Apache Solr Review

Enhance Data Handling By Activating Schema Or Schemaless Mode

Apache Solr is built with data handling capabilities and one of them is that it supports both schema and schemaless modes. Basically, the difference between the two modes is configuration. In schema mode, a schema needs to be designed and manually configured so it will be able to accommodate a particular data type. This is because not all types of data have the same field names and values.

On the other hand, when the schemaless mode is enabled, users no longer need a preconfigured schema to handle the ingested data. Instead, a schema is automatically generated, guesses are made to the type of data, and the schema is updated based on those guesses. The schemaless mode, therefore, accelerates sample deployments but it is not recommended for production use, as errors may not be detected right away,

Dynamic Fields

The search platform also uses dynamic fields. Dynamic fields permit users to add new fields that are not explicitly defined in the schema they configured. These dynamic fields are included in the schema and they are named with a wildcard character. So if they are indexing a document that contains a field that doesn’t have an exact match in the fields specified in the schema, such field will be matched with a particular dynamic field. Also, a field type is provided for the newly generated field. Dynamic fields are very useful when users forgot to define a specific field in their schema. Moreover, they will be able to have flexibility as they add documents and data.

Process Queries Written  In Multiple Languages

Apache Solr offers flexible and robust query features which streamline how searches are performed and how contents and data related to those searches are generated. For example, queries can be written in multiple languages such as English, Chinese, Japanese, German, French and more.

Flexible Query Parsing

The platform also has query parsers that can be plugged into applications. Through the aid of these query parsers, applications will have the capability to handle various types of query operations and deliver the most relevant content to app users. In addition, Apache Solr supports different query parsing techniques which include Boolean, phrase, term, numeric, and fielded

Slice And Dice Data Through Pivot Faceting

With Apache Solr, users can organize their data in multiple ways, allowing them to break the data into categories and sub-categories. This is called faceting. Faceting is a way of slicing and dicing a data so that it can be viewed and analyzed in multiple dimensions. This approach uses algorithms that define how data is broken down and grouped into facet categories.

Apache Solr implements a faceting feature called pivot faceting. With this feature, users will be able to set up different sophisticated “what if”scenarios” so that they can break down their search results, documents, and data in multiple ways. For example, if someone enters the word “restaurants” into the search box, the search result can show a list of the top 3 US states with restaurants that have 4 or 5 star ratings, indicating the number of 4 or 5 star restaurants present in each state. Through pivot faceting, this can still be broken down using a different scenario or a sub-category. So aside from showing the top 3 states, the search result can also generate a list of cities in those top 3 states that have 4 or 5 star restaurants, providing the number of 4 or 5 star restaurants for each city.

Automate Content Discovery

Apache Solr comes with content discovery features which enable searchers to quickly find and access whatever content or information they are looking for. As they type in search queries, the platform provides spelling suggestions, helping them check the spelling or words they are typing in. The platform can also automatically suggest terms or phrases related to what they are searching as well as give them the ability to instantly complete a word or phrase without the need to type the entire word or phrase.

Configurable Hit Highlighting

As part of its content discovery features, the search platform offers hit highlighting feature. With this feature, portions or fragments of the documents that match the queries made by searchers and contain the terms or phrases they typed in are highlighted. Because of this, they will be able to concentrate on specific areas of the document where matches occur. Also, this feature is configurable which means users can define the number of characters and the size of the fragments that need to be highlighted.

Enable Location-Aware Search

There is a very interesting feature in Apache Solr that can be used for generating content based on the location of the person making a query. This feature is known as spacial search. This type of search takes into account the geographical location of the searcher and uses geometric data types for finding matches. Geometric data types include points, lines and polygons. The platform supports such data types and generates results that show spatial relationships between the geometries. This location-aware search feature of Apache Solr can be extended to mobile applications. Thus, mobile apps can search, organize, and filter content depending on the locations of the app users who are doing the searches.

Enrich Your Content

With Apache Solr, the indexing process is made flexible. This is because it makes sure that users are able to gather data from different sources and come up with indices that contain rich content and documents. This is enhanced by the platform’s integration with Apache Tika, a content detection and analysis framework which can search and extract data from different file types in such a way that users no longer have to write or execute any code. In addition, the platform in itself can accommodate data even if it is not created and saved using the JSON file format, a widely-used and open-standard file format used for exchanging data between applications and generating site content. Apache Solr can process data from XML and CSV files, Microsoft Office documents, and other file types.

Rich And Powerful Administration Interfaces

Apache Solr is built with rich and powerful administration interfaces which make it easy for users to track what’s happening throughout the platform as well as gain full control of their Solr resources and instances. For instance, it has an interface where they will be able to access reports that give them insights into activities being performed within the platform. They can key check KPI metrics right straight from their web browser as well.

In case they want to review the queries they built and try alternative queries, the platform has a query interface where they can immediately test queries and apply them to their data. Interfaces are also provided for conducting data analysis and exploration. When it comes to uploading documents for indexing and enriching their indices, a user interface is also designed for that.

Open Source And Open Development Approach

As an open source search platform, Apache Solr provides developers with he with the ability to configure, enhance, and extend its capabilities so that it can function and operate according to their own processes and at the same time gives them the opportunity to be a part of a community that aims to develop the platform further. In fact, the Apache Solr code can be accessed from SVN and Git, allowing them to edit it to optimize the platform’s way of implementing its features and functionalities, fix bugs, and even apply patches.

Plugins And Extensions

Furthermore, the capabilities of Apache Solr can be enhanced by using plugins and extensions that are shipped with it. There are plugins which can be used for clustering search results, detecting languages, as well as for integrating with other tools from Apache such as Apache UIMA and Apache Velocity.