● Covered by 1 source · 1 reportMedium impact
Slater Database Enables Efficient Querying of Large Graphs with Low RAM Usage
Slater, a new low-memory graph database, can manage large graphs up to hundreds of millions of nodes with minimal RAM usage. It enables efficient querying despite high node and edge counts by serving data from an on-disk image instead of keeping the graph entirely in memory, which reduces costs for large-scale applications.
Key points
- Current version of Slater is v0.24.1.
- Enables querying of large graphs with hundreds of millions of nodes.
- Decouples graph size from memory requirements for cost efficiency.
- Supports standard Neo4j drivers over the Bolt protocol.
Introduction to Slater
Slater is a graph database designed to handle extensive graphs that exceed available memory. It aims to overcome the limitations of traditional graph databases that require substantial RAM to manage large datasets effectively.
Key Features
Current version v0.24.1 enables the storage and querying of graphs with hundreds of millions of nodes and billions of edges using only a few hundred MB of RAM. Unlike conventional graph databases which require RAM equivalent to the size of the graph, Slater utilizes on-disk images for efficient data retrieval.
Slater supports seamless integration with existing Neo4j drivers via the Bolt protocol, ensuring that applications can leverage its capabilities without extensive modifications.
Innovative Memory Management
Slater introduces a unique memory management approach where only a fixed cache budget is maintained in RAM while the rest of the graph is stored on disk. This allows Slater to serve different graph sizes with the same RAM requirements, thereby significantly lowering costs in scenarios that require replicas across multiple regions or tenants.
Use Cases and Applications
The architecture of Slater makes it suitable for various applications including knowledge graphs, recommendation systems, identity graphs, and dependency tracking. By allowing disk-native vector search alongside graph data, Slater enhances its utility in environments that require both embedding retrieval and graph query functionalities.
Conclusion
Slater's low-memory architecture represents a notable advancement in the management of large-scale graph databases. Its ability to query extensive datasets efficiently with reduced memory footprints is likely to attract attention from developers and organizations dealing with vast amounts of relational data.
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Primary sources
GitHub cmc_internal/api GitHub github/collect GitHub Hikari-Systems/slater GitHub _private/browser GitHub get-started/accessibility GitHub open-source/sponsorsReporting from
Slater, a new low-memory graph database, can manage large graphs up to hundreds of millions of nodes with minimal RAM usage. It enables efficient querying despite high node and edge counts by serving data from an on-disk image instead of keeping the graph entirely in memory, which reduces costs for large-scale applications.