Axibase Time Series Database

Statistics Time Series Database

Axibase Time Series Database is designed from the ground-up to store and analyze time series data at scale. Unlike traditional databases it comes with pre-integrated Visualization and Rule Engine. It’s a forward-looking technology to support your IoT, system monitoring and other Big Data use cases.

Streaming Data

Stream live, high-frequency data into ATSD using TCP, UDP, or long-running HTTP POST requests using text commands or CSV.

TCP: series e:srv1 m:du_pct=20.5 t:name=/sda1 | nc ...
UDP: series e:sensor1 m:ms.watt=17.5 | nc -u -w1 ...
CSV: csv p:webspace_as-csv e:nurswgvml007

Batch Data

Query and upload data on schedule from relational databases, web services, and files using industry-standard protocols and formats: JMX, SNMP, CSV/TSV, SOAP, and JSON.

Rich API

ATSD API v1 implements RESTful API allowing you to create, edit, update, and delete meta-data such as device properties and metric settings as well as to query series, properties, and messages. Implemented APIs: Network API, Data API and Meta API.

API Clients

Interact with ATSD to insert and fetch meta-data and time series using open-source API clients for major server-side programming languages including R, Java, Python, Ruby, PHP etc.

Query Language

SQL-like query language for interactive data analysis by data scientists, analysts, and report developers.

SELECT entity, TIME, AVG(cpu_busy.VALUE), MAX(disk_used.VALUE), tags.*
   FROM cpu_busy
   JOIN USING entity disk_used
WHERE TIME > now - 1*DAY  AND entity LIKE 'NURSWG*'
   GROUP BY entity, tags, PERIOD(1 HOUR)
   HAVING AVG(cpu_busy.VALUE) > 20
   LIMIT 1000

Analytical Queries

We implement queries that select raw data with support for filters and wildcards but also analytical queries with an extensive array of aggregation and grouping functions.

Rule Engine

Create alerting rules in the built-in rule engine with time/count-based sliding windows, aggregation and forecasting functions. Deliver alerts to enterprise consoles, email, ticketing systems or execute system commands.

Non-parametric Forecasting

Built-in Holt-Winters and ARIMA forecasts allow you to quickly compute expected system state and make proactive decisions if observed values are outside of confidence interval. In autopilot mode ATSD is capable of identifying best parameters for each algorithm which greatly improves forecast accuracy.