266 lines
13 KiB
Markdown
266 lines
13 KiB
Markdown
# Roadmap
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The Distribution Project consists of several components, some of which are
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still being defined. This document defines the high-level goals of the
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project, identifies the current components, and defines the release-
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relationship to the Docker Platform.
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* [Distribution Goals](#distribution-goals)
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* [Distribution Components](#distribution-components)
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* [Project Planning](#project-planning): release-relationship to the Docker Platform.
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This road map is a living document, providing an overview of the goals and
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considerations made in respect of the future of the project.
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## Distribution Goals
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- Replace the existing [docker registry](github.com/docker/docker-registry)
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implementation as the primary implementation.
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- Replace the existing push and pull code in the docker engine with the
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distribution package.
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- Define a strong data model for distributing docker images
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- Provide a flexible distribution tool kit for use in the docker platform
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- Unlock new distribution models
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## Distribution Components
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Components of the Distribution Project are managed via github [milestones](https://github.com/docker/distribution/milestones). Upcoming
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features and bugfixes for a component will be added to the relevant milestone. If a feature or
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bugfix is not part of a milestone, it is currently unscheduled for
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implementation.
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* [Registry](#registry)
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* [Distribution Package](#distribution-package)
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***
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### Registry
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The new Docker registry is the main portion of the distribution repository.
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Registry 2.0 is the first release of the next-generation registry. This was
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primarily focused on implementing the [new registry
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API](https://github.com/docker/distribution/blob/master/docs/spec/api.md),
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with a focus on security and performance.
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Following from the Distribution project goals above, we have a set of goals
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for registry v2 that we would like to follow in the design. New features
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should be compared against these goals.
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#### Data Storage and Distribution First
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The registry's first goal is to provide a reliable, consistent storage
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location for Docker images. The registry should only provide the minimal
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amount of indexing required to fetch image data and no more.
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This means we should be selective in new features and API additions, including
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those that may require expensive, ever growing indexes. Requests should be
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servable in "constant time".
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#### Content Addressability
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All data objects used in the registry API should be content addressable.
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Content identifiers should be secure and verifiable. This provides a secure,
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reliable base from which to build more advanced content distribution systems.
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#### Content Agnostic
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In the past, changes to the image format would require large changes in Docker
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and the Registry. By decoupling the distribution and image format, we can
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allow the formats to progress without having to coordinate between the two.
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This means that we should be focused on decoupling Docker from the registry
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just as much as decoupling the registry from Docker. Such an approach will
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allow us to unlock new distribution models that haven't been possible before.
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We can take this further by saying that the new registry should be content
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agnostic. The registry provides a model of names, tags, manifests and content
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addresses and that model can be used to work with content.
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#### Simplicity
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The new registry should be closer to a microservice component than its
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predecessor. This means it should have a narrower API and a low number of
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service dependencies. It should be easy to deploy.
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This means that other solutions should be explored before changing the API or
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adding extra dependencies. If functionality is required, can it be added as an
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extension or companion service.
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#### Extensibility
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The registry should provide extension points to add functionality. By keeping
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the scope narrow, but providing the ability to add functionality.
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Features like search, indexing, synchronization and registry explorers fall
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into this category. No such feature should be added unless we've found it
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impossible to do through an extension.
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#### Active Feature Discussions
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The following are feature discussions that are currently active.
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If you don't see your favorite, unimplemented feature, feel free to contact us
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via IRC or the mailing list and we can talk about adding it. The goal here is
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to make sure that new features go through a rigid design process before
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landing in the registry.
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##### Proxying to other Registries
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A _pull-through caching_ mode exists for the registry, but is restricted from
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within the docker client to only mirror the official Docker Hub. This functionality
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can be expanded when image provenance has been specified and implemented in the
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distribution project.
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##### Metadata storage
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Metadata for the registry is currently stored with the manifest and layer data on
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the storage backend. While this is a big win for simplicity and reliably maintaining
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state, it comes with the cost of consistency and high latency. The mutable registry
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metadata operations should be abstracted behind an API which will allow ACID compliant
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storage systems to handle metadata.
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##### Peer to Peer transfer
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Discussion has started here: https://docs.google.com/document/d/1rYDpSpJiQWmCQy8Cuiaa3NH-Co33oK_SC9HeXYo87QA/edit
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##### Indexing, Search and Discovery
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The original registry provided some implementation of search for use with
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private registries. Support has been elided from V2 since we'd like to both
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decouple search functionality from the registry. The makes the registry
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simpler to deploy, especially in use cases where search is not needed, and
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let's us decouple the image format from the registry.
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There are explorations into using the catalog API and notification system to
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build external indexes. The current line of thought is that we will define a
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common search API to index and query docker images. Such a system could be run
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as a companion to a registry or set of registries to power discovery.
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The main issue with search and discovery is that there are so many ways to
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accomplish it. There are two aspects to this project. The first is deciding on
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how it will be done, including an API definition that can work with changing
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data formats. The second is the process of integrating with `docker search`.
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We expect that someone attempts to address the problem with the existing tools
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and propose it as a standard search API or uses it to inform a standardization
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process. Once this has been explored, we integrate with the docker client.
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Please see the following for more detail:
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- https://github.com/docker/distribution/issues/206
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##### Deletes
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> __NOTE:__ Deletes are a much asked for feature. Before requesting this
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feature or participating in discussion, we ask that you read this section in
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full and understand the problems behind deletes.
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While, at first glance, implementing deleting seems simple, there are a number
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mitigating factors that make many solutions not ideal or even pathological in
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the context of a registry. The following paragraph discuss the background and
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approaches that could be applied to arrive at a solution.
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The goal of deletes in any system is to remove unused or unneeded data. Only
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data requested for deletion should be removed and no other data. Removing
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unintended data is worse than _not_ removing data that was requested for
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removal but ideally, both are supported. Generally, according to this rule, we
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err on holding data longer than needed, ensuring that it is only removed when
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we can be certain that it can be removed. With the current behavior, we opt to
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hold onto the data forever, ensuring that data cannot be incorrectly removed.
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To understand the problems with implementing deletes, one must understand the
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data model. All registry data is stored in a filesystem layout, implemented on
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a "storage driver", effectively a _virtual file system_ (VFS). The storage
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system must assume that this VFS layer will be eventually consistent and has
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poor read- after-write consistency, since this is the lower common denominator
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among the storage drivers. This is mitigated by writing values in reverse-
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dependent order, but makes wider transactional operations unsafe.
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Layered on the VFS model is a content-addressable _directed, acyclic graph_
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(DAG) made up of blobs. Manifests reference layers. Tags reference manifests.
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Since the same data can be referenced by multiple manifests, we only store
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data once, even if it is in different repositories. Thus, we have a set of
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blobs, referenced by tags and manifests. If we want to delete a blob we need
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to be certain that it is no longer referenced by another manifest or tag. When
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we delete a manifest, we also can try to delete the referenced blobs. Deciding
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whether or not a blob has an active reference is the crux of the problem.
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Conceptually, deleting a manifest and its resources is quite simple. Just find
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all the manifests, enumerate the referenced blobs and delete the blobs not in
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that set. An astute observer will recognize this as a garbage collection
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problem. As with garbage collection in programming languages, this is very
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simple when one always has a consistent view. When one adds parallelism and an
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inconsistent view of data, it becomes very challenging.
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A simple example can demonstrate this. Let's say we are deleting a manifest
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_A_ in one process. We scan the manifest and decide that all the blobs are
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ready for deletion. Concurrently, we have another process accepting a new
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manifest _B_ referencing one or more blobs from the manifest _A_. Manifest _B_
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is accepted and all the blobs are considered present, so the operation
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proceeds. The original process then deletes the referenced blobs, assuming
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they were unreferenced. The manifest _B_, which we thought had all of its data
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present, can no longer be served by the registry, since the dependent data has
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been deleted.
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Deleting data from the registry safely requires some way to coordinate this
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operation. The following approaches are being considered:
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- _Reference Counting_ - Maintain a count of references to each blob. This is
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challenging for a number of reasons: 1. maintaining a consistent consensus
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of reference counts across a set of Registries and 2. Building the initial
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list of reference counts for an existing registry. These challenges can be
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met with a consensus protocol like Paxos or Raft in the first case and a
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necessary but simple scan in the second..
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- _Lock the World GC_ - Halt all writes to the data store. Walk the data store
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and find all blob references. Delete all unreferenced blobs. This approach
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is very simple but requires disabling writes for a period of time while the
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service reads all data. This is slow and expensive but very accurate and
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effective.
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- _Generational GC_ - Do something similar to above but instead of blocking
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writes, writes are sent to another storage backend while reads are broadcast
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to the new and old backends. GC is then performed on the read-only portion.
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Because writes land in the new backend, the data in the read-only section
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can be safely deleted. The main drawbacks of this approach are complexity
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and coordination.
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- _Centralized Oracle_ - Using a centralized, transactional database, we can
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know exactly which data is referenced at any given time. This avoids
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coordination problem by managing this data in a single location. We trade
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off metadata scalability for simplicity and performance. This is a very good
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option for most registry deployments. This would create a bottleneck for
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registry metadata. However, metadata is generally not the main bottleneck
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when serving images.
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Please let us know if other solutions exist that we have yet to enumerate.
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Note that for any approach, implementation is a massive consideration. For
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example, a mark-sweep based solution may seem simple but the amount of work in
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coordination offset the extra work it might take to build a _Centralized
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Oracle_. We'll accept proposals for any solution but please coordinate with us
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before dropping code.
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At this time, we have traded off simplicity and ease of deployment for disk
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space. Simplicity and ease of deployment tend to reduce developer involvement,
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which is currently the most expensive resource in software engineering. Taking
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on any solution for deletes will greatly effect these factors, trading off
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very cheap disk space for a complex deployment and operational story.
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Please see the following issues for more detail:
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- https://github.com/docker/distribution/issues/422
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- https://github.com/docker/distribution/issues/461
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- https://github.com/docker/distribution/issues/462
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### Distribution Package
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At its core, the Distribution Project is a set of Go packages that make up
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Distribution Components. At this time, most of these packages make up the
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Registry implementation.
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The package itself is considered unstable. If you're using it, please take care to vendor the dependent version.
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For feature additions, please see the Registry section. In the future, we may break out a
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separate Roadmap for distribution-specific features that apply to more than
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just the registry.
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***
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### Project Planning
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An [Open-Source Planning Process](https://github.com/docker/distribution/wiki/Open-Source-Planning-Process) is used to define the Roadmap. [Project Pages](https://github.com/docker/distribution/wiki) define the goals for each Milestone and identify current progress.
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