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Apache-2.0

数据生成器

如果你在从事大数据BI的工作,想对比一下MySQL、GreenPlum、Elasticsearch、Hive、Spark SQL、Presto、Impala、Drill、HAWQ、Druid、Pinot、Kylin、ClickHouse等不同实现方案之间的表现,那你就需要一份标准的数据进行测试,这个开源项目就是为了生成这样的标准数据。

数据模型:src/main/resources/数据模型.png

src/main/resources/数据模型.png

一、编译程序:

 mvn assembly:assembly

二、在MySQL中创建一个数据库,然后执行 src/main/resources/model_ddl.sql 创建对应的表。

三、指定ES的经纬度类型:

curl -H "Content-Type: application/json" -XPUT 'http://192.168.252.193:9200/contract/contract/_bulk' -d '
{ "index":{ "_id": 1} }
{"id":1}
'

curl -H "Content-Type: application/json" -XPUT 'http://192.168.252.193:9200/contract/_mapping/contract' -d '
{
  "properties": {
    "geo_location": {
      "type": "geo_point"
    }
  }
}
'

curl -H "Content-Type: application/json" -XPUT 'http://192.168.252.193:9200/detail/detail/_bulk' -d '
{ "index":{ "_id": 1} }
{"id":1}
'

curl -H "Content-Type: application/json" -XPUT 'http://192.168.252.193:9200/detail/_mapping/detail' -d '
{
  "properties": {
    "geo_location": {
      "type": "geo_point"
    }
  }
}
'

curl -H "Content-Type: application/json" -XPUT 'http://192.168.252.193:9200/area/area/_bulk' -d '
{ "index":{ "_id": 1} }
{"id":1}
'

curl -H "Content-Type: application/json" -XPUT 'http://192.168.252.193:9200/area/_mapping/area' -d '
{
  "properties": {
    "geo_location": {
      "type": "geo_point"
    }
  }
}
'

curl -H "Content-Type: application/json" -XPUT 'http://192.168.252.193:9200/customer/customer/_bulk' -d '
{ "index":{ "_id": 1} }
{"id":1}
'

curl -H "Content-Type: application/json" -XPUT 'http://192.168.252.193:9200/customer/_mapping/customer' -d '
{
  "properties": {
    "geo_location": {
      "type": "geo_point"
    }
  }
}
'

curl -H "Content-Type: application/json" -XPUT 'http://192.168.252.193:9200/sales_staff/sales_staff/_bulk' -d '
{ "index":{ "_id": 1} }
{"id":1}
'

curl -H "Content-Type: application/json" -XPUT 'http://192.168.252.193:9200/sales_staff/_mapping/sales_staff' -d '
{
  "properties": {
    "geo_location": {
      "type": "geo_point"
    }
  }
}
'

四、在当前目录下的config.txt文件中指定配置:

#新增数据是MySQL批量提交记录数量
batchSize=1000
#订单时间开始年份
startYear=2000
#订单时间开始月份
startMonth=1
#订单时间开始天数
startDay=1
#客户数
customerCount=5000
#销售数
salesStaffCount=2000
#合同数
contractCount=20000
#商品数
itemCount=10000
#商品价格上限
priceLimit=1000
#合同最大明细数
contractDetailLimit=100
#合同明细商品最大数量
itemQuantityLimit=100
#将生成的数据保存到哪个MySQL
mysql.url=jdbc:mysql://192.168.252.193:3306/demo?useUnicode=true&characterEncoding=utf8
mysql.user=root
mysql.password=root
mysql.pageSize=10000
#将MySQL里面的数据查出来组装成JSON文档后索引到哪个ES
es.host=192.168.252.193
es.port=9200
#ES批量提交数量
es.batchSize=1000
#可选值为file或者es
#如果选择file,则在当前目录想生成相应的脚本文件,等程序执行完毕后再执行脚本文件将数据索引到ES
#如果选择es,则在数据生成完毕后直接在程序中把数据提交给ES进行索引
es.mode=es
#是否异步多线程的方式进行ES索引
output.async=true
#如果是异步多线程的方式进行ES索引,则需要几个线程
output.async.thread.count=10
#如果ES索引中断,再次索引的时候从哪一页开始索引,0代表第一页
output.start.page=0

五、运行程序:

all in one:

nohup java -Xmx2g -Xms2g -cp data-generator-1.0-jar-with-dependencies.jar org.apdplat.data.generator.Start &

或者

step by step:

1. 生成模拟数据并保存到mysql:

    nohup java -Xmx2g -Xms2g -cp data-generator-1.0-jar-with-dependencies.jar org.apdplat.data.generator.generator.Generator &

2. 将mysql中的数据生成合同文档并提交给ES:

    nohup java -Xmx2g -Xms2g -cp data-generator-1.0-jar-with-dependencies.jar org.apdplat.data.generator.mysql2es.Contract &
    如果es.mode=es则不需要执行如下两步, 只有es.mode=file才需要执行
    chmod +x contract.sh
    nohup ./contract.sh &

3. 将mysql中的数据生成合同明细文档并提交给ES:

    nohup java -Xmx2g -Xms2g -cp data-generator-1.0-jar-with-dependencies.jar org.apdplat.data.generator.mysql2es.ContractDetail &
    如果es.mode=es则不需要执行如下两步, 只有es.mode=file才需要执行
    chmod +x detail.sh
    nohup ./detail.sh &

4. 将mysql中的数据生成区域文档并提交给ES:

    nohup java -Xmx1g -Xms1g -cp data-generator-1.0-jar-with-dependencies.jar org.apdplat.data.generator.mysql2es.Area &
    如果es.mode=es则不需要执行如下两步, 只有es.mode=file才需要执行
    chmod +x area.sh
    nohup ./area.sh &

5. 将mysql中的数据生成商品文档并提交给ES:

    nohup java -Xmx2g -Xms2g -cp data-generator-1.0-jar-with-dependencies.jar org.apdplat.data.generator.mysql2es.Item &
    如果es.mode=es则不需要执行如下两步, 只有es.mode=file才需要执行
    chmod +x item.sh
    nohup ./item.sh &

6. 将mysql中的数据生成客户文档并提交给ES:

    nohup java -Xmx2g -Xms2g -cp data-generator-1.0-jar-with-dependencies.jar org.apdplat.data.generator.mysql2es.Customer &
    如果es.mode=es则不需要执行如下两步, 只有es.mode=file才需要执行
    chmod +x customer.sh
    nohup ./customer.sh &

7. 将mysql中的数据生成销售文档并提交给ES:

    nohup java -Xmx2g -Xms2g -cp data-generator-1.0-jar-with-dependencies.jar org.apdplat.data.generator.mysql2es.SalesStaff &
    如果es.mode=es则不需要执行如下两步, 只有es.mode=file才需要执行
    chmod +x sales_staff.sh
    nohup ./sales_staff.sh &

8. 将mysql中的数据生成品牌文档并提交给ES:

    nohup java -Xmx2g -Xms2g -cp data-generator-1.0-jar-with-dependencies.jar org.apdplat.data.generator.mysql2es.Brand &
    如果es.mode=es则不需要执行如下两步, 只有es.mode=file才需要执行
    chmod +x brand.sh
    nohup ./brand.sh &

9. 将mysql中的数据生成分类文档并提交给ES:

    nohup java -Xmx2g -Xms2g -cp data-generator-1.0-jar-with-dependencies.jar org.apdplat.data.generator.mysql2es.Category &
    如果es.mode=es则不需要执行如下两步, 只有es.mode=file才需要执行
    chmod +x category.sh
    nohup ./category.sh &

六、在hive中执行 src/main/resources/hive_ddl.sql 创建表。

七、执行 src/main/resources/sqoop.txt 中的命令将MySQL中的数据导入Hive。

八、在Kylin中导入Hive的表、创建Model和Cube、构建Cube。

九、在Kibana中创建索引模式,创建图表。

十、对MySQL、Kibana+ES、Kylin进行对比如下统计:

SELECT
    item. NAME ,
    sum(contract_detail.price) AS total_price ,
    sum(contract_detail.item_quantity) AS total_quantity
FROM
    contract_detail
LEFT JOIN item ON contract_detail.item_id = item.id
GROUP BY
    item. NAME
ORDER BY
    total_quantity DESC 
    
Kylin耗时0.5秒,MySQL59秒,ES5秒。
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简介

数据生成器 -- 如果你在从事大数据BI的工作,想对比一下MySQL、GreenPlum、Elasticsearch、Hive、Presto、Impala、Drill、HAWQ、Druid、Pinot、Kylin等不同实现方案之间的表现,那你就需要一份标准的数据进行测试,这个开源项目就是为了生成这样的标准数据。 展开 收起
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