Using temporality

Overview

Fauna documents are immutable, which means that once a document is created it never changes. If you update a document Fauna creates a new version of the document reflecting the changes and the original is left intact. With Fauna’s temporality features, it is possible to see the change history of a document, retrieve older versions, and perform queries based on document timestamps.

The `history_days` attribute

The history_days field of the collection determines how long Fauna retains old versions of updated documents. history_days defaults to 30, but it is possible to specify a different value when you create a collection:

CreateCollection({ name: "fruit", history_days: 10 })

{
  ref: Collection("fruit"),
  ts: 1659482501550000,
  history_days: 10,
  name: 'fruit'
}

Query metrics:

bytesIn: 67
bytesOut: 142
computeOps: 1
readOps: 0
writeOps: 1
readBytes: 804
writeBytes: 324
queryTime: 11ms
retries: 0

You can also update the history_days attribute of an existing collection:

Update(Collection("fruit"), { history_days: 5 })

{
  ref: Collection("fruit"),
  ts: 1659482502120000,
  history_days: 5,
  name: 'fruit'
}

Query metrics:

bytesIn: 72
bytesOut: 141
computeOps: 1
readOps: 0
writeOps: 1
readBytes: 128
writeBytes: 94
queryTime: 7ms
retries: 0

After the period of time specified by history_days expires, Fauna removes any version of a document which has aged out. The most recent version of a document is not affected.

Documents might also include a ttl (time-to-live) field. Documents that have aged out of the database due to a ttl expiration cannot be retrieved with temporal queries. See Create for more information about the ttl attribute.

Document removal is handled by a background task, so once a document "expires" due to the history_days setting on a collection or the ttl field on a document, it could be some time (hours or days) before the removal occurs. There is no guarantee that removal actually occurs.

Document history

You can see a document’s change history with the Events function. To illustrate this feature, let’s create a new document in the fruit collection:

Create(
  Ref(Collection("fruit"), "1"),
  {
    data: {
      type: "apple",
      colors: ["red", "green"],
      quantity: 15
    }
  }
)

{
  ref: Ref(Collection("fruit"), "1"),
  ts: 1659482501840000,
  data: { type: 'apple', colors: [ 'red', 'green' ], quantity: 15 }
}

Query metrics:

bytesIn: 146
bytesOut: 205
computeOps: 1
readOps: 0
writeOps: 1
readBytes: 14
writeBytes: 224
queryTime: 13ms
retries: 0

Now let’s update the document:

Update(
  Ref(Collection("fruit"), "1"),
  {
    data: {
      quantity: 50
    }
  }
)

{
  ref: Ref(Collection("fruit"), "1"),
  ts: 1659482502400000,
  data: { type: 'apple', colors: [ 'red', 'green' ], quantity: 50 }
}

Query metrics:

bytesIn: 106
bytesOut: 205
computeOps: 1
readOps: 0
writeOps: 1
readBytes: 88
writeBytes: 117
queryTime: 13ms
retries: 0

Now you can use Events to see the change history. The following example uses Events with the Paginate function to list all the document’s change events.

Paginate(Events(Ref(Collection("fruit"), "1")))

{
  data: [
    {
      ts: 1659482501840000,
      action: 'create',
      document: Ref(Collection("fruit"), "1"),
      data: { type: 'apple', colors: [ 'red', 'green' ], quantity: 15 }
    },
    {
      ts: 1659482502400000,
      action: 'update',
      document: Ref(Collection("fruit"), "1"),
      data: { quantity: 50 }
    }
  ]
}

Query metrics:

bytesIn: 63
bytesOut: 415
computeOps: 1
readOps: 1
writeOps: 0
readBytes: 191
writeBytes: 0
queryTime: 1ms
retries: 0

You can retrieve a specific version of a document by including its timestamp with the Get function:

Get(Ref(Collection("fruit"), "1"), ToInteger("<$TIMESTAMP>"))

{
  ref: Ref(Collection("fruit"), "1"),
  ts: 1659482760050000,
  data: { type: 'apple', colors: [ 'red', 'green' ], quantity: 15 }
}

Query metrics:

bytesIn: 86
bytesOut: 205
computeOps: 1
readOps: 1
writeOps: 0
readBytes: 88
writeBytes: 0
queryTime: 1ms
retries: 0

Replace the string <$TIMESTAMP> with the value for ts from the document creation result.

Timestamp queries

You can use the At function to retrieve data from a specified point in time. To see this feature in action, let’s add a few more documents to our collection.

Map(
  [
    ["2", "mango", ["green", "yellow"], 20],
    ["3", "melon", ["green"], 100],
    ["4", "pear", ["yellow", "brown"], 60],
  ],
  Lambda(
    ["id", "type", "colors", "quantity"],
    Create(
      Ref(Collection("fruit"), Var("id")),
      {
        data: {
          type: Var("type"),
          colors: Var("colors"),
          quantity: Var("quantity")
        }
      }
    )
  )
)

[
  {
    ref: Ref(Collection("fruit"), "2"),
    ts: 1659482827030000,
    data: { type: 'mango', colors: [ 'green', 'yellow' ], quantity: 20 }
  },
  {
    ref: Ref(Collection("fruit"), "3"),
    ts: 1659482827030000,
    data: { type: 'melon', colors: [ 'green' ], quantity: 100 }
  },
  {
    ref: Ref(Collection("fruit"), "4"),
    ts: 1659482827030000,
    data: { type: 'pear', colors: [ 'yellow', 'brown' ], quantity: 60 }
  }
]

Query metrics:

bytesIn: 353
bytesOut: 593
computeOps: 1
readOps: 0
writeOps: 3
readBytes: 42
writeBytes: 675
queryTime: 11ms
retries: 0

These new documents all have the same timestamp, because they were all added as part of the same transaction.

Now that you have several documents in our collection, create an index to retrieve them all at once:

Map(
  Paginate(Documents(Collection("fruit"))),
  Lambda("ref", Get(Var("ref")))
)

{
  data: [
    {
      ref: Ref(Collection("fruit"), "1"),
      ts: 1659482826190000,
      data: { type: 'apple', colors: [ 'red', 'green' ], quantity: 50 }
    },
    {
      ref: Ref(Collection("fruit"), "2"),
      ts: 1659482827030000,
      data: { type: 'mango', colors: [ 'green', 'yellow' ], quantity: 20 }
    },
    {
      ref: Ref(Collection("fruit"), "3"),
      ts: 1659482827030000,
      data: { type: 'melon', colors: [ 'green' ], quantity: 100 }
    },
    {
      ref: Ref(Collection("fruit"), "4"),
      ts: 1659482827030000,
      data: { type: 'pear', colors: [ 'yellow', 'brown' ], quantity: 60 }
    }
  ]
}

Query metrics:

bytesIn: 116
bytesOut: 795
computeOps: 1
readOps: 12
writeOps: 0
readBytes: 834
writeBytes: 0
queryTime: 1ms
retries: 0

To query a database at a particular point in time, you can use the At function. At adds a condition to a query which matches only documents which were created at or before a specified timestamp.

At(
  ToInteger("<$TIMESTAMP>"),
  Map(
    Paginate(Documents(Collection("fruit"))),
    Lambda("ref", Get(Var("ref")))
  )
)

{
  data: [
    {
      ref: Ref(Collection("fruit"), "1"),
      ts: 1659482825620000,
      data: { type: 'apple', colors: [ 'red', 'green' ], quantity: 15 }
    }
  ]
}

Query metrics:

bytesIn: 160
bytesOut: 216
computeOps: 1
readOps: 9
writeOps: 0
readBytes: 464
writeBytes: 0
queryTime: 1ms
retries: 0

Replace the string <$TIMESTAMP> with the value for ts from the document creation result.

Any documents which have been removed from the database due to either history_days or ttl expiration cannot be retrieved with temporal queries.

Conclusion

Temporality offers developers a rich set of features for working with versioned documents and querying collections based on document timestamps. See the following resources for more information about temporality:

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Using temporality

Overview

The history_days attribute

Document history

Timestamp queries

Conclusion

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The `history_days` attribute