hive spark conf

CREATE TABLE org_userbehavior_all_yunzhi
(
user_id Int
,event_time bigint
,behivior_id SMALLINT
,behivior_name String
,behivior_pop  String
,record_date  String
)
ROW FORMAT DELIMITED FIELDS TERMINATED BY '\t'
STORED AS TEXTFILE
location '/user/hive/warehouse/org_userbehavior_all_yunzhi';
LOAD DATA INPATH '/sparklib/data' OVERWRITE INTO TABLE org_userbehavior_all_yunzhi;



CREATE TABLE org_userbehavior_all_yunzhi_parquet
(
user_id Int
,event_time Int
,behivior_pop  String
)
partitioned by (record_date String,behivior_id SMALLINT )
STORED AS PARQUET
location '/user/hive/warehouse/org_userbehavior_all_yunzhi_parquet';

INSERT OVERWRITE TABLE org_userbehavior_funnel_dd PARTITION(record_date,behivior_id)  SELECT user_id, cast (event_time/1000 as  Int) as record_date   ,behivior_pop,record_date,behivior_id from org_userbehavior_all;
-- 4538072102  /user/hive/warehouse/org_userbehavior_funnel_dd
-- hadoop fs -ls -R  /user/hive/warehouse/org_userbehavior_funnel_dd | wc -l     3599

CREATE TABLE org_userbehavior_all_yunzhi_parquet
(
user_id Int
,event_time Int
,behivior_pop  String
,behivior_id SMALLINT
)
partitioned by (record_date String )
STORED AS PARQUET
location '/user/hive/warehouse/org_userbehavior_all_yunzhi_parquet';

set hive.exec.dynamic.partition.mode=nonstrict
set mapred.max.split.size=1024000000;
set mapred.min.split.size.per.node=1024000000;
set mapred.min.split.size.per.rack=1024000000;
set mapred.reduce.tasks=30;
set hive.exec.reducers.bytes.per.reducer=1073741824;
set hive.exec.compress.intermediate=true;
set hive.exec.compress.output=true;
set mapred.output.compression.codec=org.apache.hadoop.io.compress.GzipCodec;
set hive.exec.reducers.bytes.per.reducer=50000000;



set mapred.max.split.size=100000000;
set mapred.min.split.size.per.node=200000000;
set mapred.min.split.size.per.rack=200000000;
set mapred.reduce.tasks=100;


set spark.sql.map.partitions=100;
set spark.sql.shuffle.partitions=40;
set spark.sql.reduce.partitions=10;






org.apache.hadoop.io.compress.GzipCodec
org.apache.hadoop.io.compress.DefaultCodec
org.apache.hadoop.io.compress.Bzip2Codec
org.apache.hadoop.io.compress.SnappyCodec

set mapred.output.compression.codec=org.apache.hadoop.io.compress.GzipCodec



INSERT OVERWRITE TABLE org_userbehavior_all_yunzhi_parquet PARTITION(record_date) SELECT user_id, cast (event_time/1000 as Int) as event_time,behivior_pop,behivior_id,record_date from org_userbehavior_all_yunzhi;
-- 2908248821  /user/hive/warehouse/org_userbehavior_funnel_dd2
-- hadoop fs -ls -R  /user/hive/warehouse/org_userbehavior_funnel_dd2 | wc -l     2960

-- todo partitions 不包含 behivior_id 压缩比更好,和字典压缩有关.  生成的文件数太多,要修改 shuffer 数量来减少 文件数.



-- udf
-- funnel_merge   tuple
-- funnel_merge2  time *10  + eventid
-- funnel_sum





CREATE TABLE org_userbehavior_all_yunzhi_behiviorid_parquet
(
user_id Int
,event_time Int
,behivior_pop  String
)
partitioned by (record_date String,behivior_id SMALLINT)
STORED AS PARQUET
location '/user/hive/warehouse/org_userbehavior_all_yunzhi_behiviorid_parquet';
INSERT OVERWRITE TABLE org_userbehavior_all_yunzhi_behiviorid_parquet PARTITION(record_date,behivior_id)  SELECT user_id, cast (event_time/1000 as  Int) as event_time ,behivior_pop,record_date,behivior_id from org_userbehavior_all_yunzhi;

set mapred.output.compression.codec=org.apache.hadoop.io.compress.GzipCodec
INSERT OVERWRITE TABLE org_userbehavior_all_yunzhi_parquet PARTITION(record_date) SELECT user_id, cast (event_time/1000 as Int) as event_time,behivior_pop,behivior_id,record_date from org_userbehavior_all_yunzhi;


set hive.merge.mapfiles=true;
set hive.merge.mapredfiles=true;
set hive.merge.size.per.task=128000000;
set hive.merge.smallfiles.avgsize=15000000;

set hive.merge.mapfiles = true #在Map-only的任务结束时合并小文件
set hive.merge.mapredfiles = true #在Map-Reduce的任务结束时合并小文件
set hive.merge.size.per.task = 256*1000*1000 #合并文件的大小
set hive.merge.smallfiles.avgsize=16000000 #当输出文件的平均大小小于该值时，启动一个独立的map-reduce任务进行文件merge


set hive.exec.dynamic.partition.mode=nonstrict;
set mapred.max.split.size=256000000;
set mapred.min.split.size.per.node=256000000;
set mapred.min.split.size.per.rack=256000000;
set mapred.reduce.tasks=30;
set hive.exec.reducers.bytes.per.reducer=1073741824;
set hive.exec.compress.intermediate=true;
set hive.exec.compress.output=true;
set mapred.output.compression.codec=org.apache.hadoop.io.compress.GzipCodec;
set spark.sql.shuffle.partitions=40;
set spark.sql.reduce.partitions=10;
set hive.merge.mapfiles=true;
set hive.merge.mapredfiles=true;
set hive.merge.size.per.task=128000000;
set hive.merge.smallfiles.avgsize=50000000;


org.apache.hadoop.io.compress.GzipCodec
org.apache.hadoop.io.compress.DefaultCodec
org.apache.hadoop.io.compress.Bzip2Codec
org.apache.hadoop.io.compress.SnappyCodec


set mapred.output.compression.codec=org.apache.hadoop.io.compress.GzipCodec


select funnelsum(funneldata) from  ( select user_id,funnelmergecount(behivior_id,event_time,'10004,10007',1000000,1451577600) as funneldata  from org_userbehavior_all_yunzhi_parquet  where   behivior_id in (10004,10007)  and  (behivior_id != 10004 or  jsoninfogetstring(behivior_pop,'brand') = 'Apple')  group by  user_id ) temp2
select funnelsum(funneldata) from  ( select user_id,funnelmergecount(behivior_id,event_time,'10002,10003,10004,10007,10008',86400,1451577600) as funneldata  from org_userbehavior_all_yunzhi_parquet  where  record_date in ('20170101','20170102','20170103','20170104','20170105','20170106','20170107')  and  behivior_id in (10002,10003,10004,10007,10008)  and    (behivior_id != 10003 or  jsoninfogetstring(behivior_pop,'brand') = 'Apple')  and   (behivior_id != 10004 or  jsoninfogetdouble(behivior_pop,'price') > 5000)   group by  user_id ) temp2
select funnelsum(funneldata) from  ( select user_id,funnelmergecount(behivior_id,event_time,'10002,10003,10004,10007,10008',86400,1451577600) as funneldata  from org_userbehavior_all_yunzhi_parquet  where  record_date in ('20170101','20170102','20170103','20170104','20170105','20170106','20170107')  and  behivior_id in (10002,10003,10004,10007,10008)   group by  user_id ) temp2 ;
select funnelsum(funneldata) from  ( select user_id,funnelmergecount(behivior_id,event_time,'10002,10003,10004,10007,10008',86400,1451577600) as funneldata  from org_userbehavior_all_yunzhi_parquet  where   behivior_id in (10002,10003,10004,10007,10008)   group by  user_id ) temp2 ;



CREATE TABLE org_userbehavior_all_yunzhi_parquet_userslice
(
user_id Int
,event_time Int
,behivior_pop  String
,behivior_id SMALLINT
)
partitioned by (record_date String,user_id_slice Int )
STORED AS PARQUET
location '/user/hive/warehouse/org_userbehavior_all_yunzhi_parquet_userslice';




要如何把 天的输出 ，变成一个区间 。  一天 一天的 处理 ？  把 utdid 根据 Hash 分区



set hive.exec.dynamic.partition.mode=nonstrict;
INSERT OVERWRITE TABLE org_userbehavior_all_yunzhi_parquet_userslice PARTITION(record_date,user_id_slice) SELECT user_id, cast (event_time/1000 as Int) as event_time,behivior_pop,behivior_id,record_date,user_id%8 as mode  from org_userbehavior_all_yunzhi where record_date = 20170101;




输出小文件合并的改进 （增加自动合并结果文件）

当spark.sql.shuffle.partitions设置的比较大且结果数据集比较小时，会产生大量的小文件（文件数等同spark.sql.shuffle.partitions）。
解决办法：
在最后的执行计划中加入一个repartition transformation。通过参数控制最终的partitions数且不影响shuffle partition的数量。
使用方法：
set spark.sql.result.partitions=10;

作者：Albert陈凯
链接：http://www.jianshu.com/p/00328171b8a6
來源：简书
著作权归作者所有。商业转载请联系作者获得授权，非商业转载请注明出处。

配置属性
set hive.merge.sparkfiles=true;
set hive.merge.smallfiles.avgsize=50000000;







set spark.sql.shuffle.partitions=1;
set spark.sql.reduce.partitions=1;




# see HIVE-9153
mapreduce.input.fileinputformat.split.maxsize=750000000
hive.vectorized.execution.enabled=true

hive.cbo.enable=true
hive.optimize.reducededuplication.min.reducer=4
hive.optimize.reducededuplication=true
hive.orc.splits.include.file.footer=false
set hive.merge.mapfiles=true;
set hive.merge.sparkfiles=true;
set hive.merge.smallfiles.avgsize=16000000;
set hive.merge.size.per.task=256000000;
hive.merge.orcfile.stripe.level=true
hive.auto.convert.join=true
hive.auto.convert.join.noconditionaltask=true
hive.auto.convert.join.noconditionaltask.size=894435328
hive.optimize.bucketmapjoin.sortedmerge=false
hive.map.aggr.hash.percentmemory=0.5
hive.map.aggr=true
hive.optimize.sort.dynamic.partition=false
hive.stats.autogather=true
hive.stats.fetch.column.stats=true
hive.vectorized.execution.reduce.enabled=false
hive.vectorized.groupby.checkinterval=4096
hive.vectorized.groupby.flush.percent=0.1
hive.compute.query.using.stats=true
hive.limit.pushdown.memory.usage=0.4
hive.optimize.index.filter=true
hive.exec.reducers.bytes.per.reducer=67108864
hive.smbjoin.cache.rows=10000
hive.exec.orc.default.stripe.size=67108864
hive.fetch.task.conversion=more
hive.fetch.task.conversion.threshold=1073741824
hive.fetch.task.aggr=false
mapreduce.input.fileinputformat.list-status.num-threads=5
spark.kryo.referenceTracking=false
spark.kryo.classesToRegister=org.apache.hadoop.hive.ql.io.HiveKey,org.apache.hadoop.io.BytesWritable,org.apache.hadoop.hive.ql.exec.vector.VectorizedRowBatch

set hive.exec.dynamic.partition.mode=nonstrict
set hive.merge.mapfiles=true;
set hive.merge.sparkfiles=true;
set hive.merge.smallfiles.avgsize=128000000;
set hive.merge.size.per.task=256000000;

INSERT OVERWRITE TABLE org_userbehavior_all_yunzhi_parquet_userslice PARTITION(record_date,user_id_slice) SELECT user_id, cast (event_time/1000 as Int) as event_time,behivior_pop,behivior_id,record_date,user_id%8 as mode  from org_userbehavior_all_yunzhi