsity. Sparsity also affect the
-efficiency of the inverted index (used to index `text`/`keyword` fields) and
-dimensional points (used to index `geo_point` and numerics) but to a lesser
-extent.
-
-Here are some recommendations that can help avoid sparsity:
-
-[float]
-==== Avoid putting unrelated data in the same index
-
-You should avoid putting documents that have totally different structures into
-the same index in order to avoid sparsity. It is often better to put these
-documents into different indices, you could also consider giving fewer shards
-to these smaller indices since they will contain fewer documents overall.
-
-Note that this advice does not apply in the case that you need to use
-parent/child relations between your documents since this feature is only
-supported on documents that live in the same index.
-
-[float]
-==== Normalize document structures
-
-Even if you really need to put different kinds of documents in the same index,
-maybe there are opportunities to reduce sparsity. For instance if all documents
-in the index have a timestamp field but some call it `timestamp` and others
-call it `creation_date`, it would help to rename it so that all documents have
-the same field name for the same data.
-
-[float]
-==== Avoid types
-
-Types might sound like a good way to store multiple tenants in a single index.
-They are not: given that types store everything in a single index, having
-multiple types that have different fields in a single index will also cause
-problems due to sparsity as described above. If your types do not have very
-similar mappings, you might want to consider moving them to a dedicated index.
-
-[float]
-==== Disable `norms` and `doc_values` on sparse fields
-
-If none of the above recommendations apply in your case, you might want to
-check whether you actually need `norms` and `doc_values` on your sparse fields.
-`norms` can be disabled if producing scores is not necessary on a field, this is
-typically true for fields that are only used for filtering. `doc_values` can be
-disabled on fields that are neither used for sorting nor for aggregations.
-Beware that this decision should not be made lightly since these parameters
-cannot be changed on a live index, so you would have to reindex if you realize
-that you need `norms` or `doc_values`.
-