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- PySpark 概述
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创建pyspark对象
import warnings
warnings.filterwarnings('ignore')
#import pandas as pd
#import numpy as np
from datetime import timedelta, date, datetime
import time
import gc
import os
import argparse
import sysfrom pyspark.sql import SparkSession, functions as fn
from pyspark.ml.feature import StringIndexer
from pyspark.ml.recommendation import ALS
from pyspark.sql.types import *
from pyspark import StorageLevel
spark = SparkSession \.builder \.appName("stockout_test") \.config("hive.exec.dynamic.partition.mode", "nonstrict") \.config("spark.sql.sources.partitionOverwriteMode", "dynamic")\.config("spark.driver.memory", '20g')\.config("spark.executor.memory", '40g')\.config("spark.yarn.executor.memoryOverhead", '1g')\.config("spark.executor.instances", 8)\.config("spark.executor.cores", 8)\.config("spark.kryoserializer.buffer.max", '128m')\.config("spark.yarn.queue", 'root.algo')\.config("spark.executorEnv.OMP_NUM_THREADS", 12)\.config("spark.executorEnv.ARROW_PRE_0_15_IPC_FORMAT", 1) \.config("spark.default.parallelism", 800)\.enableHiveSupport() \.getOrCreate()
spark.sql("set hive.exec.dynamic.partition.mode = nonstrict")
spark.sql("set hive.exec.dynamic.partition=true")
spark.sql("set spark.sql.autoBroadcastJoinThreshold=-1")创建DataFrame
employee_salary = [("zhangsan", "IT", 8000),("lisi", "IT", 7000),("wangwu", "IT", 7500),("zhaoliu", "ALGO", 10000),("qisan", "IT", 8000),("bajiu", "ALGO", 12000),("james", "ALGO", 11000),("wangzai", "INCREASE", 7000),("carter", "INCREASE", 8000),("kobe", "IT", 9000)]columns= ["name", "department", "salary"]
df = spark.createDataFrame(data = employee_salary, schema = columns)
df.show()+--------+----------+------+ | name|department|salary| +--------+----------+------+ |zhangsan| IT| 8000| | lisi| IT| 7000| | wangwu| IT| 7500| | zhaoliu| ALGO| 10000| | qisan| IT| 8000| | bajiu| ALGO| 12000| | james| ALGO| 11000| | wangzai| INCREASE| 7000| | carter| INCREASE| 8000| | kobe| IT| 9000| +--------+----------+------+
row_number()
from pyspark.sql.window import Window
import pyspark.sql.functions as FwindowSpec = Window.partitionBy("department").orderBy(F.desc("salary"))
df.withColumn("row_number", F.row_number().over(windowSpec)).show(truncate=False)+--------+----------+------+----------+ |name |department|salary|row_number| +--------+----------+------+----------+ |carter |INCREASE |8000 |1 | |wangzai |INCREASE |7000 |2 | |kobe |IT |9000 |1 | |zhangsan|IT |8000 |2 | |qisan |IT |8000 |3 | |wangwu |IT |7500 |4 | |lisi |IT |7000 |5 | |bajiu |ALGO |12000 |1 | |james |ALGO |11000 |2 | |zhaoliu |ALGO |10000 |3 | +--------+----------+------+----------+
Rank()
from pyspark.sql.window import Window
import pyspark.sql.functions as FwindowSpec = Window.partitionBy("department").orderBy(F.desc("salary"))
df.withColumn("rank",F.rank().over(windowSpec)).show(truncate=False)+--------+----------+------+----+ |name |department|salary|rank| +--------+----------+------+----+ |carter |INCREASE |8000 |1 | |wangzai |INCREASE |7000 |2 | |kobe |IT |9000 |1 | |qisan |IT |8000 |2 | |zhangsan|IT |8000 |2 | |wangwu |IT |7500 |4 | |lisi |IT |7000 |5 | |bajiu |ALGO |12000 |1 | |james |ALGO |11000 |2 | |zhaoliu |ALGO |10000 |3 | +--------+----------+------+----+
dense_rank()
from pyspark.sql.window import Window
import pyspark.sql.functions as FwindowSpec = Window.partitionBy("department").orderBy(F.desc("salary"))
df.withColumn("dense_rank",F.dense_rank().over(windowSpec)).show()+--------+----------+------+----------+ | name|department|salary|dense_rank| +--------+----------+------+----------+ | carter| INCREASE| 8000| 1| | wangzai| INCREASE| 7000| 2| | kobe| IT| 9000| 1| | qisan| IT| 8000| 2| |zhangsan| IT| 8000| 2| | wangwu| IT| 7500| 3| | lisi| IT| 7000| 4| | bajiu| ALGO| 12000| 1| | james| ALGO| 11000| 2| | zhaoliu| ALGO| 10000| 3| +--------+----------+------+----------+
lag()
from pyspark.sql.window import Window
import pyspark.sql.functions as FwindowSpec = Window.partitionBy("department").orderBy(F.desc("salary"))
df.withColumn("lag",F.lag("salary",1).over(windowSpec)).show()+--------+----------+------+-----+ | name|department|salary| lag| +--------+----------+------+-----+ | carter| INCREASE| 8000| null| | wangzai| INCREASE| 7000| 8000| | kobe| IT| 9000| null| |zhangsan| IT| 8000| 9000| | qisan| IT| 8000| 8000| | wangwu| IT| 7500| 8000| | lisi| IT| 7000| 7500| | bajiu| ALGO| 12000| null| | james| ALGO| 11000|12000| | zhaoliu| ALGO| 10000|11000| +--------+----------+------+-----+
lead()
from pyspark.sql.window import Window
import pyspark.sql.functions as FwindowSpec = Window.partitionBy("department").orderBy(F.desc("salary"))
df.withColumn("lead",F.lead("salary", 1).over(windowSpec)).show()+--------+----------+------+-----+ | name|department|salary| lead| +--------+----------+------+-----+ | carter| INCREASE| 8000| 7000| | wangzai| INCREASE| 7000| null| | kobe| IT| 9000| 8000| |zhangsan| IT| 8000| 8000| | qisan| IT| 8000| 7500| | wangwu| IT| 7500| 7000| | lisi| IT| 7000| null| | bajiu| ALGO| 12000|11000| | james| ALGO| 11000|10000| | zhaoliu| ALGO| 10000| null| +--------+----------+------+-----+
Aggregate Functions
from pyspark.sql.window import Window
import pyspark.sql.functions as FwindowSpec = Window.partitionBy("department").orderBy(F.desc("salary"))
windowSpecAgg = Window.partitionBy("department")df.withColumn("row", F.row_number().over(windowSpec)) \.withColumn("avg", F.avg("salary").over(windowSpecAgg)) \.withColumn("sum", F.sum("salary").over(windowSpecAgg)) \.withColumn("min", F.min("salary").over(windowSpecAgg)) \.withColumn("max", F.max("salary").over(windowSpecAgg)) \.withColumn("count", F.count("salary").over(windowSpecAgg)) \.withColumn("distinct_count", F.approx_count_distinct("salary").over(windowSpecAgg)) \.show()+--------+----------+------+---+-------+-----+-----+-----+-----+--------------+ | name|department|salary|row| avg| sum| min| max|count|distinct_count| +--------+----------+------+---+-------+-----+-----+-----+-----+--------------+ | carter| INCREASE| 8000| 1| 7500.0|15000| 7000| 8000| 2| 2| | wangzai| INCREASE| 7000| 2| 7500.0|15000| 7000| 8000| 2| 2| | kobe| IT| 9000| 1| 7900.0|39500| 7000| 9000| 5| 4| |zhangsan| IT| 8000| 2| 7900.0|39500| 7000| 9000| 5| 4| | qisan| IT| 8000| 3| 7900.0|39500| 7000| 9000| 5| 4| | wangwu| IT| 7500| 4| 7900.0|39500| 7000| 9000| 5| 4| | lisi| IT| 7000| 5| 7900.0|39500| 7000| 9000| 5| 4| | bajiu| ALGO| 12000| 1|11000.0|33000|10000|12000| 3| 3| | james| ALGO| 11000| 2|11000.0|33000|10000|12000| 3| 3| | zhaoliu| ALGO| 10000| 3|11000.0|33000|10000|12000| 3| 3| +--------+----------+------+---+-------+-----+-----+-----+-----+--------------+
from pyspark.sql.window import Window
import pyspark.sql.functions as F
# 需要注意的是 approx_count_distinct() 函数适用于窗函数的统计，
# 而在groupby中通常用countDistinct()来代替该函数，用来求组内不重复的数值的条数。
# approx_count_distinct()取的是近似的数值，不太准确，使用需注意 windowSpec = Window.partitionBy("department").orderBy(F.desc("salary"))
windowSpecAgg = Window.partitionBy("department")df.withColumn("row", F.row_number().over(windowSpec)) \.withColumn("avg", F.avg("salary").over(windowSpecAgg)) \.withColumn("sum", F.sum("salary").over(windowSpecAgg)) \.withColumn("min", F.min("salary").over(windowSpecAgg)) \.withColumn("max", F.max("salary").over(windowSpecAgg)) \.withColumn("count", F.count("salary").over(windowSpecAgg)) \.withColumn("distinct_count", F.approx_count_distinct("salary").over(windowSpecAgg)) \.where(F.col("row")==1).select("department","avg","sum","min","max","count","distinct_count") \.show()+----------+-------+-----+-----+-----+-----+--------------+ |department| avg| sum| min| max|count|distinct_count| +----------+-------+-----+-----+-----+-----+--------------+ | INCREASE| 7500.0|15000| 7000| 8000| 2| 2| | IT| 7900.0|39500| 7000| 9000| 5| 4| | ALGO|11000.0|33000|10000|12000| 3| 3| +----------+-------+-----+-----+-----+-----+--------------+