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1. Flink 大数据实时处理设计方案
整套计划通过 Canal + Kafka 连接器 + Protobuf，实现数据的同步接入，由 Flink 服务负责对各类业务数据的实时统计解决。
2. 热销商品的统计解决
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性能
实现对热销商品的统计，统计周期为一天，每 3 秒刷新一次数据。
-
外围代码
主逻辑实现：
/** * 执行 Flink 工作解决 * @throws Exception */ private void executeFlinkTask() throws Exception { // 1. 创立运行环境 StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment(); // 2. 设置 kafka 服务连贯信息 Properties properties = new Properties(); properties.setProperty("bootstrap.servers", "10.10.20.132:9092"); properties.setProperty("group.id", "fink_group"); // 3. 创立 Kafka 生产端 FlinkKafkaConsumer kafkaProducer = new FlinkKafkaConsumer( "order_binlog", // 指标 topic new SimpleStringSchema(), // 序列化 配置 properties); // 调试，从新从最早记录生产 kafkaProducer.setStartFromEarliest(); // 尽可能从最早的记录开始 env.setStreamTimeCharacteristic(TimeCharacteristic.EventTime); env.setParallelism(1); // 4. 读取 Kafka 数据源 DataStreamSource<String> socketStr = env.addSource(kafkaProducer); // 5. 数据过滤转换解决 socketStr.filter(new FilterFunction<String>() { @Override public boolean filter(String value) throws Exception {JsonObject jsonObject = GsonConvertUtil.getSingleton().getJsonObject(value); String isDDL = jsonObject.get("isDdl").getAsString(); String type = jsonObject.get("type").getAsString(); // 过滤条件：非 DDL 操作，并且是新增的数据 return isDDL.equalsIgnoreCase("false") && "INSERT".equalsIgnoreCase(type); } }).flatMap(new FlatMapFunction<String, Order>() { @Override public void flatMap(String value, Collector<Order> out) throws Exception { // 获取 JSON 中的 data 数据 JsonArray dataArray = GsonConvertUtil.getSingleton().getJsonObject(value).getAsJsonArray("data"); // 将 data 数据转换为 java 对象 for(int i =0; i< dataArray.size(); i++) {JsonObject jsonObject = dataArray.get(i).getAsJsonObject(); Order order = GsonConvertUtil.getSingleton().cvtJson2Obj(jsonObject, Order.class); System.out.println("order =>" + order); out.collect(order); } } }) .assignTimestampsAndWatermarks(new BoundedOutOfOrdernessTimestampExtractor<Order>(Time.seconds(0)) { @Override public long extractTimestamp(Order element) {return element.getExecTime(); } }) .keyBy(Order::getGoodsId) .timeWindow(Time.hours(24), Time.seconds(3)) .aggregate(new TotalAmount(), new AmountWindow()) .keyBy(HotOrder::getTimeWindow) .process(new TopNHotOrder()); // 6. 执行工作 env.execute("job"); }
热销商品的金额累加解决：
/**
* 商品金额累加器
*/
private static class TotalAmount implements AggregateFunction<Order, Order, Order> {
@Override
public Order createAccumulator() {Order order = new Order();
order.setTotalAmount(0l);
return order;
}
/**
* 累加统计商品销售总金额
* @param value
* @param accumulator
* @return
*/
@Override
public Order add(Order value, Order accumulator) {accumulator.setGoodsId(value.getGoodsId());
accumulator.setGoodsName((value.getGoodsName()));
accumulator.setTotalAmount(accumulator.getTotalAmount() + (value.getExecPrice() * value.getExecVolume()));
return accumulator;
}
@Override
public Order getResult(Order accumulator) {return accumulator;}
@Override
public Order merge(Order a, Order b) {return null;}
}热销商品的数据转换解决，用于统计：
/**
* 热销商品，在工夫窗口内，对象数据的转换解决
*/
private static class AmountWindow implements WindowFunction<Order, HotOrder, Long, TimeWindow> {
@Override
public void apply(Long goodsId, TimeWindow window, Iterable<Order> input, Collector<HotOrder> out) throws Exception {Order order = input.iterator().next();
out.collect(new HotOrder(goodsId, order.getGoodsName(), order.getTotalAmount(), window.getEnd()));
}
}热销商品的统计排行解决逻辑：
/**
* 热销商品的统计排行实现
*/
private class TopNHotOrder extends KeyedProcessFunction<Long, HotOrder, String> {
private ListState<HotOrder> orderState;
@Override
public void processElement(HotOrder value, Context ctx, Collector<String> out) throws Exception {
// 将数据退出到状态列表外面
orderState.add(value);
// 注册定时器
ctx.timerService().registerEventTimeTimer(value.getTimeWindow());
}
@Override
public void onTimer(long timestamp, OnTimerContext ctx, Collector<String> out) throws Exception {List<HotOrder> orderList = new ArrayList<>();
for(HotOrder order : orderState.get()){orderList.add(order);
}
// 依照成交总金额，倒序排列
orderList.sort(Comparator.comparing(HotOrder::getTotalAmount).reversed());
orderState.clear();
// 将数据写入至 ES
HotOrderRepository hotOrderRepository = (HotOrderRepository) ApplicationContextUtil.getBean("hotOrderRepository");
StringBuffer strBuf = new StringBuffer();
for(HotOrder order: orderList) {order.setId(order.getGoodsId());
order.setCreateDate(new Date(order.getTimeWindow()));
hotOrderRepository.save(order);
strBuf.append(order).append("\n");
System.out.println("result =>" + order);
}
out.collect(strBuf.toString());
}
@Override
public void open(Configuration parameters) throws Exception {super.open(parameters);
orderState = getRuntimeContext().getListState(new ListStateDescriptor<HotOrder>("hot-order", HotOrder.class));
}
}3. 区域热销商品统计解决 (多维度条件)
-
性能
性能：依据不同区域（比方省份、城市），实现对热销商品的统计，统计周期为一天，每 3 秒刷新一次数据。
-
外围代码
主逻辑代码：
/** * 执行 Flink 工作解决 * @throws Exception */ private void executeFlinkTask() throws Exception { // 1. 创立运行环境 StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment(); // 2. 设置 kafka 服务连贯信息 Properties properties = new Properties(); properties.setProperty("bootstrap.servers", "10.10.20.132:9092"); properties.setProperty("group.id", "fink_group"); // 3. 创立订单的 Kafka 生产端 FlinkKafkaConsumer orderKafkaProducer = new FlinkKafkaConsumer( "order_binlog", // 指标 topic new SimpleStringSchema(), // 序列化 配置 properties); // 调试，从新从最早记录生产 orderKafkaProducer.setStartFromEarliest(); // 尽可能从最早的记录开始 env.setStreamTimeCharacteristic(TimeCharacteristic.EventTime); env.setParallelism(1); // 4. 创立地址信息的 kafka 生产端 FlinkKafkaConsumer addressKafkaProducer = new FlinkKafkaConsumer( "orderAddress_binlog", // 指标 topic new SimpleStringSchema(), // 序列化 配置 properties); // 调试，从新从最早记录生产 addressKafkaProducer.setStartFromEarliest(); // 尽可能从最早的记录开始 // 5. 读取 Kafka 数据源 ( 订单数据源和地址数据源）DataStreamSource<String> orderStream = env.addSource(orderKafkaProducer); DataStreamSource<String> addressStream = env.addSource(addressKafkaProducer); // 6. 数据过滤转换解决（订单数据）DataStream<Order> orderDataStream = orderStream.filter(new FilterFunction<String>() { @Override public boolean filter(String value) throws Exception {JsonObject jsonObject = GsonConvertUtil.getSingleton().getJsonObject(value); String isDDL = jsonObject.get("isDdl").getAsString(); String type = jsonObject.get("type").getAsString(); // 过滤条件：非 DDL 操作，并且是新增的数据 return isDDL.equalsIgnoreCase("false") && "INSERT".equalsIgnoreCase(type); } }).flatMap(new FlatMapFunction<String, Order>() { @Override public void flatMap(String value, Collector<Order> out) throws Exception { // 获取 JSON 中的 data 数据 JsonArray dataArray = GsonConvertUtil.getSingleton().getJsonObject(value).getAsJsonArray("data"); // 将 data 数据转换为 java 对象 for(int i =0; i< dataArray.size(); i++) {JsonObject jsonObject = dataArray.get(i).getAsJsonObject(); Order order = GsonConvertUtil.getSingleton().cvtJson2Obj(jsonObject, Order.class); System.out.println("order =>" + order); out.collect(order); } } }) .assignTimestampsAndWatermarks(new BoundedOutOfOrdernessTimestampExtractor<Order>(Time.seconds(0)) { @Override public long extractTimestamp(Order element) {return element.getExecTime(); } }); // 7. 过滤转换地址数据源 DataStream<OrderAddress> orderAddressDataStream = addressStream.filter(new FilterFunction<String>() { @Override public boolean filter(String value) throws Exception {JsonObject jsonObject = GsonConvertUtil.getSingleton().getJsonObject(value); String isDDL = jsonObject.get("isDdl").getAsString(); String type = jsonObject.get("type").getAsString(); // 过滤条件：非 DDL 操作，并且是新增的数据 return isDDL.equalsIgnoreCase("false") && "INSERT".equalsIgnoreCase(type); } }).flatMap(new FlatMapFunction<String, OrderAddress>() { @Override public void flatMap(String value, Collector<OrderAddress> out) throws Exception { // 获取 JSON 中的 data 数据 JsonArray dataArray = GsonConvertUtil.getSingleton().getJsonObject(value).getAsJsonArray("data"); // 将 data 数据转换为 java 对象 for(int i =0; i< dataArray.size(); i++) {JsonObject jsonObject = dataArray.get(i).getAsJsonObject(); OrderAddress orderAddress = GsonConvertUtil.getSingleton().cvtJson2Obj(jsonObject, OrderAddress.class); System.out.println("orderAddress =>" + orderAddress); out.collect(orderAddress); } } }) .assignTimestampsAndWatermarks(new BoundedOutOfOrdernessTimestampExtractor<OrderAddress>(Time.seconds(0)) { @Override public long extractTimestamp(OrderAddress element) {return element.getExecTime(); } }); // 8. 订单数据流和地址数据流的 join 解决 orderDataStream.join(orderAddressDataStream).where(new KeySelector<Order, Object>() { @Override public Object getKey(Order value) throws Exception {return value.getId(); } }).equalTo(new KeySelector<OrderAddress, Object>() { @Override public Object getKey(OrderAddress value) throws Exception {return value.getOrderId(); } }) // 这里的工夫，相比上面的工夫窗滑动值 slide 快一些 .window(TumblingEventTimeWindows.of(Time.seconds(2))) .apply(new JoinFunction<Order, OrderAddress, JoinOrderAddress>() { @Override public JoinOrderAddress join(Order first, OrderAddress second) throws Exception {return JoinOrderAddress.build(first, second); } }).assignTimestampsAndWatermarks(new BoundedOutOfOrdernessTimestampExtractor<JoinOrderAddress>(Time.seconds(0)) { @Override public long extractTimestamp(JoinOrderAddress element) {return element.getExecTime(); } }) // 9. 依据省份和商品 ID 进行数据分组 .keyBy(new KeySelector<JoinOrderAddress, Tuple2<String, Long>>() { @Override public Tuple2<String, Long> getKey(JoinOrderAddress value) throws Exception {return Tuple2.of(value.getProvince(), value.getGoodsId()); } }) .timeWindow(Time.hours(24), Time.seconds(3)) .aggregate(new TotalAmount(), new AmountWindow()) .keyBy(HotDimensionOrder::getTimeWindow) .process(new TopNDimensionOrder()); // 10. 执行工作 env.execute("job"); }
商品金额累加器：
/**
* 商品金额累加器
*/
private static class TotalAmount implements AggregateFunction<JoinOrderAddress, JoinOrderAddress, JoinOrderAddress> {
@Override
public JoinOrderAddress createAccumulator() {JoinOrderAddress order = new JoinOrderAddress();
order.setTotalAmount(0l);
return order;
}
/**
* 商品销售总金额累加解决
* @param value
* @param accumulator
* @return
*/
@Override
public JoinOrderAddress add(JoinOrderAddress value, JoinOrderAddress accumulator) {accumulator.setGoodsId(value.getGoodsId());
accumulator.setGoodsName((value.getGoodsName()));
accumulator.setProvince(value.getProvince());
accumulator.setCity(value.getCity());
accumulator.setTotalAmount(accumulator.getTotalAmount() + (value.getExecPrice() * value.getExecVolume()));
return accumulator;
}
@Override
public JoinOrderAddress getResult(JoinOrderAddress accumulator) {return accumulator;}
@Override
public JoinOrderAddress merge(JoinOrderAddress a, JoinOrderAddress b) {return null;}
}热销商品的数据转换解决:
private static class AmountWindow implements WindowFunction<JoinOrderAddress, HotDimensionOrder, Tuple2<String, Long>, TimeWindow> {
@Override
public void apply(Tuple2<String, Long> goodsId, TimeWindow window, Iterable<JoinOrderAddress> input, Collector<HotDimensionOrder> out) throws Exception {JoinOrderAddress order = input.iterator().next();
out.collect(new HotDimensionOrder(order, window.getEnd()));
}
}依据不同区域的热销商品，实现统计排行：
private class TopNDimensionOrder extends KeyedProcessFunction<Long, HotDimensionOrder, String> {
private ListState<HotDimensionOrder> orderState;
@Override
public void processElement(HotDimensionOrder value, Context ctx, Collector<String> out) throws Exception {
// 将数据退出到状态列表外面
orderState.add(value);
// 注册定时器
ctx.timerService().registerEventTimeTimer(value.getTimeWindow());
}
@Override
public void onTimer(long timestamp, OnTimerContext ctx, Collector<String> out) throws Exception {List<HotDimensionOrder> orderList = new ArrayList<>();
for(HotDimensionOrder order : orderState.get()){orderList.add(order);
}
// 依照省份和商品的成交总金额，倒序排列
orderList.sort(Comparator.comparing(HotDimensionOrder::getProvince).thenComparing(HotDimensionOrder::getTotalAmount, Comparator.reverseOrder()));
orderState.clear();
// 将数据写入至 ES
HotDimensionRepository hotDimensionRepository = (HotDimensionRepository) ApplicationContextUtil.getBean("hotDimensionRepository");
StringBuffer strBuf = new StringBuffer();
for(HotDimensionOrder order: orderList) {order.setId(order.getProvince() + order.getGoodsId());
order.setCreateDate(new Date(order.getTimeWindow()));
hotDimensionRepository.save(order);
strBuf.append(order).append("\n");
System.out.println("result =>" + order);
}
out.collect(strBuf.toString());
}
@Override
public void open(Configuration parameters) throws Exception {super.open(parameters);
orderState = getRuntimeContext().getListState(new ListStateDescriptor<HotDimensionOrder>("hot-dimension", HotDimensionOrder.class));
}
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