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 […]
多くのお客様から非常によくいただくリクエストは、たとえばスキャンした PNG ファイルのテキストなど、画像ファイル中でテキストをインデックスすることです。このチュートリアルでは、それを SOLR を使って行う方法を段階的に説明します。前提条件：Hortonworks Sandbox がダウンロードされていること、「HDP Sandbox のコツを学ぶ」のチュートリアルを完了していること。ステップバイステップ・ガイド […]
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 […]
はじめに：JReport は、Apache Hive の JDBC ドライバを使用して Hortonworks Data Platform 2.3 からデータを簡単に抽出し可視化することができる、組み込み BI レポーティングツールです。レポート、ダッシュボード、データ分析を作成することが可能で、後で自分のアプリケーションに組み込むこともできます。このチュートリアルでは、次のステップをご説明します[...]
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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