pig对null的处理（实际，对空文本处理为两种取值null或‘’），pignull
pig对null的处理（实际，对空文本处理为两种取值null或‘’），pignull
pig对文本null的处理很特殊。会处理成两种null，还会处理成''这种空值。比如，读name,age,sex日志信息。name取值处理，如果记录为“，，，”这样，会将name取值为null，如果记录为“，19，男”则name会处理为''。同样是空值，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''，另一种为null。
具体例子：读取日志中倒数第4个字段（全部为空，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)
处理代码如下：
--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如下：
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，包括数据类型的选择。这和传统的关系型数据库是有本质区别的。（传统的RDBMS必须要事先定义严格的表结构）
缺点，太过灵活。
对文本为空时，处理也比较特殊。如果Pig的函数有4种类型：计算函数（Evalfunction）比如MAX，筛选函数（Filterfunction）如ISEMPTY，加载函数（loadfunction），存储函数（stroefunction）如PigStorage函数。
这些函数对空值有着特殊处理：1，首先加载时（加载/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，比如字符‘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）取代之，同时会输出提示信息，但不终止语句执行。本质是大数据集有一定比例的损坏数据，是一种常见情况。此时我们可以通过如下语句进行筛选:
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;
比较操作（==，！=，,<,=,<=），matches，算数操作（+,-,*,/ ,包含%，？，CASE）中，如果有一个操作数为空，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另一个数据类型，结果为空
AVG，MIN，MAX，SUM，COUNT ：这几个操作将忽略空值
SIZE ：任意计算的对象为null，结果也为null
CONCAT ：任意一个字表达式为空，结果为空tuple(.) or map(#）：如果 被引用的对象为空，那么结果为空 。
三元操作符 ？ ：如果一个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 ，不过滤null数据
以下操作会产生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可以作为一个常量使用 。
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，如果有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 6b :
1 7
2 810
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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