MaxCompute:Python 2 UDAF

You can use MaxCompute Studio to write UDAF code in Python 2. The UDAF 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 UDAF code that is written in Python 2, an error is returned when you run the UDAF. To address this issue, you must add an encoding declaration to the header of the code.

• Module import: required.

  UDAF code must include at least from odps.udf import annotate and from odps.udf import BaseUDAF. from odps.udf import annotate is used to import the function signature module. This way, MaxCompute can identify the function signature that is defined in the code. from odps.udf import BaseUDAF is a base class for Python UDAFs. You must use this class to implement methods such as iterate, merge, or terminate in derived classes.

  If you want to reference file or table resources in UDAF code, UDAF 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 UDAF. For more information about function signatures, see Function signatures and data types.

• Custom Python class (derived class): required.

  A custom Python class is the organizational unit of UDAF code. This class defines the variables and methods that are used to meet your business requirements. In UDAF 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.

• Methods to implement Python classes: required.

  The following table describes the four methods that can be used to implement Python classes. You can select a method based on your business requirements.

  Method	Description
  BaseUDAF.new_buffer()	Returns the intermediate value buffer of a UDAF. buffer must be a marshallable object, such as LIST or DICT, and the buffer size cannot increase with the amount of data. In extreme cases, the buffer size cannot exceed 2 MB after the marshaling operation.
  BaseUDAF.iterate(buffer[, args, ...])	Aggregates args into the intermediate value buffer.
  BaseUDAF.merge(buffer, pbuffer)	Stores the merged results of pbuffer and the intermediate value buffer in the buffer.
  BaseUDAF.terminate(buffer)	Converts buffer into a value of a basic data type in MaxCompute SQL.

Sample code:

#coding:utf-8
# Import the function signature module and base classes. 
from odps.udf import annotate
from odps.udf import BaseUDAF
# The function signature. 
@annotate('double->double')
# The custom Python class. 
class Average(BaseUDAF):
# Methods to implement Python classes. 
    def new_buffer(self):
        return [0, 0]
    def iterate(self, buffer, number):
        if number is not None:
            buffer[0] += number
            buffer[1] += 1
    def merge(self, buffer, pbuffer):
        buffer[0] += pbuffer[0]
        buffer[1] += pbuffer[1]
    def terminate(self, buffer):
        if buffer[1] == 0:
            return 0.0
        return buffer[0] / buffer[1]

MaxCompute allows you to write Python 2 UDAFs in Python 2.7 and run the UDAF 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 the input parameters and return value. When you run a UDAF, the data types of input parameters and the return value of the UDAF must be consistent with the data types specified in the function signature. Data type consistency is checked during semantic parsing. If the data types are inconsistent, an error is returned. Format of function signature:

'arg_type_list -> type'

• arg_type_list: indicates the data types of input parameters. If multiple input parameters are used, specify multiple data types and separate them with commas (,). The following data types are supported: BIGINT, STRING, DOUBLE, BOOLEAN, DATETIME, DECIMAL, FLOAT, BINARY, DATE, DECIMAL(precision,scale), CHAR, VARCHAR, complex data types (ARRAY, MAP, and STRUCT), and nested complex data types.
  arg_type_list can also be set to an asterisk (*) or left empty.

  • If arg_type_list is set to an asterisk (*), a random number of input parameters are used.
  • If arg_type_list is left empty, no input parameters are used.
• type: specifies the data type of return values. For a UDAF, 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.

After you develop a Python 2 UDAF by following the instructions in Development process, you can use MaxCompute SQL to call this UDAF. The following steps describe how to call a Python 2 UDAF:

• 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 resource sharing across projects, see Cross-project resource access based on packages.