Distributed search using Apache Blur

Apache Blur is a distributed search engine that can work with Apache Hadoop. It is different from the traditional big data system in that it provides a relational data model-like storage, on top of HDFS. Apache Blur does not use Apache Solr; however, it consumes Apache Lucene APIs. Blur provides faster data ingestion using MapReduce and advanced searches such as a faceted search, fuzzy, pagination, and a wildcard search.

Apache Blur provides a row-based data model (similar to RDBMS), with unique row IDs. Records should have a unique record ID, row ID, and column family. Column family is a group of logical columns. For example, the personal information column family will have columns such as name, companies with which the person works, and contact information. The following figure shows how Apache Blur works closely with Apache Hadoop:

Apache Blur uses Hadoop to store its indexes in a distributed manner. It uses Thrift APIs for all interprocess communication...