OpenSearch/docs/reference/migration/migrate_5_0/mapping.asciidoc

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[[breaking_50_mapping_changes]]
=== Mapping changes
==== `string` fields replaced by `text`/`keyword` fields
The `string` field datatype has been replaced by the `text` field for full
text analyzed content, and the `keyword` field for not-analyzed exact string
values. For backwards compatibility purposes, during the 5.x series:
* `string` fields on pre-5.0 indices will function as before.
* New `string` fields can be added to pre-5.0 indices as before.
* `text` and `keyword` fields can also be added to pre-5.0 indices.
* When adding a `string` field to a new index, the field mapping will be
rewritten as a `text` or `keyword` field if possible, otherwise
an exception will be thrown. Certain configurations that were possible
with `string` fields are no longer possible with `text`/`keyword` fields
such as enabling `term_vectors` on a not-analyzed `keyword` field.
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==== Default string mappings
String mappings now have the following default mappings:
[source,js]
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---------------
{
"type": "text",
"fields": {
"keyword": {
"type": "keyword",
"ignore_above": 256
}
}
}
---------------
This allows to perform full-text search on the original field name and to sort
and run aggregations on the sub keyword field.
Use the new points API to index numeric fields. #17746 This makes all numeric fields including `date`, `ip` and `token_count` use points instead of the inverted index as a lookup structure. This is expected to perform worse for exact queries, but faster for range queries. It also requires less storage. Notes about how the change works: - Numeric mappers have been split into a legacy version that is essentially the current mapper, and a new version that uses points, eg. LegacyDateFieldMapper and DateFieldMapper. - Since new and old fields have the same names, the decision about which one to use is made based on the index creation version. - If you try to force using a legacy field on a new index or a field that uses points on an old index, you will get an exception. - IP addresses now support IPv6 via Lucene's InetAddressPoint and store them in SORTED_SET doc values using the same encoding (fixed length of 16 bytes and sortable). - The internal MappedFieldType that is stored by the new mappers does not have any of the points-related properties set. Instead, it keeps setting the index options when parsing the `index` property of mappings and does `if (fieldType.indexOptions() != IndexOptions.NONE) { // add point field }` when parsing documents. Known issues that won't fix: - You can't use numeric fields in significant terms aggregations anymore since this requires document frequencies, which points do not record. - Term queries on numeric fields will now return constant scores instead of giving better scores to the rare values. Known issues that we could work around (in follow-up PRs, this one is too large already): - Range queries on `ip` addresses only work if both the lower and upper bounds are inclusive (exclusive bounds are not exposed in Lucene). We could either decide to implement it, or drop range support entirely and tell users to query subnets using the CIDR notation instead. - Since IP addresses now use a different representation for doc values, aggregations will fail when running a terms aggregation on an ip field on a list of indices that contains both pre-5.0 and 5.0 indices. - The ip range aggregation does not work on the new ip field. We need to either implement range aggs for SORTED_SET doc values or drop support for ip ranges and tell users to use filters instead. #17700 Closes #16751 Closes #17007 Closes #11513
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==== Numeric fields
Numeric fields are now indexed with a completely different data-structure, called
BKD tree, that is expected to require less disk space and be faster for range
queries than the previous way that numerics were indexed.
Term queries will return constant scores now, while they used to return higher
scores for rare terms due to the contribution of the document frequency, which
this new BKD structure does not record. If scoring is needed, then it is advised
to map the numeric fields as <<keyword,`keyword`s>> too.
Note that this <<keyword,`keyword`>> mapping do not need to replace the numeric
mapping. For instance if you need both sorting and scoring on your numeric field,
you could map it both as a number and a `keyword` using <<multi-fields>>:
[source,js]
--------------------------------------------------
PUT my_index
Use the new points API to index numeric fields. #17746 This makes all numeric fields including `date`, `ip` and `token_count` use points instead of the inverted index as a lookup structure. This is expected to perform worse for exact queries, but faster for range queries. It also requires less storage. Notes about how the change works: - Numeric mappers have been split into a legacy version that is essentially the current mapper, and a new version that uses points, eg. LegacyDateFieldMapper and DateFieldMapper. - Since new and old fields have the same names, the decision about which one to use is made based on the index creation version. - If you try to force using a legacy field on a new index or a field that uses points on an old index, you will get an exception. - IP addresses now support IPv6 via Lucene's InetAddressPoint and store them in SORTED_SET doc values using the same encoding (fixed length of 16 bytes and sortable). - The internal MappedFieldType that is stored by the new mappers does not have any of the points-related properties set. Instead, it keeps setting the index options when parsing the `index` property of mappings and does `if (fieldType.indexOptions() != IndexOptions.NONE) { // add point field }` when parsing documents. Known issues that won't fix: - You can't use numeric fields in significant terms aggregations anymore since this requires document frequencies, which points do not record. - Term queries on numeric fields will now return constant scores instead of giving better scores to the rare values. Known issues that we could work around (in follow-up PRs, this one is too large already): - Range queries on `ip` addresses only work if both the lower and upper bounds are inclusive (exclusive bounds are not exposed in Lucene). We could either decide to implement it, or drop range support entirely and tell users to query subnets using the CIDR notation instead. - Since IP addresses now use a different representation for doc values, aggregations will fail when running a terms aggregation on an ip field on a list of indices that contains both pre-5.0 and 5.0 indices. - The ip range aggregation does not work on the new ip field. We need to either implement range aggs for SORTED_SET doc values or drop support for ip ranges and tell users to use filters instead. #17700 Closes #16751 Closes #17007 Closes #11513
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{
"mappings": {
"my_type": {
"properties": {
"my_number": {
"type": "long",
"fields": {
"keyword": {
"type": "keyword"
}
}
}
}
}
}
}
--------------------------------------------------
// CONSOLE
Use the new points API to index numeric fields. #17746 This makes all numeric fields including `date`, `ip` and `token_count` use points instead of the inverted index as a lookup structure. This is expected to perform worse for exact queries, but faster for range queries. It also requires less storage. Notes about how the change works: - Numeric mappers have been split into a legacy version that is essentially the current mapper, and a new version that uses points, eg. LegacyDateFieldMapper and DateFieldMapper. - Since new and old fields have the same names, the decision about which one to use is made based on the index creation version. - If you try to force using a legacy field on a new index or a field that uses points on an old index, you will get an exception. - IP addresses now support IPv6 via Lucene's InetAddressPoint and store them in SORTED_SET doc values using the same encoding (fixed length of 16 bytes and sortable). - The internal MappedFieldType that is stored by the new mappers does not have any of the points-related properties set. Instead, it keeps setting the index options when parsing the `index` property of mappings and does `if (fieldType.indexOptions() != IndexOptions.NONE) { // add point field }` when parsing documents. Known issues that won't fix: - You can't use numeric fields in significant terms aggregations anymore since this requires document frequencies, which points do not record. - Term queries on numeric fields will now return constant scores instead of giving better scores to the rare values. Known issues that we could work around (in follow-up PRs, this one is too large already): - Range queries on `ip` addresses only work if both the lower and upper bounds are inclusive (exclusive bounds are not exposed in Lucene). We could either decide to implement it, or drop range support entirely and tell users to query subnets using the CIDR notation instead. - Since IP addresses now use a different representation for doc values, aggregations will fail when running a terms aggregation on an ip field on a list of indices that contains both pre-5.0 and 5.0 indices. - The ip range aggregation does not work on the new ip field. We need to either implement range aggs for SORTED_SET doc values or drop support for ip ranges and tell users to use filters instead. #17700 Closes #16751 Closes #17007 Closes #11513
2016-04-01 05:07:35 -04:00
Also the `precision_step` parameter is now irrelevant and will be rejected on
indices that are created on or after 5.0.
==== `_timestamp` and `_ttl`
The `_timestamp` and `_ttl` fields were deprecated and are now removed. As a
replacement for `_timestamp`, you should populate a regular date field with the
current timestamp on application side. For `_ttl`, you should either use
time-based indices when applicable, or cron a delete-by-query with a range
query on a timestamp field
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==== `index` property
On all field datatypes (except for the deprecated `string` field), the `index`
property now only accepts `true`/`false` instead of `not_analyzed`/`no`. The
`string` field still accepts `analyzed`/`not_analyzed`/`no`.
==== Doc values on unindexed fields
Previously, setting a field to `index:no` would also disable doc-values. Now,
doc-values are always enabled on numeric and boolean fields unless
`doc_values` is set to `false`.
==== Floating points use `float` instead of `double`
When dynamically mapping a field containing a floating point number, the field
now defaults to using `float` instead of `double`. The reasoning is that
floats should be more than enough for most cases but would decrease storage
requirements significantly.
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==== `norms`
`norms` now take a boolean instead of an object. This boolean is the replacement
for `norms.enabled`. There is no replacement for `norms.loading` since eager
loading of norms is not useful anymore now that norms are disk-based.
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==== `fielddata.format`
Setting `fielddata.format: doc_values` in the mappings used to implicitly
enable doc-values on a field. This no longer works: the only way to enable or
disable doc-values is by using the `doc_values` property of mappings.
==== `fielddata.filter.regex`
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Regex filters are not supported anymore and will be dropped on upgrade.
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==== Source-transform removed
The source `transform` feature has been removed. Instead, use an ingest pipeline
==== `_parent` field no longer indexed
The join between parent and child documents no longer relies on indexed fields
and therefore from 5.0.0 onwards the `_parent` field is no longer indexed. In
order to find documents that refer to a specific parent id, the new
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`parent_id` query can be used. The GET response and hits inside the search
response still include the parent id under the `_parent` key.
==== Source `format` option
The `_source` mapping no longer supports the `format` option. It will still be
accepted for indices created before the upgrade to 5.0 for backwards
compatibility, but it will have no effect. Indices created on or after 5.0
will reject this option.
==== Object notation
Core types no longer support the object notation, which was used to provide
per document boosts as follows:
[source,js]
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---------------
{
"value": "field_value",
"boost": 42
}
---------------
==== Boost accuracy for queries on `_all`
Per-field boosts on the `_all` are now compressed into a single byte instead
of the 4 bytes used previously. While this will make the index much more
space-efficient, it also means that index time boosts will be less accurately
encoded.
==== `_ttl` and `_timestamp` cannot be created
You can no longer create indexes with `_ttl` or `_timestamp` enabled. Indexes
with them enabled created before 5.0 will continue to work.
You should replace `_timestamp` in new indexes by adding a field to your source
either in the application producing the data or with an ingest pipline like
this one:
[source,js]
---------------
PUT _ingest/pipeline/timestamp
{
"description" : "Adds a timestamp field at the current time",
"processors" : [ {
"set" : {
"field": "timestamp",
"value": "{{_ingest.timestamp}}"
}
} ]
}
PUT newindex/type/1?pipeline=timestamp
{
"example": "data"
}
GET newindex/type/1
---------------
// CONSOLE
Which produces
[source,js]
---------------
{
"_source": {
"example": "data",
"timestamp": "2016-06-21T18:48:55.560+0000"
},
...
}
---------------
// TESTRESPONSE[s/\.\.\./"found": true, "_id": "1", "_index": "newindex", "_type": "type", "_version": 1/]
// TESTRESPONSE[s/"2016-06-21T18:48:55.560\+0000"/"$body._source.timestamp"/]
If you have an old index created with 2.x that has `_timestamp` enabled then
you can migrate it to a new index with the a `timestamp` field in the source
with reindex:
[source,js]
---------------
POST _reindex
{
"source": {
"index": "oldindex"
},
"dest": {
"index": "newindex"
},
"script": {
"lang": "painless",
"inline": "ctx._source.timestamp = ctx._timestamp; ctx._timestamp = null"
}
}
---------------
// CONSOLE
// TEST[s/^/PUT oldindex\nGET _cluster\/health?wait_for_status=yellow\n/]
You can replace `_ttl` with time based index names (preferred) or by adding a
cron job which runs a delete-by-query on a timestamp field in the source
document. If you had documents like this:
[source,js]
---------------
POST index/type/_bulk
{"index":{"_id":1}}
{"example": "data", "timestamp": "2016-06-21T18:48:55.560+0000" }
{"index":{"_id":2}}
{"example": "data", "timestamp": "2016-04-21T18:48:55.560+0000" }
---------------
// CONSOLE
Then you could delete all of the documents from before June 1st with:
[source,js]
---------------
POST index/type/_delete_by_query
{
"query": {
"range" : {
"timestamp" : {
"lt" : "2016-05-01"
}
}
}
}
---------------
// CONSOLE
// TEST[continued]
IMPORTANT: Keep in mind that deleting documents from an index is very expensive
compared to deleting whole indexes. That is why time based indexes are
recommended over this sort of thing and why `_ttl` was deprecated in the first
place.