sparquet-cola

Spark data-quality library — SODA-style metric checks, free-form SQL rules, schema assertions and a valid/invalid split (quarantine), all on top of PySpark.

Cola ("glue" in Portuguese) is the layer that glues quality onto your DataFrames. It depends only on pyspark — drop it into any Spark job, notebook or Airflow task, with or without the Sparquet framework. Inside Sparquet it is the engine behind the validations block; the rule type strings are identical, so what you learn here transfers directly to the pipeline JSON.

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The import name is sparquet_cola (underscore, Python convention); the PyPI package is sparquet-cola (hyphen).

Install

pip install sparquet-cola

from sparquet_cola import Cola

pyspark>=3.4.0 comes as a dependency. The public names are Cola, ColaSplit, CheckResult, and the individual check classes.

Quickstart

One class does everything — a registry of check types with four members: run, split, register and available.

from sparquet_cola import Cola

cola = Cola()

# 1) Run checks and read the results (never raises on a failed check)
for r in cola.run(df, [
    {"type": "row_count", "min": 1},
    {"type": "not_null", "columns": ["id"]},
    {"type": "check", "metric": "missing_percent", "column": "cpf", "must_be": "< 5%", "warn": "= 0"},
    {"type": "sql", "failed_rows": "SELECT * FROM _validation_df WHERE amount < 0"},
]):
    print(r)   # [FAIL] check: missing_percent(cpf) = 8 violates must_be (< 5%)

# 2) Split valid from invalid (quarantine)
split = cola.split(df, [
    {"type": "not_null", "columns": ["id"]},
    {"type": "check", "metric": "invalid_count", "column": "email",
     "valid_format": "email", "must_be": "= 0"},
])
split.valid.write.format("delta").save(".../silver_ok")
split.invalid.write.format("delta").save(".../silver_quarantine")

A rule is a plain dict with a type key plus that check's parameters. A CheckResult carries rule_type, passed, severity (pass/warn/fail), message, failed_count, metric_value, check_name and failed_rows (a DataFrame, for sql failed-rows checks).

run only raises for a genuinely malformed rule (unknown type, a missing required parameter, an invalid threshold or metric name) — a failed check is a returned result, not an exception.

The checks

Row-level checks feed the valid/invalid split; aggregate checks don't.

Row-level failure codes

A quarantine table that does not say which rule rejected each row cannot be acted on. Every rule therefore has a code: the one you declare, or — when you omit it — the validation expression itself, rendered compactly and deterministically (the same rule always renders the same string, because it lands in your data).

split can then write those codes next to the rejected rows, and be scoped to a subset of the rules:

split = cola.split(df, rules, annotate="dq_codes", only=["AGE_RANGE", "not_null(email)"])
split.invalid.select("id", "dq_codes").show(truncate=False)
# +----+-----------------------------+
# | id | dq_codes                    |
# +----+-----------------------------+
# | 7  | [AGE_RANGE]                 |
# | 8  | [not_null(email)]           |
# +----+-----------------------------+

annotate adds an array<string> column to invalid only — on valid it would be empty by definition — built from the same predicates the split already computes, so it costs no extra pass. only restricts both the split and the annotation to the rules whose code is listed; omitted, every row-level rule takes part.

check metrics and threshold DSL

check measures one metric and compares it to a threshold:

{"type": "check", "name": "cpf completeness",
 "metric": "missing_percent", "column": "cpf", "must_be": "< 1%", "warn": "= 0"}

Metrics: row_count, distinct_count, missing_count/missing_percent, duplicate_count/duplicate_percent, invalid_count/invalid_percent, min/max/avg/sum/stddev, freshness.

Threshold DSL (used in must_be and the softer warn):

For invalid_*, validity is configured with valid_values / invalid_values / valid_format / valid_regex / valid_min / valid_max / valid_length (and min/max_length). Named valid_format values include email, uuid, cpf, cnpj, date, url, ip, and more. A warn breach is logged and reported but is not a failure.

Custom checks

Subclass BaseCheck, implement run(df) -> CheckResult, and optionally violation(df) (a boolean Spark Column, True for offending rows) to join the split. Register it under a type:

from pyspark.sql import functions as F
from sparquet_cola import Cola
from sparquet_cola.checks import BaseCheck, CheckResult

class NoFutureDateCheck(BaseCheck):
    def run(self, df):
        column = self.params["column"]
        failed = df.filter(F.col(column) > F.current_date()).count()
        if failed:
            return CheckResult("no_future_date", False, f"{failed} future dates", failed)
        return CheckResult("no_future_date", True)

    def violation(self, df):
        return F.col(self.params["column"]) > F.current_date()

cola = Cola()
cola.register("no_future_date", NoFutureDateCheck)
cola.run(df, [{"type": "no_future_date", "column": "ordered_at"}])

Inside Sparquet

The validations block of a Sparquet pipeline JSON runs on exactly this engine — the same rule type strings, threshold DSL and validity config. The framework adds report persistence, the on_failure policy and row-level quarantine via validations.outputs.

Development

pip install -e .
PYTHONPATH=. python tests/test_cola_lib.py    # pure unit tests, no Java needed
PYTHONPATH=. python tests/test_split_spark.py # split integration; skips without Java

Releasing to PyPI is automated via GitHub Actions — see docs/DEPLOY_PYPI.md. Changes per version are listed in CHANGELOG.md.

License

Apache License 2.0 — see LICENSE and NOTICE.