Hive数据导入HBase

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推荐:使用hive读取hbase数据

[ Mapr框架安装完后,安装与配置hbase、hive。其中mapr框架的安装路径为/opt/maprHbase的安装路径为/opt/mapr/hbase/hbase-0.90.4Hive的安装路径为/opt/mapr/hive/hive-0

Hive数据导入HBase大致有3中方法

  1. 在Hive创建数据保存在HBase的表方式,这种方法的特点是简单,但是数据量超过千万以后 ,数据偏移现象比较明显,效率不高
  2. 在定义Hive的UDF,将数据写入HBase,如果提前将HBase表的regen分好,这种直接put的方法效率还行
  3. 直接用MapReduce生成Hfile,然后导入HBase,这种方法的特点是程序步奏很多,但是效率高,每分钟轻松能到3000万数据


下面介绍一下我这种用MapReduce生成Hfile,然后导入HBase的方法,一共分为6步:

1. 生成Range Partitioning


这一步决定后面生成HFile时的reduce的个数,比如下面这段sql共生成137556197/100/22000=62条记录,则生成HFile时用63个reduce


select list_no,row_sequence() from ( 
        select 
            list_no, 
            row_sequence() as row 
        from (
            select 
                list_no 
            from user_info  tablesample(bucket 1 out of 100 on list_no) s order by list_no
        ) t order by list_no 
    ) x where (row % 22000)=0 order by list_no ;




2. 创建存储HFile数据表
存储HiveHFileOutputFormat数据


CREATE EXTERNAL TABLE IF NOT EXISTS hbase_user_info(
  list_no string,
asset_id double,
  ...

STORED AS INPUTFORMAT 'org.apache.hadoop.mapred.TextInputFormat' 
OUTPUTFORMAT 'org.apache.hadoop.hive.hbase.HiveHFileOutputFormat' 
TBLPROPERTIES('hfile.family.path' = '/tmp-data/user/hbase/hbase_hfiles/user_info/f');


3. 生成HFile


INSERT OVERWRITE TABLE hbase_user_info  
SELECT * FROM user_info CLUSTER BY list_no;


4. 创建HBase表


echo "create 'user_info', {NAME => 'f', COMPRESSION => 'GZ'}" | hbase shell  


5. 将HFile文件导入HBase


hadoop jar /appcom/hbase/hbase-0.94.11.jar  completebulkload  /tmp-data/user/hbase/hbase_hfiles/user_info user_info


6. 测试是否有数据


echo "scan 'user_info', { LIMIT => 1 }" | hbase shell


下面是前面6步合成的shell脚本

#!/bin/bash

hive_table=user_info
rowkey=id
hfile_path=/tmp-data/user/hbase/hbase_hfiles/

hbase_jar=$(echo "$(ls -l /software/hbase/*.jar | grep -v test)"|awk  '{print $9}')
hive_contrib_jar=$(echo "$(ls -l /software/hive/lib/hive-contrib*.jar | grep -v test)"|awk  '{print $9}')
hive_hbase_handler_jar=$(echo "$(ls -l /software/hive/lib/hive-hbase-handler*.jar | grep -v test)"|awk  '{print $9}')


echo "##################################[step 1 generate splites]#####################################"
hive -e "
    use user_db;

    CREATE EXTERNAL TABLE  IF NOT EXISTS  hbase_splits(partition STRING, count int)
    PARTITIONED BY (table STRING);

    add jar ${hive_contrib_jar};
    create temporary function row_sequence as 'org.apache.hadoop.hive.contrib.udf.UDFRowSequence';
    INSERT OVERWRITE TABLE  hbase_splits
    PARTITION (table='${hive_table}')
    select ${rowkey},row_sequence() from (
        select
            ${rowkey},
            row_sequence() as row
        from (
            select
                ${rowkey}
            from ${hive_table}  tablesample(bucket 1 out of 100 on ${rowkey}) s order by ${rowkey}
        ) t order by ${rowkey}
    ) x where (row % 22000)=0 order by ${rowkey} ;

    CREATE EXTERNAL TABLE  IF NOT EXISTS hbase_splits_file(partition STRING)
    PARTITIONED BY (table STRING)
    ROW FORMAT
      SERDE 'org.apache.hadoop.hive.serde2.binarysortable.BinarySortableSerDe'
    STORED AS
      INPUTFORMAT 'org.apache.hadoop.mapred.TextInputFormat'
      OUTPUTFORMAT 'org.apache.hadoop.hive.ql.io.HiveNullValueSequenceFileOutputFormat'
    LOCATION '/tmp-data/user/hbase/hbase_splits_file';

    INSERT OVERWRITE TABLE hbase_splits_file
    PARTITION (table='${hive_table}')
    select partition from hbase_splits where table='${hive_table}';


"

echo "##################################[step 2 create hfile table ]#####################################"

echo "DEBUG: table name is: "${hive_table}


sql_select_col="CREATE EXTERNAL TABLE IF NOT EXISTS hbase_${hive_table}("


desc_table_cols=$(hive -e "
    use user_db;

    desc ${hive_table};
")


desc_table_cols=$(echo "${desc_table_cols}" | grep -Ev "^$|^#.*$|^col_name.*$")
temp_file=`mktemp -u temp.user.XXXXXX.$$`
echo "$desc_table_cols" > ${temp_file}


while read line
do
    lgt=$(expr length "${line}")
    if [ ${lgt} -eq 0 ];
    then
        break
    fi;

    col_name=$(echo "${line}"|awk -F ' ' '{print $1}')
    col_type=$(echo "${line}"|awk -F ' ' '{print $2}')

    sql_select_col="${sql_select_col}${col_name} ${col_type},";


done < ${temp_file}


rm -rf ${temp_file}


len=$(expr length "${sql_select_col}")
let "len = len - 1"
sql_select_col=$(echo ${sql_select_col}|cut -c1-$len)


sql_select_col=${sql_select_col}") STORED AS INPUTFORMAT 'org.apache.hadoop.mapred.TextInputFormat'  OUTPUTFORMAT 'org.apache.hadoop.hive.hbase.HiveHFileOutputFormat' TBLPROPERTIES('hfile.family.path' = '${hfile_path}${hive_table}/f');"


echo "DEBUG: cols:"${sql_select_col}


hive -e "use user_db;
    ${sql_select_col};
"


echo "##################################[step 3 create hfile ]#####################################"
task_num=$(
    hive -e "
        use user_db;

        select max(count) + 1 from hbase_splits where table='${hive_table}';
    "
)


task_num_str=$(echo ${task_num})


num=$(echo "${task_num_str}" | awk '{print $2}')
echo ${num}


hive -e "
ADD JAR ${hbase_jar};
ADD JAR ${hive_hbase_handler_jar};

USE user_db;

SET mapred.reduce.tasks=${num};
SET total.order.partitioner.path=/tmp-data/user/hbase/hbase_splits_file/table=${hive_table}/000000_0;
SET hive.mapred.partitioner=org.apache.hadoop.mapred.lib.TotalOrderPartitioner;
set hive.optimize.sampling.orderby=true;
set hive.optimize.sampling.orderby.number=10000000;
set hive.optimize.sampling.orderby.percent=0.1f;


INSERT OVERWRITE TABLE hbase_${hive_table}
SELECT * FROM ${hive_table} CLUSTER BY ${rowkey};
"
status=$?
echo status=${status}
if [ ${status} -eq 0 ];
then
    echo "##################################[step 4 create hbase table  ]#####################################"
    #create 'testtable', { NAME => 'colfam1', COMPRESSION => 'GZ' }
    echo "create '${hive_table}', {NAME => 'f', COMPRESSION => 'GZ'}" | hbase shell




    echo "##################################[step 5 move hfile to hbase ]#####################################"
    hadoop jar ${hbase_jar}  completebulkload  ${hfile_path}${hive_table} ${hive_table}


    echo "##################################[step 6 test ]#####################################"
    echo "scan '${hive_table}', { LIMIT => 1 }" | hbase shell


else
    echo "ERROR:@@@@@@     generate hfile error       @@@@@@";
    exit -1;
fi


根据经验,基本上分在每个reduce的DateSize在1-2G之间效率比较高,有一个计算公式如下:

推荐:Hive 数据导入HBase的2种方法详解

[Hive数据导入到HBase基本有2个方案: 1、HBase中建表,然后Hive中建一个外部表,这样当Hive中写入数据后,HBase中也会同时更新 2、MapReduce读取Hive数据,然后写

【以user_info为例,假设每个reduce 1.5G,(591999730/100) /  (63208973969/1024/1024/1024/1.5) ) = 150846,这个值在第一步(row %  150846)时使用】

其中63208973969通过hadoop fs -du获得,591999730为记录条数



可能遇到的问题

a)     Can't read partitions file,需要设置SET mapreduce.totalorderpartitioner.path=/tmp/hbase_splits;为SETtotal.order.partitioner.path=/tmp/hbase_splits;前面那个是hadoop2用的;

b)    java.io.IOException: Added a key not lexically larger thanprevious key,可能是rowkey有重复;

c)     reduce阶段 Nofiles found...,RangePartitioning对应的区域内没有数据;

d)    Wrong number of partitions in keyset,这是因为Range Partitioning中的条数和后面SETmapred.reduce.tasks=43不一致造成的,RangePartitioning应该是mapred.reduce.tasks的值减1;


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