我有一个df：
id log
0 24 2*C316*first_field_name17*second_field_name16*third_field_name2*N311*field value1*Y5*hello2*O30*0*0*
1 25 2*C316*first_field_name17*second_field_name16*third_field_name2*N311*field value1*Y5*hello2*O30*0*0*
我有一个解析字符串的函数：
dfs = []
def parse_log(id, log):
split = log.split('*')
number_of_fields = int(split[1][1:int(split[0][0])])
i=2
string_length = int(split[1][int(split[0][0]):])
field_names_list = []
while i < number_of_fields + 2:
field_name = split[i][0:string_length]
field_names_list.append(field_name)
string_length = int(split[i][string_length:])
i+=1
i = 3 + number_of_fields
string_length = int(split[2 + number_of_fields][string_length:])
new_values_list = []
while i < 3+number_of_fields*2:
field_name = split[i][0:string_length]
new_values_list.append(field_name)
string_length = int(split[i][string_length:])
i+=1
i = 4 + number_of_fields*2
string_length = int(split[3 + number_of_fields*2][string_length:])
old_values_list = []
while i <= 3 + number_of_fields*3:
old_value = split[i][0:string_length]
old_values_list.append(old_value)
if i == 3 + number_of_fields*3:
string_length = 0
else:
string_length = int(split[i][string_length:])
i+=1
df = pd.DataFrame(
{'id':id,
'field': field_names_list,
'old_value': old_values_list,
'new_value': new_values_list
})
dfs.append(df)
此功能适用于普通大熊猫：
data.apply(lambda x: parse_audit_log(x['id'], x['log']), axis=1)
然后我尝试用dask代替（data在这种情况下是从SQL读取的dask数据框）：
out = data.map_partitions(lambda df : df.apply(lambda x : parse_audit_log(x.id,x.log),axis=1), meta=('result', int)).compute(get=get)
这会导致错误：
---------------------------------------------------------------------------
AttributeError Traceback (most recent call last)
<ipython-input-12-2468010e0124> in <module>()
----> 1 out = data.map_partitions(lambda df : df.apply(lambda x : parse_audit_log(x.ROW_ID,x.AUDIT_LOG),axis=1), meta=('result', int)).compute(get=get)
~\_installed\anaconda\lib\site-packages\dask\base.py in compute(self, **kwargs)
153 dask.base.compute
154 """
--> 155 (result,) = compute(self, traverse=False, **kwargs)
156 return result
157
~\_installed\anaconda\lib\site-packages\dask\base.py in compute(*args, **kwargs)
402 postcomputes = [a.__dask_postcompute__() if is_dask_collection(a)
403 else (None, a) for a in args]
--> 404 results = get(dsk, keys, **kwargs)
405 results_iter = iter(results)
406 return tuple(a if f is None else f(next(results_iter), *a)
~\_installed\anaconda\lib\site-packages\dask\multiprocessing.py in get(dsk, keys, num_workers, func_loads, func_dumps, optimize_graph, **kwargs)
175 get_id=_process_get_id, dumps=dumps, loads=loads,
176 pack_exception=pack_exception,
--> 177 raise_exception=reraise, **kwargs)
178 finally:
179 if cleanup:
~\_installed\anaconda\lib\site-packages\dask\local.py in get_async(apply_async, num_workers, dsk, result, cache, get_id, rerun_exceptions_locally, pack_exception, raise_exception, callbacks, dumps, loads, **kwargs)
519 _execute_task(task, data) # Re-execute locally
520 else:
--> 521 raise_exception(exc, tb)
522 res, worker_id = loads(res_info)
523 state['cache'][key] = res
~\_installed\anaconda\lib\site-packages\dask\compatibility.py in reraise(exc, tb)
64 def reraise(exc, tb=None):
65 if exc.__traceback__ is not tb:
---> 66 raise exc.with_traceback(tb)
67 raise exc
68
~\_installed\anaconda\lib\site-packages\dask\local.py in execute_task()
288 try:
289 task, data = loads(task_info)
--> 290 result = _execute_task(task, data)
291 id = get_id()
292 result = dumps((result, id))
~\_installed\anaconda\lib\site-packages\dask\local.py in _execute_task()
269 func, args = arg[0], arg[1:]
270 args2 = [_execute_task(a, cache) for a in args]
--> 271 return func(*args2)
272 elif not ishashable(arg):
273 return arg
~\_installed\anaconda\lib\site-packages\dask\dataframe\core.py in apply_and_enforce()
3402
3403 Ensures the output has the same columns, even if empty."""
-> 3404 df = func(*args, **kwargs)
3405 if isinstance(df, (pd.DataFrame, pd.Series, pd.Index)):
3406 if len(df) == 0:
<ipython-input-12-2468010e0124> in <lambda>()
----> 1 out = data.map_partitions(lambda df : df.apply(lambda x : parse_audit_log(x.ROW_ID,x.AUDIT_LOG),axis=1), meta=('result', int)).compute(get=get)
~\_installed\anaconda\lib\site-packages\pandas\core\frame.py in apply()
4875 f, axis,
4876 reduce=reduce,
-> 4877 ignore_failures=ignore_failures)
4878 else:
4879 return self._apply_broadcast(f, axis)
~\_installed\anaconda\lib\site-packages\pandas\core\frame.py in _apply_standard()
4971 try:
4972 for i, v in enumerate(series_gen):
-> 4973 results[i] = func(v)
4974 keys.append(v.name)
4975 except Exception as e:
<ipython-input-12-2468010e0124> in <lambda>()
----> 1 out = data.map_partitions(lambda df : df.apply(lambda x : parse_audit_log(x.ROW_ID,x.AUDIT_LOG),axis=1), meta=('result', int)).compute(get=get)
<ipython-input-11-08a2f8f06a76> in parse_audit_log()
1 def parse_audit_log(row_id, audit_log):
----> 2 split = audit_log.split('*')
3 number_of_fields = int(split[1][1:int(split[0][0])])
4
5 i=2
AttributeError: ("'NoneType' object has no attribute 'split'", 'occurred at index 1')
这是一个错误，但是我在调整功能以适应敏捷要求时遇到了其他几个问题。函数的dask应用程序我缺少什么？我的meta标签几乎可以肯定是不对的。尽管那里有一些示例，它们使函数难以实现，但没有显式的返回，我还没有看到任何涉及函数。
更新-所需输出：
理想情况下，我将得到一个df：
row_id field_name new_value old_value
0 24 first_field_name field value
1 24 second_field_name Y
2 24 third_field_name hello
3 25 first_field_name field value
4 25 second_field_name Y
5 25 third_field_name hello
问题（以及我尝试使用dask的原因）是数据集为5500万条记录。由于此解析过程会将这些记录分解为一个或多个记录，因此我需要一种可以容纳我的32GB内存的高效存储设备。