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How to Enable Delta Synchronization Based on TQL Query Layout
Previously Delta Synchronization for data push works based on the last_modified_time attribute only, meaning if this attribute is changed compared to the last push, it is included in the data push result that will be sent to the target system.
Starting with this version, it is possible to decide whether you want to run Delta Synchronization to push CIs to target systems or not based on the attributes defined in a TQL query layout. This means that if the last_modified_time attribute is changed but no other attributes from the layout is changed, then the CI will not be sent to the target system. If you have an attribute changed from the TQL query layout, then the CI will be sent.
Example:
You have the TQL query Node with the layout name, description, and serial_number
- If the attribute description is changed then the CI will be sent.
- If the attribute Node is changed, then the CI will not be sent.
To enable delta data push based on TQL query layout, do the following
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Create a TQL query with the layout of your choice.
For details, see How to Define a TQL Query in the Universal CMDB Modeling Guide.
Important The more attributes you add in the TQL query layout, the more processing time will be required for the delta data push jobs.
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To enable Delta Synchronization for data push jobs based on TQL query layout,
- Go to Data Flow Management > Adapter Management, from the Resources pane locate the adapter that you want to use for your data push jobs.
- Right-click the adapter and select Edit Adapter Source from the context menu.
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Locate the following setting:
<adapter-setting name="push.changes.based.on.attributes">false</adapter-setting>
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Change the value from
false
totrue
.Note If the setting does not exist, add the above line with value setting to true.
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Run Delta Synchronization for your data push jobs.
Note The laboratory performance testing of a TQL query Node --> Interface with 1.2 million CIs indicates a decrease of the performance with this feature enabled, due to the fact that the processing time is increased by 30%. Therefore, this feature should be used carefully based on how your data looks and how your data is modified by other jobs (discovery, enrichment, and so on).
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