StatusNeo

ElasticSearch – Using Python

Introduction

• ElasticSearch is a search engine platform that is used to store data and perform various analyses.
• It is based on the Apache Lucene Library.
• It stores the data in the document form, which is a non-relational form, basically a complex data structure.
• It is a distributed storage platform, that is having distributed clusters across the globe so that it can robust and easily accessible.
• Indexing is done to optimize the data storage and analyses capabilities.
• It is developed in JAVA, and is supported in many languages like JAVA, .NET, PYTHON, RUBY, C#, etc.
• It is the most popular search engine database.

Schema-Less

• Elasticsearch also has the ability to be schema-less.
• Documents can be indexed without explicitly specifying how to handle each of the different fields that might occur in a document.
• When dynamic mapping is enabled, Elasticsearch automatically detects and adds new fields to the index.
• This default behavior makes it easy to index and explore your data.
• Just start indexing documents and Elasticsearch will detect and map booleans, floating-point, and integer values, dates, and strings to the appropriate Elasticsearch data types.
• Ultimately, however, you know more about your data and how you want to use it than Elasticsearch can.
• You can define rules to control dynamic mapping and explicitly define mappings to take full control of how fields are stored and indexed.

Define Your Own Mapping

• Distinguish between full-text string fields and exact value string fields.
• Perform language-specific text analysis
• Optimize fields for partial matching
• Use custom date formats
• Use data types such as geo_point and geo_shape that cannot be automatically detected
• It’s often useful to index the same field in different ways for different purposes. For example, you might want to index a string field as both a text field for full-text search and as a keyword field for sorting or aggregating your data. Or, you might choose to use more than one language analyzer to process the contents of a string field that contains user input.

REST API’s

• Elasticsearch provides a simple, coherent REST API for managing your cluster and indexing and searching your data.
• For testing purposes, you can easily submit requests directly from the command line or through the Developer Console in Kibana.
• From your applications, you can use the Elasticsearch client for your language of choice: Java, JavaScript, Go, .NET, PHP, Perl, Python, or Ruby.

Searching Your DataEdit

• The Elasticsearch REST APIs support structured queries, full-text queries, and complex queries that combine the two.
• Structured queries are similar to the types of queries you can construct in SQL.
• For example, you could search the gender and age fields in your employee index and sort the matches by the hire_date field.
• Full-text queries find all documents that match the query string and return them sorted by relevance—how good a match they are for your search terms.
• In addition to searching for individual terms, you can perform phrase searches, similarity searches, and prefix searches, and get autocomplete suggestions.

Geo-Spatial Data

• Elasticsearch indexes non-textual data in optimized data structures that support high-performance geo and numerical queries.

Coding – Demonstration

• Install ElasticSearch on your system and start your ElasticSearch Engine. (https://www.elastic.co/guide/en/elasticsearch/reference/current/install-elasticsearch.html)
• Make a connection with ElasticSearch, and say it’s in your utils file.

• Create an index for your dataset.
• Code For checking, deleting and fetching all the indexes.

• Code for fetching, inserting, and querying dataset.

• Code to read data from CSV.

References-

• https://www.elastic.co/guide/en/elasticsearch/reference/current/search-analyze.html
• https://github.com/Utkarsh731/elasticSearch
• https://www.elastic.co/

Still Curious? Visit my website to know more!

For more interesting Blogs Visit- Utkarsh Shukla Author