同时处理多条信息
python实现并没有提供任何内置的方法来同时处理多条信息。然而，Java实现提供了parseDelimitedFrom和writeDelimitedTo等方法。为了避免重新发明轮子，我们把 Java 库的功能翻译成 Python。
ava实现将消息的大小保存在Varint值中。Varints是一种序列化方法，它以一个或多个字节存储整数。
- 序列化
import random
from google.protobuf.internal.encoder import _VarintBytes
import metric_pb2
with open('out.bin', 'wb') as f:
my_tags = ('my_tag', 'foo:bar')
for i in range(5):
my_metric = metric_pb2.Metric()
my_metric.name = 'sys.cpu'
my_metric.type = 'gauge'
my_metric.value = round(random.random(), 2)
my_metric.tags.extend(my_tags)
size = my_metric.ByteSize()
f.write(_VarintBytes(size))
f.write(my_metric.SerializeToString())
- 反序列化
from google.protobuf.internal.decoder import _DecodeVarint32
import metric_pb2
with open('out.bin', 'rb') as f:
buf = f.read()
n = 0
while n < len(buf):
msg_len, new_pos = _DecodeVarint32(buf, n)
n = new_pos
msg_buf = buf[n:n+msg_len]
n += msg_len
read_metric = metric_pb2.Metric()
read_metric.ParseFromString(msg_buf)
print(read_metric)
执行结果：
$ python pb_unpacks.py
name: "sys.cpu"
type: "gauge"
value: 0.4699999988079071
tags: "my_tag"
tags: "foo:bar"
name: "sys.cpu"
type: "gauge"
value: 0.7699999809265137
tags: "my_tag"
tags: "foo:bar"
name: "sys.cpu"
type: "gauge"
value: 0.5400000214576721
tags: "my_tag"
tags: "foo:bar"
name: "sys.cpu"
type: "gauge"
value: 0.44999998807907104
tags: "my_tag"
tags: "foo:bar"
name: "sys.cpu"
type: "gauge"
value: 0.9100000262260437
tags: "my_tag"
tags: "foo:bar"
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