我们都知道Spark比Hadoop的MR计算速度更快。到底快多少呢？我一直比较疑惑，会有官网说的那么夸张吗。
今天就拿基于Spark的Spark SQL和基于MR的Hive比较一下，因为Spark SQL也兼容了HiveQL，我们就可以通过运行相同的HiveQL语句，比较直观的看出到底快多少了。
Spark SQL只要在编译的时候引入Hive支持，就可以支持Hive表访问，UDF，SerDe，以及HiveQL/HQL--引自《Spark快速大数据分析》
Hive一般在工作站上运行，它把SQL查询转化为一系列在Hadoop集群上运行的MR作业--引自《Hadoop权威指南》
用的文件系统都是HDFS，比较的是第二条sql语句
1.运行spark-sql shell
guo@drguo:/opt/spark-1.6.1-bin-hadoop2.6/bin$ spark-sql
spark-sql> create external table cn(x bigint, y bigint, z bigint, k bigint)
> row format delimited fields terminated by ','
> location '/cn';
OK
Time taken: 0.876 seconds
spark-sql> create table p as select y, z, sum(k) as t
> from cn where x>=20141228 and x<=20150110 group by y, z;
Time taken: 20.658 seconds 查看jobs，用了15s，为什么上面显示花了接近21s？我也不清楚，先放下不管。
再看一下它的DAG图
2.运行Hive
guo@drguo:~$ hive
hive> create external table cn(x bigint, y bigint, z bigint, k bigint)
> row format delimited fields terminated by ','
> location '/cn';
OK
Time taken: 0.752 seconds
hive> create table p as select y, z, sum(k) as t
> from cn where x>=20141228 and x<=20150110 group by y, z;
Query ID = guo_20160515161032_2e035fc2-6214-402a-90dd-7acda7d638bf
Total jobs = 1
Launching Job 1 out of 1
Number of reduce tasks not specified. Estimated from input data size: 1
In order to change the average load for a reducer (in bytes):
set hive.exec.reducers.bytes.per.reducer=<number>
In order to limit the maximum number of reducers:
set hive.exec.reducers.max=<number>
In order to set a constant number of reducers:
set mapreduce.job.reduces=<number>
Starting Job = job_1463299536204_0001, Tracking URL = http://drguo:8088/proxy/application_1463299536204_0001/
Kill Command = /opt/Hadoop/hadoop-2.7.2/bin/hadoop job -kill job_1463299536204_0001
Hadoop job information for Stage-1: number of mappers: 1; number of reducers: 1
2016-05-15 16:10:52,353 Stage-1 map = 0%, reduce = 0%
2016-05-15 16:11:03,418 Stage-1 map = 100%, reduce = 0%, Cumulative CPU 8.65 sec
2016-05-15 16:11:13,257 Stage-1 map = 100%, reduce = 100%, Cumulative CPU 13.99 sec
MapReduce Total cumulative CPU time: 13 seconds 990 msec
Ended Job = job_1463299536204_0001
Moving data to: hdfs://drguo:9000/user/hive/warehouse/predict
Table default.predict stats: [numFiles=1, numRows=1486, totalSize=15256, rawDataSize=13770]
MapReduce Jobs Launched:
Stage-Stage-1: Map: 1 Reduce: 1 Cumulative CPU: 13.99 sec HDFS Read: 16551670 HDFS Write: 15332 SUCCESS
Total MapReduce CPU Time Spent: 13 seconds 990 msec
OK
Time taken: 42.887 seconds
花了接近43s。再去RM看一下，花了36s
结果非常明显
如果按shell里显示花的时间算，21：43
按网页里显示花的时间算，15：36
差不多一倍，据说即将发布的Spark2.0速度更快。
那么问题来了，Spark什么时候会完全取代MR？
后补：数据量550734，count时间为25秒比2.7秒
hive> show tables;
OK
t_log_2016
test
Time taken: 0.333 seconds, Fetched: 2 row(s)
hive> select count(1) from t_log_2016;
Query ID = hdfs_20161013143029_f605d459-878f-495f-95bf-3a3960537d00
Total jobs = 1
Launching Job 1 out of 1
Tez session was closed. Reopening...
Session re-established.
Status: Running (Executing on YARN cluster with App id application_1475896673093_0012)
--------------------------------------------------------------------------------
VERTICES STATUS TOTAL COMPLETED RUNNING PENDING FAILED KILLED
--------------------------------------------------------------------------------
Map 1 .......... SUCCEEDED 12 12 0 0 0 0
Reducer 2 ...... SUCCEEDED 1 1 0 0 0 0
--------------------------------------------------------------------------------
VERTICES: 02/02 [==========================>>] 100% ELAPSED TIME: 16.56 s
--------------------------------------------------------------------------------
OK
550734
Time taken: 25.034 seconds, Fetched: 1 row(s)
spark-sql> show tables;
t_log_2016 false
test false
Time taken: 1.964 seconds, Fetched 2 row(s)
spark-sql> select count(*) from t_log_2016;
550734
Time taken: 2.715 seconds, Fetched 1 row(s)
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原文链接：https://blog.csdn.net/dr_guo/article/details/52775070
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