what the fuck

This is where types help: types are a convenient way to store several types of data in the same index, in order to keep the total number of indices low for the reasons exposed above. In terms of implementation it works by adding a “_type” field to every document that is automatically used for filtering when searching on a specific type. One nice property of types is that searching across several types of the same index comes with no overhead compared to searching a single type: it does not change how many shard results need to be merged.

However this comes with limitations as well:

• Fields need to be consistent across types. For instance if two fields have the same name in different types of the same index, they need to be of the same field type (string, date, etc.) and have the same configuration.
• Fields that exist in one type will also consume resources for documents of types where this field does not exist. This is a general issue with Lucene indices: they don’t like sparsity. Sparse postings lists can’t be compressed efficiently because of high deltas between consecutive matches. And the issue is even worse with doc values: for speed reasons, doc values often reserve a fixed amount of disk space for every document, so that values can be addressed efficiently. This means that if Lucene establishes that it needs one byte to store all value of a given numeric field, it will also consume one byte for documents that don’t have a value for this field. Future versions of Elasticsearch will have improvements in this area but I would still advise you to model your data in a way that will limit sparsity as much as possible.
• Scores use index-wide statistics, so scores of documents in one type can be impacted by documents from other types.

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oh yeah