Scaling Your IoT Platform
Scales instantly on demand and handles big data volumes
Writes data simultaneously with an all-master architecture
Fast indexed search and range searches in space and time
Horizontal scaling means adding more servers in a data cloud to accommodate steady or
accelerated growth of data that an enterprise generates or collects. It is considered the
highest priority in selecting a database system in today's data platform and cloud
in the process of horizontal scaling, large amounts of data are moved among
computer servers. It is slow and risky. It is considered the horrifying nightmare
of distributed data systems. JaguarDB has overcome this challenge with an innovative
Zero Move technology. With JaguarDB, a database cluster can scale horizontally to any size instantly.
Automating Time Series Data
With JaguarDB, users can create tables and time tick tables optimized for time series data analysis.
Not only fast data ingestion is achieved, but also data are aggregated automatically according to user-defined
multiple time windows represented as tick tables. For example, a user can create tick tables for every 15 seconds,
60 seconds, 5 minutes, 15 minutes, 1 hour, 1 day, 10 days, 3 months, and 1 year. When the user wants to retrieve
the aggregated data (including sum, average, minimum, maximum, etc.) of some columns, one select query is
enough to get the data with only a point query. No table scan for calculation is required. Realtime analysis of
time series data is extremely fast in JaguarDB. Multiple indexes can also be automatically built for fast query
of various data columns.
In other databases that support geo-location data, only one tag data is supported at a location. In JaguarDB,
unlimited number of tags are supported for tracking all types of data at a location and at all locations.
Other databases support 6 geospatial shapes (e.g. point, line, polygon) while JaguarDB supports 18 geospatial shapes
(e.g. line, polygon, circle, square, rectangle, cube, sphere, ellipse, triangle, ellipsoid, etc). Spatial relationship
(e.g. distance, within, intersect, etc) are easily obtained by using a simplistic query statement. Indexing of spatial
data and metrics data are integrated for all applications using geolocation data.
Unifiying Space and Time
In typical IoT applications, time series data and geolocation data are inherently combined. All events generated
by mobile objects, sensors, and devices contain both time and location. JaguarDB stores time series data and location data
in one database or in one table so that data locality is maximized for fast data retrieval.
Keep in mind, in additon to all the above advangtages, JaguarDB stores all data in a distributed architecture
so that it ensures high scalability for large volumes of data. At anytime on demand, JaguarDB can be scaled out
instantly to any number of nodes.
Sharded Multiple Master Architecture
All nodes in jaguardb cluster are master nodes, which take both data writes and reads.
Write and read bandwidth are fully utilized. Data is sharded on all the nodes to increase
both write and read speed. When data is stored on one node, two other nodes also receive
data as backup for high availability and security. Geometric shapes are modeled as objects
in contrast to raw data points with coordinates. Data reads and writes visit different nodes
to increase concurrency and achieve high performance.
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