Handling huge amount of data generated every second or faster.
Toshiba GridDB™ is a highly scalable NoSQL database best suited for IoT and Big Data
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.
Take control of your time series data with data retention policy and perform aggregation in GridDB
Elastically scale to the petabyte level as your time series data grows without sacrificing performance
GridDB cluster is reliable and automatically heals itself in the case of failure without any downtime
Leverage your developers knowledge of SQL, utilize the fast NoSQL interface, and connect to a variety of 3rd party applications
1. Time Series Data Management
The Key Container data model of GridDB extends the typical NoSQL Key-Value store. The Key Container model represents data in the form of collections that are referenced by keys. The key and container are rough equivalents of the table name and table data in Relational Databases (RDB). Data modeling in GridDB is easier than with other NoSQL databases as we can define the schema and design the data similar to that of an RDB. In addition, the Key Container model allows high speed access to data through Java and C APIs. Transaction operations can be performed on a row-by-row basis within a container, ensuring transactional consistency on a container-by-container basis. The key container data model is suitable for storing sensor data.
2. Petabyte-Scale Performance
GridDB incorporates various architectural ingenuity to achieve high speed. For example, GridDB has developed an event-driven method that continuously executes asynchronous data processing, allocates an exclusive memory and DB file to each CPU core / thread, and eliminates unnecessary synchronous processing. GridDB also localizes the data that your application needs to access with a unique time-series data placement technology that arranges the main data in the same block as much as possible to quickly process large amounts of data. By setting memory aggregation hints according to the access pattern and access frequency of the application, the memory area can be effectively used and memory miss hits can be reduced.
3. Highly Available and Reliable
In general, cluster management method can adopt either the master-slave or peer-to-peer method to distributed data to multiple nodes. While the master-slave method makes it relatively easy to maintain data consistency, it is necessary to make the master node redundant in order to avoid a single point of failure (SPOF). Even if the peer-to-peer method avoids a single point of failure, it still has the major problem of high communication overhead between nodes and poor performance when trying to increase the level of consistency. GridDB overcomes the shortcomings of both master-slave and peer-to-peer methods by developing hybrid cluster management that combines the advantages of the two methods, and adding to that an autonomous data relocation technology to ensure consistency in the event of a failure. GridDB also ensures secure and non-stop operation. Node expansion or in the event of a failure, imbalanced data placement can concentrate the load on a specific node, resulting in performance degradation or reduced availability. To do this, it is necessary to relocate the data between the nodes in a well-balanced and fast manner. GridDB uses its own automatic data distribution algorithm (ADDA) to determine data placement and data replication between nodes. As a result, overhead costs such as communication and data conversion processing are significantly reduced, and system expansion can be performed quickly.
4. Developer-Friendly API
GridDB provides both NoSQL and SQL interface. You can seamlessly use the convenience of SQL typically used in RDB and the high speed of NoSQL DB. The NoSQL interface can perform operations such as CRUD (Register, Update, Delete, and Reference). In addition, various plugins are available so that GridDB can be accessed from a variety of programming languages such as Java, C, Python, Go, and Node.js. You can treat the key value container (table) like an RDB table and access it via SQL. GridDB can also be accessed via the standard RDB connection interface such as ODBC and JDBC, leveraging the power of SQL, as well as making it easy to integrate with BI and ETL tools.