MySQL 中 count(*)、count(1)、count(字段)性能对比：一次彻底搞清楚

异常君2025-06-08 13:17

团队 code review 时，一位同事把 count(*)改成了 count(1)，说这样性能更好。真的是这样吗？今天通过源码和实测数据，把这个问题说透。

本文基于 MySQL 8.0.28 版本测试，不同版本的优化器行为可能有差异

三种 count 方式的本质区别

先看看这三种写法在 MySQL 中到底做了什么：

java 复制代码 
// 模拟MySQL处理count的伪代码
public class CountProcessor {

    // count(*) 的处理逻辑
    public long countStar(Table table) {
        long count = 0;
        for (Row row : table.getAllRows()) {
            // 只要行存在就计数，不管字段值
            count++;
        }
        return count;
    }

    // count(1) 的处理逻辑
    public long countOne(Table table) {
        long count = 0;
        for (Row row : table.getAllRows()) {
            // MySQL优化器会把count(1)转换为count(*)
            count++;
        }
        return count;
    }

    // count(字段) 的处理逻辑
    public long countField(Table table, String fieldName) {
        long count = 0;
        for (Row row : table.getAllRows()) {
            Object value = row.getField(fieldName);
            // 关键：只统计非NULL值
            if (value != null) {
                count++;
            }
        }
        return count;
    }
}

性能测试：用数据说话

创建测试环境，准备 1000 万条数据，多轮测试取平均值：

java 复制代码 
@Slf4j
@SpringBootTest
@TestPropertySource(properties = {
    "spring.datasource.hikari.maximum-pool-size=5",
    "spring.datasource.hikari.minimum-idle=1"
})
public class CountPerformanceTest {

    private static final DecimalFormat df = new DecimalFormat("#.##");

    @Autowired
    private JdbcTemplate jdbcTemplate;

    @Autowired
    private PlatformTransactionManager transactionManager;

    @BeforeEach
    @Transactional(propagation = Propagation.REQUIRES_NEW)
    public void setupTestData() {
        try {
            // 创建测试表
            jdbcTemplate.execute("""
                CREATE TABLE IF NOT EXISTS user_test (
                    id BIGINT PRIMARY KEY AUTO_INCREMENT,
                    username VARCHAR(50) NOT NULL,
                    email VARCHAR(100),
                    age INT,
                    city VARCHAR(50),
                    create_time TIMESTAMP DEFAULT CURRENT_TIMESTAMP,
                    INDEX idx_username (username),
                    INDEX idx_email (email),
                    INDEX idx_city (city)
                ) ENGINE=InnoDB DEFAULT CHARSET=utf8mb4
            """);

            // 使用事务批量插入
            TransactionTemplate transactionTemplate = new TransactionTemplate(transactionManager);
            transactionTemplate.execute(status -> {
                insertTestDataInBatches();
                return null;
            });

            // 更新统计信息
            jdbcTemplate.execute("ANALYZE TABLE user_test");
            log.info("测试数据准备完成，表统计信息已更新");

        } catch (Exception e) {
            log.error("准备测试数据失败", e);
            throw new RuntimeException("测试数据初始化失败", e);
        }
    }

    private void insertTestDataInBatches() {
        List<Object[]> batchArgs = new ArrayList<>();
        String[] cities = {"北京", "上海", "广州", "深圳", "杭州"};

        for (int i = 1; i <= 10_000_000; i++) {
            String username = "user_" + i;
            String email = (i % 10 == 0) ? null : "user" + i + "@test.com";
            Integer age = (i % 20 == 0) ? null : 20 + (i % 50);
            String city = (i % 15 == 0) ? null : cities[i % cities.length];

            batchArgs.add(new Object[]{username, email, age, city});

            if (i % 10000 == 0) {
                jdbcTemplate.batchUpdate(
                    "INSERT INTO user_test (username, email, age, city) VALUES (?, ?, ?, ?)",
                    batchArgs
                );
                batchArgs.clear();
                if (i % 100000 == 0) {
                    log.info("已插入 {} 条数据", i);
                }
            }
        }
    }

    @Test
    public void testCountPerformance() {
        StopWatch stopWatch = new StopWatch("COUNT性能测试");
        try {
            warmUp();

            // 多次测试取平均值
            int testRounds = 5;
            Map<String, List<Long>> timings = new HashMap<>();

            for (int round = 0; round < testRounds; round++) {
                log.info("===== 第 {} 轮测试 =====", round + 1);
                performSingleRound(stopWatch, timings, round + 1);

                // 轮次间隔，避免缓存影响
                Thread.sleep(1000);
            }

            // 计算并输出平均值
            logAverageTimings(timings);

        } catch (InterruptedException e) {
            Thread.currentThread().interrupt();
            log.error("测试被中断", e);
        } finally {
            if (stopWatch.isRunning()) {
                stopWatch.stop();
            }
            log.info("性能测试总结:\n{}", stopWatch.prettyPrint());
        }
    }

    private void performSingleRound(StopWatch stopWatch, Map<String, List<Long>> timings, int round) {
        // 测试count(*)
        stopWatch.start("COUNT(*) - Round " + round);
        Long countStar = jdbcTemplate.queryForObject("SELECT COUNT(*) FROM user_test", Long.class);
        stopWatch.stop();
        long time = stopWatch.getLastTaskTimeMillis();
        timings.computeIfAbsent("COUNT(*)", k -> new ArrayList<>()).add(time);
        log.info("COUNT(*) 结果: {}, 耗时: {} ms", countStar != null ? countStar : "null", time);

        // 测试count(1)
        stopWatch.start("COUNT(1) - Round " + round);
        Long countOne = jdbcTemplate.queryForObject("SELECT COUNT(1) FROM user_test", Long.class);
        stopWatch.stop();
        time = stopWatch.getLastTaskTimeMillis();
        timings.computeIfAbsent("COUNT(1)", k -> new ArrayList<>()).add(time);
        log.info("COUNT(1) 结果: {}, 耗时: {} ms", countOne != null ? countOne : "null", time);

        // 测试count(主键)
        stopWatch.start("COUNT(id) - Round " + round);
        Long countId = jdbcTemplate.queryForObject("SELECT COUNT(id) FROM user_test", Long.class);
        stopWatch.stop();
        time = stopWatch.getLastTaskTimeMillis();
        timings.computeIfAbsent("COUNT(id)", k -> new ArrayList<>()).add(time);
        log.info("COUNT(id) 结果: {}, 耗时: {} ms", countId != null ? countId : "null", time);

        // 测试count(索引列)
        stopWatch.start("COUNT(username) - Round " + round);
        Long countUsername = jdbcTemplate.queryForObject("SELECT COUNT(username) FROM user_test", Long.class);
        stopWatch.stop();
        time = stopWatch.getLastTaskTimeMillis();
        timings.computeIfAbsent("COUNT(username)", k -> new ArrayList<>()).add(time);
        log.info("COUNT(username) 结果: {}, 耗时: {} ms", countUsername != null ? countUsername : "null", time);

        // 测试count(非索引列)
        stopWatch.start("COUNT(age) - Round " + round);
        Long countAge = jdbcTemplate.queryForObject("SELECT COUNT(age) FROM user_test", Long.class);
        stopWatch.stop();
        time = stopWatch.getLastTaskTimeMillis();
        timings.computeIfAbsent("COUNT(age)", k -> new ArrayList<>()).add(time);
        log.info("COUNT(age) 结果: {}, 耗时: {} ms", countAge != null ? countAge : "null", time);
    }

    private void logAverageTimings(Map<String, List<Long>> timings) {
        log.info("\n===== 性能测试平均值 =====");
        timings.forEach((query, times) -> {
            double average = times.stream()
                .mapToLong(Long::longValue)
                .average()
                .orElse(0.0);
            log.info("{} 平均耗时: {} ms", query, df.format(average));
        });
    }

    private void warmUp() {
        log.info("开始预热查询...");
        for (int i = 0; i < 5; i++) {
            jdbcTemplate.queryForObject("SELECT COUNT(*) FROM user_test", Long.class);
        }
    }

    @AfterEach
    public void cleanup() {
        try {
            jdbcTemplate.execute("DROP TABLE IF EXISTS user_test");
            log.info("测试表清理完成");
        } catch (Exception e) {
            log.error("清理测试数据失败", e);
        }
    }
}

基准测试结果（基于 AWS RDS MySQL 8.0.28）

测试环境：

  • 实例规格：db.r6g.xlarge (4 vCPU, 32 GiB RAM)
  • 存储：1000 GiB GP3 SSD
  • 测试数据：1000 万条记录
场景 COUNT(*) COUNT(1) COUNT(主键) COUNT(索引列) COUNT(非索引列)
冷查询 2.13s 2.15s 2.31s 2.45s 8.76s
热查询 0.18s 0.18s 0.21s 0.23s 7.89s
并行查询(4 线程) 0.58s 0.59s 0.65s 0.71s 2.34s

结论：COUNT(*)和 COUNT(1)性能基本一致，COUNT(非索引列)性能最差。

存储引擎差异与索引统计

不同存储引擎对 COUNT 的处理方式差异很大：

java 复制代码 
@Component
@Slf4j
public class StorageEngineCountAnalyzer {

    @Autowired
    private JdbcTemplate jdbcTemplate;

    public void analyzeEngineSpecificBehavior() {
        // 查看表的存储引擎和统计信息
        String engineQuery = """
            SELECT
                t.table_name,
                t.engine,
                t.table_rows,
                t.avg_row_length,
                t.data_length,
                t.index_length
            FROM information_schema.tables t
            WHERE t.table_schema = DATABASE()
            AND t.table_name IN ('users', 'orders', 'products')
        """;

        List<Map<String, Object>> results = jdbcTemplate.queryForList(engineQuery);

        results.forEach(row -> {
            String tableName = (String) row.get("table_name");
            String engine = (String) row.get("engine");
            Long approximateRows = (Long) row.get("table_rows");

            log.info("表: {}, 引擎: {}, 近似行数: {}", tableName, engine, approximateRows);

            // 不同引擎的COUNT行为
            switch (engine) {
                case "MyISAM":
                    log.info("MyISAM引擎维护了准确的行数，COUNT(*)是O(1)操作");
                    break;
                case "InnoDB":
                    log.info("InnoDB引擎需要扫描索引统计行数，COUNT(*)是O(n)操作");
                    updateIndexStatistics(tableName);
                    break;
                case "Memory":
                    log.info("Memory引擎类似MyISAM，维护行数元数据");
                    break;
            }
        });
    }

    /**
     * 更新表的索引统计信息
     */
    public void updateIndexStatistics(String tableName) {
        try {
            // 更新索引统计信息
            jdbcTemplate.execute("ANALYZE TABLE " + tableName);

            // 查看索引基数和选择性
            String sql = """
                SELECT
                    s.index_name,
                    s.cardinality,
                    t.table_rows,
                    ROUND((s.cardinality / t.table_rows * 100), 2) as selectivity_percent
                FROM information_schema.statistics s
                JOIN information_schema.tables t
                    ON s.table_schema = t.table_schema
                    AND s.table_name = t.table_name
                WHERE s.table_schema = DATABASE()
                AND s.table_name = ?
                AND s.seq_in_index = 1
                ORDER BY s.cardinality DESC
            """;

            List<Map<String, Object>> stats = jdbcTemplate.queryForList(sql, tableName);
            stats.forEach(stat ->
                log.info("索引: {}, 基数: {}, 选择性: {}%",
                    stat.get("index_name"),
                    stat.get("cardinality"),
                    stat.get("selectivity_percent")
                )
            );
        } catch (Exception e) {
            log.error("更新表统计信息失败: {}", tableName, e);
        }
    }
}

执行计划成本分析与监控

通过 EXPLAIN 和性能监控深入分析 COUNT 的执行成本：

java 复制代码 
@Component
@Slf4j
public class CountCostAnalyzer {

    @Autowired
    private JdbcTemplate jdbcTemplate;

    @Autowired
    private MeterRegistry meterRegistry;

    public void analyzeCountPlans() {
        String[] queries = {
            "SELECT COUNT(*) FROM user_test",
            "SELECT COUNT(1) FROM user_test",
            "SELECT COUNT(id) FROM user_test",
            "SELECT COUNT(username) FROM user_test",
            "SELECT COUNT(email) FROM user_test"
        };

        for (String query : queries) {
            analyzeQueryWithMetrics(query);
        }
    }

    private void analyzeQueryWithMetrics(String query) {
        Timer.Sample sample = Timer.Sample.start(meterRegistry);

        try {
            log.info("分析查询: {}", query);

            // EXPLAIN分析
            List<Map<String, Object>> explainResult = jdbcTemplate.queryForList("EXPLAIN " + query);

            for (Map<String, Object> row : explainResult) {
                log.info("type: {}, key: {}, rows: {}, Extra: {}",
                    row.get("type"),
                    row.get("key"),
                    row.get("rows"),
                    row.get("Extra")
                );
            }

            // 使用会话级profiling
            analyzeCostProfile(query);

            // 记录成功指标
            meterRegistry.counter("mysql.count.success", "query", extractQueryType(query))
                .increment();

        } catch (Exception e) {
            log.error("查询分析失败: {}", query, e);
            meterRegistry.counter("mysql.count.error", "query", extractQueryType(query))
                .increment();
        } finally {
            sample.stop(Timer.builder("mysql.count.duration")
                .tag("query", extractQueryType(query))
                .register(meterRegistry));
        }
    }

    public void analyzeCostProfile(String query) {
        jdbcTemplate.execute((ConnectionCallback<Void>) connection -> {
            try (Statement stmt = connection.createStatement()) {
                stmt.execute("SET profiling = 1");

                // 执行查询
                jdbcTemplate.queryForObject(query, Long.class);

                // 获取profile信息
                try (ResultSet rs = stmt.executeQuery("SHOW PROFILE")) {
                    while (rs.next()) {
                        double duration = rs.getDouble("Duration");
                        if (duration > 0.001) {
                            log.info("步骤: {}, 耗时: {}s",
                                rs.getString("Status"),
                                duration
                            );
                        }
                    }
                }
            } catch (SQLException e) {
                log.error("Profile分析失败", e);
            } finally {
                // 确保关闭profiling
                try {
                    connection.createStatement().execute("SET profiling = 0");
                } catch (SQLException e) {
                    log.warn("关闭profiling失败", e);
                }
            }
            return null;
        });
    }

    private String extractQueryType(String query) {
        if (query.contains("COUNT(*)")) return "count_star";
        if (query.contains("COUNT(1)")) return "count_one";
        if (query.contains("COUNT(id)")) return "count_id";
        if (query.contains("COUNT(email)")) return "count_email";
        return "count_other";
    }
}

生产环境实战场景

场景 1：高并发计数器实现（使用 Caffeine 缓存）

java 复制代码 
@Component
@Slf4j
public class ConcurrentCountManager {

    // 使用Caffeine缓存，更安全高效
    private final Cache<String, Long> countCache = Caffeine.newBuilder()
        .maximumSize(1000)
        .expireAfterWrite(5, TimeUnit.MINUTES)
        .recordStats()
        .build();

    @Autowired
    private JdbcTemplate jdbcTemplate;

    public long getCount(String tableName) {
        return countCache.get(tableName, this::queryCount);
    }

    private long queryCount(String tableName) {
        // 验证表名防止SQL注入
        if (!isValidTableName(tableName)) {
            throw new IllegalArgumentException("Invalid table name: " + tableName);
        }

        Long count = jdbcTemplate.queryForObject(
            "SELECT COUNT(*) FROM " + tableName, Long.class
        );
        return count != null ? count : 0L;
    }

    private boolean isValidTableName(String tableName) {
        return tableName != null && tableName.matches("^[a-zA-Z_][a-zA-Z0-9_]*$");
    }

    @Scheduled(fixedDelay = 60000)
    public void logCacheStats() {
        CacheStats stats = countCache.stats();
        log.info("缓存统计 - 命中率: {}, 加载次数: {}, 驱逐次数: {}",
            String.format("%.2f%%", stats.hitRate() * 100),
            stats.loadCount(),
            stats.evictionCount()
        );
    }
}

场景 2：分库分表场景的 COUNT 优化

java 复制代码 
@Service
@Slf4j
public class ShardingCountService {

    @Autowired
    private List<DataSource> shardDataSources;

    @Autowired
    private ExecutorService executorService;

    /**
     * 分库分表场景下的COUNT优化
     */
    public long getShardedCount(String logicalTableName) {
        CompletableFuture<Long>[] futures = new CompletableFuture[shardDataSources.size()];

        for (int i = 0; i < shardDataSources.size(); i++) {
            final int shardIndex = i;
            futures[i] = CompletableFuture.supplyAsync(() -> {
                JdbcTemplate shardJdbcTemplate = new JdbcTemplate(shardDataSources.get(shardIndex));
                String physicalTable = logicalTableName + "_" + shardIndex;

                try {
                    Long count = shardJdbcTemplate.queryForObject(
                        "SELECT COUNT(*) FROM " + physicalTable,
                        Long.class
                    );
                    log.debug("分片 {} 统计结果: {}", shardIndex, count);
                    return count != null ? count : 0L;
                } catch (Exception e) {
                    log.error("分片 {} 统计失败", shardIndex, e);
                    return 0L;
                }
            }, executorService);
        }

        // 汇总所有分片结果
        return CompletableFuture.allOf(futures)
            .thenApply(v -> Arrays.stream(futures)
                .mapToLong(f -> f.join())
                .sum()
            )
            .join();
    }
}

场景 3：COUNT 策略选择

java 复制代码 
@Service
@Slf4j
public class SmartCountStrategySelector {

    @Autowired
    private CountOptimizationConfig config;

    @Autowired
    private JdbcTemplate jdbcTemplate;

    @Autowired
    private ConcurrentCountManager countManager;

    @Autowired
    private RedisTemplate<String, Long> redisTemplate;

    /**
     * 选择最优的COUNT策略
     */
    public long getOptimizedCount(String tableName, CountContext context) {
        // 1. 评估表大小
        long approximateSize = getApproximateTableSize(tableName);

        // 2. 根据上下文选择策略
        CountOptimizationConfig.CountStrategy strategy = selectStrategy(
            approximateSize,
            context
        );

        log.debug("表 {} 选择策略: {}, 预估大小: {}",
            tableName, strategy, approximateSize);

        // 3. 执行对应策略
        return switch (strategy) {
            case DIRECT -> directCount(tableName);
            case CACHED -> countManager.getCount(tableName);
            case APPROXIMATE -> approximateCount(tableName);
            case COUNTER_TABLE -> counterTableCount(tableName);
        };
    }

    private CountOptimizationConfig.CountStrategy selectStrategy(
            long tableSize,
            CountContext context) {

        // 高精度要求
        if (context.isHighAccuracy()) {
            return tableSize < 1_000_000 ?
                CountOptimizationConfig.CountStrategy.DIRECT :
                CountOptimizationConfig.CountStrategy.COUNTER_TABLE;
        }

        // 高频查询
        if (context.getQueryFrequency() > 100) {
            return CountOptimizationConfig.CountStrategy.CACHED;
        }

        // 默认策略
        return config.selectStrategy(tableSize);
    }

    private long getApproximateTableSize(String tableName) {
        String sql = """
            SELECT table_rows
            FROM information_schema.tables
            WHERE table_schema = DATABASE()
            AND table_name = ?
        """;

        Long size = jdbcTemplate.queryForObject(sql, Long.class, tableName);
        return size != null ? size : 0L;
    }

    private long directCount(String tableName) {
        Long count = jdbcTemplate.queryForObject(
            "SELECT COUNT(*) FROM " + tableName, Long.class
        );
        return count != null ? count : 0L;
    }

    private long approximateCount(String tableName) {
        return getApproximateTableSize(tableName);
    }

    private long counterTableCount(String tableName) {
        String sql = "SELECT count_value FROM table_counters WHERE table_name = ?";
        Long count = jdbcTemplate.queryForObject(sql, Long.class, tableName);
        return count != null ? count : 0L;
    }

    @Data
    @Builder
    public static class CountContext {
        private boolean highAccuracy;
        private int queryFrequency; // 每分钟查询次数
        private boolean realTime;
        private long maxLatencyMs;
    }
}

场景 4：带熔断器的 COUNT 服务

java 复制代码 
@Component
@Slf4j
public class CountCircuitBreaker {

    private final CircuitBreaker circuitBreaker;

    @Autowired
    private RedisTemplate<String, Long> redisTemplate;

    @Autowired
    private JdbcTemplate jdbcTemplate;

    public CountCircuitBreaker() {
        CircuitBreakerConfig config = CircuitBreakerConfig.custom()
            .failureRateThreshold(50)
            .waitDurationInOpenState(Duration.ofSeconds(30))
            .slidingWindowSize(10)
            .build();

        this.circuitBreaker = CircuitBreaker.of("countService", config);

        circuitBreaker.getEventPublisher()
            .onStateTransition(event ->
                log.warn("熔断器状态变更: {} -> {}",
                    event.getStateTransition().getFromState(),
                    event.getStateTransition().getToState())
            );
    }

    public long getCountWithFallback(String tableName, Supplier<Long> countSupplier) {
        return circuitBreaker.executeSupplier(() -> {
            log.debug("执行COUNT查询: {}", tableName);
            return countSupplier.get();
        }, throwable -> {
            log.error("COUNT查询失败，使用降级策略: {}", tableName, throwable);
            return getFallbackCount(tableName);
        });
    }

    private long getFallbackCount(String tableName) {
        // 降级策略优先级
        // 1. 尝试从Redis缓存获取
        String cacheKey = "count:fallback:" + tableName;
        Long cached = redisTemplate.opsForValue().get(cacheKey);
        if (cached != null) {
            log.info("使用Redis缓存值作为降级: {} = {}", tableName, cached);
            return cached;
        }

        // 2. 使用information_schema近似值
        try {
            String sql = """
                SELECT table_rows
                FROM information_schema.tables
                WHERE table_schema = DATABASE()
                AND table_name = ?
            """;
            Long approximate = jdbcTemplate.queryForObject(sql, Long.class, tableName);
            if (approximate != null) {
                log.info("使用近似值作为降级: {} ≈ {}", tableName, approximate);
                // 缓存近似值
                redisTemplate.opsForValue().set(cacheKey, approximate, Duration.ofMinutes(10));
                return approximate;
            }
        } catch (Exception e) {
            log.error("获取近似值失败", e);
        }

        // 3. 返回-1表示无法获取
        log.warn("所有降级策略失败: {}", tableName);
        return -1L;
    }
}

COUNT 优化配置类

java 复制代码 
@Configuration
@ConfigurationProperties(prefix = "mysql.count.optimization")
@Data
@Validated
public class CountOptimizationConfig {

    @NotNull
    @Min(1)
    private Integer cacheSize = 1000;

    @NotNull
    @Min(1)
    private Integer cacheExpireMinutes = 5;

    @NotNull
    private Boolean enableParallelQuery = true;

    @NotNull
    @Min(1)
    @Max(16)
    private Integer parallelThreads = 4;

    @NotNull
    private Boolean enableApproximateCount = true;

    @NotNull
    @Min(0)
    @Max(100)
    private Integer approximateThresholdPercent = 5;

    /**
     * 根据表大小自动选择COUNT策略
     */
    public CountStrategy selectStrategy(long tableSize) {
        if (tableSize < 10_000) {
            return CountStrategy.DIRECT;
        } else if (tableSize < 1_000_000) {
            return CountStrategy.CACHED;
        } else if (enableApproximateCount) {
            return CountStrategy.APPROXIMATE;
        } else {
            return CountStrategy.COUNTER_TABLE;
        }
    }

    public enum CountStrategy {
        DIRECT,         // 直接查询
        CACHED,         // 使用缓存
        APPROXIMATE,    // 近似值
        COUNTER_TABLE   // 计数器表
    }
}

MySQL 8.0 新特性对 COUNT 的影响

java 复制代码 
@Component
@Slf4j
public class MySQL8CountOptimizer {

    @Autowired
    private JdbcTemplate jdbcTemplate;

    /**
     * MySQL 8.0 并行查询配置
     */
    @PostConstruct
    public void configureParallelQuery() {
        // 检查MySQL版本
        String version = jdbcTemplate.queryForObject(
            "SELECT VERSION()", String.class
        );

        if (version != null && version.startsWith("8.")) {
            log.info("MySQL 8.0 detected, configuring parallel query");

            // 预热查询计划缓存
            warmupQueryPlanCache();
        }
    }

    /**
     * 预热查询计划缓存
     */
    private void warmupQueryPlanCache() {
        List<String> criticalTables = Arrays.asList("users", "orders", "products");

        criticalTables.forEach(table -> {
            try {
                // 预热不同类型的COUNT查询
                jdbcTemplate.queryForObject("SELECT COUNT(*) FROM " + table, Long.class);

                // 预热带条件的COUNT
                jdbcTemplate.queryForObject(
                    "SELECT COUNT(*) FROM " + table + " WHERE id > ?",
                    Long.class,
                    0
                );

                log.info("查询计划缓存预热完成: {}", table);
            } catch (Exception e) {
                log.warn("查询计划缓存预热失败: {}", table, e);
            }
        });
    }

    public void testParallelQueryPerformance() {
        StopWatch watch = new StopWatch();

        // 禁用并行
        watch.start("COUNT without parallel");
        jdbcTemplate.execute("SET SESSION innodb_parallel_read_threads = 1");
        Long count1 = jdbcTemplate.queryForObject(
            "SELECT COUNT(*) FROM large_table", Long.class
        );
        watch.stop();

        // 启用并行
        watch.start("COUNT with parallel");
        jdbcTemplate.execute("SET SESSION innodb_parallel_read_threads = 4");
        Long count2 = jdbcTemplate.queryForObject(
            "SELECT COUNT(*) FROM large_table", Long.class
        );
        watch.stop();

        log.info("并行查询性能对比:\n{}", watch.prettyPrint());
    }
}

决策流程图

时间复杂度分析

操作类型 MyISAM InnoDB (无 WHERE) InnoDB (有 WHERE)
COUNT(*) O(1) O(n) - 扫描最小索引 O(log n + m) - m 为结果集大小
COUNT(indexed_column) O(1) O(n) - 扫描列索引 O(log n + m)
COUNT(non_indexed_column) O(n) O(n) - 全表扫描 O(n)
COUNT(DISTINCT column) O(n log n) O(n log n) O(n log n)

注：n 为表的总行数

最佳实践

  1. 统计总行数 ：使用 COUNT(*)，语义清晰，性能最优
  2. 统计非空值 ：使用 COUNT(column)，注意 NULL 值影响
  3. 大表优化：考虑缓存、近似值或分片统计
  4. 避免在循环中 COUNT：使用 GROUP BY 一次查询
  5. 注意存储引擎差异：MyISAM 的 COUNT(*)是 O(1)，InnoDB 是 O(n)
  6. 监控慢查询：设置合理的 slow_query_log 阈值
  7. 分页优化：避免每次都 COUNT，考虑游标分页
  8. 安全防护：永远不要直接拼接 SQL，使用参数化查询
  9. MySQL 8.0 优化：启用并行查询提升大表 COUNT 性能
  10. 生产环境策略：组合使用计数器表、缓存和异步更新
  11. 高可用保障：使用熔断器防止雪崩，提供降级方案
  12. 分库分表：并行统计各分片，注意连接池大小

总结

对比维度 COUNT(*) COUNT(1) COUNT(字段) COUNT(DISTINCT)
统计内容 所有行 所有行 非 NULL 值 去重后的非 NULL 值
性能表现 最优（InnoDB 会选择最小索引） 与 COUNT(*)相同 取决于字段是否有索引 需要排序去重，最慢
使用场景 统计总行数 无特殊优势，不推荐 统计字段完整性 统计唯一值数量
NULL 处理 包含 NULL 行 包含 NULL 行 排除 NULL 值 排除 NULL 值
优化器处理 标准处理 转换为 COUNT(*) 需要检查字段值 需要去重操作
推荐指数 ⭐⭐⭐⭐⭐ ⭐⭐ ⭐⭐⭐（特定场景） ⭐⭐⭐（必要时使用）
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