protobuf.wkt

Bases: Message[_AnyFields], AnyMixin

Any contains an arbitrary serialized protocol buffer message along with a URL that describes the type of the serialized message.

In its binary encoding, an Any is an ordinary message; but in other wire forms like JSON, it has a special encoding. The format of the type URL is described on the type_url field.

Protobuf APIs provide utilities to interact with Any values:

• A 'pack' operation accepts a message and constructs a generic Any wrapper around it.
• An 'unpack' operation reads the content of an Any message, either into an existing message or a new one. Unpack operations must check the type of the value they unpack against the declared type_url.
• An 'is' operation decides whether an Any contains a message of the given type, i.e. whether it can 'unpack' that type.

The JSON format representation of an Any follows one of these cases:

• For types without special-cased JSON encodings, the JSON format representation of the Any is the same as that of the message, with an additional @type field which contains the type URL.
• For types with special-cased JSON encodings (typically called 'well-known' types, listed in https://protobuf.dev/programming-guides/json/#any), the JSON format representation has a key @type which contains the type URL and a key value which contains the JSON-serialized value.

The text format representation of an Any is like a message with one field whose name is the type URL in brackets. For example, an Any containing a foo.Bar message may be written [type.googleapis.com/foo.Bar] { a: 2 }.

message google.protobuf.Any

Attributes:

string type_url = 1;

bytes value = 2;

pack(message)

is_type(type_info)

unpack(type_info: DescMessage) -> Message | None

unpack(type_info: type[T]) -> T | None

Bases: Message[_FileDescriptorProtoFields]

Describes a complete .proto file.

message google.protobuf.FileDescriptorProto

Attributes:

optional string name = 1;

optional string package = 2;

repeated string dependency = 3;

repeated int32 public_dependency = 10 [packed = false];

repeated int32 weak_dependency = 11 [packed = false];

repeated string option_dependency = 15;

repeated google.protobuf.DescriptorProto message_type = 4;

repeated google.protobuf.EnumDescriptorProto enum_type = 5;

repeated google.protobuf.ServiceDescriptorProto service = 6;

repeated google.protobuf.FieldDescriptorProto extension = 7;

optional google.protobuf.FileOptions options = 8;

optional google.protobuf.SourceCodeInfo source_code_info = 9;

optional string syntax = 12;

optional google.protobuf.Edition edition = 14;

Bases: Message[_FileDescriptorSetFields], FileDescriptorSetMixin

The protocol compiler can output a FileDescriptorSet containing the .proto files it parses.

message google.protobuf.FileDescriptorSet

Attributes:

repeated google.protobuf.FileDescriptorProto file = 1;

to_registry()

Bases: Message[_DurationFields], DurationMixin

A Duration represents a signed, fixed-length span of time represented as a count of seconds and fractions of seconds at nanosecond resolution. It is independent of any calendar and concepts like "day" or "month". It is related to Timestamp in that the difference between two Timestamp values is a Duration and it can be added or subtracted from a Timestamp. Range is approximately +-10,000 years.

Examples

Example 1: Compute Duration from two Timestamps in pseudo code.

Timestamp start = ...;
Timestamp end = ...;
Duration duration = ...;

duration.seconds = end.seconds - start.seconds;
duration.nanos = end.nanos - start.nanos;

if (duration.seconds < 0 && duration.nanos > 0) {
  duration.seconds += 1;
  duration.nanos -= 1000000000;
} else if (duration.seconds > 0 && duration.nanos < 0) {
  duration.seconds -= 1;
  duration.nanos += 1000000000;
}

Example 2: Compute Timestamp from Timestamp + Duration in pseudo code.

Timestamp start = ...;
Duration duration = ...;
Timestamp end = ...;

end.seconds = start.seconds + duration.seconds;
end.nanos = start.nanos + duration.nanos;

if (end.nanos < 0) {
  end.seconds -= 1;
  end.nanos += 1000000000;
} else if (end.nanos >= 1000000000) {
  end.seconds += 1;
  end.nanos -= 1000000000;
}

Example 3: Compute Duration from datetime.timedelta in Python.

td = datetime.timedelta(days=3, minutes=10)
duration = Duration()
duration.FromTimedelta(td)

JSON Mapping

In JSON format, the Duration type is encoded as a string rather than an object, where the string ends in the suffix "s" (indicating seconds) and is preceded by the number of seconds, with nanoseconds expressed as fractional seconds. For example, 3 seconds with 0 nanoseconds should be encoded in JSON format as "3s", while 3 seconds and 1 nanosecond should be expressed in JSON format as "3.000000001s", and 3 seconds and 1 microsecond should be expressed in JSON format as "3.000001s".

message google.protobuf.Duration

Attributes:

int64 seconds = 1;

int32 nanos = 2;

from_nanos(nanos)

from_timedelta(td)

to_timedelta()

to_nanos()

>>> from protobuf.wkt import Duration
>>> Duration(nanos=1_000).to_nanos()
1000
>>> Duration(seconds=10, nanos=100).to_nanos()
10000000100

to_seconds()

>>> from protobuf.wkt import Duration
>>> Duration(seconds=10).to_seconds()
10.0
>>> Duration(nanos=100).to_seconds()
1e-07
>>> Duration(seconds=10, nanos=100).to_seconds()
10.0000001

from_seconds(seconds)

>>> from protobuf.wkt import Duration
>>> Duration.from_seconds(10)
Duration(seconds=10)
>>> Duration.from_seconds(10.1)
Duration(seconds=10, nanos=100000000)

Bases: Message[_EmptyFields]

A generic empty message that you can re-use to avoid defining duplicated empty messages in your APIs. A typical example is to use it as the request or the response type of an API method. For instance:

service Foo {
  rpc Bar(google.protobuf.Empty) returns (google.protobuf.Empty);
}

message google.protobuf.Empty

Bases: Message[_FieldMaskFields]

FieldMask represents a set of symbolic field paths, for example:

paths: "f.a"
paths: "f.b.d"

Here f represents a field in some root message, a and b fields in the message found in f, and d a field found in the message in f.b.

Field masks are used to specify a subset of fields that should be returned by a get operation or modified by an update operation. Field masks also have a custom JSON encoding (see below).

Field Masks in Projections

When used in the context of a projection, a response message or sub-message is filtered by the API to only contain those fields as specified in the mask. For example, if the mask in the previous example is applied to a response message as follows:

f {
  a : 22
  b {
    d : 1
    x : 2
  }
  y : 13
}
z: 8

The result will not contain specific values for fields x,y and z (their value will be set to the default, and omitted in proto text output):

f {
  a : 22
  b {
    d : 1
  }
}

A repeated field is not allowed except at the last position of a paths string.

If a FieldMask object is not present in a get operation, the operation applies to all fields (as if a FieldMask of all fields had been specified).

Note that a field mask does not necessarily apply to the top-level response message. In case of a REST get operation, the field mask applies directly to the response, but in case of a REST list operation, the mask instead applies to each individual message in the returned resource list. In case of a REST custom method, other definitions may be used. Where the mask applies will be clearly documented together with its declaration in the API. In any case, the effect on the returned resource/resources is required behavior for APIs.

Field Masks in Update Operations

A field mask in update operations specifies which fields of the targeted resource are going to be updated. The API is required to only change the values of the fields as specified in the mask and leave the others untouched. If a resource is passed in to describe the updated values, the API ignores the values of all fields not covered by the mask.

If a repeated field is specified for an update operation, new values will be appended to the existing repeated field in the target resource. Note that a repeated field is only allowed in the last position of a paths string.

If a sub-message is specified in the last position of the field mask for an update operation, then new value will be merged into the existing sub-message in the target resource.

For example, given the target message:

f {
  b {
    d: 1
    x: 2
  }
  c: [1]
}

And an update message:

f {
  b {
    d: 10
  }
  c: [2]
}

then if the field mask is:

paths: ["f.b", "f.c"]

then the result will be:

f {
  b {
    d: 10
    x: 2
  }
  c: [1, 2]
}

An implementation may provide options to override this default behavior for repeated and message fields.

Note that libraries which implement FieldMask resolution have various different behaviors in the face of empty masks or the special "*" mask. When implementing a service you should confirm these cases have the appropriate behavior in the underlying FieldMask library that you desire, and you may need to special case those cases in your application code if the underlying field mask library behavior differs from your intended service semantics.

Update methods implementing https://google.aip.dev/134 - MUST support the special value * meaning "full replace" - MUST treat an omitted field mask as "replace fields which are present".

Other methods implementing https://google.aip.dev/157 - SHOULD support the special value "*" to mean "get all". - MUST treat an omitted field mask to mean "get all", unless otherwise documented.

Considerations for HTTP REST

The HTTP kind of an update operation which uses a field mask must be set to PATCH instead of PUT in order to satisfy HTTP semantics (PUT must only be used for full updates).

JSON Encoding of Field Masks

In JSON, a field mask is encoded as a single string where paths are separated by a comma. Fields name in each path are converted to/from lower-camel naming conventions.

As an example, consider the following message declarations:

message Profile {
  User user = 1;
  Photo photo = 2;
}
message User {
  string display_name = 1;
  string address = 2;
}

In proto a field mask for Profile may look as such:

mask {
  paths: "user.display_name"
  paths: "photo"
}

In JSON, the same mask is represented as below:

{
  mask: "user.displayName,photo"
}

Field Masks and Oneof Fields

Field masks treat fields in oneofs just as regular fields. Consider the following message:

message SampleMessage {
  oneof test_oneof {
    string name = 4;
    SubMessage sub_message = 9;
  }
}

The field mask can be:

mask {
  paths: "name"
}

Or:

mask {
  paths: "sub_message"
}

Note that oneof type names ("test_oneof" in this case) cannot be used in paths.

Field Mask Verification

The implementation of any API method which has a FieldMask type field in the request should verify the included field paths, and return an INVALID_ARGUMENT error if any path is unmappable.

message google.protobuf.FieldMask

Attributes:

repeated string paths = 1;

Bases: Message[_ListValueFields], ListValueMixin

Represents a JSON array.

message google.protobuf.ListValue

Attributes:

repeated google.protobuf.Value values = 1;

from_python(values)

>>> from protobuf.wkt import ListValue
>>> ListValue.from_python([1, "foo"])
ListValue(values=[Value(kind=Oneof(field='number_value', value=1.0)), Value(kind=Oneof(field='string_value', value='foo'))])

to_python()

>>> from protobuf import Oneof
>>> from protobuf.wkt import ListValue, Value
>>> ListValue(
...     values=[
...         Value(kind=Oneof(field="number_value", value=1.0)),
...         Value(kind=Oneof(field="string_value", value="foo")),
...     ]
... ).to_python()
[1.0, 'foo']

Bases: Enum

Represents a JSON null.

NullValue is a sentinel, using an enum with only one value to represent the null value for the Value type union.

A field of type NullValue with any value other than 0 is considered invalid. Most ProtoJSON serializers will emit a Value with a null_value set as a JSON null regardless of the integer value, and so will round trip to a 0 value.

enum google.protobuf.NullValue

Attributes:

NULL_VALUE = 0

Bases: Message[_StructFields], StructMixin

Represents a JSON object.

An unordered key-value map, intending to perfectly capture the semantics of a JSON object. This enables parsing any arbitrary JSON payload as a message field in ProtoJSON format.

This follows RFC 8259 guidelines for interoperable JSON: notably this type cannot represent large Int64 values or NaN/Infinity numbers, since the JSON format generally does not support those values in its number type.

If you do not intend to parse arbitrary JSON into your message, a custom typed message should be preferred instead of using this type.

message google.protobuf.Struct

Attributes:

map<string, google.protobuf.Value> fields = 1;

from_python(data)

>>> from protobuf.wkt import Struct
>>> Struct.from_python({"a": 1, "b": "bear"})
Struct(fields={'a': Value(kind=Oneof(field='number_value', value=1.0)), 'b': Value(kind=Oneof(field='string_value', value='bear'))})

to_python()

>>> from protobuf import Oneof
>>> from protobuf.wkt import Struct, Value
>>> Struct(
...     fields={
...         "a": Value(kind=Oneof(field="number_value", value=1.0)),
...         "b": Value(kind=Oneof(field="string_value", value="bear")),
...     }
... ).to_python()
{'a': 1.0, 'b': 'bear'}

Bases: Message[_ValueFields], ValueMixin

Represents a JSON value.

Value represents a dynamically typed value which can be either null, a number, a string, a boolean, a recursive struct value, or a list of values. A producer of value is expected to set one of these variants. Absence of any variant is an invalid state.

message google.protobuf.Value

Attributes:

oneof kind

from_python(value)

>>> from protobuf.wkt import Value
>>> Value.from_python(None)
Value(kind=Oneof(field='null_value', value=NullValue.NULL_VALUE))
>>> Value.from_python(True)
Value(kind=Oneof(field='bool_value', value=True))
>>> Value.from_python(2)
Value(kind=Oneof(field='number_value', value=2.0))
>>> Value.from_python(3.14)
Value(kind=Oneof(field='number_value', value=3.14))
>>> Value.from_python("hello")
Value(kind=Oneof(field='string_value', value='hello'))
>>> Value.from_python([1, "foo"])
Value(kind=Oneof(field='list_value', value=ListValue(values=[Value(kind=Oneof(field='number_value', value=1.0)), Value(kind=Oneof(field='string_value', value='foo'))])))
>>> Value.from_python({"a": 1, "b": "bear"})
Value(kind=Oneof(field='struct_value', value=Struct(fields={'a': Value(kind=Oneof(field='number_value', value=1.0)), 'b': Value(kind=Oneof(field='string_value', value='bear'))})))

to_python()

>>> from protobuf import Oneof
>>> from protobuf.wkt import ListValue, NullValue, Struct, Value
>>> Value(
...     kind=Oneof(field="null_value", value=NullValue.NULL_VALUE)
... ).to_python()
>>> Value(kind=Oneof(field="bool_value", value=True)).to_python()
True
>>> Value(kind=Oneof(field="number_value", value=3.14)).to_python()
3.14
>>> Value(kind=Oneof(field="string_value", value="hello")).to_python()
'hello'
>>> Value(
...     kind=Oneof(
...         field="list_value",
...         value=ListValue(
...             values=[
...                 Value(kind=Oneof(field="number_value", value=1.0)),
...                 Value(kind=Oneof(field="string_value", value="foo")),
...             ]
...         ),
...     )
... ).to_python()
[1.0, 'foo']
>>> Value(
...     kind=Oneof(
...         field="struct_value",
...         value=Struct(
...             fields={
...                 "a": Value(kind=Oneof(field="number_value", value=1.0)),
...                 "b": Value(
...                     kind=Oneof(field="string_value", value="bear")
...                 ),
...             }
...         ),
...     )
... ).to_python()
{'a': 1.0, 'b': 'bear'}

Bases: Message[_TimestampFields], TimestampMixin

A Timestamp represents a point in time independent of any time zone or local calendar, encoded as a count of seconds and fractions of seconds at nanosecond resolution. The count is relative to an epoch at UTC midnight on January 1, 1970, in the proleptic Gregorian calendar which extends the Gregorian calendar backwards to year one.

All minutes are 60 seconds long. Leap seconds are "smeared" so that no leap second table is needed for interpretation, using a 24-hour linear smear.

The range is from 0001-01-01T00:00:00Z to 9999-12-31T23:59:59.999999999Z. By restricting to that range, we ensure that we can convert to and from RFC 3339 date strings.

Examples

Example 1: Compute Timestamp from POSIX time().

Timestamp timestamp;
timestamp.set_seconds(time(NULL));
timestamp.set_nanos(0);

Example 2: Compute Timestamp from POSIX gettimeofday().

struct timeval tv;
gettimeofday(&tv, NULL);

Timestamp timestamp;
timestamp.set_seconds(tv.tv_sec);
timestamp.set_nanos(tv.tv_usec * 1000);

Example 3: Compute Timestamp from Win32 GetSystemTimeAsFileTime().

FILETIME ft;
GetSystemTimeAsFileTime(&ft);
UINT64 ticks = (((UINT64)ft.dwHighDateTime) << 32) | ft.dwLowDateTime;

// A Windows tick is 100 nanoseconds. Windows epoch 1601-01-01T00:00:00Z
// is 11644473600 seconds before Unix epoch 1970-01-01T00:00:00Z.
Timestamp timestamp;
timestamp.set_seconds((INT64) ((ticks / 10000000) - 11644473600LL));
timestamp.set_nanos((INT32) ((ticks % 10000000) * 100));

Example 4: Compute Timestamp from Java System.currentTimeMillis().

long millis = System.currentTimeMillis();

Timestamp timestamp = Timestamp.newBuilder().setSeconds(millis / 1000)
    .setNanos((int) ((millis % 1000) * 1000000)).build();

Example 5: Compute Timestamp from Java Instant.now().

Instant now = Instant.now();

Timestamp timestamp =
    Timestamp.newBuilder().setSeconds(now.getEpochSecond())
        .setNanos(now.getNano()).build();

Example 6: Compute Timestamp from current time in Python.

timestamp = Timestamp()
timestamp.GetCurrentTime()

JSON Mapping

In JSON format, the Timestamp type is encoded as a string in the RFC 3339 format. That is, the format is "{year}-{month}-{day}T{hour}:{min}:{sec}[.{frac_sec}]Z" where {year} is always expressed using four digits while {month}, {day}, {hour}, {min}, and {sec} are zero-padded to two digits each. The fractional seconds, which can go up to 9 digits (i.e. up to 1 nanosecond resolution), are optional. The "Z" suffix indicates the timezone ("UTC"); the timezone is required. A ProtoJSON serializer should always use UTC (as indicated by "Z") when printing the Timestamp type and a ProtoJSON parser should be able to accept both UTC and other timezones (as indicated by an offset).

For example, "2017-01-15T01:30:15.01Z" encodes 15.01 seconds past 01:30 UTC on January 15, 2017.

In JavaScript, one can convert a Date object to this format using the standard toISOString() method. In Python, a standard datetime.datetime object can be converted to this format using strftime with the time format spec '%Y-%m-%dT%H:%M:%S.%fZ'. Likewise, in Java, one can use the Joda Time's ISODateTimeFormat.dateTime() to obtain a formatter capable of generating timestamps in this format.

message google.protobuf.Timestamp

Attributes:

int64 seconds = 1;

int32 nanos = 2;

now()

from_nanos(nanos)

from_datetime(dt)

>>> from datetime import datetime, timezone, timedelta
>>> from protobuf.wkt import Timestamp
>>> Timestamp.from_datetime(datetime(1970, 1, 1, tzinfo=timezone.utc))
Timestamp()
>>> Timestamp.from_datetime(
...     datetime(1970, 1, 1, 0, 0, 10, tzinfo=timezone.utc)
... )
Timestamp(seconds=10)
>>> Timestamp.from_datetime(
...     datetime(
...         1970, 1, 1, tzinfo=timezone(timedelta(hours=5, minutes=30))
...     )
... )
Timestamp(seconds=-19800)

to_datetime()

>>> from datetime import timezone, timedelta
>>> from protobuf.wkt import Timestamp
>>> Timestamp(seconds=10).to_datetime()
datetime.datetime(1970, 1, 1, 0, 0, 10, tzinfo=datetime.timezone.utc)
>>> Timestamp(seconds=10).to_datetime().astimezone(
...     timezone(timedelta(hours=5, minutes=30))
... )
datetime.datetime(1970, 1, 1, 5, 30, 10, tzinfo=datetime.timezone(datetime.timedelta(seconds=19800)))

to_nanos()

>>> from protobuf.wkt import Timestamp
>>> Timestamp(nanos=1_000).to_nanos()
1000
>>> Timestamp(seconds=10, nanos=100).to_nanos()
10000000100

to_seconds()

>>> from protobuf.wkt import Timestamp
>>> Timestamp(seconds=10).to_seconds()
10.0
>>> Timestamp(nanos=100).to_seconds()
1e-07
>>> Timestamp(seconds=10, nanos=100).to_seconds()
10.0000001

from_seconds(seconds)

>>> from protobuf.wkt import Timestamp
>>> Timestamp.from_seconds(10)
Timestamp(seconds=10)
>>> Timestamp.from_seconds(10.1)
Timestamp(seconds=10, nanos=100000000)
>>> Timestamp.from_seconds(-10.1)
Timestamp(seconds=-11, nanos=900000000)

Bases: Message[_BoolValueFields]

Wrapper message for bool.

The JSON representation for BoolValue is JSON true and false.

Not recommended for use in new APIs, but still useful for legacy APIs and has no plan to be removed.

message google.protobuf.BoolValue

Attributes:

bool value = 1;

Bases: Message[_BytesValueFields]

Wrapper message for bytes.

The JSON representation for BytesValue is JSON string.

Not recommended for use in new APIs, but still useful for legacy APIs and has no plan to be removed.

message google.protobuf.BytesValue

Attributes:

bytes value = 1;

Bases: Message[_DoubleValueFields]

Wrapper message for double.

The JSON representation for DoubleValue is JSON number.

Not recommended for use in new APIs, but still useful for legacy APIs and has no plan to be removed.

message google.protobuf.DoubleValue

Attributes:

double value = 1;

Bases: Message[_FloatValueFields]

Wrapper message for float.

The JSON representation for FloatValue is JSON number.

Not recommended for use in new APIs, but still useful for legacy APIs and has no plan to be removed.

message google.protobuf.FloatValue

Attributes:

float value = 1;

Bases: Message[_Int32ValueFields]

Wrapper message for int32.

The JSON representation for Int32Value is JSON number.

Not recommended for use in new APIs, but still useful for legacy APIs and has no plan to be removed.

message google.protobuf.Int32Value

Attributes:

int32 value = 1;

Bases: Message[_Int64ValueFields]

Wrapper message for int64.

The JSON representation for Int64Value is JSON string.

Not recommended for use in new APIs, but still useful for legacy APIs and has no plan to be removed.

message google.protobuf.Int64Value

Attributes:

int64 value = 1;

Bases: Message[_StringValueFields]

Wrapper message for string.

The JSON representation for StringValue is JSON string.

Not recommended for use in new APIs, but still useful for legacy APIs and has no plan to be removed.

message google.protobuf.StringValue

Attributes:

string value = 1;

Bases: Message[_UInt32ValueFields]

Wrapper message for uint32.

The JSON representation for UInt32Value is JSON number.

Not recommended for use in new APIs, but still useful for legacy APIs and has no plan to be removed.

message google.protobuf.UInt32Value

Attributes:

uint32 value = 1;

Bases: Message[_UInt64ValueFields]

Wrapper message for uint64.

The JSON representation for UInt64Value is JSON string.

Not recommended for use in new APIs, but still useful for legacy APIs and has no plan to be removed.

message google.protobuf.UInt64Value

Attributes:

uint64 value = 1;