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SQL Syntax

KnowledgeFlowDB supports standard SQL queries over the graph data model, providing a familiar interface for relational-style queries.

Basic Query Structure​

SELECT columns
FROM table
[WHERE condition]
[ORDER BY field [ASC|DESC]]
[LIMIT n]
[OFFSET m]

Querying Nodes​

Simple Node Queries​

Count all nodes in the database:

SQL Query
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Find all files:

SQL Query
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Get specific properties:

SQL Query
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Filtering by Properties​

Find nodes with specific labels:

SQL Query
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Querying Edges​

Simple Edge Queries​

Count all relationships:

SQL Query
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Find all relationships by type:

SQL Query
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Get edge details:

SQL Query
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Joins Between Nodes and Edges​

Find Connected Nodes​

Get source nodes with their relationships:

SQL Query
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Find files that contain other entities:

SQL Query
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WHERE Clauses​

Label Filtering​

Find specific node types:

SQL Query
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Combined Conditions​

SQL Query
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Aggregations​

Count by Label​

SQL Query
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Edge Type Statistics​

SQL Query
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ORDER BY and LIMIT​

Pagination​

Get first 10 nodes:

SQL Query
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Skip first 10, get next 10:

SQL Query
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Order by label:

SQL Query
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Complex Queries​

Multi-Table Joins​

Find all relationships with full node details:

SQL Query
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Nested Aggregations​

SQL Query
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Try Your Own Queries!​

Experiment with your own SQL queries:

SQL Query
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Available Tables​

The SQL interface provides access to:

  • nodes: Graph nodes with columns:

    • id (String): Unique node identifier
    • label (String): Node type/label
    • properties (JSON String): Node properties

  • edges: Graph relationships with columns:

    • id (String): Unique edge identifier
    • from_node (String): Source node ID
    • to_node (String): Target node ID
    • edge_type (String): Relationship type
    • properties (JSON String): Edge properties

Comparing SQL and KQL​

Both query languages access the same underlying graph data:

SQL is great for:

  • Relational-style queries
  • Aggregations and statistics
  • Joins across nodes and edges
  • Familiar syntax for SQL users

KQL is great for:

  • Graph pattern matching
  • Multi-hop traversals
  • Relationship-centric queries
  • Cypher-like graph exploration

Try switching between SQL and KQL in the playground to see the difference!

Next Steps​