Rapid indexing & search on Hadoop
Apache Solr is the open source platform for searches of data stored in HDFS in Hadoop. Solr powers the search and navigation features of many of the world’s largest Internet sites, enabling powerful full-text search and near real-time indexing. Whether users search for tabular, text, geo-location or sensor data in Hadoop, they find it quickly with Apache Solr.
Hadoop operators put documents in Apache Solr by “indexing” via XML, JSON, CSV or binary over HTTP.
Then users can query those petabytes of data via HTTP GET. They can receive XML, JSON, CSV or binary results. Apache Solr is optimized for high volume web traffic.
Top features include:
Solr is highly reliable, scalable and fault tolerant. Both data analysts and developers in the open source community trust Solr’s distributed indexing, replication and load-balanced querying capabilities.
Solr is written in Java and runs as a standalone full-text search server within a servlet container such as Jetty. Solr uses the Apache Lucene Java search library at its core for full-text indexing and search, and has REST-like HTTP/XML and JSON APIs that make it easy to use with many programming languages.
Solr’s powerful external configuration allows it to be tailored to almost any type of application without Java coding, and it has an extensive plugin architecture when more advanced customization is required.
Apache Solr includes a deployment methodology to set up a cluster of Solr servers that combines fault tolerance and high availability. This is referred to as SolrCloud. SolrCloud provides distributed indexing and search capabilities, and provides automated failover for queries in the event of any failure to a SolrCloud server.
SolrCloud utilizes Apache ZooKeeper for cluster coordination and configuration.
Introduction Hadoop has always been associated with BigData, yet the perception is it’s only suitable for high latency, high throughput queries. With the contribution of the community, you can use Hadoop interactively for data exploration and visualization. In this tutorial you’ll learn how to analyze large datasets using Apache Hive LLAP on Amazon Web Services […]
A very common request from many customers is to be able to index text in image files; for example, text in scanned PNG files. In this tutorial we are going to walkthrough how to do this with SOLR. Prerequisites Download the Hortonworks Sandbox Complete the Learning the Ropes of the HDP Sandbox tutorial. Step-by-step guide […]
Apache Zeppelin on HDP 2.4.2 Author: Vinay Shukla In March 2016 we delivered the second technical preview of Apache Zeppelin, on HDP 2.4. Meanwhile we and the Zeppelin community have continued to add new features to Zeppelin. These features are now available in the final technical preview of Apache Zeppelin. This technical preview works with […]
Introduction JReport is a embedded BI reporting tool can easily extract and visualize data from the Hortonworks Data Platform 2.3 using the Apache Hive JDBC driver. You can then create reports, dashboards, and data analysis, which can be embedded into your own applications. In this tutorial we are going to walkthrough the folllowing steps to […]
Introduction In this tutorial, you will learn about the different features available in the HDF sandbox. HDF stands for Hortonworks DataFlow. HDF was built to make processing data-in-motion an easier task while also directing the data from source to the destination. You will learn about quick links to access these tools that way when you […]
The Hortonworks Sandbox is delivered as a Dockerized container with the most common ports already opened and forwarded for you. If you would like to open even more ports, check out this tutorial.
Introduction R is a popular tool for statistics and data analysis. It has rich visualization capabilities and a large collection of libraries that have been developed and maintained by the R developer community. One drawback to R is that it’s designed to run on in-memory data, which makes it unsuitable for large datasets. Spark is […]
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