SQL Server Table Reconciliation

SQL Server Table Reconciliation

Compare identical tables across two SQL Server instances using Python with mssql-python driver and Apache Arrow. Detect missing rows, column mismatches, schema drift, and produce a reconciliation report.

Workflow

1. Collect connection details for source and target
2. Identify primary key / composite key
3. Detect schema differences
4. Extract data via Arrow for efficient columnar transfer
5. Compare rows and columns
6. Generate reconciliation report

Collect Inputs

Bundled Script

The reconciliation logic is provided as a standalone script at scripts/reconcile.py. Invoke it with the appropriate arguments based on user inputs:

python scripts/reconcile.py \
    --source-server <source_server> \
    --source-database <source_database> \
    --target-server <target_server> \
    --target-database <target_database> \
    --tables "<table_spec>" \
    --auth <sql|entra> \
    --chunk-size <chunk_size> \
    --output <console|csv|json>

Optional arguments

Example invocations

Single table with SQL auth:

python scripts/reconcile.py \
    --source-server prod-server.database.windows.net \
    --source-database ProdDB \
    --target-server staging-server.database.windows.net \
    --target-database StagingDB \
    --tables "dbo.Orders" \
    --auth sql \
    --output console

Wildcard with Entra auth and CSV output:

python scripts/reconcile.py \
    --source-server prod-server.database.windows.net \
    --source-database ProdDB \
    --target-server staging-server.database.windows.net \
    --target-database StagingDB \
    --tables "dbo.*" \
    --auth entra \
    --output csv

Prerequisites

Install required packages before running:

pip install mssql-python pyarrow pandas

Comparison Rules

• Normalize types before comparing: cast decimals to same precision, trim strings, normalize datetime to UTC
• NULL handling: NULL == NULL is considered a match (both sides missing = no diff)
• Ignore row order: always compare by PK join, never positional
• Large tables: chunk extraction with OFFSET/FETCH or ROW_NUMBER() partitioning

Hash-Based Optimization (for large tables)

When table has >1M rows, generate a hash pre-check:

SELECT {pk_cols},
       HASHBYTES('SHA2_256', CONCAT_WS('|', col1, col2, ...)) AS row_hash
FROM {table}

Compare hashes first; only fetch full rows for mismatched hashes. This reduces data transfer significantly.

Report Format

Reconciling dbo.EMPLOYEES...
Reconciling dbo.DEPARTMENTS...
Reconciling dbo.JOBS...

--- dbo.EMPLOYEES ---
  Source: 107  Target: 107
  Missing: 0  Extra: 0  Mismatches: 0
  Result: ✓ IDENTICAL

--- dbo.DEPARTMENTS ---
  Source: 27  Target: 27
  Missing: 0  Extra: 0  Mismatches: 3
  Result: ✗ DIFFERENCES FOUND

--- dbo.JOBS ---
  Source: 19  Target: 19
  Missing: 0  Extra: 0  Mismatches: 0
  Result: ✓ IDENTICAL

=== Summary: 2 passed, 1 failed, 0 skipped / 3 tables ===

When a single table is provided, include full detail (schema drift, sample rows, mismatches). When multiple tables, use the compact per-table format above with full detail only for tables with FAIL status.

Performance Considerations

Constraints

• Always use mssql-python driver (not pyodbc, pymssql)
• Always use Apache Arrow via cursor (cursor.arrow()) for data extraction
• Connection MUST use connection string format, not keyword arguments (kwargs like encrypt=True throw errors)
• Never compare without identifying PK first — ask user if auto-detect fails
• Handle connection failures gracefully with retry logic
• Never hardcode credentials in generated scripts — use os.environ / getpass (env vars: MSSQL_USER, MSSQL_PASSWORD)
• Do not print credentials in output or logs
• Use parameterized queries (? placeholders) for metadata lookups — never f-string interpolate user input into SQL