转自:https://www.jianshu.com/p/06c6f9e50974
- 最简单的注册UDF ---- 直接将lambda表达式注册成UDF
下面是一个简单的清洗函数
from pyspark.sql.types import StringType
spark.udf.register('sex_distinct', lambda x: 'M' if x == u'男' else 'F', StringType())
spark.sql("""
select sex_distinct('男')
""").show()
结果
+---------------+
|sex_distinct(男)|
+---------------+
| M|
+---------------+
- 很多时候逻辑比较复杂,匿名函数不能完成工作,可以自己
def
一个函数,将def
的函数名填入上面lambda
函数所在位置就行
from pyspark.sql.types import StringType def sex_distinct(sex_chinese): if sex_chinese == u'男': return u'M' else: return u'F'
spark.udf.register('sex_distinct_rename', sex_distinct, StringType())
spark.sql("""
select sex_distinct_rename('女')
""").show()
源码分析
def register(self, name, f, returnType=None): """注册python的函数或自定义的函数为udf
:param name: sql语句中的函数名 :param f: 函数,可以python的,也可以是自定义的 :param returnType: ["DataType", "NullType", "StringType", "BinaryType", "BooleanType", "DateType", "TimestampType", "DecimalType", "DoubleType", "FloatType", "ByteType", "IntegerType", "LongType", "ShortType", "ArrayType", "MapType", "StructField", "StructType"] 可以看出规律了吧,和sql中的一一对应 :return: a user-defined function. To register a nondeterministic Python function, users need to first build a nondeterministic user-defined function for the Python function and then register it as a SQL function. `returnType` can be optionally specified when `f` is a Python function but not when `f` is a user-defined function. Please see below. 1. 当f是python内部的函数(所谓python内部的函数就是python自带的函数) `returnType` 默认是 string type 并且可以按需指定. 返回类型必须匹配指定类型. 这种情况约等于 `register(name, f, returnType=StringType())`. >>> strlen = spark.udf.register("stringLengthString", lambda x: len(x)) >>> spark.sql("SELECT stringLengthString('test')").collect() [Row(stringLengthString(test)=u'4')] >>> spark.sql("SELECT 'foo' AS text").select(strlen("text")).collect() [Row(stringLengthString(text)=u'3')] >>> from pyspark.sql.types import IntegerType >>> _ = spark.udf.register("stringLengthInt", lambda x: len(x), IntegerType()) >>> spark.sql("SELECT stringLengthInt('test')").collect() [Row(stringLengthInt(test)=4)] 2. 当f是用户自定义的函数 Spark uses the return type of the given user-defined function as the return type of the registered user-defined function. `returnType` should not be specified. In this case, this API works as if `register(name, f)`. >>> from pyspark.sql.types import IntegerType >>> from pyspark.sql.functions import udf >>> slen = udf(lambda s: len(s), IntegerType()) >>> _ = spark.udf.register("slen", slen) >>> spark.sql("SELECT slen('test')").collect() [Row(slen(test)=4)] >>> import random >>> from pyspark.sql.functions import udf >>> from pyspark.sql.types import IntegerType >>> random_udf = udf(lambda: random.randint(0, 100), IntegerType()).asNondeterministic() >>> new_random_udf = spark.udf.register("random_udf", random_udf) >>> spark.sql("SELECT random_udf()").collect() # doctest: +SKIP [Row(random_udf()=82)] >>> from pyspark.sql.functions import pandas_udf, PandasUDFType >>> @pandas_udf("integer", PandasUDFType.SCALAR) # doctest: +SKIP ... def add_one(x): ... return x + 1 ... >>> _ = spark.udf.register("add_one", add_one) # doctest: +SKIP >>> spark.sql("SELECT add_one(id) FROM range(3)").collect() # doctest: +SKIP [Row(add_one(id)=1), Row(add_one(id)=2), Row(add_one(id)=3)] >>> @pandas_udf("integer", PandasUDFType.GROUPED_AGG) # doctest: +SKIP ... def sum_udf(v): ... return v.sum() ... >>> _ = spark.udf.register("sum_udf", sum_udf) # doctest: +SKIP >>> q = "SELECT sum_udf(v1) FROM VALUES (3, 0), (2, 0), (1, 1) tbl(v1, v2) GROUP BY v2" >>> spark.sql(q).collect() # doctest: +SKIP [Row(sum_udf(v1)=1), Row(sum_udf(v1)=5)] .. note:: Registration for a user-defined function (case 2.) was added from Spark 2.3.0. """</span> <span class="token comment"># This is to check whether the input function is from a user-defined function or</span> <span class="token comment"># Python function.</span> <span class="token keyword">if</span> <span class="token builtin">hasattr</span><span class="token punctuation">(</span>f<span class="token punctuation">,</span> <span class="token string">'asNondeterministic'</span><span class="token punctuation">)</span><span class="token punctuation">:</span> <span class="token keyword">if</span> returnType <span class="token keyword">is</span> <span class="token keyword">not</span> <span class="token boolean">None</span><span class="token punctuation">:</span> <span class="token keyword">raise</span> TypeError<span class="token punctuation">(</span> <span class="token string">"Invalid returnType: data type can not be specified when f is"</span> <span class="token string">"a user-defined function, but got %s."</span> <span class="token operator">%</span> returnType<span class="token punctuation">)</span> <span class="token keyword">if</span> f<span class="token punctuation">.</span>evalType <span class="token keyword">not</span> <span class="token keyword">in</span> <span class="token punctuation">[</span>PythonEvalType<span class="token punctuation">.</span>SQL_BATCHED_UDF<span class="token punctuation">,</span> PythonEvalType<span class="token punctuation">.</span>SQL_SCALAR_PANDAS_UDF<span class="token punctuation">,</span> PythonEvalType<span class="token punctuation">.</span>SQL_GROUPED_AGG_PANDAS_UDF<span class="token punctuation">]</span><span class="token punctuation">:</span> <span class="token keyword">raise</span> ValueError<span class="token punctuation">(</span> <span class="token string">"Invalid f: f must be SQL_BATCHED_UDF, SQL_SCALAR_PANDAS_UDF or "</span> <span class="token string">"SQL_GROUPED_AGG_PANDAS_UDF"</span><span class="token punctuation">)</span> register_udf <span class="token operator">=</span> UserDefinedFunction<span class="token punctuation">(</span>f<span class="token punctuation">.</span>func<span class="token punctuation">,</span> returnType<span class="token operator">=</span>f<span class="token punctuation">.</span>returnType<span class="token punctuation">,</span> name<span class="token operator">=</span>name<span class="token punctuation">,</span> evalType<span class="token operator">=</span>f<span class="token punctuation">.</span>evalType<span class="token punctuation">,</span> deterministic<span class="token operator">=</span>f<span class="token punctuation">.</span>deterministic<span class="token punctuation">)</span> return_udf <span class="token operator">=</span> f <span class="token keyword">else</span><span class="token punctuation">:</span> <span class="token keyword">if</span> returnType <span class="token keyword">is</span> <span class="token boolean">None</span><span class="token punctuation">:</span> <span class="token comment">#这里指定了返回类型默认为StringType()</span> returnType <span class="token operator">=</span> StringType<span class="token punctuation">(</span><span class="token punctuation">)</span> register_udf <span class="token operator">=</span> UserDefinedFunction<span class="token punctuation">(</span>f<span class="token punctuation">,</span> returnType<span class="token operator">=</span>returnType<span class="token punctuation">,</span> name<span class="token operator">=</span>name<span class="token punctuation">,</span> evalType<span class="token operator">=</span>PythonEvalType<span class="token punctuation">.</span>SQL_BATCHED_UDF<span class="token punctuation">)</span> return_udf <span class="token operator">=</span> register_udf<span class="token punctuation">.</span>_wrapped<span class="token punctuation">(</span><span class="token punctuation">)</span> self<span class="token punctuation">.</span>sparkSession<span class="token punctuation">.</span>_jsparkSession<span class="token punctuation">.</span>udf<span class="token punctuation">(</span><span class="token punctuation">)</span><span class="token punctuation">.</span>registerPython<span class="token punctuation">(</span>name<span class="token punctuation">,</span> register_udf<span class="token punctuation">.</span>_judf<span class="token punctuation">)</span> <span class="token keyword">return</span> return_udf
-
复杂数据类型,
ArrayType
、MapType
、StructType
-
ArrayType
Demo
-
from pyspark.sql.types import *
def split_to_array(input_string):
word_list = input_string.split('|')
return word_listspark.udf.register('split_to_array', split_to_array, ArrayType(StringType()))
spark.sql("""
select split_to_array('我| shi|真的')
""").show()
结果
+-------------------------+
|split_to_array(我| shi|真的)|
+-------------------------+
| [我, shi, 真的]|
+-------------------------+
-
MapType
Demo
from pyspark.sql.types import *
def word_count(input_string):
word_dict = {}
word_list = input_string.split(' ')
for word in word_list:
word_dict[word] = 0<span class="token keyword">for</span> word <span class="token keyword">in</span> word_list<span class="token punctuation">:</span> word_dict<span class="token punctuation">[</span>word<span class="token punctuation">]</span> <span class="token operator">+=</span> <span class="token number">1</span> <span class="token keyword">return</span> word_dict
spark.udf.register('word_count', word_count, MapType(StringType(), IntegerType()))
spark.sql("""
select word_count('this apple belong to big apple')
""").show(truncate=False)
结果
+----------------------------------------------------------+
|word_count(this apple belong to big apple) |
+----------------------------------------------------------+
|Map(this -> 1, big -> 1, belong -> 1, to -> 1, apple -> 2)|
+----------------------------------------------------------+
-
StructType
Demo
from pyspark.sql.types import * import hashlib
def string_to_struct(input_string):
my_dict={}
m = hashlib.md5()
m.update(input_string.encode('utf-8'))
my_dict['id'] = m.hexdigest()
my_dict['content'] = input_string
return my_dictschema = StructType([
StructField("id", StringType(), True),
StructField("content", StringType(), True)
])spark.udf.register('string_to_struct', string_to_struct, schema)
df = spark.sql("""
select string_to_struct('my name is hello world')
""")df.show(truncate=False)
df.printSchema()
结果
+---------------------------------------------------------+ |string_to_struct(my name is hello world) | +---------------------------------------------------------+ |[1e030e259e2c7759fb24572ac4d62d3f,my name is hello world]| +---------------------------------------------------------+
root
|-- string_to_struct(my name is hello world): struct (nullable = true)
| |-- id: string (nullable = true)
| |-- content: string (nullable = true)
可以看出规律了吧,python中的类型要和自己定义的复杂类型对应起来。
此外,复杂数据类型支持嵌套,array
中可以嵌套struct
、map
、array
,其他同理。