MRS builds a reliable, secure, and easy-to-use O&M platform and provides storage and analysis capabilities for massive data, helping address enterprise data storage and processing demands. It enables you to quickly build a Hadoop, Hive, or Spark cluster to analyze massive data, or build an HBase cluster for querying massive data in a quasi real-time manner. MRS delivers the following functions:
You need only to import the data to be analyzed to OBS or HDFS and submit an analysis job. The following job types are supported:
MapReduce job: Hadoop 2.7.2 is supported. MapReduce is used to implement parallel computing of large data sets.
Spark/Spark SQL job: Spark 1.5.1 is supported. Spark provides analysis and mining and iterative memory computing capabilities and supports application development in multiple programming languages, including Scala, Java, and Python. Additionally, Spark SQL enables data to be queried and analyzed using SQL statements.
HQL job: Hive 1.3.0 is supported. Hive is a data warehouse framework built on Hadoop. It provides storage of structured data using the HQL, a language like the SQL. Hive converts HQL statements to MapReduce or Spark tasks for querying and analyzing massive data stored in clusters
- Processing of massive data in a quasi real-time manner: risk management, CDR query and so on.
HBase 1.0.2: HBase is a column-based distributed storage system that features high reliability, performance, and scalability. It is designed to eliminate the limitations of relational databases in processing massive data, and ensure quasi real-time data access to large tables.