Spring Cloud Ribbon负载均衡解析
toyiye 2024-09-12 20:59 4 浏览 0 评论
Ribbon
Ribbon是Netflix发布的云中间层服务开源项目,其主要功能是提供客户端实现负载均衡算法。Ribbon客户端组件提供一系列完善的配置项如连接超时,重试等。简单的说,Ribbon是一个客户端负载均衡器,我们可以在配置文件中Load Balancer后面的所有机器,Ribbon会自动的帮助你基于某种规则(如简单轮询,随机连接等)去连接这些机器,我们也很容易使用Ribbon实现自定义的负载均衡算法。
负载均衡策略
IRule:所有负载均衡策略的父接口,里边的核心方法就是choose方法,用来选择一个服务实例。
AbstractLoadBalancerRule:是一个抽象类,里边主要定义了一个ILoadBalancer负载均衡器,负载均衡器辅助负责均衡策略选取合适的服务端实例。
spring cloud ribbon大概提供了以下几种负载均衡策略。
1.RoundRobinRule: 轮询选择server
2.RandomRule:随机选择一个server
3.RetryRule:先按照指定的策略获取服务,如果获取服务失败则在指定时间内进行重试,获取可用的服务
4.WeightedResponseTimeRule:对RoundRobinRule的扩展,响应速度越快的实例选择权重越多大,越容易被选择
5.BestAvailableRule:会先过滤掉由于多次访问故障而处于断路器跳闸状态的服务,然后选择一个并发量最小的服务
6.AvailabilityFilteringRule:过滤掉那些因为一直连接失败的被标记为circuit tripped的后端server,并过滤掉那些高并发的的后端server(active connections 超过配置的阈值)
7.ZoneAvoidanceRule:复合判断server所在区域的性能和server的可用性,选择服务器
源码分析
RoundRobinRule:
public class RoundRobinRule extends AbstractLoadBalancerRule {
private AtomicInteger nextServerCyclicCounter;
private static final boolean AVAILABLE_ONLY_SERVERS = true;
private static final boolean ALL_SERVERS = false;
private static Logger log = LoggerFactory.getLogger(RoundRobinRule.class);
public RoundRobinRule() {
this.nextServerCyclicCounter = new AtomicInteger(0);
}
public RoundRobinRule(ILoadBalancer lb) {
this();
this.setLoadBalancer(lb);
}
public Server choose(ILoadBalancer lb, Object key) {
if (lb == null) {
log.warn("no load balancer");
return null;
} else {
Server server = null;
int count = 0;
while(true) {
if (server == null && count++ < 10) {
List<Server> reachableServers = lb.getReachableServers();
List<Server> allServers = lb.getAllServers();
int upCount = reachableServers.size();
int serverCount = allServers.size();
if (upCount != 0 && serverCount != 0) {
int nextServerIndex = this.incrementAndGetModulo(serverCount);
server = (Server)allServers.get(nextServerIndex);
if (server == null) {
Thread.yield();
} else {
if (server.isAlive() && server.isReadyToServe()) {
return server;
}
server = null;
}
continue;
}
log.warn("No up servers available from load balancer: " + lb);
return null;
}
if (count >= 10) {
log.warn("No available alive servers after 10 tries from load balancer: " + lb);
}
return server;
}
}
}
private int incrementAndGetModulo(int modulo) {
int current;
int next;
do {
current = this.nextServerCyclicCounter.get();
next = (current + 1) % modulo;
} while(!this.nextServerCyclicCounter.compareAndSet(current, next));
return next;
}
public Server choose(Object key) {
return this.choose(this.getLoadBalancer(), key);
}
public void initWithNiwsConfig(IClientConfig clientConfig) {
}
这个类的choose(ILoadBalancer lb, Object key)函数整体逻辑是这样的:
开启一个计数器count,在while循环中遍历服务清单
while(true) {
if (server == null && count++ < 10) {
List<Server> reachableServers = lb.getReachableServers();
List<Server> allServers = lb.getAllServers();
........
获取清单之前先通过incrementAndGetModulo方法获取一个下标,
private int incrementAndGetModulo(int modulo) {
int current;
int next;
do {
current = this.nextServerCyclicCounter.get();
next = (current + 1) % modulo;
} while(!this.nextServerCyclicCounter.compareAndSet(current, next));
return next;
}
这个下标是一个不断自增长的数先加1然后和服务清单总数取模之后获取到的(所以这个下标从来不会越界),
拿着下标再去服务清单列表中取服务,每次循环计数器都会加1,如果连续10次都没有取到服务,则会报一个警告No available alive servers after 10 tries from load balancer: XXXX。
RandomRule:
public class RandomRule extends AbstractLoadBalancerRule {
public RandomRule() {
}
@SuppressWarnings({"RCN_REDUNDANT_NULLCHECK_OF_NULL_VALUE"})
public Server choose(ILoadBalancer lb, Object key) {
if (lb == null) {
return null;
} else {
Server server = null;
while(server == null) {
if (Thread.interrupted()) {
return null;
}
List<Server> upList = lb.getReachableServers();
List<Server> allList = lb.getAllServers();
int serverCount = allList.size();
if (serverCount == 0) {
return null;
}
int index = this.chooseRandomInt(serverCount);
server = (Server)upList.get(index);
if (server == null) {
Thread.yield();
} else {
if (server.isAlive()) {
return server;
}
server = null;
Thread.yield();
}
}
return server;
}
}
protected int chooseRandomInt(int serverCount) {
return ThreadLocalRandom.current().nextInt(serverCount);
}
public Server choose(Object key) {
return this.choose(this.getLoadBalancer(), key);
}
public void initWithNiwsConfig(IClientConfig clientConfig) {
}
在RoundRobinRule策略前面的方法一样,通过ILoadBalancer获取所有可用的服务清单,所有注册的服务清单。
List<Server> upList = lb.getReachableServers();
List<Server> allList = lb.getAllServers();
RandomRule是根据ThreadLocalRandom获取一个随机的服务。
生成随机数是很常见的需求。 JAVA 提供 Random生成随机数。但是它在多线程环境中性能并不高。
简单来说,Random 之所以在多线程环境中性能不高的原因是多个线程共享同一个 Random 实例并进行争夺。
为了解决这个限制,JAVA 在 JDK 7 中引入了 ThreadLocalRandom 类,用于在多线程环境下生产随机数。
RetryRule:
public class RetryRule extends AbstractLoadBalancerRule {
IRule subRule = new RoundRobinRule();
long maxRetryMillis = 500L;
public RetryRule() {
}
public RetryRule(IRule subRule) {
this.subRule = (IRule)(subRule != null ? subRule : new RoundRobinRule());
}
public RetryRule(IRule subRule, long maxRetryMillis) {
this.subRule = (IRule)(subRule != null ? subRule : new RoundRobinRule());
this.maxRetryMillis = maxRetryMillis > 0L ? maxRetryMillis : 500L;
}
public void setRule(IRule subRule) {
this.subRule = (IRule)(subRule != null ? subRule : new RoundRobinRule());
}
public IRule getRule() {
return this.subRule;
}
public void setMaxRetryMillis(long maxRetryMillis) {
if (maxRetryMillis > 0L) {
this.maxRetryMillis = maxRetryMillis;
} else {
this.maxRetryMillis = 500L;
}
}
public long getMaxRetryMillis() {
return this.maxRetryMillis;
}
public void setLoadBalancer(ILoadBalancer lb) {
super.setLoadBalancer(lb);
this.subRule.setLoadBalancer(lb);
}
public Server choose(ILoadBalancer lb, Object key) {
long requestTime = System.currentTimeMillis();
long deadline = requestTime + this.maxRetryMillis;
Server answer = null;
answer = this.subRule.choose(key);
if ((answer == null || !answer.isAlive()) && System.currentTimeMillis() < deadline) {
InterruptTask task = new InterruptTask(deadline - System.currentTimeMillis());
while(!Thread.interrupted()) {
answer = this.subRule.choose(key);
if (answer != null && answer.isAlive() || System.currentTimeMillis() >= deadline) {
break;
}
Thread.yield();
}
task.cancel();
}
return answer != null && answer.isAlive() ? answer : null;
}
public Server choose(Object key) {
return this.choose(this.getLoadBalancer(), key);
}
public void initWithNiwsConfig(IClientConfig clientConfig) {
}
}
RetryRule中又定义了一个subRule,它的实现类是RoundRobinRule。
IRule subRule = new RoundRobinRule();
也可以传入其他的负载均衡策略,如果不传入默认是RoundRobinRule。
然后在RetryRule的choose(ILoadBalancer lb, Object key)方法中,每次还是采用传入的策略中的choose规则来选择一个服务实例,如果选到的实例正常就返回,如果选择的服务实例为null或者已经失效,则在失效时间deadline之前不断的进行重试,如果超过了deadline还是没取到则会返回一个null。
WeightedResponseTimeRule:
public class WeightedResponseTimeRule extends RoundRobinRule {
public static final IClientConfigKey<Integer> WEIGHT_TASK_TIMER_INTERVAL_CONFIG_KEY = new IClientConfigKey<Integer>() {
public String key() {
return "ServerWeightTaskTimerInterval";
}
public String toString() {
return this.key();
}
public Class<Integer> type() {
return Integer.class;
}
};
public static final int DEFAULT_TIMER_INTERVAL = 30000;
private int serverWeightTaskTimerInterval = 30000;
private static final Logger logger = LoggerFactory.getLogger(WeightedResponseTimeRule.class);
private volatile List<Double> accumulatedWeights = new ArrayList();
private final Random random = new Random();
protected Timer serverWeightTimer = null;
protected AtomicBoolean serverWeightAssignmentInProgress = new AtomicBoolean(false);
String name = "unknown";
public WeightedResponseTimeRule() {
}
public WeightedResponseTimeRule(ILoadBalancer lb) {
super(lb);
}
public void setLoadBalancer(ILoadBalancer lb) {
super.setLoadBalancer(lb);
if (lb instanceof BaseLoadBalancer) {
this.name = ((BaseLoadBalancer)lb).getName();
}
this.initialize(lb);
}
void initialize(ILoadBalancer lb) {
if (this.serverWeightTimer != null) {
this.serverWeightTimer.cancel();
}
this.serverWeightTimer = new Timer("NFLoadBalancer-serverWeightTimer-" + this.name, true);
this.serverWeightTimer.schedule(new WeightedResponseTimeRule.DynamicServerWeightTask(), 0L, (long)this.serverWeightTaskTimerInterval);
WeightedResponseTimeRule.ServerWeight sw = new WeightedResponseTimeRule.ServerWeight();
sw.maintainWeights();
Runtime.getRuntime().addShutdownHook(new Thread(new Runnable() {
public void run() {
WeightedResponseTimeRule.logger.info("Stopping NFLoadBalancer-serverWeightTimer-" + WeightedResponseTimeRule.this.name);
WeightedResponseTimeRule.this.serverWeightTimer.cancel();
}
}));
}
public void shutdown() {
if (this.serverWeightTimer != null) {
logger.info("Stopping NFLoadBalancer-serverWeightTimer-" + this.name);
this.serverWeightTimer.cancel();
}
}
List<Double> getAccumulatedWeights() {
return Collections.unmodifiableList(this.accumulatedWeights);
}
@SuppressWarnings({"RCN_REDUNDANT_NULLCHECK_OF_NULL_VALUE"})
public Server choose(ILoadBalancer lb, Object key) {
if (lb == null) {
return null;
} else {
Server server = null;
while(server == null) {
List<Double> currentWeights = this.accumulatedWeights;
if (Thread.interrupted()) {
return null;
}
List<Server> allList = lb.getAllServers();
int serverCount = allList.size();
if (serverCount == 0) {
return null;
}
int serverIndex = 0;
double maxTotalWeight = currentWeights.size() == 0 ? 0.0D : (Double)currentWeights.get(currentWeights.size() - 1);
if (maxTotalWeight >= 0.001D && serverCount == currentWeights.size()) {
double randomWeight = this.random.nextDouble() * maxTotalWeight;
int n = 0;
for(Iterator var13 = currentWeights.iterator(); var13.hasNext(); ++n) {
Double d = (Double)var13.next();
if (d >= randomWeight) {
serverIndex = n;
break;
}
}
server = (Server)allList.get(serverIndex);
} else {
server = super.choose(this.getLoadBalancer(), key);
if (server == null) {
return server;
}
}
if (server == null) {
Thread.yield();
} else {
if (server.isAlive()) {
return server;
}
server = null;
}
}
return server;
}
}
void setWeights(List<Double> weights) {
this.accumulatedWeights = weights;
}
public void initWithNiwsConfig(IClientConfig clientConfig) {
super.initWithNiwsConfig(clientConfig);
this.serverWeightTaskTimerInterval = (Integer)clientConfig.get(WEIGHT_TASK_TIMER_INTERVAL_CONFIG_KEY, 30000);
}
class ServerWeight {
ServerWeight() {
}
public void maintainWeights() {
ILoadBalancer lb = WeightedResponseTimeRule.this.getLoadBalancer();
if (lb != null) {
if (WeightedResponseTimeRule.this.serverWeightAssignmentInProgress.compareAndSet(false, true)) {
try {
WeightedResponseTimeRule.logger.info("Weight adjusting job started");
AbstractLoadBalancer nlb = (AbstractLoadBalancer)lb;
LoadBalancerStats stats = nlb.getLoadBalancerStats();
if (stats != null) {
double totalResponseTime = 0.0D;
ServerStats ss;
for(Iterator var6 = nlb.getAllServers().iterator(); var6.hasNext(); totalResponseTime += ss.getResponseTimeAvg()) {
Server server = (Server)var6.next();
ss = stats.getSingleServerStat(server);
}
Double weightSoFar = 0.0D;
List<Double> finalWeights = new ArrayList();
Iterator var20 = nlb.getAllServers().iterator();
while(var20.hasNext()) {
Server serverx = (Server)var20.next();
ServerStats ssx = stats.getSingleServerStat(serverx);
double weight = totalResponseTime - ssx.getResponseTimeAvg();
weightSoFar = weightSoFar + weight;
finalWeights.add(weightSoFar);
}
WeightedResponseTimeRule.this.setWeights(finalWeights);
return;
}
} catch (Exception var16) {
WeightedResponseTimeRule.logger.error("Error calculating server weights", var16);
return;
} finally {
WeightedResponseTimeRule.this.serverWeightAssignmentInProgress.set(false);
}
}
}
}
}
class DynamicServerWeightTask extends TimerTask {
DynamicServerWeightTask() {
}
public void run() {
WeightedResponseTimeRule.ServerWeight serverWeight = WeightedResponseTimeRule.this.new ServerWeight();
try {
serverWeight.maintainWeights();
} catch (Exception var3) {
WeightedResponseTimeRule.logger.error("Error running DynamicServerWeightTask for {}", WeightedResponseTimeRule.this.name, var3);
}
}
}
}
weightedResponseTimeRuleRoundRobinRule的一个子类,在WeightedResponseTimeRule中对RoundRobinRule的功能进行了扩展,WeightedResponseTimeRule中会根据每一个实例的运行情况来给计算出该实例的一个权重,然后在挑选实例的时候则根据权重进行挑选,这样能够实现更优的实例调用。WeightedResponseTimeRule中有一个名叫DynamicServerWeightTask的定时任务,默认情况下每隔30秒会计算一次各个服务实例的权重,权重的计算规则也很简单,如果一个服务的平均响应时间越短则权重越大,那么该服务实例被选中执行任务的概率也就越大。
BestAvailableRule:
public class BestAvailableRule extends ClientConfigEnabledRoundRobinRule {
private LoadBalancerStats loadBalancerStats;
public BestAvailableRule() {
}
public Server choose(Object key) {
if (this.loadBalancerStats == null) {
return super.choose(key);
} else {
List<Server> serverList = this.getLoadBalancer().getAllServers();
int minimalConcurrentConnections = 2147483647;
long currentTime = System.currentTimeMillis();
Server chosen = null;
Iterator var7 = serverList.iterator();
while(var7.hasNext()) {
Server server = (Server)var7.next();
ServerStats serverStats = this.loadBalancerStats.getSingleServerStat(server);
if (!serverStats.isCircuitBreakerTripped(currentTime)) {
int concurrentConnections = serverStats.getActiveRequestsCount(currentTime);
if (concurrentConnections < minimalConcurrentConnections) {
minimalConcurrentConnections = concurrentConnections;
chosen = server;
}
}
}
if (chosen == null) {
return super.choose(key);
} else {
return chosen;
}
}
}
public void setLoadBalancer(ILoadBalancer lb) {
super.setLoadBalancer(lb);
if (lb instanceof AbstractLoadBalancer) {
this.loadBalancerStats = ((AbstractLoadBalancer)lb).getLoadBalancerStats();
}
}
}
BestAvailableRule继承自ClientConfigEnabledRoundRobinRule,根据loadBalancerStats中保存的服务实例的状态信息来过滤掉失效的服务实例的功能,然后顺便找出并发请求最小的服务实例来使用。然而loadBalancerStats有可能为null,如果loadBalancerStats为null,则BestAvailableRule将采用它的父类即ClientConfigEnabledRoundRobinRule的服务选取策略(线性轮询)。
AvailabilityFilteringRule:
public Server choose(Object key) {
int count = 0;
for(Server server = this.roundRobinRule.choose(key); count++ <= 10; server = this.roundRobinRule.choose(key)) {
if (this.predicate.apply(new PredicateKey(server))) {
return server;
}
}
return super.choose(key);
}
轮循选一个,判读是否满足条件,如果满足则返回,超过10次,则调用父类的choose方法选择。
apply规则:
需要满足俩个条件,断路器闭合,调用服务的并发请求数小于限制。
10次之后还不满足,则调用父类的choose方法,来看下PredicateBasedRule的choose实现
public Server choose(Object key) {
ILoadBalancer lb = this.getLoadBalancer();
Optional<Server> server = this.getPredicate().chooseRoundRobinAfterFiltering(lb.getAllServers(), key);
return server.isPresent() ? (Server)server.get() : null;
}
chooseRoundRobinAfterFiltering中是如何实现的呢
- 获取所有的服务实例
- 遍历服务列表,过滤掉不满足条件的
- 在满足条件的服务列表中,再进行RoundRibbon算法,选出服务
public Optional<Server> chooseRoundRobinAfterFiltering(List<Server> servers, Object loadBalancerKey) {
List<Server> eligible = this.getEligibleServers(servers, loadBalancerKey);
return eligible.size() == 0 ? Optional.absent() : Optional.of(eligible.get(this.incrementAndGetModulo(eligible.size())));
}
public List<Server> getEligibleServers(List<Server> servers, Object loadBalancerKey) {
if (loadBalancerKey == null) {
return ImmutableList.copyOf(Iterables.filter(servers, this.getServerOnlyPredicate()));
} else {
List<Server> results = Lists.newArrayList();
Iterator var4 = servers.iterator();
while(var4.hasNext()) {
Server server = (Server)var4.next();
if (this.apply(new PredicateKey(loadBalancerKey, server))) {
results.add(server);
}
}
return results;
}
}
AvailabilityFilteringRule在RoundRibbon的基础上,选择满足条件的服务,如果10次了还没得到,则在满足条件的服务列表中,再用RoundRibbon算法选择。
ZoneAvoidanceRule:
public class ZoneAvoidanceRule extends PredicateBasedRule {
private static final Random random = new Random();
//使用CompositePredicate来进行服务实例清单过滤。
//组合过滤条件
private CompositePredicate compositePredicate;
public ZoneAvoidanceRule() {
super();
//主过滤条件
ZoneAvoidancePredicate zonePredicate = new ZoneAvoidancePredicate(this);
//次过滤条件
AvailabilityPredicate availabilityPredicate = new AvailabilityPredicate(this);
compositePredicate = createCompositePredicate(zonePredicate, availabilityPredicate);
}
......
}
public class CompositePredicate extends AbstractServerPredicate {
//主过滤条件
private AbstractServerPredicate delegate;
//次过滤条件列表
private List<AbstractServerPredicate> fallbacks = Lists.newArrayList();
private int minimalFilteredServers = 1;
private float minimalFilteredPercentage = 0;
@Override
public boolean apply(@Nullable PredicateKey input) {
return delegate.apply(input);
}
......
@Override
public List<Server> getEligibleServers(List<Server> servers, Object loadBalancerKey) {
//使用主过滤条件对所有实例过滤并返回过滤后的清单
List<Server> result = super.getEligibleServers(servers, loadBalancerKey);
Iterator<AbstractServerPredicate> i = fallbacks.iterator();
//依次使用次过滤条件对主过滤条件的结果进行过滤
//不论是主过滤条件还是次过滤条件,都需要判断下面两个条件
//只要有一个条件符合,就不再过滤,将当前结果返回供线性轮询
//算法选择
//第1个条件:过滤后的实例总数>=最小过滤实例数(默认为1)
//第2个条件:过滤互的实例比例>最小过滤百分比(默认为0)
while (!(result.size() >= minimalFilteredServers && result.size() > (int) (servers.size() * minimalFilteredPercentage))
&& i.hasNext()) {
AbstractServerPredicate predicate = i.next();
result = predicate.getEligibleServers(servers, loadBalancerKey);
}
return result;
}
}
/服务实例所在的Zone必须可用
public class ZoneAvoidancePredicate extends AbstractServerPredicate {
......
@Override
public boolean apply(@Nullable PredicateKey input) {
if (!ENABLED.get()) {
return true;
}
String serverZone = input.getServer().getZone();
if (serverZone == null) {
// there is no zone information from the server, we do not want to filter
// out this server
return true;
}
LoadBalancerStats lbStats = getLBStats();
if (lbStats == null) {
// no stats available, do not filter
return true;
}
if (lbStats.getAvailableZones().size() <= 1) {
// only one zone is available, do not filter
return true;
}
Map<String, ZoneSnapshot> zoneSnapshot = ZoneAvoidanceRule.createSnapshot(lbStats);
if (!zoneSnapshot.keySet().contains(serverZone)) {
// The server zone is unknown to the load balancer, do not filter it out
return true;
}
logger.debug("Zone snapshots: {}", zoneSnapshot);
Set<String> availableZones = ZoneAvoidanceRule.getAvailableZones(zoneSnapshot, triggeringLoad.get(), triggeringBlackoutPercentage.get());
logger.debug("Available zones: {}", availableZones);
//服务实例所在的Zone必须可用
if (availableZones != null) {
return availableZones.contains(input.getServer().getZone());
} else {
return false;
}
}
}
ZoneAvoidanceRule是PredicateBasedRule的一个实现类,只不过这里多一个过滤条件,ZoneAvoidanceRule中的过滤条件是以ZoneAvoidancePredicate为主过滤条件和以AvailabilityPredicate为次过滤条件组成的一个叫做CompositePredicate的组合过滤条件,过滤成功之后,继续采用线性轮询的方式从过滤结果中选择一个出来。
相关推荐
- 为何越来越多的编程语言使用JSON(为什么编程)
-
JSON是JavascriptObjectNotation的缩写,意思是Javascript对象表示法,是一种易于人类阅读和对编程友好的文本数据传递方法,是JavaScript语言规范定义的一个子...
- 何时在数据库中使用 JSON(数据库用json格式存储)
-
在本文中,您将了解何时应考虑将JSON数据类型添加到表中以及何时应避免使用它们。每天?分享?最新?软件?开发?,Devops,敏捷?,测试?以及?项目?管理?最新?,最热门?的?文章?,每天?花?...
- MySQL 从零开始:05 数据类型(mysql数据类型有哪些,并举例)
-
前面的讲解中已经接触到了表的创建,表的创建是对字段的声明,比如:上述语句声明了字段的名称、类型、所占空间、默认值和是否可以为空等信息。其中的int、varchar、char和decimal都...
- JSON对象花样进阶(json格式对象)
-
一、引言在现代Web开发中,JSON(JavaScriptObjectNotation)已经成为数据交换的标准格式。无论是从前端向后端发送数据,还是从后端接收数据,JSON都是不可或缺的一部分。...
- 深入理解 JSON 和 Form-data(json和formdata提交区别)
-
在讨论现代网络开发与API设计的语境下,理解客户端和服务器间如何有效且可靠地交换数据变得尤为关键。这里,特别值得关注的是两种主流数据格式:...
- JSON 语法(json 语法 priority)
-
JSON语法是JavaScript语法的子集。JSON语法规则JSON语法是JavaScript对象表示法语法的子集。数据在名称/值对中数据由逗号分隔花括号保存对象方括号保存数组JS...
- JSON语法详解(json的语法规则)
-
JSON语法规则JSON语法是JavaScript对象表示法语法的子集。数据在名称/值对中数据由逗号分隔大括号保存对象中括号保存数组注意:json的key是字符串,且必须是双引号,不能是单引号...
- MySQL JSON数据类型操作(mysql的json)
-
概述mysql自5.7.8版本开始,就支持了json结构的数据存储和查询,这表明了mysql也在不断的学习和增加nosql数据库的有点。但mysql毕竟是关系型数据库,在处理json这种非结构化的数据...
- JSON的数据模式(json数据格式示例)
-
像XML模式一样,JSON数据格式也有Schema,这是一个基于JSON格式的规范。JSON模式也以JSON格式编写。它用于验证JSON数据。JSON模式示例以下代码显示了基本的JSON模式。{"...
- 前端学习——JSON格式详解(后端json格式)
-
JSON(JavaScriptObjectNotation)是一种轻量级的数据交换格式。易于人阅读和编写。同时也易于机器解析和生成。它基于JavaScriptProgrammingLa...
- 什么是 JSON:详解 JSON 及其优势(什么叫json)
-
现在程序员还有谁不知道JSON吗?无论对于前端还是后端,JSON都是一种常见的数据格式。那么JSON到底是什么呢?JSON的定义...
- PostgreSQL JSON 类型:处理结构化数据
-
PostgreSQL提供JSON类型,以存储结构化数据。JSON是一种开放的数据格式,可用于存储各种类型的值。什么是JSON类型?JSON类型表示JSON(JavaScriptO...
- JavaScript:JSON、三种包装类(javascript 包)
-
JOSN:我们希望可以将一个对象在不同的语言中进行传递,以达到通信的目的,最佳方式就是将一个对象转换为字符串的形式JSON(JavaScriptObjectNotation)-JS的对象表示法...
- Python数据分析 只要1分钟 教你玩转JSON 全程干货
-
Json简介:Json,全名JavaScriptObjectNotation,JSON(JavaScriptObjectNotation(记号、标记))是一种轻量级的数据交换格式。它基于J...
- 比较一下JSON与XML两种数据格式?(json和xml哪个好)
-
JSON(JavaScriptObjectNotation)和XML(eXtensibleMarkupLanguage)是在日常开发中比较常用的两种数据格式,它们主要的作用就是用来进行数据的传...
你 发表评论:
欢迎- 一周热门
- 最近发表
- 标签列表
-
- r语言矩阵 (127)
- browsererror (114)
- exportexcel (119)
- cv2.bitwise_not (137)
- dump命令 (128)
- es6concat (126)
- heapify (127)
- java.security.egd (130)
- javax.annotation (117)
- jsstringsplit (117)
- js数字 (115)
- maven编译 (132)
- mysqlleft (128)
- nodejsbuffer (149)
- org.apache.commons.httpclient (126)
- org.jsoup (141)
- org.springframework.web (128)
- robotframework-ride (115)
- setnocounton (141)
- socket.gethostbyname (122)
- sqlmid (121)
- time.strptime (133)
- vscode格式化 (125)
- win32con (129)
- window.localstorage (126)