KUDU数据导入尝试一：TextFile数据导入Hive,Hive数据导入KUDU

背景

1. SQLSERVER数据库中单表数据几十亿，分区方案也已经无法查询出结果。故：采用导出功能，导出数据到Text文本(文本>40G)中。
2. 因上原因，所以本次的实验样本为：【数据量：61w条，文本大小：74M】

选择DataX原因

1. 试图维持统一的异构数据源同步方案。（其实行不通）
2. 试图进入Hive时，已经是压缩ORC格式，降低存储大小，提高列式查询效率，以便后续查询HIVE数据导入KUDU时提高效率（其实行不通）

1. 建HIVE表

进入HIVE,必须和TextFile中的字段类型保持一致

 create table event_hive_3(
`#auto_id` string
,`#product_id` int
,`#event_name` string
,`#part_date` int
,`#server_id` int
,`#account_id` bigint
,`#user_id` bigint
,part_time STRING
,GetItemID bigint
,ConsumeMoneyNum bigint
,Price bigint
,GetItemCnt bigint
,TaskState bigint
,TaskType bigint
,BattleLev bigint
,Level bigint
,ItemID bigint
,ItemCnt bigint
,MoneyNum bigint
,MoneyType bigint
,VIP bigint
,LogID bigint
)
ROW FORMAT DELIMITED
FIELDS TERMINATED BY '\t'
STORED AS ORC;

2. 建Kudu表

这个过程,自行发挥~

#Idea中，执行单元测试【EventAnalysisRepositoryTest.createTable()】即可
public void createTable() throws Exception {
        repository.getClient();
        repository.createTable(Event_Sjmy.class,true);
}

3. 建立Impala表

进入Impala-shell 或者hue;

use sd_dev_sdk_mobile;
CREATE EXTERNAL TABLE `event_sjmy_datax` STORED AS KUDU
TBLPROPERTIES(
    'kudu.table_name' = 'event_sjmy_datax',
    'kudu.master_addresses' = 'sdmain:7051')

4. 编辑Datax任务

不直接load进hive的目的是为了进行一步文件压缩，降低内存占用，转为列式存储。

# 编辑一个任务
vi /home/jobs/textToHdfs.json;
{
    "setting": {},
    "job": {
        "setting": {
            "speed": {
                "channel": 2
            }
        },
        "content": [
            {
                "reader": {
                    "name": "txtfilereader",
                    "parameter": {
                        "path": ["/home/data"],
                        "encoding": "GB2312",
                        "column": [
                            {
                                "index": 0,
                                "type": "string"
                            },
                            {
                                "index": 1,
                                "type": "int"
                            },
                            {
                                "index": 2,
                                "type": "string"
                            },
                            {
                                "index": 3,
                                "type": "int"
                            },
                            {
                                "index": 4,
                                "type": "int"
                            },
							{
                                "index": 5,
                                "type": "long"
                            },
							{
                                "index": 6,
                                "type": "long"
                            },
							{
                                "index": 7,
                                "type": "string"
                            },
							{
                                "index": 8,
                                "type": "long"
                            },
							{
                                "index": 9,
                                "type": "long"
                            },
							{
                                "index": 10,
                                "type": "long"
                            },{
                                "index": 11,
                                "type": "long"
                            },{
                                "index": 12,
                                "type": "long"
                            },
							{
                                "index": 13,
                                "type": "long"
                            },
							{
                                "index": 14,
                                "type": "long"
                            },
							{
                                "index": 15,
                                "type": "long"
                            },
							{
                                "index": 17,
                                "type": "long"
                            },
							{
                                "index": 18,
                                "type": "long"
                            },
							{
                                "index": 19,
                                "type": "long"
                            },
							{
                                "index": 20,
                                "type": "long"
                            },
							{
                                "index": 21,
                                "type": "long"
                            }

                        ],
                        "fieldDelimiter": "/t"
                    }
                },
                 "writer": {
                    "name": "hdfswriter",
                    "parameter": {
                        "column": [{"name":"#auto_id","type":" STRING"},{"name":"#product_id","type":" int"},{"name":"#event_name","type":" STRING"},{"name":"#part_date","type":"int"},{"name":"#server_id","type":"int"},{"name":"#account_id","type":"bigint"},{"name":"#user_id","type":" bigint"},{"name":"part_time","type":" STRING"},{"name":"GetItemID","type":" bigint"},{"name":"ConsumeMoneyNum","type":"bigint"},{"name":"Price ","type":"bigint"},{"name":"GetItemCnt ","type":"bigint"},{"name":"TaskState ","type":"bigint"},{"name":"TaskType ","type":"bigint"},{"name":"BattleLev ","type":"bigint"},{"name":"Level","type":"bigint"},{"name":"ItemID ","type":"bigint"},{"name":"ItemCnt ","type":"bigint"},{"name":"MoneyNum ","type":"bigint"},{"name":"MoneyType ","type":"bigint"},{"name":"VIP ","type":"bigint"},{"name":"LogID ","type":"bigint"}],
                        "compress": "NONE",
                        "defaultFS": "hdfs://sdmain:8020",
                        "fieldDelimiter": "\t",
                        "fileName": "event_hive_3",
                        "fileType": "orc",
                        "path": "/user/hive/warehouse/dataxtest.db/event_hive_3",
                        "writeMode": "append"
                    }
                }
            }
        ]
    }
}

4.1 执行datax任务

注意哦,数据源文件，先放在/home/data下哦。数据源文件必须是个数据二维表。

#textfile中数据例子如下:
{432297B4-CA5F-4116-901E-E19DF3170880}	701	获得筹码	201906	2	4974481	1344825	00:01:06	0	0	0	0	0	0	0	0	0	0	100	2	3	31640
{CAAF09C6-037D-43B9-901F-4CB5918FB774}	701	获得筹码	201906	2	5605253	1392330	00:02:25	0	0	0	0	0	0	0	0	0	0	390	2	10	33865

cd $DATAX_HOME/bin
python datax.py /home/job/textToHdfs.json

效果图：

使用Kudu从HIVE读取写入到Kudu表中

进入shell

#进入shell:
impala-shell;
#选中库--如果表名有指定库名，可省略
use sd_dev_sdk_mobile;
输入SQL:
    INSERT INTO sd_dev_sdk_mobile.event_sjmy_datax
    SELECT `#auto_id`,`#event_name`,`#part_date`,`#product_id`,`#server_id`,`#account_id`,`#user_id`,part_time,GetItemID,ConsumeMoneyNum,Price,GetItemCnt,TaskState,TaskType,BattleLev,Level,ItemID,ItemCnt,MoneyNum,MoneyType,VIP,LogID
    FROM event_hive_3 ;

效果图：

看看这可怜的结果

这速度难以接受,我选择放弃。

打脸环节-原因分析：

1. DataX读取TextFile到HIVE中的速度慢： DataX对TextFile的读取是单线程的，（2.0版本后可能会提供多线程ReaderTextFile的能力），这直接浪费了集群能力和12核的CPU。且，文件还没法手动切割任务分节点执行。
2. Hive到KUDU的数据慢：insert into xxx select * 这个【*】一定要注意，如果读取所有列，那列式查询的优势就没多少了，所以，转ORC多此一举。
3. Impala读取HIVE数据时，内存消耗大！
   
   唯一的好处： 降低硬盘资源的消耗（74M文件写到HDFS，压缩后只有15M），但是！！！这有何用？我要的是导入速度！如果只是为了压缩，应该Load进Hive，然后启用Hive的Insert到ORC新表，充分利用集群资源！

代码如下

//1. 数据加载到textfile表中
load data inpath '/home/data/event-19-201906.txt' into table event_hive_3normal;
//2. 数据查询出来写入到ORC表中。
insert into event_hive_3orc
select * from event_hive_3normal

实验失败~

优化思路：1.充分使用集群的CPU资源

2.避免大批量数据查询写入

优化方案：掏出我的老家伙，单Flume读取本地数据文件sink到Kafka, 集群中多Flume消费KAFKA集群，sink到Kudu ！下午见！