InfluxDB is an open source time series platform. This includes APIs for storing and querying data, processing it in the background for ETL or monitoring and alerting purposes, user dashboards, and visualizing and exploring the data and more. The master branch on this repo now represents the latest InfluxDB, which now includes functionality for Kapacitor (background processing) and Chronograf (the UI) all in a single binary.  It is designed to handle high write and query loads and provides a SQL-like query language called InfluxQL for interacting with data.

InfluxDB - Scalable datastore for metrics, events, and real-time analytics

Atlas was developed by Netflix to manage dimensional time series data for near real-time operational insight. Atlas features in-memory data storage, allowing it to gather and report very large numbers of metrics, very quickly. Atlas captures operational intelligence. Whereas business intelligence is data gathered for analyzing trends over time, operational intelligence provides a picture of what is currently happening within a system.

Atlas - In-memory dimensional time series database

LinDB is an open-source Time Series Database which provides high performance, high availability and horizontal scalability. LinDB takes a lot of best practice of TSDB and implements some optimizations based on the characteristics of time series data. Unlike writing a lot of Continuous-Query for InfluxDB, LinDB supports rollup in specific interval automatically after creating the database. Moreover, LinDB is extremely fast for parallel querying and computing of distributed time series data.
 
LinDB supports Metric + Tags + Fields, and is schema-free; The LinQL is handy for realtime data analytics. Tags based sharding strategy in LinDB solves the hotspots problem, and is truly horizontally expanded available by simply adding new broker and storage nodes.

LinDB is an open-source Time Series Database which provides high performance, high availability and horizontal scalability. LinDB takes a lot of best practice of TSDB and implements some optimizations based on the characteristics of time series data. Unlike writing a lot of Continuous-Query for InfluxDB, LinDB supports rollup in specific interval automatically after creating the database. Moreover, LinDB is extremely fast for parallel querying and computing of distributed time series data.


Lindb - Distributed Time Series Database

Akumuli is a time-series database for modern hardware. It can be used to capture, store and process time-series data in real-time. The word "akumuli" can be translated from Esperanto as "accumulate".
<ul>
<li>True column-oriented format (not PAX).</li>
<li>Based on novel <a href="http://akumuli.org/akumuli/2017/04/29/nbplustree/" rel="nofollow">LSM and B+tree hybrid datastructure</a> with multiversion concurrency control (no concurrency bugs, parallel writes, optimized for SSD and NVMe).</li>
<li>Crash safety and recovery.</li>
<li>Fast aggregation without pre-configured rollups or materialized views.</li>
<li>Queries can be executed without decompressing the data.</li>
<li>Fast compression algorithm (dictionary + entropy) with small memory overhead (about 2.5 bytes per element on appropriate data).</li>
<li>Compressed in-memory storage for recent data.</li>
<li>Can be used as a server application or an embedded library.</li>
<li>Simple query language based on JSON and HTTP.</li>
<li>Fast range scans and joins, read speed doesn't depend on database cardinality.</li>
<li>Fast data ingestion over the network:
<ul>
<li>4.5M data points per second on 8-core Intel Xeon E5-2670 v2 (m3.2xlarge EC2 instance).</li>
<li>16.1M data points per second on 32-core Intel Xeon E5-2680 v2 (c3.8xlarge EC2 instance).</li>
</ul>
</li>
<li>Query results are streamed to client using the chunked transfer encoding of the HTTP protocol.</li>
<li>Decompression algorithm and input parsers were fuzz-tested.</li>
<li>Grafana <a href="https://github.com/akumuli/akumuli-datasource">datasource</a> plugin.</li>
<li>Fast and compact inverted index for series lookup.</li>
</ul>

Akumuli is a time-series database for modern hardware. It can be used to capture, store and process time-series data in real-time. The word "akumuli" can be translated from Esperanto as "accumulate".

Akumuli - Time-series database

DalmatinerDB is a metric database written in pure Erlang. It takes advantage of some special properties of metrics to make some tradeoffs. Its goal is to make a store for metric data (time, value of a metric) that is fast, has a low overhead, and is easy to query and manage. DalmatinerDB allows for metric input in second or even sub-second precision. It will interpolate the missing values to the best of it’s abilities. This is usually acceptable for aggregated data.
 
DalmatinerDB is designed to run on ZFS file system and it relies heavily on taking advantage of ZFS facilities such as the ARC, ZIL, checksums and volume compression. The frontend servers give users access to a rich SQL-like query language that's easy to use for both dev and ops. It integrates nicely with Grafana making it easy to build beautiful dashboards.

DalmatinerDB is a metric database written in pure Erlang. It takes advantage of some special properties of metrics to make some tradeoffs. Its goal is to make a store for metric data (time, value of a metric) that is fast, has a low overhead, and is easy to query and manage. DalmatinerDB allows for metric input in second or even sub-second precision. It will interpolate the missing values to the best of it’s abilities. This is usually acceptable for aggregated data.

DalmatinerDB - Fast distributed metrics database in Erlang

Hawkular Metrics is a metrics collection, aggregation, visualization framework. Hawkular is a set of Open Source projects designed to be a generic solution for common monitoring problems. The Hawkular projects provide REST services that can be used for all kinds of monitoring needs. The aim is to provide a generic solution that can be used for common monitoring problems.
 
The main goal of the Hawkular organization is to provide tools to monitor, view, predict, and guide you to make informed decisions about deployed applications and infrastructure. Anyone looking for Java-based components for alerting, metric storage, or distributed tracing should be interested in Hawkular.

Hawkular Metrics is a metrics collection, aggregation, visualization framework. Hawkular is a set of Open Source projects designed to be a generic solution for common monitoring problems. The Hawkular projects provide REST services that can be used for all kinds of monitoring needs. The aim is to provide a generic solution that can be used for common monitoring problems.


Hawkular metrics - Time Series Metrics Engine based on Cassandra

Discover open source projects across all platforms

Projects

InfluxDB - Scalable datastore for metrics, events, and real-time analytics

Atlas - In-memory dimensional time series database

Lindb - Distributed Time Series Database

Akumuli - Time-series database

DalmatinerDB - Fast distributed metrics database in Erlang

Hawkular metrics - Time Series Metrics Engine based on Cassandra

TechStack

Tagcloud

License

Suggested keywords:

Projects

InfluxDB - Scalable datastore for metrics, events, and real-time analytics

Atlas - In-memory dimensional time series database

Lindb - Distributed Time Series Database

Akumuli - Time-series database

DalmatinerDB - Fast distributed metrics database in Erlang

Hawkular metrics - Time Series Metrics Engine based on Cassandra

TechStack

Tagcloud

License