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高可用服务架构设计(12) - 基于request cache请求缓存技术优化批量商品数据查询接口

2019.07.15 00:54 314浏览

回顾执行流程

1、创建command,2种command类型

2、执行command,4种执行方式

3、查找是否开启了request cache,是否有请求缓存,如果有缓存,直接取用缓存,返回结果

首先,reqeust context(请求上下文)

一般在一个web应用中,Hystrix会在一个filter里面,对每个请求都添加一个请求上下文

即Tomcat容器内,每一次请求,就是一次请求上下文

然后在这次请求上下文中,我们会去执行N多代码,调用N多依赖服务,有的依赖服务可能还会调用好几次

在一次请求上下文中,如果有多个command,参数及调用的接口也是一样的,其实结果也可以认为是一样的

那么就可以让第一次command执行返回的结果缓存在内存,然后这个请求上下文中,后续的其他对这个依赖的调用全部从内存中取用缓存结果即可

这样避免在一次请求上下文中多次执行一样的command,避免重复执行网络请求,从而提升整个请求的性能

  • request cache的原理图

对于请求缓存(request caching),请求合并(request collapsing),请求日志(request log),等等技术,都必须自己管理HystrixReuqestContext的声明周期

在一个请求执行之前,都必须先初始化一个request context

HystrixRequestContext context = HystrixRequestContext.initializeContext();

然后在请求结束之后,需要关闭request context

context.shutdown();

一般来说,在java web的应用中,都是通过filter过滤器来实现的

public class HystrixRequestContextServletFilter implements Filter {

    public void doFilter(ServletRequest request, ServletResponse response, FilterChain chain) 
     throws IOException, ServletException {
        HystrixRequestContext context = HystrixRequestContext.initializeContext();
        try {
            chain.doFilter(request, response);
        } finally {
            context.shutdown();
        }
    }
}

@Bean
public FilterRegistrationBean indexFilterRegistration() {
    FilterRegistrationBean registration = new FilterRegistrationBean(new IndexFilter());
    registration.addUrlPatterns("/");
    return registration;
}

结合业务背景,我们做了一个批量查询商品数据的接口,在这个里面,我们其实通过HystrixObservableCommand一次性批量查询多个商品id的数据

但是这里有个问题,如果说nginx在本地缓存失效了,重新获取一批缓存,传递过来的productId都没有进行去重,1,1,2,2,5,6,7

那么可能说,商品id出现了重复,如果按照我们之前的业务逻辑,可能就会重复对productId=1的商品查询两次,productId=2的商品查询两次

我们对批量查询商品数据的接口,可以用request cache做一个优化,就是说一次请求,就是一次request context,对相同的商品查询只能执行一次,其余的都走request cache

public class CommandUsingRequestCache extends HystrixCommand<Boolean> {

    private final int value;

    protected CommandUsingRequestCache(int value) {
        super(HystrixCommandGroupKey.Factory.asKey("ExampleGroup"));
        this.value = value;
    }

    @Override
    protected Boolean run() {
        return value == 0 || value % 2 == 0;
    }

    @Override
    protected String getCacheKey() {
        return String.valueOf(value);
    }

}

@Test
public void testWithCacheHits() {
    HystrixRequestContext context = HystrixRequestContext.initializeContext();
    try {
        CommandUsingRequestCache command2a = new CommandUsingRequestCache(2);
        CommandUsingRequestCache command2b = new CommandUsingRequestCache(2);

        assertTrue(command2a.execute());
        // this is the first time we've executed this command with
        // the value of "2" so it should not be from cache
        assertFalse(command2a.isResponseFromCache());

        assertTrue(command2b.execute());
        // this is the second time we've executed this command with
        // the same value so it should return from cache
        assertTrue(command2b.isResponseFromCache());
    } finally {
        context.shutdown();
    }

    // start a new request context
    context = HystrixRequestContext.initializeContext();
    try {
        CommandUsingRequestCache command3b = new CommandUsingRequestCache(2);
        assertTrue(command3b.execute());
        // this is a new request context so this 
        // should not come from cache
        assertFalse(command3b.isResponseFromCache());
    } finally {
        context.shutdown();
    }
}

缓存的手动清理

public static class GetterCommand extends HystrixCommand<String> {

    private static final HystrixCommandKey GETTER_KEY = HystrixCommandKey.Factory.asKey("GetterCommand");
    private final int id;

    public GetterCommand(int id) {
        super(Setter.withGroupKey(HystrixCommandGroupKey.Factory.asKey("GetSetGet"))
                .andCommandKey(GETTER_KEY));
        this.id = id;
    }

    @Override
    protected String run() {
        return prefixStoredOnRemoteDataStore + id;
    }

    @Override
    protected String getCacheKey() {
        return String.valueOf(id);
    }

    /**
     * Allow the cache to be flushed for this object.
     * 
     * @param id
     *            argument that would normally be passed to the command
     */
    public static void flushCache(int id) {
        HystrixRequestCache.getInstance(GETTER_KEY,
                HystrixConcurrencyStrategyDefault.getInstance()).clear(String.valueOf(id));
    }

}

public static class SetterCommand extends HystrixCommand<Void> {

    private final int id;
    private final String prefix;

    public SetterCommand(int id, String prefix) {
        super(HystrixCommandGroupKey.Factory.asKey("GetSetGet"));
        this.id = id;
        this.prefix = prefix;
    }

    @Override
    protected Void run() {
        // persist the value against the datastore
        prefixStoredOnRemoteDataStore = prefix;
        // flush the cache
        GetterCommand.flushCache(id);
        // no return value
        return null;
    }
}

回顾执行流程

1、创建command,2种command类型

2、执行command,4种执行方式

3、查找是否开启了request cache,是否有请求缓存,如果有缓存,直接取用缓存,返回结果

首先,reqeust context(请求上下文)

一般在一个web应用中,Hystrix会在一个filter里面,对每个请求都添加一个请求上下文

即Tomcat容器内,每一次请求,就是一次请求上下文

然后在这次请求上下文中,我们会去执行N多代码,调用N多依赖服务,有的依赖服务可能还会调用好几次

在一次请求上下文中,如果有多个command,参数及调用的接口也是一样的,其实结果也可以认为是一样的

那么就可以让第一次command执行返回的结果缓存在内存,然后这个请求上下文中,后续的其他对这个依赖的调用全部从内存中取用缓存结果即可

这样避免在一次请求上下文中多次执行一样的command,避免重复执行网络请求,从而提升整个请求的性能

  • request cache的原理图

对于请求缓存(request caching),请求合并(request collapsing),请求日志(request log),等等技术,都必须自己管理HystrixReuqestContext的声明周期

在一个请求执行之前,都必须先初始化一个request context

HystrixRequestContext context = HystrixRequestContext.initializeContext();

然后在请求结束之后,需要关闭request context

context.shutdown();

一般来说,在java web的应用中,都是通过filter过滤器来实现的

public class HystrixRequestContextServletFilter implements Filter {

    public void doFilter(ServletRequest request, ServletResponse response, FilterChain chain) 
     throws IOException, ServletException {
        HystrixRequestContext context = HystrixRequestContext.initializeContext();
        try {
            chain.doFilter(request, response);
        } finally {
            context.shutdown();
        }
    }
}

@Bean
public FilterRegistrationBean indexFilterRegistration() {
    FilterRegistrationBean registration = new FilterRegistrationBean(new IndexFilter());
    registration.addUrlPatterns("/");
    return registration;
}

结合业务背景,我们做了一个批量查询商品数据的接口,在这个里面,我们其实通过HystrixObservableCommand一次性批量查询多个商品id的数据

但是这里有个问题,如果说nginx在本地缓存失效了,重新获取一批缓存,传递过来的productId都没有进行去重,1,1,2,2,5,6,7

那么可能说,商品id出现了重复,如果按照我们之前的业务逻辑,可能就会重复对productId=1的商品查询两次,productId=2的商品查询两次

我们对批量查询商品数据的接口,可以用request cache做一个优化,就是说一次请求,就是一次request context,对相同的商品查询只能执行一次,其余的都走request cache

public class CommandUsingRequestCache extends HystrixCommand<Boolean> {

    private final int value;

    protected CommandUsingRequestCache(int value) {
        super(HystrixCommandGroupKey.Factory.asKey("ExampleGroup"));
        this.value = value;
    }

    @Override
    protected Boolean run() {
        return value == 0 || value % 2 == 0;
    }

    @Override
    protected String getCacheKey() {
        return String.valueOf(value);
    }

}

@Test
public void testWithCacheHits() {
    HystrixRequestContext context = HystrixRequestContext.initializeContext();
    try {
        CommandUsingRequestCache command2a = new CommandUsingRequestCache(2);
        CommandUsingRequestCache command2b = new CommandUsingRequestCache(2);

        assertTrue(command2a.execute());
        // this is the first time we've executed this command with
        // the value of "2" so it should not be from cache
        assertFalse(command2a.isResponseFromCache());

        assertTrue(command2b.execute());
        // this is the second time we've executed this command with
        // the same value so it should return from cache
        assertTrue(command2b.isResponseFromCache());
    } finally {
        context.shutdown();
    }

    // start a new request context
    context = HystrixRequestContext.initializeContext();
    try {
        CommandUsingRequestCache command3b = new CommandUsingRequestCache(2);
        assertTrue(command3b.execute());
        // this is a new request context so this 
        // should not come from cache
        assertFalse(command3b.isResponseFromCache());
    } finally {
        context.shutdown();
    }
}

缓存的手动清理

public static class GetterCommand extends HystrixCommand<String> {

    private static final HystrixCommandKey GETTER_KEY = HystrixCommandKey.Factory.asKey("GetterCommand");
    private final int id;

    public GetterCommand(int id) {
        super(Setter.withGroupKey(HystrixCommandGroupKey.Factory.asKey("GetSetGet"))
                .andCommandKey(GETTER_KEY));
        this.id = id;
    }

    @Override
    protected String run() {
        return prefixStoredOnRemoteDataStore + id;
    }

    @Override
    protected String getCacheKey() {
        return String.valueOf(id);
    }

    /**
     * Allow the cache to be flushed for this object.
     * 
     * @param id
     *            argument that would normally be passed to the command
     */
    public static void flushCache(int id) {
        HystrixRequestCache.getInstance(GETTER_KEY,
                HystrixConcurrencyStrategyDefault.getInstance()).clear(String.valueOf(id));
    }

}

public static class SetterCommand extends HystrixCommand<Void> {

    private final int id;
    private final String prefix;

    public SetterCommand(int id, String prefix) {
        super(HystrixCommandGroupKey.Factory.asKey("GetSetGet"));
        this.id = id;
        this.prefix = prefix;
    }

    @Override
    protected Void run() {
        // persist the value against the datastore
        prefixStoredOnRemoteDataStore = prefix;
        // flush the cache
        GetterCommand.flushCache(id);
        // no return value
        return null;
    }
}

参考

  • 《Java工程师面试突击第1季-中华石杉老师》
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