Run your own docker registry with token-based identification behind nginx

How to build an controlled environment to distribute docker images based on user accounts

Docker itself, AWS (just to name the biggest docker hosts right now) and many more public / private repository servers are on the marked. But sometime there is need to host an own registry for docker images. One reason can be because we can, the other is for example to give individual pull / push rights to different images to different users and control the access also based on expiration dates.

Components and the big picture

For this setup we need several software components to work orchestrated together. Starting with the firewall to block all ports except the 443 for HTTPS, the nginx reverse proxy to terminate the SSL connection and protect the underlying services against direct access and also possible load balancing, the docker registry to host the images and at last but not least the docker token authenticator to identify users and give access to images (push and/or pull) based on their rights.

Docker introduced in the second version for the registry protocol the “Docker registry authentication scheme“. This basically transfers the access control to images to an outside system and uses the bearer token mechanism to communicate. The flow is to access an docker image is:

  1. Docker daemon accesses the docker registry server as usual and gets a 401 Unauthorized in return with a “WWW-Authenticate” header pointing to the authentication server the registry server trusts.
  2. Docker daemon contacts the authentication server with the given URL and the user identifies against the server.
  3. The authentication server checks the access rights based on username, password, image name and access type (pull/push) and returns a bearer token signed with the private key.
  4. Docker daemon accesses the docker registry again with the bearer token and the docker image request.
  5. Docker registry server checks the bearer token based on the authentication server public key and grants access or doesn’t.


Ubuntu ships with a very simple firewall control script called “Uncomplicated Firewall“. The script manages the iptable configuration and lets the user configure ports with a single line. If you access the server via SSH make sure you allow ssh access before you activate the firewall. I also recommend installing fail2ban to ban script hacking.

sudo apt update
sudo apt install -y ufw fail2ban 
ufw allow ssh #only necessary when you need remote access
ufw allow https
ufw allow http
ufw enable 
ufw status

Nginx reverse proxy

We install Nginx also as a docker service because the update cycle is way faster compared to the software repository. The basic Nginx docker container is ready to be used and only needs the settings for http and https. Everything is handled via the https port but we also have http (port 80) open to have a redirect to https for everything with a 301 (moved permanently) return code.


COPY   default.conf /etc/nginx/conf.d/default.conf
COPY   ssl.conf     /etc/nginx/conf.d/ssl.conf
COPY   cert /cert 


This is a very simple Dockerfile to to add the ssl certificates and the http/https configuration. We could also mount the ssl and configuration in the docker-compose file and leave the images plain as it is. Both options are valid and just a flavour.

server {
    listen      80;
    listen [::]:80;
    return 301 https://$host$request_uri;

This is the http configuration for nginx. Accepting everything for http and returning a 301 (moved permanently) to the same server and path just with https.

SSL configuration

SSL configuration is a little bit more complicated as we also specify the ciphers and parameters for the encryption. As this topic is endless and very easy to screw up I personally relay on as a configuration source.

openssl dhparam -out dhparams.pem 4096

The recommendation is to generate own Diffie–Hellman pool bigger than 2048 bit. This process can take a very long time. We add the result file together with our keys to the cert folder.

ssl_protocols              TLSv1.2 TLSv1.3;
ssl_prefer_server_ciphers  on;
ssl_dhparam                /cert/dhparams.pem;
ssl_ciphers                "ECDHE-RSA-AES256-GCM-SHA512:DHE-RSA-AES256-GCM-SHA512:ECDHE-RSA-AES256-GCM-SHA384:DHE-RSA-AES256-GCM-SHA384:ECDHE-RSA-AES256-SHA384";
ssl_ecdh_curve             secp384r1; 
ssl_session_cache          shared:SSL:10m;
ssl_session_timeout        10m;
ssl_session_tickets        off; 
ssl_stapling               on; 
ssl_stapling_verify        on; 
resolver          valid=300s;
resolver_timeout           5s;
add_header                 Strict-Transport-Security "max-age=63072000; includeSubDomains; preload";
add_header                 X-Frame-Options DENY;
add_header                 X-Content-Type-Options nosniff;
add_header                 X-XSS-Protection "1; mode=block";

This configuration is based on the recommendation from cipherlist. Be aware one part of this setup is the Strict-Transport-Security with can cause a lot of long-time trouble if you mess it up. This completes the basic SSL setup.

map $upstream_http_docker_distribution_api_version $docker_distribution_api_version {
  '' 'registry/2.0';

This mapping helps to set the right header even when Nginx removed it because of authentication. Docker registry needs this information in the http header.

server {
    listen      443 ssl http2;
    listen [::]:443 ssl http2;


    ssl_certificate         /cert/auth/fullchain.pem;
    ssl_certificate_key     /cert/auth/privkey.pem;
    ssl_trusted_certificate /cert/auth/chain.pem;

    location /auth {

        proxy_read_timeout    90;
        proxy_connect_timeout 90;
        proxy_redirect        off;

        proxy_set_header X-Real-IP         $remote_addr;
        proxy_set_header X-Forwarded-For   $proxy_add_x_forwarded_for;
        proxy_set_header X-Forwarded-Proto https;
        proxy_set_header X-Forwarded-Port  443;
        proxy_set_header Host              $http_host;

        proxy_pass http://dockerauth:5001/auth;

In this case we are running the registry and the auth server on the same virtual machine. Therefore both configurations are in the SSL.conf file. This one is for the auth server.

server {
    listen      443 ssl http2;
    listen [::]:443 ssl http2;


    ssl_certificate         /cert/registry/fullchain.pem;
    ssl_certificate_key     /cert/registry/privkey.pem;
    ssl_trusted_certificate /cert/registry/chain.pem;

    client_max_body_size 0;
    chunked_transfer_encoding on;

    location /v2/ {

        if ($http_user_agent ~ "^(docker\/1\.(3|4|5(?!\.[0-9]-dev))|Go ).*$" ) {
          return 404;

        add_header 'Docker-Distribution-Api-Version' $docker_distribution_api_version always;

        proxy_pass http://registry:5000;
        proxy_set_header  Host              $http_host;   # required for docker client's sake
        proxy_set_header  X-Real-IP         $remote_addr; # pass on real client's IP
        proxy_set_header  X-Forwarded-For   $proxy_add_x_forwarded_for;
        proxy_set_header  X-Forwarded-Proto $scheme;
        proxy_read_timeout                  900;

And this configuration part for the registry server itself. Important here is the client_max_body_size parameter to make sure even bigger docker images are getting through. Older docker client versions getting a 404 because they can not be handled by the docker registry.

Lets encrypt

The easiest way to get a certificate is by using let’s encrypt. There are different ways how to receive a certificate, we just use a very simple one here with the standalone call. The certbot opens a mini web server on port 80 to handle the authentication request on its own. Therefore make sure the Nginx docker is not running.

certbot certonly -d --standalone
certbot certonly -d     --standalone

for i in registry auth client
 cp /etc/letsencrypt/live/${i}     /root/nginx/cert/${i}/
 cp /etc/letsencrypt/live/${i} /root/nginx/cert/${i}/
 cp /etc/letsencrypt/live/${i}   /root/nginx/cert/${i}/

Do the certificate request call for the auth and the registry certificate and copy the certificate and private key to your cert folder for the docker build to pick it up. Don’t forget the dhaprams.pem file.

Docker registry

Now as the server is configured and more or less secured, let’s configure the docker registry server and auth server. Docker inc. offers a docker registry docker container which is relatively easy to hande and to configure.

      - REGISTRY_AUTH=token
      - REGISTRY_AUTH_TOKEN_SERVICE="Docker registry"
      - REGISTRY_AUTH_TOKEN_ISSUER="Acme auth server"

The configuration is done in the docker-compose file itself. The important information is the REALM, so the docker registry can redirect the client to the auth server with the issuer and the cert bundle from the referred auth server to check the bearer token later.

Docker Token Authenticator

Docker Inc. does not provide an auth server out of the box as done with the registry itself. This is basically left for the registry provider to build their own. Luckily Cesanta stepped up and build a nice configurable auth server to be used with the registry server. docker_auth has different ways of how to store information about the user.

  • Static list of users
  • Google Sign-In
  • Github Sign-In
  • LDAP bind
  • MongoDB user collection
  • External Program (gets login parameters and returns 0 or 1)

In our case the way to go is the MongoDB user collection as we can control for each user individually who has access to which image and easily change it on the fly by modifying the user data in the DB itself.

server:  # Server settings.
  # Address to listen on.
  addr: ":5001"

  issuer: "Acme auth server" # Must match issuer in the Registry config.
  expiration: 900
  certificate: "/ssl/domain.crt"
  key: "/ssl/domain.key"

    addrs: ["authdb"]
    timeout: "10s"
    database: "23-5"
    username: "ansi"
    password_file: "/config/mongopass.txt"
    enabled_tls: false
  collection: "users"

    addrs: ["authdb"]
    timeout: "10s"
    database: "23-5"
    username: "ansi"
    password_file: "/config/mongopass.txt"
    enabled_tls: false
  collection: "acl"
  cache_ttl: "10s"

This is the configuration file for the auth server. Mainly 4 parts.

  • Server
    • Witch port to listen on
    • Nginx handles the TLS termination, therefore, this server has no TLS handling.
  • Token
    • Use the same issuer as configured in the registry server itself and provide the certificate files for signing the bearer token.
  • Mongo_auth
    • Where the user information is stored, the password is saved in a simple ASCII file and how to access the MongoDB. In our case, as we are behind a firewall in a docker network we don’t use TLS to access thMongoDBDB.
  • ACL_Mongo
    • Beside the user information, the AccessControlList (ACL) can also be stored in a MongoDB. Same configuration as the mongo_auth but there is a cache information as this information is stored in memory and refreshed every 10 seconds.


mongo --host localhost --username root --password example --authenticationDatabase admin

use 23-5

db.createUser({user: "ansi", pwd: "test", roles: ["readWrite"], mechanisms: ["SCRAM-SHA-1"]})

mongo --host localhost --username ansi --password test --authenticationDatabase 23-5

    "username" : "waldi",
    "password" : "$2y$05$hxH........Ii33Csix8hC",
    "labels" : {"full-access":["test/*"],

  { "seq": 10,
    "match": {"name": "${labels:full-access}"},
    "actions": ["*"],
    "comment": "full access"
  { "seq": 20,
    "match": {"name": "${labels:read-only-access}"},
    "actions": ["pull"],
    "comment": "pull access"

The mongoDB was initialized by the docker-compose file with an admin user “root” and passwd “example”. We use this account to create a new database called “23-5” and set a new user there with username “ansi” and passwd “test”. This database stores all user and acls. The docker registry users by themselves are stored with an bencrypted password. and some labels. Bencrypt a passwd with:

sudo apt install apache2-tools
htpasswd -nB USERNAME

Beside username and password, we can also store labels of all kind to a given user. This allows us to use these labels for the ACLs again. So in our case, the ACLs defines all docker images with a given name (the name is stored in the label with read-only or full access) to access images based on their label. In our case, the user “waldi” has full access to all docker images with “test/*” and only read access to everything in “prod/*” but nothing else. ACLs have a seq number in which they were processed. The first patching ACL will be used.

Labels can be combined so for example:

  "match": { "name": "${labels:project}/${labels:group}-${labels:tier}" },
  "actions": [ "push", "pull" ],
  "comment": "Contrived multiple label match rule"
    "username" : "busy-guy",
    "password" : "$2y$05$B.x.......CbCGtjFl7S33aCUHNBxbq",
    "labels" : {
        "group" : [
        "project" : [
        "tier" : [

Would give push and pull access to the docker image


These variables can be checked for the ACL:

  • ${account} the account name aka username
  • ${name} the repository name “*” can be used. So for example “prod/*” gives access to “prod/server”

Generating bearer SSL key

In order to sign a bearer token we need a key. This can be a self signed key done with openssl:

openssl req \
       -newkey rsa:4096 \
       -days 365 \ 
       -nodes -keyout domain.key \
       -out domain.csr \
       -subj "/C=EU/ST=Germany/L=Berlin/O=23-5/"

openssl x509 \
       -signkey domain.key \
       -in domain.csr \
       -req -days 365 -out domain.crt

openssl req \
        -x509 \
        -nodes \
        -days 365 \
        -newkey rsa:2048 \
        -keyout server.key \
        -out server.pem


We can configure and start the auth and registry server and nginx with one docker-compose file:

version: '3'


    restart: always
      context: nginx
      - 80:80
      - 443:443

    restart: always
      - authdb
      - 3000:3000
      - TZ=Europe/Berlin

    restart: always
      - /root/auth_db:/data/db
      - TZ=Europe/Berlin
      - 27017:27017
    command: --bind_ip

      - /root/auth_server/config:/config:ro
      - /root/auth_server/ssl:/ssl:ro
    command: --v=2 --alsologtostderr /config/auth_config.yml
    restart: always
      - TZ=Europe/Berlin

      - /root/auth_server/ssl:/ssl:ro
      - /root/docker_registry/data:/var/lib/registry
    restart: always
      - TZ=Europe/Berlin
      - REGISTRY_AUTH=token
      - REGISTRY_AUTH_TOKEN_SERVICE="Docker registry"
      - REGISTRY_AUTH_TOKEN_ISSUER="Acme auth server"

I also added a mongoclient docker container to have easy access to the mongodb server. Please be aware this one is not secured by the nginx reverse proxy and is only for testing. You can also access the mongodb with command line:

docker exec -it root_authdb_1 mongo --host localhost --username root --password example --authenticationDatabase admin

The MongoDB docker is also called with a different command to give access outside of localhost. (–bind_ip


docker-compose build 
docker-compose up -d

Is starting the setup. We have a docker registry user “waldi” with this setup:

[{"username": "waldi",
  "password": "$2......dKOIrAn.KxCfeEn7HhePFIO",
  "labels": {"full-access": ["test", "socke*"]}

[{"seq": 10,
  "match":{"name": "${labels:full-access}"},
  "comment": "full access"
  "seq": 20,
  "match":{"name": "${labels:read-only-access}"},
  "comment": "pull access"

So user “waldi can write and read all repositories with either “test” or anything starting with “socke“. Let’s try it.

$ docker login
Authenticating with existing credentials...
Login Succeeded

$ docker pull nginx
Using default tag: latest
latest: Pulling from library/nginx
Status: Image is up to date for nginx:latest

$ docker tag nginx:latest

$ docker push
The push refers to repository []
fc4c9f8e7dac: Pushed 
912ed487215b: Pushed 
778790 size: 948

$ docker tag nginx:latest

$ docker push            
The push refers to repository []
fc4c9f8e7dac: Mounted from test 
912ed487215b: Mounted from test 
5dacd731af1b: Mounted from test 
latest: digest: sha256:c10f4146f30fda9f40946bc114afeb1f4e867877c49283207a08ddbcf1778790 size: 948

It works. Now let’s test the negative part and try if the push gets refused:

$ docker tag nginx:latest 

$ docker push     
The push refers to repository []
fc4c9f8e7dac: Preparing 
912ed487215b: Preparing 
5dacd731af1b: Preparing 
denied: requested access to the resource is denied

It works! The user can be modified on the fly in the MongoDB and granted or revoked rights. There is one final test to check if the Nginx is secured:

Workload container for autoscaling test with kubernetes


The Idea

Every now and then you want to test your installation, your server or your setup. Specially when you want to test auto scaling functionalities. Kubernetes has an out of the box auto scaler and the official descriptions recommends a test docker container for testing with a apache and php installation. This is really great for testing a web application where you have some workload for a relatively short time frame. But I would also like to test a scenario where the workload runs for a longer time in the kubernetes setup and generates way more cpu workload then a web application. Therefore I hacked a nice docker container based on a c program load generator.

The docker container

The docker container is basically a very very simple Flask server with only one entry point “/”. The workload itself can be configured via two parameters:

  • percentage How much cpu load will be generated
  • seconds How long will the workload be active

The docker container itself uses nearly no CPU cycles as Flask is the only python process being active and waits for calls to start using CPU cycles.


I use a very nice open source tool called lookbusy from Devin Carraway which consumes memory and cpu cycles based on command line parameters. Unfortunately the program has no parameter to configure the time span it shout run. Therefore I call it the unix command timeout to terminate its execution after the given amount of seconds.

The Flask python wrapper

import subprocess
from   threading import Thread
from   flask     import Flask, request

app = Flask(__name__)

def worker(percentage, seconds):['timeout', str(seconds), '/usr/local/bin/lookbusy', '-c', str(percentage)])

def load(): 
    percentage = request.args.get('percentage') if "percentage" in request.args else 50
    seconds    = request.args.get('seconds')    if "seconds"    in request.args else 10
    Thread(target=worker, args=(percentage, seconds)).start()
    return "started"

if __name__ == "__main__":'', port=80, processes=10)

The only program is a python Flask one, very short and only takes the get call to its root folder, checks for the two parameters and starts a thread with the subprocess. The get call immediately returns as it also supports long run workload simulations.

The Dockerfile

FROM   python:latest
RUN    curl | tar xvz && \
       cd lookbusy-1.4 && ./configure && \
       make && make install && cd .. && rm -rf lookbusy-1.4
RUN    pip install Flask
CMD    python -u

The docker container is based on python latest (at this time 3.6.4). I put all the curl, make, install and rm calls into a single line in order to have a minimal footprint for the docker layer as we do not need the source code any more. As Flask is the only requirements I also call it directly without the requirements.txt file. The “-u” parameter for the python call is necessary to prevent python from buffering the output. Otherwise it can be quite disturbing when trying to read the debug log file.

Building and pushing the docker container

docker build -t ansi/lookbusy .
docker push     ansi/lookbusy

Building and pushing it to is straightforward and nothing special.

Testing it on a kubernetes cluster

I have chosen the IBM cloud to test my docker container.

Requesting a kubernetes cluster

Requesting a kubernetes cluster can be done after login with

bx cs cluster-create --name ansi-blogtest --location dal10 --workers 3 --kube-version 1.8.6 --private-vlan 1788637 --public-vlan 1788635 --machine-type b2c.4x16

This command uses the bluemix CLI with the cluster plugin to control and configure kubernetes on the IBM infrastructure. The parameters are

  • –name to give your cluster a name (will be very important later on)
  • –location which datacenter to use (in this case dallas). Use “bx cs locations” to get your possible locations for the chosen region
  • –workers how many worker nodes are requested
  • –kube-version which kubernetes version should be used. Use “bx cs kube-versions” to get the available versions. “(default)” is not part of the parameter call.
  • –private-vlan which vlan for the private network should be used. Use “bx cs vlans <location>” to get the available public and private vlans
  • –public-vlan see private vlan
  • –machine-type which kind of underlying configuration you want to use for your worker node. Use “bx cs machine-types <location>” to get the available machine types. The first number after the “.” is the amount of cores and one after “x” the the amount of RAM in GB.

This command takes some time (~1h) to generate the kubernetes cluster. BTW my bluemix cli docker container has all necessary tools and also a nice script called “” to query all parameters and start a new cluster. After the cluster is up and running we can get the kubernetes configuration with

bx cs cluster-config ansi-blog
The configuration for ansi-blogtest was downloaded successfully. Export environment variables to start using Kubernetes.

export KUBECONFIG=/root/.bluemix/plugins/container-service/clusters/ansi-blog/kube-config-dal10-ansi-blog.yml

Starting a pod and replica set

kubectl run loadtest --image=ansi/lookbusy --requests=cpu=200m

We start the pod and replica set without a yaml file because the request is very straight forward. Important here is the parameter “–requests“. Without it the autoscaler can not measure the cpu load and it never triggers.

Exposing the http port

kubectl expose deployment loadtest --type=LoadBalancer --name=loadtest --port=80

Again because the call is so simple we directly call kubectl without a yaml file to expose the Port 80. We can check for the public IP with

kubectl get svc
loadtest LoadBalancer <pending>   80:31277/TCP 23m

In case the cloud runs out of public IP addresses and the “EXTERNAL_IP” is still pending after several minutes we can use one of the workers public ip addresses and the dynamic assigned port. The port is visible with “kubectl get svc” at the “PORTS” section. The syntax is as always in docker internalport:externalport. The workers public IP can be checked with

bx cs workers ansi-blog
ID                                               Public IP     Private IP     Machine Type       State  Status Version
kube-dal10-cr1dd768315d654d4bb4340ee8159faa17-w1 b2c.4x16.encrypted normal Ready  1.8.6_1506

So instead of calling our service with a official public ip address on port 80 we can use


Kubernetes has a build in horizontal autoscaler which can be started with

kubectl autoscale deployment loadtest --cpu-percent=50 --min=1 --max=10

In this case it measures the cpu load and starts new pods when the load is over 50%. The autoscaler in this configuration never starts more than 10 and never less than 2 pods. The current measurements and parameters can be checked with

kubectl get hpa
loadtest  Deployment/loadtest 0% / 50% 1       10      1        23m

So right now the cpu load is 0 and only one replica is running.


Time to get call our container and start the load test. Depending on the URL we an use curl to start the test with

curl ""

and check the result after some time with

kubectl get hpa
loadtest  Deployment/loadtest 60%/50%  1       10      6        23m

As we see the load increases and autoscaler kicks in. More details can obtained with the “kubectl proxy” command.

Deleting the kubernetes cluster

To clean up we could either delete all pods and replica sets and services but we could also delete the complete cluster with

bx cs cluster-rm ansi-blog