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In every app’s development lifecycle, there’s a point where the underlying schema doesn’t fit the requirements and must be changed for good. That requires a migration for both schema and the underlying data. This article guides you through common migration scenarios you can encounter with Dgraph and help you avoid any pitfalls around them. These are the most common scenarios that can occur:
  • Renaming a type
  • Renaming a field
  • Changing a field’s type
  • Adding @id to an existing field
As long as you can avoid migration, avoid it. Because there can be scenarios where you might need to update downstream clients, which can be hard. So, its always best to try out things first, once you are confident enough, then only push them to production.

​
Renaming a type

Let’s say you had the following schema: 
type User {
  id: ID!
  name: String
}
and you had your app working fine with it. Now, you feel that the name AppUser would be more sensible than the name User because User seems a bit generic to you. Then you are in a situation where you need migration. This can be handled in a couple of ways:
  1. Migrate all the data for type User to use the new name AppUser. OR,
  2. Just use the @dgraph(type: ...) directive to maintain backward compatibility with the existing data.
Depending on your use-case, you might find option 1 or 2 better for you. For example, if you have accumulated very little data for the User type till now, then you might want to go with option #1. But, if you have an active app with a very large dataset then updating the node of each user may not be a thing you might want to commit to, as that can require some maintenance downtime. So, option #2 could be a better choice in such conditions. Option #2 makes your new schema compatible with your existing data. Here’s an example: 
type AppUser @dgraph(type: "User") {
  id: ID!
  name: String
}
So, no downtime required. Migration is done by just updating your schema. Fast, easy, and simple. Note that, irrespective of what option you choose for migration on Dgraph side, you still need to migrate your GraphQL clients to use the new name in queries/mutations. For example, the query getUser would now be renamed to getAppUser. So, your downstream clients need to update that bit in the code.

​
Renaming a field

Just like renaming a type, let’s say you had the following working schema: 
type User {
  id: ID!
  name: String
  phone: String
}
and now you figured that it would be better to call phone as tel. You need migration. You have the same two choices as before:
  1. Migrate all the data for the field phone to use the new name tel. OR,
  2. Just use the @dgraph(pred: ...) directive to maintain backward compatibility with the existing data.
Here’s an example if you want to go with option #2: 
type User {
  id: ID!
  name: String
  tel: String @dgraph(pred: "User.phone")
}
Again, note that, irrespective of what option you choose for migration on Dgraph side, you still need to migrate your GraphQL clients to use the new name in queries/mutations. For example, the following query: 
query {
  getUser(id: "0x05") {
    name
    phone
  }
}
would now have to be changed to: 
query {
  getUser(id: "0x05") {
    name
    tel
  }
}
So, your downstream clients need to update that bit in the code.

​
Changing a field’s type

There can be multiple scenarios in this category:
  • List -> Single item
  • String -> Int
  • Any other combination you can imagine
It is strictly advisable that you figure out a solid schema before going in production, so that you don’t have to deal with such cases later. Nevertheless, if you ended up in such a situation, you have to migrate your data to fit the new schema. There is no easy way around here. An example scenario is, if you initially had this schema: 
type Todo {
  id: ID!
  task: String
  owner: Owner
}

type Owner {
  name: String! @id
  todo: [Todo] @hasInverse(field: "owner")
}
and later you decided that you want an owner to have only one to do at a time. So, you want to make your schema look like this: 
type Todo {
  id: ID!
  task: String
  owner: Owner
}

type Owner {
  name: String! @id
  todo: Todo @hasInverse(field: "owner")
}
If you try updating your schema, you may end up getting an error like this: 
resolving updateGQLSchema failed because succeeded in saving GraphQL schema but failed to
alter Dgraph schema - GraphQL layer may exhibit unexpected behavior, reapplying the old
GraphQL schema may prevent any issues: Schema change not allowed from [uid] => uid without
deleting pred: owner.todo
This is a red flag. As the error message says, you should revert to the old schema to make your clients work correctly. In such cases, you should have migrated your data to fit the new schema before applying the new schema. The steps for such a data migration varies from case to case, and so can’t all be listed down here, but you need to migrate your data first, is all you need to keep in mind while making such changes.

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Adding @id to an existing field

Let’s say you had the following schema: 
type User {
  id: ID!
  username: String
}
and now you think that username must be unique for every user. So, you change the schema to this: 
type User {
  id: ID!
  username: String! @id
}
Now, here’s the catch: with the old schema, it was possible that there could have existed multiple users with the username Alice. If that was true, then the queries would break in such cases. Like, if you run this query after the schema change: 
query {
  getUser(username: "Alice") {
    id
  }
}
Then it might error out saying: 
A list was returned, but GraphQL was expecting just one item. This indicates an internal error - probably a mismatch between the GraphQL and Dgraph/remote schemas. The value was resolved as null (which may trigger GraphQL error propagation) and as much other data as possible returned.
So, while making such a schema change, you need to make sure that the underlying data really honors the uniqueness constraint on the username field. If not, you need to do a data migration to honor such constraints.

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Unused fields

For example, let’s assume that you have deployed the following schema: 
type TestDataMigration {
  id: ID!
  someInfo: String!
  someOtherInfo: String
}
Then you create a TestDataMigration with someOtherInfo value. Then you update the Schema and remove the field. 
type TestDataMigration {
  id: ID!
  someInfo: String!
}
The data you have previously created is still in the graph database ! Moreover if you delete the TestDataMigration object using its id, the GraphQL API delete operation is successful. If you followed the GraphQL - DQL Schema mapping, you understand that Dgraph has used the list the known list of predicates (id, someInfo) and removed them. In fact, Dgraph also removed the dgraph.typepredicate and so thisTestDataMigration node isn’t visible anymore to the GraphQL API. The point is that a node with this uid exists and has a predicate someOtherInfo. This is because this data has been created initially and nothing in the process of deploying a new version and then using a delete operation by ID instructed Dgraph to delete this predicate. You end up with a node without type (i.e without a dgraph.type predicate) and with an old predicate value which is ‘invisible’ to your GraphQL API! When doing a GraphQL schema deployment, you must take care of the data cleaning and data migration. The good news is that DQL offers you the tools to identify (search) potential issues and to correct the data (mutations). In the previous case, you can alter the database and completely delete the predicate or you can write an ‘upsert’ DQL query that searches the nodes of interest and delete the unused predicate for those nodes.

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New non-nullable field

Another obvious example appears if you deploy a new version containing a new non-nullable field for an existing type. The existing ‘nodes’ of the same type in the graph don’t have this predicate. A GraphQL query reaching those nodes returns a list of errors. You can easily write an ‘upsert’ DQL mutation to find all node of this type not having the new predicate and update them with a default value.