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Protobuf Messages

Version 14.0.0 of the Python client library introduces a new required configuration parameter called use_proto_plus that specifies whether you want the library to return proto-plus messages or protobuf messages. For details on how to set this parameter, see the configuration documentation.

Proto-plus versus protobuf messages

In version 10.0.0 the Python client library migrated to a new code generator pipeline that integrated proto-plus as a way to improve the ergonomics of the protobuf message interface by helping them behave more like native Python objects. The tradeoff of this improvement is that proto-plus introduces performance overhead.

Proto-plus performance

One of the core benefits of proto-plus is that it converts protobuf messages and well-known types to native Python types through a process called type marshaling.

Marshaling occurs when a field is accessed on a proto-plus message instance, specifically when a field is either read or set, for example, in a protobuf definition:

syntax = "proto3";

message Dog {
  string name = 1;
}

When this definition is converted to a proto-plus class, it will look something like:

import proto

class Dog(proto.Message):
    name = proto.Field(proto.STRING, number=1)

You can then initialize the Dog class and access its name field as you would any other Python object:

dog = Dog()
dog.name = "Scruffy"
print(dog.name)

When reading and setting the name field, the value is converted from a native Python str type to a string type so that the value is compatible with the protobuf runtime.

In the analysis we've conducted since the release of version 10.0.0, we've determined that the time spent doing these type conversions has a large enough performance impact that it's important to give users the option to use protobuf messages.

Use cases for proto-plus and protobuf messages

Proto-plus message use cases

Proto-plus offers a number of ergonomic improvements over protobuf messages, so they're ideal for writing maintainable, readable code. Since they expose native Python objects, they're easier to use and understand.

Protobuf message use cases

Use protobufs for performance-sensitive use cases, specifically in applications that need to process large reports quickly, or that build mutate requests with a large number of operations, for example with BatchJobService or OfflineUserDataJobService.

Dynamically changing message types

After choosing the appropriate message type for your application, you may find that you need to use the other type for a specific workflow. In this case, it's easy to switch between the two types dynamically using utilities offered by the client library. Using the same Dog message class from above:

from google.ads.googleads import util

# Proto-plus message type
dog = Dog()

# Protobuf message type
dog = util.convert_proto_plus_to_protobuf(dog)

# Back to proto-plus message type
dog = util.convert_protobuf_to_proto_plus(dog)

Protobuf message interface differences

The proto-plus interface is documented in detail in the proto-plus documentation, but here we'll highlight some key differences that affect common use cases for the Google Ads client library.

Serializing to bytes

Proto-plus messages:
serialized = type(campaign).serialize(campaign)
deserialized = type(campaign).deserialize(serialized)
Protobuf messages:
serialized = campaign.SerializeToString()
deserialized = campaign.FromString(serialized)

Serializing to JSON

Proto-plus messages:
serialized = type(campaign).to_json(campaign)
deserialized = type(campaign).from_json(serialized)
Protobuf messages:
from google.protobuf.json_format import MessageToJson, Parse

serialized = MessageToJson(campaign)
deserialized = Parse(serialized, campaign)

Field masks

The field mask helper method provided by api-core is designed to use protobuf message instances. So when using proto-plus messages, convert them to protobuf messages to utilize the helper:

Proto-plus messages:
from google.api_core.protobuf_helpers import field_mask

campaign = client.get_type("Campaign")
protobuf_campaign = util.convert_proto_plus_to_protobuf(campaign)
mask = field_mask(None, protobuf_campaign)
Protobuf messages:
from google.api_core.protobuf_helpers import field_mask

campaign = client.get_type("Campaign")
mask = field_mask(None, campaign)

Enums

Enums exposed by proto-plus messages are instances of Python's native enum type and therefore inherit a number of convenience methods.

Retrieving enum types

When using the GoogleAdsClient.get_type method to retrieve enums, the messages that are returned are slightly different depending on whether you're using proto-plus or protobuf messages. For example:

Proto-plus messages:
val = client.get_type("CampaignStatusEnum").CampaignStatus.PAUSED
Protobuf messages:
val = client.get_type("CampaignStatusEnum").PAUSED

To make retrieving enums simpler, there's a convenience attribute on GoogleAdsClient instances that has a consistent interface regardless of which message type you're using:

val = client.enums.CampaignStatusEnum.PAUSED

Retrieving enum values

Sometimes it's useful to know the value, or field ID, of a given Enum. For example PAUSED on the CampaignStatusEnum corresponds to 3:

Proto-plus messages:
campaign = client.get_type("Campaign")
campaign.status = client.enums.CampaignStatusEnum.PAUSED
# To read the value of campaign status
print(campaign.status.value)
Protobuf messages:
campaign = client.get_type("Campaign")
status_enum = client.enums.CampaignStatusEnum
campaign.status = status_enum.PAUSED
# To read the value of campaign status
print(status_enum.CampaignStatus.Value(campaign.status))

Retrieving enum names

Sometimes it's useful to know the name of an Enum field. For example, when reading objects from the API you might want to know which campaign status the int 3 corresponds to:

Proto-plus messages:
campaign = client.get_type("Campaign")
campaign.status = client.enums.CampaignStatusEnum.PAUSED
# To read the name of campaign status
print(campaign.status.name)
Protobuf messages:
campaign = client.get_type("Campaign")
status_enum = client.enums.CampaignStatusEnum
# Sets the campaign status to the int value for PAUSED
campaign.status = status_enum.PAUSED
# To read the name of campaign status
status_enum.CampaignStatus.Name(campaign.status)

Repeated fields

As described in the proto-plus documentation, repeated fields are generally equivalent to typed lists, which means that they behave almost identically to a list.

Appending to repeated scalar fields

When adding values to repeated scalar type fields, for example string or int64 fields, the interface is the same regardless of message type:

Proto-plus messages:
ad.final_urls.append("https://www.example.com")
Protobuf messages:
ad.final_urls.append("https://www.example.com")

This includes all other common list methods as well, for example extend:

Proto-plus messages:
ad.final_urls.extend(["https://www.example.com", "https://www.example.com/2"])
Protobuf messages:
ad.final_urls.extend(["https://www.example.com", "https://www.example.com/2"])

Appending message types to repeated fields

If the repeated field is not a scalar type, the behavior when adding them to repeated fields is slightly different:

Proto-plus messages:
frequency_cap = client.get_type("FrequencyCapEntry")
frequency_cap.cap = 100
campaign.frequency_caps.append(frequency_cap)
Protobuf messages:
# The add method initializes a message and adds it to the repeated field
frequency_cap = campaign.frequency_caps.add()
frequency_cap.cap = 100

Checking for empty messages

Sometimes it's useful to know whether a message instance contains any information, or has any of its fields set.

Proto-plus messages:
# When using proto-plus messages you can simply check the message for
# truthiness.
is_empty = bool(campaign)
is_empty = not campaign
Protobuf messages:
is_empty = campaign.ByteSize() == 0

Copying messages

For both proto-plus and protobuf messages, we recommend using the copy_from helper method on the GoogleAdsClient:

client.copy_from(campaign, other_campaign)

Setting empty message fields

The process for setting empty message fields is the same regardless of the message type you're using. You just need to copy an empty message into the field in question. See the Copying messages section as well as the Empty Message Fields guide. Here's an example of how to set an empty message field:

client.copy_from(campaign.manual_cpm, client.get_type("ManualCpm"))

Field names that are reserved words

When using proto-plus messages, field names will automatically appear with a trailing underscore if the name is also a reserved word in Python. Here's an example of working with an Asset instance:

asset = client.get_type("Asset")
asset.type_ = client.enums.AssetTypeEnum.IMAGE

The full list of reserved names is constructed in the gapic generator module. It can be accessed programmatically as well. First, install the module:

python -m pip install gapic-generator

Then, in a Python repl or script:

import gapic.utils
print(gapic.utils.reserved_names.RESERVED_NAMES)

Determining field presence

Because the fields on protobuf message instances have default values, it's not always intuitive to know whether a field has been set or not.

Proto-plus messages:
# Use the "in" operator.
has_field = "name" in campaign
Protobuf messages:
campaign = client.get_type("Campaign")
# Determines whether "name" is set and not just an empty string.
campaign.HasField("name")

The protobuf Message class interface has a HasField method that determines whether the field on a message has been set, even if it was set to a default value.

Accessing protobuf message methods

The protobuf message interface includes some convenience methods that are not part of the proto-plus interface; however, it's simple to access them by converting a proto-plus message to its protobuf counterpart:

# Accessing the ListFields method
protobuf_campaign = util.convert_protobuf_to_proto_plus(campaign)
print(campaign.ListFields())

# Accessing the Clear method
protobuf_campaign = util.convert_protobuf_to_proto_plus(campaign)
print(campaign.Clear())

Issue tracker

If you have any questions about these changes or any problems migrating to version 14.0.0 of the library, file an issue on our tracker.