Execution model

If you’ve read the 概览 & 教程, you should already be familiar with how Fabric operates in the base case (a single task on a single host.) However, in many situations you’ll find yourself wanting to execute multiple tasks and/or on multiple hosts. Perhaps you want to split a big task into smaller reusable parts, or crawl a collection of servers looking for an old user to remove. Such a scenario requires specific rules for when and how tasks are executed.

This document explores Fabric’s execution model, including the main execution loop, how to define host lists, how connections are made, and so forth.

Execution strategy

Fabric defaults to a single, serial execution method, though there is an alternative parallel mode available as of Fabric 1.3 (see 并行执行). This default behavior is as follows:

• A list of tasks is created. Currently this list is simply the arguments given to fab, preserving the order given.
• For each task, a task-specific host list is generated from various sources (see How host lists are constructed below for details.)
• The task list is walked through in order, and each task is run once per host in its host list.
• Tasks with no hosts in their host list are considered local-only, and will always run once and only once.

Thus, given the following fabfile:

from fabric.api import run, env

env.hosts = ['host1', 'host2']

def taskA():
    run('ls')

def taskB():
    run('whoami')

and the following invocation:

$ fab taskA taskB

you will see that Fabric performs the following:

• taskA executed on host1
• taskA executed on host2
• taskB executed on host1
• taskB executed on host2

While this approach is simplistic, it allows for a straightforward composition of task functions, and (unlike tools which push the multi-host functionality down to the individual function calls) enables shell script-like logic where you may introspect the output or return code of a given command and decide what to do next.

Defining tasks

For details on what constitutes a Fabric task and how to organize them, please see 定义任务.

Defining host lists

Unless you’re using Fabric as a simple build system (which is possible, but not the primary use-case) having tasks won’t do you any good without the ability to specify remote hosts on which to execute them. There are a number of ways to do so, with scopes varying from global to per-task, and it’s possible mix and match as needed.

Hosts

Hosts, in this context, refer to what are also called “host strings”: Python strings specifying a username, hostname and port combination, in the form of username@hostname:port. User and/or port (and the associated @ or :) may be omitted, and will be filled by the executing user’s local username, and/or port 22, respectively. Thus, admin@foo.com:222, deploy@website and nameserver1 could all be valid host strings.

IPv6 address notation is also supported, for example ::1, [::1]:1222, user@2001:db8::1 or user@[2001:db8::1]:1222. Square brackets are necessary only to separate the address from the port number. If no port number is used, the brackets are optional. Also if host string is specified via command-line argument, it may be necessary to escape brackets in some shells.

注解

The user/hostname split occurs at the last @ found, so e.g. email address usernames are valid and will be parsed correctly.

During execution, Fabric normalizes the host strings given and then stores each part (username/hostname/port) in the environment dictionary, for both its use and for tasks to reference if the need arises. See 环境字典 env for details.

Roles

Host strings map to single hosts, but sometimes it’s useful to arrange hosts in groups. Perhaps you have a number of Web servers behind a load balancer and want to update all of them, or want to run a task on “all client servers”. Roles provide a way of defining strings which correspond to lists of host strings, and can then be specified instead of writing out the entire list every time.

This mapping is defined as a dictionary, env.roledefs, which must be modified by a fabfile in order to be used. A simple example:

from fabric.api import env

env.roledefs['webservers'] = ['www1', 'www2', 'www3']

Since env.roledefs is naturally empty by default, you may also opt to re-assign to it without fear of losing any information (provided you aren’t loading other fabfiles which also modify it, of course):

from fabric.api import env

env.roledefs = {
    'web': ['www1', 'www2', 'www3'],
    'dns': ['ns1', 'ns2']
}

Role definitions are not necessary configuration of hosts only, but could hold other role specific settings of your choice. This is achieved by defining the roles as dicts and host strings under a hosts key:

from fabric.api import env

env.roledefs = {
    'web': {
        'hosts': ['www1', 'www2', 'www3'],
        'foo': 'bar'
    },
    'dns': {
        'hosts': ['ns1', 'ns2'],
        'foo': 'baz'
    }
}

In addition to list/iterable object types, the values in env.roledefs (or value of hosts key in dict style definition) may be callables, and will thus be called when looked up when tasks are run instead of at module load time. (For example, you could connect to remote servers to obtain role definitions, and not worry about causing delays at fabfile load time when calling e.g. fab --list.)

Use of roles is not required in any way – it’s simply a convenience in situations where you have common groupings of servers.

在 0.9.2 版更改: Added ability to use callables as roledefs values.

How host lists are constructed

There are a number of ways to specify host lists, either globally or per-task, and generally these methods override one another instead of merging together (though this may change in future releases.) Each such method is typically split into two parts, one for hosts and one for roles.

Globally, via env

The most common method of setting hosts or roles is by modifying two key-value pairs in the environment dictionary, env: hosts and roles. The value of these variables is checked at runtime, while constructing each tasks’s host list.

Thus, they may be set at module level, which will take effect when the fabfile is imported:

from fabric.api import env, run

env.hosts = ['host1', 'host2']

def mytask():
    run('ls /var/www')

Such a fabfile, run simply as fab mytask, will run mytask on host1 followed by host2.

Since the env vars are checked for each task, this means that if you have the need, you can actually modify env in one task and it will affect all following tasks:

from fabric.api import env, run

def set_hosts():
    env.hosts = ['host1', 'host2']

def mytask():
    run('ls /var/www')

When run as fab set_hosts mytask, set_hosts is a “local” task – its own host list is empty – but mytask will again run on the two hosts given.

注解

This technique used to be a common way of creating fake “roles”, but is less necessary now that roles are fully implemented. It may still be useful in some situations, however.

Alongside env.hosts is env.roles (not to be confused with env.roledefs!) which, if given, will be taken as a list of role names to look up in env.roledefs.

Globally, via the command line

In addition to modifying env.hosts, env.roles, and env.exclude_hosts at the module level, you may define them by passing comma-separated string arguments to the command-line switches --hosts/-H and --roles/-R, e.g.:

$ fab -H host1,host2 mytask

Such an invocation is directly equivalent to env.hosts = ['host1', 'host2'] – the argument parser knows to look for these arguments and will modify env at parse time.

注解

It’s possible, and in fact common, to use these switches to set only a single host or role. Fabric simply calls string.split(',') on the given string, so a string with no commas turns into a single-item list.

It is important to know that these command-line switches are interpreted before your fabfile is loaded: any reassignment to env.hosts or env.roles in your fabfile will overwrite them.

If you wish to nondestructively merge the command-line hosts with your fabfile-defined ones, make sure your fabfile uses env.hosts.extend() instead:

from fabric.api import env, run

env.hosts.extend(['host3', 'host4'])

def mytask():
    run('ls /var/www')

When this fabfile is run as fab -H host1,host2 mytask, env.hosts will then contain ['host1', 'host2', 'host3', 'host4'] at the time that mytask is executed.

注解

env.hosts is simply a Python list object – so you may use env.hosts.append() or any other such method you wish.

Per-task, via the command line

Globally setting host lists only works if you want all your tasks to run on the same host list all the time. This isn’t always true, so Fabric provides a few ways to be more granular and specify host lists which apply to a single task only. The first of these uses task arguments.

As outlined in fab 选项和参数, it’s possible to specify per-task arguments via a special command-line syntax. In addition to naming actual arguments to your task function, this may be used to set the host, hosts, role or roles “arguments”, which are interpreted by Fabric when building host lists (and removed from the arguments passed to the task itself.)

注解

Since commas are already used to separate task arguments from one another, semicolons must be used in the hosts or roles arguments to delineate individual host strings or role names. Furthermore, the argument must be quoted to prevent your shell from interpreting the semicolons.

Take the below fabfile, which is the same one we’ve been using, but which doesn’t define any host info at all:

from fabric.api import run

def mytask():
    run('ls /var/www')

To specify per-task hosts for mytask, execute it like so:

$ fab mytask:hosts="host1;host2"

This will override any other host list and ensure mytask always runs on just those two hosts.

Per-task, via decorators

If a given task should always run on a predetermined host list, you may wish to specify this in your fabfile itself. This can be done by decorating a task function with the hosts or roles decorators. These decorators take a variable argument list, like so:

from fabric.api import hosts, run

@hosts('host1', 'host2')
def mytask():
    run('ls /var/www')

They will also take an single iterable argument, e.g.:

my_hosts = ('host1', 'host2')
@hosts(my_hosts)
def mytask():
    # ...

When used, these decorators override any checks of env for that particular task’s host list (though env is not modified in any way – it is simply ignored.) Thus, even if the above fabfile had defined env.hosts or the call to fab uses --hosts/-H, mytask would still run on a host list of ['host1', 'host2'].

However, decorator host lists do not override per-task command-line arguments, as given in the previous section.

Order of precedence

We’ve been pointing out which methods of setting host lists trump the others, as we’ve gone along. However, to make things clearer, here’s a quick breakdown:

• Per-task, command-line host lists (fab mytask:host=host1) override absolutely everything else.
• Per-task, decorator-specified host lists (@hosts('host1')) override the env variables.
• Globally specified host lists set in the fabfile (env.hosts = ['host1']) can override such lists set on the command-line, but only if you’re not careful (or want them to.)
• Globally specified host lists set on the command-line (--hosts=host1) will initialize the env variables, but that’s it.

This logic may change slightly in the future to be more consistent (e.g. having --hosts somehow take precedence over env.hosts in the same way that command-line per-task lists trump in-code ones) but only in a backwards-incompatible release.

Combining host lists

There is no “unionizing” of hosts between the various sources mentioned in How host lists are constructed. If env.hosts is set to ['host1', 'host2', 'host3'], and a per-function (e.g. via hosts) host list is set to just ['host2', 'host3'], that function will not execute on host1, because the per-task decorator host list takes precedence.

However, for each given source, if both roles and hosts are specified, they will be merged together into a single host list. Take, for example, this fabfile where both of the decorators are used:

from fabric.api import env, hosts, roles, run

env.roledefs = {'role1': ['b', 'c']}

@hosts('a', 'b')
@roles('role1')
def mytask():
    run('ls /var/www')

Assuming no command-line hosts or roles are given when mytask is executed, this fabfile will call mytask on a host list of ['a', 'b', 'c'] – the union of role1 and the contents of the hosts call.

Host list deduplication

By default, to support Combining host lists, Fabric deduplicates the final host list so any given host string is only present once. However, this prevents explicit/intentional running of a task multiple times on the same target host, which is sometimes useful.

To turn off deduplication, set env.dedupe_hosts to False.

Excluding specific hosts

At times, it is useful to exclude one or more specific hosts, e.g. to override a few bad or otherwise undesirable hosts which are pulled in from a role or an autogenerated host list.

注解

As of Fabric 1.4, you may wish to use skip_bad_hosts instead, which automatically skips over any unreachable hosts.

Host exclusion may be accomplished globally with --exclude-hosts/-x:

$ fab -R myrole -x host2,host5 mytask

If myrole was defined as ['host1', 'host2', ..., 'host15'], the above invocation would run with an effective host list of ['host1', 'host3', 'host4', 'host6', ..., 'host15'].

注解

Using this option does not modify env.hosts – it only causes the main execution loop to skip the requested hosts.

Exclusions may be specified per-task by using an extra exclude_hosts kwarg, which is implemented similarly to the abovementioned hosts and roles per-task kwargs, in that it is stripped from the actual task invocation. This example would have the same result as the global exclude above:

$ fab mytask:roles=myrole,exclude_hosts="host2;host5"

Note that the host list is semicolon-separated, just as with the hosts per-task argument.

Combining exclusions

Host exclusion lists, like host lists themselves, are not merged together across the different “levels” they can be declared in. For example, a global -x option will not affect a per-task host list set with a decorator or keyword argument, nor will per-task exclude_hosts keyword arguments affect a global -H list.

There is one minor exception to this rule, namely that CLI-level keyword arguments (mytask:exclude_hosts=x,y) will be taken into account when examining host lists set via @hosts or @roles. Thus a task function decorated with @hosts('host1', 'host2') executed as fab taskname:exclude_hosts=host2 will only run on host1.

As with the host list merging, this functionality is currently limited (partly to keep the implementation simple) and may be expanded in future releases.

Intelligently executing tasks with execute

1.3 新版功能.

Most of the information here involves “top level” tasks executed via fab, such as the first example where we called fab taskA taskB. However, it’s often convenient to wrap up multi-task invocations like this into their own, “meta” tasks.

Prior to Fabric 1.3, this had to be done by hand, as outlined in 作为库使用. Fabric’s design eschews magical behavior, so simply calling a task function does not take into account decorators such as roles.

New in Fabric 1.3 is the execute helper function, which takes a task object or name as its first argument. Using it is effectively the same as calling the given task from the command line: all the rules given above in How host lists are constructed apply. (The hosts and roles keyword arguments to execute are analogous to CLI per-task arguments, including how they override all other host/role-setting methods.)

As an example, here’s a fabfile defining two stand-alone tasks for deploying a Web application:

from fabric.api import run, roles

env.roledefs = {
    'db': ['db1', 'db2'],
    'web': ['web1', 'web2', 'web3'],
}

@roles('db')
def migrate():
    # Database stuff here.
    pass

@roles('web')
def update():
    # Code updates here.
    pass

In Fabric <=1.2, the only way to ensure that migrate runs on the DB servers and that update runs on the Web servers (short of manual env.host_string manipulation) was to call both as top level tasks:

$ fab migrate update

Fabric >=1.3 can use execute to set up a meta-task. Update the import line like so:

from fabric.api import run, roles, execute

and append this to the bottom of the file:

def deploy():
    execute(migrate)
    execute(update)

That’s all there is to it; the roles decorators will be honored as expected, resulting in the following execution sequence:

• migrate on db1
• migrate on db2
• update on web1
• update on web2
• update on web3

警告

This technique works because tasks that themselves have no host list (this includes the global host list settings) only run one time. If used inside a “regular” task that is going to run on multiple hosts, calls to execute will also run multiple times, resulting in multiplicative numbers of subtask calls – be careful!

If you would like your execute calls to only be called once, you may use the runs_once decorator.

参见

execute, runs_once

Leveraging execute to access multi-host results

In nontrivial Fabric runs, especially parallel ones, you may want to gather up a bunch of per-host result values at the end - e.g. to present a summary table, perform calculations, etc.

It’s not possible to do this in Fabric’s default “naive” mode (one where you rely on Fabric looping over host lists on your behalf), but with execute it’s pretty easy. Simply switch from calling the actual work-bearing task, to calling a “meta” task which takes control of execution with execute:

from fabric.api import task, execute, run, runs_once

@task
def workhorse():
    return run("get my infos")

@task
@runs_once
def go():
    results = execute(workhorse)
    print results

In the above, workhorse can do any Fabric stuff at all – it’s literally your old “naive” task – except that it needs to return something useful.

go is your new entry point (to be invoked as fab go, or whatnot) and its job is to take the results dictionary from the execute call and do whatever you need with it. Check the API docs for details on the structure of that return value.

Using execute with dynamically-set host lists

A common intermediate-to-advanced use case for Fabric is to parameterize lookup of one’s target host list at runtime (when use of Roles does not suffice). execute can make this extremely simple, like so:

from fabric.api import run, execute, task

# For example, code talking to an HTTP API, or a database, or ...
from mylib import external_datastore

# This is the actual algorithm involved. It does not care about host
# lists at all.
def do_work():
    run("something interesting on a host")

# This is the user-facing task invoked on the command line.
@task
def deploy(lookup_param):
    # This is the magic you don't get with @hosts or @roles.
    # Even lazy-loading roles require you to declare available roles
    # beforehand. Here, the sky is the limit.
    host_list = external_datastore.query(lookup_param)
    # Put this dynamically generated host list together with the work to be
    # done.
    execute(do_work, hosts=host_list)

For example, if external_datastore was a simplistic “look up hosts by tag in a database” service, and you wanted to run a task on all hosts tagged as being related to your application stack, you might call the above like this:

$ fab deploy:app

But wait! A data migration has gone awry on the DB servers. Let’s fix up our migration code in our source repo, and deploy just the DB boxes again:

$ fab deploy:db

This use case looks similar to Fabric’s roles, but has much more potential, and is by no means limited to a single argument. Define the task however you wish, query your external data store in whatever way you need – it’s just Python.

The alternate approach

Similar to the above, but using fab‘s ability to call multiple tasks in succession instead of an explicit execute call, is to mutate env.hosts in a host-list lookup task and then call do_work in the same session:

from fabric.api import run, task

from mylib import external_datastore

# Marked as a publicly visible task, but otherwise unchanged: still just
# "do the work, let somebody else worry about what hosts to run on".
@task
def do_work():
    run("something interesting on a host")

@task
def set_hosts(lookup_param):
    # Update env.hosts instead of calling execute()
    env.hosts = external_datastore.query(lookup_param)

Then invoke like so:

$ fab set_hosts:app do_work

One benefit of this approach over the previous one is that you can replace do_work with any other “workhorse” task:

$ fab set_hosts:db snapshot
$ fab set_hosts:cassandra,cluster2 repair_ring
$ fab set_hosts:redis,environ=prod status

Failure handling

Once the task list has been constructed, Fabric will start executing them as outlined in Execution strategy, until all tasks have been run on the entirety of their host lists. However, Fabric defaults to a “fail-fast” behavior pattern: if anything goes wrong, such as a remote program returning a nonzero return value or your fabfile’s Python code encountering an exception, execution will halt immediately.

This is typically the desired behavior, but there are many exceptions to the rule, so Fabric provides env.warn_only, a Boolean setting. It defaults to False, meaning an error condition will result in the program aborting immediately. However, if env.warn_only is set to True at the time of failure – with, say, the settings context manager – Fabric will emit a warning message but continue executing.

Connections

fab itself doesn’t actually make any connections to remote hosts. Instead, it simply ensures that for each distinct run of a task on one of its hosts, the env var env.host_string is set to the right value. Users wanting to leverage Fabric as a library may do so manually to achieve similar effects (though as of Fabric 1.3, using execute is preferred and more powerful.)

env.host_string is (as the name implies) the “current” host string, and is what Fabric uses to determine what connections to make (or re-use) when network-aware functions are run. Operations like run or put use env.host_string as a lookup key in a shared dictionary which maps host strings to SSH connection objects.

注解

The connections dictionary (currently located at fabric.state.connections) acts as a cache, opting to return previously created connections if possible in order to save some overhead, and creating new ones otherwise.

Lazy connections

Because connections are driven by the individual operations, Fabric will not actually make connections until they’re necessary. Take for example this task which does some local housekeeping prior to interacting with the remote server:

from fabric.api import *

@hosts('host1')
def clean_and_upload():
    local('find assets/ -name "*.DS_Store" -exec rm '{}' \;')
    local('tar czf /tmp/assets.tgz assets/')
    put('/tmp/assets.tgz', '/tmp/assets.tgz')
    with cd('/var/www/myapp/'):
        run('tar xzf /tmp/assets.tgz')

What happens, connection-wise, is as follows:

1. The two local calls will run without making any network connections whatsoever;
2. put asks the connection cache for a connection to host1;
3. The connection cache fails to find an existing connection for that host string, and so creates a new SSH connection, returning it to put;
4. put uploads the file through that connection;
5. Finally, the run call asks the cache for a connection to that same host string, and is given the existing, cached connection for its own use.

Extrapolating from this, you can also see that tasks which don’t use any network-borne operations will never actually initiate any connections (though they will still be run once for each host in their host list, if any.)

Closing connections

Fabric’s connection cache never closes connections itself – it leaves this up to whatever is using it. The fab tool does this bookkeeping for you: it iterates over all open connections and closes them just before it exits (regardless of whether the tasks failed or not.)

Library users will need to ensure they explicitly close all open connections before their program exits. This can be accomplished by calling disconnect_all at the end of your script.

注解

disconnect_all may be moved to a more public location in the future; we’re still working on making the library aspects of Fabric more solidified and organized.

Multiple connection attempts and skipping bad hosts

As of Fabric 1.4, multiple attempts may be made to connect to remote servers before aborting with an error: Fabric will try connecting env.connection_attempts times before giving up, with a timeout of env.timeout seconds each time. (These currently default to 1 try and 10 seconds, to match previous behavior, but they may be safely changed to whatever you need.)

Furthermore, even total failure to connect to a server is no longer an absolute hard stop: set env.skip_bad_hosts to True and in most situations (typically initial connections) Fabric will simply warn and continue, instead of aborting.

1.4 新版功能.

Password management

Fabric maintains an in-memory, two-tier password cache to help remember your login and sudo passwords in certain situations; this helps avoid tedious re-entry when multiple systems share the same password [1], or if a remote system’s sudo configuration doesn’t do its own caching.

The first layer is a simple default or fallback password cache, env.password (which may also be set at the command line via --password or --initial-password-prompt). This env var stores a single password which (if non-empty) will be tried in the event that the host-specific cache (see below) has no entry for the current host string.

env.passwords (plural!) serves as a per-user/per-host cache, storing the most recently entered password for every unique user/host/port combination (note that you must include all three values if modifying the structure by hand - see the above link for details). Due to this cache, connections to multiple different users and/or hosts in the same session will only require a single password entry for each. (Previous versions of Fabric used only the single, default password cache and thus required password re-entry every time the previously entered password became invalid.)

Depending on your configuration and the number of hosts your session will connect to, you may find setting either or both of these env vars to be useful. However, Fabric will automatically fill them in as necessary without any additional configuration.

Specifically, each time a password prompt is presented to the user, the value entered is used to update both the single default password cache, and the cache value for the current value of env.host_string.

Leveraging native SSH config files

Command-line SSH clients (such as the one provided by OpenSSH) make use of a specific configuration format typically known as ssh_config, and will read from a file in the platform-specific location $HOME/.ssh/config (or an arbitrary path given to --ssh-config-path/env.ssh_config_path.) This file allows specification of various SSH options such as default or per-host usernames, hostname aliases, and toggling other settings (such as whether to use agent forwarding.)

Fabric’s SSH implementation allows loading a subset of these options from one’s actual SSH config file, should it exist. This behavior is not enabled by default (in order to be backwards compatible) but may be turned on by setting env.use_ssh_config to True at the top of your fabfile.

If enabled, the following SSH config directives will be loaded and honored by Fabric:

• User and Port will be used to fill in the appropriate connection parameters when not otherwise specified, in the following fashion:

  • Globally specified User/Port will be used in place of the current defaults (local username and 22, respectively) if the appropriate env vars are not set.
  • However, if env.user/env.port are set, they override global User/Port values.
  • User/port values in the host string itself (e.g. hostname:222) will override everything, including any ssh_config values.

• HostName can be used to replace the given hostname, just like with regular ssh. So a Host foo entry specifying HostName example.com will allow you to give Fabric the hostname 'foo' and have that expanded into 'example.com' at connection time.

• IdentityFile will extend (not replace) env.key_filename.

• ForwardAgent will augment env.forward_agent in an “OR” manner: if either is set to a positive value, agent forwarding will be enabled.

• ProxyCommand will trigger use of a proxy command for host connections, just as with regular ssh.

  注解

  If all you want to do is bounce SSH traffic off a gateway, you may find env.gateway to be a more efficient connection method (which will also honor more Fabric-level settings) than the typical ssh gatewayhost nc %h %p method of using ProxyCommand as a gateway.

  注解

  If your SSH config file contains ProxyCommand directives and you have set env.gateway to a non-None value, env.gateway will take precedence and the ProxyCommand will be ignored.

  If one has a pre-created SSH config file, rationale states it will be easier for you to modify env.gateway (e.g. via settings) than to work around your conf file’s contents entirely.