Axibase Time Series Database

Hadoop-based time series database with SQL, rule-engine, and visualization.

New deployment option available: AWS EMR S3.

  • Develop with Productivity

    Use open-source API clients and Data API for seamless integration with Java, Go, Ruby, Python, R, and NodeJS applications.

  • Design Without Complexity

    Leverage Meta API, Property data type, and tags to model your application domain directly in ATSD, without staging a separate database. While ATSD is a universal solution it provides tools to tailor the presentation to specific use cases such as infrastructure monitoring.

  • Deliver Analytics

    ATSD supports SQL with powerful time-series extensions for scheduled and ad-hoc reporting. It also provides a Type 4 JDBC Driver for integration with leading reporting and BI tools including IBM SPSS, Stata, Alteryx, Pentaho, MatLab, and Tableau.

  • Automate Thresholding

    Alert and react to streaming data using analytical and anomaly detection rules with support for ARIMA and Holt-Winters forecast deviation functions. Escalate alerts to upstream tools using web notifications, system commands, and email.

Save up to 50x disk space compared to other databases.

Streaming Data

Stream high-frequency data into ATSD via TCP, UDP, and HTTP protocols using line, JSON, nmon, and CSV formats.

$echo series e:srv-1 m:cpu_b=12.2 > /dev/tcp/atsd/8081
$echo "csv p:iso-parser" | cat - data.csv > /dev/tcp/atsd/8081

Batch Data

Upload CSV files directly into the database for bulk import. Use network API to upload nmon archives with wget/curl or telnet.

Query data on schedule from web services, FTP/SFTP/SCP servers, and network devices using industry-standard protocols : JMX, SNMP, CSV/TSV, and JSON.

Offload and historize data from relational databases.

Rule Engine

Configure alerts 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.

Query Language

Support for SQL with time-series extensions for scheduled reporting and ad-hoc analysis.

SELECT entity, entity.tags.location, datetime, VALUE
  FROM 'cpu_allocated_usage_pct'
WHERE entity.groups IN ('svl-2', 'svl-2')
  AND datetime >= current_hour

Rich Schema

In addition to time series, ATSD supports properties, which are a collection of key-values. This data type can be used to model relationships and describe entities in a way that is specific to the given application domain.

By storing metadata and time series in the same database, ATSD allows you to build smarter analytical queries that benefit from data locality.

Storage Efficiency

ATSD implements optimized, compressed storage for numeric time series with the ability to customize compression algorithms for short-term and long-term data. Storing a single time/value sample without precision loss in ATSD requires up to 50 times less disk space compared to other databases.

Extensive API

ATSD provides REST API methods to insert and query series, properties, and messages as well as to manage metadata about the monitored objects.

API Clients

Accelerate data analytics and web application development with open-source ATSD API clients for Ruby, PHP, NodeJSR, Python, Go, and Java.

Get started with sample open-source Data Apps on

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 the confidence interval. In autopilot mode, ATSD is capable of identifying the best parameters for each algorithm, which greatly improves forecast accuracy.