docker-hive的操作验试

1.下载docker镜像库:https://github.com/big-data-europe/docker-hive.git,并安装它。
2.修改其docker-compose.yml文件,为每个容器增加上映射。

version: "3"
 
services:
  namenode:
    image: bde2020/hadoop-namenode:2.0.0-hadoop2.7.4-java8
    volumes:
      - /data/namenode:/hadoop/dfs/name
      - /data/tools:/tools
    environment:
      - CLUSTER_NAME=test
    env_file:
      - ./hadoop-hive.env
    ports:
      - "50070:50070"
  datanode:
    image: bde2020/hadoop-datanode:2.0.0-hadoop2.7.4-java8
    volumes:
      - /data/datanode:/hadoop/dfs/data
      - /data/tools:/tools
    env_file:
      - ./hadoop-hive.env
    environment:
      SERVICE_PRECONDITION: "namenode:50070"
    ports:
      - "50075:50075"
  hive-server:
    image: bde2020/hive:2.3.2-postgresql-metastore
    volumes:
      - /data/tools:/tools
    env_file:
      - ./hadoop-hive.env
    environment:
      HIVE_CORE_CONF_javax_jdo_option_ConnectionURL: "jdbc:postgresql://hive-metastore/metastore"
      SERVICE_PRECONDITION: "hive-metastore:9083"
    ports:
      - "10000:10000"
  hive-metastore:
    image: bde2020/hive:2.3.2-postgresql-metastore
    volumes:
      - /data/tools:/tools
    env_file:
      - ./hadoop-hive.env
    command: /opt/hive/bin/hive --service metastore
    environment:
      SERVICE_PRECONDITION: "namenode:50070 datanode:50075 hive-metastore-postgresql:5432"
    ports:
      - "9083:9083"
  hive-metastore-postgresql:
    image: bde2020/hive-metastore-postgresql:2.3.0
    volumes:
      - /data/tools:/tools
 
  presto-coordinator:
    image: shawnzhu/prestodb:0.181
    volumes:
      - /data/tools:/tools
    ports:
      - "8080:8080"

2.创建测试文本

1,xiaoming,book-TV-code,beijing:chaoyang-shagnhai:pudong
2,lilei,book-code,nanjing:jiangning-taiwan:taibei
3,lihua,music-book,heilongjiang:haerbin
3,lihua,music-book,heilongjiang2:haerbin2
3,lihua,music-book,heilongjiang3:haerbin3

3.启动并连接HIVE服务。

docker-compose up -d
docker-compose exec hive-server bash
/opt/hive/bin/beeline -u jdbc:hive2://localhost:10000


4.创建外部表

create external table t2(
    id      int
   ,name    string
   ,hobby   array<string>
   ,add     map<String,string>
)
row format delimited
fields terminated by ','
collection items terminated by '-'
map keys terminated by ':'
location '/user/t2'


5.文件上传到上步骤中的目录内。
方法1:在HIVE的beeline终端中采用:
load data local inpath ‘/tools/example.txt’ overwrite into table t2; 删除已经存在的所有文件,然后写入新的文件。
load data local inpath ‘/tools/example.txt’ into table t2; 在目录中加入新的文件【差异在overwrite】。
方法2:用hadoop fs -put的文件上传功能。
hadoop fs -put /tools/example.txt /user/t2 文件名不改变。
hadoop fs -put /tools/example.txt /user/t2/1.txt 文件名为1.txt
6.在HIVE命令行中验证

select * from t2;  上传一次文件,执行一次。


7.在hadoop的文件管理器,也可以浏览到新上传的文件。

同一个文件中的记录是会自动作去重处理的。

——————————————-
如果是sequencefile呢?
1.检验sequencefile的内容。
hadoop fs -Dfs.default.name=file:/// -text /tools/mytest.gzip.sf 废弃的
hadoop fs -Dfs.defaultFS=file:/// -text /tools/mytest.txt.sf

实际内容是:

2.建表

  create external table sfgz(
     `idx` string,
     `userid` string,
     `flag` string,
     `count` string,
     `value` string,
     `memo` string)
  partitioned by (dt string)
  row format delimited fields terminated by ','
  stored as sequencefile
  location '/user/sfgz';

3.上传文件

方法一:
hadoop fs -mkdir -p /user/sfgz/dt=2010-05-06/
hadoop fs -put /tools/mytest.txt.sf /user/sfgz/dt=2019-05-17
hadoop fs -put /tools/mytest.txt.sf /user/sfgz/dt=2010-05-04
这种方法,还需要人为Reload一下才行,其reload指令是:
方法二:
load data local inpath '/tools/mytest.txt.sf' into table sfgz partition(dt='2009-03-01');这种方法是可以直接查询了。
load data local inpath '/tools/mytest.gzip.sf' into table sfgz partition(dt='2000-03-02');