Persistent indexes

It is possible to define a persistent index on one or more document attributes (or attribute paths). The index is then used in queries to locate documents with a specific index attribute value or to find documents whose index attribute value(s) are in a given range.

For example, you can create a persistent index on the attributes value1 and value2 with the following command:

collection.ensureIndex({ type: "persistent", fields: ["value1", "value2"] });

If you declare an index to be unique, then no two documents are allowed to have the same set of attribute values. Creating a new document or updating a document will fail if the uniqueness is violated:

collection.ensureIndex({ type: "persistent", fields: ["value1", "value2"], unique: true });

If you declare an index to be sparse, a document will be excluded from the index and no uniqueness checks will be performed if any index attribute value is not set or has a value of null:

collection.ensureIndex({ type: "persistent", fields: ["value1", "value2"], sparse: true });

To store additional attributes in the index, you can set the storedValues option:

collection.ensureIndex({ type: "persistent", fields: ["value1", "value2"], storedValues: ["value3"] });

To enable in-memory caching of index entries, which can be used when doing point lookups on the index, you can set the cacheEnabled option:

collection.ensureIndex({ type: "persistent", fields: [ "value1", "value2" ], cacheEnabled: true });

Storing additional values in indexes

Introduced in: v3.10.0

Persistent indexes allow you to store additional attributes in the index that can be used to satisfy projections of the document. They cannot be used for index lookups or for sorting, but for projections only. They allow persistent indexes to fully cover more queries and avoid extra document lookups. This can have a great positive effect on index scan performance if the number of scanned index entries is large.

You can set the storedValues option and specify the additional attributes as an array of attribute paths when creating a new persistent index, similar to the fields option:

db.<collection>.ensureIndex({
  type: "persistent",
  fields: ["value1"],
  storedValues: ["value2"]
});

This will index the value1 attribute in the traditional sense, so that the index can be used for looking up by value1 or for sorting by value1. The index also supports projections on value1 as usual.

In addition, due to storedValues being used here, the index can now also supply the values for the value2 attribute for projections without having to look up the full document. Non-existing attributes are stored as null values.

The maximum number of attributes that you can use in storedValues is 32. You cannot specify the same attribute path in both, the fields and the storedValues option. If there is an overlap, the index creation will abort with an error message.

In unique indexes, only the index attributes in fields are checked for uniqueness. The index attributes in storedValues are not checked for their uniqueness.

You cannot create multiple persistent indexes with the same fields attributes and uniqueness option but different storedValues settings. That means the value of storedValues is not considered by calls to ensureIndex() when checking if an index is already present or needs to be created.

Caching of index values

Introduced in: v3.10.0

You can optionally put an in-memory hash cache in front of persistent indexes. By default, persistent indexes will not have an in-memory cache. You can enable it when creating an index by setting the cacheEnabled option to true:

db.<collection>.ensureIndex({
  type: "persistent",
  fields: ["name"],
  cacheEnabled: true
});

You cannot create multiple persistent indexes with the same fields attributes and uniqueness option but different cacheEnabled settings. That means the value of cacheEnabled is not considered by calls to ensureIndex() when checking if an index is already present or needs to be created.

The in-memory cache for an index will be initially empty, even if the index contains data. The cache will be populated lazily upon querying data from the index when using equality lookups for all index attributes. Cache entries get invalidated when modifying data in the underlying collection. Only the affected index entries will get invalidated.

As the cache is hash-based and unsorted, it cannot be used for full or partial range scans, for sorting, or for lookups that do not include all index attributes.

Filling the caches upon cache misses during lookups and upon writing to the collection can mean extra overhead, so it is recommended to use an in-memory cache only for collections that are accessed mostly for reading via equality lookups, and that are not often written to.

For AQL queries that will use indexes with an enabled in-memory cache and that are known to not benefit from using using the cache, you may turn off the usage of the cache for individual query parts. This can be achieved via the useCache hint that can be provided to an AQL FOR loop:

FOR doc IN collection OPTIONS { useCache: false }
  FILTER doc.value == @lookup
  ...

Using the useCache option will have no effect for indexes that do not have a cache enabled, or for queries that are not eligible to use caches.

The number of index cache hits and misses is also reported when profiling queries. You can use this information to assess the effectiveness of the cache for particular queries.

You can control the maximum combined memory usage of all in-memory caches via the existing --cache.size startup option, which not only controls the maximum memory usage for all edge caches, but additionally also the memory usage for all caches for persistent indexes.

Accessing Persistent Indexes from the Shell

Ensures that a unique persistent index exists:

collection.ensureIndex({ type: "persistent", fields: [ "field1", ..., "fieldn" ], unique: true })

Creates a unique persistent index on all documents using field1, … fieldn as attribute paths. At least one attribute path has to be given. The index will be non-sparse by default.

All documents in the collection must differ in terms of the indexed attributes. Creating a new document or updating an existing document will will fail if the attribute uniqueness is violated.

To create a sparse unique index, set the sparse attribute to true:

collection.ensureIndex({ type: "persistent", fields: [ "field1", ..., "fieldn" ], unique: true, sparse: true })

In a sparse index all documents will be excluded from the index that do not contain at least one of the specified index attributes or that have a value of null in any of the specified index attributes. Such documents will not be indexed, and not be taken into account for uniqueness checks.

In a non-sparse index, these documents will be indexed (for non-present indexed attributes, a value of null will be used) and will be taken into account for uniqueness checks.

In case that the index was successfully created, an object with the index details, including the index-identifier, is returned.

*Examples

arangosh> db.ids.ensureIndex({ type: "persistent", fields: [ "myId" ], unique: true });
arangosh> db.ids.save({ "myId": 123 });
arangosh> db.ids.save({ "myId": 456 });
arangosh> db.ids.save({ "myId": 789 });
arangosh> db.ids.save({ "myId": 123 });

{ 
  "cacheEnabled" : false, 
  "deduplicate" : true, 
  "estimates" : true, 
  "fields" : [ 
    "myId" 
  ], 
  "id" : "ids/74845", 
  "isNewlyCreated" : true, 
  "name" : "idx_1762249899973279745", 
  "selectivityEstimate" : 1, 
  "sparse" : false, 
  "type" : "persistent", 
  "unique" : true, 
  "code" : 201 
}
{ 
  "_id" : "ids/74849", 
  "_key" : "74849", 
  "_rev" : "_fyvNH7a---" 
}
{ 
  "_id" : "ids/74851", 
  "_key" : "74851", 
  "_rev" : "_fyvNH7a--_" 
}
{ 
  "_id" : "ids/74853", 
  "_key" : "74853", 
  "_rev" : "_fyvNH7e---" 
}
[ArangoError 1210: unique constraint violated - in index idx_1762249899973279745 of type persistent over 'myId'; conflicting key: 74849]

arangosh> db.ids.ensureIndex({ type: "persistent", fields: [ "name.first", "name.last" ], unique: true });
arangosh> db.ids.save({ "name" : { "first" : "hans", "last": "hansen" }});
arangosh> db.ids.save({ "name" : { "first" : "jens", "last": "jensen" }});
arangosh> db.ids.save({ "name" : { "first" : "hans", "last": "jensen" }});
arangosh> db.ids.save({ "name" : { "first" : "hans", "last": "hansen" }});

{ 
  "cacheEnabled" : false, 
  "deduplicate" : true, 
  "estimates" : true, 
  "fields" : [ 
    "name.first", 
    "name.last" 
  ], 
  "id" : "ids/74826", 
  "isNewlyCreated" : true, 
  "name" : "idx_1762249899966988288", 
  "selectivityEstimate" : 1, 
  "sparse" : false, 
  "type" : "persistent", 
  "unique" : true, 
  "code" : 201 
}
{ 
  "_id" : "ids/74830", 
  "_key" : "74830", 
  "_rev" : "_fyvNH7---_" 
}
{ 
  "_id" : "ids/74832", 
  "_key" : "74832", 
  "_rev" : "_fyvNH7C---" 
}
{ 
  "_id" : "ids/74834", 
  "_key" : "74834", 
  "_rev" : "_fyvNH7C--_" 
}
[ArangoError 1210: unique constraint violated - in index idx_1762249899966988288 of type persistent over 'name.first, name.last'; conflicting key: 74830]

Ensures that a non-unique persistent index exists:

collection.ensureIndex({ type: "persistent", fields: [ "field1", ..., "fieldn" ] })

Creates a non-unique persistent index on all documents using field1, … fieldn as attribute paths. At least one attribute path has to be given. The index will be non-sparse by default.

To create a sparse unique index, set the sparse attribute to true.

In case that the index was successfully created, an object with the index details, including the index-identifier, is returned.

arangosh> db.names.ensureIndex({ type: "persistent", fields: [ "first" ] });
arangosh> db.names.save({ "first" : "Tim" });
arangosh> db.names.save({ "first" : "Tom" });
arangosh> db.names.save({ "first" : "John" });
arangosh> db.names.save({ "first" : "Tim" });
arangosh> db.names.save({ "first" : "Tom" });

{ 
  "cacheEnabled" : false, 
  "deduplicate" : true, 
  "estimates" : true, 
  "fields" : [ 
    "first" 
  ], 
  "id" : "names/74594", 
  "isNewlyCreated" : true, 
  "name" : "idx_1762249899923996672", 
  "selectivityEstimate" : 1, 
  "sparse" : false, 
  "type" : "persistent", 
  "unique" : false, 
  "code" : 201 
}
{ 
  "_id" : "names/74598", 
  "_key" : "74598", 
  "_rev" : "_fyvNH4e---" 
}
{ 
  "_id" : "names/74600", 
  "_key" : "74600", 
  "_rev" : "_fyvNH4e--_" 
}
{ 
  "_id" : "names/74602", 
  "_key" : "74602", 
  "_rev" : "_fyvNH4e--A" 
}
{ 
  "_id" : "names/74604", 
  "_key" : "74604", 
  "_rev" : "_fyvNH4i---" 
}
{ 
  "_id" : "names/74606", 
  "_key" : "74606", 
  "_rev" : "_fyvNH4i--_" 
}

Query by example using a persistent index

Constructs a query-by-example using a persistent index:

collection.byExample(example)

Selects all documents from the collection that match the specified example and returns a cursor. A persistent index will be used if present.

You can use toArray(), next(), or hasNext() to access the result. The result can be limited using the skip() and limit() operator.

An attribute name of the form a.b is interpreted as attribute path, not as attribute. If you use

{ "a" : { "c" : 1 } }

as example, then you will find all documents, such that the attribute a contains a document of the form { "c" : 1 }. For example the document

{ "a" : { "c" : 1 }, "b" : 1 }

will match, but the document

{ "a" : { "c" : 1, "b" : 1 } }

will not.

However, if you use

{ "a.c" : 1 }

then you will find all documents, which contain a sub-document in a that has an attribute c of value 1. Both the following documents

{ "a" : { "c" : 1 }, "b" : 1 }

and

{ "a" : { "c" : 1, "b" : 1 } }

will match.

Persistent Indexes and Server Language

The order of index entries in persistent indexes adheres to the configured server language. If, however, the server is restarted with a different language setting as when the persistent index was created, not all documents may be returned anymore and the sort order of those which are returned can be wrong (whenever the persistent index is consulted).

To fix persistent indexes after a language change, delete and re-create them.