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# Licensed to the Apache Software Foundation (ASF) under one
# or more contributor license agreements. See the NOTICE file
# distributed with this work for additional information
# regarding copyright ownership. The ASF licenses this file
# to you under the Apache License, Version 2.0 (the
# "License"); you may not use this file except in compliance
# with the License. You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing,
# software distributed under the License is distributed on an
# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
# KIND, either express or implied. See the License for the
# specific language governing permissions and limitations
# under the License.
throw "pigudf.rb only works under JRuby!" unless RUBY_PLATFORM=="java"
require 'jruby'
org.apache.pig.scripting.jruby.PigJrubyLibrary.new.load(JRuby.runtime, false)
#TODO output_schema should accept a Schema object as well, and use Schema objects
#TODO AccumulatorEvalFunc output_schema should not allow you to give a block and defined it
# This is the base class for runy of the mill EvalFuncs. A class just serves to
# contain similar jobs, as well as allow for method reuse. In the case of simple
# EvalFuncs, each method will be turned into a UDF (though they do not have to be called).
#
# TODO: EXPLAIN SYNTAX
class PigUdf
# Here we initialize the variables we'll be using at the class level (generally
# analogous to static in Java). The nice thing about this method is that these
# values are all set in the same way, even from children. Thus, all of the children
# will update PigUdf.@@functions_to_register, and on the Java side when we want to
# access this, we can return that Map. This means it is no longer necessary to keep
# track of descendent children, etc, since all that matters are the methods that
# are registered with subclasses.
#
# The @@class_object_to_name_and_add variable is used by self.evalfunc and self.filterfunc.
# See the documentation for the former to understand why it is necessary. @@schema holds
# the last schema given by output_schema or output_schema_function, see the documentation
# on output_schema for more.
@@functions_to_register = {}
@@class_object_to_name_and_add = nil
@@schema = nil
# See the documentation on self.evalfunc for why this is necessary. This takes the current class
# object and registers it. This is necessary because self.evalfunc has to return before .to_s
# will return something meaningful and not gibberish.
def self.name_and_add_class_object
if @@class_object_to_name_and_add
name = @@class_object_to_name_and_add.class_object.to_s
@@class_object_to_name_and_add.method_name = "eval"
@@functions_to_register[name] = @@class_object_to_name_and_add
end
@@class_object_to_name_and_add = nil
end
# This is the core function that registers a method as a UDF. The pig_func_name
# identifies it, and in most cases, is the method name (the exception begin
# UDFs created using self.evalfunc). The class_object is the class against an instance
# of which the method will be called. The arity is so Pig knows how many arguments
# to pass to the UDF, and the output_schema defines the Schema of the output, either
# as a string, or as a function.
def self.register_function pig_func_name, class_object, arity, output_schema
self.name_and_add_class_object
pig_func_name = pig_func_name.to_s
reg = EvalFunc.new class_object, pig_func_name, arity, output_schema
@@functions_to_register[pig_func_name] = reg
end
def self.set_class_object_to_name_and_add func
self.name_and_add_class_object
@@class_object_to_name_and_add = func
end
# This method provides the most succinct way to define a UDF. The syntax is as follows:
#
# UdfName = PigUdf.evalfunc('int') do |arg1|
# return arg.length
# end
#
# EvalFunc takes one parameter, the schema to be returned, and a block which will represent
# the method call.
#
# In the case that this will be used, then it will be one class with one function,
# and the function name will be UdfName. It is essential that UdfName begin with
# a capital letter, as this method uses a hook given to ruby where Name = Class.new
# will generate a class of name Name, but only if Name begins with a capital letter.
#
# The reason for naming the function "GETCLASSFROMOBJECT" is that the class object must first
# be returned for its name to be available. Asking it for its name before allowing "evalfunc"
# to return will not yield the name it is given. Thus, we plant "GETCLASSFROMOBJECT" so the next
# time we access @functions_to_register, we know to check.
def self.evalfunc output_schema, &blk
c=Class.new do
define_method :eval do |*args|
blk.call(*args)
end
end
self.set_class_object_to_name_and_add EvalFunc.new c, "GETFROMCLASSOBJECT", blk.arity, output_schema
c
end
# This method functions identically to evalfunc above, the only difference being that no schema
# needs to be given.
def self.filterfunc &blk
c=Class.new do
define_method :eval do |*args|
blk.call(*args)
end
end
self.set_class_object_to_name_and_add EvalFunc.new c, "GETFROMCLASSOBJECT", blk.arity, Schema.boolean
c
end
# This is the function which register the schema associated with a given function. There are
# two ways that it can be invoked, with one argument or two (thus the vague argument names).
#
# case 1: one argument
# In this case, output_schema's argument is the schema to be set for the next method declaration.
# For example:
# output_schema "long"
#
# The above would mean that the schema for the function following it would be set to long. The mechanism
# by which this is achieved is by setting a class schema variable to the schema, and the next time
# a method is declared in the class, the class uses the schema that was set to register the function being
# declared. For more information on that, see self.method_added, as this is the Ruby provided hook
# that is used to allow this disconnect between declaring a schema and the method declaration that follows.
#
# case 2: two arguments
# In this case, arg1 is the name of the function whose schema we want to set, and arg2 is
# the schema, ie
#
# output_schema :sum, "long"
#
# You can only use this after the function is declared, otherwise there will be an error.
# In this case, the information passed to the registration function is the function name,
# an instance of the class (so that on the Java side we can instantiate a version), the arity,
# and the schema. For more information on how that information is used, see self.register_function.
#
# The following two uses are identical:
#
# use 1:
# output_schema "long"
# def sum x, y
# return x + y
# end
#
# use 2:
# def sum x,y
# return x + y
# end
# output_schema :sum, "long"
def self.output_schema arg1, arg2=nil
if arg2
function_name = arg1.to_s
schema = arg2.to_s
self.register_function function_name, self, function_name, schema
else
@@schema = arg1
end
end
# This function acts identically to output_schema, except that it is not necessary to provide a schema string
# because a filter func will always have a set schema (it will return boolean).
def self.filter_func arg1=nil
schema = "FILTERFUNC"
if arg1
function_name = arg1.to_s
self.output_schema function_name, schema
else
self.output_schema schema
end
end
# output_schema is only useful when the function at hand has a deterministic schema. In the case that the schema
# needs to be dynamic, it is useful to be able to process the input schema with a function and return the appropriate
# output schema. An example of this might be a concat function, which takes two values and concatenates them together.
# This function could work for chararrays, but also for bytearrays. In that case, the output schema depends on the input schema.
#
# As with output_schema, there are two cases, and they are identical (see output_schema for a more detailed explanation).
# The difference, however, is that instead of passing a string ie "long", the user gives a function name. Note: the schema
# function does not yet have to be defined. In the case of two arguments, the same information is passed to register_function
# as in the case of output_schema, the difference being that while the schema is passed as a string, it has an identifier
# appended to it so that when this function is running in Java, we'll know that we should be using a function.
def self.output_schema_function arg1, arg2=nil #TODO allow it to also accept a block, as in ComplexPigUdf
schema_func = (arg2||arg1).to_sym
if arg2
function_name = arg1.to_s
self.register_function function_name, self, function_name, schema_func.to_sym
else
@@schema = arg1.to_sym
end
end
# Javaists love their camelCase
class << self
alias :outputSchema :output_schema
alias :filterFunc :filter_func
alias :outputSchemaFunction :output_schema_function
end
# This is a hook that Ruby provides that is called whenever a method is declared on the subclass.
# This is used so that we have visibility on the methods as they are declared, which is useful because
# every declared method will be registered as a UDF for use in Pig. In the case of a method that doesn't
# yet have a schema declared, it's return type will just be a bytearray, as in Pig.
def self.method_added function_name
if @@schema
self.register_function function_name, self, function_name, @@schema
elsif !@@functions_to_register[function_name]
self.register_function function_name, self, function_name, nil
end
@@schema = nil
end
# This returns the map that maintains the Function classes that have information on declared methods.
def self.get_functions_to_register
self.name_and_add_class_object
@@functions_to_register
end
# The Function class privates a convenient wrapper to store information about EvalFuncs, separating
# out the methods that will be used on the frontend to get information on the method registered.
class Function
attr_accessor :method_name
attr_reader :arity, :class_object
def initialize class_object, method_name, arity
@class_object = class_object
@method_name = method_name
@arity = arity
end
def required_args
if @arity.is_a? Numeric
@arity
else
@class_object.instance_method(@arity.to_sym).parameters.count {|x,y| x==:req}
end
end
def optional_args
if @arity.is_a? Numeric
0
else
params = @class_object.instance_method(@arity.to_sym).parameters
return -1 if params.any? {|x,y| x==:rest}
params.count {|x,y| x==:opt}
end
end
# This conveniently gives an instance of the class this Function wraps, so that on the Java end
# it is trivial to get the object against which method calls can be made.
def get_receiver
@class_object.new
end
# This is useful for identifying the subclass Java is dealing with (EvalFunc, FilterFunc, etc)
def name
return self.class.to_s
end
end
class EvalFunc < Function
def initialize class_object, method_name, arity, schema_or_func
super class_object, method_name, arity
@schema_or_func = schema_or_func
end
# This is the function that will be used from Java to get the proper schema of the output.
# Given that users have two options, output_schema or output_schema_function, this method
# detects which and acts appropriately. It must be given an instance of the EvalFunc (generally
# the result of "get_receiver") in the case of an output_schema_function so that it can evaluate
# the output Schema based on the input Schema.
def schema input_schema, class_instance
if !@schema_or_func
return Schema.bytearray
elsif @schema_or_func.is_a? String
return Schema.new @schema_or_func
elsif @schema_or_func.is_a? Schema
return @schema_or_func
else
func = @schema_or_func
func = @class_object.instance_method(func) if func.is_a? Symbol
return func.bind(class_instance).call input_schema
end
end
end
end
# This is the base class used for Algebraic and Accumulator functions. The reason for the different
# implementation is because there is more structure in these cases. In the case of general EvalFuncs,
# a method is equivalent to a UDF. In the case of Algebraic and Accumulator UDFs, however, a class is
# equivalent to a UDF. Thus, instead of keeping track of methods added, we keep track of classes
# that extend our Algebraic and Accumulator UDF base classes.
class ComplexUdfBase
# As with the basic PigUdf, there is a class method "output_schema" which defines the schema for the class.
# This method can be called anywhere (as there is not the issue of multiple UDFs to worry about). If it is not
# called, it will have return type bytearray.
def self.output_schema schema
@schema = schema
end
class << self
alias :outputSchema :output_schema
end
# This returns the schema, or in the case that one was not supplied, a Schema of bytearray.
def self.get_output_schema
Schema.new(@schema||Schema.bytearray)
end
# Since a class = a UDF, in this case it makes sense to traverse the tree of decendant classes
# in order to pull all of the registered classes. It's important to note
def self.classes_to_register
classes = {}
ObjectSpace.each_object(Class) do |c|
classes[c.to_s] = c if c.ancestors.include?(self) and (c != self)
end
classes
end
# This is a method that can be used by Pig to ensure that all of the necessary methods are present, so that
# the function will throw an error on parsing instead of on execution. This is a shell implementation
# to ensure that necessary_methods is called by a subclass, which will then generate the proper implementation.
def self.check_if_necessary_methods_present
throw "Need to declare the methods that should be present"
end
# This is a method that, if called at the class level, defines a set of methods that must be called
# by any child classes (ie UDFs).
def self.necessary_methods *m
self.instance_eval "def self.check_if_necessary_methods_present; #{Array(m).inspect}.all? { |m| self.method_defined? m }; end"
end
end
# This is the class that any Accumulator UDF must extend. The necessary_methods call ensures that all
# child classes have the necessary methods implemented. AccumulatorPigUdfs support dynamic output_schema.
# To do so, register a block with the schema function, as so:
# output_schema do |input|
# return input
# end
#
# In the case of a non-dyanamic output schema, it's possible to stil just set output_schema "long".
#
# an example of an accumulator UDF is:
#
# class SUM < AccumulatorPigUdf
# output_schema "long"
#
# def exec input
# @res ||= 0
# input.flatten.inject(:+)
# end
# def get
# @res
# end
# end
class AccumulatorPigUdf < ComplexUdfBase
def self.output_schema schema=nil, &blk
if block_given?
throw "Can specify block or schema but not both!" if schema
throw "Block must accept one argument!" if blk.arity != 1
@schema = blk
else
@schema = schema
end
end
class << self
alias :outputSchema :output_schema
end
def self.get_output_schema input_schema=nil
if input_schema && @schema.class == Proc
@schema.call input_schema
else
Schema.new(@schema||Schema.bytearray)
end
end
necessary_methods :exec, :get
end
# This is the class that any Accumulator UDF must extend. The necessary_methods call ensures that all
# child classes have the necessary methods implemented.
#
# an example of an Algebraic UDF is:
#
# class Count < AlgebraicPigUdf
# output_schema "long"
#
# def initial t
# t.nil? ? 0 : 1
# end
#
# def intermed t
# return 0 if t.nil?
# return t.flatten.inject(:+)
# end
#
# def final t
# return intermed(t)
# end
# end
class AlgebraicPigUdf < ComplexUdfBase
necessary_methods :initial, :intermed, :final
end
