sql-server-table-reconciliation

✓ Official★ 36,202

by github · part of github/awesome-copilot

Use when: comparing SQL Server tables across instances, data migration validation, ETL verification, row mismatch detection, schema drift, reconciliation report, production vs staging comparison. Uses mssql-python driver with Apache Arrow for fast columnar data transfer and comparison.

🧩 One of 7 skills in the github/awesome-copilot package — works on its own, and pairs well with its siblings.

This is the playbook your agent receives when the skill activates — you don't need to read it to use the skill, but it's here to audit before installing.

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

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

Collect Inputs

Parameter	Required	Description
Source server	Yes	Source SQL Server (e.g. `prod-server.database.windows.net`)
Source database	Yes	Source database name
Target server	Yes	Target SQL Server (e.g. `staging-server.database.windows.net`)
Target database	Yes	Target database name
Tables	Yes	Comma-separated `schema.table` names, or `schema.` wildcard (e.g. `dbo.Orders,dbo.Items` or `dbo.`)
Auth mode	Yes	`sql` (user/password) or `entra` (Azure AD/token)
Primary key	Auto-detect	Column(s) forming the row identity. Auto-detect from metadata if not provided.
Columns to compare	All	Subset of columns, or all non-PK columns
Chunk size	`100000`	Rows per batch for large tables
Output format	`console`	`console`, `csv`, `parquet`, or `json`

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

Argument	Description
`--primary-key`	Comma-separated PK column(s). Omit to auto-detect.
`--columns`	Comma-separated columns to compare. Omit to compare all non-PK columns.

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

Scenario	Strategy
< 100K rows	Single Arrow fetch, in-memory pandas compare
100K–1M rows	Chunked extraction (100K batches), streaming comparison
> 1M rows	Hash pre-check → only fetch mismatched rows
Wide tables (100+ cols)	Compare PK + hash first, drill into specific columns on mismatch
Network-constrained	Use Arrow columnar format (10-50x smaller than row-by-row)

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