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

Cortex provides horizontally scalable, highly available, multi-tenant, long term storage for Prometheus. Cortex can run across multiple machines in a cluster, exceeding the throughput and storage of a single machine. This enables you to send the metrics from multiple Prometheus servers to a single Cortex cluster and run "globally aggregated" queries across all data in a single place. Cortex makes your PromQL queries blazin' fast through aggressive parallelization and caching.
 
When run in a cluster, Cortex can replicate data between machines. This allows you to survive machine failure without gaps in your graphs. Cortex can isolate data and queries from multiple different independent Prometheus sources in a single cluster, allowing untrusted parties to share the same cluster.
 
Cortex supports Amazon DynamoDB, Google Bigtable, Cassandra, S3, GCS and Microsoft Azure for long term storage of metric data. This allows you to durably store data for longer than the lifetime of any single machine, and use this data for long term capacity planning.

Cortex provides horizontally scalable, highly available, multi-tenant, long term storage for Prometheus. Cortex can run across multiple machines in a cluster, exceeding the throughput and storage of a single machine. This enables you to send the metrics from multiple Prometheus servers to a single Cortex cluster and run "globally aggregated" queries across all data in a single place. Cortex makes your PromQL queries blazin' fast through aggressive parallelization and caching.



Cortex - A multitenant, horizontally scalable Prometheus as a Service

Blueflood is a high throughput, low latency, multi-tenant distributed metric processing system behind Rackspace Metrics, which is currently used in production by the Rackspace Monitoring team and Rackspace Public Cloud team to store metrics generated by their systems. Data from Blueflood can be used to construct dashboards, generate reports, graphs or for any other use involving time-series data. It focuses on near-realtime data, with data that is queryable mere milliseconds after ingestion.


Blueflood - A distributed system designed to ingest and process time series data

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

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

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

Pymetrix (previously known as Analyx) is a plug-and-play analytics library written in Python. It aims to be lightweight and effective at capturing most of the basic metrics for your website, with the possibility of adding more through the use of extensions. Pymetrix can export data as Time Series and also as Aggregate hits data. 


Pymetrix - A simple Plug and Play Library for getting analytics

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