Tech Topics

Removal of Mapping Types in Elasticsearch 6.0

Mapping types are going away. Elasticsearch 6.0 supports a single mapping type per index only, and it represents the first step on the way to removing types altogether.

This blog post explains what types are, why we are removing them, how you can migrate your indices to a single type world, and lays out the full schedule for type removal over the next three major versions.

<tldr>

• Indices created in Elasticsearch 6.0.0 or later may contain a single mapping type only.
• Indices created in 5.x with multiple mapping types will continue to function as before in Elasticsearch 6.x.
• Mapping types will be completely removed in Elasticsearch 7.0.0 although some backwards compatibility features will only be removed in 9.0.0.

</tldr>

What are mapping types?

Since the first release of Elasticsearch, each document has been stored in a single index and assigned a single mapping type. A mapping type was used to represent the type of document or entity being indexed, for instance a twitter index might have a user type and a tweet type.

Each mapping type could have its own fields, so the user type might have a full_name field, a user_name field, and an email field, while the tweet type could have a content field, a tweeted_at field and, like the user type, a user_name field.

Each document had a _type meta-field containing the type name, and searches could be limited to one or more types by specifying the type name(s) in the URL:

GET twitter/user,tweet/_search
{
"query": {
"match": {
"user_name": "kimchy"
}
}
}


The _type field was combined with the document’s _id to generate a _uid field, so documents of different types with the same _id could exist in a single index.

Mapping types were also used to establish a parent-child relationship between documents, so documents of type question could be parents to documents of type answer.

Why are mapping types being removed?

In the early days of Elasticsearch, we spoke about an “index” being similar to a “database” in an SQL database, and a “type” being equivalent to a “table”.

This was a bad analogy that led to incorrect assumptions. In an SQL database, tables are independent of each other. The columns in one table have no bearing on columns with the same name in another table. This is not the case for fields in a mapping type.

In an Elasticsearch index, fields that have the same name in different mapping types are backed by the same Lucene field internally. In other words, using the example above, the user_name field in the user type is stored in exactly the same field as the user_name field in the tweet type, and both user_name fields must have the same mapping (definition) in both types.

This can lead to frustration when, for example, you want deleted to be a date field in one type and a boolean field in another type in the same index.

On top of that, storing different entities that have few or no fields in common in the same index leads to sparse data and interferes with Lucene’s ability to compress documents efficiently.

For these reasons, we have decided to remove the concept of mapping types from Elasticsearch.

Alternatives to mapping types

Custom type field

There is a limit to how many primary shards can exist in a cluster so you may not want to waste an entire shard for a collection of only a few thousand documents. In this case, you can implement your own custom type field which will work in a similar way to the old _type.

Let’s take the user/tweet example above. Originally, the workflow would have looked something like this:

PUT twitter
{
"mappings": {
"user": {
"properties": {
"name": { "type": "text" },
"user_name": { "type": "keyword" },
"email": { "type": "keyword" }
}
},
"tweet": {
"properties": {
"content": { "type": "text" },
"user_name": { "type": "keyword" },
"tweeted_at": { "type": "date" }
}
}
}
}
{
"name": "Shay Banon",
"user_name": "kimchy",
"email": "shay@kimchy.com"
}
{
"user_name": "kimchy",
"tweeted_at": "2017-10-24T09:00:00Z",
"content": "Types are going away"
}
{
"query": {
"match": {
"user_name": "kimchy"
}
}
}


You could achieve the same thing by adding a custom type field as follows:

PUT twitter
{
"mappings": {
"doc": {
"properties": {
"type": { "type": "keyword" },
"name": { "type": "text" },
"user_name": { "type": "keyword" },
"email": { "type": "keyword" },
"content": { "type": "text" },
"tweeted_at": { "type": "date" }
}
}
}
}
{
"type": "user",
"name": "Shay Banon",
"user_name": "kimchy",
"email": "shay@kimchy.com"
}
{
"type": "tweet",
"user_name": "kimchy",
"tweeted_at": "2017-10-24T09:00:00Z",
"content": "Types are going away"
}
{
"query": {
"bool": {
"must": {
"match": {
"user_name": "kimchy"
}
},
"filter": {
"match": {
"type": "tweet"
}
}
}
}
}


If you need to run searches and aggregations across old indices (which use the _type field) and new indices (with a custom type field), you can rewrite the query as follows:

GET twitter_old,twitter_new/_search
{
"query": {
"bool": {
"must": {
"match": {
"user_name": "kimchy"
}
},
"filter": {
"bool": {
"should": [
{ "term": { "_type": "tweet" }},
{ "term": { "type":  "tweet" }}
]
}
}
}
}
}


Index per document type

The other alternative is to have an index per document type. Instead of storing tweets and users in a single twitter index, you could store tweets in the tweets index and users in the user index. Indices are completely independent of each other and so there will be no conflict of field types between indices.

This approach has two benefits:

• Data is more likely to be dense and so benefit from compression techniques used in Lucene.
• The term statistics used for scoring in full text search are more likely to be accurate because all documents in the same index represent a single entity.

Each index can be sized appropriately for the number of documents it will contain: you can use a smaller number of primary shards for users and a larger number of primary shards for tweets.

Parent/Child without mapping types

Previously, a parent-child relationship was represented by making one mapping type the parent, and one or more other mapping types the children. Without types, we can no longer use this syntax. The parent-child feature will continue to function as before, except that the way of expressing the relationship between documents has been changed to use the new join field.

Schedule for removal of mapping types

This is a big change for our users, so we have tried to make it as painless as possible. The change will roll out as follows:

Elasticsearch 5.6.0
• Setting index.mapping.single_type: true on an index enables the single-type-per-index behaviour which is enforced in 6.0.
• The join field replacement for parent-child is available on indices created in 5.6.
Elasticsearch 6.x
• Indices created in 5.x will continue to function in 6.x as they did in 5.x.
• Indices created in 6.x only allow a single-type per index. Any name can be used for the type, but there can be only one.
• The _type name can no longer be combined with the _id to form the _uid field. The _uid field has become an alias for the _id field.
• New indices no longer support the old-style of parent/child and should use the join field instead.
• The _default_ mapping type is deprecated.
Elasticsearch 7.x
• The type parameter in URLs are optional. For instance, indexing a document no longer requires a document type.
• The GET|PUT _mapping APIs support a query string parameter ( include_type_name) which indicates whether the body should include a layer for the type name. It defaults to true. 7.x indices which don’t have an explicit type will use the dummy type name _doc.
• The _default_ mapping type is removed.
Elasticsearch 8.x
• The type parameter is no longer supported in URLs.
• The include_type_name parameter defaults to false.
Elasticsearch 9.x
• The include_in_type parameter is removed.

Migrating multi-type indices to single-type

The Reindex API can be used to convert multi-type indices to single-type indices. The following examples can be used in Elasticsearch 5.6 or Elasticsearch 6.x. In 6.x, there is no need to specify index.mapping.single_type as that is the default.

Custom type field

This first example adds a custom type field and sets it to the value of the original _type. It also adds the type to the _id in case there are any documents of different types which have conflicting IDs:

PUT new_twitter
{
"mappings": {
"doc": {
"properties": {
"type": {
"type": "keyword"
},
"name": {
"type": "text"
},
"user_name": {
"type": "keyword"
},
"email": {
"type": "keyword"
},
"content": {
"type": "text"
},
"tweeted_at": {
"type": "date"
}
}
}
}
}
POST _reindex
{
"source": {
},
"dest": {
},
"script": {
"source": """
ctx._source.type = ctx._type;
ctx._id = ctx._type + '-' + ctx._id;
ctx._type = 'doc';
"""
}
}


Index per document type

This next example splits our twitter index into a tweets index and a users index:

PUT users
{
"settings": {
"index.mapping.single_type": true
},
"mappings": {
"user": {
"properties": {
"name": {
"type": "text"
},
"user_name": {
"type": "keyword"
},
"email": {
"type": "keyword"
}
}
}
}
}
PUT tweets
{
"settings": {
"index.mapping.single_type": true
},
"mappings": {
"tweet": {
"properties": {
"content": {
"type": "text"
},
"user_name": {
"type": "keyword"
},
"tweeted_at": {
"type": "date"
}
}
}
}
}
POST _reindex
{
"source": {
"type": "user"
},
"dest": {
"index": "users"
}
}
POST _reindex
{
"source": {