Hive优化策略

hive优化目标

在有限的资源下，运行效率高。

常见问题
数据倾斜、Map数设置、Reduce数设置等

hive运行

查看运行计划

explain [extended] hql

例子

explain select no,count(*) from testudf group by no;
explain extended select no,count(*) from testudf group by no;

运行阶段
STAGE DEPENDENC1ES:
Stage-1 is a root stage
Stage-0 is a root stage

Map阶段

      Map Operator Tree:
          TableScan
            alias: testudf
            Statistics: Num rows: 0 Data size: 30 Basic stats: PARTIAL Column stats:                               NONE
            Select Operator
              expressions: no (type: string)
              outputColumnNames: no
              Statistics: Num rows: 0 Data size: 30 Basic stats: PARTIAL Column stats                              : NONE
              Group By Operator
                aggregations: count()
                keys: no (type: string)
                mode: hash
                outputColumnNames: _col0, _col1
                Statistics: Num rows: 0 Data size: 30 Basic stats: PARTIAL Column sta                              ts: NONE
                Reduce Output Operator
                  key expressions: _col0 (type: string)
                  sort order: +
                  Map-reduce partition columns: _col0 (type: string)
                  Statistics: Num rows: 0 Data size: 30 Basic stats: PARTIAL Column s                              tats: NONE
                  value expressions: _col1 (type: bigint)

reduce阶段

      Reduce Operator Tree:
        Group By Operator
          aggregations: count(VALUE._col0)
          keys: KEY._col0 (type: string)
          mode: mergepartial
          outputColumnNames: _col0, _col1
          Statistics: Num rows: 0 Data size: 0 Basic stats: NONE Column stats: NONE
          Select Operator
            expressions: _col0 (type: string), _col1 (type: bigint)
            outputColumnNames: _col0, _col1
            Statistics: Num rows: 0 Data size: 0 Basic stats: NONE Column stats: NONE
            File Output Operator
              compressed: false
              Statistics: Num rows: 0 Data size: 0 Basic stats: NONE Column stats: NO                              NE
              table:
                  input format: org.apache.hadoop.mapred.TextInputFormat
                  output format: org.apache.hadoop.hive.ql.io.HiveIgnoreKeyTextOutput                              Format
                  serde: org.apache.hadoop.hive.serde2.lazy.LazySimpleSerDe

---

hive (liguodong)> explain extended select no,count(*) from testudf group by no;
OK
Explain
ABSTRACT SYNTAX TREE:

TOK_QUERY
   TOK_FROM
      TOK_TABREF
         TOK_TABNAME
            testudf
   TOK_INSERT
      TOK_DESTINATION
         TOK_DIR
            TOK_TMP_FILE
      TOK_SELECT
         TOK_SELEXPR
            TOK_TABLE_OR_COL
               no
         TOK_SELEXPR
            TOK_FUNCTIONSTAR
               count
      TOK_GROUPBY
         TOK_TABLE_OR_COL
            no


STAGE DEPENDENCIES:
  Stage-1 is a root stage
  Stage-0 is a root stage

STAGE PLANS:
  Stage: Stage-1
    Map Reduce
      Map Operator Tree:
          TableScan
            alias: testudf
            Statistics: Num rows: 0 Data size: 30 Basic stats: PARTIAL Column stats: NONE
            GatherStats: false
            Select Operator
              expressions: no (type: string)
              outputColumnNames: no
              Statistics: Num rows: 0 Data size: 30 Basic stats: PARTIAL Column stats: NONE
              Group By Operator
                aggregations: count()
                keys: no (type: string)
                mode: hash
                outputColumnNames: _col0, _col1
                Statistics: Num rows: 0 Data size: 30 Basic stats: PARTIAL Column stats: NONE
                Reduce Output Operator
                  key expressions: _col0 (type: string)
                  sort order: +
                  Map-reduce partition columns: _col0 (type: string)
                  Statistics: Num rows: 0 Data size: 30 Basic stats: PARTIAL Column stats: NONE
                  tag: -1
                  value expressions: _col1 (type: bigint)
      Path -> Alias:
        hdfs://nameservice1/user/hive/warehouse/liguodong.db/testudf [testudf]
      Path -> Partition:
        hdfs://nameservice1/user/hive/warehouse/liguodong.db/testudf
          Partition
            base file name: testudf
            input format: org.apache.hadoop.mapred.TextInputFormat
            output format: org.apache.hadoop.hive.ql.io.HiveIgnoreKeyTextOutputFormat
            properties:
              COLUMN_STATS_ACCURATE true
              bucket_count -1
              columns no,num
              columns.comments
              columns.types string:string
              field.delim
              file.inputformat org.apache.hadoop.mapred.TextInputFormat
              file.outputformat org.apache.hadoop.hive.ql.io.HiveIgnoreKeyTextOutputFormat
              line.delim

              location hdfs://nameservice1/user/hive/warehouse/liguodong.db/testudf
              name liguodong.testudf
              numFiles 1
              numRows 0
              rawDataSize 0
              serialization.ddl struct testudf { string no, string num}
              serialization.format
              serialization.lib org.apache.hadoop.hive.serde2.lazy.LazySimpleSerDe
              totalSize 30
              transient_lastDdlTime 1437374988
            serde: org.apache.hadoop.hive.serde2.lazy.LazySimpleSerDe

              input format: org.apache.hadoop.mapred.TextInputFormat
              output format: org.apache.hadoop.hive.ql.io.HiveIgnoreKeyTextOutputFormat
              properties:
                COLUMN_STATS_ACCURATE true
                bucket_count -1
                columns no,num
                columns.comments
                columns.types string:string
                field.delim
                file.inputformat org.apache.hadoop.mapred.TextInputFormat
                file.outputformat org.apache.hadoop.hive.ql.io.HiveIgnoreKeyTextOutputFormat
                line.delim

                location hdfs://nameservice1/user/hive/warehouse/liguodong.db/testudf
                name liguodong.testudf
                numFiles 1
                numRows 0
                rawDataSize 0
                serialization.ddl struct testudf { string no, string num}
                serialization.format
                serialization.lib org.apache.hadoop.hive.serde2.lazy.LazySimpleSerDe
                totalSize 30
                transient_lastDdlTime 1437374988
              serde: org.apache.hadoop.hive.serde2.lazy.LazySimpleSerDe
              name: liguodong.testudf
            name: liguodong.testudf
      Truncated Path -> Alias:
        /liguodong.db/testudf [testudf]
      Needs Tagging: false
      Reduce Operator Tree:
        Group By Operator
          aggregations: count(VALUE._col0)
          keys: KEY._col0 (type: string)
          mode: mergepartial
          outputColumnNames: _col0, _col1
          Statistics: Num rows: 0 Data size: 0 Basic stats: NONE Column stats: NONE
          Select Operator
            expressions: _col0 (type: string), _col1 (type: bigint)
            outputColumnNames: _col0, _col1
            Statistics: Num rows: 0 Data size: 0 Basic stats: NONE Column stats: NONE
            File Output Operator
              compressed: false
              GlobalTableId: 0
              directory: hdfs://nameservice1/tmp/hive-root/hive_2015-07-21_09-51-37_330_7990199479532530033-1/-mr-10000/.hive-staging_hive_2015-07-21_09-51-37_330_7990199479532530033-1/-ext-10001
              NumFilesPerFileSink: 1
              Statistics: Num rows: 0 Data size: 0 Basic stats: NONE Column stats: NONE
              Stats Publishing Key Prefix: hdfs://nameservice1/tmp/hive-root/hive_2015-07-21_09-51-37_330_7990199479532530033-1/-mr-10000/.hive-staging_hive_2015-07-21_09-51-37_330_7990199479532530033-1/-ext-10001/
              table:
                  input format: org.apache.hadoop.mapred.TextInputFormat
                  output format: org.apache.hadoop.hive.ql.io.HiveIgnoreKeyTextOutputFormat
                  properties:
                    columns _col0,_col1
                    columns.types string:bigint
                    escape.delim 
                    hive.serialization.extend.nesting.levels true
                    serialization.format 1
                    serialization.lib org.apache.hadoop.hive.serde2.lazy.LazySimpleSerDe
                  serde: org.apache.hadoop.hive.serde2.lazy.LazySimpleSerDe
              TotalFiles: 1
              GatherStats: false
              MultiFileSpray: false

  Stage: Stage-0
    Fetch Operator
      limit: -1

HIVE运行过程

hive表优化

分区

静态分区
动态分区

set hive.exec.dynamic.partition=true;
set hive.exec.dynamic.partltlon.mode=nonstrict;

分桶

set hive.enforce.bucketing=true;
set hive.enforce.sorting=true;

表优化数据目标：同样数据尽量聚集在一起

Hive job优化

并行化运行

每一个查询被hive转化成多个阶段，有些阶段关联性不大，则能够并行化运行，降低运行时问。

set hive.exec.parallel=true;
set hive.exec.parallel.thread.number=8;

eg:

select num 
from 
(select count(city) as num from city
union all
select count(province) as num from province
)tmp;

本地化运行

set hive.exec.mode.local.auto=true;

当一个job满足例如以下条件才干真正使用本地模式：
1.job的输入数据大小必须小于參数：
hive.exec.mode.local.inputbytes.max（默认128MB）
2.job的map数必须小于參数：
hive.exec.mode.local.auto.tasks.max（默认4)
3.job的reduce数必须为0或者1

job合并输入小文件

set hive.input.format=
org.apache.hadoop.hive.ql.io.CombineHiveInputFormat

合并文件数由mapred.max.split.size限制的大小决定。

job合并输出小文件

set hive.merge.smallfiles.avgsize=256000000;当输出文件平均大小小于该值。启动新job合并文件
set hive.merge.size.per.task=64000000;合并之后的文件大小

JVM重利用

set mapred.job.reuse.jvm.num.tasks=20;

JVM重利用能够是job长时间保留slot，直到作业结束，这在对于有较多任务和较多小文件的任务是很有意义的，降低运行时间。当然这个值不能设置过大，由于有些作业会有reduce任务，假设reduce任务没有完毕，则map任务占用的slot不能释放。其它的作业可能就须要等待。

压缩数据

中间压缩就是处理hive查询的多个job之间的数据。对中间压缩，
最好选择一个节省CPU耗时的压缩方式。

set hive.exec.compress.intermediate=true。
set hive.intermediate.compression.codec=org.apache.hadoop.io.compress.SnappyCodec;
set hive.intermediate.compression.type=BLOCK;

终于的输出也能够压缩,选择一个压缩效果比較好的，节省了磁盘空间，可是cpu比較耗时。

set hive.exec.compress.output=true;
set mapred.output.compression.codec=
org.apache.hadoop.io.compress.GzipCodec;
set mapred.output.compression.type=BLOCK:

Hive SQL语句优化

join优化

hive.optimize.skewjoin=true; 假设是join过程出现倾斜应该设置为true
set hive.skewjoin.key=100000; 这个是join的键相应的记录条数超过这个值则会进行优化。

mapjoin

自己主动运行
set hive.auto.convert.join=true;
hive.mapjoin.smalltable.filesize默认值是25mb   

手动运行
select /*+mapjoin(A)*/ f.a,f.b from A t join B f on(f.a==t.a)

简单总结一下，mapjoin的使用场景：
1、关联操作中有一张表很小
2、(不等值)的链接操作时

注：小表尽量设置小一点或用手动方式。

bucket join

两个表以同样方式划分捅。
两个表的桶个数是倍数关系。

create table ordertab(cid int,price,float)clustered by(cid) into 32 buckets;

create table customer(id int,first string)clustered by(id) into 32 buckets;

select price from ordertab t join customer s on t.cid=s.id

改动where的位置进行优化

join优化前
select m.cid, u.id from order m join customer u on m.cid=u.id
where m.dt='2013-12-12

join优化后
select m.cid, u.id from
(select cid from order where dt='2013-12-12') m
join customer u on m.cid=u.id;
这样就能降低join连接的数据量。

group by优化

hive.groupby.skewindata=true;
假设是group by过程出现倾斜应该设置为true。

set hive.groupby.mapaggr.checkinterval=100000;
这个是group的键相应的记录条数超过这个值则会进行优化。

count distinct优化

优化前（启动一个job，数据量大时，一个reduce负载过重）
select count(distinct id) from tablename;

优化后（启动两个job）

select count(1) from (select distinct id from tablename)tmp;
select count(1) from (select id from tablename group by id)tmp;

union all优化

优化前
select a,sum(b),count(distinct c),count(distinct d) from test group by a;

优化后
select a, sum(b) as b,count(c) as c, count(d) as d
from(
select a, 0 as b, c, null as d from test group by a,c
union all
select a, 0 as b, null as c, d from test group by a,d
union all
select a,b,null as c,null as d from test
)tmpl 
group by a;

Hive Map/Reduce优化

Map优化

改动map个数进行优化
直接设置mapred.map.tasks无效
set mapred.map.tasks=10。

map个数的计算过程
（1）默认map个数
default_num=total_size/block_size;

（2）期望大小
goal_num=mapred.map.tasks;

（3）设置处理的文件大小

split_size=max(mapred.min.split.size,b1ock_size);
split_num=total_size/split_size;

(4)计算的map个数
compute_map_num=min(split_num,max(default_num,goal_num))

经过以上的分析。在设置map个数的时候，能够简单的总结为下面几点：
1）假设想添加map个数，则设置mapred.map.tasks为一个较大的值。
2）假设想减小map个数。则设置mapred.min.split.size为一个较大的值。有例如以下两种情况：
情况1：输入文件size巨大。但不是小文件增大mapred.min.split.size的值。
情况2：输入文件数量巨大，且都是小文件，就是单个文件的size小于blockSize。
这样的情况通过增大mapred.min.spllt.size不可行，
须要使用CombineFileInputFormat将多个input path合并成一个
InputSplit送给mapper处理，从而降低mapper的数量。

map端聚合
map阶段进行combiner
set hive.map.aggr=true:

猜測运行
启动多个同样的map，谁先运行完。用谁的。
set mapred.map.tasks.speculative.execution=true

---

shuffle优化

依据须要配置相应參数。
Map端
io.sort.mb
io.sort.spill.percent
min.num.spill.for.combine
io.sort.factor
io.sort.record.percent

Reduce端
mapred.reduce.parallel.copies
mapred.reduce.copy.backoff
io.sort.factor
mapred.job.shuffle.input.buffer.percent
mapred.job.reduce.input.buffer.percent

---

Reduce优化

须要reduce操作的查询
聚合函数sum,count,distinct
高级查询group by,join,distribute by,cluster by…

order by比較特殊，仅仅须要一个reduce，设置reduce个数无效。

---

判断运行
设置mapred.reduce.tasks.speculative.execution或者hive.mapred.reduce.tasks.speculative.execution效果都一样。

设置Reduce
set mapred.reduce.tasks=10; 直接设置
hive.exec.reducers.max 默认：999
hive.exec.reducers.bytes.per.reducer 默认:1G
计算公式
maxReducers=hive.exec.reducers.max
perReducer=hive.exec.reducers.bytes.per.reducer
numRTasks=min[maxReducers,input.size/perReducer]