xoverrr (pronounced “crossover”)

A tool for cross-database and intra-source data quality checks with detailed discrepancy analysis and reporting.

Supported databases: Oracle, PostgreSQL (+ Greenplum), ClickHouse.

Features

• Four check strategies — row samples, daily counts, custom SQL, and source-only sniff checks
• Multi-DBMS — tables and views; extensible via adapters
• SQLAlchemy engines — pass any supported source / target / results connection
• Replication-lag aware — optionally skip “fresh” rows that may still be catching up
• Auto metadata — primary keys and column types from DBMS catalogs (or supply your own PK)
• Type conversion — application-side normalization across databases
• Column filters — include / exclude lists; mismatched column names skipped automatically
• Chunked date ranges — process long periods in N-day windows
• Reports — text or JSON, with example mismatched rows
• Optional persistence — write run results to a third engine for dashboards / audit
• Tests — unit coverage plus Docker-backed integration tests

Quick start

Sample check (Greenplum/PostgreSQL → Oracle):

from xoverrr import DataQualityChecker, DataReference, CHECK_SUCCESS
from sqlalchemy import create_engine
from datetime import date, timedelta

# 1. Connections
source_engine = create_engine('postgresql://user:pass@localhost:5432/source_db')
target_engine = create_engine('oracle+oracledb://user:pass@localhost:1521/target_db')
results_engine = create_engine('postgresql://user:pass@localhost:5432/dq_audit')

# 2. Checker
checker = DataQualityChecker(
    source_engine=source_engine,
    target_engine=target_engine,
    timezone='Europe/Athens',
    results_engine=results_engine,  # optional
)

# 3. Tables + date window
source_table = DataReference("employees", schema="hr")
target_table = DataReference("employees", schema="hr")
end_date = date.today()
start_date = end_date - timedelta(days=7)

# 4. Run
status, report, stats, details = checker.check_samples(
    source_table=source_table,
    target_table=target_table,
    date_column="hire_date",
    update_column="modified_at",
    date_range=(start_date.strftime('%Y-%m-%d'), end_date.strftime('%Y-%m-%d')),
    chunk_size_days=30,
    custom_primary_key=["employee_id"],
    exclude_columns=["audit_log", "temp_field"],
    tolerance_pct=0.5,
    exclude_recent_hours=3,
    max_examples=5,
    persist_result=DataReference("dq_results", "test"),
    check_name="employees_daily",
    check_tags={"env": "prod", "domain": "hr"},
    report_output_format='text',  # 'json' or 'text'
)

# 5. Result
print(report)
if status == CHECK_SUCCESS:
    print("Data quality check passed")
else:
    print("Data quality check failed")

Every check method returns the same tuple:

Which method should I use?

Check methods

1. Data sample (check_samples)

Compares row sets and column values over a date range.

status, report, stats, details = checker.check_samples(
    source_table=DataReference("table_name", "schema_name"),
    target_table=DataReference("table_name", "schema_name"),
    date_column="created_at",
    update_column="modified_date",
    date_range=("2024-01-01", "2024-12-31"),
    chunk_size_days=30,
    exclude_columns=["audit_timestamp", "internal_id"],
    include_columns=None,
    custom_primary_key=["id", "user_id"],
    tolerance_pct=1.0,
    exclude_recent_hours=24,
    max_examples=3,
)

Main parameters

If custom_primary_key is omitted, the PK is inferred from metadata (must exist on at least one side).

2. Counts (check_counts)

Daily aggregates — good for large volumes and spotting missing/extra rows.

status, report, stats, details = checker.check_counts(
    source_table=DataReference("users", "schema1"),
    target_table=DataReference("users", "schema2"),
    date_column="created_at",
    date_range=("2024-01-01", "2024-12-31"),
    chunk_size_days=30,
    tolerance_pct=2.0,
    max_examples=5,
)

Main parameters: source_table, target_table, date_column, date_range, chunk_size_days, tolerance_pct, max_examples, plus the shared persist_result / check_name / check_tags / report_output_format options described above.

3. Custom query (check_custom_queries)

Compare arbitrary SQL on both sides. Primary key is required.

status, report, stats, details = checker.check_custom_queries(
    source_query="""
        SELECT id AS user_id, name AS user_name, created_at AS created_date
        FROM scott.source_table
        WHERE status = :status
    """,
    source_params={'status': 'active'},
    target_query="""
        SELECT user_id, user_name, created_date
        FROM scott.target_table
        WHERE status = :status
    """,
    target_params={'status': 'active'},
    custom_primary_key=["user_id"],
    exclude_columns=["internal_code"],
    tolerance_pct=0.5,
    max_examples=3,
)

Chunking: when both source_params and target_params include start_date / end_date, set chunk_size_days to split the range:

status, report, stats, details = checker.check_custom_queries(
    source_query="""
        SELECT id, name, created_at
        FROM scott.source_table
        WHERE created_at >= date_trunc('day', cast(:start_date as date))
          AND created_at < date_trunc('day', cast(:end_date as date)) + interval '1 day'
    """,
    source_params={'start_date': '2024-01-01', 'end_date': '2024-12-31'},
    target_query="""
        SELECT id, name, created_at
        FROM scott.target_table
        WHERE created_at >= date_trunc('day', cast(:start_date as date))
          AND created_at < date_trunc('day', cast(:end_date as date)) + interval '1 day'
    """,
    target_params={'start_date': '2024-01-01', 'end_date': '2024-12-31'},
    custom_primary_key=["id"],
    chunk_size_days=30,
    tolerance_pct=0.5,
)

To skip recently changed rows in custom SQL, add the same flag used by sample check:

CASE WHEN updated_at > (sysdate - 3/24) THEN 'y' END AS xrecently_changed

4. Sniff query (check_sniff_query)

Source-only check. Mark each row with xsniff_passed (y = passed, n = failed).
No target engine or primary key required:

checker = DataQualityChecker(
    source_engine=source_engine,
    timezone='UTC',
)

Row-level — one flag per row:

status, report, stats, details = checker.check_sniff_query(
    source_query="""
        SELECT
            order_id,
            amount,
            CASE
                WHEN amount > 0 AND customer_id IS NOT NULL THEN 'y'
                ELSE 'n'
            END AS xsniff_passed
        FROM sales.orders
        WHERE created_at >= :start_date
    """,
    source_params={'start_date': '2024-01-01'},
    tolerance_pct=1.0,
)

Scalar pass/fail — a single xsniff_passed value:

status, report, stats, details = checker.check_sniff_query(
    source_query="""
        SELECT CASE
            WHEN EXISTS (SELECT 1 FROM sales.orders WHERE amount <= 0) THEN 'n'
            ELSE 'y'
        END AS xsniff_passed
    """,
    tolerance_pct=0.0,
)

Issues-only filter — WHERE keeps only bad rows and marks every returned row with a literal 'n'.
Empty result means pass (final_score = 100). Any returned row means fail (issue_rows_pct = 100 for that result set):

status, report, stats, details = checker.check_sniff_query(
    source_query="""
        SELECT
            order_id,
            amount,
            'n' AS xsniff_passed
        FROM sales.orders
        WHERE amount <= 0
          AND created_at >= :start_date
    """,
    source_params={'start_date': '2024-01-01'},
    tolerance_pct=0.0,
)

Use this when you only care that issue rows exist (and want their keys/attributes in the report). Prefer the row-level CASE pattern above when you need a rate over the full checked scope.

Main parameters: source_query, source_params, chunk_size_days (when params include dates), tolerance_pct, max_examples, plus shared persistence / naming / report format options.

Useful stats fields:

Metric calculation

All methods expose stats.final_diff_score and stats.final_score.

Quality score (every method):

final_score = 100 − final_diff_score

Scores are 0–100%. Higher final_score = better quality.
Pass/fail uses tolerance:

• final_diff_score > tolerance_pct → CHECK_FAILED
• otherwise → CHECK_SUCCESS

check_samples / check_custom_queries

final_diff_score =
    (dup_source_rows_pct × 0.1)
  + (dup_target_rows_pct × 0.1)
  + (source_only_rows_pct × 0.15)
  + (target_only_rows_pct × 0.15)
  + (issue_rows_pct × 0.5)

check_counts

sum_of_absolute_differences = abs(source_count − target_count)  per day
sum_of_common_counts        = min(source_count, target_count)   per day

final_diff_score = 100 × sum_of_absolute_differences
                       / (sum_of_absolute_differences + sum_of_common_counts)

check_sniff_query

issue_rows_pct = (rows with xsniff_passed = 'n') / (checked rows) × 100
final_diff_score = issue_rows_pct

Empty result → final_diff_score = 0 (and score 100).

With the issues-only filter pattern (WHERE … + literal 'n' AS xsniff_passed), checked rows are only the filtered issue rows, so any non-empty result yields issue_rows_pct = 100.

Shared behaviour

Chunked processing (chunk_size_days)

Available on all methods. Splits a date range into N-day windows, runs each chunk, then aggregates metrics and examples. Useful for long ranges or large tables.

• check_custom_queries: both sides must pass start_date and end_date in params
• check_sniff_query: chunking uses start_date / end_date in source_params

Status values

Result persistence

With results_engine set and persist_result=True (or a custom DataReference), one row is written per run. The table is created if missing; primary key is run_id. Columns cover status, metadata, stats, details JSON, and the text report.

Logging

Each run has an internal run_id (also stored when persistence is on; not in public JSON from CheckResult.to_dict()):

2024-01-15 10:30:45 - INFO - xoverrr.core - Check run started: run_id=a3f2c8b91d4e5678 check_name=employees_daily check_type=samples
2024-01-15 10:30:45 - INFO - xoverrr.core._check_samples - Query executed in 2.34s
2024-01-15 10:30:46 - INFO - xoverrr.core._check_samples - Source: 150000 rows, Target: 149950 rows
2024-01-15 10:30:47 - INFO - xoverrr.utils.compare_dataframes - Comparison completed in 1.2s
2024-01-15 10:30:47 - INFO - xoverrr.core - Check run finished: run_id=a3f2c8b91d4e5678 status=CHECK_SUCCESS

Performance notes

• DataFrame size hard limit: 3 GB per sample
• Rough benchmark: two samples of ~1M rows × 10 columns (~330 MB each) compared in ~3 s (Intel Core i5 / 16 GB RAM)

Example report

Text output (report_output_format='text'):

================================================================================
2025-11-24 20:09:40
run_id: a3f2c8b91d4e5678
version: *.*.*
source db type: postgresql
target db type: oracle
SAMPLES CHECK REPORT:
hr.employees
VS
hr.employees
================================================================================
timezone: Europe/Athens

    SELECT employee_id, first_name, last_name, salary, department_id, hire_date,
           case when updated_at > (now() - INTERVAL '3 hours') then 'y' end as xrecently_changed
    FROM hr.employees
    WHERE 1=1
        AND hire_date >= date_trunc('day', cast(:start_date as date))
        AND hire_date < date_trunc('day', cast(:end_date as date)) + interval '1 day'

    params: {'start_date': '2025-11-17', 'end_date': '2025-11-24'}
----------------------------------------

    SELECT employee_id, first_name, last_name, salary, department_id, hire_date,
           case when updated_at > (sysdate - 3/24) then 'y' end as xrecently_changed
    FROM hr.employees
    WHERE 1=1
        AND hire_date >= trunc(to_date(:start_date, 'YYYY-MM-DD'), 'dd')
        AND hire_date < trunc(to_date(:end_date, 'YYYY-MM-DD'), 'dd') + 1

    params: {'start_date': '2025-11-17', 'end_date': '2025-11-24'}
----------------------------------------

SUMMARY:
  Source rows: 105
  Target rows: 105
  Duplicated source rows: 0
  Duplicated target rows: 0
  Only source rows: 0
  Only target rows: 0
  Comparable rows: 105
  Passed rows: 103
----------------------------------------
  Source only rows %: 0.00000
  Target only rows %: 0.00000
  Duplicated source rows %: 0.00000
  Duplicated target rows %: 0.00000
  Issue rows %: 1.90476
  Final discrepancies score: 0.95238
  Final data quality score: 99.04762
  Source-only key examples:
  Target-only key examples:
  Duplicated source key examples:
  Duplicated target key examples:
  Evaluated columns: first_name, last_name, salary, department_id
  Skipped source columns: audit_log, temp_field
  Skipped target columns:

ISSUE BREAKDOWN:
  Max issue %: 1.90476
  Issue counts by column:

 column_name  issue_count
     salary                2

  Issue examples:

 primary_key column_name source_value target_value
         101      salary        50000        51000
         102      salary        60000        60500

================================================================================

Known limitations

Oracle thin client & TIMESTAMP WITH TIME ZONE

With the Oracle thin client and check_custom_queries, TIMESTAMP WITH TIME ZONE columns lose timezone context in the result set.

Workaround — cast to TIMESTAMP in SQL:

source_query = """
    SELECT
        order_id,
        CAST(created_at AT TIME ZONE 'Europe/Paris' AS TIMESTAMP) AS created_at,
        amount
    FROM orders
    WHERE status = 'completed'
"""