如何将“2017-08-06T08:00:00+08:00”字符串转换成Flink的timestamp？

可以使用Flink的`DateTimeFormatter`类来将字符串转换为`java.time.Instant`对象，然后再使用`Instant.toEpochMilli()`方法将其转换为毫秒数，最终可以通过`org.apache.flink.api.common.eventtime.TimestampAssigner`类来创建对应的timestamp。以下是示例代码：

import org.apache.flink.api.common.eventtime.TimestampAssigner;
import org.apache.flink.api.common.eventtime.Watermark;
import org.apache.flink.api.common.typeinfo.TypeInformation;
import org.apache.flink.api.java.typeutils.ResultTypeQueryable;
import org.apache.flink.streaming.api.functions.AssignerWithPunctuatedWatermarks;
import org.apache.flink.streaming.api.functions.timestamps.AscendingTimestampExtractor;
import org.apache.flink.streaming.api.windowing.time.Time;
import org.apache.flink.streaming.connectors.kafka.KafkaDeserializationSchema;
import org.apache.flink.streaming.connectors.kafka.KafkaSerializationSchema;
import org.apache.flink.streaming.connectors.kafka.KafkaTopicPartition;
import org.apache.flink.streaming.connectors.kafka.internals.KafkaTopicPartitionState;
import org.apache.flink.streaming.util.serialization.KeyedSerializationSchema;
import org.apache.flink.table.api.ValidationException;
import org.apache.flink.table.types.logical.LogicalType;
import org.apache.flink.table.types.logical.TimestampType;
import org.apache.kafka.clients.consumer.ConsumerRecord;
import org.apache.kafka.clients.producer.ProducerRecord;
import org.apache.kafka.common.TopicPartition;

import java.nio.charset.StandardCharsets;
import java.time.Instant;
import java.time.ZoneOffset;
import java.time.format.DateTimeFormatter;
import java.time.temporal.ChronoUnit;
import java.util.Collections;
import java.util.List;
import java.util.Map;
import java.util.Objects;

public class FlinkTimestampAssigner implements AssignerWithPunctuatedWatermarks<String>, ResultTypeQueryable<Long>, KafkaDeserializationSchema<String>, KafkaSerializationSchema<String>, KeyedSerializationSchema<String> {
    private static final long serialVersionUID = 1L;
    private final DateTimeFormatter formatter = DateTimeFormatter.ofPattern("yyyy-MM-dd'T'HH:mm:ssXXX");
    private final LogicalType[] argumentTypes;
    private final int[] arguments;
    private final TimestampType.TimestampKind timestampKind;
    
    public FlinkTimestampAssigner(LogicalType[] argumentTypes, int[] arguments, TimestampType.TimestampKind timestampKind) {
        this.argumentTypes = argumentTypes;
        this.arguments = arguments;
        this.timestampKind = timestampKind;
    }

    @Override
    public Long extractTimestamp(String element, long recordTimestamp) {
        Instant instant = Instant.from(formatter.parse(element));
        return instant.toEpochMilli();
    }

    @Override
    public Watermark checkAndGetNextWatermark(String lastElement, long extractedTimestamp) {
        return new Watermark(extractedTimestamp - 1);
    }

    @Override
    public TypeInformation<Long> getProducedType() {
        return TypeInformation.of(Long.class);
    }

    @Override
    public boolean isEndOfStream(String nextElement) {
        return false;
    }

    @Override
    public String deserialize(ConsumerRecord<byte[], byte[]> record) throws Exception {
        return new String(record.value(), StandardCharsets.UTF_8);
    }

    @Override
    public boolean isEndOfStream(String element, KafkaTopicPartition partition, long offset) {
        return false;
    }

    @Override
    public TypeInformation<String> getProducedType() {
        return TypeInformation.of(String.class);
    }

    @Override
    public ProducerRecord<byte[], byte[]> serialize(String element, Long timestamp) {
        return new ProducerRecord<>(getTargetTopic(element), null, timestamp, null, element.getBytes(StandardCharsets.UTF_8));
    }

    @Override
    public byte[] serializeKey(String element) {
        return null;
    }

    @Override
    public byte[] serializeValue(String element) {
        return element.getBytes(StandardCharsets.UTF_8);
    }

    @Override
    public String getTargetTopic(String element) {
        return null;
    }

    @Override
    public KafkaTopicPartition getTargetTopicPartition(String element) {
        return null;
    }

    @Override
    public void open(SerializationRuntimeContext context) throws Exception {
        // do nothing
    }

    @Override
    public void setTopicPartitionState(KafkaTopicPartition partition, KafkaTopicPartitionState state) {
        // do nothing
    }

    @Override
    public KafkaTopicPartitionState getTopicPartitionState(TopicPartition partition) {
        return null;
    }

    @Override
    public List<TopicPartition> getCommittableTopicPartitions() {
        return Collections.emptyList();
    }

    @Override
    public Map<TopicPartition, Long> getCommittableOffsets() {
        return Collections.emptyMap();
    }

    @Override
    public void close() throws Exception {
        // do nothing
    }
}

在使用时，可以将此类作为`DataStream`的`assignTimestampsAndWatermarks()`方法的参数，例如：

DataStream<String> input = ...;
DataStream<String> withTimestampsAndWatermarks = input.assignTimestampsAndWatermarks(new FlinkTimestampAssigner(new LogicalType[]{new TimestampType(3)}, new int[]{0}, TimestampType.TimestampKind.PROCTIME));

注意，在上述代码中，`TimestampType.TimestampKind`参数可以根据实际情况进行设置。另外，如果需要将`Instant`对象转换为`java.sql.Timestamp`对象，则可以使用`java.sql.Timestamp.from(instant)`方法。