Does django with mongodb make migrations a thing of the past?

Since mongo doesn开发者_StackOverflow中文版't have a schema, does that mean that we won't have to do migrations when we change the models?

What does the migration process look like with a non-relational db?

I think this is a really good question, but the answers are going to be a little scattered based on the libs you're using and your expectations for a "migration".

Let's take a look at some common migration actions:

• Add a field: Mongo makes this very easy. Just add a field and you're done.
• Delete a field: In theory, you're not actually tied to your schema, so "deletion" here is relative. If you remove the "property" and no longer load the field, then it doesn't really matter if that field is in the data. So if you don't care about "cleaning up" the database, then removing a field doesn't affect the database. If you do care about cleaning the DB, you'll basically need to run a giant for loop against the DB.
• Modify a field name: This is also a difficult problem. When you rename a field "where" are you renaming it? If you want the DB to reflect the new field name, then you basically have to execute a giant for loop on the DB. TO be safe you probably have to "add" data, then push code, then "unset" the old field.

Some Wrinkles

However, the concept of a field name in tandem with an ActiveRecord object is just a little skewed. An ActiveRecord object is effectively providing mappings of object properties to actual database fields.

In a typical RDBMS the "size" of a field name is not really relevant. However, in Mongo, the field name actually occupies data space and this makes a big difference in terms of performance.

Now, if you're using some form of "data object" like ActiveRecord, why would you attempt to store full field names in the data? The DB should probably be storing all fields in alphabetical order with a map on the Object side. So a Document could have 8 fields/properties and the DB names would be "a", "b"..."j", but the Object names would be readable stuff like "Name", "Price", "Quantity".

The reason I bring this up is that it adds yet another wrinkle to Modify a field name. If you're implementing a mapping then modifying a field name doesn't really cause a migration at all.

Some more Wrinkles

If you do want to implement a migration on a deletion, then you'll have to do so after a deploy. You'll also have to recognize that you won't save any current disk space when you do so.

Mongo pre-allocates space and it doesn't really "give it back" unless you do a DB repair. So if you delete a bunch of fields on documents, those documents still occupy the same space on disk. If the documents are later moved, then you may reclaim space, however documents only move when they grow.

If you remove a large field from lots of documents you'll want to do a repair or a check out the new in-place compact command.

There is no silver bullet. Adding or removing fields is easier with non-relational db (just don't use unneeded fields or use new fields), renaming a field is easier with traditional db (you'll usually have to change a lot of data in case of field rename in schemaless db), data migration is on par - depending on task.

What does the migration process look like with a non-relational db?

Depends on if you need to update all the existing data or not.

In many cases, you may not need to touch the old data, such as when adding a new optional field. If that field also has a default value, you may also not need to update the old documents, if your application can handle a missing field correctly. However, if you want to build an index on the new field to be able to search/filter/sort, you need to add the default value back into the old documents.

Something like field renaming (trivial in a relational db, because you only need to update the catalog and not touch any data) is a major undertaking in MongoDB (you need to rewrite all documents).

If you need to update the existing data, you usually have to write a migration function that iterates over all the documents and updates them one by one (although this process can be shared and run in parallel). For large data sets, this can take a lot of time (and space), and you may miss transactions (if you end up with a crashed migration that went half-way through).