:Develop a PyODPS 3 task

Prerequisites

A PyODPS 3 node is created. For more information, see Create and manage MaxCompute nodes.

Background information

PyODPS is MaxCompute SDK for Python. PyODPS provides a simple and convenient Python programming interface. This way, you can use Python to write code for MaxCompute jobs, query MaxCompute tables and views, and manage MaxCompute resources. For more information, see Overview. In DataWorks, you can use PyODPS nodes to schedule Python tasks and integrate Python tasks with other types of tasks.

Limits

• Due to the specifications of resources in an exclusive resource group for scheduling that you want to use to run a PyODPS node, we recommend that you use the PyODPS node to process no more than 50 MB of data from your on-premises machine. If the PyODPS node processes more than 50 MB of data from your on-premises machine, an out of memory (OOM) exception may occur, and the system may report Got killed. We recommend that you do not write excessive data processing code for a PyODPS node. For more information, see Best practices for efficient use of PyODPS nodes.

• When you use a serverless resource group to run a PyODPS node, you can configure an appropriate number of CUs for the PyODPS node based on the amount of data that needs to be processed in the node.

• If the system reports Got killed, the memory usage exceeds the limit, and the system terminates the related processes. We recommend that you do not perform operations on on-premises data. However, the limits on the memory usage do not apply to SQL or DataFrame tasks (excluding to_pandas tasks) that are initiated by PyODPS.

• You can use the NumPy and pandas libraries that are pre-installed in DataWorks to run functions other than UDFs. Third-party packages that contain binary code are not supported.

• For compatibility reasons, options.tunnel.use_instance_tunnel is set to False in DataWorks by default. If you want to globally enable InstanceTunnel, you must set this parameter to True.

• Python 3 defines bytecode differently in different subversions such as Python 3.7 and Python 3.8.

  MaxCompute is compatible with Python 3.7. A MaxCompute client that uses another subversion of Python 3 returns an error when code that has specific syntax is run. For example, a MaxCompute client that uses Python 3.8 returns an error when code that has the finally block syntax is run. We recommend that you use Python 3.7.

• PyODPS 3 nodes can run on a serverless resource group. For more information about how to purchase and use a serverless resource group, see Create and use a serverless resource group.

Simple code editing example

After you create a PyODPS node, you can write and run code. For information about the PyODPS syntax, see Overview.

• Use the MaxCompute entry point

  In DataWorks, each PyODPS node includes the global variable odps or o, which is the MaxCompute entry point. Therefore, you do not need to manually specify the MaxCompute entry point.

  print(odps.exist_table('PyODPS_iris'))

• Execute SQL statements

  You can execute SQL statements in the PyODPS node. For more information, see SQL.

  • By default, InstanceTunnel is disabled in DataWorks. In this case, instance.open_reader is run by using the Result interface, and a maximum of 10,000 data records can be read. You can use reader.count to obtain the number of data records that are read. If you need to iteratively read all data, you must remove the limit on the number of data records to read. You can execute the following statements to globally enable InstanceTunnel and remove the limit on the number of data records to read:

    options.tunnel.use_instance_tunnel = True
    options.tunnel.limit_instance_tunnel = False  # Remove the limit on the number of data records to read. 
    
    with instance.open_reader() as reader:
      # Use InstanceTunnel to read all data.

  • You can also add tunnel=True to open_reader to enable InstanceTunnel for the current open_reader operation. You can add limit=False to open_reader to remove the limit on the number of data records to read for the current open_reader operation.

    # The current open_reader operation is performed by using InstanceTunnel, and all data can be read. 
    with instance.open_reader(tunnel=True, limit=False) as reader:

• Configure runtime parameters

  • You can use the hints parameter to configure runtime parameters. The hints parameter is of the DICT type. For more information about the hints parameter, see SET operations.

    o.execute_sql('select * from PyODPS_iris', hints={'odps.sql.mapper.split.size': 16})

  • If you configure the sql.settings parameter for global configurations, you must configure runtime parameters each time you run the node code.

    from odps import options
    options.sql.settings = {'odps.sql.mapper.split.size': 16}
    o.execute_sql('select * from PyODPS_iris')  # Configure the hints parameter based on global configurations.

• Obtain query results of SQL statements

  You can use the open_reader method to obtain query results in the following scenarios:

  • The SQL statements return structured data.

    with o.execute_sql('select * from dual').open_reader() as reader:
    	for record in reader:  # Process each record.

  • SQL statements such as DESC are executed. In this case, you can use the reader.raw property to obtain raw query results.

    with o.execute_sql('desc dual').open_reader() as reader:
    	print(reader.raw)

    Note

    If you use a custom scheduling parameter and run the PyODPS 3 node on the configuration tab of the node, you must set the scheduling parameter to a constant value to specify a fixed time. The value of the custom scheduling parameter for PyODPS nodes cannot be automatically replaced.

• Use DataFrame to process data

  You can use DataFrame to process data.

  • Call a DataFrame API operation

    DataFrame API operations are not automatically called. These operations can be called only when you explicitly call an immediately executed method.

    from odps.df import DataFrame
    iris = DataFrame(o.get_table('pyodps_iris'))
    for record in iris[iris.sepal_width < 3].execute():  # Call an immediately executed method to process each data record.

    To call an immediately executed method for data display, set options.interactive to True.

    from odps import options
    from odps.df import DataFrame
    options.interactive = True  # Set options.interactive to True at the beginning of the code. 
    iris = DataFrame(o.get_table('pyodps_iris'))
    print(iris.sepal_width.sum())  # The method is immediately executed after the system displays information.

  • Display details

    To display details, you must set options.verbose to True. By default, this parameter is set to True in DataWorks. The system displays details such as the Logview URL during the running process.

Example

The following example describes how to use a PyODPS node:

1. Prepare a dataset and create a table named pyodps_iris. For more information, see DataFrame data processing.

2. Create a DataFrame object. For more information, see Create a DataFrame object from a MaxCompute table.

3. Enter the following code in the code editor of the PyODPS node and run the code:

   from odps.df import DataFrame
   
   # Create a DataFrame object by using a MaxCompute table. 
   iris = DataFrame(o.get_table('pyodps_iris'))
   print(iris.sepallength.head(5))

   The following information is returned:

      sepallength
   0          4.5
   1          5.5
   2          4.9
   3          5.0
   4          6.0

Advanced code editing example

If you want a PyODPS node to be scheduled on a regular basis, you must define scheduling properties for the node. For more information, see Overview.

print('ds=' + args['ds'])
ds=20161116

o.get_table('table_name').get_partition('ds=' + args['ds'])

For more information about how to develop a PyODPS task in other scenarios, see the following topics:

• Reference a third-party package in a PyODPS node

• Use a PyODPS node to segment Chinese text based on Jieba

• Use a PyODPS node to download data to a local directory for processing or to process data online

What to do next

• Determine whether a custom Shell script is successfully run: The logic for determining whether a custom Python script is successfully run is the same as the logic for determining whether a custom Shell script is successfully run. You can use this method to determine whether a custom Python script is successfully run.

• Deploy the PyODPS 3 node: If you use a workspace in standard mode, you must deploy the PyODPS 3 node to the production environment before the PyODPS 3 node can be scheduled on a regular basis.

• Perform O&M on the PyODPS 3 node: After the PyODPS 3 node is deployed to Operation Center in the production environment, you can perform O&M operations on the node in Operation Center.

• Learn FAQ about PyODPS: You can learn FAQ about PyODPS. This way, you can identify and troubleshoot issues in an efficient manner when exceptions occur.