python - 在 Python 数据流/Apache Beam 上启动 CloudSQL 代理

Question

我目前正在从事一项 ETL Dataflow 作业（使用 Apache Beam Python SDK），该作业从 CloudSQL（使用psycopg2和自定义ParDo）查询数据并将其写入 BigQuery。我的目标是创建一个数据流模板，我可以使用 Cron 作业从 AppEngine 开始。

我有一个使用 DirectRunner 在本地工作的版本。为此，我使用 CloudSQL (Postgres) 代理客户端，以便可以连接到 127.0.0.1 上的数据库。

当使用带有自定义命令的 DataflowRunner 在 setup.py 脚本中启动代理时，作业不会执行。它坚持重复此日志消息：

Setting node annotation to enable volume controller attach/detach

我的 setup.py 的一部分如下所示：

CUSTOM_COMMANDS = [
['echo', 'Custom command worked!'],
['wget', 'https://dl.google.com/cloudsql/cloud_sql_proxy.linux.amd64', '-O', 'cloud_sql_proxy'],
['echo', 'Proxy downloaded'],
['chmod', '+x', 'cloud_sql_proxy']]

class CustomCommands(setuptools.Command):
  """A setuptools Command class able to run arbitrary commands."""

  def initialize_options(self):
    pass

  def finalize_options(self):
    pass

  def RunCustomCommand(self, command_list):
    print('Running command: %s' % command_list)
    logging.info("Running custom commands")
    p = subprocess.Popen(
        command_list,
        stdin=subprocess.PIPE, stdout=subprocess.PIPE, stderr=subprocess.STDOUT)
    # Can use communicate(input='y\n'.encode()) if the command run requires
    # some confirmation.
    stdout_data, _ = p.communicate()
    print('Command output: %s' % stdout_data)
    if p.returncode != 0:
      raise RuntimeError(
          'Command %s failed: exit code: %s' % (command_list, p.returncode))

  def run(self):
    for command in CUSTOM_COMMANDS:
      self.RunCustomCommand(command)
    subprocess.Popen(['./cloud_sql_proxy', '-instances=bi-test-1:europe-west1:test-animal=tcp:5432'])

在从sthomp阅读Github 上的这个问题 和关于 Stackoverflo的讨论之后，我将最后一行添加为单独subprocess.Popen()的内容。我还尝试使用.run()subprocess.Popen

brodin提到的另一个解决方案是允许从每个 IP 地址访问并通过用户名和密码进行连接。据我了解，他并不认为这是最佳实践。

预先感谢您的帮助。

！！！这篇文章底部的解决方法！！！

---

更新 - 日志文件

这些是作业期间发生的错误级别的日志：

E  EXT4-fs (dm-0): couldn't mount as ext3 due to feature incompatibilities 
E  Image garbage collection failed once. Stats initialization may not have completed yet: unable to find data for container / 
E  Failed to check if disk space is available for the runtime: failed to get fs info for "runtime": unable to find data for container / 
E  Failed to check if disk space is available on the root partition: failed to get fs info for "root": unable to find data for container / 
E  [ContainerManager]: Fail to get rootfs information unable to find data for container / 
E  Could not find capacity information for resource storage.kubernetes.io/scratch 
E  debconf: delaying package configuration, since apt-utils is not installed 
E    % Total    % Received % Xferd  Average Speed   Time    Time     Time  Current 
E                                   Dload  Upload   Total   Spent    Left  Speed 
E  
  0     0    0     0    0     0      0      0 --:--:-- --:--:-- --:--:--     0
100  3698  100  3698    0     0  25674      0 --:--:-- --:--:-- --:--:-- 25860 



#-- HERE IS WHEN setup.py FOR MY JOB IS EXECUTED ---

E  debconf: delaying package configuration, since apt-utils is not installed 
E  insserv: warning: current start runlevel(s) (empty) of script `stackdriver-extractor' overrides LSB defaults (2 3 4 5). 
E  insserv: warning: current stop runlevel(s) (0 1 2 3 4 5 6) of script `stackdriver-extractor' overrides LSB defaults (0 1 6). 
E  option = Interval; value = 60.000000; 
E  option = FQDNLookup; value = false; 
E  Created new plugin context. 
E  option = PIDFile; value = /var/run/stackdriver-agent.pid; 
E  option = Interval; value = 60.000000; 
E  option = FQDNLookup; value = false; 
E  Created new plugin context. 

---

在这里您可以找到我的自定义 setup.py 启动后的所有日志（日志级别：任何；所有日志）：

https://jpst.it/1gk2Z

更新日志文件 2

作业日志（我在没有卡住一段时间后手动取消了作业）：

 2018-06-08 (08:02:20) Autoscaling is enabled for job 2018-06-07_23_02_20-5917188751755240698. The number of workers will b...
 2018-06-08 (08:02:20) Autoscaling was automatically enabled for job 2018-06-07_23_02_20-5917188751755240698.
 2018-06-08 (08:02:24) Checking required Cloud APIs are enabled.
 2018-06-08 (08:02:24) Checking permissions granted to controller Service Account.
 2018-06-08 (08:02:25) Worker configuration: n1-standard-1 in europe-west1-b.
 2018-06-08 (08:02:25) Expanding CoGroupByKey operations into optimizable parts.
 2018-06-08 (08:02:25) Combiner lifting skipped for step Save new watermarks/Write/WriteImpl/GroupByKey: GroupByKey not fol...
 2018-06-08 (08:02:25) Combiner lifting skipped for step Group watermarks: GroupByKey not followed by a combiner.
 2018-06-08 (08:02:25) Expanding GroupByKey operations into optimizable parts.
 2018-06-08 (08:02:26) Lifting ValueCombiningMappingFns into MergeBucketsMappingFns
 2018-06-08 (08:02:26) Annotating graph with Autotuner information.
 2018-06-08 (08:02:26) Fusing adjacent ParDo, Read, Write, and Flatten operations
 2018-06-08 (08:02:26) Fusing consumer Get rows from CloudSQL tables into Begin pipeline with watermarks/Read
 2018-06-08 (08:02:26) Fusing consumer Group watermarks/Write into Group watermarks/Reify
 2018-06-08 (08:02:26) Fusing consumer Group watermarks/GroupByWindow into Group watermarks/Read
 2018-06-08 (08:02:26) Fusing consumer Save new watermarks/Write/WriteImpl/WriteBundles/WriteBundles into Save new watermar...
 2018-06-08 (08:02:26) Fusing consumer Save new watermarks/Write/WriteImpl/GroupByKey/GroupByWindow into Save new watermark...
 2018-06-08 (08:02:26) Fusing consumer Save new watermarks/Write/WriteImpl/GroupByKey/Reify into Save new watermarks/Write/...
 2018-06-08 (08:02:26) Fusing consumer Save new watermarks/Write/WriteImpl/GroupByKey/Write into Save new watermarks/Write/...
 2018-06-08 (08:02:26) Fusing consumer Write to BQ into Get rows from CloudSQL tables
 2018-06-08 (08:02:26) Fusing consumer Group watermarks/Reify into Write to BQ
 2018-06-08 (08:02:26) Fusing consumer Save new watermarks/Write/WriteImpl/Map(<lambda at iobase.py:926>) into Convert dict...
 2018-06-08 (08:02:26) Fusing consumer Save new watermarks/Write/WriteImpl/WindowInto(WindowIntoFn) into Save new watermark...
 2018-06-08 (08:02:26) Fusing consumer Convert dictionary list to single dictionary and json into Remove "watermark" label
 2018-06-08 (08:02:26) Fusing consumer Remove "watermark" label into Group watermarks/GroupByWindow
 2018-06-08 (08:02:26) Fusing consumer Save new watermarks/Write/WriteImpl/InitializeWrite into Save new watermarks/Write/W...
 2018-06-08 (08:02:26) Workflow config is missing a default resource spec.
 2018-06-08 (08:02:26) Adding StepResource setup and teardown to workflow graph.
 2018-06-08 (08:02:26) Adding workflow start and stop steps.
 2018-06-08 (08:02:26) Assigning stage ids.
 2018-06-08 (08:02:26) Executing wait step start25
 2018-06-08 (08:02:26) Executing operation Save new watermarks/Write/WriteImpl/DoOnce/Read+Save new watermarks/Write/WriteI...
 2018-06-08 (08:02:26) Executing operation Save new watermarks/Write/WriteImpl/GroupByKey/Create
 2018-06-08 (08:02:26) Starting worker pool setup.
 2018-06-08 (08:02:26) Executing operation Group watermarks/Create
 2018-06-08 (08:02:26) Starting 1 workers in europe-west1-b...
 2018-06-08 (08:02:27) Value "Group watermarks/Session" materialized.
 2018-06-08 (08:02:27) Value "Save new watermarks/Write/WriteImpl/GroupByKey/Session" materialized.
 2018-06-08 (08:02:27) Executing operation Begin pipeline with watermarks/Read+Get rows from CloudSQL tables+Write to BQ+Gr...
 2018-06-08 (08:02:36) Autoscaling: Raised the number of workers to 0 based on the rate of progress in the currently runnin...
 2018-06-08 (08:02:46) Autoscaling: Raised the number of workers to 1 based on the rate of progress in the currently runnin...
 2018-06-08 (08:03:05) Workers have started successfully.
 2018-06-08 (08:11:37) Cancel request is committed for workflow job: 2018-06-07_23_02_20-5917188751755240698.
 2018-06-08 (08:11:38) Cleaning up.
 2018-06-08 (08:11:38) Starting worker pool teardown.
 2018-06-08 (08:11:38) Stopping worker pool...
 2018-06-08 (08:12:30) Autoscaling: Reduced the number of workers to 0 based on the rate of progress in the currently runni...

堆栈跟踪：

No errors have been received in this time period.

---

更新：解决方法可以在下面我的回答中找到

Answer 1

解决方法：

我终于找到了解决方法。我的想法是通过 CloudSQL 实例的公共 IP 进行连接。为此，您需要允许从每个 IP 连接到您的 CloudSQL 实例：

1. 转到 GCP 中 CloudSQL 实例的概览页面
2. 单击Authorization选项卡
3. 单击Add network并添加0.0.0.0/0（！！这将允许每个 IP 地址连接到您的实例！！）

为了增加流程的安全性，我使用了 SSL 密钥并且只允许 SSL 连接到实例：

4. 单击SSL选项卡
5. 单击Create a new certificate为您的服务器创建 SSL 证书
6. 单击Create a client certificate为您的客户端创建 SSL 证书
7. 单击Allow only SSL connections以拒绝所有非 SSL 连接尝试

之后，我将证书存储在 Google Cloud Storage 存储桶中并在连接 Dataflow 作业之前加载它们，即：

import psycopg2
import psycopg2.extensions
import os
import stat
from google.cloud import storage

# Function to wait for open connection when processing parallel
def wait(conn):
    while 1:
        state = conn.poll()
        if state == psycopg2.extensions.POLL_OK:
            break
        elif state == psycopg2.extensions.POLL_WRITE:
            pass
            select.select([], [conn.fileno()], [])
        elif state == psycopg2.extensions.POLL_READ:
            pass
            select.select([conn.fileno()], [], [])
        else:
            raise psycopg2.OperationalError("poll() returned %s" % state)

# Function which returns a connection which can be used for queries
def connect_to_db(host, hostaddr, dbname, user, password, sslmode = 'verify-full'):

    # Get keys from GCS
    client = storage.Client()

    bucket = client.get_bucket(<YOUR_BUCKET_NAME>)

    bucket.get_blob('PATH_TO/server-ca.pem').download_to_filename('server-ca.pem')
    bucket.get_blob('PATH_TO/client-key.pem').download_to_filename('client-key.pem')
    os.chmod("client-key.pem", stat.S_IRWXU)
    bucket.get_blob('PATH_TO/client-cert.pem').download_to_filename('client-cert.pem')

    sslrootcert = 'server-ca.pem'
    sslkey = 'client-key.pem'
    sslcert = 'client-cert.pem'

    con = psycopg2.connect(
        host = host,
        hostaddr = hostaddr,
        dbname = dbname,
        user = user,
        password = password,
        sslmode=sslmode,
        sslrootcert = sslrootcert,
        sslcert = sslcert,
        sslkey = sslkey)
    return con

然后我在自定义中使用这些函数ParDo来执行查询。
最小的例子：

import apache_beam as beam

class ReadSQLTableNames(beam.DoFn):
    '''
    parDo class to get all table names of a given cloudSQL database.
    It will return each table name.
    '''
    def __init__(self, host, hostaddr, dbname, username, password):
        super(ReadSQLTableNames, self).__init__()
        self.host = host
        self.hostaddr = hostaddr
        self.dbname = dbname
        self.username = username
        self.password = password

    def process(self, element):

        # Connect do database
        con = connect_to_db(host = self.host,
            hostaddr = self.hostaddr,
            dbname = self.dbname,
            user = self.username,
            password = self.password)
        # Wait for free connection
        wait_select(con)
        # Create cursor to query data
        cur = con.cursor(cursor_factory=RealDictCursor)

        # Get all table names
        cur.execute(
        """
        SELECT
        tablename as table
        FROM pg_tables
        WHERE schemaname = 'public'
        """
        )
        table_names = cur.fetchall()

        cur.close()
        con.close()
        for table_name in table_names:
            yield table_name["table"]

管道的一部分可能如下所示：

# Current workaround to query all tables: 
# Create a dummy initiator PCollection with one element
init = p        |'Begin pipeline with initiator' >> beam.Create(['All tables initializer'])

tables = init   |'Get table names' >> beam.ParDo(ReadSQLTableNames(
                                                host = known_args.host,
                                                hostaddr = known_args.hostaddr,
                                                dbname = known_args.db_name,
                                                username = known_args.user,
                                                password = known_args.password))

我希望这个解决方案可以帮助其他有类似问题的人

Answer 2

我设法找到了更好或至少更简单的解决方案。在 DoFn 设置功能中使用云代理设置预连接

class MyDoFn(beam.DoFn):
 def setup(self):
    os.system("wget https://dl.google.com/cloudsql/cloud_sql_proxy.linux.amd64 -O cloud_sql_proxy")
    os.system("chmod +x cloud_sql_proxy")
    os.system(f"./cloud_sql_proxy -instances={self.sql_args['cloud_sql_connection_name']}=tcp:3306 &")

Answer 3

2022 年要做的简单而正确的事情是使用云 sql 连接器，该连接器将与在 gcloud sql 上运行的 postgres、sqlserver 和 mysql 一起使用。

https://cloud.google.com/sql/docs/mysql/connect-connectors#python_1

https://pypi.org/project/cloud-sql-python-connector/

无需将 IP 列入白名单或让您的数据库完全开放。您对主机使用此格式：“project:region:instance”