Pivot faceting (also termed as Decision tree faceting) can be defined as tool that let us do certain automated operations such as sorting, computing count, sum and average of the information stored in a table. As a result, it displays the summarized information in another table. If you are looking for the summarized data (similar to statistics) probably for the purpose of data analysis or reporting, use of pivot faceting is the one for you.
Solr provides us the capability to return the faceted search results of the locations that fall within a specific radius from a geographic point, falls in a square with specific dimension, and falls under a rectangular area with specific dimension. In order to understand the concept better, let us learn about the filters and function queries associated based on the scenario.
Following are the parameters that are used when dealing with spatial search:
In user perspective, a facet (termed as Dynamic Navigation in Google Search Appliance) can be defined as a component of the result set when the search output is split down into multiple categories (quite similar to grouping the search results based on certain parameters) and displayed to the end user along with the document count associated that is associated to the individual component. These facets allow user to further restrict their search quite conveniently.
Before we start playing around with bigdata for Solr, let us understand bigdata briefly.
In this chapter, we will learn how Solr can be used to churn out data for analytics purpose. We will also understand bigdata and learn how to use different faceting concepts such as radius faceting, pivot faceting and so on for data analytics purpose. Additionally, we will discuss the tools we can use with Solr in order to plot graphs and charts, input to it being the Solr’s response.
We will cover:
