Axibase Time Series Database

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

  • 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.

  • 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.

  • Automate Thresholding

    Alert and react to streaming data using analytical and anomaly detection rules with support for ARIMA and Holt-Winters forecast deviation functions.

Streaming Data

Stream high-frequency data into ATSD via TCP, UDP, and HTTP protocols using text, 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

ATSD provides optimized, compressed storage for both numeric and string series. It can store properties, which are a collection of user-defined key values, organized by type. The properties don’t have a temporal dimension and are often used to describe objects in a way that is specific to the given application domain.

By co-locating metadata and temporal data you can build smarter queries, and otherwise enrich time-series data with context and meaning.

Extensive API

ATSD Data API and Meta API implement RESTful API methods allowing you to create, edit, update, and delete meta-data such as device properties and metric settings, as well as to insert and query series, properties, and messages.

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.