Abstract: Disclosed are a method and a system for optimizing data storage of query statistics of a graph database. The method includes: periodically scanning, on storage servers in which partitions are located, all edges in the partitions; determining, according to all the edges in the partitions, partitions to which start points and end points belong, and calculating outgoing-edge correlation and incoming-edge correlation between partitions; calculating relevancies between partitions through a preset correlation matrix weight according to the outgoing-edge correlation and the incoming-edge correlation between partitions; and storing partitions with high relevancies on a same storage server.

Abstract: The present application relates to a data processing method and system of a distributed graph database. The present application solves the problem of how to improve the efficiency of adding, deleting, modifying and checking data in a graph database, realizes balanced storage and concurrent computing of distributed index, reduces network overhead caused by data operation, and improves the efficiency of data operation effectively.

Abstract: The present application relates to a method and system for storing data in a graph database. The method includes: separating a key-value structure of the storage engine and separately storing a graph structure and a graph property of graph data; and reading, by the compute engine, the graph structure, caching the graph structure for a short term in a single query request scenario, and caching the graph structure for a long term in a read-only analysis scenario. The present application resolves problems that engineering is complex and deep graph traversal performance is poor when graph data is processed, reduces engineering complexity, and improves the deep graph traversal performance.

Abstract: Disclosed are a method and a system for optimizing data storage of query statistics of a graph database. The method includes: periodically scanning, on storage servers in which partitions are located, all edges in the partitions; determining, according to all the edges in the partitions, partitions to which start points and end points belong, and calculating outgoing-edge correlation and incoming-edge correlation between partitions; calculating relevancies between partitions through a preset correlation matrix weight according to the outgoing-edge correlation and the incoming-edge correlation between partitions; and storing partitions with high relevancies on a same storage server.

Abstract: The present application relates to a data processing method and system of a distributed graph database. The present application solves the problem of how to improve the efficiency of adding, deleting, modifying and checking data in a graph database, realizes balanced storage and concurrent computing of distributed index, reduces network overhead caused by data operation, and improves the efficiency of data operation effectively.