Which Index to use when

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 will also automatically create 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 persisted on disk and does thus not need to be rebuilt in memory when the server is restarted or the indexed collection is reloaded. Therefore, they don’t influence the loading time of collections.

  The operations in a persistent index have logarithmic complexity, but operations may have a higher constant factor than operations of in-memory indexes because the persistent index may need to make extra roundtrips to the primary index to fetch the actual documents.

  A persistent index can be used for equality lookups, lookups based on a leftmost prefix of the index attributes, range queries and for sorting.

• TTL index: the TTL index provided by ArangoDB can be used for automatically removing expired documents from a collection.

  The TTL index is set up by setting an expireAfter value and by picking a single document attribute which contains the documents’ reference timepoint. Documents are expired expireAfter seconds after their reference timepoint has been reached. The documents’ reference timepoint is specified as either a numeric timestamp (Unix timestamp) or a date string in format YYYY-MM-DDTHH:MM:SS with optional milliseconds. All date strings will be interpreted as UTC dates.

  For example, if expireAfter is set to 600 seconds (10 minutes) and the index attribute is “creationDate” and there is the following document:

  { "creationDate" : 1550165973 }
  

  This document will be indexed with a creation date time value of 1550165973, which translates to the human-readable date 2019-02-14T17:39:33.000Z. The document will expire 600 seconds afterwards, which is at timestamp 1550166573 (or 2019-02-14T17:49:33.000Z in the human-readable version).

  The actual removal of expired documents will not necessarily happen immediately. Expired documents will eventually be removed by a background thread that is periodically going through all TTL indexes. The frequency for invoking this background thread can be configured using the --ttl.frequency startup option.

  There is no guarantee when exactly the removal of expired documents will be carried out, so queries may still find and return documents that have already expired. These will eventually be removed when the background thread kicks in and has capacity to remove the expired documents. It is guaranteed however that only documents which are past their expiration time will actually be removed.

  Please note that the numeric date time values for the index attribute has to be specified in seconds since January 1st 1970 (Unix timestamp). To calculate the current timestamp from JavaScript in this format, there is Date.now() / 1000; to calculate it from an arbitrary Date instance, there is Date.getTime() / 1000. In AQL you can do DATE_NOW() / 1000 or divide an arbitrary Unix timestamp in milliseconds by 1000 to convert it to seconds.

  Alternatively, the index attribute values can be specified as a date string in format YYYY-MM-DDTHH:MM:SS, optionally with milliseconds after a decimal point in the format YYYY-MM-DDTHH:MM:SS.MMM and an optional timezone offset. All date strings without a timezone offset will be interpreted as UTC dates.

  The above example document using a date string attribute value would be

  { "creationDate" : "2019-02-14T17:39:33.000Z" }
  

  In case the index attribute does not contain a numeric value nor a proper date string, the document will not be stored in the TTL index and thus will not become a candidate for expiration and removal. Providing either a non-numeric value or even no value for the index attribute is a supported way of keeping documents from being expired and removed.

  TTL indexes are designed exactly for the purpose of removing expired documents from a collection. It is not recommended to rely on TTL indexes for user-land AQL queries. This is because TTL indexes internally may store a transformed, always numerical version of the index attribute value even if it was originally passed in as a datestring. As a result TTL indexes will likely not be used for filtering and sort operations in user-land AQL queries.

• Geo index: the geo index provided by ArangoDB allows searching for documents within a radius around a two-dimensional earth coordinate (point), 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 }
  

  or

  { "coords": [ 50.9406645, 6.9599115 ] }
  

  Geo indexes will be 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 will only be invoked via special functions.

• 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:

  Feature	ArangoSearch	Full-text Index
  Term search	Yes	Yes
  Prefix search	Yes	Yes
  Boolean expressions	Yes	Restricted
  Range search	Yes	No
  Phrase search	Yes	No
  Relevance ranking	Yes	No
  Configurable Analyzers	Yes	No
  AQL composable language construct	Yes	No
  Indexed attributes per collection	Unlimited	1
  Indexed collections	Unlimited	1
  Consistency	Eventual	Immediate

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 will 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 will not know beforehand whether the values which are compared to doc.attr will 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 will match against each other and thus produce large results.