Highlights
- Direct CSV-to-graph mapping eliminates need for custom import scripts
- Built-in handling of data types, null values, and multi-value fields reduces development time
- Neo4j-compatible syntax ensures smooth migration path for existing projects
Load CSV = Goodbye to Import Headaches
Most graph database import tools treat CSV files as flat data structures, forcing developers to:
- Write complex transformation logic
- Handle data type conversions manually
- Deal with multi-value fields through custom parsers
- Manage relationship creation in separate passes
Importing large datasets into graph databases is a common pain point amongst developers who often resort to writing custom scripts or using external tools, which can be time-consuming and error-prone.
These approaches also lack the flexibility to handle complex data transformations during import.
FalkordB’s new v4.6 CSV loader allows direct mapping of CSV columns to nodes, relationships, and properties. It handles common pitfalls like data type conversion, null values, and multi-value fields out of the box.
For instance, converting a pipe-separated string of genres into a list property is now a one-liner:
LOAD CSV FROM 'movies.csv' AS row
CREATE (:Movie {title: row.title, genres: split(row.genres, '|')})
For data scientists and architects working with graph-based models, this feature streamlines the data preparation pipeline.
It’s particularly useful for tasks like knowledge graph construction or social network analysis, where data often originates from tabular sources.
FalkorDB’s CSV loader goes beyond basic functionality, offering advanced features like relationship creation with properties in a single pass, as well as support for loading remote files over HTTPS.
What’s Next
This is part of our broader initiative to simplify graph database adoption. The CSV loader is available now in FalkorDB v4.6, with documentation and examples at our GitHub repository.
For developers looking to migrate from Neo4j or start a new graph project, this provides a familiar yet more powerful alternative for data ingestion
How does FalkorDB handle multi-value fields in CSV imports?
Can FalkorDB load CSV files from remote sources?
Is FalkorDB's CSV loader compatible with Neo4j syntax?
Build fast and accurate GenAI apps with GraphRAG SDK at scale
FalkorDB offers an accurate, multi-tenant RAG solution based on our low-latency, scalable graph database technology. It’s ideal for highly technical teams that handle complex, interconnected data in real-time, resulting in fewer hallucinations and more accurate responses from LLMs.