hadoop初级 pig入门经验总结

问题导读：
1.UNION与传统sql的unoin的相同与不同之处？
2.pig有哪些符合数据类型？
3.比较pig基本语法与传统数据库的区别？

• pig简介
• pig数据类型
• pig latin语法
• pig udf自定义
• pig derived衍生
• 推荐网站 http://pig.apache.org/docs/r0.10.0/basic.html
  

pig简介

pig是hadoop上层的衍生架构，与hive类似。对比hive（hive类似sql，是一种声明式的语言），pig是一种过程语言，类似于存储过程一步一步得进行数据转化。

pig数据类型

• double > float > long > int > bytearray
• tuple|bag|map|chararray > bytearray
  

double float long int chararray bytearray都相当于pig的基本类型

tuple相当于数组 ，但是可以类型不一，举例('dirkzhang','dallas',41)

Bag相当于tuple的一个集合，举例{('dirk',41),('kedde',2),('terre',31)}，在group的时候会生成bag

Map相当于哈希表，key为chararray，value为任意类型，例如['name'#dirk,'age'#36,'num'#41

nulls 表示的不只是数据不存在，他更表示数据是unkown

pig latin语法

1：load

LOAD 'data' [USING function] [AS schema];

       例如：

      load = LOAD 'sql://{SELECT MONTH_ID,DAY_ID,PROV_ID FROM zb_d_bidwmb05009_010}'    USING com.bonc.dataplatform.bbdp.geniuspig.VerticaLoader('oracle','192.168.6.5','dev','1522','vbap','vbap','1') AS (MONTH_ID:chararray,DAY_ID:chararray,PROV_ID:chararray);

Table = load ‘url’ as (id,name…..);    //table和load之间除了等号外 还必须有个空格 不然会出错，url一定要带引号，且只能是单引号。

2：filter       alias = FILTER alias BY expression;

       Table = filter Table1 by + A； //A可以是 id > 10;not name matches ‘’，is not null 等，可以用and  和or连接各条件

       例如：

       filter = filter load20 by ( MONTH_ID == '1210' and  DAY_ID == '18' and  PROV_ID == '010' );

3：group

alias = GROUP alias { ALL | BY expression} [, alias ALL | BY expression …] [USING 'collected' | 'merge'] [PARTITION BY partitioner] [PARALLEL n];

          pig的分组，不仅是数据上的分组，在数据的schema形式上也进行分组为groupcolumn:bag

         Table3 = group Table2 by id；也可以Table3 = group Table2 by （id，name）；括号必须加

         可以使用ALL实现对所有字段的分组

4：foreach

alias = FOREACH alias GENERATE expression [AS schema] [expression [AS schema]….];

alias = FOREACH nested_alias {

alias = {nested_op | nested_exp}; [{alias = {nested_op | nested_exp}; …]

GENERATE expression [AS schema] [expression [AS schema]….]

};

一般跟generate一块使用

         Table = foreach Table generate （id，name）；括号可加可不加。

avg = foreach Table generate group, AVG(age);  MAX ,MIN..

在进行数据过滤时，建议尽早使用foreach generate将多余的数据过滤掉，减少数据交换

5：join

Inner  join Syntax

alias = JOIN alias BY {expression|'('expression [, expression …]')'} (, alias BY {expression|'('expression [, expression …]')'} …) [USING 'replicated' | 'skewed' | 'merge' | 'merge-sparse'] [PARTITION BY partitioner] [PARALLEL n];
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Outer join Syntax

alias = JOIN left-alias BY left-alias-column [LEFT|RIGHT|FULL] [OUTER], right-alias BY right-alias-column [USING 'replicated' | 'skewed' | 'merge'] [PARTITION BY partitioner] [PARALLEL n];
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     join/left join / right join

daily = load 'A' as (id，name, sex);
divs  = load 'B' as (id，name, sex);
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join

jnd   = join daily by (id, name), divs by (id, name);       
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left join

jnd   = join daily by (id, name) left outer, divs by (id, name);
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也可以同时多个变量，但只用于inner join

A = load 'input1' as (x, y);
B = load 'input2' as (u, v);
C = load 'input3' as (e, f);
alpha = join A by x, B by u, C by e;
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6: union

alias = UNION [ONSCHEMA] alias, alias [, alias …];

union 相当与sql中的union，但与sql不通的是pig中的union可以针对两个不同模式的变量：如果两个变量模式相同，那么union后的变量模式与 变量的模式一样；如果一个变量的模式可以由另一各变量的模式强制类型转换，那么union后的变量模式与转换后的变量模式相同；否则，union后的变量 没有模式。

A = load 'input1' as (x:int, y:float);

B = load 'input2' as (x:int, y:float);

C = union A, B;

describe C;

C: {x: int,y: float}

A = load 'input1' as (x:double, y:float);

B = load 'input2' as (x:int, y:double);

C = union A, B;

describe C;

C: {x: double,y: double}

A = load 'input1' as (x:int, y:float);

B = load 'input2' as (x:int, y:chararray);

C = union A, B;

describe C;

Schema for C unknown.

注意：在pig 1.0中 执行不了最后一种union。

如果需要对两个具有不通列名的变量union的话，可以使用onschema关键字

A = load 'input1' as (w: chararray, x:int, y:float);

B = load 'input2' as (x:int, y:double, z:chararray);

C = union onschema A, B;

describe C;

C: {w: chararray,x: int,y: double,z: chararray}

join和union之后alias的别名会变

7：Dump

     dump alias

用于在屏幕上显示数据。

8：Order by

alias = ORDER alias BY { * [ASC|DESC] | field_alias [ASC|DESC] [, field_alias [ASC|DESC] …] } [PARALLEL n];

         A = order Table by id desc;

9：distinct

         A = distinct alias;

10：limit

         A = limit alias 10;

11：sample

SAMPLE alias size;

随机抽取指定比例（0到1）的数据。

some = sample divs 0.1;

13：cross

alias = CROSS alias, alias [, alias …] [PARTITION BY partitioner] [PARALLEL n];

将多个数据集中的数据按照字段名进行同值组合，形成笛卡尔积。

--cross.pig

daily = load 'NYSE_daily' as (exchange:chararray, symbol:chararray,date:chararray, open:float, high:float, low:float,

close:float, volume:int, adj_close:float);

divs = load 'NYSE_dividends' as (exchange:chararray, symbol:chararray,date:chararray, dividends:float);

tonsodata = cross daily, divs parallel 10;

15：split

Syntax

SPLIT alias INTO alias IF expression, alias IF expression [, alias IF expression …] [, alias OTHERWISE];

A = LOAD 'data' AS (f1:int,f2:int,f3:int);

DUMP A;

(1,2,3)

(4,5,6)

(7,8,9)

SPLIT A INTO X IF f1<7, Y IF f2==5, Z IF (f3<6 OR f3>6);

DUMP X;

(1,2,3)

(4,5,6)

DUMP Y;

(4,5,6)

DUMP Z;

(1,2,3)

(7,8,9)

16：store

         Store  … into … Using…

pig在别名维护上：

1、join

如e = join d by name,b by name;

    g = foreach e generate $0 as one:chararray, $1 as two:int, $2 as      three:chararray,$3 asfour:int;

    他生成的schemal：

        e: {d::name: chararray,d::position: int,b::name: chararray,b::age: int}

g: {one: chararray,two: int,three: chararray,four: int}

2、group

   B = GROUP A BY age;

----------------------------------

| B     | group: int | A: bag({name: chararray,age: int,gpa: float}) |

----------------------------------

|       | 18         | {(John, 18, 4.0), (Joe, 18, 3.8)}             |

|       | 20         | {(Bill, 20, 3.9)}                             |

----------------------------------

(18,{(John,18,4.0F),(Joe,18,3.8F)})

pig udf自定义

pig支持嵌入user defined function，一个简单的udf 继承于evalFunc，通常用在filter，foreach中

public class MyUDF extends EvalFunc<String> {  
  
    @Override  
    public String exec(Tuple input) throws IOException {  
        if(input == null || input.size() ==0)  
            return null;  
        try {  
            String val = (String) input.get(0);  
            return new StringBuffer(val).append(" pig").toString();  
        } catch (Exception e) {  
            throw new IOException(e.getMessage());  
        }  
    }  
  
}  
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pig支持udf in loader and store

udf loader 需要继承于LoadFunc

udf storer 需要继承于StoreFunc

这类似于hadoop中写inputformat和outputformat

其中vertica就是写了一个DB版本的

这里贴一个简单的loader的例子：

public class MyLoader extends LoadFunc{ 

protected RecordReader recordReader = null; 

private PreparedStatement ps; 
private Connection conn; 
private final String jdbcURL; 
private final String user; 
private final String pwd; 
private final String querySql; 
private ResultSet rs; 

public MyLoader(String driver,String jdbcURL,String user,String pwd,String querySql){ 
try { 
Class.forName(driver); 
} catch (Exception e) { 
// TODO: handle exception 
} 
this.jdbcURL = jdbcURL; 
this.user = user; 
this.pwd = pwd; 
this.querySql = querySql; 
} 

@Override 
public InputFormat getInputFormat() throws IOException { 
return new PigTextInputFormat(); 
} 

@Override 
public Tuple getNext() throws IOException { 
// TODO 重要的读取过程 
Text val = null; 
boolean next = false; 
try { 
next = rs.next(); 
} catch (Exception e) { 
// TODO: handle exception 
} 
if(!next) 
return null; 
ResultSetMetaData rsmd; 
try { 
// rsmd = result 
} catch (Exception e) { 
// TODO: handle exception 
} 

return null; 
} 

@Override 
public void prepareToRead(RecordReader arg0, PigSplit arg1) 
throws IOException { 
this.recordReader = arg0; 
} 

@Override 
public void setLocation(String arg0, Job arg1) throws IOException { 
//no idea 
} 

public ResourceSchema getSchema(String location,Job job) throws IOException{ 
Configuration conf = job.getConfiguration(); 
Schema schema = new Schema(); 
try { 
//TODO:reader from database table 
// Connection conn = DriverManager.getConnection(this.jdbcURL, this.user, this.pwd); 
FieldSchema fieldName = new FieldSchema("name", DataType.CHARARRAY); 
FieldSchema fieldPosition = new FieldSchema("position", DataType.INTEGER); 
schema.add(fieldName); 
schema.add(fieldPosition); 
} catch (Exception e) { 
//TODO log exception 
} 

return null; 
} 

public void prepareToRead(){ 

} 

}
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其中getNext方法就是如何处理reader读取出的数据
        getSchema可以固定读取数据的schema
        setLocation可以处理输入的数据源
        prepareToRead是读取数据之前，可以在此做标识，等等