5bei.cn大模型教程网在Apache Spark的生态系统中，RDD（弹性分布式数据集）和DataFrame是两种核心的数据抽象，支撑着海量数据的处理与分析。自2014年Spark 1.3版本引入DataFrame以来，这两种数据结构就并存于Spark的API中，各自在不同场景中发挥着重要作用。本文将从底层原理、性能表现和适用场景三个维度，通过实战案例对比RDD与DataFrame的核心差异，帮助开发者在实际项目中做出合理选择。
一、两种数据结构的底层差异
理解RDD和DataFrame的本质区别，是分析其性能差异的基础。
1. RDD：无schema的分布式集合
RDD是Spark最基础的数据抽象，本质上是分布式的Java对象集合，具有以下特点：
无schema设计：不包含数据结构信息，Spark无法提前知晓数据的字段类型和结构
面向对象编程：通过map、flatMap等方法操作Java/Scala对象
惰性计算：所有转换操作都是延迟执行的，只有行动操作才触发计算
from pyspark import SparkContext
# 初始化SparkContext
sc = SparkContext(“local”, “RDDExample”)
# 创建RDD（学生姓名、年龄、成绩）
data = [(“Alice”, 22, 85), (“Bob”, 21, 78), (“Charlie”, 23, 92)]
rdd = sc.parallelize(data)
# RDD转换操作：筛选成绩大于80的学生
filtered_rdd = rdd.filter(lambda x: x[2] > 80)
# 行动操作：收集结果
result = filtered_rdd.collect()
print(result) # [(‘Alice’, 22, 85), (‘Charlie’, 23, 92)]
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RDD的优势在于灵活性，能处理任何类型的数据，但由于缺乏结构信息，Spark无法对其进行优化。
2. DataFrame：带schema的分布式表
DataFrame是一种带schema的分布式数据集合，类似于关系型数据库中的表，具有以下特点：
强类型schema：包含字段名称和数据类型信息（如整数、字符串）
列式存储：数据按列存储，相同类型数据连续存放，提高IO效率
Catalyst优化器：基于schema信息进行查询优化，生成高效执行计划
from pyspark.sql import SparkSession
# 初始化SparkSession
spark = SparkSession.builder.appName(“DataFrameExample”).getOrCreate()
# 创建DataFrame（指定schema）
data = [(“Alice”, 22, 85), (“Bob”, 21, 78), (“Charlie”, 23, 92)]
schema = [“name”, “age”, “score”]
df = spark.createDataFrame(data, schema=schema)
# DataFrame操作：筛选成绩大于80的学生
filtered_df = df.filter(df.score > 80)
# 显示结果
filtered_df.show()
# +——-+—+—–+
# | name|age|score|
# +——-+—+—–+
# | Alice| 22| 85|
# |Charlie| 23| 92|
# +——-+—+—–+
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DataFrame的schema让Spark能像数据库一样优化查询，这是其性能优于RDD的关键原因。
二、性能对比实验
为直观展示两者的性能差异，我们通过三个典型场景进行对比实验：数据过滤、聚合计算和Join操作。
1. 实验环境
硬件：4核CPU，16GB内存
软件：Spark 3.3.0，Python 3.9
数据集：生成1000万条模拟用户数据（id, name, age, city, salary）
# 生成测试数据
def generate_test_data(size):
import random
cities = [“New York”, “London”, “Paris”, “Tokyo”, “Sydney”]
data = []
for i in range(size):
age = random.randint(18, 65)
salary = random.randint(30000, 150000)
city = random.choice(cities)
data.append((i, f”User{i}”, age, city, salary))
return data
# 创建RDD和DataFrame
large_data = generate_test_data(10_000_000)
rdd = sc.parallelize(large_data)
df = spark.createDataFrame(large_data, [“id”, “name”, “age”, “city”, “salary”])
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2. 数据过滤性能
过滤操作是最基础的数据集操作，测试筛选年龄大于30且薪资高于50000的记录：
import time
# RDD过滤
start_time = time.time()
rdd_filtered = rdd.filter(lambda x: x[2] > 30 and x[4] > 50000)
rdd_count = rdd_filtered.count() # 触发计算
rdd_time = time.time() – start_time
# DataFrame过滤
start_time = time.time()
df_filtered = df.filter((df.age > 30) & (df.salary > 50000))
df_count = df_filtered.count()
df_time = time.time() – start_time
print(f”RDD过滤时间: {rdd_time:.2f}秒”)
print(f”DataFrame过滤时间: {df_time:.2f}秒”)
print(f”DataFrame速度提升: {rdd_time/df_time:.2f}倍”)
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实验结果：RDD耗时约12.8秒，DataFrame耗时约3.5秒，DataFrame速度提升约3.6倍。
原因分析：
DataFrame的Catalyst优化器会将过滤条件合并为单一操作
列式存储使Spark只需读取需要的列（age和salary），减少IO
RDD需遍历所有字段，且Python lambda函数的执行效率较低
3. 聚合计算性能
聚合操作测试按城市分组计算平均薪资：
# RDD聚合
start_time = time.time()
# (city, salary) -> 按city分组求平均
rdd_agg = rdd.map(lambda x: (x[3], (x[4], 1)))
.reduceByKey(lambda a, b: (a[0]+b[0], a[1]+b[1]))
.map(lambda x: (x[0], x[1][0]/x[1][1]))
rdd_agg_result = rdd_agg.collect()
rdd_time = time.time() – start_time
# DataFrame聚合
start_time = time.time()
df_agg = df.groupBy(“city”).agg({“salary”: “avg”})
df_agg_result = df_agg.collect()
df_time = time.time() – start_time
print(f”RDD聚合时间: {rdd_time:.2f}秒”)
print(f”DataFrame聚合时间: {df_time:.2f}秒”)
print(f”DataFrame速度提升: {rdd_time/df_time:.2f}倍”)
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实验结果：RDD耗时约28.5秒，DataFrame耗时约5.2秒，DataFrame速度提升约5.5倍。
原因分析：
DataFrame的聚合操作由Catalyst优化为高效执行计划
底层使用Tungsten执行引擎，用二进制格式存储数据，减少内存占用
RDD的shuffle操作效率低，且Python对象的序列化开销大
4. Join操作性能
Join操作测试用户表与城市信息表的关联：
# 创建城市信息表
cities_data = [(“New York”, “USA”), (“London”, “UK”), (“Paris”, “France”),
(“Tokyo”, “Japan”), (“Sydney”, “Australia”)]
cities_rdd = sc.parallelize(cities_data)
cities_df = spark.createDataFrame(cities_data, [“city”, “country”])
# RDD Join
start_time = time.time()
# (city, (user_data, country))
rdd_join = rdd.map(lambda x: (x[3], x))
.join(cities_rdd.map(lambda x: (x[0], x[1])))
rdd_join_count = rdd_join.count()
rdd_time = time.time() – start_time
# DataFrame Join
start_time = time.time()
df_join = df.join(cities_df, on=”city”, how=”inner”)
df_join_count = df_join.count()
df_time = time.time() – start_time
print(f”RDD Join时间: {rdd_time:.2f}秒”)
print(f”DataFrame Join时间: {df_time:.2f}秒”)
m.ximalaya.com/sound/891988510?952=74
m.ximalaya.com/sound/891988510?162=550
m.ximalaya.com/sound/891988511?147=55
m.ximalaya.com/sound/891988511?717=435
m.ximalaya.com/sound/891988514?516=83
m.ximalaya.com/sound/891988514?652=880
m.ximalaya.com/sound/891988515?108=42
m.ximalaya.com/sound/891988515?851=523
m.ximalaya.com/sound/891988512?708=06
m.ximalaya.com/sound/891988512?360=014
m.ximalaya.com/sound/891988516?897=08
m.ximalaya.com/sound/891988516?836=548
m.ximalaya.com/sound/891988513?877=55
m.ximalaya.com/sound/891988513?018=666
m.ximalaya.com/sound/891988517?705=63
m.ximalaya.com/sound/891988517?178=832
m.ximalaya.com/sound/891988518?937=29
m.ximalaya.com/sound/891988518?637=487
m.ximalaya.com/sound/891988519?169=61
m.ximalaya.com/sound/891988519?738=526
m.ximalaya.com/sound/891988520?392=81
m.ximalaya.com/sound/891988520?240=096
m.ximalaya.com/sound/891988521?513=34
m.ximalaya.com/sound/891988521?747=305
m.ximalaya.com/sound/891988523?267=52
m.ximalaya.com/sound/891988523?760=222
m.ximalaya.com/sound/891988524?842=76
m.ximalaya.com/sound/891988524?018=790
m.ximalaya.com/sound/891988525?948=97
m.ximalaya.com/sound/891988525?859=381
m.ximalaya.com/sound/891988526?209=97
m.ximalaya.com/sound/891988526?442=691
m.ximalaya.com/sound/891988527?972=56
m.ximalaya.com/sound/891988527?643=875
m.ximalaya.com/sound/891988528?820=08
m.ximalaya.com/sound/891988528?563=728
m.ximalaya.com/sound/891988529?471=38
m.ximalaya.com/sound/891988529?074=294
m.ximalaya.com/sound/891988530?619=65
m.ximalaya.com/sound/891988530?278=333
m.ximalaya.com/sound/891988532?603=83
m.ximalaya.com/sound/891988532?773=779
m.ximalaya.com/sound/891988533?513=08
m.ximalaya.com/sound/891988533?884=589
m.ximalaya.com/sound/891988531?779=06
m.ximalaya.com/sound/891988531?842=559
m.ximalaya.com/sound/891988534?465=44
m.ximalaya.com/sound/891988534?118=100
m.ximalaya.com/sound/891988536?307=33
m.ximalaya.com/sound/891988536?237=403
m.ximalaya.com/sound/891988535?273=21
m.ximalaya.com/sound/891988535?831=790
m.ximalaya.com/sound/891988537?561=81
m.ximalaya.com/sound/891988537?456=911
m.ximalaya.com/sound/891988539?777=06
m.ximalaya.com/sound/891988539?912=191
m.ximalaya.com/sound/891988540?841=70
m.ximalaya.com/sound/891988540?766=338
m.ximalaya.com/sound/891988541?637=22
m.ximalaya.com/sound/891988541?248=116
m.ximalaya.com/sound/891988542?704=35
m.ximalaya.com/sound/891988542?627=960
m.ximalaya.com/sound/891988545?345=82
m.ximalaya.com/sound/891988545?412=072
m.ximalaya.com/sound/891988546?569=40
m.ximalaya.com/sound/891988546?396=473
m.ximalaya.com/sound/891988547?417=98
m.ximalaya.com/sound/891988547?600=144
m.ximalaya.com/sound/891988548?633=18
m.ximalaya.com/sound/891988548?940=877
m.ximalaya.com/sound/891988549?016=30
m.ximalaya.com/sound/891988549?194=801
m.ximalaya.com/sound/891988550?733=82
m.ximalaya.com/sound/891988550?838=106
m.ximalaya.com/sound/891988552?158=97
m.ximalaya.com/sound/891988552?404=661
m.ximalaya.com/sound/891988555?103=68
m.ximalaya.com/sound/891988555?071=586
m.ximalaya.com/sound/891988556?995=38
m.ximalaya.com/sound/891988556?895=530
m.ximalaya.com/sound/891988557?401=54
m.ximalaya.com/sound/891988557?643=128
m.ximalaya.com/sound/891988558?588=46
m.ximalaya.com/sound/891988558?613=035
m.ximalaya.com/sound/891988561?771=72
m.ximalaya.com/sound/891988561?815=811
m.ximalaya.com/sound/891988562?835=71
m.ximalaya.com/sound/891988562?467=907
m.ximalaya.com/sound/891988564?561=59
m.ximalaya.com/sound/891988564?388=892
m.ximalaya.com/sound/891988565?120=95
m.ximalaya.com/sound/891988565?989=665
m.ximalaya.com/sound/891988563?005=53
m.ximalaya.com/sound/891988563?704=858
m.ximalaya.com/sound/891988569?047=53
m.ximalaya.com/sound/891988569?355=400
m.ximalaya.com/sound/891988572?250=79
m.ximalaya.com/sound/891988572?617=833
m.ximalaya.com/sound/891988573?735=40
m.ximalaya.com/sound/891988573?867=665
m.ximalaya.com/sound/891988571?596=76
m.ximalaya.com/sound/891988571?169=114
m.ximalaya.com/sound/891988570?986=37
m.ximalaya.com/sound/891988570?948=032
m.ximalaya.com/sound/891988568?764=06
m.ximalaya.com/sound/891988568?213=877
m.ximalaya.com/sound/891988559?846=72
m.ximalaya.com/sound/891988559?496=348
m.ximalaya.com/sound/891988560?702=77
m.ximalaya.com/sound/891988560?379=616
m.ximalaya.com/sound/891988567?481=43
m.ximalaya.com/sound/891988567?945=270
m.ximalaya.com/sound/891988575?007=28
m.ximalaya.com/sound/891988575?114=284
m.ximalaya.com/sound/891988574?521=03
m.ximalaya.com/sound/891988574?335=728
m.ximalaya.com/sound/891988577?151=24
m.ximalaya.com/sound/891988577?454=265
m.ximalaya.com/sound/891988578?789=74
m.ximalaya.com/sound/891988578?533=674
m.ximalaya.com/sound/891988580?152=99
m.ximalaya.com/sound/891988580?088=697
m.ximalaya.com/sound/891988579?771=14
m.ximalaya.com/sound/891988579?731=755
m.ximalaya.com/sound/891988581?367=40
m.ximalaya.com/sound/891988581?843=421
m.ximalaya.com/sound/891988583?516=60
m.ximalaya.com/sound/891988583?789=908
m.ximalaya.com/sound/891988584?591=73
m.ximalaya.com/sound/891988584?730=392
m.ximalaya.com/sound/891988585?638=80
m.ximalaya.com/sound/891988585?451=900
m.ximalaya.com/sound/891988582?445=54
m.ximalaya.com/sound/891988582?754=805
m.ximalaya.com/sound/891988586?680=12
m.ximalaya.com/sound/891988586?321=719
m.ximalaya.com/sound/891988587?785=48
m.ximalaya.com/sound/891988587?188=568
m.ximalaya.com/sound/891988588?525=86
m.ximalaya.com/sound/891988588?494=602
m.ximalaya.com/sound/891988589?738=21
m.ximalaya.com/sound/891988589?458=250
m.ximalaya.com/sound/891988590?035=29
m.ximalaya.com/sound/891988590?564=086
m.ximalaya.com/sound/891988591?958=49
m.ximalaya.com/sound/891988591?960=239
m.ximalaya.com/sound/891988592?148=97
m.ximalaya.com/sound/891988592?367=828
m.ximalaya.com/sound/891988593?948=36
m.ximalaya.com/sound/891988593?047=450
m.ximalaya.com/sound/891988594?070=43
m.ximalaya.com/sound/891988594?250=707
m.ximalaya.com/sound/891988596?411=35
m.ximalaya.com/sound/891988596?303=563
m.ximalaya.com/sound/891988595?595=50
m.ximalaya.com/sound/891988595?827=506
m.ximalaya.com/sound/891988598?121=63
m.ximalaya.com/sound/891988598?954=764
m.ximalaya.com/sound/891988599?889=80
m.ximalaya.com/sound/891988599?822=093
m.ximalaya.com/sound/891988601?101=14
m.ximalaya.com/sound/891988601?160=797
m.ximalaya.com/sound/891988600?013=01
m.ximalaya.com/sound/891988600?995=714
m.ximalaya.com/sound/891988602?094=78
m.ximalaya.com/sound/891988602?685=651
m.ximalaya.com/sound/891988603?395=67
m.ximalaya.com/sound/891988603?422=653
m.ximalaya.com/sound/891988604?162=28
m.ximalaya.com/sound/891988604?220=576
m.ximalaya.com/sound/891988606?178=00
m.ximalaya.com/sound/891988606?390=411
m.ximalaya.com/sound/891988607?986=65
m.ximalaya.com/sound/891988607?394=378
m.ximalaya.com/sound/891988605?359=11
m.ximalaya.com/sound/891988605?474=662
m.ximalaya.com/sound/891988609?712=13
m.ximalaya.com/sound/891988609?955=629
m.ximalaya.com/sound/891988608?282=75
m.ximalaya.com/sound/891988608?324=046
m.ximalaya.com/sound/891988612?282=83
m.ximalaya.com/sound/891988612?221=245
m.ximalaya.com/sound/891988610?133=27
m.ximalaya.com/sound/891988610?256=754
m.ximalaya.com/sound/891988611?458=73
m.ximalaya.com/sound/891988611?451=566
m.ximalaya.com/sound/891988613?206=67
m.ximalaya.com/sound/891988613?124=783
m.ximalaya.com/sound/891988614?672=60
m.ximalaya.com/sound/891988614?496=099
m.ximalaya.com/sound/891988615?779=96
m.ximalaya.com/sound/891988615?275=183
m.ximalaya.com/sound/891988616?668=24
m.ximalaya.com/sound/891988616?739=292
m.ximalaya.com/sound/891988617?692=93
m.ximalaya.com/sound/891988617?275=464
m.ximalaya.com/sound/891988620?002=09
m.ximalaya.com/sound/891988620?011=077
m.ximalaya.com/sound/891988621?839=87
m.ximalaya.com/sound/891988621?710=246
m.ximalaya.com/sound/891988622?855=69
m.ximalaya.com/sound/891988622?449=965
m.ximalaya.com/sound/891988623?784=03
m.ximalaya.com/sound/891988623?147=945
m.ximalaya.com/sound/891988625?063=25
m.ximalaya.com/sound/891988625?505=586
m.ximalaya.com/sound/891988624?242=10
m.ximalaya.com/sound/891988624?295=641
m.ximalaya.com/sound/891988627?855=56
m.ximalaya.com/sound/891988627?051=235
m.ximalaya.com/sound/891988626?734=50
m.ximalaya.com/sound/891988626?521=161
m.ximalaya.com/sound/891988629?808=22
m.ximalaya.com/sound/891988629?751=493
m.ximalaya.com/sound/891988632?168=95
m.ximalaya.com/sound/891988632?224=558
m.ximalaya.com/sound/891988630?178=15
m.ximalaya.com/sound/891988630?537=983
m.ximalaya.com/sound/891988633?186=95
m.ximalaya.com/sound/891988633?413=195
m.ximalaya.com/sound/891988628?049=76
m.ximalaya.com/sound/891988628?924=671
m.ximalaya.com/sound/891988631?941=87
m.ximalaya.com/sound/891988631?160=349
m.ximalaya.com/sound/891988635?454=91
m.ximalaya.com/sound/891988635?101=404
m.ximalaya.com/sound/891988634?548=04
m.ximalaya.com/sound/891988634?768=679
m.ximalaya.com/sound/891988636?097=33
m.ximalaya.com/sound/891988636?190=756
m.ximalaya.com/sound/891988637?616=01
m.ximalaya.com/sound/891988637?180=221
m.ximalaya.com/sound/891988638?262=24
m.ximalaya.com/sound/891988638?709=613
m.ximalaya.com/sound/891988639?397=59
m.ximalaya.com/sound/891988639?768=983
m.ximalaya.com/sound/891988641?730=60
m.ximalaya.com/sound/891988641?734=498
m.ximalaya.com/sound/891988640?431=73
m.ximalaya.com/sound/891988640?266=392
m.ximalaya.com/sound/891988642?072=92
m.ximalaya.com/sound/891988642?224=694
m.ximalaya.com/sound/891988644?329=45
m.ximalaya.com/sound/891988644?590=683
m.ximalaya.com/sound/891988643?372=71
m.ximalaya.com/sound/891988643?132=489
m.ximalaya.com/sound/891988646?353=81
m.ximalaya.com/sound/891988646?378=722
m.ximalaya.com/sound/891988649?772=49
m.ximalaya.com/sound/891988649?073=579
m.ximalaya.com/sound/891988648?615=04
m.ximalaya.com/sound/891988648?316=147
m.ximalaya.com/sound/891988645?903=07
m.ximalaya.com/sound/891988645?669=256
m.ximalaya.com/sound/891988650?243=52
m.ximalaya.com/sound/891988650?422=312
m.ximalaya.com/sound/891988651?714=52
m.ximalaya.com/sound/891988651?417=465
m.ximalaya.com/sound/891988647?854=23
m.ximalaya.com/sound/891988647?604=420
m.ximalaya.com/sound/891988652?170=11
m.ximalaya.com/sound/891988652?882=044
m.ximalaya.com/sound/891988653?892=29
m.ximalaya.com/sound/891988653?497=210
m.ximalaya.com/sound/891988654?859=03
m.ximalaya.com/sound/891988654?800=980
m.ximalaya.com/sound/891988656?821=82
m.ximalaya.com/sound/891988656?738=365
m.ximalaya.com/sound/891988655?228=34
m.ximalaya.com/sound/891988655?558=484
m.ximalaya.com/sound/891988657?783=63
m.ximalaya.com/sound/891988657?728=518
m.ximalaya.com/sound/891988658?386=45
m.ximalaya.com/sound/891988658?858=144
m.ximalaya.com/sound/891988659?539=00
m.ximalaya.com/sound/891988659?504=011
m.ximalaya.com/sound/891988661?490=59
m.ximalaya.com/sound/891988661?207=539
m.ximalaya.com/sound/891988662?345=87
m.ximalaya.com/sound/891988662?282=546
m.ximalaya.com/sound/891988663?698=65
m.ximalaya.com/sound/891988663?718=750
m.ximalaya.com/sound/891988664?007=87
m.ximalaya.com/sound/891988664?461=715
m.ximalaya.com/sound/891988665?996=70
m.ximalaya.com/sound/891988665?397=579
m.ximalaya.com/sound/891988666?511=41
m.ximalaya.com/sound/891988666?184=634
m.ximalaya.com/sound/891988667?978=25
m.ximalaya.com/sound/891988667?965=928
m.ximalaya.com/sound/891988668?603=42
m.ximalaya.com/sound/891988668?914=263
m.ximalaya.com/sound/891988669?701=87
m.ximalaya.com/sound/891988669?279=004
m.ximalaya.com/sound/891988670?778=90
m.ximalaya.com/sound/891988670?735=468
m.ximalaya.com/sound/891988673?100=01
m.ximalaya.com/sound/891988673?961=148
m.ximalaya.com/sound/891988674?274=72
m.ximalaya.com/sound/891988674?884=653
m.ximalaya.com/sound/891988677?285=53
m.ximalaya.com/sound/891988677?822=151
m.ximalaya.com/sound/891988672?137=81
m.ximalaya.com/sound/891988672?307=392
m.ximalaya.com/sound/891988675?147=57
m.ximalaya.com/sound/891988675?664=914
m.ximalaya.com/sound/891988678?356=23
m.ximalaya.com/sound/891988678?209=810
m.ximalaya.com/sound/891988679?357=10
m.ximalaya.com/sound/891988679?074=700
m.ximalaya.com/sound/891988676?718=11
m.ximalaya.com/sound/891988676?262=027
m.ximalaya.com/sound/891988680?617=58
m.ximalaya.com/sound/891988680?039=352
m.ximalaya.com/sound/891988681?455=40
m.ximalaya.com/sound/891988681?254=820
m.ximalaya.com/sound/891988682?504=64
m.ximalaya.com/sound/891988682?713=163
m.ximalaya.com/sound/891988683?147=76
m.ximalaya.com/sound/891988683?675=200
m.ximalaya.com/sound/891988685?236=96
m.ximalaya.com/sound/891988685?292=535
m.ximalaya.com/sound/891988686?033=23
m.ximalaya.com/sound/891988686?238=486
m.ximalaya.com/sound/891988687?252=06
m.ximalaya.com/sound/891988687?912=410
m.ximalaya.com/sound/891988688?927=45
m.ximalaya.com/sound/891988688?674=118
m.ximalaya.com/sound/891988689?674=25
m.ximalaya.com/sound/891988689?856=572
m.ximalaya.com/sound/891988691?809=97
m.ximalaya.com/sound/891988691?597=236
m.ximalaya.com/sound/891988692?970=11
m.ximalaya.com/sound/891988692?689=254
m.ximalaya.com/sound/891988694?434=28
m.ximalaya.com/sound/891988694?925=155
m.ximalaya.com/sound/891988695?330=15
m.ximalaya.com/sound/891988695?842=460
m.ximalaya.com/sound/891988696?710=00
m.ximalaya.com/sound/891988696?865=574
m.ximalaya.com/sound/891988697?302=34
m.ximalaya.com/sound/891988697?787=994
m.ximalaya.com/sound/891988698?046=66
m.ximalaya.com/sound/891988698?595=684
m.ximalaya.com/sound/891988700?418=09
m.ximalaya.com/sound/891988700?316=516
m.ximalaya.com/sound/891988699?543=37
m.ximalaya.com/sound/891988699?041=912
m.ximalaya.com/sound/891988701?442=13
m.ximalaya.com/sound/891988701?510=553
m.ximalaya.com/sound/891988702?744=66
m.ximalaya.com/sound/891988702?014=201
m.ximalaya.com/sound/891988703?889=10
m.ximalaya.com/sound/891988703?255=764
m.ximalaya.com/sound/891988704?677=48
m.ximalaya.com/sound/891988704?072=298
m.ximalaya.com/sound/891988706?095=94
m.ximalaya.com/sound/891988706?222=390
m.ximalaya.com/sound/891988705?182=82
m.ximalaya.com/sound/891988705?469=582
m.ximalaya.com/sound/891988707?268=24
m.ximalaya.com/sound/891988707?031=205
m.ximalaya.com/sound/891988708?169=50
m.ximalaya.com/sound/891988708?634=827
m.ximalaya.com/sound/891988710?560=59
m.ximalaya.com/sound/891988710?356=931
m.ximalaya.com/sound/891988709?772=68
m.ximalaya.com/sound/891988709?553=100
m.ximalaya.com/sound/891988711?185=13
m.ximalaya.com/sound/891988711?461=298
m.ximalaya.com/sound/891988712?551=39
m.ximalaya.com/sound/891988712?740=723
m.ximalaya.com/sound/891988713?710=01
m.ximalaya.com/sound/891988713?689=538
m.ximalaya.com/sound/891988715?162=48
m.ximalaya.com/sound/891988715?336=366
m.ximalaya.com/sound/891988714?075=19
m.ximalaya.com/sound/891988714?451=243
m.ximalaya.com/sound/891988716?027=03
m.ximalaya.com/sound/891988716?149=581
m.ximalaya.com/sound/891988717?902=76
m.ximalaya.com/sound/891988717?389=496
m.ximalaya.com/sound/891988718?444=13
m.ximalaya.com/sound/891988718?976=510
m.ximalaya.com/sound/891988719?399=35
m.ximalaya.com/sound/891988719?380=025
m.ximalaya.com/sound/891988721?450=40
m.ximalaya.com/sound/891988721?242=334
m.ximalaya.com/sound/891988723?427=72
m.ximalaya.com/sound/891988723?361=892
m.ximalaya.com/sound/891988724?020=87
m.ximalaya.com/sound/891988724?934=042
m.ximalaya.com/sound/891988722?562=38
m.ximalaya.com/sound/891988722?266=971
m.ximalaya.com/sound/891988720?586=42
m.ximalaya.com/sound/891988720?720=341
m.ximalaya.com/sound/891988726?916=76
m.ximalaya.com/sound/891988726?210=715
m.ximalaya.com/sound/891988725?295=86
m.ximalaya.com/sound/891988725?354=894
m.ximalaya.com/sound/891988727?778=07
m.ximalaya.com/sound/891988727?058=680
m.ximalaya.com/sound/891988728?972=97
m.ximalaya.com/sound/891988728?355=357
m.ximalaya.com/sound/891988730?137=57
m.ximalaya.com/sound/891988730?930=614
m.ximalaya.com/sound/891988734?310=09
m.ximalaya.com/sound/891988734?122=796
m.ximalaya.com/sound/891988736?805=79
m.ximalaya.com/sound/891988736?251=651
m.ximalaya.com/sound/891988738?161=10
m.ximalaya.com/sound/891988738?310=275
m.ximalaya.com/sound/891988731?674=29
m.ximalaya.com/sound/891988731?525=902
m.ximalaya.com/sound/891988735?725=28
m.ximalaya.com/sound/891988735?376=551
m.ximalaya.com/sound/891988737?023=88
m.ximalaya.com/sound/891988737?432=480
m.ximalaya.com/sound/891988733?221=71
m.ximalaya.com/sound/891988733?189=786
m.ximalaya.com/sound/891988739?427=17
m.ximalaya.com/sound/891988739?824=312
m.ximalaya.com/sound/891988732?406=83
m.ximalaya.com/sound/891988732?082=428
m.ximalaya.com/sound/891988740?770=80
m.ximalaya.com/sound/891988740?063=986
m.ximalaya.com/sound/891988741?875=82
m.ximalaya.com/sound/891988741?321=470
m.ximalaya.com/sound/891988742?239=50
m.ximalaya.com/sound/891988742?106=580
m.ximalaya.com/sound/891988744?344=94
m.ximalaya.com/sound/891988744?602=047
m.ximalaya.com/sound/891988743?384=14
m.ximalaya.com/sound/891988743?669=170
文章来源于互联网:Spark大数据处理：RDD与DataFrame性能对比
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