LOAD CSV streams the rows of a CSV file into a query. Unlike LOAD JSONL / LOAD GML (which build a whole graph from a file), LOAD CSV is a reading statement that yields one row at a time, bound to a variable you can reference in RETURN, MATCH, or CREATE.
The file must live in the data subdirectory of the TuringDB working directory (set by -turing-dir, default $HOME/.turing). Absolute paths are re-rooted inside that directory.
Syntax
LOAD CSV "<file>" [WITH HEADERS] [ON ERROR SKIP | ON ERROR FAIL] AS <row>
| Clause | Description |
|---|---|
WITH HEADERS | Treat the first line as column names; access columns as row.<name>. Without it, access columns by 0-based index row[0], row[1], … |
ON ERROR SKIP | Skip malformed rows |
ON ERROR FAIL | Abort on the first malformed row (the default) |
AS <row> | Variable bound to each row |
All values come back as strings. Wrap them in toInteger(...) / toFloat(...) / datetime(...) if you need other types.
With headers
LOAD CSV "people.csv" WITH HEADERS AS row
RETURN row.name AS name, row.age AS age, row.city AS cityWithout headers (index access)
LOAD CSV "people.csv" AS row
RETURN row[0] AS name, row[1] AS age
LIMIT 5Read a row’s fields as row.col or row[<literal index>]. Without WITH HEADERS, the header line comes back as an ordinary data row.
Combining with the rest of a query
LOAD CSV composes with MATCH, WITH / WHERE, aggregates, ORDER BY and UNWIND. For example, join the rows with nodes that are already in the graph:
LOAD CSV "people.csv" WITH HEADERS AS row
MATCH (p:Person {name: row.name})
RETURN p.name, row.cityTo use row.x inside an OPTIONAL MATCH pattern that directly follows LOAD CSV, project the field first: ... AS row WITH row.name AS nm OPTIONAL MATCH (p:Person {name: nm}) ....
Building a graph from CSV
Because LOAD CSV yields rows, you can drive CREATE from it inside a change:
client.new_change()
client.query('LOAD CSV "people.csv" WITH HEADERS AS row CREATE (:Person {name: row.name, age: toInteger(row.age)})')
client.query("COMMIT") # make the new nodes visible to the next query
client.query("CHANGE SUBMIT")
client.checkout()This approach suits small files and additions to an existing graph. Creating edges with LOAD CSV matches both endpoints for every row, so it is best kept to a few thousand edges. For bulk loads, convert the CSV to Parquet and use LOAD PARQUET, which loads 100,000 nodes and 200,000 edges in about 0.2 seconds.
To skip malformed rows, add ON ERROR SKIP (the default, ON ERROR FAIL, stops at the first one). See Writing data for a single-pass MERGE pattern that needs no COMMIT.