大数据 Spark 架构

一.Spark的产生背景起源

1.spark特点

  1.1轻量级快速处理

 Saprk允许传统的hadoop集群中的应用程序在内存中已100倍的速度运行即使在磁盘上也比传统的hadoop10倍,Spark通过减少对磁盘的io达到性能上的提升,他将中间处理的数据放到内存中,spark使用了rddresilient distributed datasets)数据抽象

这允许他在内存中存储数据,所以减少了运行时间

1.2 易于使用

spark支持多种语言。Spark允许javascala python R语言,允许shell进行交互式查询

1.3 支持复杂的查询

除了简单的mapreduce操作之外,Spark还支持filterforeachreduceByKeyaggregate以及SQL查询、流式查询等复杂查询。Spark更为强大之处是用户可以在同一个工作流中无缝的搭配这些功能,例如Spark可以通过Spark Streaming1.2.2小节对Spark Streaming有详细介绍)获取流数据,然后对数据进行实时SQL查询或使用MLlib库进行系统推荐,而且这些复杂业务的集成并不复杂,因为它们都基于RDD这一抽象数据集在不同业务过程中进行转换,转换代价小,体现了统一引擎解决不同类型工作场景的特点。

  1.4 实时的流处理

对比maprduce只能处理离线数据。Spark还能支持实时的流计算,spark  streaming 主要用来对数据进行实时的处理,yarnnodemanger统一调度管理很厉害,在yarn产生后hadoop也可以整合资源进行实时的处理

2.时事产物

2.1    mapreduce产生时磁盘廉价,因此许多设计收回考虑到内存的使用,而spark产生时内存相对廉价,对计算速度有所要求,因此spark的产生是基于内存计算的框架结构mapreduce需要写复杂的程序进行计算,

 

 

二.Spark架构

1.spark的体系结构

Spark的体系结构不同于Hadoopmapreduce HDFS ,Spark主要包括spark core和在spark core的基础上建立的应用框架sparkSql  spark Streaming  MLlib  GraphX;

Core库主要包括上下文(spark Context)抽象的数据集(RDD),调度器(Scheduler),洗牌(shuffle) 和序列化器(Seralizer)等。Spark系统中的计算,IO,调度和shuffle等系统的基本功能都在其中

Core库之上就根据业务需求分为用于交互式查询的SQL、实时流处理Streaming、机器学习Mllib和图计算GraphX四大框架hdfs迄今是不可替代的

 

 

 

Spark架构组成图

 

 

 

 

 

一.Hivesparksql支持的对比

Hive创建数据库   创建表       true

        验证策略

脚本

    Hive

Spark-sql

创建库 删除库

Create database lvhou_hive

Create database lvhou_spark

Dorp database lvhou_hive

Dorp database lvhou_spark

True

True

创建表 删除表

Use lvhou_hive

Create table hive_test(a string,b string)

Use lvhou_spark

Create table spark_test(a string,b string)

Drop table hive_test

Drop table spark_test

 

True

True

CTAS

Create table lvhou_test as selec * from lvhou_test1;

 

true

false

Insert

 

Insert into lvhou_hive values(‘hhah’,’heheh’)

true

false

insert

Insert into lvhou_spark value(‘12’.’32’),(‘asd’,’asdf’)

True

false

Select

Select * from lvhou_hive

Select * from lvhou_spark

True

True

 

Select in

 

 

 

 

 

 

 

Select子查询

in 两条数据

 

not in 两条数据

select * from test1 where a,b in (select a,b from test2 where a = 'aa');

 

select * from test1 where a,b not

 in (select a,b from test2 where a = 'aa');

 

falese

false

Select

union查询

union all

select * from test union all select * from test0;(合一)

select * from test union select * from test0;(去重)

true

false

Select

union 3查询

union all

 

select * from (select * from test union select * from test0) a;

 

select a from (select * from test union all select * from test0) a;

false

False

Select

exit

not exit

select * from test t where exists(select * from test0 t0 where t0.a = t.a);

select * from lv_test where exists(select * tfrom test t where lv_test.a = t.a);

True

False

update

update test1 set b = 'abc' where a = 'aa';

update test1 set a = 'abc';

 

Update test1 set b = 'abc';

True

False

delete

delete from test1 where a = 'aa';

delete from test1;

True

False

TRUNCATE TABLE

 

Truncate table test;

True

False

Alter

alter table test1 add columns (d string);

alter table test drop a;

alter table test rename a to a1;

 

True

False

索引

create index index_a on table test(a)

as 'org.apache.hadoop.hive.ql.index.compact.CompactIndexHandler'

 with deferred rebuild;

True

False

INTERSECT

交集

select a from test

INTERSECT

select a from test0;

False

False

EXCEPT

 

select a from test

EXCPEPT

select a from test0;

False

False

Minus

返回第一个结果中不同的

select a from test

minus

select a from test0;

False

False

order by

排序

select a from test order by a desc;

 

True

False

sort by

排序

select a,b from test sort by b desc;

True

False

distribute by

 

select a,b from test distribute by a;

True

False

distribute by + sort by

select a,b from test distribute by a sort by b asc;

True

False

cluster by

 

select a,b from test cluster by a;

 

True

False

trim(string a)

去空格

select trim('   aaa ') from test00;

True

True

 

substrstring A,int start,int len

截取字符串

select substr('abcdefg',3,2) from test;

 

select substr('abcdefg',-3,2) from test;

True

True

like

 

select * from test where a like '%a%';

True

False

Count

 

select count(*) from test00;

 

select count(distinct *) from test00;

True

False

Sum

 

select sum(c) from test00;

 

select sum(distinct c) from test00;

True

False

Avg

 

select avg(c) from test00

select avg(distinct c) from test00

True

False

Min

 

select min(distinct c) from test00;

True

False

Max

 

select max(distinct c) from test00;

True

False

group by

 

select a from test00 group by a ;

select a,sum(c) from test00 group by a;

select a,avg(c) from test00 group by a;

True

False

Hiving

 

 

select a,avg(c) as ac from test00 group by a having ac=1;

True

False

load

load data local inpath '/tmp/qichangjian/test01.txt' overwrite into table test_load;

True

False

posted @ 2017-08-25 14:25  菜鸟的进击  阅读(8667)  评论(0编辑  收藏  举报