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DRDS is a database middleware product elaborately developed by JD Cloud. It can realize automatic sub-database and sub-table under massive data. With the advantages of high performance, distribution, flexible update, compatibility with MySQL, etc., it is applicable to online transactions of highly concurrent and large-scale data, historical data query, automatic data segmentation and other service scenarios. It has been used in large-scale within JD Group after multiple 618 and Double Eleven tests.Details
Sharding can be automatically realized through simple definition, transparent to the service, and the application does not need to be changed
There are multiple splitting methods that can support the split of numerical value and character string types
DRDS node can dynamically expand the processing capability and user’s business will not be affected during the time of expansion
The back-end MySQL instance also supports dynamic capacity expansion to further extend the storage and processing power of the entire cluster
DRDS node adopts distributed architecture and multiple nodes can provide services at the same time
The backend storage nodes adopt MySQL instance, so that it is the high available architecture with one for use one for standby naturally
It can monitor various main performance indicators of DRDS nodes, and the system running status is clear at a glance
Support self-defined alarms. The users can flexibly formulate various alarm rules, to grasp various situations immediately.
It is suitable for large-scale online transaction scenarios of E-commerce and O2O. It enables warehouse and table sorting for user, order, commodity, logistics and other data, and supports mass transactions with high concurrency. Moreover, it is also easy for horizontal expansion of database, and can improve the concurrency capacity, processing capacity and storage capacity of the whole system.
Strong sub-warehouse and sub-table capacity supports automatic data fragmentation and can store data onto the backend MySQL node by the given fragmentation policy and expand by need at any time. It is suitable for fragmental search and analysis of mass data (for instance, time-based historical information search) and summary of regional data.