When running queries, you want to get results as soon as possible. In the worst-case scenario, during query execution all nodes need to be checked to find a match.

This is how the query plan looks when there is no index on the data:

memgraph> EXPLAIN MATCH (n:Person {prop: 1}) RETURN n;
| QUERY PLAN                        |
| " * Produce {n}"                  |
| " * Filter (n :Person), {n.prop}" |
| " * ScanAllByLabel (n :Person)"   |
| " * Once"                         |

Notice the ScanAllByLabel operations. By creating indexes, query execution can be much faster because indexes partition data with a key. When a query is executed, the engine first checks if there is an index, and instead of explicitly checking every node, it can just check the indexed ones, making retrieving indexed data more efficient.

The following query creates an index on a property of a certain label:

CREATE INDEX ON :Person(prop);

The query plan of a query matching that specific node and property:

memgraph> EXPLAIN MATCH (n:Person {prop: 1}) RETURN n;
| QUERY PLAN                                          |
| " * Produce {n}"                                    |
| " * ScanAllByLabelPropertyValue (n :Person {prop})" |
| " * Once"                                           |

The ScanAllByLabel operations has been replaced by ScanAllByLabelPropertyValue, a more efficient operation.

But there are also some downsides to indexing:

  • each index requires extra storage (memory)
  • indexes slow down write operations to the database.

It is important to choose the right data to create indexes on, as indexing all of the content will not improve the database speed.

The structures in the index are dynamically updated on modifications or insertions of new nodes, slowing down the write operations.

Indexing won't bring any improvements on properties that are mostly of the same value, as they have no proper distinguishers.

For the same reason, indexing certain data types will not bring any significant performance gain. For example, for properties with boolean values, the time will be cut in half.

Create an index

Indexes are not created automatically.

You can explicitly create indexes on a data with a specific label or label-property combination using the CREATE INDEX ON syntax.

Label index

To optimize queries that fetch nodes by label, you need to create a label index:


Creating an index will optimize the following type of queries:

MATCH (n:Person) RETURN n;

Label-property index

To optimize queries that fetch nodes with a certain label and property combination, you need to create a label-property index. For the best performance, create index on properties containing unique integer values.


Creating a label-property index will not create a label index!

For example, to index nodes that are labeled as :Person and have a property named age:

CREATE INDEX ON :Person(age);

Creating an index will optimize the queries that need to match a specific label and property combination:

MATCH (n :Person {age: 42}) RETURN n;

Index will also optimize queries that filter labels and properties with the WHERE clause:

MATCH (n) WHERE n:Person AND n.age = 42 RETURN n;

Be aware that since the filter inside WHERE can contain any kind of an expression, the expression can so complicated that the index doesn't get used. If there is any suspicion that an index isn't used, we recommend writing labels and properties inside the MATCH pattern.

Analyze graph

When multiple label-property indexes exist, the database can sometimes select a non-optimal index due to the data's distribution.

The ANALYZE GRAPH; query calculates the distribution of property values so the database can select a more optimal label-property index with the smallest average property value size. The query is run only once after all indexes have been created and data inserted in the database.

Index hinting

You can also instruct the planner to use specific index(es) (if possible) by specifying which index(es) to use at the beginning of the query with USING INDEX clause.


Overriding planner behavior with index hints should be used with caution, and only by experienced developers and/or database administrators, as poor index choice may cause queries to perform poorly.

Schema-related procedures

You can also modify the indexes using the schema.assert() procedure.

Speed comparison

Below is a comparison of the same query run without an index and with an index. The query without an index took 0.015 seconds to execute, and the query with an index 0.006 seconds.

memgraph> SHOW INDEX INFO;
Empty set (0.001 sec)

memgraph> MATCH (n:Person) WHERE =~ ".*an$" RETURN;
|      |
| "Lillian"   |
| "Logan"     |
| "Susan"     |
| "Sebastian" |
4 rows in set (0.021 sec)

memgraph> CREATE INDEX ON :Person(name);
Empty set (0.015 sec)

memgraph> MATCH (n:Person) WHERE =~ ".*an$" RETURN;
|      |
| "Lillian"   |
| "Logan"     |
| "Susan"     |
| "Sebastian" |
4 rows in set (0.006 sec)

Show created indexes

To check all the labels and label-property pairs that Memgraph currently indexes, use the following query:


The query displays a table of all label and label-property indexes presently kept by Memgraph, ordered by index type, label, property and count.

Delete an index

Created indexes can be deleted using the following syntax:

DROP INDEX ON :Label(property);

These queries instruct all active transactions to abort as soon as possible. Once all transactions have finished, the index will be deleted.


Existence and unique constraints, and indexes can be recovered in parallel. To enable this behaviour, set the storage-parallel-schema-recovery configuration flag to true.

Underlying implementation

The central part of Memgraph's index data structure is a highly-concurrent skip list (opens in a new tab). Skip lists are probabilistic data structures that allow fast search within an ordered sequence of elements. The structure itself is built in layers where the bottom layer is an ordinary linked list that preserves the order. Each higher level can be imagined as a highway for layers below.

The implementation details behind skip list operations are well documented in the literature and are out of scope for this document. Nevertheless, we believe that it is important for more advanced users to understand the following implications of this data structure (n denotes the current number of elements in a skip list):

  • The average insertion time is O(log(n))
  • The average deletion time is O(log(n))
  • The average search time is O(log(n))
  • The average memory consumption is O(n)

When it comes to label-property indexes, Memgraph stores a list of specific properties that are used in label-property indexes. This list is ordered to make the search faster. All property types can be ordered. First, they are ordered based on the type and then within that type.