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

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.
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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

 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

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Atlas - In-memory dimensional time series database

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Atlas - In-memory dimensional time series database

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Akumuli - Time-series database

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