mongodb脚本执行

1.编写脚本:job.js,内容如下:

conn = new Mongo("mongodb://abc:b=abc@10.0.1.183:27017,10.0.1.184:27017/wps_credit?maxPoolSize=300&replicaSet=c45134ec-6a5f-461e-8745-7081b46b0d87");
db = conn.getDB("abc");
 
var result = db.transaction_5.find().limit(10);
while(result.hasNext()) {
    printjson(result.next());
}
var tm = new Date();
printjson(db.transaction_6.count());
db.transaction_6.remove({});
printjson(db.transaction_7.count());
db.transaction_7.remove({});
printjson(db.transaction_8.count());
db.transaction_8.remove({});
 
var t2 = new Date().getTime() - t1;
printjson(t2);

2.执行它

mongo -nodb job.js

mongodb主从数据库同步

简单点说,就是从库都是新的mongodb,通过主从切换完成空间的清理。
————————-
1)先删除数据,remove不会阻塞住整个db;
找业务不忙的时间操作。
db.collection1.remove({})
db.collection2.remove({})

2)然后,secondary上的数据重新同步,这样secondary删除的空间就释放了。
同步完之后进行一次主从切换,Secondary升级为Primary

3)新Secondary再同样进行重新同步数据,同样释放空间了
4)drop掉需要清理的空表
db.collection1.drop()
db.collection2.drop()

VC2017的运行时库提取

如果你是用Visual Studio 2015和2017来编写C或C++程序,那么就已经是基于UCRT的。
为了方便提取运行时库,请安装EveryThing搜索工具。
工具下载地址:https://www.voidtools.com/zh-cn/downloads/
以VC2017运行时库为例。
VC2017运行时库【UCRT库】包括以下几种文件。
Microsoft.VC141.CRT:
1.在EveryThing的输入界面搜索:Microsoft.VC141.CRT

2.再搜索ucrt,在Redist的子目录下,才是正常的UWP或ucrt库,文件一般是41个。

kafka容器化部署

1.参考https://github.com/wurstmeister/kafka-docker的实现。
2.参考https://github.com/simplesteph/kafka-stack-docker-compose
3.基于上述两个参考,实现以下的部署文件。

version: '3.1'
 
services:
  zoo1:
    image: zookeeper:3.4.9
    hostname: zoo1
    ports:
      - "2181:2181"
    environment:
        ZOO_MY_ID: 1
        ZOO_PORT: 2181
        ZOO_SERVERS: server.1=zoo1:2888:3888 server.2=zoo2:2888:3888 server.3=zoo3:2888:3888
    volumes:
      - ./zk-multiple-kafka-multiple/zoo1/data:/data
      - ./zk-multiple-kafka-multiple/zoo1/datalog:/datalog
 
  zoo2:
    image: zookeeper:3.4.9
    hostname: zoo2
    ports:
      - "2182:2182"
    environment:
        ZOO_MY_ID: 2
        ZOO_PORT: 2182
        ZOO_SERVERS: server.1=zoo1:2888:3888 server.2=zoo2:2888:3888 server.3=zoo3:2888:3888
    volumes:
      - ./zk-multiple-kafka-multiple/zoo2/data:/data
      - ./zk-multiple-kafka-multiple/zoo2/datalog:/datalog
 
  zoo3:
    image: zookeeper:3.4.9
    hostname: zoo3
    ports:
      - "2183:2183"
    environment:
        ZOO_MY_ID: 3
        ZOO_PORT: 2183
        ZOO_SERVERS: server.1=zoo1:2888:3888 server.2=zoo2:2888:3888 server.3=zoo3:2888:3888
    volumes:
      - ./zk-multiple-kafka-multiple/zoo3/data:/data
      - ./zk-multiple-kafka-multiple/zoo3/datalog:/datalog
 
 
  kafka1:
    image: wurstmeister/kafka:2.12-2.0.1
    container_name: kafka1
    hostname: kafka1
    ports:
      - "9092:9092"
      - "1099:1099"
    environment:
      KAFKA_ZOOKEEPER_CONNECT: "zoo1:2181,zoo2:2182,zoo3:2183"
      KAFKA_BROKER_ID: 1
      KAFKA_LOG4J_LOGGERS: "kafka.controller=INFO,kafka.producer.async.DefaultEventHandler=INFO,state.change.logger=INFO"
      KAFKA_AUTO_CREATE_TOPICS_ENABLE: "true"
      KAFKA_LISTENERS: PLAINTEXT://:9092
      KAFKA_ADVERTISED_LISTENERS: PLAINTEXT://192.168.10.100:9092
      KAFKA_OFFSETS_TOPIC_REPLICATION_FACTOR: 3
      KAFKA_DEFAULT_REPLICATION_FACTOR: 3
      KAFKA_JMX_OPTS: "-Dcom.sun.management.jmxremote -Dcom.sun.management.jmxremote.authenticate=false -Dcom.sun.management.jmxremote.ssl=false -Djava.rmi.server.hostname=127.0.0.1 -Dcom.sun.management.jmxremote.rmi.port=1099"
      JMX_PORT: 1099
    volumes:
      - ./zk-multiple-kafka-multiple/kafka1:/kafka
      - /var/run/docker.sock:/var/run/docker.sock
    depends_on:
      - zoo1
      - zoo2
      - zoo3
 
  kafka2:
    image: wurstmeister/kafka:2.12-2.0.1
    container_name: kafka2
    hostname: kafka2
    ports:
      - "9093:9092"
      - "2099:1099"
    environment:
      KAFKA_ZOOKEEPER_CONNECT: "zoo1:2181,zoo2:2182,zoo3:2183"
      KAFKA_BROKER_ID: 2
      KAFKA_LOG4J_LOGGERS: "kafka.controller=INFO,kafka.producer.async.DefaultEventHandler=INFO,state.change.logger=INFO"
      KAFKA_AUTO_CREATE_TOPICS_ENABLE: "true"
      KAFKA_LISTENERS: PLAINTEXT://:9092
      KAFKA_ADVERTISED_LISTENERS: PLAINTEXT://192.168.10.100:9093
      KAFKA_OFFSETS_TOPIC_REPLICATION_FACTOR: 3
      KAFKA_DEFAULT_REPLICATION_FACTOR: 3
      KAFKA_JMX_OPTS: "-Dcom.sun.management.jmxremote -Dcom.sun.management.jmxremote.authenticate=false -Dcom.sun.management.jmxremote.ssl=false -Djava.rmi.server.hostname=127.0.0.1 -Dcom.sun.management.jmxremote.rmi.port=1099"
      JMX_PORT: 1099
    volumes:
      - ./zk-multiple-kafka-multiple/kafka2:/kafka
      - /var/run/docker.sock:/var/run/docker.sock
    depends_on:
      - zoo1
      - zoo2
      - zoo3
 
  kafka3:
    image: wurstmeister/kafka:2.12-2.0.1
    container_name: kafka3
    hostname: kafka3
    ports:
      - "9094:9092"
      - "3099:1099"
    environment:
      KAFKA_ZOOKEEPER_CONNECT: "zoo1:2181,zoo2:2182,zoo3:2183"
      KAFKA_BROKER_ID: 3
      KAFKA_LOG4J_LOGGERS: "kafka.controller=INFO,kafka.producer.async.DefaultEventHandler=INFO,state.change.logger=INFO"
      KAFKA_AUTO_CREATE_TOPICS_ENABLE: "true"
      KAFKA_LISTENERS: PLAINTEXT://:9092
      KAFKA_ADVERTISED_LISTENERS: PLAINTEXT://192.168.10.100:9094
      KAFKA_OFFSETS_TOPIC_REPLICATION_FACTOR: 3
      KAFKA_DEFAULT_REPLICATION_FACTOR: 3
      KAFKA_JMX_OPTS: "-Dcom.sun.management.jmxremote -Dcom.sun.management.jmxremote.authenticate=false -Dcom.sun.management.jmxremote.ssl=false -Djava.rmi.server.hostname=127.0.0.1 -Dcom.sun.management.jmxremote.rmi.port=1099"
      JMX_PORT: 1099
    volumes:
      - ./zk-multiple-kafka-multiple/kafka3:/kafka
      - /var/run/docker.sock:/var/run/docker.sock
    depends_on:
      - zoo1
      - zoo2
      - zoo3
 
  manager:
    image: hlebalbau/kafka-manager:2.0.0.2
    hostname: manager
    ports:
      - "9000:9000"
    environment:
      ZK_HOSTS: "zoo1:2181,zoo2:2182,zoo3:2183"
      APPLICATION_SECRET: "random-secret"
      KAFKA_MANAGER_AUTH_ENABLED: "true"
      KAFKA_MANAGER_USERNAME: "abc"
      KAFKA_MANAGER_PASSWORD: "123"
    command: -Dpidfile.path=/dev/null

4.测试文件
基于https://github.com/segmentio/kafka-go库的示范,实现如下:

package kaf
 
import (
	"context"
	"fmt"
	"github.com/segmentio/kafka-go"
	"log"
	"time"
)
 
func LeaderProduce() {
	topic := "my-topic"
	partition := 0
 
	conn, err := kafka.DialLeader(context.Background(), "tcp", "localhost:9092", topic, partition)
	if err != nil {
		log.Fatal(err)
	}
	conn.SetWriteDeadline(time.Now().Add(10 * time.Second))
	conn.WriteMessages(
		kafka.Message{Value: []byte(fmt.Sprint("one!", time.Now()))},
		kafka.Message{Value: []byte(fmt.Sprint("two!", time.Now()))},
		kafka.Message{Value: []byte(fmt.Sprint("three!", time.Now()))},
	)
 
	conn.Close()
}
 
func LeaderConsumer() {
	topic := "my-topic"
	partition := 0
 
	conn, _ := kafka.DialLeader(context.Background(), "tcp", "localhost:9092", topic, partition)
 
	conn.SetReadDeadline(time.Now().Add(10 * time.Second))
	batch := conn.ReadBatch(10e3, 1e6) // fetch 10KB min, 1MB max
	for {
		msg, err := batch.ReadMessage()
		if err != nil {
			break
		}
		fmt.Println(string(msg.Value))
	}
 
	batch.Close()
	conn.Close()
}
 
func ClusterProduce(port int) {
	// make a writer that produces to topic-A, using the least-bytes distribution
	w := kafka.NewWriter(kafka.WriterConfig{
		Brokers:  []string{"localhost:9092", "localhost:9093", "localhost:9094"},
		Topic:    "topic-A",
		Balancer: &kafka.LeastBytes{},
	})
 
	err := w.WriteMessages(context.Background(),
		kafka.Message{
			Key:   []byte("Key-A"),
			Value: []byte(fmt.Sprint("Hello World!", time.Now())),
		},
		kafka.Message{
			Key:   []byte("Key-B"),
			Value: []byte(fmt.Sprint("One!", time.Now())),
		},
	)
	if err != nil {
		fmt.Println(port, "error", err)
	}
 
	w.Close()
}
 
func clusterConsume(port int) {
	// make a new reader that consumes from topic-A
	r := kafka.NewReader(kafka.ReaderConfig{
		Brokers:  []string{"localhost:9092", "localhost:9093", "localhost:9094"},
		GroupID:  "consumer-group-id",
		Topic:    "topic-A",
		MinBytes: 1024 * 10, // 10KB
		MaxBytes: 10e6,      // 10MB
	})
 
	for {
		m, err := r.ReadMessage(context.Background())
		if err != nil {
			fmt.Println(port, "error.....", err)
			time.Sleep(time.Second * 10)
			continue
		}
		fmt.Printf("%v--message at topic/partition/offset %v/%v/%v: %s = %s\n", port, m.Topic, m.Partition, m.Offset, string(m.Key), string(m.Value))
		// time.Sleep(time.Second)
	}
 
	r.Close()
}

YUM安装PHP7的开发环境

第一步:安装remi源
rpm -Uvh http://rpms.remirepo.net/enterprise/remi-release-7.rpm
或yum install http://rpms.famillecollet.com/enterprise/remi-release-7.rpm
第二步:配置php7.2仓库
yum -y install yum-utils
yum-config-manager –enable remi-php72 #yum -y install yum-utils
第三步:安装PHP
yum install php 因为上一步remi配置,所以这里会指向php72
第四步:安装扩展组件
yum install php php72-php-opcache php72-php-ldap php72-php-odbc php72-php-pear php72-php-xml php72-php-xmlrpc php72-php-soap curl curl-devel php72-php-mbstring php72-php-mysqlnd php72-php-fpm php72-php-gd php72-php-xdebug php72-php-pecl-mysql php72-php-pecl-memcached php72-php-pecl-memcache php72-php-pecl-redis
第五步:安装php-fpm
yum install php72-php-fpm
systemctl restart php72-php-fpm #启动php-fpm服务
netstat -tunlp|grep 9000 #查看9000端口是否正常启动了

php的配置文件及组件的安装位置
/etc/opt/remi/php72
/etc/opt/remi/php72/php-fpm.d/*.conf
—————————
安装xdebug
搜索相应库:yum search php|grep xdebug

yum install php72-php-pecl-xdebug

ZooKeeper的容器化配置

docker pull zookeeper
https://github.com/getwingm/kafka-stack-docker-compose

version: '3.1'
 
services:
  zoo1:
    image: zookeeper
    restart: always
    hostname: zoo1
    ports:
      - 2181:2181
    environment:
      ZOO_MY_ID: 1
      ZOO_SERVERS: server.1=0.0.0.0:2888:3888;2181 server.2=zoo2:2888:3888;2181 server.3=zoo3:2888:3888;2181
 
  zoo2:
    image: zookeeper
    restart: always
    hostname: zoo2
    ports:
      - 2182:2181
    environment:
      ZOO_MY_ID: 2
      ZOO_SERVERS: server.1=zoo1:2888:3888;2181 server.2=0.0.0.0:2888:3888;2181 server.3=zoo3:2888:3888;2181
 
  zoo3:
    image: zookeeper
    restart: always
    hostname: zoo3
    ports:
      - 2183:2181
    environment:
      ZOO_MY_ID: 3
      ZOO_SERVERS: server.1=zoo1:2888:3888;2181 server.2=zoo2:2888:3888;2181 server.3=0.0.0.0:2888:3888;2181

Mongo常见操作

Mongo后台建唯一索引
db.orders.ensureIndex({userid:1,reqid:1},{unique:true,background:true})
备分数据库
mongodump -h 10.0.1.106 –port 27017 -u root -p xxxxxxx -d abc_gold -o /data/backup/abc_gold –authenticationDatabase admin
数据恢复
mongorestore -h 10.0.1.107 –port 27017 -u root -p yyyyyyyyy -d abc_gold /data/backup/abc_gold –drop –authenticationDatabase admin

pika编译及运行

1.基于https://github.com/Qihoo360/pika/的v3.1.1版本,定制出适合业务要求的功能改进版。
https://github.com/kxtry/pika是在v3.1.1基础上,新增了incrbyrange(key,val,min, max)及hincrbyrange(key,val,min, max)这两个命令。
2.官方提供的编译是直接基于docker的编译。

编译Dockerfile文件。
docker build -t pika .

3.提取编译后结果,也可以直接用该容器运行应用

运行应用:
docker run pika:latest bash -c "./bin/pika -c ./conf/pika.conf"
也可提取相关应用至宿主机运行。
docker cp 容器名:/pika/output ./  #docker cp ce4541cc4627:/pika/output ./

4.提取出来的应用,在宿主机上运行,需要安装相关依赖。

安装epel源。
rpm -ivh https://mirrors.ustc.edu.cn/epel/epel-release-latest-7.noarch.rpm
安装glog和protobuf的动态连接库。
sudo yum install -y glog protobuf  #编译时,对应的是glog-devel 和protobuf-devel
如果仍然无法运行,则执行strace ./pika或ldd pika来检查缺少哪些动态库。

5. 运行:

docker run pika:latest bash -c "./bin/pika -c ./conf/pika.conf"

6. 运行脚本run-app.sh

#!/bin/sh
 
# crontab -e
# */1 * * * * sh /data/scripts/run-app.sh start
 
path_current=`pwd`
path_script=$(cd "$(dirname "$0")"; pwd)
path_data=$path_script/data
logfile=$path_data/check.log
mode=$1
 
name=pika
 
app_process=`ps -ef | grep "$name"| grep -v grep`
 
if [ ! -d $path_data ];then 
   mkdir -p $path_data
fi
 
echo `date` >> $logfile
echo "ready to check...." >> $logfile
case "$mode" in
   'install')
      if [ ! -f $path_script/.envok ]; then
         rpm -ivh https://mirrors.ustc.edu.cn/epel/epel-release-latest-7.noarch.rpm
         yum install -y glog protobuf && touch $path_script/.envok
      fi
      if [ ! -f $path_script/conf/pika.conf ]; then
         mkdir -p $path_data && /bin/cp -rf $path_script/pika.conf.template $path_script/conf/pika.conf && echo "$path_script/conf/pika.conf" | xargs /bin/sed -i "s#{{path_current}}#$path_data#g"
      fi
      ;;
   'start')
      echo "$app_process" >> $logfile
      echo "it's ready to start op...."
      if test -n "$app_process"; then
         echo ""
         echo "$app_process"
         echo ""
      else
         cd $path_script  
         nohup $path_script/bin/$name -c $path_script/conf/${name}.conf > $path_data/info.txt 2>&1 &
         echo "success to restart $name" >> $logfile
         cd $path_current
      fi
      echo 'success to start.'
      ;;
   'stop')
      echo "it's ready to check process..."
      if test -n "$app_process"; then
         echo "had find app process informaton"
         echo $app_process | awk '{print ($2)}' | xargs kill -3
      fi
      echo 'success to kill.'
      ;;
   *)
      basename=`basename "$0"`
      echo "Usage: $basename  {install|start|stop}  [ server options ]"
      exit 1
      ;;
esac
exit 1

7.原默认配置脚本pika.conf.template

# Pika port
port : 9221
# Thread Number
thread-num : 50
# Thread Pool Size
thread-pool-size : 100
# Sync Thread Number
sync-thread-num : 10
# Pika log path
log-path : {{path_current}}/log/
# Pika db path
db-path : {{path_current}}/db/
# Pika write-buffer-size
write-buffer-size : 268435456
# Pika timeout
timeout : 60
# Requirepass
requirepass : abc123
# Masterauth
masterauth : abc123
# Userpass
userpass : abc123
# User Blacklist
userblacklist :
# if this option is set to 'classic', that means pika support multiple DB, in
# this mode, option databases enable
# if this option is set to 'sharding', that means pika support multiple Table, you
# can specify partition num for each table, in this mode, option table-list enable
# Pika instance mode [classic | sharding]
instance-mode : classic
# Set the number of databases. The default database is DB 0, you can select
# a different one on a per-connection basis using SELECT <dbid> where
# dbid is a number between 0 and 'databases' - 1, limited in [1, 8]
databases : 1
# Table list
table-list : table1:1,table2:1
# Dump Prefix
dump-prefix :
# daemonize  [yes | no]
daemonize : yes
# Dump Path
dump-path : {{path_current}}/dump/
# Expire-dump-days
dump-expire : 0
# pidfile Path
pidfile : {{path_current}}/pika.pid
# Max Connection
maxclients : 20000
# the per file size of sst to compact, defalut is 2M
target-file-size-base : 20971520
# Expire-logs-days
expire-logs-days : 7
# Expire-logs-nums
expire-logs-nums : 10
# Root-connection-num
root-connection-num : 2
# Slowlog-write-errorlog
slowlog-write-errorlog : no
# Slowlog-log-slower-than
slowlog-log-slower-than : 10000
# Slowlog-max-len
slowlog-max-len : 128
# Pika db sync path
db-sync-path : {{path_current}}/dbsync/
# db sync speed(MB) max is set to 1024MB, min is set to 0, and if below 0 or above 1024, the value will be adjust to 1024
db-sync-speed : -1
# The slave priority
slave-priority : 100
# network interface
#network-interface : eth1
# replication
#slaveof : master-ip:master-port
 
# CronTask, format 1: start-end/ratio, like 02-04/60, pika will check to schedule compaction between 2 to 4 o'clock everyday
#                   if the freesize/disksize > 60%.
#           format 2: week/start-end/ratio, like 3/02-04/60, pika will check to schedule compaction between 2 to 4 o'clock
#                   every wednesday, if the freesize/disksize > 60%.
#           NOTICE: if compact-interval is set, compact-cron will be mask and disable.
#
#compact-cron : 3/02-04/60
 
# Compact-interval, format: interval/ratio, like 6/60, pika will check to schedule compaction every 6 hours,
#                           if the freesize/disksize > 60%. NOTICE:compact-interval is prior than compact-cron;
#compact-interval :
 
# server-id for hub
server-id : 1
 
###################
## Critical Settings
###################
# write_binlog  [yes | no]
write-binlog : yes
# binlog file size: default is 100M,  limited in [1K, 2G]
binlog-file-size : 104857600
# Automatically triggers a small compaction according statistics
# Use the cache to store up to 'max-cache-statistic-keys' keys
# if 'max-cache-statistic-keys' set to '0', that means turn off the statistics function
# it also doesn't automatically trigger a small compact feature
max-cache-statistic-keys : 0
# When 'delete' or 'overwrite' a specific multi-data structure key 'small-compaction-threshold' times,
# a small compact is triggered automatically, default is 5000, limited in [1, 100000]
small-compaction-threshold : 5000
# If the total size of all live memtables of all the DBs exceeds
# the limit, a flush will be triggered in the next DB to which the next write
# is issued.
max-write-buffer-size :  10737418240
# Compression
compression : snappy
# max-background-flushes: default is 1, limited in [1, 4]
max-background-flushes : 1
# max-background-compactions: default is 2, limited in [1, 8]
max-background-compactions : 2
# max-cache-files default is 5000
max-cache-files : 5000
# max_bytes_for_level_multiplier: default is 10, you can change it to 5
max-bytes-for-level-multiplier : 10
# BlockBasedTable block_size, default 4k
# block-size: 4096
# block LRU cache, default 8M, 0 to disable
# block-cache: 8388608
# whether the block cache is shared among the RocksDB instances, default is per CF
# share-block-cache: no
# whether or not index and filter blocks is stored in block cache
# cache-index-and-filter-blocks: no
# when set to yes, bloomfilter of the last level will not be built
# optimize-filters-for-hits: no
# https://github.com/facebook/rocksdb/wiki/Leveled-Compaction#levels-target-size
# level-compaction-dynamic-level-bytes: no

8.目录结构如下:

Top
 |--->bin
 |--->conf
 |--->tool
pika.conf.template
run-app.sh