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Scaling Your Data Systems


     Scalable Storage      Scales instantly on demand and handles big data volumes
     Scalable Writes      Writes data simultaneously with an all-master architecture
     Scalable Reads      Fast indexed search and range searches in space and time

Horizontal scaling involves adding more servers to a data cloud to accommodate the steady or accelerated growth of data in an enterprise. It is a top priority when selecting a database system in today's data platform and cloud environment. However, traditional methods of horizontal scaling involve moving large amounts of data among computer servers, which is slow and risky. This process is often considered the horrifying nightmare of distributed data systems. JaguarDB has overcome this challenge with its innovative ZeroMove technology, which allows a database cluster to scale horizontally instantly to any size, eliminating the need for data migration and significantly improving scalability and performance.

This article describes our revolutionary technology of scaling-out distributed database systems:

Techrxiv Zero Data Migration




AI Data Infrastructure


JaguarDB software infrastructure that is scalable in an unparalleled manner is designed to expand seamlessly and efficiently to meet growing demands and workloads without compromising performance or stability. It possesses the ability to handle increasing data volumes, user traffic, and computational requirements with exceptional effectiveness.




Automating Time Series Data


JaguarDB provides users with the ability to create tables and time tick tables that are optimized for time series data analysis. In addition to achieving fast data ingestion, data is also automatically aggregated based on user-defined multiple time windows represented as tick tables. These tick tables can be created for time intervals such as every 15 seconds, 60 seconds, 5 minutes, 15 minutes, 1 hour, 1 day, 10 days, 3 months, and 1 year. Retrieving the aggregated data, which includes sum, average, minimum, maximum, etc., of some columns, can be done with a single select query, without requiring a table scan for calculation. This makes real-time analysis of time series data extremely fast in JaguarDB. Furthermore, multiple indexes can be automatically built for fast query of various data columns.



JaguarDB sets itself apart from other databases that support geo-location data by allowing an unlimited number of tags to be associated with a location, making it ideal for tracking all types of data. While other databases support only 6 geospatial shapes, JaguarDB supports 18, including line, polygon, circle, square, rectangle, cube, sphere, ellipse, triangle, and ellipsoid. Obtaining spatial relationships, such as distance, within, and intersect, is simple and can be achieved with a single query statement. Additionally, JaguarDB integrates indexing of spatial data and metrics data for all applications that utilize geolocation data, providing an efficient and seamless experience.


Unifiying Space and Time


In IoT applications, time series data and geolocation data are often interrelated. Mobile objects, sensors, and devices generate events that include both time and location information. To ensure fast data retrieval, JaguarDB allows storage of time series and geolocation data in a single database or table, maximizing data locality. This approach enables efficient querying of data based on both time and location criteria, supporting a wide range of IoT use cases.

In addition to the above-mentioned advantages, JaguarDB stores all data in a distributed architecture that ensures high scalability for large volumes of data. With its unique ZeroMove Hashing data distribution technology, scaling out JaguarDB to any number of nodes can be done instantly on demand. This enables enterprises to handle the steady or accelerated growth of data with ease, without worrying about the limitations of traditional scaling methods. Overall, JaguarDB provides a highly scalable and efficient solution for storing and managing large volumes of data.


Sharded Multiple Master Architecture


JaguarDB operates using a unique architecture where all nodes in the cluster are master nodes, capable of both data writes and reads. This approach ensures that write and read bandwidth is fully utilized, and data is sharded across all nodes to increase both write and read speed. To ensure high availability and security, data stored on one node is also backed up on two other nodes.



In JaguarDB, geometric shapes are modeled as objects, rather than raw data points with coordinates. This enables more efficient querying and indexing of spatial data. To achieve high performance, data reads and writes are distributed across different nodes in the cluster, increasing concurrency and overall system efficiency.



Unleashing the Potential






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