Implementing Searching metrics could be challenging for every team, data could be unstructured or structured and scoring most searched keywords and texts could be really challenging, no need to mention hardship of analyzing features, this gets more harder if you need to provide Flexibility, Monitoring and Performance to your API users. Elasticsearch comes with plenty of options like scoring your query, support fuzziness, regex, wildcard, tokenizing your phrases, full-text search and many more, it also indexes your data after creation committed, and enables you to query your data in a snap. Elasticsearch uses a data structure called an inverted index that supports very fast full-text searches. It also provides Histograms Aggregation to create data visualization by intervals to enable your system to create beautiful charts with high performance. This performance comes from the underlying system in Elastic’s data structures and algorithms. Elastic could be schema-less, With elastic you do not need necessarily specify your schema explicitly and you can use dynamic mapping feature which provides your real-time mapping when you create data. Elastic can understand types with the value of your data. Elasticsearch can be Backed by Kibana and Logstash to monitor, visualize and filter data, Logstash supports famous applications and tools logs filtering by default. for example Apache web server. To visualize and monitor how apache really works on your system, it also supports scripting on your search queries which enables the system to manipulate results. Lest hear a bit of description from Creators: ``` Elasticsearch is a distributed document store. Instead of storing information as rows of columnar data, Elasticsearch stores complex data structures that have been serialized as JSON documents. When you have multiple Elasticsearch nodes in a cluster, stored documents are distributed across the cluster and can be accessed immediately from any node. You can go far beyond simple data retrieval and aggregate information to discover trends and patterns in your data. And as your data and query volume grows, the distributed nature of Elasticsearch enables your deployment to grow seamlessly right along with it. ``` All data in Elastic are indexed, which means Elasticsearch provides near real-time search and analytics for all types of data. We are gonna touch these Concepts: - What is Elastic and why we should use it - Best source to Learn - When we should use Elasticsearch - How Elastic gonna benefit your application - Let's taste a bit of Functionality(Mapping/Search API) - What is the ELK and how to use it to improve performance - Release the Engine Power