内存分析 查看内存使用 # 内存统计 INFO memory # 关键指标 used_memory: 1073741824 # 已使用内存（字节） used_memory_human: 1.00G used_memory_peak: 2147483648 maxmemory: 4294967296 # 最大内存限制 # 内存碎片率 mem_fragmentation_ratio: 1.20 # >1.5需要优化 # 查看key占用内存 MEMORY USAGE key Java内存分析 @Component public class MemoryAnalyzer { @Autowired private RedisTemplate<String, String> redis; public void analyze() { Properties info = redis.execute((RedisCallback<Properties>) connection -> connection.info("memory") ); long usedMemory = Long.parseLong(info.getProperty("used_memory")); long maxMemory = Long.parseLong(info.getProperty("maxmemory")); double fragRatio = Double.parseDouble(info.getProperty("mem_fragmentation_ratio")); log.info("内存使用: {}MB / {}MB ({}%)", usedMemory / 1024 / 1024, maxMemory / 1024 / 1024, usedMemory * 100 / maxMemory); log.info("碎片率: {}", fragRatio); if (usedMemory > maxMemory * 0.9) { log.warn("内存使用超过90%，需要优化"); } if (fragRatio > 1.5) { log.warn("内存碎片率过高，考虑重启Redis"); } } // 分析TOP 100大key public List<KeyMemory> analyzeTopKeys() { List<KeyMemory> result = new ArrayList<>(); ScanOptions options = ScanOptions.scanOptions().count(100).build(); redis.execute((RedisCallback<Object>) connection -> { Cursor<byte[]> cursor = connection.scan(options); while (cursor.hasNext()) { String key = new String(cursor.next()); Long size = connection.memoryUsage(key.getBytes()); if (size != null && size > 10240) { // > 10KB KeyMemory km = new KeyMemory(); km.setKey(key); km.setSize(size); result.add(km); } } return null; }); return result.stream() .sorted(Comparator.comparingLong(KeyMemory::getSize).reversed()) .limit(100) .collect(Collectors.toList()); } } 优化策略 1. 选择合适的数据类型 // ❌ 不好：String存储对象（JSON） redis.opsForValue().set("user:1001", "{\"name\":\"Alice\",\"age\":25,\"city\":\"Beijing\"}"); // 占用：~100字节 // ✅ 好：Hash存储（小对象） redis.opsForHash().put("user:1001", "name", "Alice"); redis.opsForHash().put("user:1001", "age", "25"); redis.opsForHash().put("user:1001", "city", "Beijing"); // 占用：~50字节，节省50% 2. 利用紧凑编码 // Hash：保持ziplist编码 // 条件：字段数<512，值长度<64字节 public void setUserInfo(Long userId, Map<String, String> info) { String key = "user:" + userId; // 确保字段值简短 Map<String, String> compactInfo = info.entrySet().stream() .filter(e -> e.getValue().length() < 64) // 值<64字节 .collect(Collectors.toMap(Map.Entry::getKey, Map.Entry::getValue)); redis.opsForHash().putAll(key, compactInfo); // 验证编码 String encoding = redis.execute((RedisCallback<String>) connection -> new String(connection.execute("OBJECT", "ENCODING".getBytes(), key.getBytes())) ); log.info("编码: {}", encoding); // 期望：ziplist或listpack } // 配置优化 // redis.conf hash-max-ziplist-entries 512 hash-max-ziplist-value 64 3. 整数优化 // ❌ 不好：字符串存储整数 redis.opsForValue().set("count", "12345"); // embstr编码，~20字节 // ✅ 好：整数存储 redis.opsForValue().set("count", 12345); // int编码，8字节 // ✅ 更好：共享整数对象（0-9999） redis.opsForValue().set("status", 1); // 共享对象，0字节（不计redisObject） 4. 压缩value @Service public class CompressedCacheService { @Autowired private RedisTemplate<String, byte[]> redis; // 存储大value时压缩 public void setLarge(String key, String value) throws IOException { byte[] data = value.getBytes(StandardCharsets.UTF_8); if (data.length > 1024) { // >1KB才压缩 // Gzip压缩 ByteArrayOutputStream bos = new ByteArrayOutputStream(); try (GZIPOutputStream gzip = new GZIPOutputStream(bos)) { gzip.write(data); } byte[] compressed = bos.toByteArray(); // 添加压缩标记 byte[] withFlag = new byte[compressed.length + 1]; withFlag[0] = 1; // 压缩标记 System.arraycopy(compressed, 0, withFlag, 1, compressed.length); redis.opsForValue().set(key, withFlag); log.info("压缩率: {}%", (1 - compressed.length * 1.0 / data.length) * 100); } else { // 不压缩 byte[] withFlag = new byte[data.length + 1]; withFlag[0] = 0; // 未压缩标记 System.arraycopy(data, 0, withFlag, 1, data.length); redis.opsForValue().set(key, withFlag); } } public String getLarge(String key) throws IOException { byte[] data = redis.opsForValue().get(key); if (data == null || data.length == 0) { return null; } byte flag = data[0]; byte[] content = new byte[data.length - 1]; System.arraycopy(data, 1, content, 0, content.length); if (flag == 1) { // 解压 ByteArrayInputStream bis = new ByteArrayInputStream(content); ByteArrayOutputStream bos = new ByteArrayOutputStream(); try (GZIPInputStream gzip = new GZIPInputStream(bis)) { byte[] buffer = new byte[1024]; int len; while ((len = gzip.read(buffer)) > 0) { bos.write(buffer, 0, len); } } return new String(bos.toByteArray(), StandardCharsets.UTF_8); } else { return new String(content, StandardCharsets.UTF_8); } } } 5. 设置合理的过期时间 // ❌ 不好：永不过期 redis.opsForValue().set("cache:data", data); // ✅ 好：设置过期时间 redis.opsForValue().set("cache:data", data, 3600, TimeUnit.SECONDS); // ✅ 更好：随机过期时间（防止雪崩） int expire = 3600 + ThreadLocalRandom.current().nextInt(300); redis.opsForValue().set("cache:data", data, expire, TimeUnit.SECONDS); 6. 定期清理过期数据 @Component public class CacheCleanup { @Autowired private RedisTemplate<String, String> redis; @Scheduled(cron = "0 0 2 * * ?") // 每天凌晨2点 public void cleanup() { // 清理超过30天的时间线数据 long threshold = System.currentTimeMillis() - 30L * 24 * 3600 * 1000; Long removed = redis.opsForZSet().removeRangeByScore("timeline", 0, threshold); log.info("清理过期时间线数据: {} 条", removed); // 清理空Hash Set<String> hashKeys = redis.keys("user:*"); if (hashKeys != null) { for (String key : hashKeys) { Long size = redis.opsForHash().size(key); if (size != null && size == 0) { redis.delete(key); log.info("删除空Hash: {}", key); } } } } } 7. 拆分BigKey // ❌ 不好：单个大Hash HSET user:1001 field1 val1 ... field10000 val10000 // 1MB // ✅ 好：分片存储 @Service public class ShardedHashService { private static final int SHARD_COUNT = 10; public void hset(String key, String field, String value) { int shard = Math.abs(field.hashCode()) % SHARD_COUNT; String shardKey = key + ":shard:" + shard; redis.opsForHash().put(shardKey, field, value); } public String hget(String key, String field) { int shard = Math.abs(field.hashCode()) % SHARD_COUNT; String shardKey = key + ":shard:" + shard; return (String) redis.opsForHash().get(shardKey, field); } } 内存淘汰策略 # redis.conf # 最大内存 maxmemory 4gb # 淘汰策略 maxmemory-policy allkeys-lru # 策略说明： # noeviction: 不淘汰，内存满时写入报错 # allkeys-lru: 所有key，LRU淘汰 # volatile-lru: 有过期时间的key，LRU淘汰 # allkeys-lfu: 所有key，LFU淘汰（访问频率） # volatile-lfu: 有过期时间的key，LFU淘汰 # allkeys-random: 所有key，随机淘汰 # volatile-random: 有过期时间的key，随机淘汰 # volatile-ttl: 有过期时间的key，TTL小的先淘汰 # LRU采样数量（越大越精确，但消耗CPU） maxmemory-samples 5 监控告警 @Component public class MemoryMonitor { @Autowired private RedisTemplate<String, String> redis; @Scheduled(fixedRate = 300000) // 每5分钟 public void monitor() { Properties info = redis.execute((RedisCallback<Properties>) connection -> connection.info("memory") ); long usedMemory = Long.parseLong(info.getProperty("used_memory")); long maxMemory = Long.parseLong(info.getProperty("maxmemory")); long evicted = Long.parseLong(info.getProperty("evicted_keys")); double usage = usedMemory * 100.0 / maxMemory; log.info("内存使用: {}%", String.format("%.2f", usage)); // 告警：内存使用超过80% if (usage > 80) { log.warn("Redis内存使用超过80%，需要扩容或优化"); sendAlert("Redis内存告警", String.format("当前使用%.2f%%", usage)); } // 告警：淘汰key过多 if (evicted > 1000) { log.warn("Redis淘汰key过多: {}", evicted); sendAlert("Redis淘汰告警", String.format("已淘汰%d个key", evicted)); } } private void sendAlert(String title, String message) { // 发送告警 } } 内存优化清单 数据结构：
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