问题一:如何保证数据按照事件时间准确的落到同一个分区;
- 使用watermark自定义分桶规则,参考链接:flink 落 hdfs 数据按照事件事件分区解决方案 https://developer.aliyun.com/article/719786
/**
* @Author:wenwei
* @Date : 2020/9/8 22:15
* 自定义分桶的规则
* 1:按照什么格式定义文件名,默认为yyyy-MM-dd-HH
*/
@PublicEvolving
public class CustomBucketAssigner<IN> implements BucketAssigner<IN, String> {
private static final long serialVersionUID = 1L;
private static final String DEFAULT_FORMAT_STRING = "yyyy-MM-dd--HH";
private final String formatString;
private final ZoneId zoneId;
private transient DateTimeFormatter dateTimeFormatter;
/**
* Creates a new {@code DateTimeBucketAssigner} with format string {@code "yyyy-MM-dd--HH"}.
*/
public CustomBucketAssigner() {
this(DEFAULT_FORMAT_STRING);
}
/**
* Creates a new {@code DateTimeBucketAssigner} with the given date/time format string.
*
* @param formatString The format string that will be given to {@code SimpleDateFormat} to determine
* the bucket id.
*/
public CustomBucketAssigner(String formatString) {
this(formatString, ZoneId.systemDefault());
}
/**
* Creates a new {@code DateTimeBucketAssigner} with format string {@code "yyyy-MM-dd--HH"} using the given timezone.
*
* @param zoneId The timezone used to format {@code DateTimeFormatter} for bucket id.
*/
public CustomBucketAssigner(ZoneId zoneId) {
this(DEFAULT_FORMAT_STRING, zoneId);
}
/**
* Creates a new {@code DateTimeBucketAssigner} with the given date/time format string using the given timezone.
*
* @param formatString The format string that will be given to {@code DateTimeFormatter} to determine
* the bucket path.
* @param zoneId The timezone used to format {@code DateTimeFormatter} for bucket id.
*/
public CustomBucketAssigner(String formatString, ZoneId zoneId) {
this.formatString = Preconditions.checkNotNull(formatString);
this.zoneId = Preconditions.checkNotNull(zoneId);
}
//将分桶的规则写成按照事件时间;
@Override
public String getBucketId(IN element, BucketAssigner.Context context) {
if (dateTimeFormatter == null) {
dateTimeFormatter = DateTimeFormatter.ofPattern(formatString).withZone(zoneId);
}
//固定格式命名文件夹名称
return "p_data_day="+dateTimeFormatter.format(Instant.ofEpochMilli(context.currentWatermark()));
}
@Override
public SimpleVersionedSerializer<String> getSerializer() {
return SimpleVersionedStringSerializer.INSTANCE;
}
@Override
public String toString() {
return "DateTimeBucketAssigner{" +
"formatString='" + formatString + '\'' +
", zoneId=" + zoneId +
'}';
}
}
问题二: flink 如何准确的划分窗口的?
如何正确定义window的窗口时间,保证数据都会准确的按照事件分区,不会将前一天的数据,落入到下一个时间分区里面;可以参考windows 中的源码,其中定义start时间,值得参考
/**
* Method to get the window start for a timestamp.
*
* @param timestamp epoch millisecond to get the window start. 事件发生的时间
* @param offset The offset which window start would be shifted by. 定义TumblingEventTimeWindows 设置云讯的offset的值,默认都为零
* @param windowSize The size of the generated windows. 窗口大小
* @return window start
对应的数据应windows
例如 windows Size = 5s ,offset = 0 ; 例如当前的 timestamp = 2s ; 7s
2 - (2-0+5) % 5 = 0 ,
7 - (7 - 0 + 5) % 5 = 5 ,
例如 windows Size = 7s ,offset = 0 ; 例如当前的 timestamp = 2s ; 7s
2 - (2-0+7) % 7 = 0;
7 - (7-0+7)%7= 7
例如 windows Size = 5s ,offset = 1s ; 例如当前的 timestamp = 2s ; 7s
2 - (2-1+5) % 5 = 1 ,
7 - (7 - 0 + 5) % 5 = 6 ,
例如 windows Size = 7s ,offset = 0 ; 例如当前的 timestamp = 2s ; 7s
2 - (2-1+7) % 7 = 1;
7 - (7-1+7)%7= 8
*/
public static long getWindowStartWithOffset(long timestamp, long offset, long windowSize) {
return timestamp - (timestamp - offset + windowSize) % windowSize;
}
问题三 : 由于数据量不断增大,解析IP地址的时候,导致文件句柄过多;
- 将解析ip的类改造成单例类,有待优化
public class Ip2regionSingleton {
private static Logger logger = LoggerFactory.getLogger(Ip2regionSingleton.class);
private static Ip2regionSingleton instance = new Ip2regionSingleton();
private static DbConfig config;
private static DbSearcher searcher;
public DbSearcher getSearcher() {
return searcher;
}
// 私有化构造方法
private Ip2regionSingleton() {
String path = Ip2regionSingleton.class.getResource("/").getPath();
String dbPath = path + "plugins/ip2region.db";
File file = new File(dbPath);
logger.info("singleton count:{}","-------------------------------------------------------");
if ( file.exists() ) {
try{
config = new DbConfig();
searcher = new DbSearcher(config, dbPath);
}catch (Exception e){
logger.error("Ip2regionSingleton:{}",e.getMessage());
e.printStackTrace();
}
}
}
public static Ip2regionSingleton getInstance() {
return instance;
}
}
问题四: 如何解决flink pom文件中 ,包依赖的问题;
- maven helper ;找到相应冲突的jar类;
- 通过exclude方式,去除掉冲突的jar类
问题五: 如何保证flink中的,端到端数据的一致性,顺序性;
-
保证kafka中数据的顺序性;(做到全局的数据的顺序性基本上不可能,但是可以做到单分区的数据一致性)
-
kaka中,每个机器有个broker,broker里面有多个partition,partition之间通过主从方式复制;这样保证数据的一致性;
-
flink中设置 exactly oncely的语义;
env.enableCheckpointing(parameter.getLong("checkpoint.cycle",300*1000L),CheckpointingMode.EXACTLY_ONCE);
问题六: 如何保证在无事件数据更新的时候,更新watermark的值,然后触发窗口的计算
-
在处理某些数据的时候,数据流的时间更新时间间隔大于窗口的大小,如果使用PunctuatedWatermarks 会导致watermark一直不更新;改成AssignerWithPeriodicWatermarks周期性的更新的watermark即可
private static class CustomWatermarks<T> implements AssignerWithPunctuatedWatermarks<PageActivityDO> { private static final long serialVersionUID = 1L; private Long currentTime = 0L; //允许2分钟的延迟 private Long allowDelayTime = 120L; @Override public Watermark checkAndGetNextWatermark(PageActivityDO topic, long l) { return new Watermark(currentTime - allowDelayTime); } @Override public long extractTimestamp(PageActivityDO topic, long l) { DateTimeFormatter formatter = DateTimeFormatter.ofPattern("yyyy-MM-dd HH:mm:ss"); if(StringUtils.isNullOrWhitespaceOnly(topic.getPoint_time())){ return currentTime; } LocalDateTime localDateTime = LocalDateTime.parse(topic.getPoint_time(), formatter); currentTime = Math.max(localDateTime.toInstant(ZoneOffset.of("+8")).toEpochMilli(), currentTime); return currentTime; } }
private static class CustomWatermarksPeriodc<T> implements AssignerWithPeriodicWatermarks<ActivityInfoDO> {
private static final long serialVersionUID = 1L;
//允许30s的延迟
private Long allowDelayTime = 30000L;
@Override
public long extractTimestamp(ActivityInfoDO topic, long l) {
DateTimeFormatter formatter = DateTimeFormatter.ofPattern("yyyy-MM-dd HH:mm:ss");
if(StringUtils.isNullOrWhitespaceOnly(topic.getPush_time())){
return System.currentTimeMillis();
}
LocalDateTime localDateTime = LocalDateTime.parse(topic.getPush_time(), formatter);
logger.info("extractTimestamp,currentWatermark:{}",localDateTime );
return localDateTime.toInstant(ZoneOffset.of("+8")).toEpochMilli();
}
@Nullable
@Override
public Watermark getCurrentWatermark() {
logger.info("getCurrentWatermark, currentWatermark:{}",System.currentTimeMillis() - allowDelayTime);
return new Watermark(System.currentTimeMillis() - allowDelayTime);
}
}
- 特别注意 选用 Periodic WatermarkGenerator 需要设置自动watermark更新机制, setAutoWatermarkInterval(1000)
问题七:如何保证两阶段提交的实现,保证数据能够幂等性写入和事务性的写入
- 保证数据源数据可重放
- 数据sink支持事务处理(预提交,回滚,提交)
- 或者通过sink的地方,支持唯一性去重
问题八:sink to mysql 的时候,经常报错
- 报错类型 : The last packet successfully received from the server was 1,203,500 milliseconds ago.
- 有可能是jdbc版本出现,同时最好采用mysql 连接池
正确的使用valueState
flink 对于不是大规模的中间态的管理,可以选用 fsStateBackend ;StateBackend fsStateBackend = new FsStateBackend(parameter.get("flink.state.path"));
- 其中包括状态的保留时间;更新类型;是否可见
StateTtlConfig ttlConfig = StateTtlConfig
.newBuilder(Time.days(ttlDays))
.setUpdateType(StateTtlConfig.UpdateType.OnReadAndWrite)
.setStateVisibility(StateTtlConfig.StateVisibility.NeverReturnExpired)
// .cleanupInRocksdbCompactFilter(1000L)
.build();
参考链接:
1: Flink最难知识点再解析 | 时间/窗口/水印/迟到数据处理
2:Flink 中 timeWindow 滚动窗口边界和数据延迟问题调研
5:两阶段提交(2PC)与其在Flink exactly once中的应用
提醒,使用的是flink 1.9的版本
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