气流 mysql 到 gcp Dag 错误
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【中文标题】气流 mysql 到 gcp Dag 错误【英文标题】:Airflow mysql to gcp Dag error 【发布时间】:2017-07-20 23:09:47 【问题描述】:我最近开始使用 Airflow。我正在研究 DAG:
-
查询 mysql 数据库
提取查询并将其作为 JSON 文件存储在云存储桶中
将存储的 JSON 文件上传到 BigQuery
Dag 导入三个运算符:MySqlOperator
、MySqlToGoogleCloudStorageOperator
和 GoogleCloudStorageToBigQueryOperator
我正在使用 Airflow 1.8.0、Python 3 和 Pandas 0.19.0。
这是我的 Dag 代码:
sql2gcp_csv = MySqlToGoogleCloudStorageOperator(
task_id='sql2gcp_csv',
sql='airflow_gcp/aws_sql_extract_7days.sql',
bucket='gs://var.value.gcs_bucket/ ds_nodash /',
filename=' ds_nodash -account-*.json',
schema_filename='support/file.json',
approx_max_file_size_bytes=1900000000,
mysql_conn_id='aws_mysql',
google_cloud_storage_conn_id='airflow_gcp',
)
但是,当我运行它时,我收到以下错误:
[2017-07-20 22:38:07,478] models.py:1441 INFO - Marking task as FAILED.
[2017-07-20 22:38:07,490] models.py:1462 ERROR - a bytes-like object is required, not 'str'
/home/User/airflow/workspace/env/lib/python3.5/site-packages/airflow/models.py:1927: PendingDeprecationWarning: Invalid arguments were passed to MySqlOperator. Support for passing such arguments will be dropped in Airflow 2.0. Invalid arguments were:
*args: ()
**kwargs: 'database': 'test'
category=PendingDeprecationWarning
/home/User/airflow/workspace/env/lib/python3.5/site-
packages/airflow/ti_deps/deps/base_ti_dep.py:94: PendingDeprecationWarning: generator '_get_dep_statuses' raised StopIteration
for dep_status in self._get_dep_statuses(ti, session, dep_context):
Traceback (most recent call last):
File "/home/User/airflow/workspace/env/bin/airflow", line 28, in <module> args.func(args)
File "/home/User/airflow/workspace/env/lib/python3.5/site-packages/airflow/bin/cli.py", line 422, in run pool=args.pool,
File "/home/User/airflow/workspace/env/lib/python3.5/site-packages/airflow/utils/db.py", line 53, in wrapper result = func(*args, **kwargs)
File "/home/User/airflow/workspace/env/lib/python3.5/site-packages/airflow/models.py", line 1374, in run result = task_copy.execute(context=context)
File "/home/User/airflow/workspace/env/lib/python3.5/site-packages/airflow/contrib/operators/mysql_to_gcs.py", line 91, in execute files_to_upload = self._write_local_data_files(cursor)
File "/home/User/airflow/workspace/env/lib/python3.5/site-packages/airflow/contrib/operators/mysql_to_gcs.py", line 136, in _write_local_data_files
json.dump(row_dict, tmp_file_handle)
File "/usr/lib/python3.5/json/__init__.py", line 179, in dump
TypeError: a bytes-like object is required, not 'str'
有谁知道为什么会抛出这个异常?
【问题讨论】:
【参考方案1】:根据您的回溯,您的代码在this point 处中断。如您所见,它处理代码:
json.dump(row_dict, tmp_file_handle)
tmp_file_handle
是一个NamedTemporaryFile
initialized 带有默认输入参数,也就是说,它模拟一个以w+b
模式打开的文件(因此只接受字节类数据作为输入)。
问题在于,在 Python 2 中,所有字符串都是字节,而在 Python 3 中,字符串是文本(默认编码为 utf-8
)。
如果您打开 Python 2 并运行此代码:
In [1]: from tempfile import NamedTemporaryFile
In [2]: tmp_f = NamedTemporaryFile(delete=True)
In [3]: import json
In [4]: json.dump('1': 1, tmp_f)
效果很好。
但如果你打开一个 Python 3 并运行相同的代码:
In [54]: from tempfile import NamedTemporaryFile
In [55]: tmp_f = NamedTemporaryFile(delete=True)
In [56]: import json
In [57]: json.dump('1': 1, tmp_f)
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
<ipython-input-57-81743b9013c4> in <module>()
----> 1 json.dump('1': 1, tmp_f)
/usr/local/lib/python3.6/json/__init__.py in dump(obj, fp, skipkeys, ensure_ascii, check_circular, allow_nan, cls, indent, separators, default, sort_keys, **kw)
178 # a debuggability cost
179 for chunk in iterable:
--> 180 fp.write(chunk)
181
182
/usr/local/lib/python3.6/tempfile.py in func_wrapper(*args, **kwargs)
481 @_functools.wraps(func)
482 def func_wrapper(*args, **kwargs):
--> 483 return func(*args, **kwargs)
484 # Avoid closing the file as long as the wrapper is alive,
485 # see issue #18879.
TypeError: a bytes-like object is required, not 'str'
我们得到了和你一样的错误。
这意味着 Python 3 仍然不完全支持 Airflow(正如您在 test coverage 中看到的那样,模块 airflow/contrib/operators/mysql_to_gcs.py
尚未在 python 2 或 3 中测试)。确认这一点的一种方法是使用 python 2 运行您的代码并查看它是否有效。
我建议在their JIRA 上创建一个问题,请求两个版本的 Python 的可移植性。
【讨论】:
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