Docker-Registry
About this document
As the documentation evolves with different registry versions, be sure that before reading any further you:
- check which version of the registry you are running
- switch to the corresponding tag to access the README that matches your product version
The stable, released version is the 0.9.0 tag.
Please also have a quick look at the FAQ before reporting bugs.
Table of Contents
- Quick Start
- Configuration mechanism overview
- Configuration flavors
- Available configuration options
- Your own config
- Advanced use
- Drivers
- For developers
Quick start
The fastest way to get running:
- install docker
- run the registry:
docker run -p 5000:5000 registry
That will use the official image from the Docker hub.
Here is a slightly more complex example that launches a registry on port 5000, using an Amazon S3 bucket to store images with a custom path, and enables the search endpoint:
docker run \
-e SETTINGS_FLAVOR=s3 \
-e AWS_BUCKET=mybucket \
-e STORAGE_PATH=/registry \
-e AWS_KEY=myawskey \
-e AWS_SECRET=myawssecret \
-p 5000:5000 \
registry
Configuration mechanism overview
By default, the registry will use the config_sample.yml configuration to start.
Individual configuration options from that file may be overridden using environment variables. Example: docker run -e STORAGE_PATH=/registry
.
You may also use different "flavors" from that file (see below).
Finally, you can use your own configuration file (see below).
Configuration flavors
The registry can be instructed to use a specific flavor from a configuration file.
This mechanism lets you define different running "mode" (eg: "development", "production" or anything else).
In the config_sample.yml
file, you'll see several sample flavors:
common
: used by all other flavors as base settingslocal
: stores data on the local filesystems3
: stores data in an AWS S3 bucketceph-s3
: stores data in a Ceph cluster via a Ceph Object Gateway, using the S3 APIazureblob
: stores data in an Microsoft Azure Blob Storage ((docs))dev
: basic configuration using thelocal
flavortest
: used by unit testsprod
: production configuration (basically a synonym for thes3
flavor)gcs
: stores data in Google cloud storageswift
: stores data in OpenStack Swiftglance
: stores data in OpenStack Glance, with a fallback to local storageglance-swift
: stores data in OpenStack Glance, with a fallback to Swiftelliptics
: stores data in Elliptics key/value storage
You can define your own flavors by adding a new top-level yaml key.
To specify which flavor you want to run, set the SETTINGS_FLAVOR
environment variable: export SETTINGS_FLAVOR=dev
The default flavor is dev
.
NOTE: it's possible to load environment variables from within the config file
with a simple syntax: _env:VARIABLENAME[:DEFAULT]
. Check this syntax
in action in the example below...
Example config
common: &common
standalone: true
loglevel: info
search_backend: "_env:SEARCH_BACKEND:"
sqlalchemy_index_database:
"_env:SQLALCHEMY_INDEX_DATABASE:sqlite:////tmp/docker-registry.db"
prod:
<<: *common
loglevel: warn
storage: s3
s3_access_key: _env:AWS_S3_ACCESS_KEY
s3_secret_key: _env:AWS_S3_SECRET_KEY
s3_bucket: _env:AWS_S3_BUCKET
boto_bucket: _env:AWS_S3_BUCKET
storage_path: /srv/docker
smtp_host: localhost
from_addr: docker@myself.com
to_addr: my@myself.com
dev:
<<: *common
loglevel: debug
storage: local
storage_path: /home/myself/docker
test:
<<: *common
storage: local
storage_path: /tmp/tmpdockertmp
Available configuration options
When using the config_sample.yml
, you can pass all options through as environment variables. See config_sample.yml
for the mapping.
General options
loglevel
: string, level of debugging. Any of python's logging module levels:debug
,info
,warn
,error
orcritical
debug
: boolean, make the/_ping
endpoint output more useful information, such as library versions and host information.storage_redirect
: Redirect resource requested if storage engine supports this, e.g. S3 will redirect signed URLs, this can be used to offload the server.boto_host
/boto_port
: If you are usingstorage: s3
the standard boto config file locations (/etc/boto.cfg, ~/.boto
) will be used. If you are using a non-Amazon S3-compliant object store (such as Ceph), in one of the boto config files'[Credentials]
section, setboto_host
,boto_port
as appropriate for the service you are using. Alternatively, setboto_host
andboto_port
in the config file.
Authentication options
-
standalone
: boolean, run the server in stand-alone mode. This means that the Index service on index.docker.io will not be used for anything. This impliesdisable_token_auth
. -
index_endpoint
: string, configures the hostname of the Index endpoint. This is used to verify passwords of users that log in. It defaults to https://index.docker.io. You should probably leave this to its default. -
disable_token_auth
: boolean, disable checking of tokens with the Docker index. You should provide your own method of authentication (such as Basic auth).
Search-engine options
The Docker Registry can optionally index repository information in a
database for the GET /v1/search
endpoint. You
can configure the backend with a configuration like:
The search_backend
setting selects the search backend to use. If
search_backend
is empty, no index is built, and the search endpoint always
returns empty results.
search_backend
: The name of the search backend engine to use. Currently supported backends are:sqlalchemy
If search_backend
is neither empty nor one of the supported backends, it
should point to a module.
Example:
common:
search_backend: foo.registry.index.xapian
In this case, the module is imported, and an instance of its Index
class is used as the search backend.
sqlalchemy
Use SQLAlchemy as the search backend.
sqlalchemy_index_database
: The database URL passed through to create_engine.
Example:
common:
search_backend: sqlalchemy
sqlalchemy_index_database: sqlite:////tmp/docker-registry.db
On initialization, the SQLAlchemyIndex
class checks the database
version. If the database doesn't exist yet (or does exist, but lacks
a version
table), the SQLAlchemyIndex
creates the database and
required tables. To avoid several Gunicorn workers racing to create
the database, you should launch your registry with
--preload. For example:
$ docker run -e GUNICORN_OPTS=[--preload] -p 5000:5000 registry
Mirroring Options
All mirror options are placed in a mirroring
section.
mirroring
:source
:source_index
:tags_cache_ttl
:
Example:
common:
mirroring:
source: https://registry-1.docker.io
source_index: https://index.docker.io
tags_cache_ttl: 172800 # 2 days
Cache options
It's possible to add an LRU cache to access small files. In this case you need
to spawn a redis-server configured in
LRU mode. The config file "config_sample.yml"
shows an example to enable the LRU cache using the config directive cache_lru
.
Once this feature is enabled, all small files (tags, meta-data) will be cached in Redis. When using a remote storage backend (like Amazon S3), it will speed things up dramatically since it will reduce roundtrips to S3.
All config settings are placed in a cache
or cache_lru
section.
cache
/cache_lru
:host
: Host address of serverport
: Port server listens onpassword
: Authentication password
Storage options
storage
selects the storage engine to use. The registry ships with two storage engine by default (file
and s3
).
If you want to find other (community provided) storages: pip search docker-registry-driver
To use and install one of these alternate storages:
pip install docker-registry-driver-NAME
- in the configuration set
storage
toNAME
- add any other storage dependent configuration option to the conf file
- review the storage specific documentation for additional dependency or configuration instructions.
Currently, we are aware of the following storage drivers:
storage file
storage_path
: Path on the filesystem where to store data
Example:
local:
storage: file
storage_path: /mnt/registry
Persistent storage
If you use any type of local store along with a registry running within a docker
remember to use a data volume for the storage_path
. Please read the documentation
for data volumes for more information.
Example:
docker run -p 5000 -v /tmp/registry:/tmp/registry registry
storage s3
AWS Simple Storage Service options
s3_access_key
: string, S3 access keys3_secret_key
: string, S3 secret keys3_bucket
: string, S3 bucket names3_region
: S3 region where the bucket is locateds3_encrypt
: boolean, if true, the container will be encrypted on the server-side by S3 and will be stored in an encrypted form while at rest in S3.s3_secure
: boolean, true for HTTPS to S3s3_use_sigv4
: boolean, true for USE_SIGV4 (boto_host needs to be set or use_sigv4 will be ignored by boto.)boto_bucket
: string, the bucket name for non-Amazon S3-compliant object storeboto_host
: string, host for non-Amazon S3-compliant object storeboto_port
: for non-Amazon S3-compliant object storeboto_debug
: for non-Amazon S3-compliant object storeboto_calling_format
: string, the fully qualified class name of the boto calling format to use when accessing S3 or a non-Amazon S3-compliant object storestorage_path
: string, the sub "folder" where image data will be stored.
Example:
prod:
storage: s3
s3_region: us-west-1
s3_bucket: acme-docker
storage_path: /registry
s3_access_key: AKIAHSHB43HS3J92MXZ
s3_secret_key: xdDowwlK7TJajV1Y7EoOZrmuPEJlHYcNP2k4j49T
Your own config
Start from a copy of config_sample.yml.
Then, start your registry with a mount point to expose your new configuration inside the container (-v /home/me/myfolder:/registry-conf
), and point to it using the DOCKER_REGISTRY_CONFIG
environment variable:
sudo docker run -p 5000:5000 -v /home/me/myfolder:/registry-conf -e DOCKER_REGISTRY_CONFIG=/registry-conf/mysuperconfig.yml registry
Advanced use
For more features and advanced options, have a look at the advanced features documentation
Drivers
For more backend drivers, please read drivers.md
For developers
Read contributing