OpenSearch/docs/reference/how-to/general.asciidoc

[[general-recommendations]]
== General recommendations

[float]
[[large-size]]
=== Don't return large result sets

Elasticsearch is designed as a search engine, which makes it very good at
getting back the top documents that match a query. However, it is not as good
for workloads that fall into the database domain, such as retrieving all
documents that match a particular query. If you need to do this, make sure to
use the <<search-request-scroll,Scroll>> API.

[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`.