Kafka To HBase To Hive

目录

1.在HBase中创建表

2.写入API

2.1普通模式写入hbase（逐条写入）

2.2普通模式写入hbase（buffer写入）

2.3设计模式写入hbase（buffer写入）

3.HBase表映射至Hive中

1.在HBase中创建表

hbase(main):003:0> create_namespace 'events_db'                                                 

hbase(main):004:0> create 'events_db:users','profile','region','registration'

hbase(main):005:0> create 'events_db:user_friend','uf'

hbase(main):006:0> create 'events_db:events','schedule','location','creator','remark'

hbase(main):007:0> create 'events_db:event_attendee','euat'

hbase(main):008:0> create 'events_db:train','eu'

hbase(main):011:0> list_namespace_tables 'events_db'

TABLE                                                                               

event_attendee                                                                       

events                                                                              

train                                                                               

user_friend                                                                         

users                                                                               

5 row(s)

2.写入API

2.1普通模式写入hbase（逐条写入）

import org.apache.hadoop.conf.Configuration;
import org.apache.hadoop.hbase.HBaseConfiguration;
import org.apache.hadoop.hbase.HConstants;
import org.apache.hadoop.hbase.TableName;
import org.apache.hadoop.hbase.client.Connection;
import org.apache.hadoop.hbase.client.ConnectionFactory;
import org.apache.hadoop.hbase.client.Put;
import org.apache.hadoop.hbase.client.Table;
import org.apache.hadoop.hbase.util.Bytes;
import org.apache.kafka.clients.consumer.ConsumerConfig;
import org.apache.kafka.clients.consumer.ConsumerRecord;
import org.apache.kafka.clients.consumer.ConsumerRecords;
import org.apache.kafka.clients.consumer.KafkaConsumer;
import org.apache.kafka.common.serialization.StringDeserializer;import java.io.IOException;
import java.time.Duration;import java.util.ArrayList;
import java.util.Collections;
import java.util.Properties;/*** 将Kafka中的topic为userfriends中的数据消费到hbase中* hbase中的表为events_db:user_friend*/
public class UserFriendToHB {static int num = 0; //计数器public static void main(String[] args) {Properties properties = new Properties();properties.put(ConsumerConfig.BOOTSTRAP_SERVERS_CONFIG, "kb129:9092");properties.put(ConsumerConfig.KEY_DESERIALIZER_CLASS_CONFIG, StringDeserializer.class);properties.put(ConsumerConfig.VALUE_DESERIALIZER_CLASS_CONFIG,StringDeserializer.class);properties.put(ConsumerConfig.AUTO_OFFSET_RESET_CONFIG,"earliest");properties.put(ConsumerConfig.ENABLE_AUTO_COMMIT_CONFIG,"false");properties.put(ConsumerConfig.GROUP_ID_CONFIG, "user_friend_group1");KafkaConsumer<String, String> consumer = new KafkaConsumer<>(properties);consumer.subscribe(Collections.singleton("userfriends"));//配置hbase信息，连接hbase数据库Configuration conf = HBaseConfiguration.create();conf.set(HConstants.HBASE_DIR, "hdfs://kb129:9000/hbase");conf.set(HConstants.ZOOKEEPER_QUORUM, "kb129");conf.set(HConstants.CLIENT_PORT_STR, "2181");Connection connection = null;try {connection = ConnectionFactory.createConnection(conf);Table ufTable = connection.getTable(TableName.valueOf("events_db:user_friend"));ArrayList<Put> datas = new ArrayList<>();while (true){ConsumerRecords<String, String> poll = consumer.poll(Duration.ofMillis(100));//每次for循环前清空datasdatas.clear();for (ConsumerRecord<String, String> record : poll) {//System.out.println(record.value());String[] split = record.value().split(",");int i = (split[0] + split[1]).hashCode();Put put = new Put(Bytes.toBytes(i));put.addColumn(Bytes.toBytes("uf"), Bytes.toBytes("userid"), split[0].getBytes());put.addColumn("uf".getBytes(), "friend".getBytes(),split[1].getBytes());datas.add(put);}num = num + datas.size();System.out.println("---------num:" + num);if (datas.size() > 0){ufTable.put(datas);}try {Thread.sleep(10);} catch (InterruptedException e) {throw new RuntimeException(e);}}} catch (IOException e) {throw new RuntimeException(e);}}
}

2.2普通模式写入hbase（buffer写入）

import org.apache.hadoop.conf.Configuration;
import org.apache.hadoop.hbase.HBaseConfiguration;
import org.apache.hadoop.hbase.HConstants;
import org.apache.hadoop.hbase.TableName;
import org.apache.hadoop.hbase.client.*;
import org.apache.hadoop.hbase.util.Bytes;
import org.apache.kafka.clients.consumer.ConsumerConfig;
import org.apache.kafka.clients.consumer.ConsumerRecord;
import org.apache.kafka.clients.consumer.ConsumerRecords;
import org.apache.kafka.clients.consumer.KafkaConsumer;
import org.apache.kafka.common.serialization.StringDeserializer;import java.io.IOException;
import java.time.Duration;
import java.util.ArrayList;
import java.util.Collections;
import java.util.Properties;/*** 将Kafka中的topic为userfriends中的数据消费到hbase中* hbase中的表为events_db:user_friend*/
public class UserFriendToHB2 {static int num = 0; //计数器public static void main(String[] args) {Properties properties = new Properties();properties.put(ConsumerConfig.BOOTSTRAP_SERVERS_CONFIG, "kb129:9092");properties.put(ConsumerConfig.KEY_DESERIALIZER_CLASS_CONFIG, StringDeserializer.class);properties.put(ConsumerConfig.VALUE_DESERIALIZER_CLASS_CONFIG,StringDeserializer.class);properties.put(ConsumerConfig.AUTO_OFFSET_RESET_CONFIG,"earliest");properties.put(ConsumerConfig.ENABLE_AUTO_COMMIT_CONFIG,"false");properties.put(ConsumerConfig.GROUP_ID_CONFIG, "user_friend_group1");KafkaConsumer<String, String> consumer = new KafkaConsumer<>(properties);consumer.subscribe(Collections.singleton("userfriends"));//配置hbase信息，连接hbase数据库Configuration conf = HBaseConfiguration.create();conf.set(HConstants.HBASE_DIR, "hdfs://kb129:9000/hbase");conf.set(HConstants.ZOOKEEPER_QUORUM, "kb129");conf.set(HConstants.CLIENT_PORT_STR, "2181");Connection connection = null;try {connection = ConnectionFactory.createConnection(conf);BufferedMutatorParams bufferedMutatorParams = new BufferedMutatorParams(TableName.valueOf("events_db:user_friend"));bufferedMutatorParams.setWriteBufferPeriodicFlushTimeoutMs(10000);//设置超时flush时间最大值bufferedMutatorParams.writeBufferSize(10*1024*1024);//设置缓存大小flushBufferedMutator bufferedMutator = connection.getBufferedMutator(bufferedMutatorParams) ;ArrayList<Put> datas = new ArrayList<>();while (true){ConsumerRecords<String, String> poll = consumer.poll(Duration.ofMillis(100));datas.clear();  //每次for循环前清空datasfor (ConsumerRecord<String, String> record : poll) {//System.out.println(record.value());String[] split = record.value().split(",");int i = (split[0] + split[1]).hashCode();Put put = new Put(Bytes.toBytes(i));put.addColumn(Bytes.toBytes("uf"), Bytes.toBytes("userid"), split[0].getBytes());put.addColumn("uf".getBytes(), "friend".getBytes(),split[1].getBytes());datas.add(put);}num = num + datas.size();System.out.println("---------num:" + num);if (datas.size() > 0){bufferedMutator.mutate(datas);}}} catch (IOException e) {throw new RuntimeException(e);}}
}

2.3设计模式写入hbase（buffer写入）

（1）Iworker接口

public interface IWorker {void fillData(String targetName);
}

（2）worker实现类

import nj.zb.kb23.kafkatohbase.oop.writer.IWriter;
import org.apache.kafka.clients.consumer.ConsumerConfig;
import org.apache.kafka.clients.consumer.ConsumerRecords;
import org.apache.kafka.clients.consumer.KafkaConsumer;
import org.apache.kafka.common.serialization.StringDeserializer;import java.time.Duration;
import java.util.Collections;
import java.util.Properties;public class Worker implements IWorker {private KafkaConsumer<String, String> consumer = null;private IWriter writer = null;public Worker(String topicName, String consumerGroupId, IWriter writer) {this.writer = writer;Properties properties = new Properties();properties.put(ConsumerConfig.BOOTSTRAP_SERVERS_CONFIG, "kb129:9092");properties.put(ConsumerConfig.KEY_DESERIALIZER_CLASS_CONFIG, StringDeserializer.class);properties.put(ConsumerConfig.VALUE_DESERIALIZER_CLASS_CONFIG, StringDeserializer.class);properties.put(ConsumerConfig.AUTO_OFFSET_RESET_CONFIG, "earliest");properties.put(ConsumerConfig.ENABLE_AUTO_COMMIT_CONFIG, "false");properties.put(ConsumerConfig.GROUP_ID_CONFIG, consumerGroupId);consumer = new KafkaConsumer<>(properties);consumer.subscribe(Collections.singleton(topicName));}@Overridepublic void fillData(String targetName) {int num = 0;while (true) {ConsumerRecords<String, String> records = consumer.poll(Duration.ofMillis(100));int returnNum = writer.write(targetName, records);num += returnNum;System.out.println("---------num:" + num);}}
}

（3）IWriter接口

import org.apache.kafka.clients.consumer.ConsumerRecords;/*** 完成kafka消费出的数据  ConsumerRecords 的组装和写入到指定类型的数据库 指定table 的工作*/
public interface IWriter {int write(String targetTableName, ConsumerRecords<String, String> records);
}

（4）writer实现类

import nj.zb.kb23.kafkatohbase.oop.handler.IParseRecord;
import org.apache.hadoop.conf.Configuration;
import org.apache.hadoop.hbase.HBaseConfiguration;
import org.apache.hadoop.hbase.HConstants;
import org.apache.hadoop.hbase.TableName;
import org.apache.hadoop.hbase.client.*;
import org.apache.kafka.clients.consumer.ConsumerRecords;import java.io.IOException;
import java.util.List;public class HBaseWriter implements IWriter{private Connection connection = null;private BufferedMutator bufferedMutator = null;private IParseRecord handler = null;/*** 初始化HBaseWriter对象*/public HBaseWriter(IParseRecord handler) {this.handler = handler;Configuration conf = HBaseConfiguration.create();conf.set(HConstants.HBASE_DIR, "hdfs://kb129:9000/hbase");conf.set(HConstants.ZOOKEEPER_QUORUM, "kb129");conf.set(HConstants.CLIENT_PORT_STR, "2181");try {connection = ConnectionFactory.createConnection(conf);} catch (IOException e) {throw new RuntimeException(e);}}private void getBufferedMutator(String targetTableName){BufferedMutatorParams bufferedMutatorParams = new BufferedMutatorParams(TableName.valueOf(targetTableName));bufferedMutatorParams.setWriteBufferPeriodicFlushTimeoutMs(10000);//设置超时flush时间最大值bufferedMutatorParams.writeBufferSize(10*1024*1024);//设置缓存大小flushif (bufferedMutator == null){try {bufferedMutator = connection.getBufferedMutator(bufferedMutatorParams);} catch (IOException e) {throw new RuntimeException(e);}}}@Overridepublic int write(String targetTableName, ConsumerRecords<String, String> records) {if (records.count() > 0) {this.getBufferedMutator(targetTableName);List<Put> datas = handler.parse(records);try {bufferedMutator.mutate(datas);} catch (IOException e) {throw new RuntimeException(e);}return datas.size();}else {return 0;}}
}

（5）IParseRecord接口

import org.apache.hadoop.hbase.client.Put;
import org.apache.kafka.clients.consumer.ConsumerRecords;import java.util.List;/*** 将record 装配成 put*/
public interface IParseRecord {List<Put> parse(ConsumerRecords<String, String> records);
}

（6）具体表对应的handler类（包装Put）

UsersHandler

import org.apache.hadoop.hbase.client.Put;
import org.apache.kafka.clients.consumer.ConsumerRecord;
import org.apache.kafka.clients.consumer.ConsumerRecords;import java.util.ArrayList;
import java.util.List;public class UsersHandler implements IParseRecord{List<Put> datas = new ArrayList<>();@Overridepublic List<Put> parse(ConsumerRecords<String, String> records) {datas.clear();for (ConsumerRecord<String, String> record : records) {String[] users = record.value().split(",");Put put = new Put(users[0].getBytes());put.addColumn("profile".getBytes(), "birthyear".getBytes(), users[2].getBytes());put.addColumn("profile".getBytes(), "gender".getBytes(), users[3].getBytes());put.addColumn("region".getBytes(), "locale".getBytes(), users[1].getBytes());if (users.length > 4){put.addColumn("registration".getBytes(), "joinedAt".getBytes(), users[4].getBytes());}if (users.length > 5){put.addColumn("region".getBytes(), "location".getBytes(), users[5].getBytes());}if (users.length > 6){put.addColumn("region".getBytes(), "timezone".getBytes(), users[6].getBytes());}datas.add(put);}return datas;}
}

TrainHandler

import org.apache.hadoop.hbase.client.Put;
import org.apache.kafka.clients.consumer.ConsumerRecord;
import org.apache.kafka.clients.consumer.ConsumerRecords;import java.util.ArrayList;
import java.util.List;public class TrainHandler implements IParseRecord{List<Put> datas = new ArrayList<>();@Overridepublic List<Put> parse(ConsumerRecords<String, String> records) {datas.clear();for (ConsumerRecord<String, String> record : records) {String[] trains = record.value().split(",");double random = Math.random();Put put = new Put((trains[0]+trains[1]+random).getBytes());put.addColumn("eu".getBytes(), "user".getBytes(), trains[0].getBytes());put.addColumn("eu".getBytes(), "event".getBytes(), trains[1].getBytes());put.addColumn("eu".getBytes(), "invited".getBytes(), trains[2].getBytes());put.addColumn("eu".getBytes(), "timestamp".getBytes(), trains[3].getBytes());put.addColumn("eu".getBytes(), "interested".getBytes(), trains[4].getBytes());put.addColumn("eu".getBytes(), "not_interested".getBytes(), trains[5].getBytes());datas.add(put);}return datas;}
}

EventsHandler

import org.apache.hadoop.hbase.client.Put;
import org.apache.kafka.clients.consumer.ConsumerRecord;
import org.apache.kafka.clients.consumer.ConsumerRecords;import java.util.ArrayList;
import java.util.List;public class EventsHandler implements IParseRecord {List<Put> datas = new ArrayList<>();@Overridepublic List<Put> parse(ConsumerRecords<String, String> records) {datas.clear();for (ConsumerRecord<String, String> record : records) {String[] events = record.value().split(",");Put put = new Put(events[0].getBytes());put.addColumn("creator".getBytes(), "user_id".getBytes(),events[1].getBytes());put.addColumn("schedule".getBytes(), "start_time".getBytes(),events[2].getBytes());put.addColumn("location".getBytes(), "city".getBytes(),events[3].getBytes());put.addColumn("location".getBytes(), "state".getBytes(),events[4].getBytes());put.addColumn("location".getBytes(), "zip".getBytes(),events[5].getBytes());put.addColumn("location".getBytes(), "country".getBytes(),events[6].getBytes());put.addColumn("location".getBytes(), "lat".getBytes(),events[7].getBytes());put.addColumn("location".getBytes(), "lng".getBytes(),events[8].getBytes());put.addColumn("remark".getBytes(), "common_words".getBytes(),events[9].getBytes());datas.add(put);}return datas;}
}

EventAttendHandler

import org.apache.hadoop.hbase.client.Put;
import org.apache.hadoop.hbase.util.Bytes;
import org.apache.kafka.clients.consumer.ConsumerRecord;
import org.apache.kafka.clients.consumer.ConsumerRecords;import java.util.ArrayList;
import java.util.List;public class EventAttendHandler implements IParseRecord{@Overridepublic List<Put> parse(ConsumerRecords<String, String> records) {List<Put> datas = new ArrayList<>();for (ConsumerRecord<String, String> record : records) {String[] splits = record.value().split(",");Put put = new Put((splits[0] + splits[1] + splits[2]).getBytes());put.addColumn(Bytes.toBytes("euat"), Bytes.toBytes("eventid"), splits[0].getBytes());put.addColumn("euat".getBytes(), "friendid".getBytes(),splits[1].getBytes());put.addColumn("euat".getBytes(), "state".getBytes(),splits[2].getBytes());datas.add(put);}return datas;}
}

（7）主程序

import nj.zb.kb23.kafkatohbase.oop.handler.*;
import nj.zb.kb23.kafkatohbase.oop.worker.Worker;
import nj.zb.kb23.kafkatohbase.oop.writer.HBaseWriter;
import nj.zb.kb23.kafkatohbase.oop.writer.IWriter;
/*** 将Kafka中的topic为...中的数据消费到hbase中* hbase中的表为events_db:...*/
public class KfkToHbTest {static int num = 0; //计数器public static void main(String[] args) {//IParseRecord handler = new EventAttendHandler();//IWriter writer = new HBaseWriter(handler);//String topic = "eventattendees";//String consumerGroupId = "eventattendees_group1";//String targetName = "events_db:event_attendee";//Worker worker = new Worker(topic, consumerGroupId, writer);//worker.fillData(targetName);/*EventsHandler eventsHandler = new EventsHandler();IWriter writer = new HBaseWriter(eventsHandler);Worker worker = new Worker("events", "events_group1", writer);worker.fillData("events_db:eventsb");*//*UsersHandler usersHandler = new UsersHandler();IWriter writer = new HBaseWriter(usersHandler);Worker worker = new Worker("users_raw", "users_group1", writer);worker.fillData("events_db:users");*/TrainHandler trainHandler = new TrainHandler();IWriter writer = new HBaseWriter(trainHandler);Worker worker = new Worker("train", "train_group1", writer);worker.fillData("events_db:train2");}
}

3.HBase表映射至Hive中

create database if not exists events;
use events;create external table hb_users(userId string,birthyear int,gender string,locale string,location string,timezone string,joinedAt string
)stored by 'org.apache.hadoop.hive.hbase.HBaseStorageHandler'
with SERDEPROPERTIES ('hbase.columns.mapping'=':key,profile:birthyear,profile:gender,region:locale,region:location,region:timezone,registration:joinedAt')
tblproperties ('hbase.table.name'='events_db:users');select * from hb_users limit 3;
select count(1) from hb_users;--orc格式创建内部表存储映射外部表，安全保存数据，创建好可以直接删除hbase中的表
create table users stored as orc as select * from hb_users;
select * from users limit 3;
select count(1) from users;
drop table hb_users;--38209  1494
select count(*) from users where birthyear is null;select round(avg(birthyear), 0) from users;
select `floor`(avg(birthyear)) from users;-- 处理空字段，覆盖写入
withtb as ( select `floor`(avg(birthyear)) avgAge from users ),tb2 as ( select userId, nvl(birthyear, tb.avgAge),gender,locale,location,timezone,joinedAt from users,tb)
insert overwrite table users
select * from tb2;-- 查询到性别中空字符串109个
select count(gender) count from users where gender is null or gender = "";--------------------------------------------------------
create external table hb_events(event_id string,user_id string,start_time string,city string,state string,zip string,country string,lat float,lng float,common_words string
)stored by 'org.apache.hadoop.hive.hbase.HBaseStorageHandler'
with SERDEPROPERTIES ('hbase.columns.mapping'=':key,creator:user_id,schedule:start_time,location:city,location:state,location:zip,location:country,location:lat,location:lng,remark:common_words')
tblproperties ('hbase.table.name'='events_db:events');select * from hb_events limit 10;
create table events stored as orc as select * from hb_events;
select count(*) from hb_events;
select count(*) from events;
drop table hb_events;select event_id from events group by event_id having count(event_id) >1;
withtb as (select event_id, row_number() over (partition by event_id) rn from events)
select event_id from tb where rn > 1;select user_id, count(event_id) num from events group by user_id order by num desc;-----------------------------------------------------
create external table if not exists hb_user_friend(row_key string,userid string,friendid string
)stored by 'org.apache.hadoop.hive.hbase.HBaseStorageHandler'
with SERDEPROPERTIES ('hbase.columns.mapping'=':key,uf:userid,uf:friend')
tblproperties ('hbase.table.name'='events_db:user_friend');select * from hb_user_friend limit 3;
create table user_friend stored as orc as select * from hb_user_friend;
select count(*) from hb_user_friend;
select count(*) from user_friend;
drop table hb_user_friend;-----------------------------------------------------------
create external table if not exists hb_event_attendee(row_key string,eventid string,friendid string,attendtype string
)stored by 'org.apache.hadoop.hive.hbase.HBaseStorageHandler'
with SERDEPROPERTIES ('hbase.columns.mapping'=':key,euat:eventid,euat:friendid,euat:state')
tblproperties ('hbase.table.name'='events_db:event_attendee');select * from hb_event_attendee limit 3;
select count(*) from hb_event_attendee;
create table event_attendee stored as orc as select * from hb_event_attendee;
select * from event_attendee limit 3;
select count(*) from event_attendee;
drop table hb_event_attendee;--------------------------------------------------------------
create external table if not exists hb_train(row_key string,userid string,eventid string,invited string,`timestamp` string,interested string
)stored by 'org.apache.hadoop.hive.hbase.HBaseStorageHandler'
with SERDEPROPERTIES ('hbase.columns.mapping'=':key,eu:user,eu:event,eu:invited,eu:timestamp,eu:interested')
tblproperties ('hbase.table.name'='events_db:train');select * from hb_train limit 3;
select count(*) from hb_train;
create table train stored as orc as select * from hb_train;
select * from train limit 3;
select count(*) from train;
drop table hb_train;-----------------------------------------------
create external table locale(locale_id int,locale string
)
row format delimited fields terminated by '\t'
location '/events/data/locale';select * from locale;create external table time_zone(time_zone_id int,time_zone string
)
row format delimited fields terminated by ','
location '/events/data/timezone';select * from time_zone;

本文来自互联网用户投稿，该文观点仅代表作者本人，不代表本站立场。本站仅提供信息存储空间服务，不拥有所有权，不承担相关法律责任。如若转载，请注明出处：http://www.mzph.cn/news/119140.shtml

如若内容造成侵权/违法违规/事实不符，请联系多彩编程网进行投诉反馈email:809451989@qq.com，一经查实，立即删除！

相关文章

电商行业的代言人合伙人模式：创新营销策略的新革命

电商行业的代言人合伙人模式：创新营销策略的新革命

在当今的电商行业中&#xff0c;传统的营销策略已经无法满足品牌日益增长的需求。为了在激烈的市场竞争中脱颖而出&#xff0c;品牌需要寻找更加创新和有效的营销策略。代言人合伙人模式作为一种新型的营销策略&#xff0c;结合了代言人和合伙人模式的特点&#xff0c;为电商行…
阅读更多...
Python学习笔记第七十二天（Matplotlib imread）

Python学习笔记第七十二天（Matplotlib imread）

Python学习笔记第七十二天 Matplotlib imread读取图像数据修改图像裁剪图像图像颜色 后记 Matplotlib imread imread() 方法是 Matplotlib 库中的一个函数&#xff0c;用于从图像文件中读取图像数据。 imread() 方法返回一个 numpy.ndarray 对象&#xff0c;其形状是 (nrows,…
阅读更多...
点击弹出实现模拟百度那样子

点击弹出实现模拟百度那样子

<uni-section title"输入框示例" type"line" padding><view class"dialog-box"><text class"dialog-text">输入内容&#xff1a;{{ value }}</text></view><button class"button" type&qu…
阅读更多...
PL/SQL工具下载地址

PL/SQL工具下载地址

https://www.allroundautomations.com/registered-plsqldev/ 选择需要下载的版本即可
阅读更多...
LuaTable转C#的列表List和字典Dictionary

LuaTable转C#的列表List和字典Dictionary

LuaTable转C#的列表List和字典Dictionaty 介绍lua中创建表测试lua中list表表转成List表转成Dictionary 键值对表表转成Dictionary 多类型键值对表表转成Dictionary 总结 介绍 之前基本都是从C#中的List或者Dictionary转成luaTable&#xff0c;很少会把LuaTable转成C#的List或者…
阅读更多...
Android managed configurations(设置受管理的配置)

Android managed configurations(设置受管理的配置)

文章目录 Android managed configurations(设置受管理的配置)远程配置概览定义托管配置检查托管配置读取和应用托管配置监听托管配置更改向 EMM 发送托管配置反馈键控应用状态的组成部分发送托管配置反馈报告已解决的错误为您的应用添加对键控应用状态的支持第 1 步：将 Google…
阅读更多...
微信小程序通过startLocationUpdate，onLocationChange获取当前地理位置信息，配合腾讯地图解析获取到地址

微信小程序通过startLocationUpdate，onLocationChange获取当前地理位置信息，配合腾讯地图解析获取到地址

先创建个getLocation.js文件 //获取用户当前所在的位置 const getLocation () > {return new Promise((resolve, reject) > {let _locationChangeFn (res) > {resolve(res) // 回传地里位置信息wx.offLocationChange(_locationChangeFn) // 关闭实时定位wx.stopLoc…
阅读更多...
深入浅出排序算法之简单选择排序

深入浅出排序算法之简单选择排序

目录 1. 原理和执行流程 2. 代码实现 3. 性能分析 4. 双向选择排序&#xff08;了解&#xff09; 1. 原理和执行流程 选择排序包含了堆排序和简单选择排序。 每一次从无序区间选出最大&#xff08;或最小&#xff09;的一个元素&#xff0c;存放在无序区间的最后&#xff0…
阅读更多...
Echarts-3D柱状图

Echarts-3D柱状图

通过Echarts的echarts.graphic.extendShape实现真正的3D柱状图 思路就是通过调整顶部面(CubeTop)、左侧面(CubeLeft)、右侧面(CubeRight)来决定柱状图的宽窄 建议优先调整顶部面&#xff0c;一般c1不需要动 // echarts-3D-bar-config.js import Vue from "vue";cons…
阅读更多...
道路数据汇总，全国（2021年+2022年）+重点城市（深圳、上海、武汉、杭州、广州、南京、东莞），格式有shp+xlsx

道路数据汇总，全国（2021年+2022年）+重点城市（深圳、上海、武汉、杭州、广州、南京、东莞），格式有shp+xlsx

昨天推了上海道路数据&#xff0c;今天把已收集到的道路数据打包推给大家&#xff0c;后续有新数据会持续更新&#xff01; 废话不多说&#xff0c;先给数据地址再介绍数据情况&#xff1a; 2021年全国道路数据&#xff1a; 2021年全国道路数据https://www.xcitybox.com/dat…
阅读更多...
uni-app医院智能导诊系统源码

uni-app医院智能导诊系统源码

随着科技的迅速发展&#xff0c;人工智能已经逐渐渗透到我们生活的各个领域。在医疗行业中&#xff0c;智能导诊系统成为了一个备受关注的应用。本文将详细介绍智能导诊系统的概念、技术原理以及在医疗领域中的应用&#xff0c;分析其优势和未来发展趋势。 智能导诊系统通过人工…
阅读更多...
迭代器的封装与反向迭代器

迭代器的封装与反向迭代器

一、反向迭代器 在list模拟实现的过程中&#xff0c;第一次接触了迭代器的封装&#xff0c;将list的指针封装成了一个新的类型&#xff0c;并且以迭代器的基本功能对其进行了运算符重载 反向迭代器是对正向迭代器的封装&#xff0c;并且体现了泛型编程的思想&#xff0c;任意…
阅读更多...
如何在 openSUSE 中使用 Zypper Configuration 设置代理

如何在 openSUSE 中使用 Zypper Configuration 设置代理

如何在 openSUSE 中使用 Zypper Configuration 设置代理 首先&#xff0c;确定问题&#xff1a;设置代理服务器以便 Zypper 能够访问互联网并下载软件包。 亲身经验&#xff1a;我曾在使用 openSUSE 时遇到过类似问题&#xff0c;通过设置代理服务器成功解决。 数据和引证&…
阅读更多...
深入理解JVM虚拟机第十三篇：详解JVM中的程序计数器

深入理解JVM虚拟机第十三篇：详解JVM中的程序计数器

文章目录 一：程序计数器 1：概念 2：官方说法 3：图解 4：特点
阅读更多...
C++初阶：C/C++内存管理

C++初阶：C/C++内存管理

一.C/C内存分布 先来回顾一下C语言内存分区示意图如下&#xff1a; 代码区&#xff1a; 程序执行代码一般存放在代码区&#xff0c;字符串常量以及define定义的常量也可能存放在代码区。 常量区&#xff1a; 字符串&#xff0c;数字等常量以及const修饰的全局变量往往存放在…
阅读更多...
day51 --动态规划10

day51 --动态规划10

121. 买卖股票的最佳时机 122.买卖股票的最佳时机II 第一题&#xff1a;买卖股票的最佳时机 给定一个数组 prices &#xff0c;它的第 i 个元素 prices[i] 表示一支给定股票第 i 天的价格。 你只能选择 某一天 买入这只股票&#xff0c;并选择在 未来的某一个不同的日子 卖出…
阅读更多...
员工福利平台设计方案

员工福利平台设计方案

需求背景&#xff1a; 1、杭州行政希望给员工有一个福利平台&#xff0c;可以通过该福利平台&#xff0c;一方面可以结合公司周围的实体店&#xff0c;给到员工一些福利的商品&#xff0c;员工可以自行去这些商家进行消费。 2、公司可以通过福利平台&#xff0c;给员工账户进…
阅读更多...
K8s 部署 CNI 网络组件+k8s 多master集群部署+负载均衡

K8s 部署 CNI 网络组件+k8s 多master集群部署+负载均衡

------------------------------ 部署 CNI 网络组件 ------------------------------ ---------- 部署 flannel ---------- K8S 中 Pod 网络通信&#xff1a; ●Pod 内容器与容器之间的通信 在同一个 Pod 内的容器&#xff08;Pod 内的容器是不会跨宿主机的&#xff09;共享同一…
阅读更多...
实验六：DHCP、DNS、Apache、FTP服务器的安装和配置

实验六：DHCP、DNS、Apache、FTP服务器的安装和配置

1. (其它) 掌握Linux下DHCP、DNS、Apache、FTP服务器的安装和配置&#xff0c;在Linux服务器上部署JavaWeb应用 完成单元八的实训内容。 1、安装 JDK 2、安装 MySQL 3、部署JavaWeb应用 安装jdk 教程连接&#xff1a;linux安装jdk8详细步骤-CSDN博客 Jdk来源&#xff1a;linu…
阅读更多...
数仓经典面试题

数仓经典面试题

1.什么是数据仓库&#xff1f;请谈谈你对数据仓库的理解。 数据仓库是一个用于存储和管理数据的系统&#xff0c;它可以将分散的、异构的数据源中的数据进行抽取、转换、清洗和整合&#xff0c;然后按照一定的模型和架构进行组织和存储&#xff0c;以便更好地支持决策分析和业…
阅读更多...
最新文章

Copyright @ 2022~2023