Clickhouse数据库部署、Python3压测实践

Clickhouse数据库部署、Python3压测实践

一、Clickhouse数据库部署
  • 版本:yandex/clickhouse-server:latest

  • 部署方式:docker

  • 内容

    yml 复制代码
    version: "3"
    
    services:
      clickhouse:
        image: yandex/clickhouse-server:latest
        container_name: clickhouse    
        ports:
          - "8123:8123"
          - "9000:9000"
          - "9009:9009"
          - "9004:9004"
        volumes:
          - ./data/config:/var/lib/clickhouse
        ulimits:
          nproc: 65535
          nofile:
            soft: 262144
            hard: 262144
        healthcheck:
          test: ["CMD", "wget", "--spider", "-q", "localhost:8123/ping"]
          interval: 30s
          timeout: 5s
          retries: 3
        deploy:
          resources:
            limits:
              cpus: '4'
              memory: 4096M
            reservations:
              memory: 4096M
  • 建表语句

    CREATE TABLE test_table (id int,
        feild1 String, feild2 String, feild3 String
        , feild4 String, feild5 String, feild6 String
        , feild7 String, feild8 String, feild9 String
        , feild10 String, feild11 String, feild12 String
        , feild13 String, feild14 String, feild15 String
        , feild16 String, feild17 String, feild18 String
        , feild19 String, feild20 String
        ) ENGINE = MergeTree:
    
二、Python3插入数据压测
  • 关键库:clickhouse_driver、 concurrent.futures

  • 代码:

    python 复制代码
    import random
    import time
    from clickhouse_driver import Client
    from concurrent.futures import ThreadPoolExecutor, as_completed
    
    
    client = Client(host='ip')
    
    # 采用多个连接,避免单个连接被打死
    clients = [
        Client(host='ip'),
        Client(host='ip'),
        Client(host='ip'),
        Client(host='ip')
    ]
    
    
    # 采用批量插入,经过测试,单条并发插入支持差,每秒只能执行2-5次insert
    def task(i):
        sql = "INSERT INTO ck_table (id, feild1, feild2,feild3,feild4,feild5,feild6,feild7,feild8,feild9,feild10,feild11,feild12,feild13,feild14,feild15,feild16,feild17,feild18,feild19,feild20) VALUES"
        values = []
        for i in range(1000):
            values.append((random.randint(1,10000000),"feild1-"+str((random.randint(1,10000000))),"feild2-"+str(i),"feild3-"+str(i), "feild4-"+str(i), "feild5-"+str(i), "feild6-"+str(i), "feild7-"+str(i)
                           , "feild8-"+str(i), "feild9-"+str(i), "feild10-"+str(i), "feild11-"+str(i), "feild12-"+str(i), "feild13-"+str(i), "feild14-"+str(i)
                           , "feild15-"+str(i), "feild16-"+str(i), "feild17-"+str(i), "feild18-"+str(i), "feild19-"+str(i)
                           , "feild20-"+str(i)
                           ))
        clid = random.randint(1, len(clients)-1)
        clients[clid].execute(sql, values)
        return '第',clid, "插入",i, '条数据成功'
    
    
    if __name__ == '__main__':
        print ("程序开始运行")
        exec = ThreadPoolExecutor(max_workers=2)
        #ress = []
        start_time = time.perf_counter()
        for j in range(4000000):  # 总共需要执行的次数
            res = exec.submit(task,j)
            #ress.append(res)
        # for i in as_completed(ress):
        #     print("执行状态",i.result())
        print("执行耗时", time.perf_counter()-start_time,"s")
三、Python3查询数据测试
  • 关键库:clickhouse_driver、 concurrent.futures

  • 代码

    python 复制代码
    import time
    from concurrent.futures import ThreadPoolExecutor, as_completed
    from clickhouse_driver import Client
    
    client = Client(host='10.10.16.110')
    
    query_sql = """select * from ck_table where feild2='feild2-1009' """
    
    
    def new_task(i):
        count_sql = """ select count(*) from ck_table"""
        time.sleep(1)
        return "执行第",i,"个任务",client.execute(count_sql)
    
    
    if __name__ == '__main__':
        print ("程序开始运行")
        thd_ques = []
        exec = ThreadPoolExecutor(max_workers=1)
        ress = []
        start_time = time.perf_counter()
        for j in range(1000):
            res = exec.submit(new_task,j)
            ress.append(res)
        for i in as_completed(ress):
            print("执行状态",i.result())
        print("执行耗时", time.perf_counter()-start_time,"s")
四、测试结论

clickhouse:21个字段表插入-查询测试, CPU200w数据以内 >100,峰值:133.6, 均值: 约110

  • 1、不支持频繁插入(一般1-2次/s),否则会断联等报错,只能批插入(脚本使用2协程每次1000条没有报错,2个协程或者以上会出现断联等报错)

  • 2、不支持频发查询,QPS官方建议100以内,否则CPU占用会很高,拉高服务器负载

  • 3、查询效率:

    • 一个条件where查询(Memery):60W 0.33s

    • 5个条件where查询(Memery):80W 0.57s

    • 5个条件where查询(Memery):100W 0.54s

    • 5个条件where查询(Memery):112W 0.56s

    • 5个条件where查询(Memery):200W 0.565s

    • 5个条件where查询(Memery):500W 1.2s(停止插入的情况下)

    • 5个条件where查询(Memery):560W 1.97s(停止插入的情况下)

    • 5个条件where查询(TinyLog):7000W条 1分47秒

    • 2个条件where查询(TinyLog):1亿零460万条 89s

    • 5个条件where查询(TinyLog):1亿零460万条 84s

    • 10个条件where查询(TinyLog):1亿零460万条 87s

备注 450w条数据后,数据插入线程和查询线程只能存在一个,慢查询的内存消耗很高,16G内存不够用。5个条件where查询还能执行,在1-2s

  • (1)500w数据量服务器情况:(COPU均值在320左右,16G内存剩余在500-800M之间,停止写入/查询后,CPU恢复正常水平,内存剩余在800M左右)

    total used free shared buff/cache available

    15G 5.9G 519M 9.2M 9.1G 9.2G

    %CPU %MEM

    429.5 26.0

  • (2)1亿数据量服务器情况(1T磁盘消耗共38%,预计消耗6% )

    total used free shared buff/cache available

    15G 2.7G 181M 9.2M 12G 12G

    %CPU %MEM

    103.7 3.6

总结:

  • 1、不支持并发单条频繁插入,否则会报错,断联等造成数据丢失
  • 2、不支持高并发查询,官方建议QPS<= 100,否则会增加服务器负载,CPU,内存等消耗过高
  • 3、对服务器要求高,亿级CPU一般建议16核心以上,内存64G以上
  • 4、优点是查询快,批量插入效率高,建议低频大批量插入
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