Hadoop vs Spark性能对比

基于Spark-0.4和Hadoop-0.20.2

1. Kmeans

数据：自己产生的三维数据，分别围绕正方形的8个顶点

{0, 0, 0}, {0, 10, 0}, {0, 0, 10}, {0, 10, 10},

{10, 0, 0}, {10, 0, 10}, {10, 10, 0}, {10, 10, 10}

Point number	189,918,082 （1亿9千万个三维点）
Capacity	10GB
HDFS Location	/user/LijieXu/Kmeans/Square-10GB.txt

程序逻辑：

读取HDFS上的block到内存，每个block转化为RDD，里面包含vector。

然后对RDD进行map操作，抽取每个vector（point）对应的类号，输出（K,V）为（class，（Point，1）），组成新的RDD。

然后再reduce之前，对每个新的RDD进行combine，在RDD内部算出每个class的中心和。使得每个RDD的输出只有最多K个KV对。

最后进行reduce得到新的RDD（内容的Key是class，Value是中心和，再经过map后得到最后的中心。

先上传到HDFS上，然后在Master上运行

root@master:/opt/spark# ./run spark.examples.SparkKMeans master@master:5050 hdfs://master:9000/user/LijieXu/Kmeans/Square-10GB.txt 8 2.0

迭代执行Kmeans算法。

一共160个task。（160 * 64MB = 10GB）

利用了32个CPU cores，18.9GB的内存。

每个机器的内存消耗为4.5GB （共40GB）（本身points数据10GB*2，Map后中间数据(K, V) => (int, (vector, 1)) （大概10GB）

最后结果：

0.505246194 s

Final centers: Map(5 -> (13.997101228817169, 9.208875044622895, -2.494072457488311), 8 -> (-2.33522333047955, 9.128892414676326, 1.7923150585737604), 7 -> (8.658031587043952, 2.162306996983008, 17.670646829079146), 3 -> (11.530154433698268, 0.17834347219956842, 9.224352885937776), 4 -> (12.722903153986868, 8.812883284216143, 0.6564509961064319), 1 -> (6.458644369071984, 11.345681702383024, 7.041924994173552), 6 -> (12.887793408866614, -1.5189406469928937, 9.526393664105957), 2 -> (2.3345459304412164, 2.0173098597285533, 1.4772489989976143))

50MB/s 10GB => 3.5min

10MB/s 10GB => 15min

在20GB的数据上测试

Point number	377,370,313 （3亿7千万个三维点）
Capacity	20GB
HDFS Location	/user/LijieXu/Kmeans/Square-20GB.txt

运行测试命令：

root@master:/opt/spark# ./run spark.examples.SparkKMeans master@master:5050 hdfs://master:9000/user/LijieXu/Kmeans/Square-20GB.txt 8 2.0 | tee mylogs/sqaure-20GB-kmeans.log

得到聚类结果：

Final centers: Map(5 -> (-0.47785701742763115, -1.5901830956323306, -0.18453046159033773), 8 -> (1.1073911553593858, 9.051671594514225, -0.44722211311446924), 7 -> (1.4960397239284795, 10.173412443492643, -1.7932911100570954), 3 -> (-1.4771114031182642, 9.046878176063172, -2.4747981387714444), 4 -> (-0.2796747780312184, 0.06910629855122015, 10.268115903887612), 1 -> (10.467618592186486, -1.168580362309453, -1.0462842137817263), 6 -> (0.7569895433952736, 0.8615441990490469, 9.552726007309518), 2 -> (10.807948500515304, -0.5368803187391366, 0.04258123037074164))

基本就是8个中心点

内存消耗：（每个节点大约5.8GB），共50GB左右。

内存分析：

20GB原始数据，20GB的Map输出

迭代次数	时间
1	108 s
2	0.93 s

12/06/05 11:11:08 INFO spark.CacheTracker: Looking for RDD partition 2:302

12/06/05 11:11:08 INFO spark.CacheTracker: Found partition in cache!

在20GB的数据上测试（迭代更多的次数）

root@master:/opt/spark# ./run spark.examples.SparkKMeans master@master:5050 hdfs://master:900

0/user/LijieXu/Kmeans/Square-20GB.txt 8 0.8

Task数目：320

时间：

迭代次数	时间
1	100.9 s
2	0.93 s
3	4.6 s
4	3.9 s
5	3.9 s
6	3.9 s

迭代轮数对内存容量的影响：

基本没有什么影响，主要内存消耗：20GB的输入数据RDD，20GB的中间数据。

Final centers: Map(5 -> (-4.728089224526789E-5, 3.17334874733142E-5, -2.0605806380414582E-4), 8 -> (1.1841686358289191E-4, 10.000062966002101, 9.999933240005394), 7 -> (9.999976672588097, 10.000199556926772, -2.0695123602840933E-4), 3 -> (-1.3506815993198176E-4, 9.999948270638338, 2.328148782609023E-5), 4 -> (3.2493629851483764E-4, -7.892413981250518E-5, 10.00002515017671), 1 -> (10.00004313126956, 7.431996896171192E-6, 7.590402882208648E-5), 6 -> (9.999982611661382, 10.000144597573051, 10.000037734639696), 2 -> (9.999958673426654, -1.1917651103354863E-4, 9.99990217533504))

结果可视化

2. HdfsTest

测试逻辑：

package spark.examples

import spark._

object HdfsTest {

def main(args: Array[String]) {

val sc = new SparkContext(args(0), "HdfsTest")

val file = sc.textFile(args(1))

val mapped = file.map(s => s.length).cache()

for (iter <- 1 to 10) {

val start = System.currentTimeMillis()

for (x <- mapped) { x + 2 }

// println("Processing: " + x)

val end = System.currentTimeMillis()

println("Iteration " + iter + " took " + (end-start) + " ms")

}

}

}

首先去HDFS上读取一个文本文件保存在file

再次计算file中每行的字符数，保存在内存RDD的mapped中

然后读取mapped中的每一个字符数，将其加2，计算读取+相加的耗时

只有map，没有reduce。

测试10GB的Wiki

实际测试的是RDD的读取性能。

root@master:/opt/spark# ./run spark.examples.HdfsTest master@master:5050 hdfs://master:9000:/user/LijieXu/Wikipedia/txt/enwiki-20110405.txt

测试结果：

Iteration 1 took 12900 ms = 12s

Iteration 2 took 388 ms

Iteration 3 took 472 ms

Iteration 4 took 490 ms

Iteration 5 took 459 ms

Iteration 6 took 492 ms

Iteration 7 took 480 ms

Iteration 8 took 501 ms

Iteration 9 took 479 ms

Iteration 10 took 432 ms

每个node的内存消耗为2.7GB （共9.4GB * 3）

实际测试的是RDD的读取性能。

root@master:/opt/spark# ./run spark.examples.HdfsTest master@master:5050 hdfs://master:9000/user/LijieXu/Wikipedia/txt/enwiki-20110405.txt

测试90GB的RandomText数据

root@master:/opt/spark# ./run spark.examples.HdfsTest master@master:5050 hdfs://master:9000/user/LijieXu/RandomText90GB/RandomText90GB

耗时：

迭代次数	耗时
1	111.905310882 s
2	4.681715228 s
3	4.469296148 s
4	4.441203887 s
5	1.999792125 s
6	2.151376037 s
7	1.889345699 s
8	1.847487668 s
9	1.827241743 s
10	1.747547323 s

内存总消耗30GB左右。

单个节点的资源消耗：

3. 测试WordCount

写程序：

import spark.SparkContext

import SparkContext._

object WordCount {

def main(args: Array[String]) {

if (args.length < 2) {

System.err.println("Usage: wordcount <master> <jar>")

System.exit(1)

}

val sp = new SparkContext(args(0), "wordcount", "/opt/spark", List(args(1)))

val file = sp.textFile("hdfs://master:9000/user/LijieXu/Wikipedia/txt/enwiki-20110405.txt");

val counts = file.flatMap(line => line.split(" ")).map(word => (word, 1)).reduceByKey(_ + _)

counts.saveAsTextFile("hdfs://master:9000/user/Output/WikiResult3")

}

}

打包成mySpark.jar，上传到Master的/opt/spark/newProgram。

运行程序：

root@master:/opt/spark# ./run -cp newProgram/mySpark.jar WordCount master@master:5050 newProgram/mySpark.jar

Mesos自动将jar拷贝到执行节点，然后执行。

内存消耗：（10GB输入file + 10GB的flatMap + 15GB的Map中间结果（word，1））

还有部分内存不知道分配到哪里了。

耗时：50 sec（未经过排序）

Hadoop WordCount耗时：120 sec到140 sec

结果未排序

单个节点：

Hadoop测试

Kmeans

运行Mahout里的Kmeans

root@master:/opt/mahout-distribution-0.6# bin/mahout org.apache.mahout.clustering.syntheticcontrol.kmeans.Job -Dmapred.reduce.tasks=36 -i /user/LijieXu/Kmeans/Square-20GB.txt -o output -t1 3 -t2 1.5 -cd 0.8 -k 8 -x 6

在运行（320个map，1个reduce）

Canopy Driver running buildClusters over input: output/data

时某个slave的资源消耗情况

Completed Jobs

Jobid	Name	Map Total	Reduce Total	Time
job_201206050916_0029	Input Driver running over input: /user/LijieXu/Kmeans/Square-10GB.txt	160	0	1分2秒
job_201206050916_0030	KMeans Driver running runIteration over clustersIn: output/clusters-0/part-randomSeed	160	1	1分6秒
job_201206050916_0031	KMeans Driver running runIteration over clustersIn: output/clusters-1	160	1	1分7秒
job_201206050916_0032	KMeans Driver running runIteration over clustersIn: output/clusters-2	160	1	1分7秒
job_201206050916_0033	KMeans Driver running runIteration over clustersIn: output/clusters-3	160	1	1分6秒
job_201206050916_0034	KMeans Driver running runIteration over clustersIn: output/clusters-4	160	1	1分6秒
job_201206050916_0035	KMeans Driver running runIteration over clustersIn: output/clusters-5	160	1	1分5秒
job_201206050916_0036	KMeans Driver running clusterData over input: output/data	160	0	55秒
job_201206050916_0037	Input Driver running over input: /user/LijieXu/Kmeans/Square-20GB.txt	320	0	1分31秒
job_201206050916_0038	KMeans Driver running runIteration over clustersIn: output/clusters-0/part-randomSeed	320	36	1分46秒
job_201206050916_0039	KMeans Driver running runIteration over clustersIn: output/clusters-1	320	36	1分46秒
job_201206050916_0040	KMeans Driver running runIteration over clustersIn: output/clusters-2	320	36	1分46秒
job_201206050916_0041	KMeans Driver running runIteration over clustersIn: output/clusters-3	320	36	1分47秒
job_201206050916_0042	KMeans Driver running clusterData over input: output/data	320	0	1分34秒

运行多次10GB、20GB上的Kmeans，资源消耗

Hadoop WordCount测试

Spark交互式运行

进入Master的/opt/spark

运行

MASTER=master@master:5050 ./spark-shell

打开Mesos版本的spark

在master:8080可以看到framework

Active Frameworks

ID	User	Name	Running Tasks	CPUs	MEM	Max Share	Connected
201206050924-0-0018	root	Spark shell	0	0	0.0 MB	0.00	2012-06-06 21:12:56

scala> val file = sc.textFile("hdfs://master:9000/user/LijieXu/Wikipedia/txt/enwiki-20110405.txt")

scala> file.first

scala> val words = file.map(_.split(' ')).filter(_.size < 100) //得到RDD[Array[String]]

scala> words.cache

scala> words.filter(_.contains("Beijing")).count

12/06/06 22:12:33 INFO SparkContext: Job finished in 10.862765819 s

res1: Long = 855

scala> words.filter(_.contains("Beijing")).count

12/06/06 22:12:52 INFO SparkContext: Job finished in 0.71051464 s

res2: Long = 855

scala> words.filter(_.contains("Shanghai")).count

12/06/06 22:13:23 INFO SparkContext: Job finished in 0.667734427 s

res3: Long = 614

scala> words.filter(_.contains("Guangzhou")).count

12/06/06 22:13:42 INFO SparkContext: Job finished in 0.800617719 s

res4: Long = 134

由于GC的问题，不能cache很大的数据集。