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Fix 2720 -- Expanded documentation for docker run.

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Docker-DCO-1.1-Signed-off-by: Andy Rothfusz <github@developersupport.net> (github: metalivedev)
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security
baseimages
runmetrics

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:title: Runtime Metrics
:description: Measure the behavior of running containers
:keywords: docker, metrics, CPU, memory, disk, IO, run, runtime
.. _run_metrics:
Runtime Metrics
===============
Linux Containers rely on `control groups
<https://www.kernel.org/doc/Documentation/cgroups/cgroups.txt>`_ which
not only track groups of processes, but also expose metrics about CPU,
memory, and block I/O usage. You can access those metrics and obtain
network usage metrics as well. This is relevant for "pure" LXC
containers, as well as for Docker containers.
Control Groups
--------------
Control groups are exposed through a pseudo-filesystem. In recent
distros, you should find this filesystem under
``/sys/fs/cgroup``. Under that directory, you will see multiple
sub-directories, called devices, freezer, blkio, etc.; each
sub-directory actually corresponds to a different cgroup hierarchy.
On older systems, the control groups might be mounted on ``/cgroup``,
without distinct hierarchies. In that case, instead of seeing the
sub-directories, you will see a bunch of files in that directory, and
possibly some directories corresponding to existing containers.
To figure out where your control groups are mounted, you can run:
::
grep cgroup /proc/mounts
.. _run_findpid:
Ennumerating Cgroups
--------------------
You can look into ``/proc/cgroups`` to see the different control group
subsystems known to the system, the hierarchy they belong to, and how
many groups they contain.
You can also look at ``/proc/<pid>/cgroup`` to see which control
groups a process belongs to. The control group will be shown as a path
relative to the root of the hierarchy mountpoint; e.g. ``/`` means
“this process has not been assigned into a particular group”, while
``/lxc/pumpkin`` means that the process is likely to be a member of a
container named ``pumpkin``.
Finding the Cgroup for a Given Container
----------------------------------------
For each container, one cgroup will be created in each hierarchy. On
older systems with older versions of the LXC userland tools, the name
of the cgroup will be the name of the container. With more recent
versions of the LXC tools, the cgroup will be ``lxc/<container_name>.``
For Docker containers using cgroups, the container name will be the
full ID or long ID of the container. If a container shows up as
ae836c95b4c3 in ``docker ps``, its long ID might be something like
``ae836c95b4c3c9e9179e0e91015512da89fdec91612f63cebae57df9a5444c79``. You
can look it up with ``docker inspect`` or ``docker ps -notrunc``.
Putting everything together to look at the memory metrics for a Docker
container, take a look at ``/sys/fs/cgroup/memory/lxc/<longid>/``.
Metrics from Cgroups: Memory, CPU, Block IO
-------------------------------------------
For each subsystem (memory, cpu, and block i/o), you will find one or
more pseudo-files containing statistics.
Memory Metrics: ``memory.stat``
...............................
Memory metrics are found in the "memory" cgroup. Note that the memory
control group adds a little overhead, because it does very
fine-grained accounting of the memory usage on your system. Therefore,
many distros chose to not enable it by default. Generally, to enable
it, all you have to do is to add some kernel command-line parameters:
``cgroup_enable=memory swapaccount=1``.
The metrics are in the pseudo-file ``memory.stat``. Here is what it
will look like:
::
cache 11492564992
rss 1930993664
mapped_file 306728960
pgpgin 406632648
pgpgout 403355412
swap 0
pgfault 728281223
pgmajfault 1724
inactive_anon 46608384
active_anon 1884520448
inactive_file 7003344896
active_file 4489052160
unevictable 32768
hierarchical_memory_limit 9223372036854775807
hierarchical_memsw_limit 9223372036854775807
total_cache 11492564992
total_rss 1930993664
total_mapped_file 306728960
total_pgpgin 406632648
total_pgpgout 403355412
total_swap 0
total_pgfault 728281223
total_pgmajfault 1724
total_inactive_anon 46608384
total_active_anon 1884520448
total_inactive_file 7003344896
total_active_file 4489052160
total_unevictable 32768
The first half (without the ``total_`` prefix) contains statistics
relevant to the processes within the cgroup, excluding
sub-cgroups. The second half (with the ``total_`` prefix) includes
sub-cgroups as well.
Some metrics are "gauges", i.e. values that can increase or decrease
(e.g. swap, the amount of swap space used by the members of the
cgroup). Some others are "counters", i.e. values that can only go up,
because they represent occurrences of a specific event (e.g. pgfault,
which indicates the number of page faults which happened since the
creation of the cgroup; this number can never decrease).
cache
the amount of memory used by the processes of this control group
that can be associated precisely with a block on a block
device. When you read and write files from and to disk, this amount
will increase. This will be the case if you use "conventional" I/O
(``open``, ``read``, ``write`` syscalls) as well as mapped files
(with ``mmap``). It also accounts for the memory used by ``tmpfs``
mounts, though the reasons are unclear.
rss
the amount of memory that *doesn't* correspond to anything on
disk: stacks, heaps, and anonymous memory maps.
mapped_file
indicates the amount of memory mapped by the processes in the
control group. It doesn't give you information about *how much*
memory is used; it rather tells you *how* it is used.
pgpgin and pgpgout
correspond to *charging events*. Each time a page is "charged"
(=added to the accounting) to a cgroup, pgpgin increases. When a
page is "uncharged" (=no longer "billed" to a cgroup), pgpgout
increases.
pgfault and pgmajfault
indicate the number of times that a process of the cgroup triggered
a "page fault" and a "major fault", respectively. A page fault
happens when a process accesses a part of its virtual memory space
which is inexistent or protected. The former can happen if the
process is buggy and tries to access an invalid address (it will
then be sent a ``SIGSEGV`` signal, typically killing it with the
famous ``Segmentation fault`` message). The latter can happen when
the process reads from a memory zone which has been swapped out, or
which corresponds to a mapped file: in that case, the kernel will
load the page from disk, and let the CPU complete the memory
access. It can also happen when the process writes to a
copy-on-write memory zone: likewise, the kernel will preempt the
process, duplicate the memory page, and resume the write operation
on the process' own copy of the page. "Major" faults happen when the
kernel actually has to read the data from disk. When it just has to
duplicate an existing page, or allocate an empty page, it's a
regular (or "minor") fault.
swap
the amount of swap currently used by the processes in this cgroup.
active_anon and inactive_anon
the amount of *anonymous* memory that has been identified has
respectively *active* and *inactive* by the kernel. "Anonymous"
memory is the memory that is *not* linked to disk pages. In other
words, that's the equivalent of the rss counter described above. In
fact, the very definition of the rss counter is **active_anon** +
**inactive_anon** - **tmpfs** (where tmpfs is the amount of memory
used up by ``tmpfs`` filesystems mounted by this control
group). Now, what's the difference between "active" and "inactive"?
Pages are initially "active"; and at regular intervals, the kernel
sweeps over the memory, and tags some pages as "inactive". Whenever
they are accessed again, they are immediately retagged
"active". When the kernel is almost out of memory, and time comes to
swap out to disk, the kernel will swap "inactive" pages.
active_file and inactive_file
cache memory, with *active* and *inactive* similar to the *anon*
memory above. The exact formula is cache = **active_file** +
**inactive_file** + **tmpfs**. The exact rules used by the kernel to
move memory pages between active and inactive sets are different
from the ones used for anonymous memory, but the general principle
is the same. Note that when the kernel needs to reclaim memory, it
is cheaper to reclaim a clean (=non modified) page from this pool,
since it can be reclaimed immediately (while anonymous pages and
dirty/modified pages have to be written to disk first).
unevictable
the amount of memory that cannot be reclaimed; generally, it will
account for memory that has been "locked" with ``mlock``. It is
often used by crypto frameworks to make sure that secret keys and
other sensitive material never gets swapped out to disk.
memory and memsw limits
These are not really metrics, but a reminder of the limits applied
to this cgroup. The first one indicates the maximum amount of
physical memory that can be used by the processes of this control
group; the second one indicates the maximum amount of RAM+swap.
Accounting for memory in the page cache is very complex. If two
processes in different control groups both read the same file
(ultimately relying on the same blocks on disk), the corresponding
memory charge will be split between the control groups. It's nice, but
it also means that when a cgroup is terminated, it could increase the
memory usage of another cgroup, because they are not splitting the
cost anymore for those memory pages.
CPU metrics: ``cpuacct.stat``
.............................
Now that we've covered memory metrics, everything else will look very
simple in comparison. CPU metrics will be found in the ``cpuacct``
controller.
For each container, you will find a pseudo-file ``cpuacct.stat``,
containing the CPU usage accumulated by the processes of the
container, broken down between ``user`` and ``system`` time. If you're
not familiar with the distinction, ``user`` is the time during which
the processes were in direct control of the CPU (i.e. executing
process code), and ``system`` is the time during which the CPU was
executing system calls on behalf of those processes.
Those times are expressed in ticks of 1/100th of second. Actually,
they are expressed in "user jiffies". There are ``USER_HZ``
*"jiffies"* per second, and on x86 systems, ``USER_HZ`` is 100. This
used to map exactly to the number of scheduler "ticks" per second; but
with the advent of higher frequency scheduling, as well as `tickless
kernels <http://lwn.net/Articles/549580/>`_, the number of kernel
ticks wasn't relevant anymore. It stuck around anyway, mainly for
legacy and compatibility reasons.
Block I/O metrics
.................
Block I/O is accounted in the ``blkio`` controller. Different metrics
are scattered across different files. While you can find in-depth
details in the `blkio-controller
<https://www.kernel.org/doc/Documentation/cgroups/blkio-controller.txt>`_
file in the kernel documentation, here is a short list of the most
relevant ones:
blkio.sectors
contain the number of 512-bytes sectors read and written by the
processes member of the cgroup, device by device. Reads and writes
are merged in a single counter.
blkio.io_service_bytes
indicates the number of bytes read and written by the cgroup. It has
4 counters per device, because for each device, it differentiates
between synchronous vs. asynchronous I/O, and reads vs. writes.
blkio.io_serviced
the number of I/O operations performed, regardless of their size. It
also has 4 counters per device.
blkio.io_queued
indicates the number of I/O operations currently queued for this
cgroup. In other words, if the cgroup isn't doing any I/O, this will
be zero. Note that the opposite is not true. In other words, if
there is no I/O queued, it does not mean that the cgroup is idle
(I/O-wise). It could be doing purely synchronous reads on an
otherwise quiescent device, which is therefore able to handle them
immediately, without queuing. Also, while it is helpful to figure
out which cgroup is putting stress on the I/O subsystem, keep in
mind that is is a relative quantity. Even if a process group does
not perform more I/O, its queue size can increase just because the
device load increases because of other devices.
Network Metrics
---------------
Network metrics are not exposed directly by control groups. There is a
good explanation for that: network interfaces exist within the context
of *network namespaces*. The kernel could probably accumulate metrics
about packets and bytes sent and received by a group of processes, but
those metrics wouldn't be very useful. You want per-interface metrics
(because traffic happening on the local ``lo`` interface doesn't
really count). But since processes in a single cgroup can belong to
multiple network namespaces, those metrics would be harder to
interpret: multiple network namespaces means multiple ``lo``
interfaces, potentially multiple ``eth0`` interfaces, etc.; so this is
why there is no easy way to gather network metrics with control
groups.
Instead we can gather network metrics from other sources:
IPtables
........
IPtables (or rather, the netfilter framework for which iptables is
just an interface) can do some serious accounting.
For instance, you can setup a rule to account for the outbound HTTP
traffic on a web server:
::
iptables -I OUTPUT -p tcp --sport 80
There is no ``-j`` or ``-g`` flag, so the rule will just count matched
packets and go to the following rule.
Later, you can check the values of the counters, with:
::
iptables -nxvL OUTPUT
Technically, ``-n`` is not required, but it will prevent iptables from
doing DNS reverse lookups, which are probably useless in this
scenario.
Counters include packets and bytes. If you want to setup metrics for
container traffic like this, you could execute a ``for`` loop to add
two ``iptables`` rules per container IP address (one in each
direction), in the ``FORWARD`` chain. This will only meter traffic
going through the NAT layer; you will also have to add traffic going
through the userland proxy.
Then, you will need to check those counters on a regular basis. If you
happen to use ``collectd``, there is a nice plugin to automate
iptables counters collection.
Interface-level counters
........................
Since each container has a virtual Ethernet interface, you might want
to check directly the TX and RX counters of this interface. You will
notice that each container is associated to a virtual Ethernet
interface in your host, with a name like ``vethKk8Zqi``. Figuring out
which interface corresponds to which container is, unfortunately,
difficult.
But for now, the best way is to check the metrics *from within the
containers*. To accomplish this, you can run an executable from the
host environment within the network namespace of a container using
**ip-netns magic**.
The ``ip-netns exec`` command will let you execute any program
(present in the host system) within any network namespace visible to
the current process. This means that your host will be able to enter
the network namespace of your containers, but your containers won't be
able to access the host, nor their sibling containers. Containers will
be able to “see” and affect their sub-containers, though.
The exact format of the command is::
ip netns exec <nsname> <command...>
For example::
ip netns exec mycontainer netstat -i
``ip netns`` finds the "mycontainer" container by using namespaces
pseudo-files. Each process belongs to one network namespace, one PID
namespace, one ``mnt`` namespace, etc., and those namespaces are
materialized under ``/proc/<pid>/ns/``. For example, the network
namespace of PID 42 is materialized by the pseudo-file
``/proc/42/ns/net``.
When you run ``ip netns exec mycontainer ...``, it expects
``/var/run/netns/mycontainer`` to be one of those
pseudo-files. (Symlinks are accepted.)
In other words, to execute a command within the network namespace of a
container, we need to:
* find out the PID of any process within the container that we want to
investigate;
* create a symlink from ``/var/run/netns/<somename>`` to
``/proc/<thepid>/ns/net``
* execute ``ip netns exec <somename> ....``
Please review :ref:`run_findpid` to learn how to find the cgroup of a
pprocess running in the container of which you want to measure network
usage. From there, you can examine the pseudo-file named ``tasks``,
which containes the PIDs that are in the control group (i.e. in the
container). Pick any one of them.
Putting everything together, if the "short ID" of a container is held
in the environment variable ``$CID``, then you can do this::
TASKS=/sys/fs/cgroup/devices/$CID*/tasks
PID=$(head -n 1 $TASKS)
mkdir -p /var/run/netns
ln -sf /proc/$PID/ns/net /var/run/netns/$CID
ip netns exec $CID netstat -i
Tips for high-performance metric collection
-------------------------------------------
Note that running a new process each time you want to update metrics
is (relatively) expensive. If you want to collect metrics at high
resolutions, and/or over a large number of containers (think 1000
containers on a single host), you do not want to fork a new process
each time.
Here is how to collect metrics from a single process. You will have to
write your metric collector in C (or any language that lets you do
low-level system calls). You need to use a special system call,
``setns()``, which lets the current process enter any arbitrary
namespace. It requires, however, an open file descriptor to the
namespace pseudo-file (remember: that’s the pseudo-file in
``/proc/<pid>/ns/net``).
However, there is a catch: you must not keep this file descriptor
open. If you do, when the last process of the control group exits, the
namespace will not be destroyed, and its network resources (like the
virtual interface of the container) will stay around for ever (or
until you close that file descriptor).
The right approach would be to keep track of the first PID of each
container, and re-open the namespace pseudo-file each time.
Collecting metrics when a container exits
-----------------------------------------
Sometimes, you do not care about real time metric collection, but when
a container exits, you want to know how much CPU, memory, etc. it has
used.
Docker makes this difficult because it relies on ``lxc-start``, which
carefully cleans up after itself, but it is still possible. It is
usually easier to collect metrics at regular intervals (e.g. every
minute, with the collectd LXC plugin) and rely on that instead.
But, if you'd still like to gather the stats when a container stops,
here is how:
For each container, start a collection process, and move it to the
control groups that you want to monitor by writing its PID to the
tasks file of the cgroup. The collection process should periodically
re-read the tasks file to check if it's the last process of the
control group. (If you also want to collect network statistics as
explained in the previous section, you should also move the process to
the appropriate network namespace.)
When the container exits, ``lxc-start`` will try to delete the control
groups. It will fail, since the control group is still in use; but
that’s fine. You process should now detect that it is the only one
remaining in the group. Now is the right time to collect all the
metrics you need!
Finally, your process should move itself back to the root control
group, and remove the container control group. To remove a control
group, just ``rmdir`` its directory. It's counter-intuitive to
``rmdir`` a directory as it still contains files; but remember that
this is a pseudo-filesystem, so usual rules don't apply. After the
cleanup is done, the collection process can exit safely.

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:title: Build Images (Dockerfile Reference)
:title: Dockerfile Reference
:description: Dockerfiles use a simple DSL which allows you to automate the steps you would normally manually take to create an image.
:keywords: builder, docker, Dockerfile, automation, image creation
.. _dockerbuilder:
===================================
Build Images (Dockerfile Reference)
===================================
====================
Dockerfile Reference
====================
**Docker can act as a builder** and read instructions from a text
``Dockerfile`` to automate the steps you would otherwise take manually

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...
.. _cli_options:
Types of Options
----------------
Boolean
~~~~~~~
Boolean options look like ``-d=false``. The value you see is the
default value which gets set if you do **not** use the boolean
flag. If you do call ``run -d``, that sets the opposite boolean value,
so in this case, ``true``, and so ``docker run -d`` **will** run in
"detached" mode, in the background. Other boolean options are similar
-- specifying them will set the value to the opposite of the default
value.
Multi
~~~~~
Options like ``-a=[]`` indicate they can be specified multiple times::
docker run -a stdin -a stdout -a stderr -i -t ubuntu /bin/bash
Sometimes this can use a more complex value string, as for ``-v``::
docker run -v /host:/container example/mysql
Strings and Integers
~~~~~~~~~~~~~~~~~~~~
Options like ``-name=""`` expect a string, and they can only be
specified once. Options like ``-c=0`` expect an integer, and they can
only be specified once.
----
Commands
--------
.. _cli_daemon:
``daemon``

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commandline/index
builder
run
api/index

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:title: Docker Run Reference
:description: Configure containers at runtime
:keywords: docker, run, configure, runtime
.. _run_docker:
====================
Docker Run Reference
====================
**Docker runs processes in isolated containers**. When an operator
executes ``docker run``, she starts a process with its own file
system, its own networking, and its own isolated process tree. The
:ref:`image_def` which starts the process may define defaults related
to the binary to run, the networking to expose, and more, but ``docker
run`` gives final control to the operator who starts the container
from the image. That's the main reason :ref:`cli_run` has more options
than any other ``docker`` command.
Every one of the :ref:`example_list` shows running containers, and so
here we try to give more in-depth guidance.
.. contents:: Table of Contents
.. _run_running:
General Form
============
As you've seen in the :ref:`example_list`, the basic `run` command
takes this form::
docker run [OPTIONS] IMAGE[:TAG] [COMMAND] [ARG...]
To learn how to interpret the types of ``[OPTIONS]``, see
:ref:`cli_options`.
The list of ``[OPTIONS]`` breaks down into two groups:
* options that define the runtime behavior or environment, and
* options that override image defaults.
Since image defaults usually get set in :ref:`Dockerfiles
<dockerbuilder>` (though they could also be set at :ref:`cli_commit`
time too), we will group the runtime options here by their related
Dockerfile commands so that it is easier to see how to override image
defaults and set new behavior.
We'll start, though, with the options that are unique to ``docker
run``, the options which define the runtime behavior or the container
environment.
.. note:: The runtime operator always has final control over the
behavior of a Docker container.
Detached or Foreground
======================
When starting a Docker container, you must first decide if you want to
run the container in the background in a "detached" mode or in the
default foreground mode::
-d=false: Detached mode: Run container in the background, print new container id
Detached (-d)
.............
In detached mode (``-d=true`` or just ``-d``), all IO should be done
through network connections or shared volumes because the container is
no longer listening to the commandline where you executed ``docker
run``. You can reattach to a detached container with ``docker``
:ref:`cli_attach`. If you choose to run a container in the detached
mode, then you cannot use the ``-rm`` option.
Foreground
..........
In foreground mode (the default when ``-d`` is not specified),
``docker run`` can start the process in the container and attach the
console to the process's standard input, output, and standard
error. It can even pretend to be a TTY (this is what most commandline
executables expect) and pass along signals. All of that is
configurable::
-a=[] : Attach to stdin, stdout and/or stderr
-t=false : Allocate a pseudo-tty
-sig-proxy=true: Proxify all received signal to the process (even in non-tty mode)
-i=false : Keep stdin open even if not attached
If you do not specify ``-a`` then Docker will `attach everything
(stdin,stdout,stderr)
<https://github.com/dotcloud/docker/blob/master/commands.go#L1797>`_. You
can specify which of the three standard streams (stdin, stdout,
stderr) you'd like to connect between your instead, as in::
docker run -a stdin -a stdout -i -t ubuntu /bin/bash
For interactive processes (like a shell) you will typically want a tty
as well as persistent standard in, so you'll use ``-i -t`` together in
most interactive cases.
Clean Up (-rm)
--------------
By default a container's file system persists even after the container
exits. This makes debugging a lot easier (since you can inspect the
final state) and you retain all your data by default. But if you are
running short-term **foreground** processes, these container file
systems can really pile up. If instead you'd like Docker to
**automatically clean up the container and remove the file system when
the container exits**, you can add the ``-rm`` flag::
-rm=false: Automatically remove the container when it exits (incompatible with -d)
Name (-name)
============
The operator can identify a container in three ways:
* UUID long identifier ("f78375b1c487e03c9438c729345e54db9d20cfa2ac1fc3494b6eb60872e74778")
* UUID short identifier ("f78375b1c487")
* name ("evil_ptolemy")
The UUID identifiers come from the Docker daemon, and if you do not
assign a name to the container with ``-name`` then the daemon will
also generate a random string name too. The name can become a handy
way to add meaning to a container since you can use this name when
defining :ref:`links <working_with_links_names>` (or any other place
you need to identify a container). This works for both background and
foreground Docker containers.
PID Equivalent
==============
And finally, to help with automation, you can have Docker write the
container id out to a file of your choosing. This is similar to how
some programs might write out their process ID to a file (you've seen
them as .pid files)::
-cidfile="": Write the container ID to the file
Overriding Dockerfile Image Defaults
====================================
When a developer builds an image from a :ref:`Dockerfile
<dockerbuilder>` or when she commits it, the developer can set a
number of default parameters that take effect when the image starts up
as a container.
Four of the Dockerfile commands cannot be overridden at runtime:
``FROM, MAINTAINER, RUN``, and ``ADD``. Everything else has a
corresponding override in ``docker run``. We'll go through what the
developer might have set in each Dockerfile instruction and how the
operator can override that setting.
CMD
...
Remember the optional ``COMMAND`` in the Docker commandline::
docker run [OPTIONS] IMAGE[:TAG] [COMMAND] [ARG...]
This command is optional because the person who created the ``IMAGE``
may have already provided a default ``COMMAND`` using the Dockerfile
``CMD``. As the operator (the person running a container from the
image), you can override that ``CMD`` just by specifying a new
``COMMAND``.
If the image also specifies an ``ENTRYPOINT`` then the ``CMD`` or
``COMMAND`` get appended as arguments to the ``ENTRYPOINT``.
ENTRYPOINT
..........
::
-entrypoint="": Overwrite the default entrypoint set by the image
The ENTRYPOINT of an image is similar to a COMMAND because it
specifies what executable to run when the container starts, but it is
(purposely) more difficult to override. The ENTRYPOINT gives a
container its default nature or behavior, so that when you set an
ENTRYPOINT you can run the container *as if it were that binary*,
complete with default options, and you can pass in more options via
the COMMAND. But, sometimes an operator may want to run something else
inside the container, so you can override the default ENTRYPOINT at
runtime by using a string to specify the new ENTRYPOINT. Here is an
example of how to run a shell in a container that has been set up to
automatically run something else (like ``/usr/bin/redis-server``)::
docker run -i -t -entrypoint /bin/bash example/redis
or two examples of how to pass more parameters to that ENTRYPOINT::
docker run -i -t -entrypoint /bin/bash example/redis -c ls -l
docker run -i -t -entrypoint /usr/bin/redis-cli example/redis --help
EXPOSE (``run`` Networking Options)
...................................
The *Dockerfile* doesn't give much control over networking, only
providing the EXPOSE instruction to give a hint to the operator about
what incoming ports might provide services. At runtime, however,
Docker provides a number of ``run`` options related to networking::
-n=true : Enable networking for this container
-dns=[] : Set custom dns servers for the container
-expose=[]: Expose a port from the container
without publishing it to your host
-P=false : Publish all exposed ports to the host interfaces
-p=[] : Publish a container's port to the host (format:
ip:hostPort:containerPort | ip::containerPort |
hostPort:containerPort)
(use 'docker port' to see the actual mapping)
-link="" : Add link to another container (name:alias)
By default, all containers have networking enabled and they can make
any outgoing connections. The operator can completely disable
networking with ``run -n`` which disables all incoming and outgoing
networking. In cases like this, you would perform IO through files or
stdin/stdout only.
Your container will use the same DNS servers as the host by default,
but you can override this with ``-dns``.
As mentioned previously, ``EXPOSE`` (and ``-expose``) make a port
available **in** a container for incoming connections. The port number
on the inside of the container (where the service listens) does not
need to be the same number as the port exposed on the outside of the
container (where clients connect), so inside the container you might
have an HTTP service listening on port 80 (and so you ``EXPOSE 80`` in
the Dockerfile), but outside the container the port might be 42800.
To help a new client container reach the server container's internal
port operator ``-expose'd`` by the operator or ``EXPOSE'd`` by the
developer, the operator has three choices: start the server container
with ``-P`` or ``-p,`` or start the client container with ``-link``.
If the operator uses ``-P`` or ``-p`` then Docker will make the
exposed port accessible on the host and the ports will be available to
any client that can reach the host. To find the map between the host
ports and the exposed ports, use ``docker port``)
If the operator uses ``-link`` when starting the new client container,
then the client container can access the exposed port via a private
networking interface. Docker will set some environment variables in
the client container to help indicate which interface and port to use.
ENV (Environment Variables)
...........................
The operator can **set any environment variable** in the container by
using one or more ``-e``, even overriding those already defined by the
developer with a Dockefile ``ENV``::
$ docker run -e "deep=purple" -rm ubuntu /bin/bash -c export
declare -x HOME="/"
declare -x HOSTNAME="85bc26a0e200"
declare -x OLDPWD
declare -x PATH="/usr/local/sbin:/usr/local/bin:/usr/sbin:/usr/bin:/sbin:/bin"
declare -x PWD="/"
declare -x SHLVL="1"
declare -x container="lxc"
declare -x deep="purple"
Similarly the operator can set the **hostname** with ``-h``.
``-link name:alias`` also sets environment variables, using the
*alias* string to define environment variables within the container
that give the IP and PORT information for connecting to the service
container. Let's imagine we have a container running Redis::
# Start the service container, named redis-name
$ docker run -d -name redis-name dockerfiles/redis
4241164edf6f5aca5b0e9e4c9eccd899b0b8080c64c0cd26efe02166c73208f3
# The redis-name container exposed port 6379
$ docker ps
CONTAINER ID IMAGE COMMAND CREATED STATUS PORTS NAMES
4241164edf6f dockerfiles/redis:latest /redis-stable/src/re 5 seconds ago Up 4 seconds 6379/tcp redis-name
# Note that there are no public ports exposed since we didn't use -p or -P
$ docker port 4241164edf6f 6379
2014/01/25 00:55:38 Error: No public port '6379' published for 4241164edf6f
Yet we can get information about the redis container's exposed ports with ``-link``. Choose an alias that will form a valid environment variable!
::
$ docker run -rm -link redis-name:redis_alias -entrypoint /bin/bash dockerfiles/redis -c export
declare -x HOME="/"
declare -x HOSTNAME="acda7f7b1cdc"
declare -x OLDPWD
declare -x PATH="/usr/local/sbin:/usr/local/bin:/usr/sbin:/usr/bin:/sbin:/bin"
declare -x PWD="/"
declare -x REDIS_ALIAS_NAME="/distracted_wright/redis"
declare -x REDIS_ALIAS_PORT="tcp://172.17.0.32:6379"
declare -x REDIS_ALIAS_PORT_6379_TCP="tcp://172.17.0.32:6379"
declare -x REDIS_ALIAS_PORT_6379_TCP_ADDR="172.17.0.32"
declare -x REDIS_ALIAS_PORT_6379_TCP_PORT="6379"
declare -x REDIS_ALIAS_PORT_6379_TCP_PROTO="tcp"
declare -x SHLVL="1"
declare -x container="lxc"
And we can use that information to connect from another container as a client::
$ docker run -i -t -rm -link redis-name:redis_alias -entrypoint /bin/bash dockerfiles/redis -c '/redis-stable/src/redis-cli -h $REDIS_ALIAS_PORT_6379_TCP_ADDR -p $REDIS_ALIAS_PORT_6379_TCP_PORT'
172.17.0.32:6379>
VOLUME (Shared Filesystems)
...........................
::
-v=[]: Create a bind mount with: [host-dir]:[container-dir]:[rw|ro].
If "container-dir" is missing, then docker creates a new volume.
-volumes-from="": Mount all volumes from the given container(s)
The volumes commands are complex enough to have their own
documentation in section :ref:`volume_def`. A developer can define one
or more VOLUMEs associated with an image, but only the operator can
give access from one container to another (or from a container to a
volume mounted on the host).
USER
....
::
-u="": Username or UID
WORKDIR
.......
::
-w="": Working directory inside the container
Performance
===========
The operator can also adjust the performance parameters of the container::
-c=0 : CPU shares (relative weight)
-m="": Memory limit (format: <number><optional unit>, where unit = b, k, m or g)
-lxc-conf=[]: Add custom lxc options -lxc-conf="lxc.cgroup.cpuset.cpus = 0,1"
-privileged=false: Give extended privileges to this container