Axibase Time Series Database (ATSD) is a non-relational database optimized for collecting, storing, and analyzing temporal data from IT infrastructure, industrial equipment, smart meters, and IoT devices.

Technology Stack

  FileSystem: Local / HDFS / EMRFS
  • ATSD is written in Java. It is supported on Linux distributions in 64-bit mode and requires a Java 8 runtime environment.
  • Underneath ATSD is Apache HBase which persists key-values in the underlying file system such as ext4, Hadoop Distributed File System (HDFS), and Amazon EMRFS.

ATSD can be installed from deb and rpm packages or launched as a Docker container.

docker run -d -p 8088:8088 -p 8443:8443 -p 8081:8081 \

Compute Scalability

Single-node ATSD instance can process up to 200,000 metrics per second with millisecond accuracy, support for out-of-order writes, and without numeric precision loss.

Number of metrics inserted per second is scaled by adding region servers to the underlying HBase cluster.

Storage Scalability

Storage efficiency determines how many metrics and individual series can be stored in the system.

Compared to traditional databases, ATSD requires up to 50 times less disk space. Refer to compression tests for more details.

Storage capacity can be scaled by adding data nodes to the underlying HDFS cluster. In ATSD on AWS EMR, the storage capacity is right-sized automatically and independently of the processing capacity.

Use Cases

  • High-performance metrics backend.
  • Consolidated statistics repository.
  • Centralized monitoring system.
  • EDM database.
  • Data Lake component for time series data.
  • Econometrics data store.


ATSD is a modular systems which includes the following components in addition to the core storage engine:

Inserting Data

  • Upload CSV
  • Stream CSV
  • Stream network commands via TCP/UDP
  • Stream network commands with Kafka
  • Insert data using REST API
  • Use API clients or storage drivers
  • Install pre-integrated collectors/agents
  • Deploy Axibase Collector instance

Line Protocols

The database provides its own compact line protocol for inserting high volumes of metrics with arbitrary dimensions which we call network commands.

echo "series e:sns-001 m:temperature=15.4 m:rpm=302 t:panel=front" \
  > /dev/tcp/atsd_hostname/8081

The commands can be streamed into ATSD on ports 8081/tcp and port 8082/udp. Alternatively, the commands can be uploaded by posting them to the /api/v1/command REST API endpoint.

New objects and attributes are registered automatically and allow collecting data from different domain models in a single extensible schema.

The following protocols are supported for compatibility with external sources:

  • tcollector
  • graphite
  • statsd
  • osisoft pi


The table schema in ATSD, displayed on the Settings > Storage > Database Tables page, is self-managed by the database and as such does not require changes when inserting data from objects of different types.


  • Entity - Name of the object being monitored.
  • Metric - Name of the numeric attribute describing the object.
  • Sample - Timestamped metric value, time:number.
  • Series - Sequence of Samples.
  • Tag - Custom attribute describing the Entity, Metric, or Series, consists of a name and a value, name:value.


To store some temperature observations for bioreactor BR1740 enclosure located at site SVL2 in Sunnyvale, as well as room temperature at the same site, we might send the following commands into ATSD.

Metadata commands contain descriptive attributes and can be sent only once (or whenever these attributes change).

entity e:BR1740 t:type=Bioreactor t:city=Sunnyvale t:site=SVL2
entity e:SVL2   t:type=site       t:city=Sunnyvale
metric m:Temperature t:units=Celsius

The series commands carry the actual measurements and contain only those attributes that are necessary to identify the series.

series d:2018-05-20T00:15:00Z e:BR1740 m:Temperature=70.5 t:part=enclosure
series d:2018-05-20T00:15:00Z e:SVL2   m:Temperature=25.2
series d:2018-05-20T00:16:00Z e:BR1740 m:Temperature=72.5 t:part=enclosure
series d:2018-05-20T00:16:00Z e:SVL2   m:Temperature=25.1

By separating inserted data into metadata and time series data, each type of information can stored and processed separately and efficiently. At the same time, both types of data are readily available and efficiently joined when querying the records from the database.

SELECT datetime, value, entity, entity.tags.type
  FROM atsd_series
WHERE metric = 'Temperature'
  AND entity = 'BR1740'
  ORDER BY datetime