kontra 0.6.0

Developer-first data quality engine

Kontra

Fast data quality validation for files, databases, and DataFrames.

Kontra validates data against declarative rules. It stays fast on large datasets by resolving checks from metadata when possible, then running the rest via batched SQL pushdown (DuckDB / PostgreSQL / SQL Server).

pip install kontra

Quick Start

import kontra
from kontra import rules

result = kontra.validate("users.parquet", rules=[
    rules.not_null("user_id"),
    rules.unique("email"),
    rules.range("age", min=0, max=120),
])

result.passed        # True
result.to_dict()     # Structured output for CI/services
result.to_llm()      # Token-optimized summary for agents

DataFrames work too:

result = kontra.validate(df, rules=[...])  # Polars or pandas

CLI

kontra profile users.parquet --draft > contract.yml
kontra validate contract.yml

✅ users — PASSED (4 of 4 rules)
  ✅ COL:user_id:not_null [metadata]
  ✅ COL:age:range [metadata]
  ✅ COL:email:unique [sql]
  ✅ COL:status:allowed_values [sql]

Execution

Metadata (preplan) resolves what it can prove. Remaining rules run via SQL pushdown when available, or locally (Polars). Preplan and pushdown are configurable.

Contracts

Rules can also be defined in YAML:

name: users
datasource: users.parquet

rules:
  - name: not_null
    params: { column: user_id }

  - name: unique
    params: { column: email }
    severity: warning

  - name: allowed_values
    params:
      column: status
      values: [active, inactive, pending]

  - name: range
    params: { column: age, min: 0, max: 120 }

What You Get

• 18 built-in rules for nulls, uniqueness, ranges, regex, freshness, and more (reference)
• Fast execution: metadata analysis + batched SQL pushdown
• Multiple sources: Parquet, CSV, PostgreSQL, SQL Server, S3, Azure ADLS Gen2
• Agent-friendly: structured, token-optimized summaries via .to_llm()
• Debuggable failures: collect failing rows during validation, fetch more later on demand
• Track drift: save runs and compare over time with kontra diff

Fail Fast vs Exact Counts

By default, Kontra runs in fail-fast mode: it stops at the first violation per rule and reports failed_count: 1 as a lower bound. This enables early termination and metadata-only resolution — large Parquet tables can validate in milliseconds when Parquet statistics are sufficient to prove a rule passes.

When you need exact counts, enable tally:

result = kontra.validate("users.parquet", rules=[...], tally=True)

Or per-rule in YAML:

rules:
  - name: not_null
    params: { column: user_id }
    tally: true      # scan all rows, count all violations

Results:

• default (fail fast) → failed_count: 1 (≥1 violation exists)
• tally: true → failed_count: 23741 (exact)

Failure Samples

# Collect samples during validation
result = kontra.validate("users.parquet", rules=[...], sample=5)

# Access what was collected
for rule in result.rules:
    if not rule.passed and rule.samples:
        print(rule.rule_id, rule.samples)

# Need more? Fetch on demand
result.sample_failures("COL:user_id:not_null", n=20)

Install Extras

pip install "kontra[postgres]"     # PostgreSQL
pip install "kontra[sqlserver]"    # SQL Server
pip install "kontra[s3]"           # S3 / MinIO

Documentation

Doc	Audience
Getting Started	New users
Python API	Library users
Rules Reference	All 18 rules
Configuration	Project setup
Advanced Topics	Agents, state, performance
Architecture	Contributors

License

MIT