JanusGraph aggregate query

JanusGraph is an open source distributed Graph database that supports multiple aggregate queries. The following are some common types of aggregate queries and their implementation in JanusGraph. 1. Counting: Used to count the number of vertices or edges that meet specific conditions. Example: Counting the number of all edges in a graph. g.E().count() 2. Summing: Used to sum the attribute values of vertices or edges. Example: Calculate the total age of all vertices. g.V().values('age').sum() 3. Average: Used to calculate the average value of attribute values. Example: Calculate the average age of all vertices. g.V().values('age').mean() 4. Max and Min: Used to find the maximum and minimum values of attribute values. Example: Find the maximum value among all vertex ages. g.V().values('age').max() 5. Grouping: Used to group vertices or edges based on attribute values. Example: Grouping vertices based on their gender and counting the number of each group. g.V().group().by('gender').by(count()) 6. Sorting: Used to sort vertices or edges based on attribute values. Example: Arrange in ascending order of vertex age. g.V().order().by('age', incr) 7. Filtering: Used to filter vertices or edges based on specific conditions. Example: Find all vertices aged 18 years or older. g.V().has('age', gte(18)) It should be noted that JanusGraph does not directly support aggregate queries, but uses the graph Query language Gremlin to perform aggregate queries. Gremlin is a graph traversal language that enables various complex aggregation query operations by using multiple steps. In the above example, 'g' represents the graph object, 'V()' represents selecting all vertices, 'E()' represents selecting all edges, 'count()' represents counting, 'values ()' represents obtaining attribute values, etc. In actual use, query statements can be designed according to specific business requirements and the table structure of the Graph database to meet specific aggregate query requirements.