Hive优化策略
2017-07-23 14:02
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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]
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