Next, let’s create a class representing our document. Var client = new ElasticClient(settings) ![]() Var node = new Uri(" var settings = new ConnectionSettings(node) First of all we need to create a client to communicate with Elasticsearch. The Elasticsearch reference contains samples in cURL format: for example the request to get high level statistics for all our indices looks like this:īut Sense offers a nice copy and paste feature that translates cURL requests to the proper Sense syntax:Įlasticsearch is document-oriented, meaning that it stores entire documents in its index. Sense offers syntax highlighting, autocomplete, formatting and code folding. Once it is installed, Elasticsearch should be available by default under It exposes a HTTP API so it is possible to use cURL to make requests but I recommend using Sense which is Chrome extension. Just go to its web page, download an installer, unzip it and install in three simple steps. The page after applying HTML and styles may look like this:Įlasticsearch is very easy to install. Web API 2 code is just a boilerplate code so I decided to skip it, as well as the AngularJS code which you can replace with your favourite UI framework. ![]() The Web API 2 controller will use NEST to communicate with Elasticsearch.Ĭode snippets in this article will only show the service implementation. The client side will make AJAX requests to ASP.NET Web API 2. In order to build a web page, I will use Single Page Application (SPA) approach with AngularJS as MVVM framework. NEST provides the alternatives of either a fluent syntax for building queries, which resembles structure of raw JSON requests to API, or the use of object initializer syntax. All the request and response objects have been mapped. NEST is a high-level client which maps closely to Elasticsearch API. To interact with Elasticsearch, we will use NEST 2.3.0 which is one of two official. I decided to test Elasticsearch because it does not require that we create an up-front schema file and it exposes Web-friendly APIs (REST and JSON). It offers greater scalability than SQL Server’s full-text search: After all, Stack Exchange initially grew on SQL Server Full-Text Search, but the limitations of its feature and performance forced them migrate to Elasticsearch for their search requirements. It is currently the most popular search engine. ![]() The search page will have following features:Įlasticsearch is an open source search engine, written in Java and based on Lucene. ![]() The dump has considerable amount of data (7 million questions) and it is easy for developers to test the relevance of the search results. In order to get more familiar with the way they work, I will show how to build a search page that will query the dump of StackOverflow questions. This is where search engines come into play. NET developers might now ask: ‘Why would we need other search engines when we are happy with SQL Server’s Full-Text Search feature?’ The answer is that it might be enough for simple searches, but other search engines are a better choice when we need to index and search unstructured data from different sources or when we need custom functionality such as spellchecking, hit-highlighting, autocomplete or advanced scoring. They want their employees and clients to be able to search and analyze it through one user interface.Īt the same time, Internet users, who are used to Google-like search, expect every bespoke search to be as fast and precise: They need autocomplete, they assume that the search tolerates misspellings, and they expect to be able to use filters and many other advanced search features. We need search engines to query and analyse the massive amounts of data that many organizations are required to access: We have no great problem in storing it but how can we then find what we need? Large organizations store many types of structured and unstructured content such as documents in different formats, e-mails, CMS pages or Microsoft Office files. Ryszard takes ElasticSearch, and seven million questions from StackOverflow, in order to show you how to get started with one of the most popular search engines around. Although SQL Server's Full-Text search is good for searching text that is within a database, there are better ways of implementing search if the text is less-well structured, or comes from a wide variety of sources or formats.
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