Naming conventions and LAVA architecture

Certain terms used in LAVA have specific meanings. Please be consistent in the use of the following terms:


The physical hardware sitting in a rack or on a desk. The difference between a board and a device is that a board can be replaced if it fails completely, yet the device remains unchanged in the database and the replacement board can pick up running LAVA test jobs as soon as the device is set back to “good health”. For example, a board can suffer an electrical short or connectors being sheared off. A device only changes state (Idle and Running) or health (Good, Bad, Maintenance, Retired).

Integrating new types of board into LAVA can be a difficult and protracted process. CI requires high levels of reliability from boards, including when stressed at high load. Not all boards can be integrated into LAVA.


A means of communicating with a device. This will often involve using a serial port, but can also be SSH or another way of obtaining a shell-type interactive interface. Connections will typically require a POSIX type shell.

See also



In lava-server, a device is a database object in LAVA which stores configuration, information and status relating to a single board. The device information can be represented in export formats like YAML for use when the database is not accessible.

In lava-dispatcher, the database is not accessible so the scheduler prepares a simple dictionary of values derived from the database and the template to provide the information about the device.

Devices are typically expected to persist long enough to provide long term comparative data, for example to support LTS teams.

device tag

If specified boards have peripherals added (USB flash storage, SATA, HDMI etc.) then admins can choose to create a device tag so that test writers can write test jobs which use those peripherals and the scheduler will assign the device according to the required device tags.

A device tag is not meant to be a method of running specific test jobs on specific boards. LAVA is not a board farm and a board can be replaced at any time without affecting ongoing CI. There is no support in LAVA for allocating specific boards to specific test jobs, the two models are incompatible.

Device tags frequently apply to multiple devices. This allows the queue of test jobs to be optimized and get results back to the developers as quickly as possible without compromising reliability.

See also

device tag


A database object which collates similar devices into a group for purposes of scheduling. Devices of a single type are often the same vendor model but not all boards of the same model will necessarily be of the same device-type.

See also

Device types


The dispatcher software relates to the lava_dispatcher module in git and the lava-dispatcher binary package in Debian. The dispatcher software for LAVA can be installed without the server or the scheduler and a machine configured in this way is also called a dispatcher. Such machines are typically expensive, especially in a busy instance, which can have a big impact on how a LAVA lab is built.

dynamic data

The Action base class provides access to dynamic data stores which other actions can access. This provides the way for action classes to share information like temporary paths of downloaded and / or modified files and other data which is generated or calculated during the operation of the pipeline. Use self.set_common_data to set the namespace, key and value and self.get_common_data to retrieve the value using the namespace and the key.


A static, read-only, dictionary of values which are available for the job and the device. Parameters must not be modified by the codebase - use the common_data primitives of the Action base class to copy parameters and store the modified values as dynamic data.


The internal name for the design of LAVA V2, based on how the actions to be executed by the dispatcher are arranged in a unidirectional pipeline object. The contents of the pipe are validated before the job starts and the description of all elements in the pipe is retained for later reference.

See also

Pipeline construction and flow and pipeline in the Glossary.


An API used by the python code inside lava_dispatcher to interact with external systems and daemons when a shell like environment is not supported. Protocols need to be supported within the python codebase and currently include multinode, LXC and vland.

server software

The server software relates to the lava_server, lava_scheduler_app and lava_results_app source code in git and the lava-server binary package in Debian. It includes LAVA components covering the UI and the scheduler daemon.


A daemon running on each dispatcher machine which communicates with the lava-server-gunicorn using HTTP. The worker in LAVA uses whatever device configuration the server provides. Commands in the device configuration often use scripts and utilities which are only installed on that dispatcher.

The objective of the worker is to run the specified jobs as reliably as possible. Each worker spawns one process for each job, executing the code in lava_dispatcher.

test job

A database object which is created for each submission and retains the logs and pipeline information generated when the slave executes the job on the device.

Test jobs are not intended to test devices or boards. Test jobs exist to test software on multiple devices as part of continuous development of software, e.g. the Linux kernel. Each test job is used to test one software build using the first available device of the requested device-type. LAVA is not best suited to QA operations at the end of a production line.


A database object providing a connection to a slave daemon on a dispatcher. Each device must be assigned to a worker to run a test job. One device can only be assigned to one worker at any one time. A single dispatcher can operate more than one worker, typically by hosting one or more slaves inside a docker container.

Admins need to balance the number of devices on each worker according to the load caused when all devices on that worker are running test jobs simultaneously.


It is common to find that all devices on a worker could be executing at high load at precisely the same time. For example, decompressing downloaded files (causing high CPU load / RAM usage) or writing large files (high I/O load). Some test jobs may also cause high network load. Admins need to monitor and balance the load on each worker according to the specific workload of each instance.