To find relevant deals near the Groupon users, a large number of geospatial searches are performed. These searches are performed on geospatial entities like postal codes, timezones, neighborhoods, or points of interest. Serving millions of queries per minute with low latency requires an efficient spatial indexer for optimization.
This article describes how Groupon uses Redis to power 2 main types of geospatial searches — find the nearest entity and find all nearby entities within a radius. We will also see how Redis clusters provide scalable and performant solutions.
In this article, we’re going to discuss various machine learning techniques for analysis and forecasting of web service metrics and its applications. Auto-scaling is a good application of this, where forecasting techniques can be applied to estimate the request rates for a web service. Similarly, forecasting techniques can be applied to service metrics to predict alerts and anomalies.
In this article, I’ll first talk about Time Series data and its role in forecasting techniques. Later on, I’ll illustrate a predictive model for forecasting request rate of a web service. …