Play with Docker
If you are learning docker or if you want to try out a quick thing in docker, Play with Docker is the best place to do it. Play with Docker is a lab environment provided by Docker itself. To start playing around with docker, all you need to do is log in with your free docker id and start using it. You will get 4 hours of time to play around with it in a single session.
The best thing is you can add up to 5 nodes and try out all the cool stuff that docker provides us. In this article, we will learn how to create a swarm. How to add manager and worker nodes to the swarm. How to create and assign a service to the swarm etc.,
Objective
Our objective in this Play with Docker session is to create swarm of managers and workers and see how a service task is distributed across all nodes.
As part of our objective we are going to perform below steps:
- Initialize Swarm
- Add Manager Nodes to the Swarm
- Add Worker Nodes to the Swarm
- Promote a Worker Node as Manager
- Create Service with replicas
- Scaling the Service to increase the replicas
- Updating the Service to further increase the replicas
Play with Docker includes templates, using which you can create predefined swarm with a set of managers and nodes, like “3 Managers and 2 Workers”, “5 Managers and no workers” and “1 Manager and 1 Worker”. However, in this example, we are going to initialize the swarm, add manager and worker nodes using the docker commands all by ourselves.
Once the session is started, click on “+ ADD NEW INSTANCE” to create nodes. Let’s add 5 nodes which are the max number nodes that we can create using Play with Docker.
Once all 5 nodes are available, we’ll initialize the swarm. We’ll join node1 and node2 as managers. We’ll join node3 as a worker and promote it as a manager. Then we’ll proceed with adding node4 and node5 as worker nodes.
Then we create a service using docker/getting-started image with 3 replicas. Once this is done, we’ll see how docker efficiently make sure to keep 3 replicas available always even in case of shutdown of a node that was assigned to the service.
Lastly, we’ll see how we can scale the number of replicas. Now, let’s get started.
If you are copying the commands from this article to try things out, please make sure to change the IPs according to your session.
Initializing the Swarm
Let’s hit our first command to initialize the swarm using docker swarm init command.
[node1] (local) root@192.168.0.15 ~
docker swarm init --advertise-addr 192.168.0.15:2377 --listen-addr 192.168.0.15:2377
Swarm initialized: current node (lbnhzlhks1cahurf5x39iw6vk) is now a manager.
To add a worker to this swarm, run the following command:
docker swarm join --token SWMTKN-1-5fuc7d0f1ofzm7p162bmijovuryv2wb1az6wy3jb0a27ghn0if-cp7j1632prhcgm9b6wqy219x6 192.168.0.15:2377
To add a manager to this swarm, run 'docker swarm join-token manager' and follow the instructions.This command not only initializes swarm but also makes current node as a leader manager.
Here, flag --advertise-addr tells the docker which IP it needs to advertise to other members of the swarm for API access and overlay networking. If the machine is got a single IP, technically you don’t have to specify this. But if the system has multiple IP addresses, --advertise-addr must be specified so that the correct address is chosen for inter-manager communication and overlay networking.
Using flag--listen-addr we are specifying the IP that needs to be used by the node to listen for inbound swarm manager traffic.
For both these flags we are using the current node IP address. When you run this command, you make sure to change it to your instance IP.
Native engine port is 2375 and the secure engine port is 2376, so we gave port 2377 as swarm port. Once you press enter we get the output saying that swarm has been initialized and this node has been marked as manager.
This also returned us an exact command that we need to run on a worker node that we want to join to this swarm. You can see that it also includes a token that must be used when we are adding a worker.
It also includes another instruction to add a manager to this swarm. Let’s copy that command and run it.
[node1] (local) root@192.168.0.15 ~
$ docker swarm join-token manager
To add a manager to this swarm, run the following command:
docker swarm join --token SWMTKN-1-5fuc7d0f1ofzm7p162bmijovuryv2wb1az6wy3jb0a27ghn0if-2xeu2vqppn1oj6kk6p3kj9lxj 192.168.0.15:2377Now we got another command to add a manager to this swarm. However, the token in this command is different than the worker node token.
You can also use docker swarm join-token to get the command which joins a node as a worker in the swarm as below:
[node1] (local) root@192.168.0.15 ~
$ docker swarm join-token worker
To add a worker to this swarm, run the following command:
docker swarm join --token SWMTKN-1-5fuc7d0f1ofzm7p162bmijovuryv2wb1az6wy3jb0a27ghn0if-cp7j1632prhcgm9b6wqy219x6 192.168.0.15:2377Let’s see what docker info returns as of now:
[node1] (local) root@192.168.0.15 ~
$ docker info
Client:
Debug Mode: false
Plugins:
app: Docker App (Docker Inc., v0.9.1-beta3)
Server:
Containers: 0
Running: 0
Paused: 0
Stopped: 0
Images: 0
Server Version: 19.03.11
Storage Driver: overlay2
Backing Filesystem: xfs
Supports d_type: true
Native Overlay Diff: true
Logging Driver: json-file
Cgroup Driver: cgroupfs
Plugins:
Volume: local
Network: bridge host ipvlan macvlan null overlay
Log: awslogs fluentd gcplogs gelf journald json-file local logentries splunk syslog
Swarm: active
NodeID: lbnhzlhks1cahurf5x39iw6vk
Is Manager: true
ClusterID: yjfkmxwayrvazt9a2kcec7teu
Managers: 1
Nodes: 1
Default Address Pool: 10.0.0.0/8
SubnetSize: 24
Data Path Port: 4789
Orchestration:
Task History Retention Limit: 5
Raft:
Snapshot Interval: 10000
Number of Old Snapshots to Retain: 0
Heartbeat Tick: 1
Election Tick: 10
Dispatcher:
Heartbeat Period: 5 seconds
CA Configuration:
Expiry Duration: 3 months
Force Rotate: 0
Autolock Managers: false
Root Rotation In Progress: false
Node Address: 192.168.0.15
Manager Addresses:
192.168.0.15:2377
Runtimes: runc
Default Runtime: runc
Init Binary: docker-init
containerd version: 7ad184331fa3e55e52b890ea95e65ba581ae3429
runc version: dc9208a3303feef5b3839f4323d9beb36df0a9dd
init version: fec3683
Security Options:
apparmor
seccomp
Profile: default
Kernel Version: 4.4.0-184-generic
Operating System: Alpine Linux v3.12 (containerized)
OSType: linux
Architecture: x86_64
CPUs: 8
Total Memory: 31.4GiB
Name: node1
ID: TPPH:JE75:EBRD:INWN:E6C6:XSGZ:2FJV:EA4W:L7FB:AAFF:T5HQ:DXBD
Docker Root Dir: /var/lib/docker
Debug Mode: true
File Descriptors: 38
Goroutines: 155
System Time: 2020-06-29T12:00:33.421784818Z
EventsListeners: 0
Registry: https://index.docker.io/v1/
Labels:
Experimental: true
Insecure Registries:
127.0.0.1
127.0.0.0/8
Live Restore Enabled: false
Product License: Community EngineThis output says that swarm is active now. Since manager nodes are also worker nodes, the output shows that there are one manager and one worker.
Adding Nodes as Managers to the Swarm
Let us switch over to node2 which we would like to make it a manager and paste the manager token command from docker swarm join-token manager output instructions. Just make sure that the --advertise-addr and --listen-addr are the IPs of the current node.
[node2] (local) root@192.168.0.14 ~
$ docker swarm join --token SWMTKN-1-5fuc7d0f1ofzm7p162bmijovuryv2wb1az6wy3jb0a27ghn0if-2xeu2vqppn1oj6kk6p3kj9lxj 192.168.0.15:2377 --advertise-addr 192.168.0.14:2377 --listen-addr 192.168.0.14:2377
This node joined a swarm as a manager.Let us switch over to node3 and make it a worker node.
[node3] (local) root@192.168.0.16 ~
$ docker swarm join --token SWMTKN-1-5fuc7d0f1ofzm7p162bmijovuryv2wb1az6wy3jb0a27ghn0if-cp7j1632prhcgm9b6wqy219x6 192.168.0.15:2377 --advertise-addr 192.168.0.16:2377 --listen-addr 192.168.0.16:2377
This node joined a swarm as a worker.Let’s switch over to node1 and see how our swarm looks now.
[node1] (local) root@192.168.0.15 ~
$ docker node ls
ID HOSTNAME STATUS AVAILABILITY MANAGER STATUS ENGINE VERSION
lbnhzlhks1cahurf5x39iw6vk * node1 Ready Active Leader 19.03.11
kpo5tz14zmzf9lzk2jeqajk65 node2 Ready Active Reachable 19.03.11
jkn253f6b4n1m0dto1j0xzlmo node3 Ready Active 19.03.11You can see that all 3 nodes are “Active” and are in “Ready” status. Also, it shows that Manager Status of node1 is Leader, that is, it’s the lead manager. The star next to its ID signifies that we are currently running this command from node1. Manager Status Reachable indicates that this node is a manager node and is reachable. Manager Status for non-manager nodes will be blank.
Promoting a worker node as a manager
Now let’s promote node3 as a manager using docker node promote command followed by node3 id.
[node1] (local) root@192.168.0.15 ~
$ docker node promote jkn253f6b4n1m0dto1j0xzlmo
Node jkn253f6b4n1m0dto1j0xzlmo promoted to a manager in the swarm.Let’s look at the nodes in the swarm again.
[node1] (local) root@192.168.0.15 ~
$ docker node ls
ID HOSTNAME STATUS AVAILABILITY MANAGER STATUS ENGINE VERSION
lbnhzlhks1cahurf5x39iw6vk * node1 Ready Active Leader 19.03.11
kpo5tz14zmzf9lzk2jeqajk65 node2 Ready Active Reachable 19.03.11
jkn253f6b4n1m0dto1j0xzlmo node3 Ready Active Reachable 19.03.11It shows that node3’s Manager Status is Reachable, meaning node3 is a manager.
Adding Nodes as Workers to the Swarm
Let’s make node4 and node5 as worker nodes now.
Switch over to node4 and type the command which we get it from docker swarm join-token worker command.
[node4] (local) root@192.168.0.18 ~
$ docker swarm join --token SWMTKN-1-5fuc7d0f1ofzm7p162bmijovuryv2wb1az6wy3jb0a27ghn0if-cp7j1632prhcgm9b6wqy219x6 192.168.0.15:2377 --advertise-addr 192.168.0.18:2377 --listen-addr 192.168.0.18:2377
This node joined a swarm as a worker.Switch over to node5 and join it as a worker
[node5] (local) root@192.168.0.17 ~
docker swarm join --token SWMTKN-1-5fuc7d0f1ofzm7p162bmijovuryv2wb1az6wy3jb0a27ghn0if-cp7j1632prhcgm9b6wqy219x6 192.168.0.15:2377 --advertise-addr 192.168.0.17:2377 --listen-addr 192.168.0.17:2377
This node joined a swarm as a worker.Let’s switch over to node1 and see how the nodes in our swarm looks now.
[node1] (local) root@192.168.0.15 ~
$ docker node ls
ID HOSTNAME STATUS AVAILABILITY MANAGER STATUS ENGINE VERSION
lbnhzlhks1cahurf5x39iw6vk * node1 Ready Active Leader 19.03.11
kpo5tz14zmzf9lzk2jeqajk65 node2 Ready Active Reachable 19.03.11
jkn253f6b4n1m0dto1j0xzlmo node3 Ready Active Reachable 19.03.11
wt2hydofwcgl532mn6q5smo4n node4 Ready Active 19.03.11
x7h8bto56lwrmokow1wpbfyl9 node5 Ready Active 19.03.11We now have 5 nodes in our swarm, having node1, node2 and node3 as Managers and node4 and node5 as workers.
Let’s run docker info and see how it shows up:
[node1] (local) root@192.168.0.15 ~
$ docker info
Client:
Debug Mode: false
Plugins:
app: Docker App (Docker Inc., v0.9.1-beta3)
Server:
Containers: 0
Running: 0
Paused: 0
Stopped: 0
Images: 0
Server Version: 19.03.11
Storage Driver: overlay2
Backing Filesystem: xfs
Supports d_type: true
Native Overlay Diff: true
Logging Driver: json-file
Cgroup Driver: cgroupfs
Plugins:
Volume: local
Network: bridge host ipvlan macvlan null overlay
Log: awslogs fluentd gcplogs gelf journald json-file local logentries splunk syslog
Swarm: active
NodeID: lbnhzlhks1cahurf5x39iw6vk
Is Manager: true
ClusterID: yjfkmxwayrvazt9a2kcec7teu
Managers: 3
Nodes: 5
Default Address Pool: 10.0.0.0/8
SubnetSize: 24
Data Path Port: 4789
Orchestration:
Task History Retention Limit: 5
Raft:
Snapshot Interval: 10000
Number of Old Snapshots to Retain: 0
Heartbeat Tick: 1
Election Tick: 10
Dispatcher:
Heartbeat Period: 5 seconds
CA Configuration:
Expiry Duration: 3 months
Force Rotate: 0
Autolock Managers: false
Root Rotation In Progress: false
Node Address: 192.168.0.15
Manager Addresses:
192.168.0.14:2377
192.168.0.15:2377
192.168.0.16:2377
Runtimes: runc
Default Runtime: runc
Init Binary: docker-init
containerd version: 7ad184331fa3e55e52b890ea95e65ba581ae3429
runc version: dc9208a3303feef5b3839f4323d9beb36df0a9dd
init version: fec3683
Security Options:
apparmor
seccomp
Profile: default
Kernel Version: 4.4.0-184-generic
Operating System: Alpine Linux v3.12 (containerized)
OSType: linux
Architecture: x86_64
CPUs: 8
Total Memory: 31.4GiB
Name: node1
ID: TPPH:JE75:EBRD:INWN:E6C6:XSGZ:2FJV:EA4W:L7FB:AAFF:T5HQ:DXBD
Docker Root Dir: /var/lib/docker
Debug Mode: true
File Descriptors: 46
Goroutines: 211
System Time: 2020-06-29T12:18:05.081382343Z
EventsListeners: 0
Registry: https://index.docker.io/v1/
Labels:
Experimental: true
Insecure Registries:
127.0.0.1
127.0.0.0/8
Live Restore Enabled: false
Product License: Community EngineThis docker info command shows that we have 5 nodes out of it 3 nodes are managers. It also lists out the IP and Port of all 3 manager nodes.
Creating the Service
Services is one of the major constructs that was introduced in Docker 1.12.
Services bring the concept called the desired state vs the actual state, where a form of reconciliation process runs in the background to make sure that actual matches desired.
Let’s create a service.
[node1] (local) root@192.168.0.15 ~
$ docker service create --name dockergs -p 80:80 --replicas 3 docker/getting-started:pwd
9qpjyalgi8q873sl37bb033j8
overall progress: 3 out of 3 tasks
1/3: running [==================================================>]
2/3: running [==================================================>]
3/3: running [==================================================>]
verify: Service converged- Using
create, we are telling Docker to create a service - Using
--name, we are telling Docker to name the service asdockergs - Using option
-pwe want port 80 to be used internally and externally - Using option
--replicaswe are defining overall 3 replicas across all of the nodes
Any traffic hitting any node in the swarm on that port is going to reach our service
Let’s list out the services using docker service ls
[node1] (local) root@192.168.0.15 ~
$ docker service ls
ID NAME MODE REPLICAS IMAGE PORTS
9qpjyalgi8q8 dockergs replicated 3/3 docker/getting-started:pwd *:80->80/tcpLet’s check how our service is spread across all the nodes using docker service ps command:
[node1] (local) root@192.168.0.15 ~
$ docker service ps dockergs
ID NAME IMAGE NODE DESIRED STATE CURRENT STATE ERROR PORTS
xheajn5nfwo0 dockergs.1 docker/getting-started:pwd node4 Running Running 2 minutes ago
ciietu78rfye dockergs.2 docker/getting-started:pwd node5 Running Running 2 minutes ago
t6frv2zu4ig7 dockergs.3 docker/getting-started:pwd node3 Running Running 2 minutes agoYou can see that service is assigned to the node1, node2 and node3.
If we click on the link “80” which is shown just beside the “OPEN PORT” button, it opens the application in a new tab.
Swarm mode is intelligent enough to reroute the request even if we try to access a node that is not assigned with the service task. For example, node2 is not assigned with the service task, but if you click on the port “80” link, it will still open the application page.
Scaling up the service using scale
Using docker service scale command we can either scale up or down the number of replicas served across all the nodes.
[node1] (local) root@192.168.0.15 ~
$ docker service scale dockergs=6
dockergs scaled to 6
overall progress: 6 out of 6 tasks
1/6: running [==================================================>]
2/6: running [==================================================>]
3/6: running [==================================================>]
4/6: running [==================================================>]
5/6: running [==================================================>]
6/6: running [==================================================>]
verify: Service convergedThe above command tells docker to scale up the replicas to 6 from the current 3. 6 replicas will be spread across 5 nodes, so we may expect one node to have 2 service tasks. Let’s check that by running docker service ps again.
[node1] (local) root@192.168.0.15 ~
$ docker service ps dockergs
ID NAME IMAGE NODE DESIRED STATE CURRENT STATE ERROR PORTS
xheajn5nfwo0 dockergs.1 docker/getting-started:pwd node4 Running Running 6 minutes ago
ciietu78rfye dockergs.2 docker/getting-started:pwd node5 Running Running 6 minutes ago
t6frv2zu4ig7 dockergs.3 docker/getting-started:pwd node3 Running Running 6 minutes ago
pmm6pkxr8yyk dockergs.4 docker/getting-started:pwd node2 Running Running 41 seconds ago
uen7sldfy6yp dockergs.5 docker/getting-started:pwd node1 Running Running 41 seconds ago
zgs9dwzpjyex dockergs.6 docker/getting-started:pwd node1 Running Running 41 seconds agoHere, we can see that node1 is serving 2 tasks.
Let’s delete node5 and run docker service ls to see what happens to the replicas.
[node1] (local) root@192.168.0.15 ~
$ docker service ls
ID NAME MODE REPLICAS IMAGE PORTS
9qpjyalgi8q8 dockergs replicated 6/6 docker/getting-started:pwd *:80->80/tcpEven after deleting the node which was assigned the service task, 6 of 6 replicas are still running. Let’s see how the tasks are allocated to nodes by running docker service ps command.
[node1] (local) root@192.168.0.15 ~
$ docker service ps dockergs
ID NAME IMAGE NODE DESIRED STATE CURRENT STATE ERROR PORTS
xheajn5nfwo0 dockergs.1 docker/getting-started:pwd node4 Running Running 11 minutes ago
bppgi82kq1of dockergs.2 docker/getting-started:pwd node2 Running Running about a minute ago
ciietu78rfye \_ dockergs.2 docker/getting-started:pwd node5 Shutdown Running 11 minutes ago
t6frv2zu4ig7 dockergs.3 docker/getting-started:pwd node3 Running Running 11 minutes ago
pmm6pkxr8yyk dockergs.4 docker/getting-started:pwd node2 Running Running 6 minutes ago
uen7sldfy6yp dockergs.5 docker/getting-started:pwd node1 Running Running 6 minutes ago
zgs9dwzpjyex dockergs.6 docker/getting-started:pwd node1 Running Running 6 minutes agoYou can see that even after deleting node5, its task has been now delegated to node2 and docker continues to maintain 6/6 replicas to match with the defined replica count.
Scaling up the service using update
You can also scale up the service replicas using the docker service update command.
[node1] (local) root@192.168.0.15 ~
$ docker service update --replicas 12 dockergs
dockergs
overall progress: 12 out of 12 tasks
1/12: running [==================================================>]
2/12: running [==================================================>]
3/12: running [==================================================>]
4/12: running [==================================================>]
5/12: running [==================================================>]
6/12: running [==================================================>]
7/12: running [==================================================>]
8/12: running [==================================================>]
9/12: running [==================================================>]
10/12: running [==================================================>]
11/12: running [==================================================>]
12/12: running [==================================================>]
verify: Service convergedWe’ve scaled up 12 replicas now. Let’s list out service details using docker service ls
[node1] (local) root@192.168.0.15 ~
$ docker service ls
ID NAME MODE REPLICAS IMAGE PORTS
9qpjyalgi8q8 dockergs replicated 12/12 docker/getting-started:pwd *:80->80/tcpDocker made all 12 replicas available. Let’s see how these tasks are assigned across all the nodes using docker service ps.
[node1] (local) root@192.168.0.15 ~
$ docker service ps dockergs
ID NAME IMAGE NODE DESIRED STATE CURRENT STATE ERROR PORTS
xheajn5nfwo0 dockergs.1 docker/getting-started:pwd node4 Running Running 22 minutes ago
bppgi82kq1of dockergs.2 docker/getting-started:pwd node2 Running Running 11 minutes ago
ciietu78rfye \_ dockergs.2 docker/getting-started:pwd node5 Shutdown Running 22 minutes ago
t6frv2zu4ig7 dockergs.3 docker/getting-started:pwd node3 Running Running 22 minutes ago
pmm6pkxr8yyk dockergs.4 docker/getting-started:pwd node2 Running Running 16 minutes ago
uen7sldfy6yp dockergs.5 docker/getting-started:pwd node1 Running Running 16 minutes ago
zgs9dwzpjyex dockergs.6 docker/getting-started:pwd node1 Running Running 16 minutes ago
rhqq8dvre1zs dockergs.7 docker/getting-started:pwd node2 Running Running about a minute ago
pifkveiabi1g dockergs.8 docker/getting-started:pwd node1 Running Running about a minute ago
awsd4qa2v2p0 dockergs.9 docker/getting-started:pwd node4 Running Running about a minute ago
a0eu3w2k61gv dockergs.10 docker/getting-started:pwd node4 Running Running about a minute ago
ah2iz8ay195o dockergs.11 docker/getting-started:pwd node3 Running Running about a minute ago
ng61z5vrkusk dockergs.12 docker/getting-started:pwd node3 Running Running about a minute agoAs we see this output, Docker efficiently allocated the tasks evenly across all the nodes.
Adding a new node to swarm after service task allocation
Now let’s add a new instance by clicking on “+ ADD INSTANCE” and add it to the swarm.
Once you’ve done this, let’s list out allocated services using docker service ps.
[node1] (local) root@192.168.0.15 ~
$ docker service ps dockergs
ID NAME IMAGE NODE DESIRED STATE CURRENT STATE ERROR PORTS
xheajn5nfwo0 dockergs.1 docker/getting-started:pwd node4 Running Running 24 minutes ago
bppgi82kq1of dockergs.2 docker/getting-started:pwd node2 Running Running 13 minutes ago
ciietu78rfye \_ dockergs.2 docker/getting-started:pwd node5 Shutdown Running 24 minutes ago
t6frv2zu4ig7 dockergs.3 docker/getting-started:pwd node3 Running Running 24 minutes ago
pmm6pkxr8yyk dockergs.4 docker/getting-started:pwd node2 Running Running 18 minutes ago
uen7sldfy6yp dockergs.5 docker/getting-started:pwd node1 Running Running 18 minutes ago
zgs9dwzpjyex dockergs.6 docker/getting-started:pwd node1 Running Running 18 minutes ago
rhqq8dvre1zs dockergs.7 docker/getting-started:pwd node2 Running Running 3 minutes ago
pifkveiabi1g dockergs.8 docker/getting-started:pwd node1 Running Running 3 minutes ago
awsd4qa2v2p0 dockergs.9 docker/getting-started:pwd node4 Running Running 3 minutes ago
a0eu3w2k61gv dockergs.10 docker/getting-started:pwd node4 Running Running 3 minutes ago
ah2iz8ay195o dockergs.11 docker/getting-started:pwd node3 Running Running 3 minutes ago
ng61z5vrkusk dockergs.12 docker/getting-started:pwd node3 Running Running 3 minutes agoThat newly added node is not getting listed over here. Newly added nodes don’t get existing tasks automatically rebalanced across them.
Adding new nodes to the swarm or bringing the old ones back does not automatically re-balance existing running tasks.
References:
