This chapter describes some of the planned features, that we at Memgraph are working on.
Excellent database performance is one of Memgraph's long-standing goals. We will be continually working on improving the performance. This includes:
- query compilation;
- query execution;
- core engine performance;
- algorithmic improvements (i.e. bidirectional breadth-first search);
- memory usage and
- other improvements.
Label-Property Index Usage Improvements
Currently, indexing combinations of labels and properties can be created, but cannot be deleted. We plan to add a new query language construct which will allow deletion of created indices.
Improving openCypher Support
Although we have implemented the most common features of the openCypher query language, there are other useful features we are still working on.
Memgraph's openCypher implementation supports the most useful functions, but there are more which openCypher provides. Some are related to not yet implemented features like paths, while some may use the features Memgraph already supports. Out of the remaining functions, some are more useful than others and as such they will be supported sooner.
List comprehensions are similar to the supported
collect function, which generates a list out of multiple values. But unlike
collect, list comprehensions offer a powerful mechanism for filtering or otherwise manipulating values which are collected into a list.
For example, getting numbers between 0 and 10 and squaring them:
RETURN [x IN range(0, 10) | x^2] AS squares
Another example, to collect
:Person nodes with
age less than 42, without list comprehensions can be achieved with:
MATCH (n :Person) WHERE n.age < 42 RETURN collect(n)
Using list comprehensions, the same can be done with the query:
MATCH (n :Person) RETURN [n IN collect(n) WHERE n.age < 42]