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Pulp 3 DB Transcription Guide Draft


Relational Pulp Translation Guide


Converting from mongo to postgres should be recognized from the outset as a monumental task. In choosing Django as our ORM, this task has hopefully been made a little bit easier by bringing in such a mature and well-supported framework. Great care has been taken to make full use of Django's offerings when coming up with techniques and guidelines for converting our non-relational mongo database over to postgres.

This document intends to serve as a reference guide for developers taking on this conversion task, with particular focus on Repositories and Content Units.

This document does not intend to take on other aspects of the move to Pulp 3, such as the REST API, the plugin API, the tasking system, RBAC, search, or other aspects of Pulp 3 that are likely to be completely revamped/replaced rather than migrated.

Many links are provided in-line to relevant documentation that can be used to aid in translating MongoEngine Documents to Django Models. This document avoids repeating information found at these links, opting instead to point out how the information at these links applies to Pulp and its transition to a relational database.

What is "Relational Pulp"?

We currently have no strategy for updating our codebase from Pulp 2 to Pulp 3, and Django is so drastically different from MongoEngine (and pymongo) that iteratively converting Pulp in-place from mongo to postgres looks very difficult. The Relational Pulp project aims to reimplement the Pulp 2 data model using the Django framework and sound relational design, and provide a useful and working code base that can be used when we do have a strategy for how to get from Pulp 2 to Pulp 3 as developers. How the code base gets used depends on that strategy, so at the moment the relational Pulp project is little more than a sandbox/testbed for proving the viability of converting Pulp to a relational database.

All of the findings documented here are the result of work on the Relational Pulp project, and are subject to change (but hopefully not a lot) as requirements are discovered, clarified, or otherwise altered as part of the Pulp 3 development effort.


While we are targeting postgres specifically for Pulp 3, no postgres-specific functionality should be needed to migrate Pulp to a relational database, allowing for a database- agnostic data model. This is analogous to using celery with kombu, allowing Pulp to (probably!) function on any backend supported by kombu, rather than explicitly coding Pulp to work on qpid. All of the links in this document are to the "current" version of postgres, but all of the features described in this document are written under the assumption that postgres 8.4 or higher will be available.

While being database-agnostic is a laudable goal, it is not a hard requirement for Pulp 3. In the event that postgres provides some django-supported "killer feature", we can drop database-agnosticism as a goal.

That all said, full-text search is a pretty killer feature, and might lock us to postgres anyway.
This feature appears in Django 1.10 and might end up forcing us to completely rewrite this
section and the Django Version section of this document. Figuring out how to do search is a
big next step for Pulp 3, and has not been thoroughly investigated as part of the Relational
Pulp effort.


This section has nothing to do with the data model, and should be discussed elsewhere.

Django and most of its dependencies work well in Python 3. Our last informal audit of Python 3 compatibility in our dependencies showed that the last major problem was twisted, which has since become Python 3 compatible. Specifically, the existence of a python 3.4 SCL with packages for el6 and el7 makes Python 3.4 the most likely minimum supported version of Python for Pulp 3.

If Python 2.7 support is still necessary for Pulp stakeholders, then compatibility libraries should be employed to make Pulp work in both major versions of Python (2 and 3).

Not all of twisted works in python 3, but the pieces that we use are among those that have been
updated to work in both python 2 and 3.


Django has many layers, including the data model layer, the view layer, etc. This document focuses on the model layer, and uses Django's terminology where applicable. Furthermore, every effort should be made to adhere to existing Django functionality so that the full benefits of adopting this framework can be realized.


If you aren't already familiar with some of the features that Django provides, part 1 of the Django tutorial provides an excellent introduction. It covers things like starting a project for the first time, starting the development web server, and accessing models. The tutorial pages that come after part 1 are largely irrelevant for Pulp 3, but are a good exercise nonetheless for someone looking to get to know Django a little bit better.


At the moment, Django 1.8 is the front-runner for our Django version of choice. All links to Django documentation are directed to Django 1.8 as a result.


  • Most recent "LTS" release of Django.
  • Supports Postgres 9+, which is available in every distribution Pulp supports.

Cons (sorta):

  • Not the latest version, so we miss out on specific features
    • ...but we gain API stability in return
  • Requires using SCLs on distributions that don't provide postgres 9
    • ...but we also need SCLs for python if we support el6

Django Concepts

There are specific Django concepts that deserve special attention before getting into some of the finer details of how the relation data model should be structured, which are outlined in subsections below.

Model Inheritance

Django provides three different mechanisms for supporting object-oriented inheritance in its Model classes. Each method provides specific benefits to Pulp that are outlined here.

Additionally, each of these model inheritance mechanisms can be combined with the other mechanisms as-needed to create a sound object-oriented design that also generates a reasonable database schema.

Abstract Base classes

Many of our ContentUnit classes have common fields and/or behavior. Abstract base classes make it trivial for us to put those common bits of code in a single place, to be inherited by Model classes in completely standard and pythonic ways, such as with subclassing or mixins.

The many different RPM-like ContentUnit subclasses use this extensively, combining in the "detail" classes to make the complete unit model for a given type.

Multi-table Inheritance

This is probably the most important Model inheritance mechanism, as it is used to implement the ContentUnit "master-detail" relationship, where the "master" content units contain all fields common to all (or most) ContentUnits, and the "detail" content units contain all of the type-specific fields for that unit type.

On the database level, the information representing a ContentUnit resides in at least two database tables (the master ContentUnit table and the detail table), and is seamlessly joined by Django on instances of the detail ContentUnit model (e.g. RPM, a puppet Module, etc).

More information on this is documented later in the section describing ContentUnit changes.

If you go by what wikipedia has to say about master-detail relationships, this isn't quite that.
However, it makes it easy to refer to both sides of the master/detail relationship with terms that
are easy to understand in the context of ContentUnits, so it's worth appropriating those
terms for Pulp's purposes here.

Proxy Models

Proxy models have no effect on the database; they have all of the same database state as the models they proxy back to, only the behavior is changed. Proxy models are a convenient middle ground between typed and untyped repositories, and solve a potential namespacing issue:

When creating relationships between Django models, Django does us a favor, and creates what is known as a "reverse relation" on the object bein related to. The reverse relation is created on the relationship's target model automatically. Creating several models that relate to Repository, for example, will create several of these reverse relations on the Repository model, cluttering its namespace. One solution, involving a proxy model, would be to create a plugin-specific repository proxy model (e.g. RPMRepositoryProxy for the RPM plugin), and then any Model in the RPM plugin that would ForeignKey to Repository targets that proxy model instead instead. The reverse relations will be created on the proxy model, but the "normal" Repository model is unaffected.

Sticking with Repository for another example, an RPMRepositoryProxy also gives us the ability to add RPM-specific methods, properties, etc to a Repository without making any changes to the platform Repository API.

RPMRepositoryProxy is not really a typed repository, per se. Its name, following the scheme of
<plugin>RepositoryProxy, still allows for an "RPMRepository" Model to be created at some
point in the future if/when we implement typed repositories.

Generic Relations

Django's Generic Relations give us the ability to associate many models to one model in a more flexible was than a normal ForeignKey. Normally used for things like object tagging, the Generic Relations that come with Django's contenttypes framework can be used by Pulp to easily associate a generic Django Model with any number of other models that can benefit from storing the information captured by the generic model.

A good example of this are the various Generic Key/Value stores that can be associated with any other Model, "Notes", "Config", and "Scratchpad".

MongoEngine to Django Field Conversions

These are the MongoEngine field types currently used by Pulp, and guidelines on converting them to Postgres. Since MongoEngine started out to get mongodb working as a Django backend, most fields have direct counterparts in Django. The following subsections are the MongoEngine fields currently used in Pulp, with applicable postgres datatypes and Django field alternatives listed inside.

Simple Field Types

These field types are directly supported by postgres/Django, and have a clear migration path.


This field can be represented by one of two Django fields, depending on which Postgres column is a better fit for the data being stored in it.

Postgres datatype reference:

For our purposes, only varchar and text are interesting, the character type will be ignored. While some database engines have differences in performance between the varchar and text data types, this tip from the linked postgres docs is good to keep in mind:

"There are no performance differences between these three types, apart from the increased storage size when using the blank-padded type. While character(n) has performance advantages in some other database systems, it has no such advantages in PostgreSQL. In most situations text or character varying should be used instead."

The "blank-padded" type mentioned in that quote is the character type, so for our purposes there is no difference in performance between varchar and text.


Represented by a varchar field in postgres, the max_length argument is required.

When the maximum length of a string is known, such as when storing hash values of a known type (or types), this is the field to use. String length validation is done at the database level.


Represented by a text field in postgres.

When the maximum length of a string is unknown, such as when storing large chunks of text like errata descriptions/summaries, this is the field to use.

IntField, LongField

There are more numeric types supported by postgres + Django than are offered by MongoEngine, so converting from one of these MongoEngine fields to a postgres field should take the available Django field types into account to ensure that the most appropriate postgres data type is being used.

The only known MongoEngine FloatField in Pulp is a timestamp field on the Distribution document, which could reasonably be converted to a DateTimeField.

IntegerField, SmallIntegerField, BigIntegerField

2-byte, 4-byte, and 8-byte (respectively) storage for signed integers.

PositiveIntegerField, PositiveSmallIntegerField

Positive-only variants of SmallIntegerField and IntegerField. These use the same postgres data types as their non-"Positive" counterparts, but use database validation to enforce values >= 0.


Also numeric types, just like IntField and LongField, but there are some python representation options when it comes to floats that are available in django fields.


Stored as the "double precision" data type, using 8 bytes of storage. Represents the python "float" type.


Stored as the "numeric" data type, storage size varies based on the field precision declared when the field is created. Very similar to FloatField, but values are represented by the python "decimal.Decimal" type. Use this field instead of FloatField in cases where the "decimal.Decimal" type is more appropriate.

For reference:

The postgres docs state that "The actual storage requirement is two bytes for each group of four decimal digits, plus three to eight bytes overhead," so there's no obvious storage efficiency benefit the be gained by using this field.


A normal BooleanField, represented a True/False value in python.

BooleanField, NullBooleanField

Represented by the "boolean" data type in postgres. "BooleanField" stores only True or False, and cannot be null/None, so a default must be specified. The "NullBooleanField" alternative additionally allows for null/None values, useful in cases where a boolean value might be unknown, or not required.

DateTimeField, UTCDateTimeField, ISO8601StringField

All mongoengine DateTimeFields should, at this point, be storing UTC datetime stamps, represented in python as "datetime.datetime" instances. UTCDateTimeField and ISO8601StringField are custom fields with special behavior for storage, but all datetimes should be stored in postgres as postgres's native data type. Any custom serialization/deserialization of datetime data should be done at the API layer.


Represented in postgres as the "timestamp with time zone" data type. Django is configured to use the UTC timezone, so tz-aware datetime objects will be properly converted to UTC timestamps when stored, our custom UTCDateTimeField is not required with Django.

DateField, TimeField

MongoEngine does not provide equivalents for these field types, but they're worth mentioning as possible alternatives to DateTimeField in case only a date or time component of a datetime object needs to be stored.

DateField represents the postgres "date" data type, and is the "" type in python.

TimeField represents the postgres "time" data type, and is the "datetime.time" type in python. Unlike DateTimeField, TimeField appears to be unaware of time zones; the column type is "time with


UUIDs, represented by instances of the "uuid.UUID" data type.


Postgres has native support for UUIDs with the "uuid" data type, storing the value as the UUID's 128-bit/16-byte value, rather than the UUID string representation.

Complex Field Types

These field types are mongo-specific, do not have a postgres/Django counterpart.


The EmbeddedDocumentField, as the name indicates, stores a document embedded in another MongoEngine document. To convert to postgres, the embedded document should be properly modeled as a Django Model, or as part of the Django Model that formerly embedded the document in MongoEngine.

The only EmbeddedDocumentField in Pulp can be found in the OSTree plugin, as an attribute of the Manifest Document. Its purpose appears to be referential, and can most likely be replaced with a standard ForeignKey relationship.


DynamicField supports multiple field types as potential values, owing to mongodb's schemaless nature. Given postgres's schema-full nature, instances of this field type must be converted to one of the available Django field types.

The only DynamicField in Pulp is in the platform TaskStatus Model, as its "result" attribute, which will need to be remodeled as part of this transition.


In general, elements of ListField arrays should be turned into their own Django Model, with a ForeignKey relationship back to the Model that originally contained the ListField.

A sort of case-study regarding converting ListFields to models can be found in the "ListField Conversion Example" section of this document.


There are many and varied instances of DictFields in Pulp. DictFields can usually either be reduced to key/value stores, or should (like with ListField) be turned into Django Models that ForeignKey back to the Model that originally contained the DictField. For the case of key/value stores, see the "Arbitrary User Data" section for details on how to handle that case.

UUID Primary Keys

Postgres has native support for the UUID datatype, as does Django, making a UUID a viable option for primary keys. Most Models in the Relational Pulp sandbox project use UUID Primary Keys, but the main motiviation behind using them is that they're already being used at the de-facto Primary Key of some of our MongoEngine, most notably including ContentUnit. Given that we are using UUID Primary Keys already, keeping them when migrating to Postgres makes it so that users integrating with Pulp will be able to keep any references they may have in their own data stores to Pulp ContentUnit by their ID (UUID).

ContentUnit Changes

The primary focus of this initial "Relational Pulp" exploration was to address the relationship between Repositories and ContentUnits. The most notable of those changes, generally those not easily managed by translating a single field, are outlined here.

ContentUnit vs. Repository Metadata

Many Pulp 2 ContentUnit subclasses are actually just metadata related to repositories that provide additional information or structure about the content stored in that Repository. For example, a Yum Repository's "comps.xml" contains various lists of packages, but doesn't contain any content itself. This was most likely done because subclassing ContentUnit in Pulp 2 is the simplest way to have a Document be related to a Repository. With Django, you just ForeignKey to the Repository (or to a plugin-specific Repository proxy).


ContentUnits now have a ManyToMany relationship with the Repository instances that contain them, which Django exposes on ContentUnit instances as related object manager attribute named "repositories". Similarly, Repository instances have a "units" attribute representing this relationship.

While this is a pretty "normal" Django ManyToMany relationship, it bears mentioning here since it's probably the biggest single reason we're doing all of this.


In Pulp 2, ContentUnits are generally associated with a single file, but in some cases ContentUnits have zero, one, or many files associated with them. In Pulp 3, a new "ContentUnitFile" Model has been created that has a ForeignKey relationship back to ContentUnit. As a result, all ContentUnits can have zero, one, or many files associated with them without any further customization to ContentUnit required to deal with it. Since the storage path for a ContentUnitFile is based on the unit key of its parent ContentUnit, all files associated with a ContentUnit will have the same base storage path.

Checksums Denormalized?

A closer look at the ContentUnitFile Model shows that there are a number of checksum fields present on that Model. At first glance, this appears to be a denormalization. There are potentially infinite checksum types, with some properties of each type unknown. Should this be a related "Checksum" Model, or maybe some sort of Generic Relation that can handle checksums for all models that need to support them? Maybe!

In reality, though, the set of checksum types supported by Pulp is finite, and is limited to the algorithms available when using python's hashlib module. Also, each checksum type value has a known length associated with it. While each checksum type is very similar, they are distinct, and the number of types is finite. Specifically, the values of hashlib.algorithms_guaranteed are the field attribute names exposed on ContentUnitFile.

Assuming no other model would benefit from these checksum fields, and this appear to be true looking at how checksums are used in Pulp 2, then exposing the checksums as Model fields is reasonable, and not a denormalization.

Arbitrary User Data

Pulp 2 supports arbitrary user data thanks to MongoEngine DictFields. DictFields allow for complex, nested data structures, with the only requirement be that the values in the field be something that mongo can store as a BSON object/embdeeded document. Examples include the "notes" and "scratchpad" fields on some models, and the "pulp_user_metadata" field on ContentUnit.

Pulp 3 will not support arbitrary user metadata. Instead, Pulp 3 will support arbitrary user key/value pairs, where both keys and values are strings. The migration from Pulp 2 to Pulp 3 will do its best to convert arbitrary user data to these key/value string pairs by flattening the contents of MongoEngine DictFields into string key/value representations and storing the result.

ListField Conversion Example (Errata)

In Pulp 2, the Errata model has many ListFields associated with it:

  • references, a list of items to which this Errata refers, such as BZ bugs and CVEs
  • pkglist, a list of package collections (themselves a list) referred to by this errata

As a result, both "references" and "pkglist" should become their own Model with a corresponding table in the database with a ForeignKey relationship back to Errata. Furthermore, because the "pkglist" element in updateinfo.xml can contains package collections, another Model is needed to represent those package collections, which then has a ForeignKey relationship back to the pkglist that contains it.

To sum up, the single Pulp 2 Errata model, with its two ListFields, becomes four Django Models:

  • Errata
    • ErrataReference - Exposed on Errata instances at the "references" attribute
    • ErrataCollection - Exposed on Errata instances as the "pkglist" attribute
      • ErrataPackage - Exposed on ErrataCollection instances as the "packages" attribute

These models (probably!) meet the requirements for errata:

  • Pulp can store all data found in errata updateinfo XML files when syncing repos.
  • Pulp can generate equivalent updateinfo XML files when publishing repos.

Model-writing "Do"s and "Don't"s

None of these are official policies. Instead, they are intended to be helpful guidelines that can assist in decision making when translating MongoEngine Documents to Django Models.


Do Reduce, Reuse, Recycle

As described in the "Model Inheritance" section, Django makes it possible for us to use good object-oriented design to create a good relational database design. Make use of good OO principles to avoid duplicating work that's already been done, or can be done in common ancestors.

Do Let Django do it Django's way

Related to the previous point, most of the problems encountered in using a relational data model are well known, and solutions to them exist in the Django ORM. Before spending a lot of time implementing an intricate solution to a tricky problem, make sure Django doesn't already provide a solution to the problem at hand.

Django's docs are very good, but they aren't always organized in the most intuitive way. It can be difficult to find the documentation related to the specific problem at hand, but it's worth the effort to look and ask around to make Django doesn't already have a solution before implementing something custom.

Do Make tables

Don't be afraid to make tables, especially when breaking down complex mongo fields into Django Models with simple field types. Most, if not all, MongoEngine ListFields will end up becoming at least one postgres table.

Do Make columns

Pulp should not be the arbiter of what information is or is not interesting to its users on a ContentUnit. As a result, Pulp should attempt to store as much available data as possible in a ContentUnit's detail table from that ContentUnit's data source(s) while still keeping a good and consistent data model.

Do Forget about the API Representation

Store data in the database column type best suited for that data, regardless of how it might need to be represented in the API. The API layer depends on the data model layer, but the data model layer does not depend on the API layer.

The data model layer should be entirely focused on being a good model, written as good python. The API layer will do what it needs to represent database values properly to users.

Do Care about uniqueness for detail ContentUnit types

alternate heading: "postgres doesn't solve the duplicate NEVRA problem"

Uniqueness contraints don't cross table boundaries, so there's no simple way to enforce ContentUnit-specific constraints at a database level, such as enforcing the uniqueness of fields in a content unit's unit key in a single repository. (e.g. All NEVRA in a repository are unique.)

Django does provide a mechanism for validating uniqueness in "special" ways, but this is part of Django's validation system which may not always be called if we're using custom views (which we will).

validate_unique is clearly the correct place to enforce correct uniqueness for model instances, but we will still need to have mechanisms in place to ensure it is being called at the correct times.


Don't Use Meaningful Primary Keys

DB primary keys should not be meaningful in any context other than relations between tables. The properties that make primary keys interesting as identity fields, which is that they are unique, indexed, and not NULL, are all properties that can be individually or collectivley assigned to most Django model fields. Separating an object's meaningful identity from its referential identity (its otherwise meaningless primary key) frees us to be able to change that object's meaningful identity, if required, in the future.

For example, a repository's meaningful identity is its repo_id. Making the repo_id field the primary key of a Repository would make it difficult to rename a repository, if that ever became a requirement that Pulp wanted to fulfill.

If a meaningful natural key is desired for a Model (and it should be, this is nice to have), implement a natural key on your Model:

Don't Index everything

Indexes are mainly used to find rows in a database quickly. If a field is not normally used to identify a particular row (or particular rows), it probably doesn't need an index. If implementing nature keys, a subset of those natural keys should be indexed.

For example, in the set of NEVRA fields, "name" is very likely to be used to find a set of rows in the RPM unit's detail table. None of the remaining fields are likely to be used separate from the "name" field to locate RPMs (epoch, version, releas, arch).

Don't Arbitrarily denormalize relations

In the relational context, normalization is a Good Thing. However, there may be times where strict normalization might seem cumbersome, and a desire to "denormalize" relations creeps into the design process. Sometimes denormalization is absolutely the right solution, but often it will be an instance of premature optimization. Unless a normalized relationship proves to be too slow, too cumbersome, or otherwise less useful than desired, avoid the temptation to fix something that may not be broken.

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seandst commented Jun 27, 2016

Trivial point, but it's the translation guide, not the transcription guide, so the file needs to be renamed at some point (or we can opt not to care since this is trivial).

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jortel commented Jun 29, 2016

Nice job, Sean.

Few notes:

  • Fix instead instead in Proxy Models 2nd paragraph.
  • I don't think the EmbeddedDocumentField is used in the OSTree model. Perhaps you meant Docker?
  • Not sure I saw this mentioned explicitly but we should not be storing any dates or timestamps as ISO8601 strings. They should all be converted to DateField or DateTimeField.

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