RedisGraph and Streams Debut
latest release of Redis Enterprise with two key functionalities:
RedisGraph and Streams. RedisGraph is a new module designed to solve
complicated, practical graph problems in real-time. Streams is a new
data structure that enables ingestion and analysis of huge volumes of
unstructured data that are generated at extremely fast rates. Both of
these functionalities support faster decision-making by businesses and
are poised to accelerate the arrival of zero latency apps and services
for their customers.
Designed to make performing critical graph-based calculations easy and efficient, and to empower enterprises to make decisions to solve problems quickly.
Minimizes the need for specialized databases to address different use cases.
Eliminates the need for manual, step-by-step calculations and permits users to store values, search millions of graph nodes and edges, and perform multiple calculations simultaneously.
Sample use cases include tracing the source of contamination in a complex supply chain, tracking user behaviors to boost efficiency of recommendation engines or analyzing traffic flow on multiple roadways at once.
“RedisGraph is an extremely
performant graph database,” said Daniel Howard, senior researcher at
Bloor. “Thanks to the matrix representation it uses and the linear
algebra algorithms it implements, it is able to create over one million
nodes in under half a second, and form three million relationships in
the same time frame. Early benchmarks performed by Redis Labs suggest
that their graph processing times are orders of magnitude faster than
Streams is a new data structure that
was introduced with open source Redis 5.0 and is now available in the
latest version of Redis Enterprise. With the power of Streams, simple
monitoring tools can be transformed to process and act upon data that is
being generated in real time. For example, a baby monitor could go from
streaming the live feed of a baby’s crib to analyzing the real-time
status and health of the infant including sounding an alarm if the
child’s breathing or heartbeat is irregular.
Enables the creation of queues to share information among different applications and systems or to unify logs from different systems.
Sample use cases include real-time sentiment analysis of social media feeds, processing data from the environment and other surroundings to inform and guide the actions of autonomous vehicles, facial recognition technology, machine learning, evaluating real-time events to spot fraud or a cyber attack, and supporting better IoT and Smart Home devices.