Axibase Time Series Database

Axibase Time Series Database represents a new class of distributed systems designed to store and analyze time series data at scale.

ATSD provides extensive APIs to simplify the process of consolidating timestamped data from multiple sources in a single repository.

Once you have statistics in one place, ATSD can help you build analytics and monitoring applications
by providing built-in Visualization for dashboards, integrated Rule Engine for alerting and automation, and Forecasting for operational analytics.

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.

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.