105 lines
5.2 KiB
Plaintext
105 lines
5.2 KiB
Plaintext
[[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`.
|
|
|