Flink状态包括:算子状态和按键分区状态,简单理解就是记录任务的中间状态或者数值
基于 KeyedStream 上的状态。这个状态是跟特定的 key 绑定的,对 KeyedStream 流上的每一个 key,都对应一个 state。
按键分区状态分为:ValueState、ListState、ReducingState、MapState、AggregatingState
即类型为T的单值状态
package com.xx.state;
import com.xx.entity.WaterSensor;
import com.xx.functions.WaterSensorMapFunction;
import org.apache.flink.api.common.eventtime.WatermarkStrategy;
import org.apache.flink.api.common.state.ValueState;
import org.apache.flink.api.common.state.ValueStateDescriptor;
import org.apache.flink.api.common.typeinfo.Types;
import org.apache.flink.configuration.Configuration;
import org.apache.flink.streaming.api.datastream.SingleOutputStreamOperator;
import org.apache.flink.streaming.api.environment.StreamExecutionEnvironment;
import org.apache.flink.streaming.api.functions.KeyedProcessFunction;
import org.apache.flink.util.Collector;
import java.time.Duration;
/**
* @author xiaxing
* @describe Flink状态管理
* 算子状态(Keyed State):状态是跟特定的 key 绑定的,对 KeyedStream 流上的每一个 key,都对应一个 state
* ValueState:即类型为T的单值状态
* ListState:即key上的状态值为一个列表
* MapState:状态值为一个 map
* ReducingState:这种状态通过用户传入的 reduceFunction,每次调用 add 方法添加值的时候,会调用 reduceFunction,最后合并到一个单一的状态值
* 按键分区状态(Operator State):与 Key 无关的 State,与 Operator 绑定的 state,整个 operator 只对应一个 state
* @since 2024/3/29 11:10
*/
public class KeyedValueStateDemo {
public static void main(String[] args) throws Exception {
StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment();
env.setParallelism(1);
SingleOutputStreamOperator<WaterSensor> sensorDS = env.socketTextStream("127.0.0.1", 7777)
.map(new WaterSensorMapFunction())
.assignTimestampsAndWatermarks(
WatermarkStrategy.<WaterSensor>forBoundedOutOfOrderness(Duration.ofSeconds(3))
.withTimestampAssigner((element, ts) -> element.getTs() * 1000L));
// 数值差超过10则告警
sensorDS.keyBy(WaterSensor::getId).process(new KeyedProcessFunction<String, WaterSensor, String>() {
ValueState<Integer> lastVcState;
@Override
public void open(Configuration parameters) throws Exception {
super.open(parameters);
lastVcState = getRuntimeContext()
.getState(new ValueStateDescriptor<>("lastVcState", Types.INT));
}
@Override
public void processElement(WaterSensor value, KeyedProcessFunction<String, WaterSensor, String>.Context ctx, Collector<String> out) throws Exception {
// 1.取出上一条数据的水位值
Integer lastVc = lastVcState.value() == null ? 0 : lastVcState.value();
// 2.就差值绝对值,判断是否超过10
int abs = Math.abs(value.getVc() - lastVc);
if (abs > 10) {
out.collect("id为:" + value.getId() + ",当前水位值:" + value.getVc() + ",上一条水位值:" + lastVc + ",相差超过10!!!");
}
// 3.保存自身水位值
lastVcState.update(value.getVc());
}
}).print();
env.execute();
}
}
即key上的状态值为一个列表
package com.xx.state;
import com.xx.entity.WaterSensor;
import com.xx.functions.WaterSensorMapFunction;
import org.apache.flink.api.common.eventtime.WatermarkStrategy;
import org.apache.flink.api.common.state.ListState;
import org.apache.flink.api.common.state.ListStateDescriptor;
import org.apache.flink.api.common.typeinfo.Types;
import org.apache.flink.configuration.Configuration;
import org.apache.flink.streaming.api.datastream.SingleOutputStreamOperator;
import org.apache.flink.streaming.api.environment.StreamExecutionEnvironment;
import org.apache.flink.streaming.api.functions.KeyedProcessFunction;
import org.apache.flink.util.Collector;
import java.time.Duration;
import java.util.ArrayList;
import java.util.List;
/**
* @author xiaxing
* @describe Flink状态管理
* 算子状态(Keyed State):状态是跟特定的 key 绑定的,对 KeyedStream 流上的每一个 key,都对应一个 state
* ValueState:即类型为T的单值状态
* ListState:即key上的状态值为一个列表
* MapState:状态值为一个 map
* ReducingState:这种状态通过用户传入的 reduceFunction,每次调用 add 方法添加值的时候,会调用 reduceFunction,最后合并到一个单一的状态值
* 按键分区状态(Operator State):与 Key 无关的 State,与 Operator 绑定的 state,整个 operator 只对应一个 state
* @since 2024/3/29 11:10
*/
public class KeyedListStateDemo {
public static void main(String[] args) throws Exception {
StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment();
env.setParallelism(1);
SingleOutputStreamOperator<WaterSensor> sensorDS = env.socketTextStream("127.0.0.1", 7777)
.map(new WaterSensorMapFunction())
.assignTimestampsAndWatermarks(
WatermarkStrategy.<WaterSensor>forBoundedOutOfOrderness(Duration.ofSeconds(3))
.withTimestampAssigner((element, ts) -> element.getTs() * 1000L));
// 取最大的三个数值
sensorDS.keyBy(WaterSensor::getId).process(new KeyedProcessFunction<String, WaterSensor, String>() {
ListState<Integer> listState;
@Override
public void open(Configuration parameters) throws Exception {
super.open(parameters);
listState = getRuntimeContext()
.getListState(new ListStateDescriptor<>("vcListState", Types.INT));
}
@Override
public void processElement(WaterSensor value, KeyedProcessFunction<String, WaterSensor, String>.Context ctx, Collector<String> out) throws Exception {
// 1.写数据
listState.add(value.getVc());
// 2.降序排序
List<Integer> result = new ArrayList<>();
for (Integer vc : listState.get()) {
result.add(vc);
}
result.sort((o1, o2) -> o2 - o1);
// 3.只保留最大的三个
if (result.size() > 3) {
result.remove(3);
}
out.collect("id为:" + value.getId() + ",最大的三个水位值:" + result);
// 4.更新数据
listState.update(result);
}
}).print();
env.execute();
}
}
状态值为一个map
package com.xx.state;
import com.xx.entity.WaterSensor;
import com.xx.functions.WaterSensorMapFunction;
import org.apache.flink.api.common.eventtime.WatermarkStrategy;
import org.apache.flink.api.common.state.MapState;
import org.apache.flink.api.common.state.MapStateDescriptor;
import org.apache.flink.api.common.typeinfo.Types;
import org.apache.flink.configuration.Configuration;
import org.apache.flink.streaming.api.datastream.SingleOutputStreamOperator;
import org.apache.flink.streaming.api.environment.StreamExecutionEnvironment;
import org.apache.flink.streaming.api.functions.KeyedProcessFunction;
import org.apache.flink.util.Collector;
import java.time.Duration;
/**
* @author xiaxing
* @describe Flink状态管理
* 算子状态(Keyed State):状态是跟特定的 key 绑定的,对 KeyedStream 流上的每一个 key,都对应一个 state
* ValueState:即类型为T的单值状态
* ListState:即key上的状态值为一个列表
* MapState:状态值为一个 map
* ReducingState:这种状态通过用户传入的 reduceFunction,每次调用 add 方法添加值的时候,会调用 reduceFunction,最后合并到一个单一的状态值
* 按键分区状态(Operator State):与 Key 无关的 State,与 Operator 绑定的 state,整个 operator 只对应一个 state
* @since 2024/3/29 11:10
*/
public class KeyedMapStateDemo {
public static void main(String[] args) throws Exception {
StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment();
env.setParallelism(1);
SingleOutputStreamOperator<WaterSensor> sensorDS = env.socketTextStream("127.0.0.1", 7777)
.map(new WaterSensorMapFunction())
.assignTimestampsAndWatermarks(
WatermarkStrategy.<WaterSensor>forBoundedOutOfOrderness(Duration.ofSeconds(3))
.withTimestampAssigner((element, ts) -> element.getTs() * 1000L));
// 统计每个key出现的次数
sensorDS.keyBy(WaterSensor::getId).process(new KeyedProcessFunction<String, WaterSensor, String>() {
MapState<Integer, Integer> mapState;
@Override
public void open(Configuration parameters) throws Exception {
super.open(parameters);
mapState = getRuntimeContext()
.getMapState(new MapStateDescriptor<>("vcMapState", Types.INT, Types.INT));
}
@Override
public void processElement(WaterSensor value, KeyedProcessFunction<String, WaterSensor, String>.Context ctx, Collector<String> out) throws Exception {
Integer vc = value.getVc();
if (mapState.contains(vc)) {
Integer count = mapState.get(vc);
count ++;
mapState.put(vc, count);
} else {
mapState.put(vc, 1);
}
StringBuilder str = new StringBuilder();
str.append("id为:").append(value.getId());
for (Integer key : mapState.keys()) {
str.append(",key:").append(key).append(",value:").append(mapState.get(key));
}
out.collect(str.toString());
}
}).print();
env.execute();
}
}
这种状态通过用户传入的 reduceFunction,每次调用 add 方法添加值的时候,会调用 reduceFunction,最后合并到一个单一的状态值
package com.xx.state;
import com.xx.entity.WaterSensor;
import com.xx.functions.WaterSensorMapFunction;
import org.apache.flink.api.common.eventtime.WatermarkStrategy;
import org.apache.flink.api.common.functions.ReduceFunction;
import org.apache.flink.api.common.state.ReducingState;
import org.apache.flink.api.common.state.ReducingStateDescriptor;
import org.apache.flink.api.common.typeinfo.Types;
import org.apache.flink.configuration.Configuration;
import org.apache.flink.streaming.api.datastream.SingleOutputStreamOperator;
import org.apache.flink.streaming.api.environment.StreamExecutionEnvironment;
import org.apache.flink.streaming.api.functions.KeyedProcessFunction;
import org.apache.flink.util.Collector;
import java.time.Duration;
/**
* @author xiaxing
* @describe Flink状态管理
* 算子状态(Keyed State):状态是跟特定的 key 绑定的,对 KeyedStream 流上的每一个 key,都对应一个 state
* ValueState:即类型为T的单值状态
* ListState:即key上的状态值为一个列表
* MapState:状态值为一个 map
* ReducingState:这种状态通过用户传入的 reduceFunction,每次调用 add 方法添加值的时候,会调用 reduceFunction,最后合并到一个单一的状态值
* 按键分区状态(Operator State):与 Key 无关的 State,与 Operator 绑定的 state,整个 operator 只对应一个 state
* @since 2024/3/29 11:10
*/
public class KeyedReducingStateDemo {
public static void main(String[] args) throws Exception {
StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment();
env.setParallelism(1);
SingleOutputStreamOperator<WaterSensor> sensorDS = env.socketTextStream("127.0.0.1", 7777)
.map(new WaterSensorMapFunction())
.assignTimestampsAndWatermarks(
WatermarkStrategy.<WaterSensor>forBoundedOutOfOrderness(Duration.ofSeconds(3))
.withTimestampAssigner((element, ts) -> element.getTs() * 1000L));
// 累加
sensorDS.keyBy(WaterSensor::getId).process(new KeyedProcessFunction<String, WaterSensor, String>() {
ReducingState<Integer> reducingState;
@Override
public void open(Configuration parameters) throws Exception {
super.open(parameters);
reducingState = getRuntimeContext()
.getReducingState(new ReducingStateDescriptor<>("vcReduceState", (ReduceFunction<Integer>) Integer::sum, Types.INT));
}
@Override
public void processElement(WaterSensor value, KeyedProcessFunction<String, WaterSensor, String>.Context ctx, Collector<String> out) throws Exception {
reducingState.add(value.getVc());
out.collect("id为:" + value.getId() + ",水位线总和:" + reducingState.get());
}
}).print();
env.execute();
}
}
package com.xx.state;
import com.xx.entity.WaterSensor;
import com.xx.functions.WaterSensorMapFunction;
import org.apache.flink.api.common.eventtime.WatermarkStrategy;
import org.apache.flink.api.common.functions.AggregateFunction;
import org.apache.flink.api.common.state.AggregatingState;
import org.apache.flink.api.common.state.AggregatingStateDescriptor;
import org.apache.flink.api.common.typeinfo.Types;
import org.apache.flink.api.java.tuple.Tuple2;
import org.apache.flink.configuration.Configuration;
import org.apache.flink.streaming.api.datastream.SingleOutputStreamOperator;
import org.apache.flink.streaming.api.environment.StreamExecutionEnvironment;
import org.apache.flink.streaming.api.functions.KeyedProcessFunction;
import org.apache.flink.util.Collector;
import java.time.Duration;
/**
* @author xiaxing
* @describe Flink状态管理
* 算子状态(Keyed State):状态是跟特定的 key 绑定的,对 KeyedStream 流上的每一个 key,都对应一个 state
* ValueState:即类型为T的单值状态
* ListState:即key上的状态值为一个列表
* MapState:状态值为一个 map
* ReducingState:这种状态通过用户传入的 reduceFunction,每次调用 add 方法添加值的时候,会调用 reduceFunction,最后合并到一个单一的状态值
* 按键分区状态(Operator State):与 Key 无关的 State,与 Operator 绑定的 state,整个 operator 只对应一个 state
* 状态生存时间(ttl)
* @since 2024/3/29 11:10
*/
public class KeyedAggregatingStateDemo {
public static void main(String[] args) throws Exception {
StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment();
env.setParallelism(1);
SingleOutputStreamOperator<WaterSensor> sensorDS = env.socketTextStream("127.0.0.1", 7777)
.map(new WaterSensorMapFunction())
.assignTimestampsAndWatermarks(
WatermarkStrategy.<WaterSensor>forBoundedOutOfOrderness(Duration.ofSeconds(3))
.withTimestampAssigner((element, ts) -> element.getTs() * 1000L));
// 累加
sensorDS.keyBy(WaterSensor::getId).process(new KeyedProcessFunction<String, WaterSensor, String>() {
AggregatingState<Integer, Double> AggregatingState;
@Override
public void open(Configuration parameters) throws Exception {
super.open(parameters);
AggregatingState = getRuntimeContext()
.getAggregatingState(new AggregatingStateDescriptor<>("aggregatingState", new AggregateFunction<Integer, Tuple2<Integer, Integer>, Double>() {
@Override
public Tuple2<Integer, Integer> createAccumulator() {
return Tuple2.of(0, 0);
}
@Override
public Tuple2<Integer, Integer> add(Integer value, Tuple2<Integer, Integer> accumulator) {
return Tuple2.of(accumulator.f0 + value, accumulator.f1 + 1);
}
@Override
public Double getResult(Tuple2<Integer, Integer> accumulator) {
return accumulator.f0 * 1D / accumulator.f1;
}
@Override
public Tuple2<Integer, Integer> merge(Tuple2<Integer, Integer> a, Tuple2<Integer, Integer> b) {
return null;
}
}, Types.TUPLE(Types.INT, Types.INT)));
}
@Override
public void processElement(WaterSensor value, KeyedProcessFunction<String, WaterSensor, String>.Context ctx, Collector<String> out) throws Exception {
AggregatingState.add(value.getVc());
out.collect("id为:" + value.getId() + ",平均水位值:" + AggregatingState.get());
}
}).print();
env.execute();
}
}
避免状态数据大量积累浪费资源
package com.xx.state;
import com.xx.entity.WaterSensor;
import com.xx.functions.WaterSensorMapFunction;
import org.apache.flink.api.common.eventtime.WatermarkStrategy;
import org.apache.flink.api.common.state.StateTtlConfig;
import org.apache.flink.api.common.state.ValueState;
import org.apache.flink.api.common.state.ValueStateDescriptor;
import org.apache.flink.api.common.time.Time;
import org.apache.flink.api.common.typeinfo.Types;
import org.apache.flink.configuration.Configuration;
import org.apache.flink.streaming.api.datastream.SingleOutputStreamOperator;
import org.apache.flink.streaming.api.environment.StreamExecutionEnvironment;
import org.apache.flink.streaming.api.functions.KeyedProcessFunction;
import org.apache.flink.util.Collector;
import java.time.Duration;
/**
* @author xiaxing
* @describe Flink状态管理
* 算子状态(Keyed State):状态是跟特定的 key 绑定的,对 KeyedStream 流上的每一个 key,都对应一个 state
* ValueState:即类型为T的单值状态
* ListState:即key上的状态值为一个列表
* MapState:状态值为一个 map
* ReducingState:这种状态通过用户传入的 reduceFunction,每次调用 add 方法添加值的时候,会调用 reduceFunction,最后合并到一个单一的状态值
* 按键分区状态(Operator State):与 Key 无关的 State,与 Operator 绑定的 state,整个 operator 只对应一个 state
* @since 2024/3/29 11:10
*/
public class KeyedValueTtlStateDemo {
public static void main(String[] args) throws Exception {
StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment();
env.setParallelism(1);
SingleOutputStreamOperator<WaterSensor> sensorDS = env.socketTextStream("127.0.0.1", 7777)
.map(new WaterSensorMapFunction())
.assignTimestampsAndWatermarks(
WatermarkStrategy.<WaterSensor>forBoundedOutOfOrderness(Duration.ofSeconds(3))
.withTimestampAssigner((element, ts) -> element.getTs() * 1000L));
// 数值差超过10则告警
sensorDS.keyBy(WaterSensor::getId).process(new KeyedProcessFunction<String, WaterSensor, String>() {
ValueState<Integer> lastVcState;
@Override
public void open(Configuration parameters) throws Exception {
super.open(parameters);
// 创建ttl config
StateTtlConfig ttlConfig = StateTtlConfig
// 过期时间:5s
.newBuilder(Time.seconds(5))
// 状态更新和写入会刷新过期时间
.setUpdateType(StateTtlConfig.UpdateType.OnCreateAndWrite)
// 不返回过期的状态值
.setStateVisibility(StateTtlConfig.StateVisibility.NeverReturnExpired)
.build();
// 状态描述其启用ttl
ValueStateDescriptor<Integer> valueState = new ValueStateDescriptor<>("lastVcState", Types.INT);
valueState.enableTimeToLive(ttlConfig);
this.lastVcState = getRuntimeContext().getState(valueState);
}
@Override
public void processElement(WaterSensor value, KeyedProcessFunction<String, WaterSensor, String>.Context ctx, Collector<String> out) throws Exception {
Integer lastVc = lastVcState.value();
out.collect("id为:" + value.getId() + ",状态值:" + lastVc);
lastVcState.update(value.getVc());
}
}).print();
env.execute();
}
}
与 Key 无关的 State,与 Operator 绑定的 state,整个 operator 只对应一个 state,常用于Source和Sink等与外部系统链接的算子上,实际使用不多。
比如Flink中的Kafka Connector,它会在每个 connector 实例中,保存该实例中消费 topic 的所有(partition, offset)映射
算子状态包括:ListState、Broadcast State
package com.xx.state;
import org.apache.flink.api.common.functions.MapFunction;
import org.apache.flink.api.common.state.ListState;
import org.apache.flink.api.common.state.ListStateDescriptor;
import org.apache.flink.api.common.typeinfo.Types;
import org.apache.flink.runtime.state.FunctionInitializationContext;
import org.apache.flink.runtime.state.FunctionSnapshotContext;
import org.apache.flink.streaming.api.checkpoint.CheckpointedFunction;
import org.apache.flink.streaming.api.environment.StreamExecutionEnvironment;
/**
* @author xiaxing
* @describe 在map算子中计算数据个数
* @since 2024/3/29 15:34
*/
public class OperatorListStateDemo {
public static void main(String[] args) throws Exception {
StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment();
env.setParallelism(1);
env.socketTextStream("127.0.0.1", 7777)
.map(new MyCountMapFunction()).print();
env.execute();
}
public static class MyCountMapFunction implements MapFunction<String, Long>, CheckpointedFunction {
private Long count = 0L;
private ListState<Long> state;
@Override
public Long map(String value) throws Exception {
return count ++;
}
/**
* 将本地变量拷贝到算子状态中
*/
@Override
public void snapshotState(FunctionSnapshotContext context) throws Exception {
System.out.println("snapshotState...");
// 清空算子状态
state.clear();
// 将本地变量添加到状态算子中
state.add(count);
}
/**
* 初始化本地变量,从状态中,把数据添加到本地变量,每个子任务调用一次
*/
@Override
public void initializeState(FunctionInitializationContext context) throws Exception {
System.out.println("initializeState...");
// 从上下文初始化算子状态
state = context
.getOperatorStateStore()
.getListState(new ListStateDescriptor<>("state", Types.LONG));
// 从算子状态中将数据拷贝到本地变量
if (context.isRestored()) {
for (Long aLong : state.get()) {
count += aLong;
}
}
}
}
}
Broadcast State 是 Flink 1.5 引入的新特性。在开发过程中,如果遇到需要下发/广播配置、规则等低吞吐事件流到下游所有 task 时,就可以使用 Broadcast State 特性。下游的 task 接收这些配置、规则并保存为 BroadcastState, 将这些配置应用到另一个数据流的计算中 。
package com.xx.state;
import com.xx.entity.WaterSensor;
import com.xx.functions.WaterSensorMapFunction;
import org.apache.flink.api.common.state.BroadcastState;
import org.apache.flink.api.common.state.MapStateDescriptor;
import org.apache.flink.api.common.state.ReadOnlyBroadcastState;
import org.apache.flink.api.common.typeinfo.Types;
import org.apache.flink.streaming.api.datastream.BroadcastConnectedStream;
import org.apache.flink.streaming.api.datastream.BroadcastStream;
import org.apache.flink.streaming.api.datastream.DataStreamSource;
import org.apache.flink.streaming.api.datastream.SingleOutputStreamOperator;
import org.apache.flink.streaming.api.environment.StreamExecutionEnvironment;
import org.apache.flink.streaming.api.functions.co.BroadcastProcessFunction;
import org.apache.flink.util.Collector;
/**
* @author xiaxing
* @describe
* @since 2024/3/29 15:34
*/
public class OperatorBroadcastStateDemo {
public static void main(String[] args) throws Exception {
StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment();
env.setParallelism(1);
// 数据流
SingleOutputStreamOperator<WaterSensor> sensorDS = env.socketTextStream("127.0.0.1", 7777)
.map(new WaterSensorMapFunction());
// 配置流(用于广播配置)
DataStreamSource<String> configSource = env.socketTextStream("127.0.0.1", 8888);
// 将配置流广播
MapStateDescriptor<String, String> broadcastMapState = new MapStateDescriptor<>("broadcast-state", Types.STRING, Types.STRING);
BroadcastStream<String> broadcast = configSource.broadcast(broadcastMapState);
// 将数据流和广播后的配置链接
BroadcastConnectedStream<WaterSensor, String> connect = sensorDS.connect(broadcast);
connect.process(new BroadcastProcessFunction<WaterSensor, String, String>() {
/**
* 数据流处理方法
*/
@Override
public void processElement(WaterSensor value, BroadcastProcessFunction<WaterSensor, String, String>.ReadOnlyContext ctx, Collector<String> out) throws Exception {
// 通过上下文获取广播状态
ReadOnlyBroadcastState<String, String> broadcastState = ctx.getBroadcastState(broadcastMapState);
String config = broadcastState.get("config") == null ? "0" : broadcastState.get("config");
if (Integer.parseInt(config) < value.getVc()) {
out.collect("水位超过指定的预置:" + config + ",当前水位:" + value.getVc());
}
}
/**
* 广播后的配置流处理方法
*/
@Override
public void processBroadcastElement(String value, BroadcastProcessFunction<WaterSensor, String, String>.Context ctx, Collector<String> out) throws Exception {
// 通过上下文获取广播状态
BroadcastState<String, String> broadcastState = ctx.getBroadcastState(broadcastMapState);
broadcastState.put("config", value);
}
}).print();
env.execute();
}
}
文章浏览阅读5.8k次。在大数据的发展当中,大数据技术生态的组件,也在不断地拓展开来,而其中的Hive组件,作为Hadoop的数据仓库工具,可以实现对Hadoop集群当中的大规模数据进行相应的数据处理。今天我们的大数据入门分享,就主要来讲讲,Hive应用场景。关于Hive,首先需要明确的一点就是,Hive并非数据库,Hive所提供的数据存储、查询和分析功能,本质上来说,并非传统数据库所提供的存储、查询、分析功能。Hive..._hive应用场景
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文章浏览阅读1k次,点赞2次,收藏6次。这个项目是基于STM32的LED闪烁项目,主要目的是让学习者熟悉STM32的基本操作和编程方法。在这个项目中,我们将使用STM32作为控制器,通过对GPIO口的控制实现LED灯的闪烁。这个STM32 LED闪烁的项目是一个非常简单的入门项目,但它可以帮助学习者熟悉STM32的编程方法和GPIO口的使用。在这个项目中,我们通过对GPIO口的控制实现了LED灯的闪烁。LED闪烁是STM32入门课程的基础操作之一,它旨在教学生如何使用STM32开发板控制LED灯的闪烁。_嵌入式stm32闪烁led实验总结
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文章浏览阅读4.4k次。需求:在诗词曲文项目中,诗词整篇朗读的时候,文章没有读完会因为屏幕熄灭停止朗读。要求:在文章没有朗读完毕之前屏幕常亮,读完以后屏幕常亮关闭;1.权限配置:设置电源管理的权限。
文章浏览阅读2.3k次。目标检测简介、评估标准、经典算法_目标检测
文章浏览阅读6.3k次,点赞4次,收藏9次。实训时需要安装SQL server2008 R所以我上网上找了一个.exe 的安装包链接:https://pan.baidu.com/s/1_FkhB8XJy3Js_rFADhdtmA提取码:ztki注:解压后1.04G安装时Microsoft需下载.NET,更新安装后会自动安装如下:点击第一个傻瓜式安装,唯一注意的是在修改路径的时候如下不可修改:到安装实例的时候就可以修改啦数据..._sqlserver 127 0 01 无法连接
文章浏览阅读7.4k次。1. Object.keys(item); 获取到了key之后就可以遍历的时候直接使用这个进行遍历所有的key跟valuevar infoItem={ name:'xiaowu', age:'18',}//的出来的keys就是[name,age]var keys=Object.keys(infoItem);2. 通常用于以下实力中 <div *ngFor="let item of keys"> <div>{{item}}.._js 遍历对象的key
文章浏览阅读2.2w次,点赞51次,收藏310次。粒子群算法求解路径规划路径规划问题描述 给定环境信息,如果该环境内有障碍物,寻求起始点到目标点的最短路径, 并且路径不能与障碍物相交,如图 1.1.1 所示。1.2 粒子群算法求解1.2.1 求解思路 粒子群优化算法(PSO),粒子群中的每一个粒子都代表一个问题的可能解, 通过粒子个体的简单行为,群体内的信息交互实现问题求解的智能性。 在路径规划中,我们将每一条路径规划为一个粒子,每个粒子群群有 n 个粒 子,即有 n 条路径,同时,每个粒子又有 m 个染色体,即中间过渡点的_粒子群算法路径规划
文章浏览阅读353次。所谓稳健的评估指标,是指在评估的过程中数据的轻微变化并不会显著的影响一个统计指标。而不稳健的评估指标则相反,在对交易系统进行回测时,参数值的轻微变化会带来不稳健指标的大幅变化。对于不稳健的评估指标,任何对数据有影响的因素都会对测试结果产生过大的影响,这很容易导致数据过拟合。_rar 海龟
文章浏览阅读607次,点赞2次,收藏7次。–基于STM32F103ZET6的UART通讯实现一、什么是IAP,为什么要IAPIAP即为In Application Programming(在应用中编程),一般情况下,以STM32F10x系列芯片为主控制器的设备在出厂时就已经使用J-Link仿真器将应用代码烧录了,如果在设备使用过程中需要进行应用代码的更换、升级等操作的话,则可能需要将设备返回原厂并拆解出来再使用J-Link重新烧录代码,这就增加了很多不必要的麻烦。站在用户的角度来说,就是能让用户自己来更换设备里边的代码程序而厂家这边只需要提供给_value line devices connectivity line devices