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Go Faster, Go BIGGER


We live in the era of the Internet of Things (IoT) where billions of devices are interconnected and are generating petabytes of data at an increasing rate. Gaining insights and information from that data and generating value out of it gives a tangible competitive advantage to businesses, organizations, governments, and even individuals.

Organizations should focus more on creating value from data that will enhance their core products, services or even operational processes rather than spend time in dealing with the complexity surrounding Big Data. Big data, in this case, means data in large quantities, high frequencies, and vast varieties.

GridDB is an innovative solution built in Toshiba to solve these complex problems. The foundation of GridDB’s principles is based upon offering a versatile data store that is optimized for IoT, provides high scalability, is tuned for high performance, and ensures high reliability.

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mascot01Optimized for IoT

KeyContainerKey Container data model

Key Container data model of GridDB extends the typical NoSQL Key-Value store. The key and the container are rough equivalents of the table name and table data in the Relational Database (RDB). Data modeling in GridDB is easier compared to other NoSQL databases as we can define the schema and design the data similar to that of an RDB.

AlgorithmTime Series Data Placement Algorithm

In order to manage data from a sensor etc. occurring at a high frequency, data is placed in accordance with the data placement algorithm (TDPA: Time Series Data Placement Algorithm) making maximum effective use of the memory .


mascot02High Performance

DatabaseIn Memory Architecture

I/O is a common bottleneck in any DBMS that can cause CPU to be under-utilized. GridDB overcomes this bottleneck with the ‘Memory first, Storage second’ structure where the ‘primary’ data that is frequently accessed resides in memory and rest is passed on to disks (SSD and HDD)

Reducing the OverheadReducing the Overhead

Operational and communication overhead occurs in multi-threaded operations due to lock and synchronization. GridDB eliminates this by allocating an exclusive memory and DB file to each CPU core / thread. As a result, execution time gets shortened and better performance is achieved.

ParallelParallel Processing

GridDB achieves high performance through parallel processing within a node and across nodes. Parallel processing across nodes is done by distributing a large dataset among multiple nodes (partitioning). Parallelism is made possible by the event-driven engine which processes multiple requests using least amount of resources.

mascot03High Scalability

AutonomousAutonomous Data Distribution technology

Based on the autonomous data distribution algorithm (ADDA: Autonomous Data Distribution Algorithm) developed independently, it is possible to determine data layout and data replication between DB servers.
We were able to remove the management server, intermediary server, etc. located between the client and the DB server.
As a result, costs such as communication and data conversion processing are significantly reduced, and high data consistency, high availability, and high performance are simultaneously achieved.

ServerCommodity hardware

GridDB scales out horizontally with commodity hardware maintaining same level of performance.
GridDB offers dual advantage for businesses that need a scale-out database for large amounts of data but still want to maintain data consistency.

Autonomous Data Distribution
High Reliabilty

mascot04High Reliabilty

HybridHybrid cluster management

GridDB’s autonomous control cluster architecture integrates the advantages of and overcomes the disadvantages of both Master-Slave and Peer-to-Peer styles. GridDB’s algorithms select the master node automatically among peers, and, in case of master node failure, operations remain intact as a new master is appointed automatically and immediately.

Failure to splitElimination of single points of failure and split brain

There is no management server in the cluster, and single point of failure (SPOF) is completely eliminated. Distribution policy is completely eliminated by the quorum policy, which allows only the majority of nodes to serve subclusters.

mascot05High Usability

NoSQL and SQL Dual InterfaceNoSQL and SQL dual interface

Seamlessly combine NoSQL (key-value type) and SQL interface. The fast NoSQL interface can be used data registration, whereas the SQL interface offer many useful functions for analysis such as aggregation.

Long term archiveLong term archive

It is possible to save data that needs to be stored for a long time to an external archive file without putting additional load on the database. This achieves a balance between database space reduction and long-term data retention.

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