CDH5.13快速体验

相对于易用性很差Apache Hadoop，其他商业版Hadoop的性能易用性都有更好的表现，如Cloudera、Hortonworks、MapR以及国产的星环，下面使用CDH(Cloudera Distribution Hadoop)快速体验下。

首先从，从Cloudera官网下载部署好的虚拟机环境https://www.cloudera.com/downloads/quickstart_vms/5-13.html.html，解压后用虚拟机打开，官方推荐至少8G内存2cpu，由于笔记本性能足够，我改为8G内存8cpu启动，虚拟机各种账号密码都是cloudera

打开虚拟机的浏览器访问http://quickstart.cloudera/#/

点击Get Started以体验

Tutorial Exercise 1：导入、查询关系数据

利用sqoop工具将mysql数据导入HDFS中

[cloudera@quickstart ~]$ sqoop import-all-tables 
>     -m 1 
>     --connect jdbc:mysql://quickstart:3306/retail_db 
>     --username=retail_dba 
>     --password=cloudera 
>     --compression-codec=snappy 
>     --as-parquetfile 
>     --warehouse-dir=/user/hive/warehouse 
>     --hive-import
Warning: /usr/lib/sqoop/../accumulo does not exist! Accumulo imports will fail.
Please set $ACCUMULO_HOME to the root of your Accumulo installation.
19/04/29 18:31:46 INFO sqoop.Sqoop: Running Sqoop version: 1.4.6-cdh5.13.0
19/04/29 18:31:46 WARN tool.BaseSqoopTool: Setting your password on the command-line is insecure. Consider using -P instead.
19/04/29 18:31:46 INFO tool.BaseSqoopTool: Using Hive-specific delimiters for output. You can override
19/04/29 18:31:46 INFO tool.BaseSqoopTool: delimiters with --fields-terminated-by, etc.
19/04/29 18:31:46 WARN tool.BaseSqoopTool: It seems that you're doing hive import directly into default

(many more lines suppressed)

                Failed Shuffles=0
                Merged Map outputs=0
                GC time elapsed (ms)=87
                CPU time spent (ms)=3690
                Physical memory (bytes) snapshot=443174912
                Virtual memory (bytes) snapshot=1616969728
                Total committed heap usage (bytes)=352845824
        File Input Format Counters 
                Bytes Read=0
        File Output Format Counters 
                Bytes Written=0
19/04/29 18:38:27 INFO mapreduce.ImportJobBase: Transferred 46.1328 KB in 85.1717 seconds (554.6442 bytes/sec)
19/04/29 18:38:27 INFO mapreduce.ImportJobBase: Retrieved 1345 records.
[cloudera@quickstart ~]$ hadoop fs -ls /user/hive/warehouse/
Found 6 items
drwxrwxrwx   - cloudera supergroup          0 2019-04-29 18:32 /user/hive/warehouse/categories
drwxrwxrwx   - cloudera supergroup          0 2019-04-29 18:33 /user/hive/warehouse/customers
drwxrwxrwx   - cloudera supergroup          0 2019-04-29 18:34 /user/hive/warehouse/departments
drwxrwxrwx   - cloudera supergroup          0 2019-04-29 18:35 /user/hive/warehouse/order_items
drwxrwxrwx   - cloudera supergroup          0 2019-04-29 18:36 /user/hive/warehouse/orders
drwxrwxrwx   - cloudera supergroup          0 2019-04-29 18:38 /user/hive/warehouse/products
[cloudera@quickstart ~]$ hadoop fs -ls /user/hive/warehouse/categories/
Found 3 items
drwxr-xr-x   - cloudera supergroup          0 2019-04-29 18:31 /user/hive/warehouse/categories/.metadata
drwxr-xr-x   - cloudera supergroup          0 2019-04-29 18:32 /user/hive/warehouse/categories/.signals
-rw-r--r--   1 cloudera supergroup       1957 2019-04-29 18:32 /user/hive/warehouse/categories/6e701a22-4f74-4623-abd1-965077105fd3.parquet
[cloudera@quickstart ~]$ 

然后访问http://quickstart.cloudera:8888/，来访问表（invalidate metadata;是用来刷新元数据的）

Tutorial Exercise 2 :外部表方式导入访问日志数据到HDFS并查询

通过hive建表

CREATE EXTERNAL TABLE intermediate_access_logs (
    ip STRING,
    date STRING,
    method STRING,
    url STRING,
    http_version STRING,
    code1 STRING,
    code2 STRING,
    dash STRING,
    user_agent STRING)
ROW FORMAT SERDE 'org.apache.hadoop.hive.contrib.serde2.RegexSerDe'
WITH SERDEPROPERTIES (
    'input.regex' = '([^ ]*) - - \[([^\]]*)\] "([^ ]*) ([^ ]*) ([^ ]*)" (\d*) (\d*) "([^"]*)" "([^"]*)"',
    'output.format.string' = "%1$$s %2$$s %3$$s %4$$s %5$$s %6$$s %7$$s %8$$s %9$$s")
LOCATION '/user/hive/warehouse/original_access_logs';

CREATE EXTERNAL TABLE tokenized_access_logs (
    ip STRING,
    date STRING,
    method STRING,
    url STRING,
    http_version STRING,
    code1 STRING,
    code2 STRING,
    dash STRING,
    user_agent STRING)
ROW FORMAT DELIMITED FIELDS TERMINATED BY ','
LOCATION '/user/hive/warehouse/tokenized_access_logs';

ADD JAR /usr/lib/hive/lib/hive-contrib.jar;

INSERT OVERWRITE TABLE tokenized_access_logs SELECT * FROM intermediate_access_logs;

impala中刷新元数据后访问表

Tutorial Exercise 3:使用spark进行关联分析

Tutorial Exercise 4:利用flume收集日志，并用solr做全文索引

Tutorial Exercise 5：可视化

Tutorial is over!