Apache Solr adds enterprise search capabilities to your big data deployment

Apache Solr is a powerful enterprise search package that lets you run a variety of advanced and complex searches on textual content. It is often used for search and discovery, predictive analytics, and fast DBMS-like retrievals.

Key Features

  • Advanced Full-Text Search Capabilities: You can use Solr to add a wide range of popular full-text search capabilities to your MapR deployment including phrases, wildcards, joins, grouping, and much more. The fast retrieval of Solr queries lets you search and analyze large volumes of data.
  • Easy Integration: You can run Solr directly on top of MapR-FS to leverage the automatic high availability and performance features of MapR. This reduces the number of distinct clusters you need to deploy for your big data architecture. In addition to Solr, the MapR Converged Data Platform also integrates with Elasticsearch to give you the flexibility to run the search engine of your choice.
  • Near Real-Time Indexing: In conjunction with the real-time capabilities in MapR, you can get near real-time indexing with Solr so that updated content is immediately available for search.

Use Cases

  • Enterprise Data Hub: Enterprise Data Hubs (EDH) typically store many different types of data, and Solr is an ideal tool to search, retrieve, and analyze the text-heavy data types.
  • Security Analytics: Analyzing log data from networking equipment often requires a variety of search and retrieval tools. Solr can be used for quickly searching through massive volumes of log data.
  • Recommendation Engines: Solr in conjunction with machine learning tools like Apache Mahout provides a way to quickly retrieve information about recommendations based applying collective preferences to specific individuals.

All Training Information
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Overview of an Enterprise Data Hub on MapR
Overview of an Enterprise Data Hub on MapR


Practical Machine Learning


MapR Documentation