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pig对null的处理(实际,对空文本处理为两种取值null或‘’)

sanyeah 2024-03-30 12:59:19 gitee 8 ℃ 0 评论

pig对文本null的处理非常特殊。会处理成两种null,还会处理成''这样的空值。
比方,读name,age,sex日志信息。name取值处理,假设记录为“。,,”这样,会将name取值为null,假设记录为“,19。男”则name会处理为''。相同是空值。pig读取后的取值却不一样。所以一定要小心。




pig读取日志信息。遇到取值为空的字段会处理为两种,一种取值为''。还有一种为null。
详细样例:读取日志中倒数第4个字段(所有为空,两个逗号之间无值",,"),
pig读入后处理为两种值(''和 null)。


1日志中空处理为null:
(5,148,b84daa9b-194e-4c4c-9595-ce4bfabca918,605378805132617404,2014-11-05 18:31:05,2014-11-05 18:31:05,1,62052,2,,,,,,,,239.130.237.121,2,-1,,,-1,e15b6c6675c6d6e8eb7851ccc866608787daeadd,b84daa9b-194e-4c4c-9595-ce4bfabca918,02:00:00:00:00:00,-991608703440210811,,,,,75061,200,,2,2,1,7.0,,,,,,)
(5,148,b84daa9b-194e-4c4c-9595-ce4bfabca918,605378805132617404,2014-11-05 18:31:05,2014-11-05 18:31:05,2,62052,2,,,,,,,,239.130.237.121,2,-1,,,-1,e15b6c6675c6d6e8eb7851ccc866608787daeadd,b84daa9b-194e-4c4c-9595-ce4bfabca918,02:00:00:00:00:00,-991608703440210811,,,,,75061,200,,2,2,1,7.0,,,,,,)


2日志中的空处理为'':
(3,90,864616028213476,1412364855586,2014-08-25 15:07:42,,1,14999,2,,,,,,460,00,112.5.236.229,2,864616028213476,3ff1c154fb35073a,,864616028213476|3ff1c154fb35073a,,,,864616028213476|3ff1c154fb35073a,,,,,311,35,-1,,1,3,2.x,1,91,,35.0,105.0,132012121230123)
(5,148,ddeb5f0f-09a7-456e-a9dc-5fb5e96c5453,682937329735483418,2014-11-04 20:08:37,2014-11-04 20:08:37,1,62052,2,,,,,,,,160.35.136.117,1,-1,,,-1,e72da4be06382bd0826be09927f650ca2570add9,ddeb5f0f-09a7-456e-a9dc-5fb5e96c5453,02:00:00:00:00:00,-3733654770696849299,,,,,66454,206,,2,2,1,7.1,,,,38.878998,-76.9898,032010032322002)
(3,90,864616028213476,1412364855586,2014-08-25 15:07:42,,2,14999,2,,,,,,460,00,112.5.236.229,2,864616028213476,3ff1c154fb35073a,,864616028213476|3ff1c154fb35073a,,,,864616028213476|3ff1c154fb35073a,,,,,311,35,-1,,1,3,2.x,1,91,,35.0,105.0,132012121230123)
(5,148,ddeb5f0f-09a7-456e-a9dc-5fb5e96c5453,682937329735483418,2014-11-04 20:08:37,2014-11-04 20:08:37,2,62052,2,,,,,,,,160.35.136.117,1,-1,,,-1,e72da4be06382bd0826be09927f650ca2570add9,ddeb5f0f-09a7-456e-a9dc-5fb5e96c5453,02:00:00:00:00:00,-3733654770696849299,,,,,66454,206,,2,2,1,7.1,,,,38.878998,-76.9898,032010032322002)


处理代码例如以下:
--citylevel report analysis:pig -p date=2014-07-30 -p year=2014 -p file_path=/user/wizad/test -f
SET job.name 'test_citylevel_reporth_istorical';
SET job.priority HIGH;


--REGISTER piggybank.jar;
REGISTER wizad-etl-udf-0.1.jar;


--DEFINE SequenceFileLoader org.apache.pig.piggybank.storage.SequenceFileLoader();
DEFINE SequenceFileLoader com.XXX.xxx.etl.pig.SequenceFileCSVLoader();




%default Cleaned_Log /user/wizad/test/wizad/cleaned/2014-10*/*/part*


%default AD_Data /user/wizad/data/wizad/metadata/ad/part*
%default Campaign_Data /user/wizad/data/wizad/metadata/campaign/part*
%default Region_Template /user/wizad/data/wizad/metadata/region_template/part-m-00000
%default Addtion_Data /user/wizad/data/report/region_addition/addition_data.txt


%default Industry_Path $file_path/report/historical/citylevel/$year/industry
%default Industry_Path $file_path/report/historical/citylevel/$year/industry
%default Industry_SUM $file_path/report/historical/citylevel/$year/industry_sum
%default Industry_TMP $file_path/report/historical/citylevel/$year/industry_tmp


%default Industry_Brand_Path $file_path/report/historical/citylevel/$year/industry_brand
%default Industry_Brand_SUM $file_path/report/historical/citylevel/$year/industry_brand_sum
%default Industry_Brand_TMP $file_path/report/historical/citylevel/$year/industry_brand_tmp


%default ALL_Path $file_path/report/historical/citylevel/$year/all
%default ALL_SUM $file_path/report/historical/citylevel/$year/all_sum
%default ALL_TMP $file_path/report/historical/citylevel/$year/all_tmp


%default output_path /user/wizad/tmp/result




--origin_cleaned_data = LOAD '$Cleaned_Log' USING PigStorage(',')
origin_cleaned_data = LOAD '$Cleaned_Log' USING SequenceFileLoader 
AS (ad_network_id:chararray,
    wizad_ad_id:chararray,
    guid:chararray,
    id:chararray,
    create_time:chararray,
    action_time:chararray,
    log_type:chararray, 
    ad_id:chararray,
    positioning_method:chararray,
    location_accuracy:chararray,
    lat:chararray, 
    lon:chararray,
    cell_id:chararray,
    lac:chararray,
    mcc:chararray,
    mnc:chararray,
    ip:chararray,
    connection_type:chararray,
    imei:chararray,
    android_id:chararray,
    android_advertising_id:chararray,
    udid:chararray,
    openudid:chararray,
    idfa:chararray,
    mac_address:chararray,
    uid:chararray,
    density:chararray,
    screen_height:chararray,
    screen_width:chararray,
    user_agent:chararray,
    app_id:chararray,
    app_category_id:chararray,
    device_model_id:chararray,
    carrier_id:chararray,
    os_id:chararray,
    device_type:chararray,
    os_version:chararray,
    country_region_id:chararray,
    province_region_id:chararray,
    city_region_id:chararray,
    ip_lat:chararray,
    ip_lon:chararray,
    quadkey:chararray);


my_test1 = filter origin_cleaned_data by guid == 'b84daa9b-194e-4c4c-9595-ce4bfabca918';
dump my_test1;
describe my_test1;
--store my_test into '$output_path/mytest' using PigStorage(',');


my_test2 = filter origin_cleaned_data by guid == '864616028213476' or guid == 'ddeb5f0f-09a7-456e-a9dc-5fb5e96c5453';
dump my_test2;
describe my_test2;
--store my_test into '$output_path/mytest' using PigStorage(',');


--将第2种空取值''过滤为unknown
unknown_data = FOREACH origin_cleaned_data GENERATE wizad_ad_id,guid,log_type,
((city_region_id == '') ? 'unknown' : city_region_id) AS city_region_id;  --(wizad_ad_id,guid,log_type,city_region_id)
--将第1种空取值null过滤为isnull
null_data =  FOREACH origin_cleaned_data GENERATE wizad_ad_id,guid,log_type,
((city_region_id is NULL) ? 'isnull' : city_region_id) AS city_region_id;  --(wizad_ad_id,guid,log_type,city_region_id)


--看看unknown和isnull的数据
all_unknown = filter unknown_data by city_region_id == 'unknown';
dump all_unknown;
--store all_unknown into '$output_path/unknown' using PigStorage(',');
all_null = filter null_data by city_region_id == 'isnull';
dump all_null;
--store all_null into '$output_path/isnull' using PigStorage(',');


--把两种都过滤为no_use
origin_historical = FOREACH origin_cleaned_data GENERATE wizad_ad_id,guid,log_type,
((city_region_id == '') or (city_region_id == null) or (city_region_id is null) ?

'no_use' : city_region_id) AS city_region_id;  --(wizad_ad_id,guid,log_type,city_region_id)
dump origin_historical;
describe origin_historical;




两种数据分别的结果例如以下:
unknown数据:
(90,864616028213476,1,unknown)
(90,862624024878336,1,unknown)
(90,990001402489819,1,unknown)
(90,862343020727070,1,unknown)
(201,1ff90f55-f5cd-4b2a-9357-5bde0e3ff526,1,unknown)
(201,c3916c92-a70c-4d34-babd-d3fc021cf642,1,unknown)
(201,00:c6:10:dd:81:17,1,unknown)
(201,88:53:95:da:9e:03,1,unknown)
......


而null数据:
(148,b84daa9b-194e-4c4c-9595-ce4bfabca918,1,isnull)
(148,13fbe940-7cd0-44a1-b637-a0df8ea83621,1,isnull)
(148,b84daa9b-194e-4c4c-9595-ce4bfabca918,2,isnull)

(148,13fbe940-7cd0-44a1-b637-a0df8ea83621,2,isnull)




看了一些资料,pig对null处理的一些总结,部分引自网络:


Pig数据流的语言。擅长于处理纯文本信息,在模式定义方面提供了更大的灵活性,包含数据类型的选择。这和传统的关系型数据库是有本质差别的。(传统的RDBMS必需要事先定义严格的表结构)

缺点,太过灵活。

对文本为空时。处理也比較特殊。

假设Pig的函数有4种类型:计算函数(Evalfunction)比方MAX。筛选函数(Filterfunction)如ISEMPTY。载入函数(loadfunction)。存储函数(stroefunction)如PigStorage函数。

这些函数对空值有着特殊处理:

1,首先载入时(载入/存储函数:PigStorage, BinStorage, BinaryStorage, TextLoader, PigDump),传统的关系数据库写入一个类型不符的数据到数据库。比方字符‘a’写到定义为int的字段。会报错。
Pig不同,如load时。字符‘a’传到在模式定义中int字段。pig会用空值(null)代替之,同一时候会输出提示信息,但不终止语句运行。本质是大数据集有一定比例的损坏数据,是一种常见情况。此时我们能够通过例如以下语句进行筛选:
corrupt_records= FILTER records BY temperature is not null;
或者用split语句:
Splitrecords into good_one if temperature is not null, bad_one is temperature is null;



2,其它函数遇到空值的特殊处理。pig函数空值的殊处理:
比較操作(==。!=,,<,=,<=)。matches。算数操作(+,-,*,/ ,包括%。?,CASE)中,假设有一个操作数为空,那么结果为空。 


CAST 操作:将一个null数据从一个数据类型转换到还有一个数据类型。结果为空 


AVG。MIN,MAX。SUM。COUNT :这几个操作将忽略空值 

SIZE :随意计算的对象为null。结果也为null 

CONCAT :随意一个字表达式为空,结果为空

tuple(.) or map(#):假设 被引用的对象为空,那么结果为空 。

 


FILTER 操作:filter的表达式为空,不会拒绝操作。

如b = filter a by X!=5 ,假设X为空,。X也为空,X!=5 将为空,那么filter将不会处理这一行数据。
 
三元操作符 ? :假设一个bool表达式的结果为null,结果将为空。

 
   null == 2 ? 1 : 4 -- returns null
   2 == 2 ? 1 : 'fred' -- type error; both values must be of the same type

COUNT_STAR ,只是滤null数据 

下面操作会产生null :

1、除0 

2、用户的UDFs 

3、引用一个不存在的字段 

4、引用一个map中不存在的字段 

5、引用一个tuple中不存在的字段 

6、load不存在的数据时产生null。空字符串不会被load。会被替换成null null能够作为一个常量使用 。 

7、load时数据类型不匹配产生null 



GROUP /COGROUP/JOIN: 用group处理一个关系时,一个关系中的null会被聚集在一起当做一个null处理 。

当cogroup来处理多个关系是,假设有key为空的情况时,多个关系之间的的空是不一样的。会被分别当做不同的null key来处理。 


如数据 : 

a :

  1 5 4

  3 6 

b :

  1 7

  2 8
  10


JOIN【inner】如:join中空和空是匹配不上的,会被过滤掉 。在join之前过滤出key为空的数据 ,有助于提高join的速度。 a = load './t1.txt' as (a1:int,a2:int); b = load './t2.txt' as (b1:int,b2:int); c= join a by a1,b by b1 ; dump c ; (1,5,1,7) 

JOIN【outer】 d = join a by a1 left,b by b1 ; dump d ; (1,5,1,7)
(3,6,,)
(,4,,) 
d = join a by a1 right,b by b1; dump d ; (1,5,1,7)
(,,2,8)
(,,,10) 
d = join a by a1 full,b by b1; dump d ; (1,5,1,7)
(,,2,8)
(3,6,,)
(,4,,)
(,,,10) 



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