#CODE ON TIME DATA FLOW CODE#
When you select individual nodes, you can see "stages" that represent code that was executed together on the cluster. Therefore, the monitoring graph represents the design of your flow, taking into account the execution path of your transformations. Additionally, the execution paths may occur on different scale-out nodes and data partitions. When your Data Flow is executed in Spark, the service determines optimal code paths based on the entirety of your data flow. You can also use the search button on the right side to find parts of your data flow logic in the graph. To see the details view with larger graph nodes that include transformation stage labels, use the zoom slider on the right side of your canvas. When you're in the graphical node monitoring view, you can see a simplified view-only version of your data flow graph. Selecting the eyeglasses gives you deep details on your data flow execution. The Run ID at the previous level is for the pipeline. The Run ID at the activity level is different than the Run ID at the pipeline level.
![code on time data flow code on time data flow](https://thumbs.dreamstime.com/x/spy-hacker-wathing-eye-encrypted-fast-long-scrolling-programming-security-hacking-code-data-flow-stream-display-new-138867772.jpg)
You see statistics at this level as well including the run times and status. The highlighted icons allow you to drill into the activities in the pipeline, including the Data Flow activity.
![code on time data flow code on time data flow](https://www.researchgate.net/publication/276489582/figure/fig3/AS:615042143768578@1523648882813/Real-time-data-flow-diagram.png)
You can see a screen similar to the one below. Click on the monitor icon in the left-hand UI panel.
![code on time data flow code on time data flow](https://codeontime.com/blog/2015/05/create-single-page-app-without-changing/image03.png)
When you execute your pipeline, you can monitor the pipeline and all of the activities contained in the pipeline including the Data Flow activity.