hadoop - Hive 映射器需要很长时间才能完成 SpillThread 映射输出日志记录

Question

我在 mapreduce 上运行配置单元，一些映射器运行了大约 8 小时(主要是最后几个映射器)。我可以看到很多 [SpillThread] org.apache.hadoop.mapred.MapTask: Finished spill 59
org.apache.hadoop.mapred.MapTask:在日志中溢出映射输出。需要你帮忙调整吗？

请在下面找到我正在运行的示例查询。

示例查询

CREATE TABLE schema.test_t AS
SELECT
demo,
col1,
col2 as col2,
col3 as col3,
col4,
col5,
col6,
col7,
SUM(col8) AS col8,
COUNT(1) AS col9,
count(distinct col10) as col10,
col11,
col12
FROM
schema.srce_t
WHERE col13 IN ('a','b')
GROUP BY
col1,col2,col3,col4,col5,col6,col7,col11,col12
GROUPING SETS ((col1,col2,col3,col4,col5,col6,col7,col11,col12),
(col1,col11,col2,col3,col5,col6,col12,col7),
(col1,col11,col2,col3,col6,col12,col7),
(col1,col11,col2,col3,col4,col6,col12,col7),
(col1,col11,col2,col4,col5,col6,col12,col7),
(col1,col11,col2,col4,col6,col12,col7),
(col1,col11,col2,col5,col6,col12,col7),
(col1,col11,col4,col5,col6,col12,col7),
(col1,col11,col3,col4,col5,col6,col12,col7),
(col1,col11,col3,col5,col6,col12,col7),
(col1,col11,col3,col4,col6,col12,col7),
(col1,col11,col4,col6,col12,col7),
(col1,col11,col3,col6,col12,col7),
(col1,col11,col5,col6,col12,col7),
(col1,col11,col2, col6,col12,col7),
(col1,col11,col6, col12,col7));

Hive 属性。

SET mapreduce.reduce.memory.mb=10240;
SET mapreduce.reduce.java.opts=-Xmx9216m;
SET mapreduce.map.memory.mb=10240;
SET mapreduce.map.java.opts=-Xmx9216m;
SET mapreduce.task.io.sort.mb=1536

日志:

   2019-05-15 05:34:32,600 INFO [main] org.apache.hadoop.mapred.MapTask: bufstart = 0; bufend = 714424619; bufvoid = 1073741824
2019-05-15 05:34:32,600 INFO [main] org.apache.hadoop.mapred.MapTask: kvstart = 268435452(1073741808); kvend = 232293228(929172912); length = 36142225/67108864
2019-05-15 05:34:32,600 INFO [main] org.apache.hadoop.mapred.MapTask: (EQUATOR) 750592747 kvi 187648180(750592720)
2019-05-15 05:34:41,305 INFO [main] org.apache.hadoop.hive.ql.exec.ReduceSinkOperator: RS[4]: records written - 10000000
2019-05-15 05:35:01,944 INFO [SpillThread] org.apache.hadoop.io.compress.CodecPool: Got brand-new compressor [.snappy]
2019-05-15 05:35:07,479 INFO [SpillThread] org.apache.hadoop.mapred.MapTask: Finished spill 0
2019-05-15 05:35:07,480 INFO [main] org.apache.hadoop.mapred.MapTask: (RESET) equator 750592747 kv 187648180(750592720) kvi 178606160(714424640)
2019-05-15 05:35:34,178 INFO [main] org.apache.hadoop.hive.ql.exec.MapOperator: MAP[13]: records read - 1000000
2019-05-15 05:35:58,140 INFO [main] org.apache.hadoop.mapred.MapTask: Spilling map output
2019-05-15 05:35:58,140 INFO [main] org.apache.hadoop.mapred.MapTask: bufstart = 750592747; bufend = 390854476; bufvoid = 1073741791
2019-05-15 05:35:58,140 INFO [main] org.apache.hadoop.mapred.MapTask: kvstart = 187648180(750592720); kvend = 151400696(605602784); length = 36247485/67108864
2019-05-15 05:35:58,141 INFO [main] org.apache.hadoop.mapred.MapTask: (EQUATOR) 427407372 kvi 106851836(427407344)
2019-05-15 05:36:31,831 INFO [SpillThread] org.apache.hadoop.mapred.MapTask: Finished spill 1
2019-05-15 05:36:31,833 INFO [main] org.apache.hadoop.mapred.MapTask: (RESET) equator 427407372 kv 106851836(427407344) kvi 97806648(391226592)
2019-05-15 05:37:19,180 INFO [main] org.apache.hadoop.mapred.MapTask: Spilling map output

Answer 1

检查这些参数的当前值并减少数字，直到您有更多的并行映射器:

set hive.input.format=org.apache.hadoop.hive.ql.io.HiveInputFormat;
set mapreduce.input.fileinputformat.split.minsize=16000; -- 16 KB
set mapreduce.input.fileinputformat.split.maxsize=128000000; -- 128Mb
--files bigger than max size will be splitted.
--files smaller than min size will be processed on the same mapper combined 

如果您的文件不是可拆分格式，例如 gzip。这无济于事。尝试使用这些设置以获得更小的映射器。

此外，这些设置可能有助于提高查询的性能

set hive.optimize.distinct.rewrite=true;
set hive.map.aggr=true;
--if files are ORC, check PPD:
SET hive.optimize.ppd=true;
SET hive.optimize.ppd.storage=true;