CSV vs TONL: Tabular Data Format Showdown for AI Applications

CSV excels at simple tabular data, but lacks the advanced features modern AI applications demand. TONL offers comparable efficiency for flat data while adding powerful query capabilities, indexing, schema validation, and streaming. Let's compare these formats for enterprise-grade data handling.

The Contenders

Query Capabilities

This is where TONL truly outshines CSV. While CSV requires external tools for data analysis, TONL has queries built in.

CSV Analysis (requires pandas or SQL):

import pandas as pd

# Load CSV
df = pd.read_csv('users.csv')

# Filter
admins = df[df['role'] == 'admin']

# Aggregate
avg_age = df['age'].mean()
grouped = df.groupby('role').size()

TONL Analysis (built-in):

import { parse } from 'tonl';

const doc = parse(tonlString);

// Filter - built into the format
const admins = doc.query('users[?(@.role == "admin")]');

// Aggregate - native API
const avgAge = doc.avg('users[*]', 'age');
const grouped = doc.groupBy('users[*]', 'role');

// Fuzzy search
import { fuzzySearch } from 'tonl/query';
const matches = fuzzySearch('Jon', doc.query('users[*].name'));

Indexing for Fast Lookups

TONL supports indexes that CSV simply cannot match:

// Create indexes for fast lookups
const doc = parse(tonlString, {
  indexes: {
    byId: { type: 'hash', path: 'users[*].id' },
    byAge: { type: 'btree', path: 'users[*].age' },
    byRoleAndName: {
      type: 'compound',
      paths: ['users[*].role', 'users[*].name']
    }
  }
});

// O(1) lookup by ID
const user = doc.getByIndex('byId', 123);

// Range query on age
const adults = doc.rangeByIndex('byAge', 18, 65);

Schema Validation

CSV has no schema support. TONL includes powerful validation:

@schema v1
@strict true

User: obj
  id: u32 required
  email: str required pattern:email lowercase:true
  age: u32? min:13 max:150
  roles: list<str> required min:1 unique:true

users: list<User> required min:1

TSL (TONL Schema Language) supports 13 built-in constraints including:

• required, optional - presence validation
• min, max - numeric and string length bounds
• pattern - regex validation (with presets like email)
• unique - array element uniqueness
• lowercase, uppercase - string normalization

Streaming Comparison

Both formats support streaming, but with different capabilities:

Optimization Strategies

TONL includes built-in optimizations that can compress data even further:

Performance Benchmarks

Testing with 10,000 user records:

When to Use Which?

Stick with CSV if:

• Your data is purely flat with no need for queries.
• You're exporting for Excel or traditional databases.
• Maximum simplicity is the priority.
• You don't need validation or indexing.

Switch to TONL if:

• You need to query or aggregate data in your LLM pipeline.
• Fast lookups by ID or other fields are required.
• You want schema validation for data integrity.
• You're processing large datasets that need streaming.
• Your data includes nested structures.

Final Verdict

CSV remains excellent for simple data exchange and spreadsheet workflows. However, for modern LLM applications requiring query capabilities, indexing, schema validation, and streaming, TONL provides enterprise-grade features while maintaining competitive token efficiency.

For simpler token optimization without advanced features, see our CSV vs TOON comparison. Learn more about TONL features or explore API cost optimization strategies.