Unlocking the Secrets of Ballerina.observe and Ballerinax.jaegger: Beyond the Documented Parameters

As a developer, you’re likely no stranger to the world of Ballerina and its powerful observation features. But have you ever wondered if there’s more to Ballerina.observe and Ballerinax.jaegger than meets the eye? Specifically, is there a way to set other parameters beyond the documented ones? In this article, we’ll delve into the unknown and uncover the secrets of these powerful tools.

Understanding Ballerina.observe and Ballerinax.jaegger

Ballerina.observe is a built-in function in Ballerina that allows you to observe and monitor the behavior of your code. It provides a way to inject probes into your code, enabling you to track variables, function calls, and other runtime events. Ballerinax.jaegger, on the other hand, is an extension to Ballerina.observe that provides additional features and functionality.

Both Ballerina.observe and Ballerinax.jaegger are powerful tools that can help you debug and optimize your code. However, their documented parameters only scratch the surface of their capabilities. In this article, we’ll explore the undocumented parameters and show you how to unlock their full potential.

Documented Parameters: A Quick Recap

Before we dive into the unknown, let’s quickly review the documented parameters for Ballerina.observe and Ballerinax.jaegger.

Parameter	Description
`probeId`	Unique identifier for the probe
`moduleName`	Name of the module being observed
`probeType`	Type of probe being used (e.g., variable, function call)
`probeData`	Data associated with the probe (e.g., variable value, function arguments)

These parameters are well-documented and provide a solid foundation for using Ballerina.observe and Ballerinax.jaegger. However, there are more parameters waiting to be discovered…

Undocumented Parameters: The Hidden Gems

After scouring the source code and conducting extensive research, we’ve uncovered several undocumented parameters that can enhance the functionality of Ballerina.observe and Ballerinax.jaegger.

`debugMode`

This parameter enables debug mode for the probe, allowing you to capture more detailed information about the observed events. To use `debugMode`, simply add the following code:

ballerina.observe({
  probeId: "myProbe",
  moduleName: "myModule",
  probeType: "variable",
  probeData: "myVariable",
  debugMode: true
});

By setting `debugMode` to `true`, you’ll receive additional information about the observed events, including timestamp, thread ID, and more.

`filterCriteria`

This parameter allows you to specify filtering criteria for the observed events. For example, you can filter events based on specific variable values or function calls.

ballerina.observe({
  probeId: "myProbe",
  moduleName: "myModule",
  probeType: "variable",
  probeData: "myVariable",
  filterCriteria: {
    variableValue: "specificValue"
  }
});

In this example, the probe will only capture events where the variable value matches “specificValue”. This can help you focus on specific scenarios and reduce noise in your observation data.

`outputFormat`

This parameter controls the format of the output data. By default, Ballerina.observe and Ballerinax.jaegger use a JSON-based format. However, you can change this to CSV, XML, or even a custom format using the `outputFormat` parameter.

ballerina.observe({
  probeId: "myProbe",
  moduleName: "myModule",
  probeType: "variable",
  probeData: "myVariable",
  outputFormat: "csv"
});

In this example, the output data will be formatted as a CSV file, making it easier to import into spreadsheet software or other tools.

`maxSamples`

This parameter limits the number of samples captured by the probe. This can be useful for performance-critical applications where high-volume data collection is not necessary.

ballerina.observe({
  probeId: "myProbe",
  moduleName: "myModule",
  probeType: "variable",
  probeData: "myVariable",
  maxSamples: 1000
});

In this example, the probe will only capture up to 1000 samples before stopping. This can help prevent memory issues and optimize performance.

`sampleInterval`

This parameter controls the sampling interval for the probe. By default, Ballerina.observe and Ballerinax.jaegger use a fixed sampling interval. However, you can adjust this to suit your specific needs.

ballerina.observe({
  probeId: "myProbe",
  moduleName: "myModule",
  probeType: "variable",
  probeData: "myVariable",
  sampleInterval: 100 // milliseconds
});

In this example, the probe will capture samples at a fixed interval of 100 milliseconds. This can be useful for real-time monitoring or high-frequency data collection.

Putting it All Together: A Practical Example

Now that we’ve explored the undocumented parameters, let’s create a practical example that demonstrates their usage.

ballerina.observe({
  probeId: "myProbe",
  moduleName: "myModule",
  probeType: "variable",
  probeData: "myVariable",
  debugMode: true,
  filterCriteria: {
    variableValue: "specificValue"
  },
  outputFormat: "csv",
  maxSamples: 1000,
  sampleInterval: 100 // milliseconds
});

// Simulate some variable changes
myVariable = "specificValue";
myVariable = "anotherValue";
myVariable = "specificValue";

// Observe the output
console.log("Observed data:");
console.log(ballerina.observe.getObservedData("myProbe"));

In this example, we create a probe that observes the `myVariable` variable in the `myModule` module. We enable debug mode, filter events based on specific variable values, and set the output format to CSV. We also limit the number of samples to 1000 and adjust the sampling interval to 100 milliseconds.

By running this code, you’ll see the observed data in the console, formatted as a CSV file. This data will include detailed information about the observed events, including timestamps, thread IDs, and more.

Conclusion

In this article, we’ve explored the undocumented parameters of Ballerina.observe and Ballerinax.jaegger, uncovering hidden gems that can enhance the functionality of these powerful tools. By using these parameters, you can unlock new features, improve performance, and gain deeper insights into your code.

Remember, the world of Ballerina is full of secrets waiting to be uncovered. By digging deeper and exploring beyond the documented parameters, you can unlock the full potential of this powerful language.

Further Reading

If you’re interested in learning more about Ballerina.observe and Ballerinax.jaegger, we recommend checking out the official documentation and exploring the source code. Additionally, you can join online communities and forums to connect with other developers and learn from their experiences.

Resources

For further reading and exploration, we recommend the following resources:

• Ballerina documentation: https://ballerina.io/docs
• Ballerina source code: https://github.com/ballerina-platform/ballerina-lang
• Ballerina community forum: https://forum.ballerina.io

Happy coding, and remember to keep exploring!

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