public class HoodieRecord<T> {
    private HoodieKey key;           // 记录的唯一标识
    private T data;                  // 记录的数据内容
    private HoodieOperation operation; // 操作类型(INSERT/UPSERT/DELETE)
    private HoodieRecordLocation currentLocation; // 当前位置
    private HoodieRecordLocation newLocation;     // 新位置
}

public class HoodieKey {
    private String recordKey;    // 记录的业务主键
    private String partitionPath; // 分区路径
    
    // 生成全局唯一的键
    public String getKey() {
        return partitionPath + "+" + recordKey;
    }
}

// 推荐的Key设计
HoodieKey userKey = new HoodieKey(
    "user_" + userId,           // 有意义的前缀
    "year=2024/month=01/day=15" // 时间分区
);

// 避免的Key设计
HoodieKey badKey = new HoodieKey(
    UUID.randomUUID().toString(), // 无业务含义
    "default"                     // 单一分区
);

public abstract class HoodieIndex<T> {
    // 标记记录位置
    public abstract JavaRDD<HoodieRecord<T>> tagLocation(
        JavaRDD<HoodieRecord<T>> recordRDD,
        JavaSparkContext jsc,
        HoodieTable<T> hoodieTable
    );
    
    // 更新索引
    public abstract JavaRDD<WriteStatus> updateLocation(
        JavaRDD<WriteStatus> writeStatusRDD,
        JavaSparkContext jsc,
        HoodieTable<T> hoodieTable
    );
}

# BloomFilter配置
hoodie.index.bloom.num_entries=60000
hoodie.index.bloom.fpp=0.000000001
hoodie.bloom.index.parallelism=200

public class HoodieWriteClient<T> {
    // 插入数据
    public JavaRDD<WriteStatus> insert(
        JavaRDD<HoodieRecord<T>> records,
        String commitTime
    );
    
    // 更新插入数据
    public JavaRDD<WriteStatus> upsert(
        JavaRDD<HoodieRecord<T>> records,
        String commitTime
    );
    
    // 删除数据
    public JavaRDD<WriteStatus> delete(
        JavaRDD<HoodieKey> keys,
        String commitTime
    );
    
    // 批量插入
    public JavaRDD<WriteStatus> bulkInsert(
        JavaRDD<HoodieRecord<T>> records,
        String commitTime
    );
}

# 写入性能优化
hoodie.upsert.shuffle.parallelism=200
hoodie.insert.shuffle.parallelism=200
hoodie.bulkinsert.shuffle.parallelism=200

# 内存优化
hoodie.memory.merge.max.size=1073741824
hoodie.memory.spillable.map.path=/tmp/hudi-spillable-map

# 提交优化
hoodie.commits.archival.batch=10
hoodie.cleaner.commits.retained=30

// Spark环境下的Insert操作
JavaSparkContext jsc = new JavaSparkContext(conf);
HoodieWriteClient<HoodieAvroPayload> client = 
    new HoodieWriteClient<>(jsc, writeConfig);

// 准备数据
JavaRDD<HoodieRecord<HoodieAvroPayload>> records = 
    prepareRecords(jsc);

// 执行Insert
String commitTime = client.startCommit();
JavaRDD<WriteStatus> writeStatuses = 
    client.insert(records, commitTime);

// 提交事务
client.commit(commitTime, writeStatuses);

// 配置BloomFilter索引参数
writeConfig = HoodieWriteConfig.newBuilder()
    .withPath(basePath)
    .withSchema(schema)
    .withParallelism(200, 200)
    .withIndexConfig(
        HoodieIndexConfig.newBuilder()
            .withIndexType(HoodieIndex.IndexType.BLOOM)
            .withBloomIndexParallelism(100)
            .build()
    )
    .build();

// 软删除：添加删除标记
Schema schemaWithDelete = SchemaBuilder.record("User")
    .fields()
    .name("id").type().stringType().noDefault()
    .name("name").type().stringType().noDefault()
    .name("_hoodie_is_deleted").type().booleanType().booleanDefault(false)
    .endRecord();

// 查询时过滤已删除记录
Dataset<Row> activeRecords = spark.read()
    .format("hudi")
    .load(tablePath)
    .filter("_hoodie_is_deleted = false");

// 硬删除：物理删除记录
JavaRDD<HoodieKey> keysToDelete = prepareKeysToDelete(jsc);
String commitTime = client.startCommit();
JavaRDD<WriteStatus> writeStatuses = 
    client.delete(keysToDelete, commitTime);
client.commit(commitTime, writeStatuses);

# Bulk Insert优化配置
hoodie.bulkinsert.shuffle.parallelism=400
hoodie.bulkinsert.sort.mode=PARTITION_SORT
hoodie.parquet.max.file.size=134217728
hoodie.parquet.small.file.limit=104857600

-- 注册Hudi表
CREATE TABLE hudi_users
USING HUDI
LOCATION '/path/to/hudi/table'

-- 快照查询
SELECT user_id, name, age, city
FROM hudi_users
WHERE age > 25
  AND city = 'Beijing'

# 查询性能优化配置
hoodie.datasource.query.type=snapshot
hoodie.datasource.read.extract.partition.values.in.path=true
hoodie.datasource.read.schema.use.end.schema=true

# 文件读取优化
hoodie.parquet.compression.codec=snappy
hoodie.parquet.block.size=134217728

// Java API增量查询
Dataset<Row> incrementalDF = spark.read()
    .format("hudi")
    .option(QUERY_TYPE_OPT_KEY, QUERY_TYPE_INCREMENTAL_OPT_VAL)
    .option(BEGIN_INSTANTTIME_OPT_KEY, "20240101000000")
    .option(END_INSTANTTIME_OPT_KEY, "20240102000000")
    .load(tablePath);

// SQL方式增量查询
spark.sql("""
    SELECT * FROM hudi_table_changes('hudi_users', 
                                   'earliest', 
                                   'latest')
""");

-- Spark SQL时间点查询
SELECT * FROM hudi_users 
TIMESTAMP AS OF '2024-01-15 10:30:00'

-- 或使用commit时间
SELECT * FROM hudi_users 
VERSION AS OF '20240115103000'

# 启用时间点查询
hoodie.datasource.query.type=snapshot
hoodie.datasource.read.begin.instanttime=20240115103000

# 保留足够的历史版本
hoodie.cleaner.commits.retained=50
hoodie.keep.min.commits=30

-- 利用分区字段进行过滤
SELECT * FROM hudi_users
WHERE year = '2024' 
  AND month = '01'
  AND day = '15'
  AND user_id = 'user123'

-- 只查询需要的列
SELECT user_id, name, last_updated
FROM hudi_users
WHERE status = 'active'

# 启用数据跳跃优化
hoodie.datasource.read.data.skipping.enabled=true
hoodie.datasource.read.data.skipping.failure.mode=strict

# 启用Z-Order优化
hoodie.layout.optimize.enable=true
hoodie.layout.optimize.strategy=z-order

// 缓存频繁查询的数据
Dataset<Row> cachedDF = spark.read()
    .format("hudi")
    .load(tablePath)
    .cache();

// 使用广播变量优化Join
Dataset<Row> result = largeDF.join(
    broadcast(smallDF), 
    "user_id"
);

# 内联压缩配置
hoodie.compact.inline=true
hoodie.compact.inline.max.delta.commits=5
hoodie.compact.inline.trigger.strategy=NUM_COMMITS

# 异步压缩配置
hoodie.datasource.compaction.async.enable=true
hoodie.compaction.strategy=org.apache.hudi.table.action.compact.strategy.LogFileSizeBasedCompactionStrategy

# 压缩并行度
hoodie.compaction.parallelism=200
hoodie.compaction.target.io=5120

# 清理策略配置
hoodie.cleaner.policy=KEEP_LATEST_COMMITS
hoodie.cleaner.commits.retained=10
hoodie.cleaner.parallelism=200

# 自动清理
hoodie.clean.automatic=true
hoodie.clean.async=true
hoodie.clean.trigger.strategy=NUM_COMMITS
hoodie.clean.max.commits=1

// 清理策略实现示例
public class KeepLatestCommitsCleaningPolicy {
    
    public List<CleanFileInfo> getDeletePaths(
            HoodieCleanerPlan cleanerPlan) {
        
        List<CleanFileInfo> deletePaths = new ArrayList<>();
        
        // 获取需要保留的提交
        List<String> commitsToRetain = getCommitsToRetain();
        
        // 遍历所有分区
        for (String partition : getAllPartitions()) {
            // 获取分区内的所有文件组
            for (FileGroup fileGroup : getFileGroups(partition)) {
                // 标记过期文件为删除
                deletePaths.addAll(
                    markExpiredFilesForDeletion(fileGroup, commitsToRetain)
                );
            }
        }
        
        return deletePaths;
    }
}

# 归档触发条件
hoodie.commits.archival.batch=20
hoodie.commits.archival.batch.size=10

# 保留策略
hoodie.keep.min.commits=30
hoodie.keep.max.commits=40

# 自动归档
hoodie.archive.automatic=true
hoodie.archive.async=false

# I/O优化
hoodie.compaction.target.io=5120
hoodie.compaction.reverse.log.read=false
hoodie.compaction.lazy.block.read=true

# 内存优化
hoodie.memory.compaction.max.size=1073741824
hoodie.memory.merge.max.size=1073741824

# 并发优化
hoodie.compaction.parallelism=400
hoodie.compaction.small.file.size=104857600

# 批量处理
hoodie.cleaner.parallelism=400
hoodie.cleaner.incremental.mode=true

# 文件系统优化
hoodie.cleaner.delete.bootstrap.base.file=false
hoodie.filesystem.view.cache.enable=true

// 关键监控指标
public class HudiMetrics {
    // 压缩相关指标
    private long compactionDuration;
    private long compactionInputBytes;
    private long compactionOutputBytes;
    private int compactionFilesProcessed;
    
    // 清理相关指标
    private long cleaningDuration;
    private int filesDeleted;
    private long spaceReclaimed;
    
    // 归档相关指标
    private int commitsArchived;
    private long archivalDuration;
}

<!-- Maven依赖 -->
<dependency>
    <groupId>org.apache.hudi</groupId>
    <artifactId>hudi-spark3.2-bundle_2.12</artifactId>
    <version>0.14.0</version>
</dependency>

# Spark配置文件
spark.sql.extensions=org.apache.spark.sql.hudi.HoodieSparkSessionExtension
spark.sql.catalog.spark_catalog=org.apache.spark.sql.hudi.catalog.HoodieCatalog
spark.serializer=org.apache.spark.serializer.KryoSerializer
spark.sql.hive.convertMetastoreParquet=false

import org.apache.hudi.DataSourceWriteOptions._
import org.apache.hudi.config.HoodieWriteConfig._

// 写入Hudi表
df.write
  .format("hudi")
  .option(RECORDKEY_FIELD_OPT_KEY, "user_id")
  .option(PARTITIONPATH_FIELD_OPT_KEY, "partition_date")
  .option(TABLE_NAME_OPT_KEY, "user_table")
  .option(OPERATION_OPT_KEY, "upsert")
  .mode(SaveMode.Append)
  .save("/path/to/hudi/table")

// 读取Hudi表
val hudiDF = spark.read
  .format("hudi")
  .load("/path/to/hudi/table")

-- 创建Hudi表
CREATE TABLE hudi_users (
    user_id STRING,
    name STRING,
    age INT,
    city STRING,
    partition_date STRING
) USING HUDI
TBLPROPERTIES (
    'primaryKey' = 'user_id',
    'preCombineField' = 'age'
)
PARTITIONED BY (partition_date)
LOCATION '/path/to/hudi/table'

-- 插入数据
INSERT INTO hudi_users VALUES 
('user1', 'Alice', 25, 'Beijing', '2024-01-15'),
('user2', 'Bob', 30, 'Shanghai', '2024-01-15')

-- 更新数据
MERGE INTO hudi_users AS target
USING (
    SELECT 'user1' as user_id, 'Alice Smith' as name, 
           26 as age, 'Beijing' as city, '2024-01-15' as partition_date
) AS source
ON target.user_id = source.user_id
WHEN MATCHED THEN UPDATE SET *
WHEN NOT MATCHED THEN INSERT *

<dependency>
    <groupId>org.apache.hudi</groupId>
    <artifactId>hudi-flink1.15-bundle</artifactId>
    <version>0.14.0</version>
</dependency>

// Flink流式写入Hudi
StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment();

// 配置Hudi Sink
HudiStreamWriteOperatorCoordinator.Options options = 
    HudiStreamWriteOperatorCoordinator.Options.builder()
        .withPath("/path/to/hudi/table")
        .withTableType(HoodieTableType.MERGE_ON_READ)
        .withWriteOperation(WriteOperationType.UPSERT)
        .build();

// 创建数据流
DataStream<RowData> dataStream = env.addSource(kafkaSource)
    .map(new RowDataMapper());

// 写入Hudi表
dataStream.sinkTo(HudiSink.forRowData(options));

-- 创建Hudi表
CREATE TABLE hudi_users (
    user_id STRING,
    name STRING,
    age INT,
    city STRING,
    partition_date STRING,
    PRIMARY KEY (user_id) NOT ENFORCED
) PARTITIONED BY (partition_date)
WITH (
    'connector' = 'hudi',
    'path' = '/path/to/hudi/table',
    'table.type' = 'MERGE_ON_READ',
    'write.operation' = 'upsert'
)

-- 流式写入
INSERT INTO hudi_users 
SELECT user_id, name, age, city, partition_date
FROM kafka_source

-- 快照查询
SELECT * FROM hudi_users /*+ OPTIONS('query.type'='snapshot') */

-- 增量查询
SELECT * FROM hudi_users /*+ OPTIONS(
    'query.type'='incremental',
    'query.incremental.start-commit'='20240115000000',
    'query.incremental.end-commit'='20240115120000'
) */

<!-- Hive辅助jar包 -->
<property>
    <name>hive.aux.jars.path</name>
    <value>/path/to/hudi-hadoop-mr-bundle-0.14.0.jar</value>
</property>

// 使用HiveSyncTool同步元数据
HiveSyncConfig hiveSyncConfig = new HiveSyncConfig();
hiveSyncConfig.databaseName = "default";
hiveSyncConfig.tableName = "hudi_users";
hiveSyncConfig.basePath = "/path/to/hudi/table";
hiveSyncConfig.partitionFields = Arrays.asList("partition_date");

HiveSyncTool hiveSyncTool = new HiveSyncTool(hiveSyncConfig, hadoopConf);
hiveSyncTool.syncHoodieTable();

-- 在Hive中查询Hudi表
SELECT user_id, name, age, city
FROM hudi_users
WHERE partition_date = '2024-01-15'
  AND age > 25

-- 查看表结构
DESCRIBE FORMATTED hudi_users

-- 查看分区信息
SHOW PARTITIONS hudi_users

# 1. Java环境
export JAVA_HOME=/usr/lib/jvm/java-8-openjdk
export PATH=$JAVA_HOME/bin:$PATH

# 2. Hadoop环境
export HADOOP_HOME=/opt/hadoop
export HADOOP_CONF_DIR=$HADOOP_HOME/etc/hadoop
export PATH=$HADOOP_HOME/bin:$PATH

# 3. Spark环境
export SPARK_HOME=/opt/spark
export PATH=$SPARK_HOME/bin:$PATH

# 4. Hudi配置
export HUDI_HOME=/opt/hudi
export HUDI_CONF_DIR=$HUDI_HOME/conf

# JVM参数优化
spark.executor.memory=8g
spark.executor.cores=4
spark.executor.instances=20
spark.driver.memory=4g
spark.driver.maxResultSize=2g

# Hudi特定优化
hoodie.memory.merge.max.size=1073741824
hoodie.memory.spillable.map.path=/tmp/hudi-spillable-map
hoodie.embed.timeline.server=true
hoodie.filesystem.view.cache.enable=true

# 存储优化
hoodie.parquet.compression.codec=snappy
hoodie.parquet.block.size=134217728
hoodie.parquet.page.size=1048576

# Prometheus监控配置
hudi_metrics:
  - job_name: 'hudi-metrics'
    static_configs:
      - targets: ['localhost:9090']
    metrics_path: '/metrics'
    scrape_interval: 30s
    
# Grafana仪表板
dashboard:
  - title: "Hudi Table Metrics"
    panels:
      - write_throughput
      - query_latency
      - compaction_duration
      - storage_usage

# 启用指标收集
hoodie.metrics.on=true
hoodie.metrics.reporter.type=PROMETHEUS

# Prometheus配置
hoodie.metrics.prometheus.port=9090
hoodie.metrics.prometheus.pushgateway.host=localhost
hoodie.metrics.prometheus.pushgateway.port=9091

# 指标收集间隔
hoodie.metrics.reporter.metricsname.prefix=hudi
hoodie.metrics.reporter.interval=30

// 自定义监控指标实现
public class HudiCustomMetrics {
    private final MetricRegistry metricRegistry;
    private final Timer writeLatencyTimer;
    private final Counter writeSuccessCounter;
    private final Counter writeFailureCounter;
    private final Histogram fileSizeHistogram;
    
    public HudiCustomMetrics() {
        this.metricRegistry = new MetricRegistry();
        this.writeLatencyTimer = metricRegistry.timer("hudi.write.latency");
        this.writeSuccessCounter = metricRegistry.counter("hudi.write.success");
        this.writeFailureCounter = metricRegistry.counter("hudi.write.failure");
        this.fileSizeHistogram = metricRegistry.histogram("hudi.file.size");
    }
    
    public void recordWriteLatency(long latencyMs) {
        writeLatencyTimer.update(latencyMs, TimeUnit.MILLISECONDS);
    }
    
    public void recordWriteSuccess() {
        writeSuccessCounter.inc();
    }
    
    public void recordWriteFailure() {
        writeFailureCounter.inc();
    }
}

# 检查Timeline状态
hdfs dfs -ls /path/to/hudi/table/.hoodie/.inflight/

# 查看失败的提交信息
hdfs dfs -cat /path/to/hudi/table/.hoodie/20240115103000.inflight

# 检查Spark日志
grep -i "error\|exception" /path/to/spark/logs/application_*.log

-- 检查文件分布情况
SELECT 
    partition_path,
    COUNT(*) as file_count,
    AVG(file_size) as avg_file_size,
    MIN(file_size) as min_file_size,
    MAX(file_size) as max_file_size
FROM hudi_table_file_info
GROUP BY partition_path
ORDER BY file_count DESC

# 检查存储使用情况
hdfs dfs -du -h /path/to/hudi/table/

# 查看清理历史
hdfs dfs -ls /path/to/hudi/table/.hoodie/.clean/

# 手动触发清理
spark-submit --class org.apache.hudi.utilities.HoodieCleanerTool \
  hudi-utilities-bundle.jar \
  --base-path /path/to/hudi/table \
  --hoodie-conf hoodie.cleaner.commits.retained=5

// Hudi诊断工具
public class HudiDiagnosticTool {
    
    // 检查Timeline健康状态
    public void checkTimelineHealth(String basePath) {
        HoodieTableMetaClient metaClient = 
            HoodieTableMetaClient.builder()
                .setConf(hadoopConf)
                .setBasePath(basePath)
                .build();
                
        HoodieTimeline timeline = metaClient.getActiveTimeline();
        
        // 检查是否有长时间运行的操作
        List<HoodieInstant> inflightInstants = 
            timeline.getInflightInstants().getInstants().collect(Collectors.toList());
            
        for (HoodieInstant instant : inflightInstants) {
            long duration = System.currentTimeMillis() - 
                Long.parseLong(instant.getTimestamp());
            if (duration > TimeUnit.HOURS.toMillis(1)) {
                System.out.println("Long running operation detected: " + instant);
            }
        }
    }
    
    // 检查文件系统一致性
    public void checkFileSystemConsistency(String basePath) {
        // 验证元数据与实际文件的一致性
        // 检查孤儿文件
        // 验证分区完整性
    }
}

# 并行度优化
hoodie.upsert.shuffle.parallelism=400
hoodie.insert.shuffle.parallelism=400
hoodie.bulkinsert.shuffle.parallelism=800

# 内存优化
hoodie.memory.merge.max.size=2147483648
hoodie.memory.spillable.map.path=/tmp/hudi-spillable-map
hoodie.memory.compaction.max.size=2147483648

# 索引优化
hoodie.index.type=BLOOM
hoodie.index.bloom.num_entries=60000
hoodie.index.bloom.fpp=0.000000001
hoodie.bloom.index.parallelism=400

# 文件大小优化
hoodie.parquet.max.file.size=268435456
hoodie.parquet.small.file.limit=134217728
hoodie.copyonwrite.record.size.estimate=1024

# 数据跳跃优化
hoodie.datasource.read.data.skipping.enabled=true
hoodie.datasource.read.data.skipping.failure.mode=strict

# 缓存优化
hoodie.filesystem.view.cache.enable=true
hoodie.filesystem.view.cache.max.size=10240

# 并行读取优化
hoodie.datasource.read.extract.partition.values.in.path=true
hoodie.datasource.read.schema.use.end.schema=true

# 1. 定期健康检查脚本
#!/bin/bash
HUDI_TABLE_PATH="/path/to/hudi/table"

# 检查Timeline状态
echo "Checking timeline health..."
hdfs dfs -ls $HUDI_TABLE_PATH/.hoodie/.inflight/

# 检查存储使用
echo "Checking storage usage..."
hdfs dfs -du -h $HUDI_TABLE_PATH/

# 检查小文件数量
echo "Checking small files..."
hdfs dfs -find $HUDI_TABLE_PATH -name "*.parquet" -exec hdfs dfs -stat "%n %b" {} \; | \
awk '$2 < 134217728 {count++} END {print "Small files count:", count}'

# 2. 自动化压缩脚本
spark-submit \
  --class org.apache.hudi.utilities.HoodieCompactor \
  --master yarn \
  --deploy-mode cluster \
  hudi-utilities-bundle.jar \
  --base-path $HUDI_TABLE_PATH \
  --table-name hudi_table \
  --compaction-parallelism 200

# 3. 监控告警脚本
#!/bin/bash
# 检查失败的提交
FAILED_COMMITS=$(hdfs dfs -ls $HUDI_TABLE_PATH/.hoodie/.inflight/ | wc -l)
if [ $FAILED_COMMITS -gt 5 ]; then
    echo "Alert: Too many failed commits detected"
    # 发送告警通知
fi

# 1. 备份关键元数据
hdfs dfs -cp $HUDI_TABLE_PATH/.hoodie /backup/location/

# 2. 创建表快照
spark-submit \
  --class org.apache.hudi.utilities.HoodieSavepointTool \
  hudi-utilities-bundle.jar \
  --base-path $HUDI_TABLE_PATH \
  --savepoint-time 20240115103000

# 3. 恢复到指定时间点
spark-submit \
  --class org.apache.hudi.utilities.HoodieRollbackTool \
  hudi-utilities-bundle.jar \
  --base-path $HUDI_TABLE_PATH \
  --rollback-to-time 20240115103000

// 多表事务示例
public class MultiTableTransaction {
    
    public void executeMultiTableTransaction() {
        // 创建事务管理器
        HoodieMultiTableTransactionManager txnManager = 
            new HoodieMultiTableTransactionManager();
        
        try {
            // 开始事务
            String txnId = txnManager.beginTransaction();
            
            // 操作表1
            HoodieWriteClient<HoodieAvroPayload> client1 = 
                createWriteClient("table1", txnId);
            JavaRDD<WriteStatus> writeStatus1 = 
                client1.upsert(records1, commitTime1);
            
            // 操作表2
            HoodieWriteClient<HoodieAvroPayload> client2 = 
                createWriteClient("table2", txnId);
            JavaRDD<WriteStatus> writeStatus2 = 
                client2.upsert(records2, commitTime2);
            
            // 预提交所有表
            txnManager.preCommit(txnId, Arrays.asList(
                new TableCommitInfo("table1", commitTime1, writeStatus1),
                new TableCommitInfo("table2", commitTime2, writeStatus2)
            ));
            
            // 最终提交
            txnManager.commit(txnId);
            
        } catch (Exception e) {
            // 回滚事务
            txnManager.rollback(txnId);
            throw new RuntimeException("Multi-table transaction failed", e);
        }
    }
}

// Schema演进管理
public class HudiSchemaEvolution {
    
    // 添加新字段（向后兼容）
    public Schema addOptionalField(Schema currentSchema, 
                                  String fieldName, 
                                  Schema.Type fieldType, 
                                  Object defaultValue) {
        SchemaBuilder.FieldAssembler<Schema> fieldAssembler = 
            SchemaBuilder.record(currentSchema.getName())
                .namespace(currentSchema.getNamespace())
                .fields();
        
        // 复制现有字段
        for (Schema.Field field : currentSchema.getFields()) {
            fieldAssembler = fieldAssembler
                .name(field.name())
                .type(field.schema())
                .withDefault(field.defaultVal());
        }
        
        // 添加新字段
        return fieldAssembler
            .name(fieldName)
            .type(fieldType)
            .withDefault(defaultValue)
            .endRecord();
    }
    
    // 字段类型演进
    public Schema evolveFieldType(Schema currentSchema, 
                                 String fieldName, 
                                 Schema.Type newType) {
        // 验证类型兼容性
        if (!isTypeCompatible(getFieldType(currentSchema, fieldName), newType)) {
            throw new SchemaEvolutionException(
                "Incompatible type evolution for field: " + fieldName);
        }
        
        // 创建新Schema
        return createSchemaWithUpdatedField(currentSchema, fieldName, newType);
    }
}

// 兼容性检查工具
public class SchemaCompatibilityChecker {
    
    public CompatibilityResult checkCompatibility(Schema writerSchema, 
                                                Schema readerSchema) {
        List<String> errors = new ArrayList<>();
        List<String> warnings = new ArrayList<>();
        
        // 检查字段兼容性
        for (Schema.Field writerField : writerSchema.getFields()) {
            Schema.Field readerField = readerSchema.getField(writerField.name());
            
            if (readerField == null) {
                if (!writerField.hasDefaultValue()) {
                    errors.add("Required field missing: " + writerField.name());
                }
            } else {
                // 检查类型兼容性
                if (!areTypesCompatible(writerField.schema(), readerField.schema())) {
                    errors.add("Incompatible types for field: " + writerField.name());
                }
            }
        }
        
        return new CompatibilityResult(errors, warnings);
    }
}

// 数据血缘追踪
public class HudiLineageTracker {
    
    public void recordLineage(String tableName, 
                            String commitTime, 
                            LineageInfo lineageInfo) {
        // 构建血缘记录
        DataLineageRecord record = DataLineageRecord.builder()
            .tableName(tableName)
            .commitTime(commitTime)
            .sourceDatasets(lineageInfo.getSourceDatasets())
            .transformations(lineageInfo.getTransformations())
            .outputDatasets(lineageInfo.getOutputDatasets())
            .build();
        
        // 存储血缘信息
        lineageStore.store(record);
        
        // 更新血缘图
        lineageGraph.addEdges(record.getSourceDatasets(), 
                             Arrays.asList(tableName));
    }
    
    public List<String> getUpstreamTables(String tableName) {
        return lineageGraph.getUpstreamNodes(tableName);
    }
    
    public List<String> getDownstreamTables(String tableName) {
        return lineageGraph.getDownstreamNodes(tableName);
    }
    
    public ImpactAnalysisResult analyzeImpact(String tableName, 
                                            String fieldName) {
        // 分析字段变更的影响范围
        List<String> affectedTables = new ArrayList<>();
        List<String> affectedApplications = new ArrayList<>();
        
        // 递归查找下游依赖
        findDownstreamDependencies(tableName, fieldName, 
                                 affectedTables, affectedApplications);
        
        return new ImpactAnalysisResult(affectedTables, affectedApplications);
    }
}

# Kerberos认证配置
hoodie.security.kerberos.enabled=true
hoodie.security.kerberos.principal=[email protected]
hoodie.security.kerberos.keytab=/path/to/hudi.keytab

# 授权配置
hoodie.security.authorization.enabled=true
hoodie.security.authorization.provider=org.apache.hudi.security.RangerAuthorizationProvider

# 加密配置
hoodie.security.encryption.enabled=true
hoodie.security.encryption.algorithm=AES-256-GCM
hoodie.security.encryption.key.provider=org.apache.hudi.security.HadoopKeyProvider

// 敏感字段加密
public class FieldLevelEncryption {
    
    @EncryptedField(algorithm = "AES-256-GCM")
    private String socialSecurityNumber;
    
    @EncryptedField(algorithm = "AES-256-GCM")
    private String creditCardNumber;
    
    @MaskedField(maskType = MaskType.EMAIL)
    private String emailAddress;
    
    // 加密处理器
    public GenericRecord encryptSensitiveFields(GenericRecord record) {
        GenericRecord encryptedRecord = new GenericData.Record(record.getSchema());
        
        for (Schema.Field field : record.getSchema().getFields()) {
            Object value = record.get(field.name());
            
            if (field.hasAnnotation(EncryptedField.class)) {
                // 加密敏感字段
                value = encryptField(value, field.getAnnotation(EncryptedField.class));
            } else if (field.hasAnnotation(MaskedField.class)) {
                // 脱敏处理
                value = maskField(value, field.getAnnotation(MaskedField.class));
            }
            
            encryptedRecord.put(field.name(), value);
        }
        
        return encryptedRecord;
    }
}

// 审计日志记录
public class HudiAuditLogger {
    
    public void logDataAccess(String user, 
                            String tableName, 
                            String operation, 
                            List<String> accessedFields) {
        AuditLogEntry entry = AuditLogEntry.builder()
            .timestamp(System.currentTimeMillis())
            .user(user)
            .tableName(tableName)
            .operation(operation)
            .accessedFields(accessedFields)
            .sourceIP(getCurrentUserIP())
            .sessionId(getCurrentSessionId())
            .build();
        
        auditLogStore.store(entry);
        
        // 实时监控异常访问
        if (isAnomalousAccess(entry)) {
            alertManager.sendAlert("Anomalous data access detected", entry);
        }
    }
    
    public List<AuditLogEntry> queryAuditLogs(AuditQuery query) {
        return auditLogStore.query(query);
    }
}

// 使用复合分区键
String partitionPath = String.format("year=%s/month=%s/bucket=%s", 
    year, month, hashCode(recordKey) % 100);

// 数据预处理，添加随机后缀
val preprocessedDF = originalDF
  .withColumn("salt", (rand() * 100).cast("int"))
  .withColumn("salted_key", concat(col("record_key"), lit("_"), col("salt")))

# 增加Shuffle并行度
hoodie.upsert.shuffle.parallelism=800
hoodie.insert.shuffle.parallelism=800

# 启用自适应查询执行
spark.sql.adaptive.enabled=true
spark.sql.adaptive.coalescePartitions.enabled=true

# 使用Global索引避免热点
hoodie.index.type=GLOBAL_SIMPLE
hoodie.index.global.simple.update.partition.path=true

# 定期备份.hoodie目录
hdfs dfs -cp /hudi/table/.hoodie /backup/metadata/$(date +%Y%m%d)

# 备份Hive元数据
mysqldump -u hive -p hive_metastore > metastore_backup_$(date +%Y%m%d).sql

// 创建数据快照点
HoodieSavepointTool savepointTool = new HoodieSavepointTool();
savepointTool.createSavepoint(basePath, commitTime, user, comments);

// 基于Timeline的增量备份
val incrementalDF = spark.read
  .format("hudi")
  .option(QUERY_TYPE_OPT_KEY, QUERY_TYPE_INCREMENTAL_OPT_VAL)
  .option(BEGIN_INSTANTTIME_OPT_KEY, lastBackupTime)
  .load(tablePath)

incrementalDF.write
  .mode("append")
  .parquet(backupPath)

# 并行度优化
hoodie.upsert.shuffle.parallelism=400
hoodie.insert.shuffle.parallelism=400
hoodie.bulkinsert.shuffle.parallelism=800

# 内存优化
hoodie.memory.merge.max.size=2147483648
hoodie.memory.spillable.map.path=/tmp/hudi-spillable-map

# 索引优化
hoodie.index.type=BLOOM
hoodie.index.bloom.num_entries=60000
hoodie.index.bloom.fpp=0.000000001

# 文件大小优化
hoodie.parquet.max.file.size=268435456
hoodie.parquet.small.file.limit=134217728

# 压缩优化
hoodie.compact.inline=false
hoodie.datasource.compaction.async.enable=true

# 启用数据跳跃
hoodie.datasource.read.data.skipping.enabled=true

# Z-Order布局优化
hoodie.layout.optimize.enable=true
hoodie.layout.optimize.strategy=z-order
hoodie.layout.optimize.build.curve.sample.number=200000

-- 利用分区字段过滤
SELECT * FROM hudi_table
WHERE year = '2024' AND month = '01' AND day = '15'
  AND user_id = 'user123'

-- 只查询需要的列
SELECT user_id, name, last_updated
FROM hudi_table
WHERE status = 'active'

# 启用BloomFilter索引
hoodie.index.type=BLOOM
hoodie.bloom.index.parallelism=200

# 启用列统计信息
hoodie.datasource.write.drop.partition.columns=false

// 缓存频繁查询的数据
val cachedDF = spark.read.format("hudi").load(tablePath).cache()

// 使用广播变量优化Join
val result = largeDF.join(broadcast(smallDF), "user_id")

// 自定义压缩策略
public class IntelligentCompactionStrategy extends CompactionStrategy {
    
    @Override
    public List<HoodieCompactionOperation> orderAndFilter(
            HoodieCompactionPlan compactionPlan) {
        
        List<HoodieCompactionOperation> operations = 
            compactionPlan.getOperations();
        
        // 按优先级排序
        return operations.stream()
            .sorted((op1, op2) -> {
                // 优先压缩小文件多的文件组
                int fileCount1 = op1.getDeltaFilePaths().size();
                int fileCount2 = op2.getDeltaFilePaths().size();
                
                if (fileCount1 != fileCount2) {
                    return Integer.compare(fileCount2, fileCount1);
                }
                
                // 其次考虑数据大小
                long size1 = calculateTotalSize(op1);
                long size2 = calculateTotalSize(op2);
                return Long.compare(size2, size1);
            })
            .limit(getMaxConcurrentCompactions())
            .collect(Collectors.toList());
    }
}

# 压缩专用资源池
hoodie.compaction.parallelism=100
hoodie.compaction.target.io=10240

# 错峰执行
hoodie.compact.schedule.enabled=true
hoodie.compact.schedule.cron=0 2 * * *  # 凌晨2点执行

-- 用户画像表设计
CREATE TABLE user_profile (
    user_id STRING,
    age_group STRING,
    income_level STRING,
    interest_tags ARRAY<STRING>,
    behavior_score MAP<STRING, DOUBLE>,
    last_updated TIMESTAMP,
    partition_date STRING
) USING HUDI
TBLPROPERTIES (
    'primaryKey' = 'user_id',
    'preCombineField' = 'last_updated',
    'type' = 'mor'
)
PARTITIONED BY (partition_date)

// 用户画像实时更新
public class UserProfileUpdater {
    
    public void updateUserProfile(UserBehaviorEvent event) {
        // 获取当前用户画像
        UserProfile currentProfile = getUserProfile(event.getUserId());
        
        // 计算新的特征值
        UserProfile updatedProfile = calculateFeatures(currentProfile, event);
        
        // 写入Hudi表
        HoodieRecord<UserProfile> record = new HoodieRecord<>(
            new HoodieKey(event.getUserId(), getCurrentPartition()),
            updatedProfile
        );
        
        writeClient.upsert(Arrays.asList(record), getCurrentCommitTime());
    }
    
    private UserProfile calculateFeatures(UserProfile current, 
                                        UserBehaviorEvent event) {
        // 更新行为计数
        current.incrementBehaviorCount(event.getEventType());
        
        // 更新兴趣标签
        current.updateInterestTags(event.getCategories());
        
        // 重新计算用户分群
        current.setUserSegment(calculateUserSegment(current));
        
        return current;
    }
}

# 画像查询优化配置
hoodie.datasource.read.data.skipping.enabled=true
hoodie.datasource.read.extract.partition.values.in.path=true

# 缓存热点数据
hoodie.filesystem.view.cache.enable=true
hoodie.filesystem.view.cache.max.size=10240

// 数据质量验证器
public class DataQualityValidator {
    
    public ValidationResult validate(Dataset<Row> dataset) {
        List<String> errors = new ArrayList<>();
        List<String> warnings = new ArrayList<>();
        
        // Schema验证
        if (!validateSchema(dataset.schema())) {
            errors.add("Schema validation failed");
        }
        
        // 空值检查
        long nullCount = dataset.filter(col("user_id").isNull()).count();
        if (nullCount > 0) {
            errors.add("Found " + nullCount + " null user_id records");
        }
        
        // 重复数据检查
        long totalCount = dataset.count();
        long distinctCount = dataset.select("user_id").distinct().count();
        if (totalCount != distinctCount) {
            warnings.add("Found duplicate records");
        }
        
        // 业务规则验证
        long invalidAgeCount = dataset.filter(col("age").lt(0).or(col("age").gt(150))).count();
        if (invalidAgeCount > 0) {
            errors.add("Found " + invalidAgeCount + " invalid age records");
        }
        
        return new ValidationResult(errors, warnings);
    }
}

// 数据清洗管道
val cleanedDF = rawDF
  .filter(col("user_id").isNotNull)  // 过滤空值
  .dropDuplicates("user_id")         // 去重
  .withColumn("age", 
    when(col("age").between(0, 150), col("age"))
    .otherwise(null))                // 异常值处理
  .withColumn("email", 
    regexp_replace(col("email"), "\\s+", ""))  // 格式标准化

// 数据质量监控
public class DataQualityMonitor {
    
    public void monitorTableQuality(String tableName) {
        Dataset<Row> df = spark.read().format("hudi").load(getTablePath(tableName));
        
        // 计算质量指标
        QualityMetrics metrics = calculateQualityMetrics(df);
        
        // 检查告警条件
        if (metrics.getNullRate() > 0.05) {
            alertManager.sendAlert("High null rate detected in " + tableName);
        }
        
        if (metrics.getDuplicateRate() > 0.01) {
            alertManager.sendAlert("High duplicate rate detected in " + tableName);
        }
        
        // 记录质量指标
        qualityMetricsStore.store(tableName, metrics);
    }
    
    private QualityMetrics calculateQualityMetrics(Dataset<Row> df) {
        long totalCount = df.count();
        long nullCount = df.filter(col("user_id").isNull()).count();
        long duplicateCount = totalCount - df.dropDuplicates("user_id").count();
        
        return QualityMetrics.builder()
            .totalRecords(totalCount)
            .nullRate((double) nullCount / totalCount)
            .duplicateRate((double) duplicateCount / totalCount)
            .completeness(1.0 - (double) nullCount / totalCount)
            .build();
    }
}