Automated monitoring checks

Last modified on 29-Mar-23

Use automated monitoring checks to instruct Soda to automatically check for row count anomalies and schema changes in a dataset.
Requires Soda Cloud

automated monitoring:
  datasets:
    - include %
    - exclude test%

About automated monitoring checks
Prerequisites
Define an automated monitoring check
Optional check configurations
Go further

About automated monitoring checks

When you add automated monitoring checks to your checks.yml file, Soda Core prepares and executes two checks on all the datasets you indicate as included in your checks YAML file.

Anomaly score check on row count: This check counts the number of rows in a dataset during scan and registers anomalous counts relative to previous measurements for the row count metric. Refer to Anomaly score checks for details.
Anomaly score checks require a minimum of four data points (four scans at stable intervals) to establish a baseline against which to gauge anomalies. If you do not see check results immediately, allow Soda Core to accumulate the necessary data points.

Schema checks: This check monitors schema changes in datasets, including column addition, deletion, data type changes, and index changes. By default, this automated check results in a failure if a column is deleted, its type changes, or its index changes; it results in a warning if a column is added. Refer to Schema checks for details.
Schema checks require a minimum of one data point to use as a baseline against which to gauge schema changes. If you do not see check results immediately, wait until after you have scanned the dataset twice.

If you have connected Soda Core to a Soda Cloud account, Soda Core pushes check results to your cloud account where Soda Cloud stores all the previously-measured, historic values for your checks in the Cloud Metric Store. SodaCL can then use these stored values to establish a baseline of normal metric values against which to evaluate future metric values to identify anomalies and schema changes. Therefore, you must have a created and connected a Soda Cloud account to use automated monitoring checks.

Prerequisites

Configure in Soda Cloud Configure using Soda Core

You have signed up for a Soda Cloud account.
You have Administrator rights within your organization’s Soda Cloud account.
You, or an Administrator in your organization’s Soda Cloud account, has deployed a Soda Agent which enables you to connect to a data source in Soda Cloud.

To define automated monitoring checks, follow the guided steps to create a new data source. Reference the section below for how to define the checks themselves.

You have installed a Soda Core package in your environment.
You have configured Soda Core to connect to a data source using a configuration.yml file.
You have created and connected a Soda Cloud account to Soda Core.
You have installed Soda Core Scientific in the same directory or virtual environment in which you installed Soda Core; see instructions below.

Install Soda Core Scientific

To use automated monitoring, you must install Soda Core Scientific in the same directory or virtual environment in which you installed Soda Core.

Set up a virtual environment, as described in the Soda Core install documentation.
Install Soda Core in your new virtual environment.
Use the following command to install Soda Core Scientific.

pip install soda-core-scientific

Note that installing the Soda Core Scientific package also installs several scientific dependencies. Reference the soda-core-scientific setup file in the public GitHub repository for details.

Refer to Troubleshoot Soda Core Scientific installation for help with issues during installation.

Troubleshoot Soda Core Scientific installation

While installing Soda Core Scientific works on Linux, you may encounter issues if you install Soda Core Scientific on Mac OS (particularly, machines with the M1 ARM-based processor) or any other operating system. If that is the case, consider using one of the following alternative installation procedures.

Install Soda Core locally
Use Docker to run Soda Core

Need help? Ask the team in the Soda community on Slack.

Use Docker to run Soda Core

Use Soda’s Docker image in which Soda Core Scientific is pre-installed.

If you have not already done so, install Docker in your local environment.
From Terminal, run the following command to pull the latest Soda Core’s official Docker image.
```
docker pull sodadata/soda-core
```

Verify the pull by running the following command.

docker run sodadata/soda-core --help

Output:

 Usage: soda [OPTIONS] COMMAND [ARGS]...

 Soda Core CLI version 3.0.xxx

 Options:
 --help  Show this message and exit.

 Commands:
 scan    runs a scan
 update-dro  updates a distribution reference file

When you run the Docker image on a non-Linux/amd64 platform, you may see the following warning from Docker, which you can ignore.

WARNING: The requested image's platform (linux/amd64) does not match the detected host platform (linux/arm64/v8) and no specific platform was requested

When you are ready to run a Soda scan, use the following command to run the scan via the docker image. Replace the placeholder values with your own file paths and names.
```
docker run -v /path/to/your_soda_directory:/sodacl sodadata/soda-core scan -d your_data_source -c /sodacl/your_configuration.yml /sodacl/your_checks.yml
```
Optionally, you can specify the version of Soda Core to use to execute the scan. This may be useful when you do not wish to use the latest released version of Soda Core to run your scans. The example scan command below specifies Soda Core version 3.0.0.
```
docker run -v /path/to/your_soda_directory:/sodacl sodadata/soda-core:v3.0.0 scan -d your_data_source -c /sodacl/your_configuration.yml /sodacl/your_checks.yml
```

What does the scan command do?

docker run ensures that the docker engine runs a specific image.
-v mounts your SodaCL files into the container. In other words, it makes the configuration.yml and checks.yml files in your local environment available to the docker container. The command example maps your local directory to /sodacl inside of the docker container.
sodadata/soda-core refers to the image that docker run must use.
scan instructs Soda Core to execute a scan of your data.
-d indicates the name of the data source to scan.
-c specifies the filepath and name of the configuration YAML file.

Error: Mounts denied

If you encounter the following error, follow the procedure below.

docker: Error response from daemon: Mounts denied: 
The path /soda-core-test/files is not shared from the host and is not known to Docker.
You can configure shared paths from Docker -> Preferences... -> Resources -> File Sharing.
See https://docs.docker.com/desktop/mac for more info.

You need to give Docker permission to acccess your configuration.yml and checks.yml files in your environment. To do so:

Access your Docker Dashboard, then select Preferences (gear symbol).
Select Resources, then follow the Docker instructions to add your Soda project directory – the one you use to store your configuration.yml and checks.yml files – to the list of directories that can be bind-mounted into Docker containers.
Click Apply & Restart, then repeat steps 2 - 4 above.

Error: Configuration path does not exist

If you encounter the following error, double check the syntax of the scan command in step 4 above.

Be sure to prepend /sodacl/ to both the congifuration.yml filepath and the checks.yml filepath.
Be sure to mount your files into the container by including the -v option. For example, -v /Users/MyName/soda_core_project:/sodacl.

Soda Core 3.0.xxx
Configuration path 'configuration.yml' does not exist
Path "checks.yml" does not exist
Scan summary:
No checks found, 0 checks evaluated.
2 errors.
Oops! 2 errors. 0 failures. 0 warnings. 0 pass.
ERRORS:
Configuration path 'configuration.yml' does not exist
Path "checks.yml" does not exist

Install Soda Core Scientific Locally

Set up a virtual environment, as described in the Soda Core install documentation.
Install Soda Core in your new virtual environment.
Use the following command to install Soda Core Scientific.

pip install soda-core-scientific

Note that installing the Soda Core Scientific package also installs several scientific dependencies. Reference the soda-core-scientific setup file in the public GitHub repository for details.

Define an automated monitoring check

In the context of SodaCL check types, automated monitoring checks are unique. This check employs the anomaly score and schema checks, but is limited in its syntax variation, with only a couple of mutable parts to specify which datasets to automatically apply the anomaly and schema checks.

The example check below uses a wildcard character (%) to specify that Soda Core executes automated monitoring checks against all datasets with names that begin with prod, and not to execute the checks against any dataset with a name that begins with test.

automated monitoring:
  datasets:
    - include prod%
    - exclude test%

You can also specify individual datasets to include or exclude, as in the following example.

automated monitoring:
  datasets:
    - include orders

Scan results in Soda Cloud

To review the checks results for automated monitoring checks in Soda Cloud, navigate to the Checks dashboard to see the automated monitoring check results with an INSIGHT tag.

Optional check configurations

Supported	Configuration	Documentation
	Define a name for an automated monitoring check.	-
	Define alert configurations to specify warn and fail thresholds.	-
	Apply an in-check filter to return results for a specific portion of the data in your dataset.	-
	Use quotes when identifying dataset names.	-
✓	Use wildcard characters ( % with dataset names in the check; see example.	-
	Use for each to apply anomaly score checks to multiple datasets in one scan.	-
	Apply a dataset filter to partition data during a scan.	-

Example with wildcards

automated monitoring:
  datasets:
    - include prod%
    - exclude test%

Go further

Need help? Join the Soda community on Slack.
Reference tips and best practices for SodaCL.
Use a freshness check to gauge how recently your data was captured.
Use reference checks to compare the values of one column to another.

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Last modified on 29-Mar-23