Data model

Xata has a relational data model, with a strict schema. Records are grouped into tables, which are grouped into databases. Xata supports rich column types and relations between tables can be represented via link columns, which are similar to foreign keys.

Internally, we are storing the data both in a transactional database (OLTP) as well as in a search/analytics engine (OLAP). This is done transparently for you and the different stores are exposed via the same API. You can read more about how Xata works behind the scenes on the How it Works page.

Data is organized in "tables" which are grouped into "databases". Tables have a strict schema, which contains a list of columns.

Xata supports many column types and the type is used, among other things, to generate type-safe clients.

You can represent relations between tables of the same database by using columns with type link, and it is possible to "join" tables at query time.

The following is an example of a record that you can store natively in Xata:

{
  "name": "John Doe",
  "email": "john@example.com",
  "age": 42,
  "address": "123 Main St, New York",
  "labels": ["admin", "user"]
}

The keys in the JSON are the column names. The values are the data. The corresponding schema file for the above record, looks like the following:

{
  "tables": [
    {
      "name": "users",
      "columns": [
        {
          "name": "name",
          "type": "string"
        },
        {
          "name": "email",
          "type": "email"
        },
        {
          "name": "age",
          "type": "int"
        },
        {
          "name": "address",
          "type": "string"
        },
        {
          "name": "labels",
          "type": "multiple"
        }
      ]
    }
  ]
}

In the above, it's worth noting:

• Each column has a well-defined type. Xata has a strongly typed schema, as opposed to a schemaless model.
• Some of the Xata data types are higher level than what is typically in a database. For example, the email type performs automatic validation of email addresses.

The string type represents a simple string. The values of this type are indexed both for quick exact matching and for full-text search. Set unique to ensure the strings you insert are unique. Set notNull to require a value.

Example definition:

{
  "name": "name",
  "type": "string"
}

The text type represents a long-form text. Unlike the string type, text column values are indexed and optimized for efficient free-text searches, rather than exact matches. If a value must be provided, you can use the notNull setting to enforce this requirement.

Example definition:

{
  "name": "address",
  "type": "text"
}

The int type represents an integer. Internally, it's represented as a 64-bit signed integer. This means the minimum value is -9223372036854775808 and the maximum value is 9223372036854775807. Set to unique to ensure the integers you insert are unique. Set notNull to require a value.

Example definition:

{
  "name": "age",
  "type": "int"
}

The float type represents a double precision floating point number. Internally, it's represented as a 64-bit signed number with up to 15 decimal digit precision. Set unique to make sure the floats you insert are unique. Set notNull to require a value.

Example definition:

{
  "name": "distance",
  "type": "float"
}

The datetime type represents a point in time up to millisecond precision. It has the date part and the time part, and both are mandatory. A RFC 3339-compliant string is used for input and output representation. Set unique to make sure the values you insert are unique. Set notNull so the column value cannot be empty. Select a default value using defaultValue. If you set now as the default value, the default value of the column will be the time when the record was inserted.

Example definition:

{
  "name": "publishedAt",
  "type": "datetime",
  "notNull": true,
  "defaultValue": "now"
}

Example values:

{
  "publishedAt": "2020-11-10T10:38:16Z"
}

{
  "publishedAt": "2020-11-10T12:38:16+02:00"
}

To learn more about automatic tracking of record creation and manipulation, refer to the createdAt and updatedAt fields, which contain relevant date information.

The bool type represents a boolean.

Example definition:

{
  "name": "isPublished",
  "type": "bool"
}

The email type represents an email address. Valid email addresses are not longer than 254 characters and respect this regular expression:

^[a-zA-Z0-9.!#$%&'*+\\/=?^_`{|}~-]+@
[a-zA-Z0-9](?:[a-zA-Z0-9-]{0,61}[a-zA-Z0-9])?
(?:\\.[a-zA-Z0-9](?:[a-zA-Z0-9-]{0,61}[a-zA-Z0-9])?)*$

Set unique to make sure the strings you insert are unique. Set notNull to require a value.

Example definition:

{
  "name": "emailAddress",
  "type": "email"
}

Example value:

{
  "emailAddress": "test@example.com"
}

The multiple type represents an array of strings.

Example definition:

{
  "name": "labels",
  "type": "multiple"
}

To insert a value into a multiple column, use a JSON array of strings, for example:

{
  "labels": ["admin", "user"]
}

The vector type represents a vector of floating point numbers with a fixed dimension. This type can be used to store embeddings computed via machine learning, for example by calling the OpenAI embeddings API. The dimension of the vector must be defined in the schema, and must be a number between 2 and 10,000. For example:

{
  "name": "embedding",
  "type": "vector",
  "vector": {
    "dimension": 1536
  }
}

In the example above we set the dimension to 1536, which is the output dimension produced by the OpenAI embeddings API, however, you can store embeddings produced by any other machine learning model. Once you store the embeddings, you can use vector search for a number of use cases, see the Similarity/Vector search section for more information.

Warning: The object type is no longer supported since December 13th, 2023. If you are using the object type in your workspace, we strongly encourage you to review your implementations and update them accordingly, replacing the object type with suitable alternatives as recommended in our migration options documentation.

The link type represents a link to another table. See the links and relationships section for more information.

Example definition:

{
  "name": "author",
  "type": "link",
  "link": {
    "table": "users"
  }
}

In the above example, the link.table property is mandatory and indicates the target table.

To insert the value for a link column, use the target record ID as the value. For example:

{
  "author": "rec_1234567890"
}

The file type is the representation of a file in the Xata database. For every file, the column stores JSON objects with a predefined schema. The file object is used to upload a file, retrieve its content, as well as update or query its metadata.

The file object consists of the following fields:

• name refers to the file name.
• mediaType follows the definition provided by RFC 6838. Xata may utilize it as a hint, but it does not validate the content against the declared media type. If the value is missing, the default is set to application/octet-stream.
• base64Content represents the file content encoded using base64 encoding.
• enablePublicUrl is an optional boolean field that indicates whether Xata should generate a public access URL for this file. The default value is false.
• signedUrlTimeout is an optional integer field that sets the time to live for a signed URL. The value is measured in seconds and the default is set to 1 minute.
• uploadUrlTimeout is an optional integer field that sets the time to live for an upload URL. The value is measured in seconds and the default is set to 24 hours.

All the fields above are input fields and can be used in insert or update requests. For example:

{
  "photo": {
    "name": "sample.png",
    "mediaType": "image/png",
    "base64Content": "iVBORw0KGgoAAAANSUhEUgAAAAIAAAACCAYAAABytg0kAAAAEklEQVR42mNk+M9QzwAEjDAGACCDAv8cI7IoAAAAAElFTkSuQmCC"
  }
}

When reading or querying a file object, Xata adds a set of read only fields:

• size - represents the file's size in bytes. It's important to note that this size refers to the original file, not the size of the base64 encoding.
• version - keeps track of the updates made to the specific file.
• url - a generated URL that provides direct access to the file. Depending on the enablePublicUrl flag, this URL can either be accessible to the public or require the Xata API key for access.
• signedUrl - a download URL generated with a signature. This URL allows read access without a key but has an expiration time determined by the duration configured in signedUrlTimeout. The field is generated on demand and needs to be explicitly requested in the query.
• uploadUrl - an upload URL generated with a signature. This URL allows write only access without a key but has an expiration time determined by the duration configured in uploadUrlTimeout. The field is generated on demand and needs to be explicitly requested in the query.
• attributes - a JSON object that stores key-value properties specific to the file's media type. For instance, recognized image types will have properties like height and width stored within the attributes.

When the file field is requested in a query, the returned file object will look like the following:

{
  "photo": {
    "name": "sample.png",
    "mediaType": "image/png",
    "enablePublicUrl": true,
    "signedUrlTimeout": 60,
    "uploadUrlTimeout": 86400,
    "size": 1885,
    "version": 1,
    "url": "https://eu-west-1.storage.xata.sh/nj42n37o4l3dd19fe6vsh4plkk",
    "attributes": {
      "height": 131,
      "width": 131
    }
  }
}

The object fields, with the exception of base64Content, url, signedUrl and uploadUrl, can be used in query filters and summaries.

The file column type does not support constraints like not null or unique or parameters like default value. The default value for a file column is null.

The file[] (file array) type represents a collection of files stored in the same table cell. Every array item is a JSON object similar to the file object, with the addition of an id field, which is used to refer to the item.

The id is automatically generated if it is not passed during the time of creation.

The following is an example of adding multiple files in a single column value:

{
  "images": [
    {
      "id": "i1",
      "name": "image1.png",
      "mediaType": "image/png",
      "base64Content": "iVBORw0KGgoAAAANSUhEUgAAAAIAAAACCAYAAABytg0kAAAAEklEQVR42mNk+M9QzwAEjDAGACCDAv8cI7IoAAAAAElFTkSuQmCC"
    },
    {
      "name": "image2.png",
      "mediaType": "image/png",
      "base64Content": "iVBORw0KGgoAAAANSUhEUgAAAAIAAAACCAYAAABytg0kAAAAEklEQVR42mNk+M9QzwAEjDAGACCDAv8cI7IoAAAAAElFTkSuQmCC"
    }
  ]
}

All file fields described above are available for file array items. The fields are returned if selected in the query. For example selecting images.id, images.name, images.mediaType, images.size, images.attributes will return the following:

{
  "images": [
    {
      "id": "i1",
      "name": "image1.png",
      "mediaType": "image/png",
      "size": 75,
      "attributes": {
        "height": 2,
        "width": 2
      }
    },
    {
      "id": "c9tr9u8g317pl42jd0lgbs0qh4",
      "name": "image2.png",
      "mediaType": "image/png",
      "size": 75,
      "attributes": {
        "height": 2,
        "width": 2
      }
    }
  ]
}

Note that although the file fields can be queried, because the column value is an array it cannot be used in filters or summaries.

The file[] column type does not support constraints like not null or unique or parameters like default values. The default value for a file[] column is an empty array [].

The json type represents any RFC 7159-compliant JSON document. To ensure efficient processing of input and optimal performance in storage and query operations, take note of the following considerations:

• White spaces are not preserved.
• Key order is not preserved.
• In case of duplicate keys, only the last one is stored.

Example definition:

{
  "name": "jsonField",
  "type": "json"
}

Xata maintains a special set of columns that don't have to be defined in the schema. The id and the xata.* columns names are reserved and you cannot create your own columns with these names. No other column names are reserved.

The id and xata.* are not explicitely represented in the schema file, because they are present for all tables and maintained by Xata.

The id column contains the record ID. It is of type string, limited to 255 chars and it is guaranteed to be unique within the table. If you don't explicitly provide an id value, Xata will generate one for you, with the following properties:

• globally unique
• sortable, with the newest record having the highest ID

It is possible to use your own ID values, by using the insert record with ID API.

The xata.version column contains the current version of the record. It is of type int and it is automatically incremented any time the record is updated. A newly inserted record will have a version of 0.

This column is meant to be used for optimistic concurrency control. For more information, see the docs.

The xata.createdAt column contains the timestamp of when the row was created. This timestamp is set when the row is inserted into the table and never updated.

The xata.updatedAt column contains the timestamp of when the row was last updated.

A column of type link describes a relationship between two entities that you can navigate in both directions.

You can represent many-to-one relationships between tables by using columns of the link type. The value of the link column points to a record in the target table, referenced by the ID.

At query time, you can easily include columns from the target table by specifying the "columns" field in the request. For an example, see Selecting columns from the linked tables.

Also, you can navigate the link in the opposite direction, from the target table to the source table. For example, if you have a posts table with an author column of type link that points to a users table. You are able to navigate the link from the users table to the posts table. At query time, you can get a list of the related posts in a single request by specifying the <-posts.author field. The arrow (<-) token indicates we are using a backwards relationship established by the author column (of type link) from posts. An example request that retrieves the name and related posts from the users table would look like this:

const page = await xata.db.users
    .select([
        "name",
        {
            name: "<-posts.author",
            columns: ["title"],
            as: "posts",
            limit: 10,
            offset: 0,
            sort: [
                {
                    createdAt: "desc",
                },
            ],
        },
    ]).getAll();

// POST /db/blogs:main/tables/users/query
{
  "columns": [
    "name",
    {
      "name": "<-posts.author",
      "columns": ["title"],
      "as": "posts",
      "limit": 10,
      "offset": 0,
      "order": [
        {
          "column": "createdAt",
          "order": "desc"
        }
      ]
    }
  ]
}

users = self.client.data().query("Users", {
  "table": "users",
  "columns": [
    "name",
    {
      "name": "<-posts.author",
      "columns": ["title"],
      "as": "posts",
      "limit": 10,
      "offset": 0,
      "order": [
        {
          "column": "createdAt",
          "order": "desc"
        }
      ]
    }
  ]
})

The response would look like this (notice the nested records array under the posts field):

{
  "meta": {
    "page": {
      "cursor": "XM4xTsUwDAbgnWP8c0DpAxZvnONRoZA6JSVtwHZZqt4dpUUIMdmWP_n31G1Qk7yMoCuWMDPcdlbC8-r9fbz9qGp6F1Z7qwKHWMs6L9q8ZSuM3iEoCIeDQ8lzNlDnHWpKygZqrQwsoOv2cwCEKByMhyfD7xoDa8Te772DVrGWkocWkbLoMQrHl5jZP77LZHV6nR-69HX59A2V8M-U6a8ZD3O-d_EOqYRRQX6_-Q4AAP__",
      "more": false,
      "size": 20
    }
  },
  "records": [
    {
      "id": "rec_cie05krjtojbm41fv2q0",
      "name": "John Doe",
      "posts": {
        "records": [
          {
            "id": "rec_cie05srjtojbm41fv2t0",
            "title": "Introduction to Xata",
            "xata": {
              "createdAt": "2023-06-28T09:52:51.474094+00:00",
              "updatedAt": "2023-06-29T10:39:18.430212+00:00",
              "version": 1
            }
          },
          {
            "id": "rec_cie05v3jtojbm41fv2tg",
            "title": "Working with relationships",
            "xata": {
              "createdAt": "2023-06-28T09:53:00.398131+00:00",
              "updatedAt": "2023-06-29T10:39:31.148422+00:00",
              "version": 1
            }
          }
        ]
      },
      "xata": {
        "createdAt": "2023-06-28T11:52:19.763366+02:00",
        "updatedAt": "2023-06-29T12:38:41.212738+02:00",
        "version": 1
      }
    },
    {
      "id": "rec_cie05ljjtojbm41fv2qg",
      "name": "Keanu Reeves",
      "postsauthor": {
        "records": [
          {
            "id": "rec_cie060jjtojbm41fv2u0",
            "title": "Blue or Red?",
            "xata": {
              "createdAt": "2023-06-28T09:53:06.568533+00:00",
              "updatedAt": "2023-06-29T10:39:10.7602+00:00",
              "version": 1
            }
          }
        ]
      },
      "xata": {
        "createdAt": "2023-06-28T11:52:22.660549+02:00",
        "updatedAt": "2023-06-29T12:38:50.456018+02:00",
        "version": 2
      }
    }
  ]
}

It is also possible to use link columns that target the same table. For example, you can have a parent column that points to the parent record of the current record.

You can represent many-to-many relationships between two tables by using a third table dedicated to storing the relationships between the two entities. For example, you have a mentors table and a mentee table, and you want to represent an N:M relationship where a mentor can have multiple mentees and a mentee can have multiple mentors. You can create a relationships table with a schema looking like this:

{
  "name": "relationships",
  "columns": [
    {
      "name": "mentor",
      "type": "link",
      "link": {
        "table": "mentors"
      }
    },
    {
      "name": "mentee",
      "type": "link",
      "link": {
        "table": "mentees"
      }
    },
    {
      "name": "status",
      "type": "string"
    }
  ]
}

In the above, you have links to the mentors and mentees tables, but you can also add other columns that are specific for the relationship. In the SQL world, this is called an associative table or a junction table.

Refer to the many-to-many relationships docs for guidance on formulating, joining and managing relations using junction tables.

You can set a default value for every data type. For each data type, you can assign a default value that will be used if a column is left blank. Note, a string with no characters is not the same as an empty value. You can set these default values using the defaultValue setting.

You can add constraints to protect the integrity of your data. We support the following constraints:

• unique: All values of a column must be different within the same table. In case of creating or updating a record with a value that does not satisfy the uniqueness contraint, the record operation will be rejected with an error.
• notNull: Column value cannot be NULL. Requests to create or update a record are rejected with an error if they set this column to NULL. When notNull is set, you must also set a default value for the column using defaultValue. In case no value is set for this column when creating a record, the defaultValue will be applied.

Limitations: You can add constraints only to new columns of empty tables.

Branches in Xata are replicated from a primary store to multiple replica stores. Data operations such as insert/update/delete are first performed in the Primary Store and changes are subsequently propagated to the replica stores, making them eventually consistent.

The propagation delay may vary and there is no strict guarantee but it is typically much less than 100 milliseconds for the vast majority of requests.

Xata has concurrency slots on the Primary and the eventually consistent replica stores. Read operations performed by the /query and /summarize endpoints can run against either of the primary or replica stores. While the default method for those operations is consistency: strong, so they use the Primary Store to ensure the Xata API returns most accurate data by default, it is strongly advised to use the replica store wherever possible.

This allows the available concurrency slots in the primary store to serve write operations and queries that purposely retrieve the latest state of content. Thus making it less likely for the operations that use the Primary Store to reach the concurrency limit and allowing you to get the best possible performance out of your plan by utilizing both Store types.

In order to change the consistency level from strong to eventual, you must add the property consistency: eventual to your /query or /summarize request. For example:

const result = await xata.db.Companies.summarize({
  columns: ['exchange', 'ceo'],
  consistency: 'eventual'
});

resp = self.xata.data().summarizeTable("Companies", {
  "columns": ["exchange", "ceo"],
  "consistency": "eventual"
});

curl -X POST "https://REDACTED.us-east-1.xata.sh/db/mydb:main/tables/Companies/summarize" \
  -H "content-type: application/json" \
  -H "authorization: Bearer xau_REDACTED" \
  -d '{"columns": ["exchange", "ceo"], "consistency": "eventual"}'

Using consistency: eventual is beneficial if you are not bound to real time data. In the given example, you are running a report from yesterday's data. With the delay it is not possible to guarantee consistency or ensure that a record inserted less than 100 milliseconds ago will be part of the result set. By using leveraging consistency: eventual for read requests, you are doubling your concurrency limits.

This is the Xata data model as it stands today. Knowing what's available to you, we encourage exploring more concepts such as the schema.

The object type is unsupported since December 13th, 2023 as mentioned here. We recommend you explore various migration options to transition away from this type. For a migration case, we listed three options for you to explore and change your schema.

Flattening a schema simplifies a database or table structure by removing nested objects. After flattening, all columns are at the same level, making it easier to represent in a tabular format.

You can flatten your table schema by adding the columns from the object column to the table itself. To simplify your table structure, you can incorporate the columns from the object column directly into the table itself, using an underscore (_) to separate them.

The column name is defined as follows:

{
  "name": "name",
  "type": "object",
  "columns": [
    {
      "name": "first",
      "type": "string"
    },
    {
      "name": "middle",
      "type": "string"
    },
    {
      "name": "last",
      "type": "string"
    }
  ]
}

To flatten the structure, the column name should be split into three new columns: first_name, middle_name, and last_name, which will represent the corresponding fields first, middle, and last.

{
  "name": "first_name",
  "type": "string"
},
{
  "name": "middle_name",
  "type": "string"
},
{
  "name": "last_name",
  "type": "string"
}

The second option is to normalize your schema and create a joined table by establishing a link. For example, if you have a table named social_content with a column profile of the type object, you can extract the columns of the profile object into their own table. This option is particularly good if you expect that the profile table might be linked to from other tables.

{
  "name": "profile",
  "type": "object",
  "columns": [
    {
      "name": "name",
      "type": "string"
    },
    {
      "name": "email",
      "type": "email"
    },
    {
      "name": "homepage",
      "type": "string"
    }
  ]
}

Create a new table called profiles with the same schema as the object type:

{
  "columns": [
    {
      "name": "name",
      "type": "string"
    },
    {
      "name": "email",
      "type": "email"
    },
    {
      "name": "homepage",
      "type": "string"
    }
  ]
}

Link the profiles table in the original social_content table:

{
  "name": "profile",
  "type": "link",
  "link": {
    "table": "profiles"
  }
}

As indicated in our roadmap, we are working on introducing support for a dedicated JSON type. As an interim solution, we suggest storing the data from an object column as serialized JSON in a column of type text.

{
  "name": "location",
  "type": "object",
  "columns": [
    {
      "name": "lat",
      "type": "float"
    },
    {
      "name": "lng",
      "type": "float"
    }
  ]
}

Change to the following:

{
  "name": "location",
  "type": "text"
}

Store the following JSON body:

{
  "lat": 47.259659,
  "lng": 11.400375
}