From 51fe7e76e41dca061ebb5c972f7af3c73375688a Mon Sep 17 00:00:00 2001 From: zeekling Date: Sun, 15 Jan 2023 05:04:40 +0000 Subject: [PATCH] =?UTF-8?q?=E6=95=B0=E6=8D=AE=E5=80=BE=E6=96=9C=E5=AD=A6?= =?UTF-8?q?=E4=B9=A0?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- 调优/dataSkew.md | 119 ++++++++++++++++++++++++++++++++++++++++++ 调优/dataSkew0001.png | Bin 0 -> 44833 bytes 调优/dataSkew0002.png | Bin 0 -> 107921 bytes 调优/dataSkew0003.png | Bin 0 -> 130925 bytes 4 files changed, 119 insertions(+) create mode 100644 调优/dataSkew.md create mode 100644 调优/dataSkew0001.png create mode 100644 调优/dataSkew0002.png create mode 100644 调优/dataSkew0003.png diff --git a/调优/dataSkew.md b/调优/dataSkew.md new file mode 100644 index 0000000..da71793 --- /dev/null +++ b/调优/dataSkew.md @@ -0,0 +1,119 @@ + + + +# 数据倾斜原理 + + +数据倾斜就是数据的分布严重不均,流入部分算子的数据明显多余其他算子,造成这部分算子压力过大。 + +![pic](./dataSkew0001.png) + + +# 影响 + +## 单点问题 + +数据集中在某些分区上(Subtask),导致数据严重不平衡。 + +## GC 频繁 + +过多的数据集中在某些 JVM(TaskManager),使得JVM 的内存资源短缺,导致频繁 GC。 + +## 吞吐下降、延迟增大 + +数据单点和频繁 GC 导致吞吐下降、延迟增大。 + +## 系统崩溃 + +严重情况下,过长的 GC 导致 TaskManager 失联,系统崩溃。 + +# Flink数据倾斜问题定位 + +## 定位反压 + +定位反压有2种方式:Flink Web UI 自带的反压监控(直接方式)、Flink Task Metrics(间接方式)。通过监控反压的信息 +,可以获取到数据处理瓶颈的 Subtask。 + +## 确定数据倾斜 + +Flink Web UI 自带Subtask 接收和发送的数据量。当 Subtasks 之间处理的数据量有较大的差距,则该 Subtask 出现数据倾斜。 + +# Flink 如何处理常见数据倾斜 + +## 数据源 source 消费不均匀 + +解决思路:通过调整并发度,解决数据源消费不均匀或者数据源反压的情况。 + +例如kafka数据源,可以调整 KafkaSource 的并发度解决消费不均匀。 + +调整并发度的原则:KafkaSource 并发度与 kafka 分区数是一样的,或者 kafka 分区数是KafkaSource 并发度的整数倍。 + +## key 分布不均匀的无统计场景 + +说明:key 分布不均匀的无统计场景,例如上游数据分布不均匀,使用keyBy来打散数据。 + +解决思路: 通过添加随机前缀,打散 key 的分布,使得数据不会集中在几个 Subtask。 + +![pic](./dataSkew0002.png) + + +具体措施: +① 在原来分区 key/uid 的基础上,加上随机的前缀或者后缀。 +② 使用数据到达的顺序seq,作为分区的key。 + +## key 分布不均匀的统计场景 + +解决思路:聚合统计前,先进行预聚合,例如两阶段聚合(加盐局部聚合+去盐全局聚合)。 + +![pic](./dataSkew0003.png) + +两阶段聚合的具体措施: +① 预聚合:加盐局部聚合,在原来的 key 上加随机的前缀或者后缀。 +② 聚合:去盐全局聚合,删除预聚合添加的前缀或者后缀,然后进行聚合统计。 + + +## SQL 样例 + +在下面SQL里面,我们统计一个网站各个端的每分钟的pv,从kafka消费过来的数据首先会按照端进行分组,然后执行聚合函 +数count来进行pv的计算。 + +```plsql +select + TUMBLE_END(proc_time, INTERVAL '1' MINUTE) as winEnd, + plat, + count(*) as pv +from + source_kafka_table +group by + TUMBLE(proc_time, INTERVAL '1' MINUTE) ,plat +``` + +如果某一个端产生的数据特别大,比如我们的微信小程序端产生数据远远大于其他app端的数据,那么把这些数据分组到某一 +个算子之后,由于这个算子的处理速度跟不上,就会产生数据倾斜。 + +```plsql +select + winEnd, + split_index(plat1,'_',0) as plat2, + sum(pv) +from ( + select + TUMBLE_END(proc_time, INTERVAL '1' MINUTE) as winEnd, + plat1, + count(*) as pv + from ( + -- 最内层,将分组的key,也就是plat加上一个随机数打散 + select + plat || '_' || cast(cast(RAND()*100 as int) as string) as plat1 , + proc_time from source_kafka_table + ) group by + TUMBLE(proc_time, INTERVAL '1' MINUTE), plat1 +) group by winEnd,split_index(plat1,'_',0) +``` +在这个sql的最内层,将分组的key,也就是plat加上一个随机数打散,然后求打散后的各个分组(也就是sql中的plat1)的 +pv值,然后最外层,将各个打散的pv求和。 + +注意:最内层的sql,给分组的key添加的随机数,范围不能太大,也不能太小,太大的话,分的组太多,增加checkpoint的 +压力,太小的话,起不到打散的作用。 + + diff --git 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