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Last updated: October 22, 2024

How to detect invalid IP4 addresses using a data quality check

This sample shows how to use data quality checks in the DQOps platform to detect invalid IP4 addresses and view the results on data quality dashboards.

Overview

The following example shows how to verify that the number of invalid IP4 address in a column does not exceed the maximum accepted count.

PROBLEM

Here is a table with some sample data. In this example, we will monitor the ip4 column.

The ip4 column contains IP4 address values. We want to verify the number of invalid IP4 address values on ip4 column.

SOLUTION

We will verify the data using monitoring invalid_ip4_address_format_found column check. Our goal is to verify if the number of invalid IP4 address values in ip4 column does not exceed set thresholds.

In this example, we will set the maximum count threshold level for the check:

  • warning: 0

If you want to learn more about checks and threshold levels, please refer to the DQOps concept section.

VALUE

If the number of IP4 address values exceed 0, a warning alert will be triggered.

Data structure

The following is a fragment of the DQOps dataset. Some columns were omitted for clarity.
The ip4 column of interest contains both valid and invalid IP4 address values.

ip4 result date
256.212.62.31 0 2/12/2023
206212177195 0 3/13/2022
206-212-177-195 0 5/15/2022
138.181.31.220 1 1/11/2023
225.129.88.137000 1 1/11/2023
116.229.97.20 text 1 1/11/2023
111218.203.183.163 1 1/11/2023
239.62.26.116 1 1/11/2023
189.133.75.23 1 1/11/2023
63.219.239.5 1 1/11/2023
(67.170.154.241) 1 1/11/2023
206.212.177.195 1 1/11/2023
217.22.25.65 1 1/11/2023
198.235.37.157 1 1/11/2023
text 127.186.60.232 1 1/11/2023
55.190.92.1 1 1/11/2023
150.238.182.105 1 1/11/2023
233.227.62.33 1 1/11/2023

Run the example using the user interface

A detailed explanation of how to start DQOps platform and run the example is described here.

To navigate to a list of checks prepared in the example using the user interface:

Navigating to a list of checks

  1. Go to the Monitoring section.

    The Monitoring Checks section enables the configuration of data quality checks that are designed for the daily and monthly monitoring of your data source.

  2. Select the table or column mentioned in the example description from the tree view on the left.

    On the tree view you can find the tables that you have imported. Here is more about adding connection and importing tables.

  3. Select the Daily checkpoints tab.

    This tab displays a list of data quality checks in the check editor. Learn more about navigating the check editor.

Run checks

Run the activated check using the Run check button.

You can also run all the checks for an entire subcategory of checks using the Run check button at the end of the line with the check subgroup name.

Run check

View detailed check results

Access the detailed results by clicking the Results button. The results should be similar to the one below.

String-invalid-ip4-address-count check results

Within the Results window, you will see four categories: Check results, Sensor readouts, Execution errors, and Error sampling. The Check results category shows the severity level that result from the verification of sensor readouts by set rule thresholds. The Sensor readouts category displays the values obtained by the sensors from the data source. The Execution errors category displays any error that occurred during the check's execution. The Error sampling category displays examples of invalid values in the column.

The actual value in this example is 5, which is above the maximum threshold level set in the warning (0). The check gives a warning (notice the yellow square to the left of the check name).

Synchronize the results with the cloud account

Synchronize the results with your DQOps cloud account using the Synchronize button located in the upper right corner of the user interface.

Synchronization ensures that the locally stored results are synced with your DQOps Cloud account, allowing you to view them on the dashboards.

Change a schedule at the connection level

With DQOps, you can easily customize when checks are run by setting schedules. You can set schedules for an entire connection, table, or individual check.

After importing new tables, DQOps sets the schedule for 12:00 P.M. (noon) every day. Follow the steps below to change the schedule.

Change a schedule at the connection level

  1. Navigate to the Data Source section.

  2. Choose the connection from the tree view on the left.

  3. Click on the Schedule tab.

  4. Select the Monitoring daily tab

  5. Select the Run every day at and change the time, for example, to 10:00. You can also select any other option.

  6. Once you have set the schedule, click on the Save button to save your changes.

    By default, scheduler is active. You can turn it off by clicking on notification icon in the top right corner of the screen, and clicking the toggle button.

    Turn off job scheduler

Once a schedule is set up for a particular connection, it will execute all the checks that have been configured across all tables associated with that connection.

You can read more about scheduling here.

You might also want to check the Running checks with a scheduler example.

YAML configuration file

The YAML configuration file stores both the table details and checks configurations.

In this example, we have set the maximum count threshold level for the check:

  • warning: 0

The highlighted fragments in the YAML file below represent the segment where the monitoring daily_invalid_ip4_address_format_found check is configured.

If you want to learn more about checks and threshold levels, please refer to the DQOps concept section.

apiVersion: dqo/v1
kind: table
spec:
  incremental_time_window:
    daily_partitioning_recent_days: 7
    monthly_partitioning_recent_months: 1
  columns:
    ip4:
      type_snapshot:
        column_type: STRING
        nullable: true
      monitoring_checks:
        daily:
          patterns:
            daily_invalid_ip4_address_format_found:
              warning:
                max_count: 0
    result:
      type_snapshot:
        column_type: INT64
        nullable: true
    date:
      type_snapshot:
        column_type: DATE
        nullable: true

In this example, we have demonstrated how to use DQOps to verify the validity of data in a column. By using the invalid_ip4_address_format_found column check, we can monitor that the number of invalid IP4 address in a column does not exceed the maximum accepted count. If it does, you will get a warning result.

Next steps