1.测试sqoop任务
1.1 测试全量抽取
1.1.1.直接执行命令
1.1.2.以shell文件方式执行sqoop或hive任务
1.2 测试增量抽取
2.测试hive任务
3.总结

当前生产上的任务主要分为两部分:sqoop任务和hive计算任务,测试这两种任务,分别以shell文件和直接执行命令的方式来测试.

本次测试的表是airflow.code_library.

1.测试sqoop任务

1.1 测试全量抽取

1.1.1.直接执行命令

from airflow import DAG
from airflow.operators.bash_operator import BashOperator
from datetime import datetime, timedelta

default_args = {
    ‘owner‘: ‘yangxw‘,
    ‘depends_on_past‘: False,
    ‘start_date‘: datetime(2017, 5, 23),
}
dag = DAG(‘sqoop4‘, default_args=default_args,schedule_interval=None)
bash_cmd = ‘‘‘
sqoop import --connect jdbc:oracle:thin:@//XX.XX.XX.XX/aaaa --username bbbb --password ‘cccc‘ --query " select CODENO, ITEMNO, ITEMNAME, BANKNO, SORTNO, ISINUSE, ITEMDESCRIBE, ITEMATTRIBUTE, RELATIVECODE, ATTRIBUTE1, ATTRIBUTE2, ATTRIBUTE3, ATTRIBUTE4, ATTRIBUTE5, ATTRIBUTE6, ATTRIBUTE7, ATTRIBUTE8, INPUTUSER, INPUTORG, INPUTTIME, UPDATEUSER, UPDATETIME, REMARK, HELPTEXT , to_char(SysDate,‘YYYY-MM-DD HH24:mi:ss‘) as etl_in_dt from XDGL.CODE_LIBRARY where \$CONDITIONS " --hcatalog-database airflow --hcatalog-table CODE_LIBRARY --hcatalog-storage-stanza ‘stored as ORC‘ --hive-overwrite --hive-delims-replacement " " -m 1
‘‘‘
t1 = BashOperator(
    task_id=‘sqoopshell‘,
    bash_command=bash_cmd,
    dag=dag)

测试成功,数据导入到表中.

1.1.2.以shell文件方式执行sqoop或hive任务

上述步骤虽然可以执行成功,但是如果要truncate 表,那么还要需要再增加一个task来执行truncate命令,这样一个ETL任务就要分成两个task很不方便.通过shell将truncate和import放在一起执行.

1)创建dag

from airflow import DAG
from airflow.operators.bash_operator import BashOperator
from datetime import datetime, timedelta

default_args = {
    ‘owner‘: ‘yangxw‘,
    ‘depends_on_past‘: False,
    ‘start_date‘: datetime(2017, 5, 23)
}

dag = DAG(‘sqoop7‘, default_args=default_args,schedule_interval=None)

bash_cmd = ‘sh /home/airflow/sqoop3.sh‘
t1 = BashOperator(
    task_id=‘sqoop7‘,
    bash_command=bash_cmd,
    dag=dag)

2)创建shell文件

hive -e "truncate table airflow.CODE_LIBRARY"
sqoop import --connect jdbc:oracle:thin:@//AAAA/BBB --username CCC --password ‘DDD‘ --query " select CODENO, ITEMNO, ITEMNAME, BANKNO, SORTNO, ISINUSE, ITEMDESCRIBE, ITEMATTRIBUTE, RELATIVECODE, ATTRIBUTE1, ATTRIBUTE2, ATTRIBUTE3, ATTRIBUTE4, ATTRIBUTE5, ATTRIBUTE6, ATTRIBUT
E7, ATTRIBUTE8, INPUTUSER, INPUTORG, INPUTTIME, UPDATEUSER, UPDATETIME, REMARK, HELPTEXT , to_char(SysDate,‘YYYY-MM-DD HH24:mi:ss‘) as etl_in_dt from XDGL.CODE_LIBRARY where \$CONDITIONS " --hcatalog-database airflow --hcatalog-table CODE_LIBRARY --hcatalog-storage-stanza ‘stored as ORC‘ --hive-overwrite --hive-delims-replacement " " -m 1 

将这些文件分发到scheduler和worker节点上,然后执行:

查看日志会报错:

…………
[2017-05-24 10:55:52,853] {base_task_runner.py:95} INFO - Subtask:   File "/opt/anaconda2/lib/python2.7/site-packages/jinja2/loaders.py", line 187, in get_source
[2017-05-24 10:55:52,853] {base_task_runner.py:95} INFO - Subtask:     raise TemplateNotFound(template)
[2017-05-24 10:55:52,854] {base_task_runner.py:95} INFO - Subtask: jinja2.exceptions.TemplateNotFound: sh /home/airflow/sqoop3.sh

这是airflow的一个bug,默认会使用jinja2的语法来解析task.

将

bash_cmd = ‘sh /home/airflow/sqoop3.sh‘ 修改为
bash_cmd = ‘{{"sh /home/airflow/sqoop3.sh"}}‘ 即可

测试成功.或者使用:

bash_cmd = ‘‘‘
sh /home/airflow/sqoop3.sh
‘‘‘

也可以执行成功.

1.2 测试增量抽取

新建个dag,sqoop8.

dag = DAG(‘sqoop8‘, default_args=default_args,schedule_interval=None)

bash_cmd = ‘‘‘
sh /home/airflow/sqoop4.sh %s
‘‘‘ % ‘2017-05-24‘

t1 = BashOperator(
    task_id=‘sqoop8‘,
    bash_command=bash_cmd,
    dag=dag)

创建shell:

hive -e "alter table airflow.ACCT_FEE_ARCH drop partition(p_day=‘$1‘);"
sqoop import --connect jdbc:oracle:thin:@//AAA/BBB --username CCC --password ‘DDD‘ --query " select SERIALNO,  ……
to_char(SYNCHDATE, ‘YYYY-MM-DD HH24:mi:ss‘) as SYNCHDATE , to_char(SysDate,‘YYYY-MM-DD HH24:mi:ss‘) as ETL_IN_DT from XDGL.ACCT_FEE_ARCH where SYNCHDATE < (TO_DATE(‘$1‘, ‘YYYY-MM-DD‘) +1) and SYNCHDATE >= (TO_DATE(‘$1‘, ‘YYYY-MM-DD‘)) and \$CONDITIONS " --hcatalog-database airflow --hcatalog-table ACCT_FEE_ARCH --hcatalog-storage-stanza ‘stored as ORC‘ --hive-partition-key p_day --hive-partition-value $1  --hive-delims-replacement " " -m 1

2.测试hive任务

上面以shell方式执行了hive truncate任务,下面以命令的方式执行sql文件.

创建sqoop9:

from airflow import DAG
from airflow.operators.bash_operator import BashOperator
from datetime import datetime, timedelta
from airflow.models import Variable

default_args = {
    ‘owner‘: ‘yangxw‘,
    ‘depends_on_past‘: False,
    ‘start_date‘: datetime(2017, 5, 23)
}

dag = DAG(‘hivesh2‘, default_args=default_args,schedule_interval=None)
str1 = Variable.get("str1")
bash_cmd = ‘‘‘
hive -f "/home/airflow/hive1.sql"  -hivevar tbname=%s
‘‘‘ % str1

t1 = BashOperator(
    task_id=‘hivesh2‘,
    bash_command=bash_cmd,
    dag=dag)

创建hive sql文件:

insert overwrite table airflow.tab_cnt select ‘${tbname}‘,  count(*) from ${tbname}

在页面上创建变量 str1=airflow.ACCT_FEE_ARCH

执行成功.

3.总结

1.如果执行shell,一定要用jinja2语法或者‘‘‘ ‘‘‘:

bash_cmd = ‘{{" sh /home/airflow/sqoop1.sh"}}‘ 或者

bash_cmd = ‘‘‘

sh /home/airflow/sqoop1.sh

‘‘‘

2.所有的文件必须复制到所有节点

python文件\shell文件\sql文件,必须复制到所有的webserver scheduler worker节点

3.有时候使用python命令编译不出来pyc文件,在页面上只能看到dag名称,不能看到代码及调度等.这时使用

python -m py_compile XXX.py 来编译

4.airflow的dag一旦创建就无法删除,错误的或者多余的dag可以设置为pause模式并隐藏.

5.shell的方式适合执行sqoop任务,可以将truncate table\drop partition和import一步执行完成,不用起两个task来执行.命令的方式适合执行hive 任务,通过hive -f XXX.sql --hivevar a=%s b=%s的方式,动态的传递参数给hive.

来自为知笔记(Wiz)