Vector index memory configuration

Vector indexes are based on Lucene. Lucene does not use Neo4j page cache memory, it uses OS memory as described in the Memory configuration section. When you have a vector index, you must ensure that there is sufficient memory for the JVM heap, Neo4j page cache, and the Lucene vector indexes to perform optimally. If Lucene has insufficient memory, the OS will perform page swapping and read data from disk, which will dramatically degrade the Neo4j vector index search performance. Tools like IOTOP can assist in understanding disk I/O usage.

Optimal Neo4j memory configuration for vector indexes

The memory configuration recommendations are Heap + Neo4j PageCache + .25(Vector Index Size) + Additional OS Managed Memory.

Considerations and caveats

You can reduce the Neo4j page cache if you plan not to return vectors to the user or calling application, as the vectors are not needed and should not be loaded into memory. For example, the vector storage in a database with a total size of 459 GB is 402 GB. By setting the page cache to 100 GB, the important part of the graph is still in memory, and the server requirements are reduced. The ratio of memory to storage will be high, but Neo4j will still be able to maintain its performance. A 1:4 ratio should perform well.

You must increase the memory if you plan to return vectors to the user or call the application and use them for a refined search. If you use the vectors for further searching or refining search results, the page cache memory allocation must also be increased.

Example calculations

The following examples show how to calculate the memory requirements when vectors are only used for searching and will not be returned to the user or application.

Table 1. Disk storage requirements
Neo4j DB	10M	~40GB
Vector Index (single index)	(1.1 * (4 * 768 + 8 * 16) * 10M)/1048576000	33.5GB
Total DB Size		73.5GB

Table 2. Memory requirements
Heap	10-20GB	20GB
Page Cache	DB Size * 1.2	50GB
OS Memory for Index	.4 of the Vector Index	12GB
Total		82GB

Table 3. Aura vector specified cluster memory configurations
Instance Size	Disk Storage	Heap	Page Cache	Remaining Memory
32GB	64GB	7.58GB	9.01GB	15.41GB
64GB	128GB	16.17GB	17.56GB	30.27GB
128GB	256GB	26.90GB	49.94GB	51.16GB
256GB	512GB	31GB	132.34GB	92.66GB
384GB	768GB	31GB	220.25GB	132.75GB
512GB	1024GB	31GB	308.55GB	172.45GB

Table 4. Aura non-vector specified cluster memory configurations
Instance Size	Disk Storage	Heap	Page Cache	Remaining Memory
32GB	64GB	10.39GB	11.13GB	10.48GB
64GB	128GB	20.57GB	23.43GB	20GB
128GB	256GB	29.60GB	70.40GB	28GB
256GB	512GB	31GB	180.20GB	44.8GB
384GB	768GB	31GB	293.20GB	59.8GB
512GB	1024GB	31GB	410.5GB	70.5GB

Warming up the vector index

The Neo4j vector index is only loaded into memory when it is accessed. Ideally, the Lucene vector index is preloaded into OS-managed memory before quering the index. However, you can also warm up the index by running a few random queries to help the OS load the index into memory. The number of queries required to warm up the index depends on the size of the index and the amount of memory available. For a smaller index (up to 1M entries), five queries should be sufficient to load the index into memory.