在这个例子中,iterencode的潜在问题是你希望通过使用生成器对大数据集进行迭代处理,而json编码不会序列化生成器.

解决这个问题的方法是子类列表类型并覆盖__iter__魔术方法,以便您可以生成生成器的输出.

以下是此列表子类的示例.

class StreamArray(list):
    """
    Converts a generator into a list object that can be json serialisable
    while still retaining the iterative nature of a generator.
 
    IE. It converts it to a list without having to exhaust the generator
    and keep it's contents in memory.
    """
    def __init__(self,generator):
        self.generator = generator
        self._len = 1
 
    def __iter__(self):
        self._len = 0
        for item in self.generator:
            yield item
            self._len += 1
 
    def __len__(self):
        """
        Json parser looks for a this method to confirm whether or not it can
        be parsed
        """
        return self._len

从这里开始使用非常简单.获取生成器句柄,将其传递到StreamArray类,将流数组对象传递给iterencode()并迭代块.块将是json格式输出,可以直接写入文件.

用法示例：

#Function that will iteratively generate a large set of data.
def large_list_generator_func():
    for i in xrange(5):
        chunk = {'hello_world': i}
        print 'Yielding chunk: ',chunk
        yield chunk
 
#Write the contents to file:
with open('/tmp/streamed_write.json','w') as outfile:
    large_generator_handle = large_list_generator_func()
    stream_array = StreamArray(large_generator_handle)
    for chunk in json.JSONEncoder().iterencode(stream_array):
        print 'Writing chunk: ',chunk
        outfile.write(chunk)

显示产量和写入的输出连续发生.

Yielding chunk:  {'hello_world': 0}
Writing chunk:  [
Writing chunk:  {
Writing chunk:  "hello_world"
Writing chunk:  : 
Writing chunk:  0
Writing chunk:  }
Yielding chunk:  {'hello_world': 1}
Writing chunk:,Writing chunk:  {
Writing chunk:  "hello_world"
Writing chunk:  : 
Writing chunk:  1
Writing chunk:  }