data-diff 0.9.0

Command-line tool and Python library to efficiently diff rows across two different databases.

data-diff: Compare datasets fast, within or across SQL databases

Use Cases

Data Migration & Replication Testing

Compare source to target and check for discrepancies when moving data between systems:

• Migrating to a new data warehouse (e.g., Oracle > Snowflake)
• Converting SQL to a new transformation framework (e.g., stored procedures > dbt)
• Continuously replicating data from an OLTP DB to OLAP DWH (e.g., MySQL > Redshift)

Data Development Testing

Test SQL code and preview changes by comparing development/staging environment data to production:

1. Make a change to some SQL code
2. Run the SQL code to create a new dataset
3. Compare the dataset with its production version or another iteration

data-diff integrates with dbt Core to seamlessly compare local development to production datasets

dbt Cloud users should check out Datafold's out-of-the-box deployment testing integration

:eyes: Watch 4-min demo video

Get started with data-diff & dbt

Also available in a VS Code Extension

Reach out on the dbt Slack in #tools-datafold for advice and support

How it works

When comparing the data, data-diff utilizes the resources of the underlying databases as much as possible. It has two primary modes of comparison:

joindiff

• Recommended for comparing data within the same database
• Uses the outer join operation to diff the rows as efficiently as possible within the same database
• Fully relies on the underlying database engine for computation
• Requires both datasets to be queryable with a single SQL query
• Time complexity approximates JOIN operation and is largely independent of the number of differences in the dataset

hashdiff

• Recommended for comparing datasets across different databases
• Can also be helpful in diffing very large tables with few expected differences within the same database
• Employs a divide-and-conquer algorithm based on hashing and binary search
• Can diff data across distinct database engines, e.g., PostgreSQL <> Snowflake
• Time complexity approximates COUNT(*) operation when there are few differences
• Performance degrades when datasets have a large number of differences

More information about the algorithm and performance considerations can be found here

Get started

Validating dbt model changes between dev and prod

⚡ Looking to use data-diff in dbt development? Head over to our data-diff + dbt documentation to get started!

Compare data tables between databases

🔀 To compare data between databases, install data-diff with specific database adapters, e.g.:

pip install data-diff 'data-diff[postgresql,snowflake]' -U

Run data-diff with connection URIs. In the following example, we compare tables between PostgreSQL and Snowflake using hashdiff algorithm:

data-diff \
  postgresql://<username>:'<password>'@localhost:5432/<database> \
  <table> \
  "snowflake://<username>:<password>@<password>/<DATABASE>/<SCHEMA>?warehouse=<WAREHOUSE>&role=<ROLE>" \
  <TABLE> \
  -k <primary key column> \
  -c <columns to compare> \
  -w <filter condition>

Check out documentation for the full command reference.

Supported databases

Database	Status	Connection string
PostgreSQL >=10	🟢	postgresql://<user>:<password>@<host>:5432/<database>
MySQL	🟢	mysql://<user>:<password>@<hostname>:5432/<database>
Snowflake	🟢	"snowflake://<user>[:<password>]@<account>/<database>/<SCHEMA>?warehouse=<WAREHOUSE>&role=<role>[&authenticator=externalbrowser]"
BigQuery	🟢	bigquery://<project>/<dataset>
Redshift	🟢	redshift://<username>:<password>@<hostname>:5439/<database>
Oracle	🟡	oracle://<username>:<password>@<hostname>/servive_or_sid
Presto	🟡	presto://<username>:<password>@<hostname>:8080/<database>
Databricks	🟡	databricks://<http_path>:<access_token>@<server_hostname>/<catalog>/<schema>
Trino	🟡	trino://<username>:<password>@<hostname>:8080/<database>
Clickhouse	🟡	clickhouse://<username>:<password>@<hostname>:9000/<database>
Vertica	🟡	vertica://<username>:<password>@<hostname>:5433/<database>
DuckDB	🟡
ElasticSearch	📝
Planetscale	📝
Pinot	📝
Druid	📝
Kafka	📝
SQLite	📝

• 🟢: Implemented and thoroughly tested.
• 🟡: Implemented, but not thoroughly tested yet.
• ⏳: Implementation in progress.
• 📝: Implementation planned. Contributions welcome.

Your database not listed here?

• Contribute a new database adapter – we accept pull requests!
• Get in touch about enterprise support and adding new adapters and features

Contributors

We thank everyone who contributed so far!

Analytics

• Usage Analytics & Data Privacy

License

This project is licensed under the terms of the MIT License.