Hive导入10G数据的测试

 

前言

Hadoop和Hive的环境已经搭建起来了，开始导入数据进行测试。我的数据1G大概对应500W行，MySQL的查询500W行大概3.29秒，用hive同样的查询大概30秒。如果我们把数据增加到10G，100G，让我们来看看Hive的表现吧。

目录

1. 导出MySQL数据
2. 导入到Hive
3. 优化导入过程Hive Bucket
4. 执行查询

1. 导出MySQL数据

下面是我的表，每天会产生一新表，用日期的方式命名。今天是2013年7月19日，对应的表是cb_hft，记录数646W条记录。

mysql> show tables;
+-----------------+
| Tables_in_CB    |
+-----------------+
| NSpremium       |
| cb_hft          |
| cb_hft_20130710 |
| cb_hft_20130712 |
| cb_hft_20130715 |
| cb_hft_20130716 |
+-----------------+
6 rows in set (0.00 sec)

mysql> select count(1) from cb_hft;
+----------+
| count(1) |
+----------+
|  6461338 |
+----------+
1 row in set (3.29 sec)

快速复制表：
由于这个表是离线系统的，没有线上应用，我重命名表cb_hft为cb_hft_20130719，再复制表结构。

mysql> RENAME TABLE cb_hft TO cb_hft_20130719;
Query OK, 0 rows affected (0.00 sec)

mysql> CREATE TABLE cb_hft like cb_hft_20130719;
Query OK, 0 rows affected (0.02 sec)

mysql> show tables;
+-----------------+
| Tables_in_CB    |
+-----------------+
| NSpremium       |
| cb_hft          |
| cb_hft_20130710 |
| cb_hft_20130712 |
| cb_hft_20130715 |
| cb_hft_20130716 |
| cb_hft_20130719 |
+-----------------+
7 rows in set (0.00 sec)

导出表到csv
以hft_20130712表为例

mysql> SELECT
  SecurityID,TradeTime,PreClosePx,OpenPx,HighPx,LowPx,LastPx, 
  BidSize1,BidPx1,BidSize2,BidPx2,BidSize3,BidPx3,BidSize4,BidPx4,BidSize5,BidPx5,
  OfferSize1,OfferPx1,OfferSize2,OfferPx2,OfferSize3,OfferPx3,OfferSize4,OfferPx4,OfferSize5,OfferPx5,
  NumTrades,TotalVolumeTrade,TotalValueTrade,PE,PE1,PriceChange1,PriceChange2,Positions
FROM cb_hft_20130712
INTO OUTFILE '/tmp/export_cb_hft_20130712.csv'  FIELDS TERMINATED BY ',' LINES TERMINATED BY '\n'; 

Query OK, 6127080 rows affected (2 min 55.04 sec)

查看数据文件

~ ls -l /tmp
-rw-rw-rw- 1 mysql mysql 1068707117 Jul 19 15:59 export_cb_hft_20130712.csv

2. 导入到Hive

登陆c1.wtmart.com机器，下载数据文件

~ ssh [email protected]
~ cd /home/cos/hadoop/sqldb
~ scp -P 10003 [email protected]:/tmp/export_cb_hft_20130712.csv .
export_cb_hft_20130712.csv                                                                 100% 1019MB  39.2MB/s   00:26

在hive上建表

~ bin/hive shell

#删除已存在的表
hive> DROP TABLE IF EXISTS t_hft_tmp;
Time taken: 4.898 seconds

#创建t_hft_tmp表
hive> CREATE TABLE t_hft_tmp(
  SecurityID STRING,TradeTime STRING,
  PreClosePx DOUBLE,OpenPx DOUBLE,HighPx DOUBLE,LowPx DOUBLE,LastPx DOUBLE,
  BidSize1 DOUBLE,BidPx1 DOUBLE,BidSize2 DOUBLE,BidPx2 DOUBLE,BidSize3 DOUBLE,BidPx3 DOUBLE,BidSize4 DOUBLE,BidPx4 DOUBLE,BidSize5 DOUBLE,BidPx5 DOUBLE,
  OfferSize1 DOUBLE,OfferPx1 DOUBLE,OfferSize2 DOUBLE,OfferPx2 DOUBLE,OfferSize3 DOUBLE,OfferPx3 DOUBLE,OfferSize4 DOUBLE,OfferPx4 DOUBLE,OfferSize5 DOUBLE,OfferPx5 DOUBLE,
  NumTrades INT,TotalVolumeTrade DOUBLE,TotalValueTrade DOUBLE,PE DOUBLE,PE1 DOUBLE,PriceChange1 DOUBLE,PriceChange2 DOUBLE,Positions DOUBLE
) PARTITIONED BY (tradeDate INT)
ROW FORMAT DELIMITED FIELDS TERMINATED BY ',';

Time taken: 0.189 seconds

#导入数据
hive> LOAD DATA LOCAL INPATH '/home/cos/hadoop/sqldb/export_cb_hft_20130712.csv' OVERWRITE INTO TABLE t_hft_tmp PARTITION (tradedate=20130712);
Copying data from file:/home/cos/hadoop/sqldb/export_cb_hft_20130712.csv
Copying file: file:/home/cos/hadoop/sqldb/export_cb_hft_20130712.csv
Loading data to table default.t_hft_tmp partition (tradedate=20130712)
Time taken: 16.535 seconds

这个时候，我们会发现这个表只有一个文件，文件没有切分成多份。这样子我们就没有办法利用到并行的MapReduce进行计算。一个文件只能由一个Map任务和一个Reduce任务去处理。

hive> dfs -ls /user/hive/warehouse/t_hft_tmp/tradedate=20130712;
Found 1 items
-rw-r--r--   1 cos supergroup 1068707117 2013-07-19 16:07 /user/hive/warehouse/t_hft_tmp/tradedate=20130712/export_cb_hft_20130712.csv

3. 优化导入过程Hive Bucket

第二步导入，我们要把刚才的一个大文件切分成多少小文件，大概按照64M一个block的要求。我们设置做16个Bucket。

新建数据表t_hft_day，并定义CLUSTERED BY,SORTED BY,16 BUCKETS

hive> CREATE TABLE t_hft_day(
  SecurityID STRING,TradeTime STRING,
  PreClosePx DOUBLE,OpenPx DOUBLE,HighPx DOUBLE,LowPx DOUBLE,LastPx DOUBLE,
  BidSize1 DOUBLE,BidPx1 DOUBLE,BidSize2 DOUBLE,BidPx2 DOUBLE,BidSize3 DOUBLE,BidPx3 DOUBLE,BidSize4 DOUBLE,BidPx4 DOUBLE,BidSize5 DOUBLE,BidPx5 DOUBLE,
  OfferSize1 DOUBLE,OfferPx1 DOUBLE,OfferSize2 DOUBLE,OfferPx2 DOUBLE,OfferSize3 DOUBLE,OfferPx3 DOUBLE,OfferSize4 DOUBLE,OfferPx4 DOUBLE,OfferSize5 DOUBLE,OfferPx5 DOUBLE,
  NumTrades INT,TotalVolumeTrade DOUBLE,TotalValueTrade DOUBLE,PE DOUBLE,PE1 DOUBLE,PriceChange1 DOUBLE,PriceChange2 DOUBLE,Positions DOUBLE
) PARTITIONED BY (tradeDate INT)
CLUSTERED BY(SecurityID) SORTED BY(TradeTime) INTO 16 BUCKETS
ROW FORMAT DELIMITED FIELDS TERMINATED BY ',';

从t_hft_tmp临时数据表导入到t_hft_day数据表

#强制执行装桶的操作
hive> set hive.enforce.bucketing = true;

#数据导入
hive> FROM t_hft_tmp
INSERT OVERWRITE TABLE t_hft_day
PARTITION (tradedate=20130712)
SELECT SecurityID , TradeTime ,
  PreClosePx ,OpenPx ,HighPx ,LowPx ,LastPx ,
  BidSize1 ,BidPx1 ,BidSize2 ,BidPx2 ,BidSize3 ,BidPx3 ,BidSize4 ,BidPx4 ,BidSize5 ,BidPx5 ,
  OfferSize1 ,OfferPx1 ,OfferSize2 ,OfferPx2 ,OfferSize3 ,OfferPx3 ,OfferSize4 ,OfferPx4 ,OfferSize5 ,OfferPx5 ,
  NumTrades,TotalVolumeTrade ,TotalValueTrade ,PE ,PE1 ,PriceChange1 ,PriceChange2 ,Positions 
WHERE tradedate=20130712;

MapReduce Total cumulative CPU time: 8 minutes 5 seconds 810 msec
Ended Job = job_201307191356_0016
Loading data to table default.t_hft_day partition (tradedate=20130712)
Partition default.t_hft_day{tradedate=20130712} stats: [num_files: 16, num_rows: 0, total_size: 1291728298, raw_data_size: 0]
Table default.t_hft_day stats: [num_partitions: 11, num_files: 176, num_rows: 0, total_size: 10425980914, raw_data_size: 0]
6127080 Rows loaded to t_hft_day
MapReduce Jobs Launched:
Job 0: Map: 4  Reduce: 16   Cumulative CPU: 485.81 sec   HDFS Read: 1068771008 HDFS Write: 1291728298 SUCCESS
Total MapReduce CPU Time Spent: 8 minutes 5 seconds 810 msec
OK
Time taken: 172.617 seconds

导入操作累计CPU时间是8分05秒，8*60+5=485秒。由于有4个Map并行，16个Reduce并行，所以实际消耗时间是172秒。

我们再看一下新表的文件是否被分片：

hive> dfs -ls /user/hive/warehouse/t_hft_day/tradedate=20130712;
Found 16 items
-rw-r--r--   1 cos supergroup   95292536 2013-07-19 16:18 /user/hive/warehouse/t_hft_day/tradedate=20130712/000000_0
-rw-r--r--   1 cos supergroup   97136495 2013-07-19 16:18 /user/hive/warehouse/t_hft_day/tradedate=20130712/000001_0
-rw-r--r--   1 cos supergroup   90695623 2013-07-19 16:18 /user/hive/warehouse/t_hft_day/tradedate=20130712/000002_0
-rw-r--r--   1 cos supergroup   84132171 2013-07-19 16:18 /user/hive/warehouse/t_hft_day/tradedate=20130712/000003_0
-rw-r--r--   1 cos supergroup   81552397 2013-07-19 16:18 /user/hive/warehouse/t_hft_day/tradedate=20130712/000004_0
-rw-r--r--   1 cos supergroup   80580028 2013-07-19 16:18 /user/hive/warehouse/t_hft_day/tradedate=20130712/000005_0
-rw-r--r--   1 cos supergroup   73195335 2013-07-19 16:18 /user/hive/warehouse/t_hft_day/tradedate=20130712/000006_0
-rw-r--r--   1 cos supergroup   68648786 2013-07-19 16:18 /user/hive/warehouse/t_hft_day/tradedate=20130712/000007_0
-rw-r--r--   1 cos supergroup   72210159 2013-07-19 16:18 /user/hive/warehouse/t_hft_day/tradedate=20130712/000008_0
-rw-r--r--   1 cos supergroup   66851502 2013-07-19 16:18 /user/hive/warehouse/t_hft_day/tradedate=20130712/000009_0
-rw-r--r--   1 cos supergroup   69292538 2013-07-19 16:18 /user/hive/warehouse/t_hft_day/tradedate=20130712/000010_0
-rw-r--r--   1 cos supergroup   75282272 2013-07-19 16:18 /user/hive/warehouse/t_hft_day/tradedate=20130712/000011_0
-rw-r--r--   1 cos supergroup   79572724 2013-07-19 16:18 /user/hive/warehouse/t_hft_day/tradedate=20130712/000012_0
-rw-r--r--   1 cos supergroup   78151866 2013-07-19 16:19 /user/hive/warehouse/t_hft_day/tradedate=20130712/000013_0
-rw-r--r--   1 cos supergroup   86850954 2013-07-19 16:18 /user/hive/warehouse/t_hft_day/tradedate=20130712/000014_0
-rw-r--r--   1 cos supergroup   92282912 2013-07-19 16:19 /user/hive/warehouse/t_hft_day/tradedate=20130712/000015_0

一共16个分片。

4. 执行查询

当前1G的文件，使用Hive执行一个简单的查询：34.974秒

hive> select count(1) from t_hft_day where tradedate=20130712;

MapReduce Total cumulative CPU time: 34 seconds 670 msec
Ended Job = job_201307191356_0017
MapReduce Jobs Launched:
Job 0: Map: 7  Reduce: 1   Cumulative CPU: 34.67 sec   HDFS Read: 1291793812 HDFS Write: 8 SUCCESS
Total MapReduce CPU Time Spent: 34 seconds 670 msec
6127080
Time taken: 34.974 seconds

MySQL执行同样的查询，在开始时我已经测试过3.29秒。
相差了10倍的时间，不过只有1G的数据量，是发挥不出hadoop的优势的。

接下来，按照上面的方法，我们把十几天的数据都导入到hive里面，然后再进行比较。

查看已导入hive的数据集

hive> SHOW PARTITIONS t_hft_day;
tradedate=20130627
tradedate=20130628
tradedate=20130701
tradedate=20130702
tradedate=20130703
tradedate=20130704
tradedate=20130705
tradedate=20130708
tradedate=20130709
tradedate=20130710
tradedate=20130712
tradedate=20130715
tradedate=20130716
tradedate=20130719
Time taken: 0.099 seconds

在MySQL中，对5张表进行查询。(5G数据量)

#单表：由于PreClosePx不是索引列，第一次查询
mysql> select SecurityID,20130719 as tradedate,count(1) as count from cb_hft_20130716 where PreClosePx>8.17 group by SecurityID limit 10;
+------------+-----------+-------+
| SecurityID | tradedate | count |
+------------+-----------+-------+
| 000001     |  20130719 |  5200 |
| 000002     |  20130719 |  5193 |
| 000003     |  20130719 |  1978 |
| 000004     |  20130719 |  3201 |
| 000005     |  20130719 |  1975 |
| 000006     |  20130719 |  1910 |
| 000007     |  20130719 |  3519 |
| 000008     |  20130719 |  4229 |
| 000009     |  20130719 |  5147 |
| 000010     |  20130719 |  2176 |
+------------+-----------+-------+
10 rows in set (24.60 sec)

#多表查询
select t.SecurityID,t.tradedate,t.count 
from (
select SecurityID,20130710 as tradedate,count(1) as count from cb_hft_20130710 where PreClosePx>8.17 group by SecurityID
union
select SecurityID,20130712 as tradedate,count(1) as count from cb_hft_20130712 group by SecurityID
union
select SecurityID,20130715 as tradedate,count(1) as count from cb_hft_20130715 where PreClosePx>8.17 group by SecurityID
union
select SecurityID,20130716 as tradedate,count(1) as count from cb_hft_20130716 where PreClosePx>8.17 group by SecurityID
union
select SecurityID,20130719 as tradedate,count(1) as count from cb_hft_20130719 where PreClosePx>8.17 group by SecurityID ) as t 
limit 10

#超过3分钟，无返回结果。
....

在Hive中，对同样的5张表进行查询。(5G数据量)

select SecurityID,tradedate,count(1) from t_hft_day where tradedate in (20130710,20130712,20130715,20130716,20130719) and PreClosePx>8.17 group by SecurityID,tradedate limit 10;

MapReduce Total cumulative CPU time: 3 minutes 56 seconds 540 msec
Ended Job = job_201307191356_0023
MapReduce Jobs Launched:
Job 0: Map: 25  Reduce: 7   Cumulative CPU: 236.54 sec   HDFS Read: 6577084486 HDFS Write: 1470 SUCCESS
Total MapReduce CPU Time Spent: 3 minutes 56 seconds 540 msec
OK
000001  20130710        5813
000004  20130715        3546
000005  20130712        1820
000005  20130719        2364
000006  20130716        1910
000008  20130710        2426
000011  20130715        2113
000012  20130712        3554
000012  20130719        3756
000013  20130716        1646
Time taken: 66.32 seconds

#对以上14张表的查询
MapReduce Total cumulative CPU time: 8 minutes 40 seconds 380 msec
Ended Job = job_201307191356_0022
MapReduce Jobs Launched:
Job 0: Map: 53  Reduce: 15   Cumulative CPU: 520.38 sec   HDFS Read: 14413501282 HDFS Write: 3146 SUCCESS
Total MapReduce CPU Time Spent: 8 minutes 40 seconds 380 msec
OK
000001  20130716        5200
000002  20130715        5535
000003  20130705        1634
000004  20130704        2173
000005  20130703        996
000005  20130712        1820
000006  20130702        1176
000007  20130701        2973
000007  20130710        4084
000010  20130716        2176
Time taken: 119.161 seconds

我们看到hadoop对以G为单位量级的数据增长是不敏感的，多了3倍的数据(15G)，执行查询的时间是原来(5G)的两倍。而MySQL数据增长到5G，查询时间几乎是不可忍受的。

1G以下的数据是单机可以处理的，MySQL会非常好的完成查询任务。Hadoop只有在数据量大的情况下才能发挥出优势，当数据量到达10G时，MySQL的单表查询就显得就会性能不足。如果数据量到达了100G，MySQL就已经解决不了了，要通过各种优化的程序才能完成查询。

测试过程已经描述的很清楚了，我们接下来的工作就是把过程自动化。