![]() curl -X DELETE " curl -X POST " -d ' Īs you can see, with a simple transformation of JSON data returned from ElasticSearch, we're able to create rich, attractive visualization of tag distribution among our articles. This facet returns the most frequent terms for a field, together with occurence counts. Pie charts with a terms facetįor the first chart, we'll use a simple terms facet in ElasticSearch. In this article, we'll learn how to retrieve data for charts like these from ElasticSearch, and how to create the charts themselves. The dashboard is not a static snapshot of the data, pre-calculated periodically, but a truly interactive tool for data exploration. When the user drills down into the data, adds a keyword, uses a custom query, all the charts change in real-time, thanks to the way how facet aggregation works. The screenshot below is from a social media monitoring application which uses ElasticSearch not only to search and mine the data, but also to provide data aggregation for the interactive dashboard. As it happens, we can use facets as a pretty powerful analytical engine for our data, without writing any OLAP implementations. Because everybody loves dashboards, whether they're useful or just pretty. In almost any analytical, monitoring or data-mining service you'll hit the requirement “We need a dashboard!” sooner or later. We can use the data for makings charts, which is exactly what we'll do in this article. When you search for “camera” at an online store, you can refine your search by choosing different manufacturers, price ranges, or features, usually by clicking on a link, not by fiddling with the query syntax.Ī canonical example of a faceted navigation at LinkedIn is pictured below.įaceted search is one of the few ways to make powerful queries accessible to your users: see Moritz Stefaner's experiments with “Elastic Lists” for inspiration.īut, we can do much more with facets then just displaying these links and checkboxes. The usual purpose of facets is to offer the user a faceted navigation, or faceted search. But it also allows to compute complex aggregations of our data, called facets. At its core lies the inverted index, a highly optimized data structure for efficient lookup of documents matching the query. Nevertheless, a modern full-text search engine can do much more than that. ![]() We can get creative with query construction, experimenting with different analyzers for our documents, and the search engine tries hard to provide best results. You pass it a query, and it returns bunch of matching documents, in the order of relevance. ![]() The primary purpose of a search engine is, quite unsurprisingly: searching. ![]()
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