DBMS > IRONdb
IRONdb System Properties
Please select another system to compare it with IRONdb.
|Editorial information provided by DB-Engines|
|Description||A distributed Time Series DBMS with a focus on scalability, fault tolerance and operational simplicity|
|Primary database model||Time Series DBMS|
|Current release||V0.10.20, January 2018|
|License Commercial or Open Source||commercial|
|Cloud-based only Only available as a cloud service||no|
|DBaaS offerings (sponsored links) Database as a Service|
Providers of DBaaS offerings, please contact us to be listed.
|Implementation language||C and C++|
|Server operating systems||Linux|
|Typing predefined data types such as float or date||yes text, numeric, histograms|
|XML support Some form of processing data in XML format, e.g. support for XML data structures, and/or support for XPath, XQuery or XSLT.||no|
|SQL Support of SQL||SQL-like query language (Circonus Analytics Query Language: CAQL)|
|APIs and other access methods||HTTP API|
|Supported programming languages||.Net|
|Server-side scripts Stored procedures||yes, in Lua|
|Partitioning methods Methods for storing different data on different nodes||Automatic, metric affinity per node|
|Replication methods Methods for redundantly storing data on multiple nodes||configurable replication factor, datacenter aware|
|MapReduce Offers an API for user-defined Map/Reduce methods||no|
|Consistency concepts Methods to ensure consistency in a distributed system||Immediate consistency per node, eventual consistency across nodes|
|Foreign keys Referential integrity||no|
|Transaction concepts Support to ensure data integrity after non-atomic manipulations of data||no|
|Concurrency Support for concurrent manipulation of data||yes|
|Durability Support for making data persistent||yes|
|In-memory capabilities Is there an option to define some or all structures to be held in-memory only.||no|
|User concepts Access control||no|
|More information provided by the system vendor|
IRONdb is a highly available, distributed Time Series Database. It can support dozens of nodes per availability zone, and ingest billions of metrics per second. IRONdb is one of the highest performing TSDBs due to the highly optimized C and Assembly implementation.
IRONdb can act as a remote storage destination for Prometheus, and integrate with the Grafana visualization tool. Data can be analyzed with the CAQL (Circonus Analytics Query Language) functional language, which provides a wide range of statistical and numeric analytical capabilities.
IRONdb is unique among TSDBs in that it does not use a consensus algorithm for distributed operations. Rather, it relies on operations being commutative in nature, so that data may be applied to any node in any order. This approach avoids the difficult technical problems inherent to distributed systems which require consensus algorithms to operate, and the reliability issues that come with them. As a result, IRONdb can operate with far more nodes than other solutions, and still remain highly performant. Installations with dozens of nodes per availability zone are standard, and operate with no single point of failure.
IRONdb is written in C and Assembly for optimal performance. By minimizing memory allocations, and avoiding copying data wherever possible, IRONdb is able to achieve significant runtime performance over other solutions. Additionally, IRONdb is optimized for a minimum memory footprint per node through the use of highly optimized data structures, allowing each node to handle many more metrics per node than other solutions.
Unmatched Operational Efficiency and Data Safety
IRONdb was designed such that it can be operated without any bespoke administrator skillsets. Standard Linux administration knowledge is sufficient to get IRONdb up and running and accomplish standard node maintenance operations. You don't need a domain specialist with hard to find skills, or a large team of operators.
IRONdb is capable of operating with data replicated in upwards of 4 different availability zones for survivability in double fault loss scenarios. Additionally, IRONdb supports the ZFS filesystem, to take advantage of the unique data safety features offered there such as block checksumming and bitrot detection.
|Typical application scenarios|
Real Systems Monitoring
Monitor your systems infrastructure in real time across thousands of hosts. IRONdb is capable of ingesting tens of thousands of metrics per host across tens of thousands of hosts. Additionally, because IRONdb is capable of ingesting histogram based metrics, it can easily handle billions of metrics per second from APIs and third party integrations.
IoT Systems Monitoring
IRONdb is uniquely suited to fulfill the telemetry needs of large scale Internet of Things systems. IRONdb's stream tags and histogram based data structures are optimized for the ephemeral nature of these systems which generate high frequency metrics with high cardinality metadata. These characteristics also lend IRONdb to be exceptionally suited for industrial scale device monitoring.
IRONdb serves the needs of the world's largest Time Series Database customers. One customer, Xandr, uses IRONdb to ingest and analyze nearly half a billion metrics per minute.
Only TSDB capable of scaling to billions of metrics.
Only TSDB to scale to dozens of nodes across multiple availability zones.
Only TSDB to store histogram based data.
Highest performing Time Series Database.
|Licensing and pricing models|
IRONdb is licensed under a subscription model based on the number of active metrics in a given time window.
Related products and services
We invite representatives of vendors of related products to contact us for presenting information about their offerings here.
Share this page