CSV Toolkit¶

This extension provides direct CSV parsing and encoding capabilities without having to use COPY, enabling inline CSV processing within SQL queries and programmatic workflows.

Installation and Setup¶

-- Install the extension
create extension omni_csv;

-- All functions are available in the omni_csv schema

Core Functions¶

`omni_csv.csv_info(csv text)` - Inspect CSV Structure¶

Analyze the structure of CSV data by extracting column names from the header row:

select *
from
    omni_csv.csv_info(E'name,age,city\nJohn,25,NYC\nJane,30,LA');

Returns:

 column_name 
-------------
 name
 age
 city

This function is essential for working with dynamic or unknown CSV structures, allowing you to discover the schema before parsing the data.

Use cases:

Validating CSV uploads before processing
Building dynamic queries based on CSV structure
API endpoints that accept arbitrary CSV formats

`omni_csv.parse(csv text)` - Parse CSV Data¶

Transform CSV text into queryable records:

select *
from
    omni_csv.parse(E'name,age,city\nJohn,25,NYC\nJane,30,LA')
        as t(name text, age text, city text);

Returns:

 name | age | city 
------+-----+------
 John |  25 | NYC
 Jane |  30 | LA

Key Requirements:

Must specify column structure using as t(column_name text, ...)
The number of columns should match

`omni_csv.csv_agg(record)` - Generate CSV from records¶

Convert query results back into CSV format:

select
    omni_csv.csv_agg(t)
from
    (select
         name,
         age,
         city
     from
         employees
     where
         department = 'Engineering'
     order by name) t;

Returns: A complete CSV string with headers and properly escaped values.

Limitations & caveats

Currently, on an empty set, this aggregate won't encode headers either, returning an empty string.

Limitations and Considerations¶

Memory Usage: Large CSV strings are processed entirely in memory
Type Declaration: Column types must be explicitly specified in as t(...) clause
No Streaming: Not suitable for extremely large datasets that exceed memory limits
Schema Rigidity: Once declared, the column structure cannot be changed within the same query
Performance: For repeated access to the same CSV, consider materializing the parsed data

Comparison with Alternatives¶

Method	Use Case	Pros	Cons
`omni_csv`	Programmatic, inline processing	No temp files, full SQL integration	Memory limited
`COPY`	Bulk data loading	Very fast, streaming	Requires file system access
External tools	Very large datasets	Can handle any size	Additional dependencies

The omni_csv extension excels in scenarios where CSV data needs to be processed as part of complex SQL workflows, API endpoints, or data transformation pipelines without the overhead of temporary files or external processes.