Boost Monitor API: Add Geo Filters For Better Data

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Boost Monitor API: Add Geo Filters for Better Data

Hey everyone! Let's talk about leveling up the Monitor API, specifically by adding some geo/location filtering. Right now, if you're using the API, you're kinda stuck with broad queries. You can't easily narrow things down to specific regions. This is a real pain, especially if you're trying to do anything location-specific like market research, keeping an eye on risks, or making sure you're compliant with local regulations. So, let's dive into why this is important and how we can make things better.

The Current State of the Monitor API

So, what's the deal with the Monitor API as it stands? Well, currently, there's no way to directly tell the API, "Hey, I only want to see data from this specific country, state, or even a particular city." The documentation for creating monitors (specifically the POST /v1alpha/monitors endpoint) only mentions the query itself, how often you want to check (cadence), optional webhooks for notifications, and some metadata. There's nothing in there about geographical regions or locations.

This means that if you're using the parallel-sdk-python (or other SDKs), you're not going to find any built-in options to specify locations. As a workaround, some people try to bake location terms directly into their query strings. For example, if you wanted to monitor mentions of a brand in California, you might try including "California" in your query. The problem? It's super unreliable. It's prone to errors, and it doesn't give you any real control over the precision of your results. What if someone mentions "California Pizza Kitchen" in New York? You'd get that, even though you're only interested in California. This workaround makes it difficult to get accurate and relevant data.

Plus, when you get the results back, there's no metadata telling you where the event originated from. This means you can't filter the results yourself after the fact in a meaningful way. You're left with a bunch of data you don't need, wasting API calls, and making your analysis harder than it should be. So, yeah, the current situation isn't ideal.

SDK Limitations and Workarounds

Let's take a closer look at what this means in practice, especially if you're using the parallel-sdk-python. If you grab data with a command like:

group = client.get(
    f"/v1alpha/monitors/{monitor_id}/event_groups/{event_group_id}",
    cast_to=httpx.Response,
).json()

You're not getting any extra details about where the events happened. This makes client-side geo-filtering pretty much impossible. You'd have to process all the results on your end and try to figure out the location based on the text. Imagine trying to filter a massive dataset manually! It's like finding a needle in a haystack, except the haystack keeps growing.

What We Want: The Geo-Filtering Dream

So, what's the ideal scenario? We want to be able to easily specify geographical controls when we create and manage our monitors. Here's what that could look like:

  • Region Specificity: The ability to specify one or more regions. This could include countries, states/provinces, cities, or even a latitude/longitude coordinate with a radius around it. Think of it like drawing a circle on a map and saying, "I only want data from within this circle."
  • Persistent Settings: The geo settings should be saved, so the monitor knows to restrict its searches to those areas every time it runs. You set it once, and the monitor always respects those boundaries. No more manually adjusting queries every time.
  • SDK Mirroring: The new geo fields should be mirrored in the client SDKs (Python, TypeScript, etc.). This makes it easy for developers to use these new features directly within their code, without having to wrestle with the API's raw requests and responses.

Benefits of Geo-Filtering

Adding these features would be a massive win for a bunch of reasons:

  • Use Case Expansion: Suddenly, a whole new world of use cases opens up. You can use the Monitor API for highly specific market intelligence (what's trending in a particular city?), risk monitoring (is there a crisis unfolding in a specific region?), or even making sure you're compliant with local regulations (are you meeting the privacy requirements of a specific state?).
  • Reduced Noise: Filtering by location would drastically cut down on irrelevant results. Instead of wading through a sea of data, you'd get exactly what you need. Less noise means less processing time, lower API usage costs, and faster insights.
  • Competitive Parity: Many competing services already offer some form of geo-filtering. Adding this feature would make the API more competitive and attractive to a wider audience. This would help in user adoption and overall value.

The Why: Why Geo-Filtering is Crucial

Alright, so why is this so important? Why should the API team prioritize adding geo-filtering? Well, it boils down to a few key areas:

  • Enhanced Data Relevance: The whole point of monitoring is to gather relevant information. Without geo-filtering, you're forced to sift through a lot of noise. This wastes your time and resources.
  • Improved Accuracy: Let's say you're monitoring brand mentions. Without geo-filtering, you might get results from all over the world, even if your business only operates in a specific country. This can give you a false picture of your brand's performance.
  • Cost Efficiency: Each API call costs resources. By reducing the number of irrelevant results, you reduce the number of API calls you need to make. This saves money and improves the efficiency of your monitoring efforts.
  • Expanded Use Cases: Geo-filtering unlocks a whole new set of potential applications for the API. It allows you to track local trends, monitor regional events, and make more informed decisions based on geographical data. Think about the possibilities!

Alternatives Considered and Why They Don't Cut It

We've touched on some of the workarounds people are using, but let's dive a little deeper into why these aren't good long-term solutions.

Encoding Location Constraints in the Query String

This is the most common workaround, and it's a disaster waiting to happen. It involves adding location-specific terms (like "California" or "London") directly into the query string. The problems are numerous:

  • Unreliability: The accuracy of this method depends heavily on the query terms and how well they match the location. Misspellings, different ways of phrasing the location (e.g., "CA" vs. "California"), or ambiguous terms can lead to missed results.
  • Lack of Precision: You can't specify a precise geographic area. You're limited to keywords, so you can't, for example, define a radius around a specific point.
  • Inefficiency: Even if you get some results, you'll likely get a lot of irrelevant ones, wasting API calls and processing time.

Post-Filtering Results

This involves getting all the results and then trying to filter them based on location. This is even worse for a few reasons:

  • Impracticality: This is only feasible if the service provides rich location metadata with each result, and, as we've already noted, that's currently not the case. Without this metadata, you'd have to rely on complex natural language processing (NLP) to extract location information from the text, which is resource-intensive and often inaccurate.
  • Wasteful: You're still paying for API calls for all the results, even the ones you ultimately throw away.
  • Scalability Issues: As your data volume grows, post-filtering becomes exponentially more difficult and resource-intensive.

Call to Action

I'm hoping to hear some guidance on planned support or any schema changes. I would like to contribute. Let's make the Monitor API even more powerful and useful! By adding these geo-filtering features, we can create a much better experience for everyone.

Let me know what you think! Are there any other features that would make the API even more awesome? Let's discuss in the comments!