Asynchronous parallel SSH library
Non-blocking, asynchronous parallel SSH client library.
Run SSH commands over many - hundreds/hundreds of thousands - number of servers asynchronously and with minimal system load on the client host.
Native code based client with extremely high performance - based on libssh2 C library.
pip install parallel-ssh
See documentation on read the docs for more complete examples.
Run uname on two remote hosts in parallel with sudo.
from pssh.pssh_client import ParallelSSHClient hosts = ['myhost1', 'myhost2'] client = ParallelSSHClient(hosts) output = client.run_command('uname') for host, host_output in output.items(): for line in host_output.stdout: print(line)
Starting from version 1.2.0, a new client is supported in parallel-ssh which offers much greater performance and reduced overhead than the current default client.
The new client is based on libssh2 via the ssh2-python extension library and supports non-blocking mode natively. Binary wheel packages with libssh2 included are provided for Linux, OSX and Windows platforms and all supported Python versions.
See this post for a performance comparison of the available clients.
To make use of this new client, ParallelSSHClient can be imported from pssh.pssh2_client instead. Their respective APIs are almost identical.
The new client will become the default and will replace the current pssh.pssh_client in a new major version of the library - 2.0.0 - once remaining features have been implemented. The native client should be considered as beta status until the 2.0.0 release when it is made the default.
The current default client will remain available as an option under a new name.
from pprint import pprint from pssh.pssh2_client import ParallelSSHClient hosts = ['myhost1', 'myhost2'] client = ParallelSSHClient(hosts) output = client.run_command('uname') for host, host_output in output.items(): for line in host_output.stdout: print(line)
See documentation for a feature comparison of the two clients.
- Highest performance and least overhead of any Python SSH libraries
- Thread safe - makes use of native threads for blocking calls like authentication
- Natively non-blocking utilising libssh2 via ssh2-python - no monkey patching of the Python standard library
- Significantly reduced overhead in CPU and memory usage
Once either standard output is iterated on to completion, or client.join(output) is called, exit codes become available in host output. Iteration ends only when remote command has completed, though it may be interrupted and resumed at any point.
for host in output: print(output[host].exit_code)
The client’s join function can be used to block and wait for all parallel commands to finish:
Similarly, output and exit codes are available after client.join is called:
output = client.run_command('exit 0') # Wait for commands to complete and gather exit codes. # Output is updated in-place. client.join(output) pprint(output.values().exit_code) # Output remains available in output generators for host, host_output in output.items(): for line in host_output.stdout: pprint(line)
There is also a built in host logger that can be enabled to log output from remote hosts. The helper function pssh.utils.enable_host_logger will enable host logging to stdout.
To log output without having to iterate over output generators, the consume_output flag can be enabled - for example:
from pssh.utils import enable_host_logger enable_host_logger() client.join(client.run_command('uname'), consume_output=True)
SFTP is supported natively, no scp binary required.
For example to copy a local file to remote hosts in parallel:
from pssh.pssh_client import ParallelSSHClient from pssh import utils from gevent import joinall utils.enable_logger(utils.logger) hosts = ['myhost1', 'myhost2'] client = ParallelSSHClient(hosts) cmds = client.copy_file('../test', 'test_dir/test') joinall(cmds, raise_error=True)
Copied local file ../test to remote destination myhost1:test_dir/test Copied local file ../test to remote destination myhost2:test_dir/test
There is similar capability to copy remote files to local ones suffixed with the host’s name with the copy_remote_file function.
Directory recursion is supported in both cases via the recurse parameter - defaults to off.
See SFTP documentation for more examples.
ParallelSSH’s design goals and motivation are to provide a library for running non-blocking asynchronous SSH commands in parallel with little to no load induced on the system by doing so with the intended usage being completely programmatic and non-interactive.
To meet these goals, API driven solutions are preferred first and foremost. This frees up developers to drive the library via any method desired, be that environment variables, CI driven tasks, command line tools, existing OpenSSH or new configuration files, from within an application et al.
Some guide lines on scaling ParallelSSH client and pool size numbers.
In general, long lived commands with little or no output gathering will scale better. Pool sizes in the multiple thousands have been used successfully with little CPU overhead in the single process running them in these use cases.
Conversely, many short lived commands with output gathering will not scale as well. In this use case, smaller pool sizes in the hundreds are likely to perform better with regards to CPU overhead in the event loop. Multiple python processes, each with its own event loop, may be used to scale this use case further as CPU overhead allows.
Gathering is highlighted here as output generation does not affect scaling. Only when output is gathered either over multiple still running commands, or while more commands are being triggered, is overhead increased.
To understand why this is, consider that in co-operative multi tasking, which is being used in this project via the gevent library, a co-routine (greenlet) needs to yield the event loop to allow others to execute - co-operation. When one co-routine is constantly grabbing the event loop in order to gather output, or when co-routines are constantly trying to start new short-lived commands, it causes overhead with other co-routines that also want to use the event loop.
This manifests itself as increased CPU usage in the process running the event loop and reduced performance with regards to scaling improvements from increasing pool size.
On the other end of the spectrum, long lived remote commands that generate no output only need the event loop at the start, when they are establishing connections, and at the end, when they are finished and need to gather exit codes, which results in practically zero CPU overhead at any time other than start or end of command execution.
Output generation is done remotely and has no effect on the event loop until output is gathered - output buffers are iterated on. Only at that point does the event loop need to be held.
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