海量数据迁移之通过rowid切分大表

在之前的章节中，讨论过了通过 分区+并行等方式来进行超大的表的切分，通过这种方式能够极大的提高数据的平均分布，但是不是最完美的。
比如在数据量再提高几个层次，我们假设这个表目前有1T的大小。有10个分区，最大的分区有400G,那么如果我们想尽可能的平均的导出数据，使用并行就不一定能够那么奏效了。
比方说我们要求每个dump文件控制在200M总有，那样的话400G的分区就需要800个并行才能完成，在实际的数据库维护中，我们知道默认的并行数只有64个，提高几倍，也不可能超过800
所以在数据量极大的情况下，如果资源紧张，可能生成的dump就会比较大。

我们考虑使用rowid来满足我们的需求。
我们可以根据需要来指定需要生成几个dump文件。比如表subscriber有600M,那么如果按照200M为一个单位，我们需要生成3个dump文件。
如果想数据足够平均，就需要在rowid上做点功夫。
我们先设定一个参数文件，如下的格式。
可以看到表memo数据量极大，按照200M一个单位，最大的分区（P9_A3000_E5）需要800个并行。
表ICE_AGREEMENT比较小，不是分区表，我们以x来临时作为分区表的代名，在处理的时候可以方便的甄别

MEMO                                 P9_A3000_E0                           156
MEMO                                 P9_A3000_E1                           170
MEMO                                 P9_A3000_E2                           190
MEMO                                 P9_A3000_E3                           200
MEMO                                 P9_A3000_E4                           180
MEMO                                 P9_A3000_E5                           800
MEMO                                 PMAXVALUE_AMAXVALUE_EMAXVALUE         1
ICE_AGREEMENT                        x                                    36
CRIBER_HISTORY                       x                                    11

可以使用如下的脚本来完成rowid的切分。

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#### $1 dba conn details
#### $2 table owner
#### $3 table_name
#### $4 subobject_name
#### $5 parallel_no
function normal_split
{
sqlplus -s $1 <<1eof
 set linesize 200
set pages 0
set feedback off
spool list/rowid_range_$3_x.lst
select rownum || ', ' ||' rowid between '||
chr(39)||dbms_rowid.rowid_create( 1, DOI, lo_fno, lo_block, 0 ) ||chr(39)|| ' and  ' ||
chr(39)||dbms_rowid.rowid_create( 1, DOI, hi_fno, hi_block, 1000000 )||chr(39) data
from (
SELECT DISTINCT DOI, grp,
first_value(relative_fno) over (partition BY DOI,grp order by relative_fno, block_id rows BETWEEN unbounded preceding AND unbounded following) lo_fno,
first_value(block_id ) over (partition BY DOI,grp order by relative_fno, block_id rows BETWEEN unbounded preceding AND unbounded following) lo_block,
last_value(relative_fno) over (partition BY DOI,grp order by relative_fno, block_id rows BETWEEN unbounded preceding AND unbounded following) hi_fno,
last_value(block_id+blocks-1) over (partition BY DOI,grp order by relative_fno, block_id rows BETWEEN unbounded preceding AND unbounded following) hi_block,
SUM(blocks) over (partition BY DOI,grp) sum_blocks,SUBOBJECT_NAME
     FROM(
SELECT   obj.OBJECT_ID,
                 obj.SUBOBJECT_NAME,
                 obj.DATA_OBJECT_ID     as DOI,
                 ext.relative_fno,
         ext.block_id,
         ( SUM(blocks) over () ) SUM,
         (SUM(blocks) over (ORDER BY DATA_OBJECT_ID,relative_fno, block_id)-0.01 ) sum_fno ,
         TRUNC( (SUM(blocks) over (ORDER BY DATA_OBJECT_ID,relative_fno, block_id)-0.01) / (SUM(blocks) over ()/ $5 ) ) grp,
         ext.blocks
FROM     dba_extents ext, dba_objects obj
WHERE    ext.segment_name = UPPER('$3')
AND      ext.owner        = UPPER('$2')
AND      obj.owner       =  ext.owner
AND      obj.object_name     = ext.segment_name
AND      obj.DATA_OBJECT_ID IS NOT NULL
ORDER BY DATA_OBJECT_ID, relative_fno, block_id
) order by  DOI,grp
);
spool off;
EOF
}

function partition_split
{
sqlplus -s $1 <<1eof
 set linesize 200
set pages 0
set feedback off
spool list/rowid_range_$3_$4.lst
select rownum || ', ' ||' rowid between '||
chr(39)||dbms_rowid.rowid_create( 1, DOI, lo_fno, lo_block, 0 ) ||chr(39)|| ' and  ' ||
chr(39)||dbms_rowid.rowid_create( 1, DOI, hi_fno, hi_block, 1000000 )||chr(39) data
from (
SELECT DISTINCT DOI, grp,
first_value(relative_fno) over (partition BY DOI,grp order by relative_fno, block_id rows BETWEEN unbounded preceding AND unbounded following) lo_fno,
first_value(block_id ) over (partition BY DOI,grp order by relative_fno, block_id rows BETWEEN unbounded preceding AND unbounded following) lo_block,
last_value(relative_fno) over (partition BY DOI,grp order by relative_fno, block_id rows BETWEEN unbounded preceding AND unbounded following) hi_fno,
last_value(block_id+blocks-1) over (partition BY DOI,grp order by relative_fno, block_id rows BETWEEN unbounded preceding AND unbounded following) hi_block,
SUM(blocks) over (partition BY DOI,grp) sum_blocks,SUBOBJECT_NAME
     FROM(
SELECT   obj.OBJECT_ID,
                 obj.SUBOBJECT_NAME,
                 obj.DATA_OBJECT_ID     as DOI,
                 ext.relative_fno,
         ext.block_id,
         ( SUM(blocks) over () ) SUM,
         (SUM(blocks) over (ORDER BY DATA_OBJECT_ID,relative_fno, block_id)-0.01 ) sum_fno ,
         TRUNC( (SUM(blocks) over (ORDER BY DATA_OBJECT_ID,relative_fno, block_id)-0.01) / (SUM(blocks) over ()/ $5 ) ) grp,
         ext.blocks
FROM     dba_extents ext, dba_objects obj
WHERE    ext.segment_name = UPPER('$3')
AND      ext.owner        = UPPER('$2')
AND      obj.owner       =  ext.owner
AND      obj.object_name     = ext.segment_name
AND      obj.DATA_OBJECT_ID IS NOT NULL
AND      obj.subobject_name=UPPER('$4')
ORDER BY DATA_OBJECT_ID, relative_fno, block_id
) order by  DOI,grp
);
spool off
EOF
}

sub_partition_name=$4

if [[ $sub_partition_name = 'x' ]]
then
normal_split $1 $2 $3 x $5
else
partition_split $1 $2 $3 $4 $5
fi

脚本比较长，需要的参数有5个，因为访问dba_extents,dba_objects需要一定的权限，可以使用dba权限的账号即可。
第2个参数是表的owner，第3个参数是表名，第4个参数是分区表名（如果是分区表就是分区表名，如果不是就填x）,第5个参数就是期望使用的并行度，能够在一定程度上加快速度
简单演示一下，可以通过下面的方式来运行脚本，我们指定生成10个dump这个表不是分区表。

ksh gen_rowid.sh n1/n1 prdowner subscriber_history x 10
1,  where  rowid between 'AAB4VPAAJAAD7qAAAA' and  'AAB4VPAAJAAD/R/EJA'
2,  where  rowid between 'AAB4VPAAJAAD/SAAAA' and  'AAB4VPAAKAABV5/EJA'
3,  where  rowid between 'AAB4VPAAKAABV6AAAA' and  'AAB4VPAALAAE/p/EJA'
4,  where  rowid between 'AAB4VPAALAAE/qAAAA' and  'AAB4VPAAMAAFFh/EJA'
5,  where  rowid between 'AAB4VPAAMAAFFiAAAA' and  'AAB4VPAAyAACuh/EJA'
6,  where  rowid between 'AAB4VPAAyAACuiAAAA' and  'AAB4VPAAzAACe5/EJA'
7,  where  rowid between 'AAB4VPAAzAACe6AAAA' and  'AAB4VPAA1AACZR/EJA'
8,  where  rowid between 'AAB4VPAA1AACZSAAAA' and  'AAB4VPAA2AACWR/EJA'
9,  where  rowid between 'AAB4VPAA2AACWSAAAA' and  'AAB4VPAA4AACP5/EJA'
10,  where  rowid between 'AAB4VPAA4AACQCAAAA' and  'AAB4VPAA5AACHx/EJA'
然后我们来看看数据是否足够平均。
可以类似下面的方式验证，我们抽第1,2,10个。

SQL> select count(*)from subscriber_history  where  rowid between 'AAB4VPAAJAAD7qAAAA' and  'AAB4VPAAJAAD/R/EJA'
  2  ;

  COUNT(*)
----------
    328759

SQL> select count(*)from  subscriber_history   where  rowid between 'AAB4VPAAJAAD/SAAAA' and  'AAB4VPAAKAABV5/EJA'
  2  /

  COUNT(*)
----------
    318021

SQL> select count(*)from subscriber_history  where  rowid between 'AAB4VPAA4AACQCAAAA' and  'AAB4VPAA5AACHx/EJA';

  COUNT(*)
----------
    332638

可以看到数据还是很平均的，达到了我们的期望。