SOQL Floats To Integers In New Relic: A Conversion Bug

Hey guys! Ever faced a situation where your meticulously gathered data just doesn't show up the way it should? Let's dive into a quirky issue some of us have encountered while using New Relic with SOQL queries. Specifically, we're talking about SOQL float columns being incorrectly converted to integers in New Relic events. This can be a real headache when you're trying to analyze precise, fractional data. So, let's break down what's happening, why it's happening, and what we can do about it.

Understanding the Issue

So, you've crafted this brilliant custom SOQL query, pulling data from Salesforce, maybe dealing with some financial metrics or usage statistics that naturally come as float values. You're all set to pipe this into New Relic, visualize it, and get those insights flowing. But then, bam! The float values – those lovely numbers with decimal points – are showing up as plain old integers. It's like losing half the story, right?

When you're dealing with numeric data, precision is key. Imagine tracking average order values, where the cents matter, or measuring response times in milliseconds. Converting these floats to integers can skew your results, leading to incorrect analyses and potentially flawed decisions. It's like trying to measure something with a broken ruler.

This problem crops up when these float columns are included in the numeric field mappings during the process of converting log entries into events for New Relic. It's a sneaky little bug that can easily slip under the radar until you notice the discrepancies in your data. The crux of the issue lies in how the New Relic Salesforce exporter handles these conversions, which we'll delve into shortly. But first, let's see how to reproduce this issue and confirm if you're facing the same problem.

Steps to Reproduce the Float to Integer Conversion

Okay, so how do you actually see this thing in action? Reproducing the bug is pretty straightforward, and walking through the steps helps solidify our understanding. Here’s the recipe:

1. Create a Custom Query: First, you need a SOQL query that selects a column which returns float values. This could be anything from average prices to calculated percentages. The key is that the underlying Salesforce field is a Number or Currency type that allows for decimal places.
2. Add to Numeric Field Mappings: Next, you'll need to configure your New Relic Salesforce exporter to recognize this column as a numeric field. This usually involves adding the column's name to a list or mapping within your exporter's configuration.
3. Set Configuration to Generate Events: Make sure your exporter is set up to send data to New Relic as events. This is the format that New Relic uses for storing and querying data, and it's where the conversion happens.
4. Execute the Integration: Now, run your integration and let the data flow from Salesforce to New Relic.

Once the data is in New Relic, you can query it and see if the float values have been truncated to integers. If you see whole numbers where you expected decimals, you've successfully reproduced the issue. Congrats! (Well, not really, but at least you know what's going on.)

Expected vs. Actual Behavior: A Tale of Two Values

So, what should actually happen, and what does happen? Let's paint a clear picture.

Expected Behavior

Ideally, when your SOQL query returns a float value, the corresponding event attribute in New Relic should also be a float value. No surprises, no data loss. If your Salesforce field holds 123.45, that's exactly what you should see in New Relic. This ensures that your analyses and visualizations are accurate and reflect the true nature of your data. After all, we're striving for data integrity here, right?

Actual Behavior

Unfortunately, what you often get is a truncated integer. That 123.45 magically transforms into 123. The decimal part vanishes, taking valuable information with it. This can be incredibly frustrating, especially when you're relying on that precision for meaningful insights. Imagine the impact on financial reports or performance metrics!

This discrepancy between the expected and actual behavior highlights the core problem: a data type mismatch during the conversion process. It's like trying to fit a square peg in a round hole – something's gotta give, and in this case, it's the decimal places.

Diving into the Technical Details

Okay, let's get a little nerdy and peek under the hood. Understanding why this happens can help us grasp the solution better. The issue stems from a specific part of the newrelic-salesforce-exporter code, particularly within the pipeline.py file.

The culprit lies in how the exporter handles the conversion of columns to numeric values. Specifically, there's a function called maybe_convert_str_to_num that's used in the pack_log_into_event function. The exporter is assuming that the input to maybe_convert_str_to_num is always a string. This assumption is where things go south for float values.

In the pack_log_into_event function (around line 73 in the pipeline.py file), the code iterates through the columns and attempts to convert those marked as numeric. If a column contains a float, it's already in numeric form, but the maybe_convert_str_to_num function still tries to treat it like a string. This leads to the float being inadvertently converted to an integer.

To put it simply, the code is trying to parse a number as if it were a string, and in the process, it's losing the decimal part. It's a classic case of misinterpreting data types, which can lead to unexpected and unwanted results.

Proposed Solutions: Fixing the Float Fiasco

Alright, we've identified the problem and understand its roots. Now, let's talk solutions! There are a couple of ways we can tackle this, and both revolve around ensuring the maybe_convert_str_to_num function handles non-string inputs gracefully.

Solution 1: Input Type Checking

The first, and perhaps most straightforward, approach is to modify the maybe_convert_str_to_num function itself. We can add a check at the beginning of the function to see if the input value is actually a string. If it's not, we can simply return the value directly, bypassing the string conversion logic.

This approach is elegant because it directly addresses the core issue. By checking the input type, we prevent the function from attempting to convert non-string values, preserving our precious float precision.

Solution 2: Preemptive Type Check

Another option is to perform the type check before calling maybe_convert_str_to_num within the pack_log_into_event function. This would involve adding a conditional statement that checks if the column value is a string before passing it to the conversion function. If it's not a string, we skip the conversion step altogether.

This solution is equally valid and achieves the same outcome: preventing the erroneous conversion of floats to integers. The choice between these two solutions often comes down to coding style and preference. Both are effective in resolving the issue.

Practical Implications and Workarounds

So, while we wait for a permanent fix (perhaps a patch in a future release of the exporter), what can you do in the meantime? Here are a few practical implications and potential workarounds.

Data Validation and Awareness

First and foremost, be aware of this issue! If you're working with float values in your SOQL queries, be sure to validate your data in New Relic to ensure it's being represented correctly. This might involve comparing values in Salesforce with their counterparts in New Relic to identify any discrepancies.

String Representation

One workaround is to represent your float values as strings in your SOQL query. You can use the FORMAT() function in SOQL to explicitly convert the float to a string. While this might seem counterintuitive (we're trying to preserve numeric precision, after all), it can prevent the erroneous conversion to integers. You'll then need to handle these string representations appropriately in New Relic, potentially converting them back to numbers for analysis and visualization.

Adjusting Numeric Field Mappings

Another approach is to carefully consider your numeric field mappings. If a column is not strictly numeric (i.e., it can sometimes contain non-numeric values), you might want to avoid mapping it as a numeric field. This will prevent the exporter from attempting to convert it, preserving its original value (whatever that may be).

Conclusion: A Call for Precision

In the world of data analysis, precision matters. The incorrect conversion of SOQL float columns to integers in New Relic events can lead to skewed results and flawed insights. By understanding the issue, its root cause, and potential solutions, we can take steps to mitigate its impact and ensure the accuracy of our data.

Whether it's through code fixes or temporary workarounds, the goal remains the same: to preserve the integrity of our data and empower ourselves with reliable insights. So, keep an eye out for those pesky float conversions, and let's work together to make our data pipelines as precise as possible!