Windowed nodes

The following windowed nodes are available in Event Processing:

Aggregate

Use an aggregate node to divide the stream of events into time-based chunks and then run an aggregate function on each of these chunks.

Adding an aggregate node

To add an aggregate node, complete the following steps:

  1. Ensure that a stream of events is available, from an Event Source node or from the output of any previously configured node.
  2. In the Palette, under Processor, drag the Aggregate node into the canvas.
  3. Connect the node to an event source by dragging the Output Port from a source node into the Input Port of this node. A purple checkbox unconfigured_node icon is displayed on the aggregate node indicating that the node is yet to be configured.

  4. Hover over the node, and click Edit icon Edit to configure the node.

    The Configure aggregate window appears.

Configuring an aggregate node

To configure an aggregate node, complete the following steps:

  1. In the Details section, enter a name for your node. The output stream of events from this node will be referred with the name you entered.
  2. Click Next to open the Time window pane.
  3. In the Specify the property to use for start of time window field, select the property of the event that corresponds to an event time. This property is used internally to define the start of the first time window.

  4. In the Specify the time window duration field, specify the duration of each time window.

    Note: A time window is defined by a start time and an end time based on the event time of the event, not the wall-clock time. The start time and the end time values are timestamps. An event is assigned to a time window if its event time is between the start time and the end time of this time window. The aggregate calculation is performed for all events contained in every time window. The closure of a time window triggers the top number calculation when either of the following conditions are met:

    • An event having an event time value greater than the end of the windows that is processed by the node.
    • A timeout period elapses after an event source becomes idle. For more information about event source idleness, see the Kafka SQL connector link in Configuring Flink.

    In a standard time window, events are assigned to time windows of a fixed-size that do not overlap. For example, “Calculate the average price of products sold every day”. To assign events to one or more time windows, you need to enable a rolling time window. An example of a rolling time window is “Calculate the average price of products sold in the last twenty four hours and repeat this calculation every hour.”.

  5. Optional: To enable a rolling time window, in the Calculate a rolling time window field, select Yes.
    1. In the Specify the offset from the start of the time window, specify a time offset to every time window start time, thereby defining additional overlapping time windows.
  6. Click Next to open the Aggregate function pane.
  7. To define how you would like to aggregate events over time windows, in the Aggregate function field, select an aggregate function.
  8. In the Property to aggregate field, select the event property that you want to use.
  9. Optional: To compute aggregate operations on additional properties over each time window, click Add another aggregate function and repeat steps 7 and 8.
  10. Optional: You can group events by one or more properties over each time window. By doing so, all aggregate functions you specified are applied in each time window on group of events having the same property value. For example, “Compute the sum of product prices every hour, grouped by order identifier”.
  11. Click Next to open the Output properties pane. You can manage the properties that are displayed in the output when you view the results after running the flow.

  12. Optional: To remove a property so that it is not displayed in the output, click the Remove property icon remove icon.

    Note: By default, the following properties are available:

    • aggregateStartTime: The start of the time window.
    • aggregateEndTime: The end of the time window.
    • aggregateResultTime: The end of the time window minus one millisecond. This is an event time.

    Warning:

    • Do not use properties named window_start, window_end and window_time because they are used internally in Flink.
    • Ensure you have not specified any of these properties in any node upstream in your graph.
    • Do not rename any properties using any of these names.
  13. Optional: To rename a property, hover over the property name, and click the Edit icon edit icon.
    1. In the text-box, enter a new name for your property.
    2. Click outside the text-box or press Enter on your keyboard to rename the property.
  14. To complete the aggregate node configuration, click Configure.

A green checkbox green checkbox appears on the aggregate node if the node is configured correctly. If there is any error in your configuration, a red checkbox red checkbox appears.

Event Processing 1.0.5 icon User actions are saved automatically. For save status updates, see the canvas header.

  • Saving Saving indicates that saving is in progress.
  • Saved Save successful confirms success.
  • Failed Save failed indicates that there are errors. If an action fails to save automatically, you receive a notification to try the save again. Click Retry to re-attempt the save. When a valid flow is saved, you can proceed to run the job.

If you are running versions earlier than 1.0.5, click Save in the navigation banner to save the flow.

Top-n

A top-n node is a filter for the highest or lowest values of a particular property within a specified time window. It divides a stream of events into time-based chunks and then selects the events with the top number (top-n) of highest or lowest values on each chunk. You specify the top number and then decide whether to sort the top number of rows into ascending or descending order so that the highest or lowest values of a particular property are returned.

Adding a top-n node

To add a top-n node, complete the following steps:

  1. Ensure that a stream of events is available, from an Event Source node or from the output of any previously configured node.
  2. In the Palette, under Processor, drag the Top-n node into the canvas.
  3. Connect the node to an event source by dragging the Output Port from a source node into the Input Port of this node. A purple checkbox unconfigured_node icon appears on the top-n node indicating that the node is yet to be configured.

  4. Hover over the node, and click Edit icon Edit to configure the node.

    The Configure top-n window appears.

Configuring a top-n node

To configure a top-n node, complete the following steps:

  1. In the Details section, enter a name for your node. The output stream of events from this node will be referred with the name you entered.
  2. Click Next to open the Time window pane.
  3. In the Specify the property to use for start of time window field, select the property of the event that corresponds to an event time. This property is used internally to define the start of the first time window.

  4. In the Specify the time window duration field, specify the duration of each time window.

    Note: A time window is defined by a start time and an end time based on the event time of the event, not the wall-clock time. The start time and the end time values are timestamps. An event is assigned to a time window if its event time is between the start time and the end time of this time window. The top number calculation is performed for all events contained in every time window. The closure of a time window triggers the top number calculation when either of the following conditions are met:

    • An event having an event time value greater than the end of the windows that is processed by the node.
    • A timeout period elapses after an event source becomes idle. For more information about event source idleness, see the Kafka SQL connector link in configuring Flink.

    In a standard time window, events are assigned to time windows of a fixed-size that do not overlap. For example, “Get the 5 most valuable orders placed every day”. To assign events to one or more time windows, you need to enable a rolling time window. An example of a rolling time window is “Get the 5 most valuable orders placed in the last 24 hours and repeat this calculation every hour”.

  5. Optional: To enable a rolling time window, in the Calculate a rolling time window field, select Yes.
    1. In the Specify the offset from the start of the time window, specify a time offset to every time window start time, thereby defining additional overlapping time windows.
  6. Click Next to open the Condition pane.
  7. In the Specify the number of results to keep on each window field, specify the number of results that you want to see for each time period.
  8. In the Number of results to keep on each window field, select the property to use for sorting the events.

  9. Select Ascending or Descending according to the following rule:

    • If you select Ascending, you get the events with the top number of lowest values of the selected property, for instance from 1 to 9. For strings, it would sort in the order A to Z.
    • If you select Descending, you get the events with the top number of highest values of the selected property, for instance from 9 to 1. For strings, it would sort in the order Z to A.

  10. Event Processing 1.0.5 icon (Optional) To group events by one or more properties over each time window, in the Grouped by field, select the property that you want to group by.

    Note: When you do this, the top-n condition is applied in each time window on a group of events that have the same property value. For example, “Get the 5 most valuable orders placed every day, grouped by customer identifier”.

  11. Event Processing 1.0.5 icon (Optional) To select an additional group to group by, click Group by another property.
  12. Click Next to open the Output properties pane. You can manage the properties that are displayed in the output when you view the results after running the flow.

  13. Optional: To remove a property so that it is not displayed in the output, click the Remove property icon remove icon.

    Note: The following properties are present by default:

    • windowStartTime: The start of the time window.
    • windowEndTime: The end of the time window.
    • windowResultTime: The end of the time window minus one millisecond. This is an event time.
    • topN: A sequential number between 1 and N according to the ordering of the events within the time window.

    Warning:

    • Do not use properties named window_start, window_end and window_time because they are used internally in Flink.
    • Ensure you have not specified any of these properties in any node upstream in your graph.
    • Do not rename any properties using any of these names.
  14. Optional: To rename a property, hover over the property name, and click the Edit icon edit icon.
    1. In the text-box, enter a new name for your property.
    2. Click outside the text-box or press Enter on your keyboard to rename the property.
  15. To complete the top-n node configuration, click Configure.

A green checkbox green checkbox appears on the top-n node if the node is configured correctly. If there is any error in your configuration, a red checkbox red checkbox appears.

Event Processing 1.0.5 icon User actions are saved automatically. For save status updates, see the canvas header.

  • Saving Saving indicates that saving is in progress.
  • Saved Save successful confirms success.
  • Failed Save failed indicates that there are errors. If an action fails to save automatically, you receive a notification to try the save again. Click Retry to re-attempt the save. When a valid flow is saved, you can proceed to run the job.

If you are running versions earlier than 1.0.5, click Save in the navigation banner to save the flow.