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Index basics

Indexes allow fast access to documents, provided the indexed attribute(s) are used in a query. While ArangoDB automatically indexes some system attributes, users are free to create extra indexes on non-system attributes of documents.

User-defined indexes can be created on collection level. Most user-defined indexes can be created by specifying the names of the index attributes. Some index types allow indexing just one attribute (e.g. fulltext index) whereas other index types allow indexing multiple attributes at the same time.

Learn how to use different indexes efficiently by going through the ArangoDB Performance Course.

The system attributes _id, _key, _from and _to are automatically indexed by ArangoDB, without the user being required to create extra indexes for them. _id and _key are covered by a collection’s primary key, and _from and _to are covered by an edge collection’s edge index automatically.

Using the system attribute _id in user-defined indexes is not possible, but indexing _key, _rev, _from, and _to is.

ArangoDB provides the following index types:

Primary Index

For each collection there will always be a primary index which is a hash index for the document keys (_key attribute) of all documents in the collection. The primary index allows quick selection of documents in the collection using either the _key or _id attributes. It will be used from within AQL queries automatically when performing equality lookups on _key or _id.

There are also dedicated functions to find a document given its _key or _id that will always make use of the primary index:

db.collection.document("<document-key>");
db._document("<document-id>");

As the primary index is an unsorted hash index, it cannot be used for non-equality range queries or for sorting.

The primary index of a collection cannot be dropped or changed, and there is no mechanism to create user-defined primary indexes.

Edge Index

Every edge collection also has an automatically created edge index. The edge index provides quick access to documents by either their _from or _to attributes. It can therefore be used to quickly find connections between vertex documents and is invoked when the connecting edges of a vertex are queried.

Edge indexes are used from within AQL when performing equality lookups on _from or _to values in an edge collections. There are also dedicated functions to find edges given their _from or _to values that will always make use of the edge index:

db.collection.edges("<from-value>");
db.collection.edges("<to-value>");
db.collection.outEdges("<from-value>");
db.collection.outEdges("<to-value>");
db.collection.inEdges("<from-value>");
db.collection.inEdges("<to-value>");

Internally, the edge index is implemented as a hash index, which stores the union of all _from and _to attributes. It can be used for equality lookups, but not for range queries or for sorting. Edge indexes are automatically created for edge collections. It is not possible to create user-defined edge indexes. However, it is possible to freely use the _from and _to attributes in user-defined indexes.

An edge index cannot be dropped or changed.

Hash Index

A hash index can be used to quickly find documents with specific attribute values. The hash index is unsorted, so it supports equality lookups but no range queries or sorting.

A hash index can be created on one or multiple document attributes. A hash index will only be used by a query if all index attributes are present in the search condition, and if all attributes are compared using the equality (==) operator. Hash indexes are used from within AQL and several query functions, e.g. byExample, firstExample etc.

Hash indexes can optionally be declared unique, then disallowing saving the same value(s) in the indexed attribute(s). Hash indexes can optionally be sparse.

The different types of hash indexes have the following characteristics:

unique hash index: all documents in the collection must have different values for the attributes covered by the unique index. Trying to insert a document with the same key value as an already existing document will lead to a unique constraint violation.

This type of index is not sparse. Documents that do not contain the index attributes or that have a value of null in the index attribute(s) will still be indexed. A key value of null may only occur once in the index, so this type of index cannot be used for optional attributes.

The unique option can also be used to ensure that no duplicate edges are created, by adding a combined index for the fields _from and _to to an edge collection.
unique, sparse hash index: all documents in the collection must have different values for the attributes covered by the unique index. Documents in which at least one of the index attributes is not set or has a value of null are not included in the index. This type of index can be used to ensure that there are no duplicate keys in the collection for documents which have the indexed attributes set. As the index will exclude documents for which the indexed attributes are null or not set, it can be used for optional attributes.
non-unique hash index: all documents in the collection will be indexed. This type of index is not sparse. Documents that do not contain the index attributes or that have a value of null in the index attribute(s) will still be indexed. Duplicate key values can occur and do not lead to unique constraint violations.
non-unique, sparse hash index: only those documents will be indexed that have all the indexed attributes set to a value other than null. It can be used for optional attributes.

The amortized complexity of lookup, insert, update, and removal operations in unique hash indexes is O(1).

Non-unique hash indexes have an amortized complexity of O(1) for insert, update, and removal operations. That means non-unique hash indexes can be used on attributes with low cardinality.

If a hash index is created on an attribute that is missing in all or many of the documents, the behavior is as follows:

if the index is sparse, the documents missing the attribute will not be indexed and not use index memory. These documents will not influence the update or removal performance for the index.
if the index is non-sparse, the documents missing the attribute will be contained in the index with a key value of null.

Hash indexes support indexing array values if the index attribute name is extended with a [*].

Skiplist Index

A skiplist is a sorted index structure. It can be used to quickly find documents with specific attribute values, for range queries and for returning documents from the index in sorted order. Skiplists will be used from within AQL and several query functions, e.g. byExample, firstExample etc.

Skiplist indexes will be used for lookups, range queries and sorting only if either all index attributes are provided in a query, or if a leftmost prefix of the index attributes is specified.

For example, if a skiplist index is created on attributes value1 and value2, the following filter conditions can use the index (note: the <= and >= operators are intentionally omitted here for the sake of brevity):

FILTER doc.value1 == ...
FILTER doc.value1 < ...
FILTER doc.value1 > ...
FILTER doc.value1 > ... && doc.value1 < ...

FILTER doc.value1 == ... && doc.value2 == ...
FILTER doc.value1 == ... && doc.value2 > ...
FILTER doc.value1 == ... && doc.value2 > ... && doc.value2 < ...

In order to use a skiplist index for sorting, the index attributes must be specified in the SORT clause of the query in the same order as they appear in the index definition. Skiplist indexes are always created in ascending order, but they can be used to access the indexed elements in both ascending or descending order. However, for a combined index (an index on multiple attributes) this requires that the sort orders in a single query as specified in the SORT clause must be either all ascending (optionally omitted as ascending is the default) or all descending.

For example, if the skiplist index is created on attributes value1 and value2 (in this order), then the following sorts clauses can use the index for sorting:

SORT value1 ASC, value2 ASC (and its equivalent SORT value1, value2)
SORT value1 DESC, value2 DESC
SORT value1 ASC (and its equivalent SORT value1)
SORT value1 DESC

The following sort clauses cannot make use of the index order, and require an extra sort step:

SORT value1 ASC, value2 DESC
SORT value1 DESC, value2 ASC
SORT value2 (and its equivalent SORT value2 ASC)
SORT value2 DESC (because first indexed attribute value1 is not used in sort clause)

Note: the latter two sort clauses cannot use the index because the sort clause does not refer to a leftmost prefix of the index attributes.

Skiplists can optionally be declared unique, disallowing saving the same value in the indexed attribute. They can be sparse or non-sparse.

The different types of skiplist indexes have the following characteristics:

unique skiplist index: all documents in the collection must have different values for the attributes covered by the unique index. Trying to insert a document with the same key value as an already existing document will lead to a unique constraint violation.

This type of index is not sparse. Documents that do not contain the index attributes or that have a value of null in the index attribute(s) will still be indexed. A key value of null may only occur once in the index, so this type of index cannot be used for optional attributes.
unique, sparse skiplist index: all documents in the collection must have different values for the attributes covered by the unique index. Documents in which at least one of the index attributes is not set or has a value of null are not included in the index. This type of index can be used to ensure that there are no duplicate keys in the collection for documents which have the indexed attributes set. As the index will exclude documents for which the indexed attributes are null or not set, it can be used for optional attributes.
non-unique skiplist index: all documents in the collection will be indexed. This type of index is not sparse. Documents that do not contain the index attributes or that have a value of null in the index attribute(s) will still be indexed. Duplicate key values can occur and do not lead to unique constraint violations.
non-unique, sparse skiplist index: only those documents will be indexed that have all the indexed attributes set to a value other than null. It can be used for optional attributes.

The operational amortized complexity for skiplist indexes is logarithmically correlated with the number of documents in the index.

Skiplist indexes support indexing array values if the index attribute name is extended with a [*]`.

Persistent Index

The persistent index is a sorted index with persistence. The index entries are written to disk when documents are stored or updated. That means the index entries do not need to be rebuilt from the collection data when the server is restarted or the indexed collection is initially loaded. Thus using persistent indexes may reduce collection loading times.

The persistent index type can be used for secondary indexes at the moment. That means the persistent index currently cannot be made the only index for a collection, because there will always be the in-memory primary index for the collection in addition, and potentially more indexes (such as the edges index for an edge collection).

The index implementation is using the RocksDB engine, and it provides logarithmic complexity for insert, update, and remove operations. As the persistent index is not an in-memory index, it does not store pointers into the primary index as all the in-memory indexes do, but instead it stores a document’s primary key. To retrieve a document via a persistent index via an index value lookup, there will therefore be an additional O(1) lookup into the primary index to fetch the actual document.

As the persistent index is sorted, it can be used for point lookups, range queries and sorting operations, but only if either all index attributes are provided in a query, or if a leftmost prefix of the index attributes is specified.

Geo Index

Users can create additional geo indexes on one or multiple attributes in collections. A geo index is used to find places on the surface of the earth fast.

The geo index stores two-dimensional coordinates. It can be created on either two separate document attributes (latitude and longitude) or a single array attribute that contains both latitude and longitude. Latitude and longitude must be numeric values.

The geo index provides operations to find documents with coordinates nearest to a given comparison coordinate, and to find documents with coordinates that are within a specifiable radius around a comparison coordinate.

The geo index is used via dedicated functions in AQL, the simple queries functions and it is implicitly applied when in AQL a SORT or FILTER is used with the distance function. Otherwise it will not be used for other types of queries or conditions.

Fulltext Index

A fulltext index can be used to find words, or prefixes of words inside documents. A fulltext index can be created on a single attribute only, and will index all words contained in documents that have a textual value in that attribute. 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. Words are indexed in their lower-cased form. The index supports complete match queries (full words) and prefix queries, plus basic logical operations such as and, or and not for combining partial results.

The fulltext index is sparse, meaning it will only index documents for which the index attribute is set and contains a string value. Additionally, only words with a configurable minimum length will be included in the index.

The fulltext index is used via dedicated functions in AQL or the simple queries, but will not be enabled for other types of queries or conditions.

Indexing attributes and sub-attributes

Top-level as well as nested attributes can be indexed. For attributes at the top level, the attribute names alone are required. To index a single field, pass an array with a single element (string of the attribute key) to the fields parameter of the ensureIndex() method. To create a combined index over multiple fields, simply add more members to the fields array:

// { name: "Smith", age: 35 }
db.posts.ensureIndex({ type: "hash", fields: [ "name" ] })
db.posts.ensureIndex({ type: "hash", fields: [ "name", "age" ] })

To index sub-attributes, specify the attribute path using the dot notation:

// { name: {last: "Smith", first: "John" } }
db.posts.ensureIndex({ type: "hash", fields: [ "name.last" ] })
db.posts.ensureIndex({ type: "hash", fields: [ "name.last", "name.first" ] })

Indexing array values

If an index attribute contains an array, ArangoDB will store the entire array as the index value by default. Accessing individual members of the array via the index is not possible this way.

To make an index insert the individual array members into the index instead of the entire array value, a special array index needs to be created for the attribute. Array indexes can be set up like regular hash or skiplist indexes using the collection.ensureIndex() function. To make a hash or skiplist index an array index, the index attribute name needs to be extended with [*] when creating the index and when filtering in an AQL query using the IN operator.

The following example creates an array hash index on the tags attribute in a collection named posts:

db.posts.ensureIndex({ type: "hash", fields: [ "tags[*]" ] });
db.posts.insert({ tags: [ "foobar", "baz", "quux" ] });

This array index can then be used for looking up individual tags values from AQL queries via the IN operator:

FOR doc IN posts
  FILTER 'foobar' IN doc.tags
  RETURN doc

It is possible to add the array expansion operator [*], but it is not mandatory. You may use it to indicate that an array index is used, it is purely cosmetic however:

FOR doc IN posts
  FILTER 'foobar' IN doc.tags[*]
  RETURN doc

The following FILTER conditions will not use the array index:

FILTER doc.tags ANY == 'foobar'
FILTER doc.tags ANY IN 'foobar'
FILTER doc.tags IN 'foobar'
FILTER doc.tags == 'foobar'
FILTER 'foobar' == doc.tags

It is also possible to create an index on subattributes of array values. This makes sense if the index attribute is an array of objects, e.g.

db.posts.ensureIndex({ type: "hash", fields: [ "tags[*].name" ] });
db.posts.insert({ tags: [ { name: "foobar" }, { name: "baz" }, { name: "quux" } ] });

The following query will then use the array index (this does require the array expansion operator):

FOR doc IN posts
  FILTER 'foobar' IN doc.tags[*].name
  RETURN doc

If you store a document having the array which does contain elements not having the subattributes this document will also be indexed with the value null, which in ArangoDB is equal to attribute not existing.

ArangoDB supports creating array indexes with a single [*] operator per index attribute. For example, creating an index as follows is not supported:

db.posts.ensureIndex({ type: "hash", fields: [ "tags[*].name[*].value" ] });

Array values will automatically be de-duplicated before being inserted into an array index. For example, if the following document is inserted into the collection, the duplicate array value bar will be inserted only once:

db.posts.insert({ tags: [ "foobar", "bar", "bar" ] });

This is done to avoid redundant storage of the same index value for the same document, which would not provide any benefit.

If an array index is declared unique, the de-duplication of array values will happen before inserting the values into the index, so the above insert operation with two identical values bar will not necessarily fail

It will always fail if the index already contains an instance of the bar value. However, if the value bar is not already present in the index, then the de-duplication of the array values will effectively lead to bar being inserted only once.

To turn off the deduplication of array values, it is possible to set the deduplicate attribute on the array index to false. The default value for deduplicate is true however, so de-duplication will take place if not explicitly turned off.

db.posts.ensureIndex({ type: "hash", fields: [ "tags[*]" ], deduplicate: false });

// will fail now
db.posts.insert({ tags: [ "foobar", "bar", "bar" ] }); 

If an array index is declared and you store documents that do not have an array at the specified attribute this document will not be inserted in the index. Hence the following objects will not be indexed:

db.posts.ensureIndex({ type: "hash", fields: [ "tags[*]" ] });
db.posts.insert({ something: "else" });
db.posts.insert({ tags: null });
db.posts.insert({ tags: "this is no array" });
db.posts.insert({ tags: { content: [1, 2, 3] } });

An array index is able to index explicit null values. When queried for nullvalues, it will only return those documents having explicitly null stored in the array, it will not return any documents that do not have the array at all.

db.posts.ensureIndex({ type: "hash", fields: [ "tags[*]" ] });
db.posts.insert({tags: null}) // Will not be indexed
db.posts.insert({tags: []})  // Will not be indexed
db.posts.insert({tags: [null]}); // Will be indexed for null
db.posts.insert({tags: [null, 1, 2]}); // Will be indexed for null, 1 and 2

Declaring an array index as sparse does not have an effect on the array part of the index, this in particular means that explicit null values are also indexed in the sparse version. If an index is combined from an array and a normal attribute the sparsity will apply for the attribute e.g.:

db.posts.ensureIndex({ type: "hash", fields: [ "tags[*]", "name" ], sparse: true });
db.posts.insert({tags: null, name: "alice"}) // Will not be indexed
db.posts.insert({tags: [], name: "alice"}) // Will not be indexed
db.posts.insert({tags: [1, 2, 3]}) // Will not be indexed
db.posts.insert({tags: [1, 2, 3], name: null}) // Will not be indexed
db.posts.insert({tags: [1, 2, 3], name: "alice"})
// Will be indexed for [1, "alice"], [2, "alice"], [3, "alice"]
db.posts.insert({tags: [null], name: "bob"})
// Will be indexed for [null, "bob"] 

Please note that filtering using array indexes only works from within AQL queries and only if the query filters on the indexed attribute using the IN operator. The other comparison operators (==, !=, >, >=, <, <=, ANY, ALL, NONE) currently cannot use array indexes.

Vertex centric indexes

As mentioned above, the most important indexes for graphs are the edge indexes, indexing the _from and _to attributes of edge collections. They provide very quick access to all edges originating in or arriving at a given vertex, which allows to quickly find all neighbors of a vertex in a graph.

In many cases one would like to run more specific queries, for example finding amongst the edges originating from a given vertex only those with a timestamp greater than or equal to some date and time. Exactly this is achieved with “vertex centric indexes”. In a sense these are localized indexes for an edge collection, which sit at every single vertex.

Technically, they are implemented in ArangoDB as indexes, which sort the complete edge collection first by _from and then by other attributes for OUTBOUND traversals, or first by _to and then by other attributes for INBOUND traversals. For traversals in ANY direction two indexes are needed, one with _from and the other with _to as first indexed field.

If we for example have a skiplist index on the attributes _from and timestamp of an edge collection, we can answer the above question very quickly with a single range lookup in the index.

Since ArangoDB 3.0 one can create sorted indexes (type “skiplist” and “persistent”) that index the special edge attributes _from or _to and additionally other attributes. Since ArangoDB 3.1, these are used in graph traversals, when appropriate FILTER statements are found by the optimizer.

For example, to create a vertex centric index of the above type, you would simply do

db.edges.ensureIndex({"type":"skiplist", "fields": ["_from", "timestamp"]});

in arangosh. Then, queries like

FOR v, e, p IN 1..1 OUTBOUND "V/1" edges
  FILTER e.timestamp >= "2018-07-09"
  RETURN p

will be considerably faster in case there are many edges originating from vertex "V/1" but only few with a recent time stamp. Note that the optimizer may prefer the default edge index over vertex centric indexes based on the costs it estimates, even if a vertex centric index might in fact be faster. Vertex centric indexes are more likely to be chosen for highly connected graphs and with RocksDB storage engine.

❮ Indexing Which index to use when ❯