使用RedisCacheWriter#clean在线异步地批量扫描匹配删除缓存数据-spring-data-redis_数据分析

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1.背景

生产环境，某云的某个业务Redis实例，触发内存使用率，连续 3 次平均值 >= 85 %告警。

运维同学告知，看看需要怎么优化或者升级配置？分享了其实例RDB的内存剖析链接。

通过内存剖析详情发现，存在某类未设置过期时间且无用的keys，其内存占用约3.8GB，内存占比25%。

内存占比挺大，有确定的成本经济收益。

做事有动力啦！

Redis实例信息

某云Redis实例的基本信息

实例规格：16G主从版

版本：Redis 2.8（兼容3.0特性）

使用RedisCacheWriter#clean在线异步地批量扫描匹配删除缓存数据-spring-data-redis,image.png,第1张

某云的Redis RDB内存剖析

基本信息

分析方法：使用已有备份集 (选择的备份文件:完成于)
详情
Key内存占有情况
Key数量分布情况
Elements内存占用情况
Elements数量分布情况
Key过期时间分布 (内存)

Key过期时间分布 (数量)

使用RedisCacheWriter#clean在线异步地批量扫描匹配删除缓存数据-spring-data-redis,image.png,第2张

2.目标

在线异步地删除缓存数据

不影响线上业务，不确定的风险可控(风险紧急预案)

3.结论先行

在线清理了5GB+内存

已使用内存总量，15.5GB -> 10.2GB

4.技术方案

变更三板斧：可灰度、可观测/可监控、可回滚

使用spring-data-redis提供的RedisCacheWriter#clean开源解决方案，在其基础上加入异步和并发控制。

【批量策略】在线异步地批量扫描匹配删除，每批10/20个key

先SCAN匹配，再批量DEL
SCAN(keyPattern) + DEL(allMatchKeys)
【执行策略】预发环境，业务低峰时期执行
【可观测】Redis实例性能监控，业务监控

【风险紧急预案-兜底方案】删除容器实例，kill杀掉异步守护线程，停止执行（可回滚）

spring-boot版本

spring-data-redis-2.7.16

spring-boot-starter-data-redis-2.7.16

可观测-Redis实例性能监控

key模式: “message:queue:*_lock”

清理时间: [2023-12-04 21:15:39.405, 2023-12-05 00:28:24.21]

清理途中，觉得每批10个key有些慢，调整到每批20个key。

【注意】应用重启后，会重新从头开始扫描，存在一段时间未删除keys，需要等一会才能看到删除效果。

不建议中途调整每批key数量！

CPU使用率 (%，平均值)

CPU使用率，增长1~3%

已使用内存总量 (Byte，求和)

已使用内存总量，15.5GB -> 10.22GB

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使用RedisCacheWriter#clean在线异步地批量扫描匹配删除缓存数据-spring-data-redis,image.png,第4张

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平均时延 (us，平均值)

每批10个key，时延增长2~3微秒

每批20个key，时延增长7~13微秒

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Keys 监控组 (Counts/s，求和)

del: 200

scan: 375

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具体实现

scan批量策略，先批量扫描匹配，再批量删除，每批10/20个key，不断地迭代以上操作，直到数据被全部清理。

import java.nio.charset.StandardCharsets;
import java.util.concurrent.ArrayBlockingQueue;
import java.util.concurrent.ConcurrentHashMap;
import java.util.concurrent.ConcurrentMap;
import java.util.concurrent.ExecutorService;
import java.util.concurrent.ThreadPoolExecutor;
import java.util.concurrent.TimeUnit;
import cn.hutool.core.thread.ThreadFactoryBuilder;
import com.spring.boot.redis.example.model.CacheKey;
import com.spring.boot.redis.example.service.CacheService;
import lombok.extern.slf4j.Slf4j;
import org.springframework.data.redis.cache.BatchStrategies;
import org.springframework.data.redis.cache.RedisCacheWriter;
import org.springframework.data.redis.connection.RedisConnectionFactory;
import org.springframework.stereotype.Service;
import org.springframework.util.StopWatch;
/**
 * 缓存服务实现
 *
 * @author guang.yi
 * @since 2023/7/30
 */
@Slf4j
@Service("cacheService")
public class CacheServiceImpl implements CacheService {
    /**
     * 并发开关
     */
    private final ConcurrentMap concurrentSwitch = new ConcurrentHashMap<>(16);
    private final ExecutorService executorService = new ThreadPoolExecutor(
            1, 1, 5L, TimeUnit.MINUTES,
            new ArrayBlockingQueue<>(1),
            new ThreadFactoryBuilder().setNamePrefix("cache-clean-")
                    .setDaemon(true).build()
    );
    private final RedisConnectionFactory redisConnectionFactory;
    public CacheServiceImpl(
            RedisConnectionFactory redisConnectionFactory
    ) {
        this.redisConnectionFactory = redisConnectionFactory;
        log.info("create CacheServiceImpl");
    }
    @Override
    public boolean cleanCache(CacheKey cacheKey) {
        String keyPattern = cacheKey.getKeyPattern();
        // 避免多次重复地操作
        if (concurrentSwitch.putIfAbsent(keyPattern, Boolean.TRUE) == null) {
            // 异步地执行
            executorService.execute(() -> this.clean(cacheKey));
            return true;
        }
        return false;
    }
    private void clean(CacheKey cacheKey) {
        log.info("cleanCache start, cacheKey={}", cacheKey);
        StopWatch stopWatch = new StopWatch("cleanCache");
        stopWatch.start();
        this.clean(cacheKey.getCacheName(), cacheKey.getKeyPattern());
        stopWatch.stop();
        log.info("cleanCache end, cacheKey={}, stopWatch={}", cacheKey, stopWatch);
    }
    /**
     * 缓存Redis的历史数据清理
     *      * 【批量策略】在线异步地批量扫描匹配删除，每批10个key
     * 先SCAN，再批量DEL
     * 【执行策略】预发环境，业务低峰时期
     * 
     *
     * @see org.springframework.data.redis.cache.RedisCacheWriter#clean
     * @see org.springframework.data.redis.cache.DefaultRedisCacheWriter#clean
     */
    private void clean(String cacheName, String keyPattern) {
        // 【批量策略】SCAN，每批10个key
        RedisCacheWriter redisCacheWriter = RedisCacheWriter.nonLockingRedisCacheWriter(
                redisConnectionFactory, BatchStrategies.scan(10));
        // 先SCAN，再批量DEL
        redisCacheWriter.clean(cacheName, keyPattern.getBytes(StandardCharsets.UTF_8));
    }
}

# .A.1. Core Properties
spring:
  # RedisProperties
  redis:
    database: 0
    host: "localhost"
    port: 6379
    timeout: 1s
    connect-timeout: 300ms
#    client-name: "user-cache"
#    client-type: lettuce
#    sentinel:
#      master: ""
#      nodes: "host:port"
#    cluster:
#      nodes: "host:port"
#      max-redirects: 3
#    jedis:
#      pool:
#        enabled: true
#        max-idle: 8
#        min-idle: 0
#        max-active: 8
#        max-wait: 300ms
#        time-between-eviction-runs: 5m
    lettuce:
      shutdown-timeout: 100ms
      pool:
        enabled: true
        max-idle: 8
        min-idle: 0
        max-active: 8
        max-wait: -1
        time-between-eviction-runs: 5m

开源解决方案有哪些坑？

深入源代码，深究实现细节，趴开裤子看看底细。

源代码做了简化

开源解决方案结论

深入源代码看，scan批量策略的实现方案靠谱。keys批量策略存在大坑，不靠谱。

scan批量策略，先批量扫描匹配，再批量删除，每批10/20个key，不断地迭代以上操作，直到数据被全部清理。

RedisCacheWriter#clean

org.springframework.data.redis.cache.RedisCacheWriter#clean

BatchStrategy批量策略，有keys和scan两种，分别对应Redis的KEYS和SCAN命令。

批量策略默认使用keys，对于真实业务使用场景，一点都不实用。

因为KEYS命令会先收集所有满足匹配条件的keys，等所有都收集好了，再一次性全量DEL删除命令。

对于大量的keys需要删除时，其操作可能夯住线上Redis实例，存在严重影响Redis实例干活的风险。

package org.springframework.data.redis.cache;
import java.time.Duration;
import org.springframework.data.redis.connection.RedisConnectionFactory;
import org.springframework.lang.Nullable;
import org.springframework.util.Assert;
/**
 * {@link RedisCacheWriter} provides low level access to Redis commands ({@code SET, SETNX, GET, EXPIRE,...}) used for
 * caching. 

 * The {@link RedisCacheWriter} may be shared by multiple cache implementations and is responsible for writing / reading
 * binary data to / from Redis. The implementation honors potential cache lock flags that might be set.
 * 
 * The default {@link RedisCacheWriter} implementation can be customized with {@link BatchStrategy} to tune performance
 * behavior.
 *
 * @author Christoph Strobl
 * @author Mark Paluch
 * @since 2.0
 */
public interface RedisCacheWriter extends CacheStatisticsProvider {
    /**
     * Create new {@link RedisCacheWriter} without locking behavior.
     *
     * @param connectionFactory must not be {@literal null}.
     * @return new instance of {@link DefaultRedisCacheWriter}.
     */
    static RedisCacheWriter nonLockingRedisCacheWriter(RedisConnectionFactory connectionFactory) {
        return nonLockingRedisCacheWriter(connectionFactory, BatchStrategies.keys());
    }
    /**
     * Create new {@link RedisCacheWriter} without locking behavior.
     *
     * @param connectionFactory must not be {@literal null}.
     * @param batchStrategy must not be {@literal null}.
     * @return new instance of {@link DefaultRedisCacheWriter}.
     * @since 2.6
     */
    static RedisCacheWriter nonLockingRedisCacheWriter(RedisConnectionFactory connectionFactory,
            BatchStrategy batchStrategy) {
        Assert.notNull(connectionFactory, "ConnectionFactory must not be null!");
        Assert.notNull(batchStrategy, "BatchStrategy must not be null!");
        return new DefaultRedisCacheWriter(connectionFactory, batchStrategy);
    }
    /**
     * Remove all keys following the given pattern.
     * 按照给定模式删除所有键。
     *
     * @param name The cache name must not be {@literal null}.
     * @param pattern The pattern for the keys to remove. Must not be {@literal null}.
     */
    void clean(String name, byte[] pattern);
}

DefaultRedisCacheWriter#clean

源代码做了简化

RedisCacheWriter#clean默认实现是org.springframework.data.redis.cache.DefaultRedisCacheWriter#clean

通过批量策略清理缓存数据batchStrategy.cleanCache(connection, name, pattern)

package org.springframework.data.redis.cache;
import java.nio.charset.StandardCharsets;
import java.time.Duration;
import java.util.concurrent.TimeUnit;
import java.util.function.Consumer;
import java.util.function.Function;
import org.springframework.dao.PessimisticLockingFailureException;
import org.springframework.data.redis.connection.RedisConnection;
import org.springframework.data.redis.connection.RedisConnectionFactory;
import org.springframework.data.redis.connection.RedisStringCommands.SetOption;
import org.springframework.data.redis.core.types.Expiration;
import org.springframework.lang.Nullable;
import org.springframework.util.Assert;
/**
 * {@link RedisCacheWriter} implementation capable of reading/writing binary data from/to Redis in {@literal standalone}
 * and {@literal cluster} environments. Works upon a given {@link RedisConnectionFactory} to obtain the actual
 * {@link RedisConnection}. 

 * {@link DefaultRedisCacheWriter} can be used in
 * {@link RedisCacheWriter#lockingRedisCacheWriter(RedisConnectionFactory) locking} or
 * {@link RedisCacheWriter#nonLockingRedisCacheWriter(RedisConnectionFactory) non-locking} mode. While
 * {@literal non-locking} aims for maximum performance it may result in overlapping, non atomic, command execution for
 * operations spanning multiple Redis interactions like {@code putIfAbsent}. The {@literal locking} counterpart prevents
 * command overlap by setting an explicit lock key and checking against presence of this key which leads to additional
 * requests and potential command wait times.
 *
 * @author Christoph Strobl
 * @author Mark Paluch
 * @author André Prata
 * @since 2.0
 */
class DefaultRedisCacheWriter implements RedisCacheWriter {
    private final RedisConnectionFactory connectionFactory;
    private final Duration sleepTime;
    private final CacheStatisticsCollector statistics;
    private final BatchStrategy batchStrategy;
    /*
     * (non-Javadoc)
     * @see org.springframework.data.redis.cache.RedisCacheWriter#clean(java.lang.String, byte[])
     */
    @Override
    public void clean(String name, byte[] pattern) {
        Assert.notNull(name, "Name must not be null!");
        Assert.notNull(pattern, "Pattern must not be null!");
        execute(name, connection -> {
            boolean wasLocked = false;
            try {
                if (isLockingCacheWriter()) {
                    doLock(name, connection);
                    wasLocked = true;
                }
                // 通过批量策略清理缓存数据
                long deleteCount = batchStrategy.cleanCache(connection, name, pattern);
                while (deleteCount > Integer.MAX_VALUE) {
                    statistics.incDeletesBy(name, Integer.MAX_VALUE);
                    deleteCount -= Integer.MAX_VALUE;
                }
                statistics.incDeletesBy(name, (int) deleteCount);
            } finally {
                if (wasLocked && isLockingCacheWriter()) {
                    doUnlock(name, connection);
                }
            }
            return "OK";
        });
    }
}

BatchStrategy批量策略

org.springframework.data.redis.cache.BatchStrategy

package org.springframework.data.redis.cache;
import org.springframework.data.redis.connection.RedisConnection;
/**
 * A {@link BatchStrategy} to be used with {@link RedisCacheWriter}.
 * 
 * Mainly used to clear the cache.
 * 

 * Predefined strategies using the {@link BatchStrategies#keys() KEYS} or {@link BatchStrategies#scan(int) SCAN}
 * commands can be found in {@link BatchStrategies}.
 *
 * @author Mark Paluch
 * @author Christoph Strobl
 * @since 2.6
 */
public interface BatchStrategy {
    /**
     * Remove all keys following the given pattern.
     *
     * @param connection the connection to use. Must not be {@literal null}.
     * @param name The cache name. Must not be {@literal null}.
     * @param pattern The pattern for the keys to remove. Must not be {@literal null}.
     * @return number of removed keys.
     */
    long cleanCache(RedisConnection connection, String name, byte[] pattern);
}

BatchStrategies批量策略实现

org.springframework.data.redis.cache.BatchStrategies

BatchStrategy批量策略，有keys和scan两种，分别对应Redis的KEYS和SCAN命令。

scan批量策略，先批量扫描匹配，再批量删除，每批10/20个key，不断地迭代以上操作，直到数据被全部清理。

keys批量策略，对于真实业务使用场景，一点都不实用。

因为KEYS命令会先收集所有满足匹配条件的keys，等所有都收集好了，再一次性全量DEL删除命令。

对于大量的keys需要删除时，其操作可能夯住线上Redis实例，存在严重影响Redis实例干活的风险。

package org.springframework.data.redis.cache;
import java.util.ArrayList;
import java.util.Collections;
import java.util.Iterator;
import java.util.List;
import java.util.NoSuchElementException;
import java.util.Optional;
import org.springframework.data.redis.connection.RedisConnection;
import org.springframework.data.redis.core.Cursor;
import org.springframework.data.redis.core.ScanOptions;
import org.springframework.util.Assert;
/**
 * A collection of predefined {@link BatchStrategy} implementations using {@code KEYS} or {@code SCAN} command.
 *
 * @author Mark Paluch
 * @author Christoph Strobl
 * @since 2.6
 */
public abstract class BatchStrategies {
    private BatchStrategies() {
        // can't touch this - oh-oh oh oh oh-oh-oh
    }
    /**
     * A {@link BatchStrategy} using a single {@code KEYS} and {@code DEL} command to remove all matching keys.
     * {@code KEYS} scans the entire keyspace of the Redis database and can block the Redis worker thread for a long time
     * on large keyspaces.
     * 
     * {@code KEYS} is supported for standalone and clustered (sharded) Redis operation modes.
     *
     * @return batching strategy using {@code KEYS}.
     */
    public static BatchStrategy keys() {
        return Keys.INSTANCE;
    }
    /**
     * A {@link BatchStrategy} using a {@code SCAN} cursors and potentially multiple {@code DEL} commands to remove all
     * matching keys. This strategy allows a configurable batch size to optimize for scan batching.
     * 

     * Note that using the {@code SCAN} strategy might be not supported on all drivers and Redis operation modes.
     *
     * @return batching strategy using {@code SCAN}.
     */
    public static BatchStrategy scan(int batchSize) {
        Assert.isTrue(batchSize > 0, "Batch size must be greater than zero!");
        return new Scan(batchSize);
    }
    /**
     * {@link BatchStrategy} using {@code KEYS}.
     */
    static class Keys implements BatchStrategy {
        static Keys INSTANCE = new Keys();
        @Override
        public long cleanCache(RedisConnection connection, String name, byte[] pattern) {
            // `KEYS`命令会先收集所有满足匹配条件的keys，等所有都收集好了，再一次性全量`DEL`删除命令
            byte[][] keys = Optional.ofNullable(connection.keys(pattern)).orElse(Collections.emptySet())
                    .toArray(new byte[0][]);
            if (keys.length > 0) {
                connection.del(keys);
            }
            return keys.length;
        }
    }
    /**
     * {@link BatchStrategy} using {@code SCAN}.
     */
    static class Scan implements BatchStrategy {
        private final int batchSize;
        Scan(int batchSize) {
            this.batchSize = batchSize;
        }
        @Override
        public long cleanCache(RedisConnection connection, String name, byte[] pattern) {
            // 批量扫描匹配删除，每批10/20个key
            // 先SCAN匹配，再批量DEL
            // SCAN(keyPattern, match, batchSize) + DEL(allMatchKeys, batchSize)
            Cursor cursor = connection.scan(ScanOptions.scanOptions().count(batchSize).match(pattern).build());
            long count = 0;
            PartitionIterator partitions = new PartitionIterator<>(cursor, batchSize);
            while (partitions.hasNext()) {
                List keys = partitions.next();
                count += keys.size();
                if (keys.size() > 0) {
                    connection.del(keys.toArray(new byte[0][]));
                }
            }
            return count;
        }
    }
    /**
     * Utility to split and buffer outcome from a {@link Iterator} into {@link List lists} of {@code T} with a maximum
     * chunks {@code size}.
     *
     * @param 
     */
    static class PartitionIterator implements Iterator> {
        private final Iterator iterator;
        private final int size;
        PartitionIterator(Iterator iterator, int size) {
            this.iterator = iterator;
            this.size = size;
        }
        @Override
        public boolean hasNext() {
            return iterator.hasNext();
        }
        @Override
        public List next() {
            if (!hasNext()) {
                throw new NoSuchElementException();
            }
            List list = new ArrayList<>(size);
            while (list.size() < size && iterator.hasNext()) {
                list.add(iterator.next());
            }
            return list;
        }
    }
}

5.参考引用

Spring Data Redis / Redis / Redis Cache
redis-spring-boot-starter-example

献给杭州2023年的第一场雪❄️

2023.12.18

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