Which Index to use when

Pick the right index type for your use case, choosing from persistent, inverted, TTL, and multi-dimensional indexes, as well as Views

ArangoDB automatically indexes the _key attribute in each collection. There is no need to index this attribute separately. Please note that a document’s _id attribute is derived from the _key attribute, and is thus implicitly indexed, too.

ArangoDB also automatically creates an index on _from and _to in any edge collection, meaning incoming and outgoing connections can be determined efficiently.

Index types

Users can define additional indexes on one or multiple document attributes. Several different index types are provided by ArangoDB. These indexes have different usage scenarios:

  • Persistent index: a persistent index is a general purpose index type that can be used for equality lookups, lookups based on a leftmost prefix of the index attributes, range queries and for sorting.

    The operations in a persistent index have logarithmic complexity.

    Persistent indexes allow you to store additional attributes in the index that can be used to cover more queries (storedValues index option). These additional attributes can be used for projections but not for lookups/filtering or sorting.

    You can optionally let persistent indexes cache equality lookups (==) to speed up index lookups for queries that cover all index attributes (cacheEnabled index option).

  • Inverted index: an inverted index lets you accelerate a broad range of queries, from simple lookups to complex information retrieval. It can cover filter conditions of the following types:

    • Exact value matching
    • Range queries
    • Prefix matching
    • Accent- and case-insensitive matching
    • Wildcard and fuzzy matching
    • Token and phrase queries for full-text search
    • Geo-spatial queries
    • Nested matching

    The inverted index type is eventually consistent like arangosearch Views, but unlike other index types. It may thus not be a suitable index type depending on your requirements.

    You can add one more more inverted indexes to a search-alias View for federated searching over multiple collections, for ranking results by relevance, and for search highlighting capabilities. It is a lightweight alternative to an arangosearch View.

  • TTL index: a time-to-live index can be used for automatically removing expired documents from a collection.

    You can choose a document attribute that specifies a point in time and whether documents shall expire at the point in time or a given number of seconds after the point in time.

  • multi-dimensional index (ZKD): a multi dimensional index allows to efficiently intersect multiple range queries. Typical use cases are querying intervals that intersect a given point or interval. For example, if intervals are stored in documents like

    { "from": 12, "to": 45 }

    then you can create an index over from, to utilize it with this query:

    FOR i IN intervals FILTER i.from <= t && t <= i.to RETURN i

    Currently only floating-point numbers (doubles) are supported as underlying type for each dimension.

  • Geo index: the geo index provided by ArangoDB allows searching for documents within a radius around a two-dimensional location (point) on Earth, or to find documents with are closest to a point. Document coordinates can either be specified in two different document attributes or in a single attribute, e.g.

    { "latitude": 50.9406645, "longitude": 6.9599115 }


    { "coords": [ 50.9406645, 6.9599115 ] }

    Geo indexes are invoked via special functions or AQL optimization. The optimization can be triggered when a collection with geo index is enumerated and a SORT or FILTER statement is used in conjunction with the distance function.

    Furthermore, a geo index can also index standard GeoJSON objects . GeoJSON uses the JSON syntax to describe geometric objects on the surface of the Earth. It supports points, lines, and polygons. See Geo-Spatial Indexes.

  • fulltext index: a fulltext index can be used to index all words contained in a specific attribute of all documents in a collection. Only words with a (specifiable) minimum length are indexed. Word tokenization is done using the word boundary analysis provided by libicu, which is taking into account the selected language provided at server start.

    The index supports complete match queries (full words) and prefix queries. Fulltext indexes are invoked via special functions.

    Please note that the fulltext index type is deprecated from version 3.10 onwards and is superseded by ArangoSearch.

  • View: ArangoSearch is a sophisticated search engine for full-text, with text pre-processing, ranking capabilities and more. It offers more features and configuration options than a fulltext index. It indexes documents near real-time, but not immediately as other indexes.

    Comparison with the full-text Index:

    FeatureArangoSearchFull-text Index
    Term searchYesYes
    Prefix searchYesYes
    Boolean expressionsYesRestricted
    Range searchYesNo
    Phrase searchYesNo
    Relevance rankingYesNo
    Configurable AnalyzersYesNo
    AQL composable language constructYesNo
    Indexed attributes per collectionUnlimited1
    Indexed collectionsUnlimited1

Sparse vs. non-sparse indexes

Persistent indexes can optionally be created sparse. A sparse index does not contain documents for which at least one of the index attribute is not set or contains a value of null.

As such documents are excluded from sparse indexes, they may contain fewer documents than their non-sparse counterparts. This enables faster indexing and can lead to reduced memory usage in case the indexed attribute does occur only in some, but not all documents of the collection.

In order to create a sparse index, an object with the attribute sparse can be added to the index creation commands:

db.collection.ensureIndex({ type: "persistent", fields: [ "attributeName" ], sparse: true }); 
db.collection.ensureIndex({ type: "persistent", fields: [ "attributeName1", "attributeName2" ], sparse: true }); 
db.collection.ensureIndex({ type: "persistent", fields: [ "attributeName" ], unique: true, sparse: true }); 
db.collection.ensureIndex({ type: "persistent", fields: [ "attributeName1", "attributeName2" ], unique: true, sparse: true }); 

When not explicitly set, the sparse attribute defaults to false for new indexes. Indexes other than persistent do not support the sparse option.

As sparse indexes may exclude some documents from the collection, they cannot be used for all types of queries. Sparse hash indexes cannot be used to find documents for which at least one of the indexed attributes has a value of null. For example, the following AQL query cannot use a sparse index, even if one was created on attribute attr:

FOR doc In collection
  FILTER doc.attr == null
  RETURN doc

If the lookup value is non-constant, a sparse index may or may not be used, depending on the other types of conditions in the query. If the optimizer can safely determine that the lookup value cannot be null, a sparse index may be used. When uncertain, the optimizer does not make use of a sparse index in a query in order to produce correct results.

For example, the following queries cannot use a sparse index on attr because the optimizer does not know beforehand whether the values which are compared to doc.attr include null:

FOR doc In collection 
  FILTER doc.attr == SOME_FUNCTION(...) 
  RETURN doc
FOR other IN otherCollection
  FOR doc In collection
    FILTER doc.attr == other.attr
    RETURN doc

Sparse persistent indexes can be used for sorting if the optimizer can safely detect that the index range does not include null for any of the index attributes.

Note that if you intend to use joins it may be clever to use non-sparsity and maybe even uniqueness for that attribute, else all items containing the null value match against each other and thus produce large results.