Detailed Component Design

Database

The database can likely be a simple key-value database like dynamo db. Weather events do not have sql like relationships or tree shaped objects of document databases. This helps us optimize for performance too. We should allow sorting by timestamp & location. Additionall, weather events should have a TTL, perhaps of a year. There's no need for us to store these long term.

User data will not have a TTL. Arugably users could be in a different database because of their different shape, but for feasability it will be easier to have one data layer solution. The key things to store for user beyond standard personal & security info will be to track the locations they care about for notification purposes. We may ask users to tell us directly, store their most recent location queries, or track their current/recent locations.

This database will not need to scale writes unless we cahnge how we fetch data or add new regions to our service. We should design instead to heavily optimize for reads. This will involve denormalizing data, caching & a focus on indices. We can scale horizontally since its a key-value db

Reader

A key thing is that we do not want the database to care about the different options a user can select a location. This app could evolve to add an increasing number of options such as: county, city, state, region, zipcodes, etc. So the reader needs to translate from those inputs to the raw longtitude-latitdude coordinates that we care about. The reader should also have a cache system, as users will likely ask for similar coordinate info in slightly different ways. I.E. calculating Miami Dade county's weather uses the same data as calculating Miami's weather. This service will focus on query optimization, caching and I/O operations. Once it fetches the data it will send them to a seperate anaylzer that will compute the data.

The reader will want to be optimized for I/O networking operations, while the analyzer is optimized for compute internsive operations (multi-threading)

Weather event listener

We could implement this a few ways. It could read from our weather service as the same time as our database, read before the database (if we wanted to optimize to be the first ones to give you alerts), could recieve events from our database, or could scan our database for the latest changes.

Of these I prefer scanning our database. 1. it is simple, it only has to look at the updated_at field on weather_data rows and the database will already be read optimized. And it ensures that we only alert on events that are in our database. It would be embarassing for users to recieve a storm notice before our app shows (its storming) data. I also do not think that seconds matter for an operation like this. The weather event listener will scan the latest updates and do the following logic:

a. is this indicating severe weather we have not already notified about.

b. if this a large deviation from what we previously predicted this weather to be?

To answer question a we will likely want an additional table in the database indicating the broadstrokes of weather alerts we've already done. This is tricky to get right, as we don't want to respam alerts when a 3 day storm is going to last 1 additional hour. but we do want to send alerts if its going to last an additional day or hit a new city. We will likely have to track the events we've alerted on by the range of coordinates we've given an alert to and the time thresholds we don't want to alert for. I.E. for a 3 day storm in Miami, we may have our threshold for notification be if the storm is for one fewer or one additional day.

We may also want tiers of notifications: emergency, warning etc.

Answering b is tricky as well as we need to set thresholds for what is a large deviation and decide whether we compare it to the last recorded event, or the record of a certain time. If the weather gained 3 degrees 10 times today, then each deviation is small but a user that checked the forecast yesterday would see a 30 degree difference!

Submissions

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