Designed for scale.

The masterless architecture of Riak TS makes it easy to add and remove nodes from your cluster. You can achieve predictable and near-linear scale by adding nodes using commodity hardware.

When nodes are added or removed, data is rebalanced automatically without requiring human intervention. New machines assume ownership of some of the partitions, and existing machines hand off relevant partitions and associated data until data ownership is equal across nodes.

Replicating data eliminates manual sharding, Riak TS makes it significantly easier for applications to scale.

Riak TS is designed to handle massive amounts of IoT or time series data. Clusters can scale to multi-petabytes of data.

Riak TS scales near-linearly
Riak TS scales near-linearly

DISTRIBUTED SYSTEMS MUST SCALE EASILY

Riak TS is designed for scalability. If your dataset grows beyond your current capacity, Riak TS scales horizontally using commodity hardware. No need to upgrade to a prohibitively expensive high-end server.

Adding capacity using other databases often requires manual sharding, which involves dividing data into logical parts (called shards) and then distributing the data across multiple machines. Sharding is not only difficult, but it can also cause certain machines to be responsible for storing and serving a disproportionately high amount of both data and requests. This can cause unpredictable latency and degraded performance. With Riak TS there is no need for sharding. Riak TS allows you to elastically grow and shrink your cluster while evenly balancing the load on each node.

Intelligent Replication
Data replication is a core feature of the Riak TS basic architecture. Riak TS was designed to operate as a clustered system containing multiple nodes (commodity servers or cloud instances).

This replication allows data to live on multiple machines at once with a single write request. Riak TS automatically distributes data across nodes in a Riak TS cluster with time series data co-located for performance.

Co-location is important to scale your queries and analytics to ensure fast reads of large data sets. Riak TS with data co-location, range queries, and Apache Spark integration, ensures your queries and analysis of time series data scales.

BENEFITS OF SCALE IN RIAK TS

With Riak TS your application scales cost effectively even during peak times. You can seamlessly handle IoT or time series data streams with low latency and zero downtime.

Manage massive amounts of data
Ensure your application can support millions of IoT devices or time series data points. Riak TS scales to enable hundreds of thousands of writes per second supporting billions of objects.

Make real-time decisions
Making real-time decisions in the high-speed, real-time IoT world can give you a competitive advantage. Riak TS ensures you can query, aggregate, and analyze massive amounts of data.

Reduce costs of scale
Scaling quickly and easily reduces costs of scale. Riak TS scales horizontally using commodity hardware and reduces the operational and hardware costs associated with scaling up or down.

temetra.com“It’s clear the only way to scale for the Internet of Things is with very distributed applications and data stores. We manage data from millions of smart meter endpoints supporting hundreds of utilities and look to distributed systems leaders like Basho for optimized solutions for high volume time series data.”

– Paul Barry, Director, Temetra

  1.  RESILIENCY
  2.  SCALABILITY
  3. OPERATIONALSIMPLICITY
  4. DATACO-LOCATION
  5. SQLCOMMANDS
  6. SQL RANGEQUERIES
  7.  AGGREGATIONS
  8. GLOBAL OBJECTEXPIRATION
  9. APACHE SPARKCONNECTOR
  10. APIS/CLIENTLIBRARIES
  11. MULTI-CLUSTERREPLICATION
  12. APACHE MESOSFRAMEWORK