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:TableScanalias: testudfStatistics: Num rows: 0 Data size: 30 Basic stats: PARTIAL Column stats: NONESelect Operatorexpressions: no (type: string)outputColumnNames: noStatistics: Num rows: 0 Data size: 30 Basic stats: PARTIAL Column stats : NONEGroup By Operatoraggregations: count()keys: no (type: string)mode: hashoutputColumnNames: _col0, _col1Statistics: Num rows: 0 Data size: 30 Basic stats: PARTIAL Column sta ts: NONEReduce Output Operatorkey 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: NONEvalue expressions: _col1 (type: bigint)reduce阶段
Reduce Operator Tree:Group By Operatoraggregations: count(VALUE._col0)keys: KEY._col0 (type: string)mode: mergepartialoutputColumnNames: _col0, _col1Statistics: Num rows: 0 Data size: 0 Basic stats: NONE Column stats: NONESelect Operatorexpressions: _col0 (type: string), _col1 (type: bigint)outputColumnNames: _col0, _col1Statistics: Num rows: 0 Data size: 0 Basic stats: NONE Column stats: NONEFile Output Operatorcompressed: falseStatistics: Num rows: 0 Data size: 0 Basic stats: NONE Column stats: NO NEtable:input format: org.apache.hadoop.mapred.TextInputFormatoutput format: org.apache.hadoop.hive.ql.io.HiveIgnoreKeyTextOutput Formatserde: org.apache.hadoop.hive.serde2.lazy.LazySimpleSerDehive (liguodong)> explain extended select no,count(*) from testudf group by no;
OK
Explain
ABSTRACT SYNTAX TREE:TOK_QUERYTOK_FROMTOK_TABREFTOK_TABNAMEtestudfTOK_INSERTTOK_DESTINATIONTOK_DIRTOK_TMP_FILETOK_SELECTTOK_SELEXPRTOK_TABLE_OR_COLnoTOK_SELEXPRTOK_FUNCTIONSTARcountTOK_GROUPBYTOK_TABLE_OR_COLnoSTAGE DEPENDENCIES:Stage-1 is a root stageStage-0 is a root stageSTAGE PLANS:Stage: Stage-1Map ReduceMap Operator Tree:TableScanalias: testudfStatistics: Num rows: 0 Data size: 30 Basic stats: PARTIAL Column stats: NONEGatherStats: falseSelect Operatorexpressions: no (type: string)outputColumnNames: noStatistics: Num rows: 0 Data size: 30 Basic stats: PARTIAL Column stats: NONEGroup By Operatoraggregations: count()keys: no (type: string)mode: hashoutputColumnNames: _col0, _col1Statistics: Num rows: 0 Data size: 30 Basic stats: PARTIAL Column stats: NONEReduce Output Operatorkey 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: NONEtag: -1value expressions: _col1 (type: bigint)Path -> Alias:hdfs://nameservice1/user/hive/warehouse/liguodong.db/testudf [testudf]Path -> Partition:hdfs://nameservice1/user/hive/warehouse/liguodong.db/testudfPartitionbase file name: testudfinput format: org.apache.hadoop.mapred.TextInputFormatoutput format: org.apache.hadoop.hive.ql.io.HiveIgnoreKeyTextOutputFormatproperties:COLUMN_STATS_ACCURATE truebucket_count -1columns no,numcolumns.commentscolumns.types string:stringfield.delimfile.inputformat org.apache.hadoop.mapred.TextInputFormatfile.outputformat org.apache.hadoop.hive.ql.io.HiveIgnoreKeyTextOutputFormatline.delimlocation hdfs://nameservice1/user/hive/warehouse/liguodong.db/testudfname liguodong.testudfnumFiles 1numRows 0rawDataSize 0serialization.ddl struct testudf { string no, string num}serialization.formatserialization.lib org.apache.hadoop.hive.serde2.lazy.LazySimpleSerDetotalSize 30transient_lastDdlTime 1437374988serde: org.apache.hadoop.hive.serde2.lazy.LazySimpleSerDeinput format: org.apache.hadoop.mapred.TextInputFormatoutput format: org.apache.hadoop.hive.ql.io.HiveIgnoreKeyTextOutputFormatproperties:COLUMN_STATS_ACCURATE truebucket_count -1columns no,numcolumns.commentscolumns.types string:stringfield.delimfile.inputformat org.apache.hadoop.mapred.TextInputFormatfile.outputformat org.apache.hadoop.hive.ql.io.HiveIgnoreKeyTextOutputFormatline.delimlocation hdfs://nameservice1/user/hive/warehouse/liguodong.db/testudfname liguodong.testudfnumFiles 1numRows 0rawDataSize 0serialization.ddl struct testudf { string no, string num}serialization.formatserialization.lib org.apache.hadoop.hive.serde2.lazy.LazySimpleSerDetotalSize 30transient_lastDdlTime 1437374988serde: org.apache.hadoop.hive.serde2.lazy.LazySimpleSerDename: liguodong.testudfname: liguodong.testudfTruncated Path -> Alias:/liguodong.db/testudf [testudf]Needs Tagging: falseReduce Operator Tree:Group By Operatoraggregations: count(VALUE._col0)keys: KEY._col0 (type: string)mode: mergepartialoutputColumnNames: _col0, _col1Statistics: Num rows: 0 Data size: 0 Basic stats: NONE Column stats: NONESelect Operatorexpressions: _col0 (type: string), _col1 (type: bigint)outputColumnNames: _col0, _col1Statistics: Num rows: 0 Data size: 0 Basic stats: NONE Column stats: NONEFile Output Operatorcompressed: falseGlobalTableId: 0directory: 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-10001NumFilesPerFileSink: 1Statistics: Num rows: 0 Data size: 0 Basic stats: NONE Column stats: NONEStats 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.TextInputFormatoutput format: org.apache.hadoop.hive.ql.io.HiveIgnoreKeyTextOutputFormatproperties:columns _col0,_col1columns.types string:bigintescape.delim \hive.serialization.extend.nesting.levels trueserialization.format 1serialization.lib org.apache.hadoop.hive.serde2.lazy.LazySimpleSerDeserde: org.apache.hadoop.hive.serde2.lazy.LazySimpleSerDeTotalFiles: 1GatherStats: falseMultiFileSpray: falseStage: Stage-0Fetch Operatorlimit: -1HIVE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='2013-12-12join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]
)
