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

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

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:

Wildcard with Entra auth and CSV output:

Prerequisites

Install required packages before running:

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:

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

Report Format

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