Greenplum HD: Enterprise-Ready Apache Hadoop

Innovation with Application Portability

Greenplum MR is a 100 percent interface-compatible implementation of the Apache Hadoop stack. By maintaining Hadoop interface compatibility, Greenplum MR provides seamless application portability while delivering the advanced features that organizations require.

Faster Performance

Greenplum MR achieves up to two times the performance of standard Apache Hadoop, and can reduce your equipment costs by more than half.

Greenplum MR Direct Shuffle uses the distributed NameNode to improve Reduce phase performance drastically. Unlike Hadoop distributions that use the local file system for shuffle and HTTP to transport shuffle data, Greenplum MR shuffle data is readable directly from anywhere on the network. The system stores data using a shared system that eliminates contention and overhead from data transport and retrieval. Automatic, transparent client-side compression reduces network overhead and reduces footprint on disk, while direct block device I/O provides throughput at hardware speed with no additional overhead. As an additional performance boost, you can read files while they are still being written.

Improved Data Access

NFS access makes Hadoop radically easier and less expensive to use. Greenplum MR allows files to be modified and overwritten, and enables multiple concurrent reads and writes on any file.

Users can simply browse files, automatically open associated applications with a mouse click, or drag and drop files and directories into and out of the cluster. Additionally, standard command-line tools and UNIX applications and utilities (such as Grep, Tar, Sort, or Tail) can be used directly on data in the cluster. With other Hadoop distributions, the user must copy the data out of the cluster in order to use standard tools.

Intuitive Resource Management

Greenplum MR includes the MapR Control System (MCS), which enables full visibility into cluster resources and activity. The tool provides visual insight into node health, service status, and resource utilization, organized by cluster topology (such as datacenters and racks).

Designed to manage large clusters with thousands of nodes, the MapR Heatmap shows the health of the entire cluster at a glance. Filters and group actions are provided to select specific components and perform administrative actions directly since the number of nodes, files, and volumes can be very high. The interfaces also include CLI and REST access.