MaxCompute:Develop a UDF in Python 2

You can use MaxCompute Studio to write UDF code in Python 2. The UDF code can contain the following information:

• Encoding declaration: optional.

  The declaration format is #coding:utf-8 or # -*- coding: utf-8 -*-. The two formats are equivalent. If Chinese characters appear in UDF code that is written in Python 2, an error is returned when you run the UDF. To address this issue, you must add an encoding declaration to the header of the code.

• Module import: required.

  UDF code must include from odps.udf import annotate, which is used to import the function signature. This way, MaxCompute can identify the function signature that is defined in the code. If you want to reference files or tables in UDF code, the UDF code must include from odps.distcache import get_cache_file or from odps.distcache import get_cache_table.

• Function signature: required.

  The function signature is in the @annotate(<signature>) format. The signature parameter is used to define the data types of the input parameters and return value of the UDF. For more information about function signatures, see Function signatures and data types.

• Custom Python class: required.

  A custom Python class is the organizational unit of UDF code. This class defines the variables and methods that are used to meet your business requirements. In UDF code, you can also reference third-party libraries that are installed in MaxCompute or reference files or tables. For more information, see Third-party libraries or Reference resources.

• evaluate method: required.

  The evaluate method is contained in the custom Python class. The evaluate method defines the input parameters and return value of the UDF. Each Python class can contain only one evaluate method.

Sample code:

#coding:utf-8
# Import the function signature. 
from odps.udf import annotate
# The function signature. 
@annotate("bigint,bigint->bigint")
# The custom Python class. 
class MyPlus(object):
# The evaluate method. 
   def evaluate(self, arg0, arg1):
       if None in (arg0, arg1):
           return None
       return arg0 + arg1

MaxCompute allows you to write Python 2 UDFs in Python 2.7 and run the UDF code in a sandbox environment. In this environment, the following operations are prohibited:

• Read data from and write data to local files.

• Start subprocesses.

• Start threads.

• Enable socket communication.

• Use other systems to call Python 2 UDFs.

Due to these limits, the code that you upload must be written by using Python standard libraries. If modules or C extension modules in Python standard libraries are involved in the preceding operations, these modules cannot be used. Take note of the following points about modules in Python standard libraries:

• All the modules that are implemented based on Python standard libraries and do not depend on extension modules are available.

• The following C extension modules are available:

  • array and audioop

  • binascii and bisect

  • cmath, _codecs_cn, _codecs_hk, _codecs_iso2022, _codecs_jp, _codecs_kr, _codecs_tw, _collections, and cStringIO

  • datetime

  • _functools and future_builtins

  • _heapq and _hashlib

  • itertools

  • _json

  • _locale and _lsprof

  • math, _md5, and _multibytecodec

  • operator

  • _random

  • _sha256, _sha512, _sha, _struct, and strop

  • time

  • unicodedata

  • _weakref

  • cPickle

• When you run UDF code in a sandbox environment, the maximum size of data that can be written to the standard output (sys.stdout) or standard error output (sys.stderr) is 20 KB. If the size exceeds 20 KB, extra characters are ignored.

Third-party libraries, such as NumPy, are installed in the Python 2 environment of MaxCompute as supplements to standard libraries.

Format of function signatures:

@annotate(<signature>)

The signature parameter is a string that specifies the data types of input parameters and return value. When you run a UDF, the data types of the input parameters and return value of the UDF must be consistent with the data types specified in the function signature. The data type consistency is checked during semantic parsing. If the data types are inconsistent, an error is returned. Format of a signature:

'arg_type_list -> type'

Parameter description:

• arg_type_list: specifies the data types of input parameters. If multiple input parameters are used, their data types are separated by commas (,). The following data types are supported: BIGINT, STRING, DOUBLE, BOOLEAN, DATETIME, DECIMAL, FLOAT, BINARY, DATE, DECIMAL(precision,scale), CHAR, and VARCHAR. Complex data types, such as ARRAY, MAP, and STRUCT, and nested complex data types are also supported.

  arg_type_list can be represented by an asterisk (*) or left empty ('').

  • If arg_type_list is represented by an asterisk (*), a random number of input parameters are allowed.

  • If arg_type_list is left empty (''), no input parameters are used.

• type: specifies the data type of the return value. For a UDF, only one column of values is returned. The following data types are supported: BIGINT, STRING, DOUBLE, BOOLEAN, DATETIME, DECIMAL, FLOAT, BINARY, DATE, and DECIMAL(precision,scale). Complex data types, such as ARRAY, MAP, and STRUCT, and nested complex data types are also supported.

The following table provides examples of valid function signatures.

After you develop a Python 2 UDF, you can use MaxCompute SQL to call this UDF. You can call a Python 2 UDF by using one of the following methods:

• Use a UDF in a MaxCompute project: The method is similar to that of using built-in functions.

• Use a UDF across projects: Use a UDF of Project B in Project A. The following statement shows an example: select B:udf_in_other_project(arg0, arg1) as res from table_t;. For more information about cross-project sharing, see Cross-project resource access based on packages.