MapR Performance Benchmark Exceeds 100 Million Data Points Per Second Ingest
New Delhi, India, September 10, 2014: MapR Technologies, Inc., provider of the top-ranked distribution for Apache Hadoop, today announced at the Tableau Conference, breakthrough performance results achieved using standard open source software, Open TSDB, running on the MapR Distribution. Using only four-nodes of a 10-node cluster, the MapR Distribution with its in-Hadoop NoSQL database, MapR-DB, ingested over 100 million data points per second.
By accelerating OpenTSDB performance by 1,000 times on such a small cluster, MapR opens the doors to cost-effectively manage massive volumes of data and enable new applications such as Internet of Things (IoT) and other real-time data analysis applications, including industrial monitoring of manufacturing facilities, predictive maintenance of distributed hardware systems and data center monitoring.
Ted Dunning, chief application architect for MapR Technologies, said, “The accelerated performance for Open TSDB validates the differentiated efficiency and scale that MapR brings to the table. OpenTSDB is a widely used database intended to store and analyze time-series data. Originally designed for only data center monitoring, poor ingest performance had limited the expansion of its use. This benchmark demonstrates a viable option for new applications, such as IoT and other real-time data-analysis applications, using Open TSDB running on MapR.”
According to estimates from Cisco, there will be approximately 50 billion devices connected by 2020. These IoT devices include sensors and other embedded data capturing devices that are communicating information continuously and pushing the boundaries of traditional data management platforms. Healthcare, manufacturing and utilities are examples of industries where decisions based on continuous data analysis can improve business operations. These devices will be phoning home and sending data. Time series databases will be critical to store and analyze these data sets.
