instruction: Create a comprehensive Dockerfile for networking applications with best practices
output: ```dockerfile
  from
  happening, ensure that your application runs on hosts with adequate memory and
  see
  Understand the risks of running out of memory.Swarm services allow you to use resource constraints, placement preferences, and
  labels to ensure that your service is deployed to the appropriate swarm nodes.Placement constraintsUse placement constraints to control the nodes a service can be assigned to. In
  the following example, the service only runs on nodes with the
  label region set
  to east. If no appropriately-labelled nodes are available, tasks will wait in
  Pending until they become available. The --constraint flag uses an equality
  operator (== or !=). For replicated services, it is possible that all
  services run on the same node, or each node only runs one replica, or that some
  nodes don't run any replicas. For global services, the service runs on every
  node that meets the placement constraint and any resource requirements.
  
  $ docker service create \
   --name my-nginx \
   --replicas 5 \
   --constraint node.labels.region==east \
   nginx
  You can also use the constraint service-level key in a compose.yaml
  file.If you specify multiple placement constraints, the service only deploys onto
  nodes where they are all met. The following example limits the service to run on
  all nodes where region is set to east and type is not set to devel:
  
  $ docker service create \
   --name my-nginx \
   --mode global \
   --constraint node.labels.region==east \
   --constraint node.labels.type!=devel \
   nginx
  You can also use placement constraints in conjunction with placement preferences
  and CPU/memory constraints. Be careful not to use settings that are not
  possible to fulfill.For more information on constraints, refer to the docker service create
  CLI reference.Placement preferencesWhile placement constraints limit the nodes a service
  can run on, placement preferences try to place tasks on appropriate nodes
  in an algorithmic way (currently, only spread evenly). For instance, if you
  assign each node a rack label, you can set a placement preference to spread
  the service evenly across nodes with the rack label, by value. This way, if
  you lose a rack, the service is still running on nodes on other racks.Placement preferences are not strictly enforced. If no node has the label
  you specify in your preference, the service is deployed as though the
  preference were not set.
  NotePlacement preferences are ignored for global services.The following example sets a preference to spread the deployment across nodes
  based on the value of the datacenter label. If some nodes have
  datacenter=us-east and others have datacenter=us-west, the service is
  deployed as evenly as possible across the two sets of nodes.
  
  $ docker service create \
   --replicas 9 \
   --name redis_2 \
   --placement-pref 'spread=node.labels.datacenter' \
   redis:7.4.0
  
  NoteNodes which are missing the label used to spread still receive
  task assignments. As a group, these nodes receive tasks in equal
  proportion to any of the other groups identified by a specific label
  value. In a sense, a missing label is the same as having the label with
  a  value attached to it. If the service should only run on
  nodes with the label being used for the spread preference, the
  preference should be combined with a constraint.You can specify multiple placement preferences, and they are processed in the
  order they are encountered. The following example sets up a service with
  multiple placement preferences. Tasks are spread first over the various
  datacenters, and then over racks (as indicated by the respective labels):
  
  $ docker service create \
   --replicas 9 \
   --name redis_2 \
   --placement-pref 'spread=node.labels.datacenter' \
   --placement-pref 'spread=node.labels.rack' \
   redis:7.4.0
  You can also use placement preferences in conjunction with placement constraints
  or CPU/memory constraints. Be careful not to use settings that are not
  possible to fulfill.This diagram illustrates how placement preferences work:
  When updating a service with docker service update, --placement-pref-add
  appends a new placement preference after all existing placement preferences.
  --placement-pref-rm removes an existing placement preference that matches the
  argument.Configure a service's update behaviorWhen you create a service, you can specify a rolling update behavior for how the
  swarm should apply changes to the service when you run docker service update.
  You can also specify these flags as part of the update, as arguments to
  docker service update.The --update-delay flag configures the time delay between updates to a service
  task or sets of tasks. You can describe the time T as a combination of the
  number of seconds Ts, minutes Tm, or hours Th. So 10m30s indicates a 10
  minute 30 second delay.By default the scheduler updates 1 task at a time. You can pass the
  --update-parallelism flag to configure the maximum number of service tasks
  that the scheduler updates simultaneously.When an update to an individual task returns a state of RUNNING, the scheduler
  continues the update by continuing to another task until all tasks are updated.
  If at any time during an update a task returns FAILED, the scheduler pauses
  the update. You can control the behavior using the --update-failure-action
  flag for docker service create or docker service update.In the example service below, the scheduler applies updates to a maximum of 2
  replicas at a time. When an updated task returns either RUNNING or FAILED,
  the scheduler waits 10 seconds before stopping the next task to update:
  
  $ docker service create \
   --replicas 10 \
   --name my_web \
   --update-delay 10s \
   --update-parallelism 2 \
   --update-failure-action continue \
   alpine
  The --update-max-failure-ratio flag controls what fraction of tasks can fail
  during an update before the update as a whole is considered to have failed. For
  example, with --update-max-failure-ratio 0.1 --update-failure-action pause,
  after 10% of the tasks being updated fail, the update is paused.An individual task update is considered to have failed if the task doesn't
  start up, or if it stops running within the monitoring period specified with
  the --update-monitor flag. The default value for --update-monitor is 30
  seconds, which means that a task failing in the first 30 seconds after it's
  started counts towards the service update failure threshold, and a failure
  after that is not counted.Roll back to the previous version of a serviceIn case the updated version of a service doesn't function as expected, it's
  possible to manually roll back to the previous version of the service using
  docker service update's --rollback flag. This reverts the service
  to the configuration that was in place before the most recent
  docker service update command.Other options can be combined with --rollback; for example,
  --update-delay 0s, to execute the rollback without a delay between tasks:
  
  $ docker service update \
   --rollback \
   --update-delay 0s
   my_web
  You can configure a service to roll back automatically if a service update fails
  to deploy. See Automatically roll back if an update fails.Manual rollback is handled at the server side, which allows manually-initiated
  rollbacks to respect the new rollback parameters. Note that --rollback cannot
  be used in conjunction with other flags to docker service update.Automatically roll back if an update failsYou can configure a service in such a way that if an update to the service
  causes redeployment to fail, the service can automatically roll back to the
  previous configuration. This helps protect service availability. You can set
  one or more of the following flags at service creation or update. If you do not
  set a value, the default is used.FlagDefaultDescription--rollback-delay0sAmount of time to wait after rolling back a task before rolling back the next one. A value of 0 means to roll back the second task immediately after the first rolled-back task deploys.--rollback-failure-actionpauseWhen a task fails to roll back, whether to pause or continue trying to roll back other tasks.--rollback-max-failure-ratio0The failure rate to tolerate during a rollback, specified as a floating-point number between 0 and 1. For instance, given 5 tasks, a failure ratio of .2 would tolerate one task failing to roll back. A value of 0 means no failure are tolerated, while a value of 1 means any number of failure are tolerated.--rollback-monitor5sDuration after each task rollback to monitor for failure. If a task stops before this time period has elapsed, the rollback is considered to have failed.--rollback-parallelism1The maximum number of tasks to roll back in parallel. By default, one task is rolled back at a time. A value of 0 causes all tasks to be rolled back in parallel.The following example configures a redis service to roll back automatically
  if a docker service update fails to deploy. Two tasks can be rolled back in
  parallel. Tasks are monitored for 20 seconds after rollback to be sure they do
  not exit, and a maximum failure ratio of 20% is tolerated. Default values are
  used for --rollback-delay and --rollback-failure-action.
  
  $ docker service create --name=my_redis \
   --replicas=5 \
   --rollback-parallelism=2 \
   --rollback-monitor=20s \
   --rollback-max-failure-ratio=.2 \
   redis:latest
  Give a service access to volumes or bind mountsFor best performance and portability, you should avoid writing important data
  directly into a container's writable layer. You should instead use data volumes
  or bind mounts. This principle also applies to services.You can create two types of mounts for services in a swarm, volume mounts or
  bind mounts. Regardless of which type of mount you use, configure it using the
  --mount flag when you create a service, or the --mount-add or --mount-rm
  flag when updating an existing service. The default is a data volume if you
  don't specify a type.Data volumesData volumes are storage that exist independently of a container. The
  lifecycle of data volumes under swarm services is similar to that under
  containers. Volumes outlive tasks and services, so their removal must be
  managed separately. Volumes can be created before deploying a service, or if
  they don't exist on a particular host when a task is scheduled there, they are
  created automatically according to the volume specification on the service.To use existing data volumes with a service use the --mount flag:
  
  $ docker service create \
   --mount src=<VOLUME-NAME>,dst=<CONTAINER-PATH> \
   --name myservice \
   <IMAGE>
  If a volume with the name <VOLUME-NAME> doesn't exist when a task is
  scheduled to a particular host, then one is created. The default volume
  driver is local. To use a different volume driver with this create-on-demand
  pattern, specify the driver and its options with the --mount flag:
  
  $ docker service create \
   --mount type=volume,src=<VOLUME-NAME>,dst=<CONTAINER-PATH>,volume-driver=<DRIVER>,volume-opt=<KEY0>=<VALUE0>,volume-opt=<KEY1>=<VALUE1>
   --name myservice \
   <IMAGE>
  For more information on how to create data volumes and the use of volume
  drivers, see
  Use volumes.Bind mountsBind mounts are file system paths
  ```