Skip to main content

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

Project description

Datafold

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

data-diff-logo


Make sure to join us at our virtual hands-on lab series where our team walks through live how to get set-up with it!

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

dbt

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

data-development-testing

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

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 the hashdiff algorithm:

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

Run data-diff with a toml configuration file. In the following example, we compare tables between MotherDuck(hosted DuckDB) and Snowflake using the hashdiff algorithm:

## DATABASE CONNECTION ##
[database.duckdb_connection] 
  driver = "duckdb"
  # filepath = "datafold_demo.duckdb" # local duckdb file example
  # filepath = "md:" # default motherduck connection example
  filepath = "md:datafold_demo?motherduck_token=${motherduck_token}" # API token recommended for motherduck connection
  database = "datafold_demo"

[database.snowflake_connection]
  driver = "snowflake"
  database = "DEV"
  user = "sung"
  password = "${SNOWFLAKE_PASSWORD}" # or "<PASSWORD_STRING>"
  # the info below is only required for snowflake
  account = "${ACCOUNT}" # by33919
  schema = "DEVELOPMENT"
  warehouse = "DEMO"
  role = "DEMO_ROLE"

## RUN PARAMETERS ##
[run.default]
  verbose = true

## EXAMPLE DATA DIFF JOB ##
[run.demo_xdb_diff]
  # Source 1 ("left")
  1.database = "duckdb_connection"
  1.table = "development.raw_orders"

  # Source 2 ("right")
  2.database = "snowflake_connection"
  2.table = "RAW_ORDERS" # note that snowflake table names are case-sensitive

  verbose = false
# export relevant environment variables, example below
export motherduck_token=<MOTHERDUCK_TOKEN>

# run the configured data-diff job
data-diff --conf datadiff.toml \
  --run demo_xdb_diff \
  -k "id" \
  -c status

# output example
- 1, completed
+ 1, returned

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>
DuckDB 🟢 duckdb://<dbname>@<filepath>
MotherDuck 🟢 duckdb://<dbname>@<filepath>
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>
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?


Contributors

We thank everyone who contributed so far!


Analytics


License

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

Project details


Release history Release notifications | RSS feed

Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

data_diff-0.10.1.tar.gz (101.2 kB view hashes)

Uploaded Source

Built Distribution

data_diff-0.10.1-py3-none-any.whl (126.3 kB view hashes)

Uploaded Python 3

Supported by

AWS AWS Cloud computing and Security Sponsor Datadog Datadog Monitoring Fastly Fastly CDN Google Google Download Analytics Microsoft Microsoft PSF Sponsor Pingdom Pingdom Monitoring Sentry Sentry Error logging StatusPage StatusPage Status page