hive 集群初探，查询比较
在slave3安装MySQL作为hive 的matastore
[root@slave3 hadoop]# yum search mysql
[root@slave3 hadoop]# yum install mysql-server.x86_64
成功安装MySQL
Installed:
mysql-server.x86_64 0:5.1.73-5.el6_6
Dependency Installed:
mysql.x86_64 0:5.1.73-5.el6_6 perl.x86_64 4:5.10.1-136.el6_6.1 perl-DBD-MySQL.x86_64 0:4.013-3.el6
perl-DBI.x86_64 0:1.609-4.el6 perl-Module-Pluggable.x86_64 1:3.90-136.el6_6.1 perl-Pod-Escapes.x86_64 1:1.04-136.el6_6.1
perl-Pod-Simple.x86_64 1:3.13-136.el6_6.1 perl-libs.x86_64 4:5.10.1-136.el6_6.1 perl-version.x86_64 3:0.77-136.el6_6.1
Dependency Updated:
mysql-libs.x86_64 0:5.1.73-5.el6_6
Complete!
[root@slave3 hadoop]# service mysqld start
启动MySQL
修改MySQL登录密码
mysql> set password =password(‘root’);
Query OK, 0 rows affected (0.00 sec)
slave3cpu信息
内存
[hadoop@slave4 ~]$ free -m
total used free shared buffers cached
Mem: 1866 1798 68 0 7 1500
-/+ buffers/cache: 290 1575
Swap: 3999 0 3999
单位为ＭＢ
详细内存
[hadoop@slave4 ~]$ cat /proc/meminfo
MemTotal: 1911400 kB
MemFree: 66904 kB
Buffers: 7308 kB
Cached: 1539760 kB
SwapCached: 0 kB
Active: 135492 kB
Inactive: 1603924 kB
Active(anon): 93060 kB
Inactive(anon): 99604 kB
Active(file): 42432 kB
Inactive(file): 1504320 kB
Unevictable: 0 kB
Mlocked: 0 kB
SwapTotal: 4095992 kB
SwapFree: 4095992 kB
Dirty: 48 kB
Writeback: 0 kB
AnonPages: 192444 kB
Mapped: 15264 kB
Shmem: 220 kB
Slab: 73204 kB
SReclaimable: 17448 kB
SUnreclaim: 55756 kB
KernelStack: 1264 kB
PageTables: 2504 kB
NFS_Unstable: 0 kB
Bounce: 0 kB
WritebackTmp: 0 kB
CommitLimit: 5051692 kB
Committed_AS: 292260 kB
VmallocTotal: 34359738367 kB
VmallocUsed: 540012 kB
VmallocChunk: 34359184520 kB
HardwareCorrupted: 0 kB
AnonHugePages: 159744 kB
HugePages_Total: 0
HugePages_Free: 0
HugePages_Rsvd: 0
HugePages_Surp: 0
Hugepagesize: 2048 kB
DirectMap4k: 9792 kB
DirectMap2M: 2076672 kB
磁盘
查看硬盘和分区分布
[hadoop@slave4 ~]$ lsblk
NAME MAJ:MIN RM SIZE RO TYPE MOUNTPOINT
sda 8:0 0 232.9G 0 disk
鈹溾攢sda1 8:1 0 500M 0 part /boot
鈹斺攢sda2 8:2 0 232.4G 0 part
鈹溾攢vg_slave4-lv_root (dm-0) 253:0 0 50G 0 lvm /
鈹溾攢vg_slave4-lv_swap (dm-1) 253:1 0 3.9G 0 lvm [SWAP]
鈹斺攢vg_slave4-lv_home (dm-2) 253:2 0 178.5G 0 lvm /home
[root@slave4 hadoop]# fdisk -l
Disk /dev/sda: 250.1 GB, 250058268160 bytes
255 heads, 63 sectors/track, 30401 cylinders
Units = cylinders of 16065 * 512 = 8225280 bytes
Sector size (logical/physical): 512 bytes / 512 bytes
I/O size (minimum/optimal): 512 bytes / 512 bytes
Disk identifier: 0x2c63be37
Device Boot Start End Blocks Id System
/dev/sda1 * 1 64 512000 83 Linux
Partition 1 does not end on cylinder boundary.
/dev/sda2 64 30402 243684352 8e Linux LVM
Disk /dev/mapper/vg_slave4-lv_root: 53.7 GB, 53687091200 bytes
255 heads, 63 sectors/track, 6527 cylinders
Units = cylinders of 16065 * 512 = 8225280 bytes
Sector size (logical/physical): 512 bytes / 512 bytes
I/O size (minimum/optimal): 512 bytes / 512 bytes
Disk identifier: 0x00000000
Disk /dev/mapper/vg_slave4-lv_swap: 4194 MB, 4194304000 bytes
255 heads, 63 sectors/track, 509 cylinders
Units = cylinders of 16065 * 512 = 8225280 bytes
Sector size (logical/physical): 512 bytes / 512 bytes
I/O size (minimum/optimal): 512 bytes / 512 bytes
Disk identifier: 0x00000000
Disk /dev/mapper/vg_slave4-lv_home: 191.7 GB, 191650332672 bytes
255 heads, 63 sectors/track, 23300 cylinders
Units = cylinders of 16065 * 512 = 8225280 bytes
Sector size (logical/physical): 512 bytes / 512 bytes
I/O size (minimum/optimal): 512 bytes / 512 bytes
Disk identifier: 0x00000000
dfs系统节点
[hadoop@slave4 ~]$ hadoop dfsadmin -report
Configured Capacity: 985770651648 (918.07 GB)
Present Capacity: 934353133568 (870.18 GB)
DFS Remaining: 590443302912 (549.89 GB)
DFS Used: 343909830656 (320.29 GB)
DFS Used%: 36.81%
Under replicated blocks: 0
Blocks with corrupt replicas: 0
Missing blocks: 0
Datanodes available: 7 (7 total, 0 dead)
Name: 192.168.2.6:50010
Decommission Status : Normal
Configured Capacity: 188643102720 (175.69 GB)
DFS Used: 75069222912 (69.91 GB)
Non DFS Used: 9779052544 (9.11 GB)
DFS Remaining: 103794827264(96.67 GB)
DFS Used%: 39.79%
DFS Remaining%: 55.02%
Last contact: Sun Jul 05 10:52:17 CST 2015
Name: 192.168.2.13:50010
Decommission Status : Normal
Configured Capacity: 21277569024 (19.82 GB)
DFS Used: 136781824 (130.45 MB)
Non DFS Used: 1261211648 (1.17 GB)
DFS Remaining: 19879575552(18.51 GB)
DFS Used%: 0.64%
DFS Remaining%: 93.43%
Last contact: Sun Jul 05 10:52:17 CST 2015
Name: 192.168.2.9:50010
Decommission Status : Normal
Configured Capacity: 188643102720 (175.69 GB)
DFS Used: 58468474880 (54.45 GB)
Non DFS Used: 9779011584 (9.11 GB)
DFS Remaining: 120395616256(112.13 GB)
DFS Used%: 30.99%
DFS Remaining%: 63.82%
Last contact: Sun Jul 05 10:52:18 CST 2015
Name: 192.168.2.10:50010
Decommission Status : Normal
Configured Capacity: 188643102720 (175.69 GB)
DFS Used: 74225582080 (69.13 GB)
Non DFS Used: 9778978816 (9.11 GB)
DFS Remaining: 104638541824(97.45 GB)
DFS Used%: 39.35%
DFS Remaining%: 55.47%
Last contact: Sun Jul 05 10:52:17 CST 2015
Name: 192.168.2.11:50010
Decommission Status : Normal
Configured Capacity: 188643102720 (175.69 GB)
DFS Used: 63778144256 (59.4 GB)
Non DFS Used: 9779015680 (9.11 GB)
DFS Remaining: 115085942784(107.18 GB)
DFS Used%: 33.81%
DFS Remaining%: 61.01%
Last contact: Sun Jul 05 10:52:15 CST 2015
Name: 192.168.2.12:50010
Decommission Status : Normal
Configured Capacity: 21277569024 (19.82 GB)
DFS Used: 966615040 (921.84 MB)
Non DFS Used: 1261236224 (1.17 GB)
DFS Remaining: 19049717760(17.74 GB)
DFS Used%: 4.54%
DFS Remaining%: 89.53%
Last contact: Sun Jul 05 10:52:17 CST 2015
Name: 192.168.2.7:50010
Decommission Status : Normal
Configured Capacity: 188643102720 (175.69 GB)
DFS Used: 71265009664 (66.37 GB)
Non DFS Used: 9779011584 (9.11 GB)
DFS Remaining: 107599081472(100.21 GB)
DFS Used%: 37.78%
DFS Remaining%: 57.04%
Last contact: Sun Jul 05 10:52:17 CST 2015
安装之后修改配置文件，
[hadoop@slave3 bin]$ ./hive
15/07/06 10:58:20 WARN conf.HiveConf: DEPRECATED: Configuration property hive.metastore.local no longer has any effect. Make sure to provide a valid value for hive.metastore.uris if you are connecting to a remote metastore.
Logging initialized using configuration in file:/opt/hadoop/hive-0.12.0/conf/hive-log4j.properties
hive> set javax.jdo.option.ConnectionURL
;
javax.jdo.option.ConnectionURL=jdbc:mysql://slave3:3306/hive?createDatabaseIfNoExist=true
hive>
hive> show tables;
FAILED: Execution Error, return code 1 from org.apache.hadoop.hive.ql.exec.DDLTask. java.lang.RuntimeException: Unable to instantiate org.apache.hadoop.hive.metastore.HiveMetaStoreClient
hive>
原因是没有driver
加上之后还是不行
2015-07-06 11:04:16,786 WARN bonecp.BoneCPConfig (BoneCPConfig.java:sanitize(1537)) - Max Connections < 1. Setting to 20
2015-07-06 11:04:16,911 ERROR Datastore.Schema (Log4JLogger.java:error(125)) - Failed initialising database.
Unable to open a test connection to the given database. JDBC url = jdbc:mysql://slave3:3306/hive?createDatabaseIfNoExist=true, username = root. Terminating connection pool. Original Exception: ——
java.sql.SQLException: Access denied for user ‘root’@’slave3’ (using password: YES)
MySQL连接不上
https://hadooptutorial.info/unable-open-test-connection-given-database/
根据这篇文章找到问题，原来是写错了
下面惊醒ncdc 的数据分析阶段
根据我的博客中间写的流程创建表；
http://blog.csdn.net/mrcharles/article/details/46514359
create table ncdc (
year string,
month string,
data string,
time string,
air string,
a string,
b string,
c string,
d string,
e string,
f string,
g string
)ROW FORMAT DELIMITED FIELDS TERMINATED BY ‘\t’ STORED AS TEXTFILE;
ncdc的数据通过一个简单的java程序进行处理，将空格变为制表符
- 导入数据到hive中
load data local inpath ‘/opt/software/ncdc/summary’ into table ncdc
hive> load data local inpath ‘/opt/hadoop/hadoopDATA/summary’ into table ncdc;
Copying data from file:/opt/hadoop/hadoopDATA/summary
Copying file: file:/opt/hadoop/hadoopDATA/summary
Loading data to table default.ncdc
Table default.ncdc stats: [num_partitions: 0, num_files: 1, num_rows: 0, total_size: 17013314, raw_data_size: 0]
OK
Time taken: 2.231 seconds
FAILED: RuntimeException java.net.UnknownHostException: unknown host: node1
出现以上的错误
修改配置文件，之前的配置文件是我直接从一台机器中拷贝出来的，没有修改hdfs的url
- 查询数据
可以查询每一年的平均气温，最高气温，最低气温等等，也可以使用分组函数，和MySQL操作差不多
select year,avg(air) from ncdc group by year;
hive> select year,avg(air) from ncdc group by year;
Total MapReduce jobs = 1
Launching Job 1 out of 1
Number of reduce tasks not specified. Estimated from input data size: 1
In order to change the average load for a reducer (in bytes):
set hive.exec.reducers.bytes.per.reducer=
In order to limit the maximum number of reducers:
set hive.exec.reducers.max=
In order to set a constant number of reducers:
set mapred.reduce.tasks=
Starting Job = job_201507050950_0001, Tracking URL = http://master:50030/jobdetails.jsp?jobid=job_201507050950_0001
Kill Command = /opt/hadoop/hadoop-1.2.1/libexec/../bin/hadoop job -kill job_201507050950_0001
Hadoop job information for Stage-1: number of mappers: 2; number of reducers: 1
2015-07-06 13:05:52,921 Stage-1 map = 0%, reduce = 0%
2015-07-06 13:05:59,965 Stage-1 map = 50%, reduce = 0%, Cumulative CPU 7.02 sec
2015-07-06 13:06:00,972 Stage-1 map = 50%, reduce = 0%, Cumulative CPU 7.02 sec
2015-07-06 13:06:01,978 Stage-1 map = 50%, reduce = 0%, Cumulative CPU 7.02 sec
2015-07-06 13:06:02,985 Stage-1 map = 100%, reduce = 0%, Cumulative CPU 11.62 sec
2015-07-06 13:06:03,992 Stage-1 map = 100%, reduce = 0%, Cumulative CPU 11.62 sec
2015-07-06 13:06:04,998 Stage-1 map = 100%, reduce = 0%, Cumulative CPU 11.62 sec
2015-07-06 13:06:06,005 Stage-1 map = 100%, reduce = 0%, Cumulative CPU 11.62 sec
2015-07-06 13:06:07,012 Stage-1 map = 100%, reduce = 0%, Cumulative CPU 11.62 sec
2015-07-06 13:06:08,019 Stage-1 map = 100%, reduce = 33%, Cumulative CPU 11.62 sec
2015-07-06 13:06:09,025 Stage-1 map = 100%, reduce = 33%, Cumulative CPU 11.62 sec
2015-07-06 13:06:10,033 Stage-1 map = 100%, reduce = 100%, Cumulative CPU 14.53 sec
2015-07-06 13:06:11,040 Stage-1 map = 100%, reduce = 100%, Cumulative CPU 14.53 sec
2015-07-06 13:06:12,046 Stage-1 map = 100%, reduce = 100%, Cumulative CPU 14.53 sec
MapReduce Total cumulative CPU time: 14 seconds 530 msec
Ended Job = job_201507050950_0001
MapReduce Jobs Launched:
Job 0: Map: 2 Reduce: 1 Cumulative CPU: 14.53 sec HDFS Read: 51040494 HDFS Write: 537 SUCCESS
Total MapReduce CPU Time Spent: 14 seconds 530 msec
OK
1901 45.16831683168317
1902 21.659558263518658
1903 -17.67699115044248
1904 33.32224247948952
1905 43.3322664228014
1906 47.0834855681403
1907 28.09189090243456
1908 28.80607441154138
1909 25.24907112526539
1910 29.00013071895425
1911 28.088644112247575
1912 16.801145236855803
1913 8.191569568197396
1914 26.378301131816624
1915 2.811635615498914
1916 21.42393787117405
1917 22.895140080045742
1918 27.712506047411708
1919 23.67520250849229
1920 43.508667830133795
1921 31.834957020057306
1922 -44.03716409376787
1923 26.79247747159462
Time taken: 33.348 seconds, Fetched: 23 row(s)
select year,max(air) from ncdc group by year;
结果：
hive> select year,max(air) from ncdc group by year;
Total MapReduce jobs = 1
Launching Job 1 out of 1
Number of reduce tasks not specified. Estimated from input data size: 1
In order to change the average load for a reducer (in bytes):
set hive.exec.reducers.bytes.per.reducer=
In order to limit the maximum number of reducers:
set hive.exec.reducers.max=
In order to set a constant number of reducers:
set mapred.reduce.tasks=
Starting Job = job_201507050950_0002, Tracking URL = http://master:50030/jobdetails.jsp?jobid=job_201507050950_0002
Kill Command = /opt/hadoop/hadoop-1.2.1/libexec/../bin/hadoop job -kill job_201507050950_0002
Hadoop job information for Stage-1: number of mappers: 2; number of reducers: 1
2015-07-06 13:07:28,648 Stage-1 map = 0%, reduce = 0%
2015-07-06 13:07:34,675 Stage-1 map = 50%, reduce = 0%, Cumulative CPU 6.57 sec
2015-07-06 13:07:35,681 Stage-1 map = 50%, reduce = 0%, Cumulative CPU 6.57 sec
2015-07-06 13:07:36,687 Stage-1 map = 50%, reduce = 0%, Cumulative CPU 6.57 sec
2015-07-06 13:07:37,693 Stage-1 map = 50%, reduce = 0%, Cumulative CPU 6.57 sec
2015-07-06 13:07:38,699 Stage-1 map = 100%, reduce = 0%, Cumulative CPU 11.26 sec
2015-07-06 13:07:39,705 Stage-1 map = 100%, reduce = 0%, Cumulative CPU 11.26 sec
2015-07-06 13:07:40,711 Stage-1 map = 100%, reduce = 0%, Cumulative CPU 11.26 sec
2015-07-06 13:07:41,716 Stage-1 map = 100%, reduce = 0%, Cumulative CPU 11.26 sec
2015-07-06 13:07:42,722 Stage-1 map = 100%, reduce = 0%, Cumulative CPU 11.26 sec
2015-07-06 13:07:43,727 Stage-1 map = 100%, reduce = 33%, Cumulative CPU 11.26 sec
2015-07-06 13:07:44,734 Stage-1 map = 100%, reduce = 100%, Cumulative CPU 14.04 sec
2015-07-06 13:07:45,740 Stage-1 map = 100%, reduce = 100%, Cumulative CPU 14.04 sec
2015-07-06 13:07:46,746 Stage-1 map = 100%, reduce = 100%, Cumulative CPU 14.04 sec
MapReduce Total cumulative CPU time: 14 seconds 40 msec
Ended Job = job_201507050950_0002
MapReduce Jobs Launched:
Job 0: Map: 2 Reduce: 1 Cumulative CPU: 14.04 sec HDFS Read: 51040494 HDFS Write: 184 SUCCESS
Total MapReduce CPU Time Spent: 14 seconds 40 msec
OK
1901 94
1902 94
1903 94
1904 94
1905 94
1906 94
1907 94
1908 94
1909 94
1910 94
1911 94
1912 94
1913 94
1914 94
1915 94
1916 94
1917 94
1918 94
1919 94
1920 94
1921 94
1922 94
1923 94
Time taken: 26.002 seconds, Fetched: 23 row(s)
select count(*) from ncdc;
hive> select count(*) from ncdc;
Total MapReduce jobs = 1
Launching Job 1 out of 1
Number of reduce tasks determined at compile time: 1
In order to change the average load for a reducer (in bytes):
set hive.exec.reducers.bytes.per.reducer=
In order to limit the maximum number of reducers:
set hive.exec.reducers.max=
In order to set a constant number of reducers:
set mapred.reduce.tasks=
Starting Job = job_201507050950_0004, Tracking URL = http://master:50030/jobdetails.jsp?jobid=job_201507050950_0004
Kill Command = /opt/hadoop/hadoop-1.2.1/libexec/../bin/hadoop job -kill job_201507050950_0004
Hadoop job information for Stage-1: number of mappers: 2; number of reducers: 1
2015-07-06 13:08:56,762 Stage-1 map = 0%, reduce = 0%
2015-07-06 13:09:03,803 Stage-1 map = 50%, reduce = 0%, Cumulative CPU 6.26 sec
2015-07-06 13:09:04,809 Stage-1 map = 100%, reduce = 0%, Cumulative CPU 10.21 sec
2015-07-06 13:09:05,816 Stage-1 map = 100%, reduce = 0%, Cumulative CPU 10.21 sec
2015-07-06 13:09:06,825 Stage-1 map = 100%, reduce = 0%, Cumulative CPU 10.21 sec
2015-07-06 13:09:07,831 Stage-1 map = 100%, reduce = 0%, Cumulative CPU 10.21 sec
2015-07-06 13:09:08,838 Stage-1 map = 100%, reduce = 0%, Cumulative CPU 10.21 sec
2015-07-06 13:09:09,844 Stage-1 map = 100%, reduce = 0%, Cumulative CPU 10.21 sec
2015-07-06 13:09:10,850 Stage-1 map = 100%, reduce = 0%, Cumulative CPU 10.21 sec
2015-07-06 13:09:11,857 Stage-1 map = 100%, reduce = 33%, Cumulative CPU 10.21 sec
2015-07-06 13:09:12,866 Stage-1 map = 100%, reduce = 100%, Cumulative CPU 13.14 sec
2015-07-06 13:09:13,872 Stage-1 map = 100%, reduce = 100%, Cumulative CPU 13.14 sec
2015-07-06 13:09:14,878 Stage-1 map = 100%, reduce = 100%, Cumulative CPU 13.14 sec
MapReduce Total cumulative CPU time: 13 seconds 140 msec
Ended Job = job_201507050950_0004
MapReduce Jobs Launched:
Job 0: Map: 2 Reduce: 1 Cumulative CPU: 13.14 sec HDFS Read: 51040494 HDFS Write: 8 SUCCESS
Total MapReduce CPU Time Spent: 13 seconds 140 msec
OK
1006038
Time taken: 28.984 seconds, Fetched: 1 row(s)
版权声明：本文为博主原创文章，未经博主允许不得转载。
hive 集群初探，查询比较的更多相关文章
- hive集群安装配置
hive 是JAVA写的的一个数据仓库,依赖hadoop.没有安装hadoop的,请参考http://blog.csdn.net/lovemelovemycode/article/details/91 ...
- presto集成kerberos以及访问集成了kerberos的hive集群
1.创建主体 注: 192.168.0.230 为单节点集群 192.168.4.50为kdc服务器 192.168.0.9为客户端 1.1.Kdc服务器创建主体 # kadmin.local -q ...
- PXC集群信息查询
目录 PXC集群信息查询 pxc流量控制 PXC节点状态 PXC集群状态 节点与集群的相关信息 PXC集群事务相关信息 PXC集群信息查询 show status like "%wsrep% ...
- 大数据学习系列之七 ----- Hadoop+Spark+Zookeeper+HBase+Hive集群搭建 图文详解
引言 在之前的大数据学习系列中,搭建了Hadoop+Spark+HBase+Hive 环境以及一些测试.其实要说的话,我开始学习大数据的时候,搭建的就是集群,并不是单机模式和伪分布式.至于为什么先写单 ...
- HADOOP+SPARK+ZOOKEEPER+HBASE+HIVE集群搭建(转)
原文地址:https://www.cnblogs.com/hanzhi/articles/8794984.html 目录 引言 目录 一环境选择 1集群机器安装图 2配置说明 3下载地址 二集群的相关 ...
- elasticsearch 口水篇（5）es分布式集群初探
es有很多特性,分布式.副本集.负载均衡.容灾等. 我们先搭建一个很简单的分布式集群(伪),在同一机器上配置三个es,配置分别如下: cluster.name: foxCluster node.nam ...
- [经验交流] Apache Mesos Docker集群初探
前言 因工作需要,我对基于Apache Mesos 的 Docker 集群作了一点研究,并搭建了一套环境,以下是资料分享. 1. Apache Mesos概述 Apache Mesos是一款开源群集管 ...
- 使用hive客户端java api读写hive集群上的信息
上文介绍了hdfs集群信息的读取方式,本文说hive 1.先解决依赖 <properties> <hive.version>1.2.1</hive.version> ...
- hive集群模式安装
hadoop3.2.0 完全分布式安装 hive-3.1.1 #解压缩tar -zxvf /usr/local/soft/apache-hive-3.1.1-bin.tar.gz -C /usr/lo ...
随机推荐
- 【TFS】解决TFS编译中文乱码问题
前言; TFS2018做程序集成非常方便,线上编译然后直接生成docker镜像,但是在使用过程中遇到编译窗口中文乱码的问题,这个问题找了好久没人知道怎么解决.如下: 这个问题不解决,每次编译失败,研发 ...
- c#命名规则参考
命名规则参考:1.从组件类型名中移去T前缀.例如TButton变成Button.2.除了第一个元音,删去所有元音字母.例如,Button变成bttn,Edit变成edt.3.压缩双字母.例如,bttn ...
- php生成各种验证码
片段 1 片段 2 片段 3 index.html ```<script type="text/javascript" src="jquery.min.js&quo ...
- shell单例-处理方案
shell单例:当某一个shell脚本需要重复执行时(shell定时任务 etc),为了避免多个相同任务之间交叉,造成数据的混乱或者错误,需要脚本单例执行. 就是前一个进程执行时,后一个进程需要阻塞等 ...
- Spring Cloud之Feign客户端调用工具
feign介绍 Feign客户端是一个web声明式http远程调用工具,提供了接口和注解方式进行调用. Spring Cloud 支持 RestTemplate Fetin Feign客户端实际开发 ...
- linux 压缩和归档
在linux下有几种压缩方式:gzip.bzip2.xz.zip gzip 压缩文件以.gz结尾, 只能压缩文件,不能压缩目录 用法: gzip:/path/to/somefile 用来压缩,完成 ...
- HDU 2089 不要62：数位dp
题目链接:http://acm.hdu.edu.cn/showproblem.php?pid=2089 题意: 问你在区间[n,m]中,有多少个数字不含"4"且不含"62 ...
- python ddt 重写
对此方法重写 def mk_test_name(name, value, index=0): 重写前 index = "{0:0{1}}".format(index + 1, in ...
- POJ 3349 Snowflake Snow Snowflakes（哈希表）
题意:判断有没有两朵相同的雪花.每朵雪花有六瓣,比较花瓣长度的方法看是否是一样的,如果对应的arms有相同的长度说明是一样的.给出n朵,只要有两朵是一样的就输出有Twin snowflakes fou ...
- 串行总线 —— I2C、UART、SPI
I2C,也叫 IIC,是一种常见的串行总线,它只需要两根线即可在连接于总线上的器件之间传送信息. 0. 电气知识 开漏输出:Open drain output,不输出电压,低电平时接地,高电平时不接地 ...