覆盖迁移工具选型、增量同步策略与数据一致性校验

# 测试脚本核心逻辑
def benchmark_tool(tool_name, record_count):
    start_time = time.time()
    tool = MigrationToolFactory.create(tool_name)
    stats = tool.migrate(record_count)
    duration = time.time() - start_time
    
    return {
        "tool": tool_name,
        "throughput": record_count / duration,
        "cpu_usage": stats.cpu_avg,
        "mem_peak": stats.mem_peak,
        "network_usage": stats.network_total
    }

# 测试结果
results = []
for tool in ["Debezium", "Canal", "FlinkCDC", "DataX"]:
    results.append(benchmark_tool(tool, 100_000_000))

// Flink 乱序处理示例
DataStream<OrderEvent> stream = env
    .addSource(kafkaSource)
    .keyBy(OrderEvent::getOrderId) // 按订单ID分区
    .window(TumblingEventTimeWindows.of(Time.seconds(5)))
    .allowedLateness(Time.seconds(2)) // 允许迟到
    .process(new OrderBufferProcess());

class OrderBufferProcess extends ProcessWindowFunction<OrderEvent> {
    public void process(OrderEvent event, Context ctx, Iterable<OrderEvent> out) {
        TreeMap<Long, OrderEvent> sorted = new TreeMap<>();
        for (OrderEvent e : events) {
            sorted.put(e.getSequenceId(), e);
        }
        // 按sequenceId顺序输出
        sorted.values().forEach(out::collect);
    }
}

class Deduplicator:
    def __init__(self, capacity=1000000, error_rate=0.001):
        self.filter = BloomFilter(capacity, error_rate)
        self.lock = threading.Lock()
    
    def is_duplicate(self, record_id):
        with self.lock:
            if record_id in self.filter:
                return True
            self.filter.add(record_id)
            return False

# 消费端使用
if not deduplicator.is_duplicate(msg.id):
    process_and_save(msg)

// 延迟统计Exporter
func recordLatency(event Event) {
    latency := time.Now().UnixMilli() - event.Timestamp
    metrics.WithLabelValues(event.Table).Observe(latency)
}

// Grafana查询
avg_over_time(sync_latency_ms{table="orders"}[5m])

alert: SyncLagHigh
expr: avg(sync_latency_ms) by (table) > 5000
for: 5m
labels:
  severity: critical
annotations:
  summary: "同步高延迟: {{ $labels.table }}"

// 使用Arrow格式优化传输
try (VectorSchemaRoot root = VectorSchemaRoot.create(schema, allocator)) {
    // 批量设置列数据
    VarCharVector idVector = (VarCharVector) root.getVector("id");
    IntVector amountVector = (IntVector) root.getVector("amount");
    
    for (int i = 0; i < batchSize; i++) {
        idVector.setSafe(i, records[i].getId().getBytes());
        amountVector.set(i, records[i].getAmount());
    }
    
    root.setRowCount(batchSize);
    // 写入Arrow流
    ArrowStreamWriter writer = new ArrowStreamWriter(root, null, socket.getOutputStream());
    writer.writeBatch();
}

/* MySQL校验函数 */
CHECKSUM TABLE orders EXTENDED;

/* PostgreSQL校验 */
SELECT pg_catalog.pg_relation_checksum('orders');

def chunk_verify(table, chunk_size=10000):
    mismatch = []
    max_id = source_db.query(f"SELECT MAX(id) FROM {table}")[0][0]
    
    for start in range(0, max_id, chunk_size):
        end = start + chunk_size
        source_hash = source_db.query(f"""
            SELECT MD5(GROUP_CONCAT(id, amount, status ORDER BY id))
            FROM orders WHERE id BETWEEN {start} AND {end}
        """)
        
        target_hash = target_db.query(f"..." )
        
        if source_hash != target_hash:
            mismatch.append((start, end))
    
    return mismatch

// 并行校验工具
ExecutorService executor = Executors.newFixedThreadPool(8);
List<Future<DiffResult>> futures = new ArrayList<>();

for (int i = 0; i < CHUNKS; i++) {
    futures.add(executor.submit(() -> {
        return compareRecords(startId, endId);
    }));
}

// 差异合并
List<DiffResult> allDiffs = new ArrayList<>();
for (Future<DiffResult> future : futures) {
    allDiffs.addAll(future.get());
}

// Spark Structured Streaming实现
val sourceStream = spark.readStream.format("jdbc")...
val targetStream = spark.readStream.format("jdbc")...

val sourceFingerprint = sourceStream
  .selectExpr("MD5(CONCAT_WS('|', *)) AS hash")
  .groupBy(window($"timestamp", "5 minutes"))
  .agg(approx_count_distinct($"hash").alias("distinct_count"))

val targetFingerprint = ... // 相同逻辑

val diff = sourceFingerprint.join(targetFingerprint, "window")
  .filter($"source.distinct_count" !== $"target.distinct_count")

diff.writeStream.outputMode("complete")
  .format("console")
  .start()

def auto_repair(diff):
    if diff.diff_type == DiffType.MISSING_IN_TARGET:
        target_db.insert(diff.source_record)
    elif diff.diff_type == DiffType.VALUE_MISMATCH:
        if diff.source_version > diff.target_version:
            target_db.update(diff.source_record)
    elif diff.diff_type == DiffType.CONFLICT_UPDATE:
        notify_ops(diff)  # 通知运维人员

# 迁移检查清单
checklist = {
    "pre_migration": [
        "schema兼容性验证",
        "字符集统一",
        "索引预创建",
        "权限矩阵检查"
    ],
    "during_migration": [
        "增量延迟监控<5s",
        "源库负载<60%",
        "网络带宽监控",
        "错误队列告警"
    ],
    "post_migration": [
        "全表记录数校验",
        "关键字段采样校验",
        "业务报表比对",
        "72小时实时校验"
    ]
}