From 10b5ad9d097b88b1d670d3e7af5c72ad2c3fdea8 Mon Sep 17 00:00:00 2001
From: heibaiying <31504331+heibaiying@users.noreply.github.com>
Date: Sat, 18 May 2019 16:19:10 +0800
Subject: [PATCH] =?UTF-8?q?Update=20Spark-Streaming=E4=B8=8E=E6=B5=81?=
 =?UTF-8?q?=E5=A4=84=E7=90=86.md?=
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---
 notes/Spark-Streaming与流处理.md | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

diff --git a/notes/Spark-Streaming与流处理.md b/notes/Spark-Streaming与流处理.md
index dfbce30..b29fc50 100644
--- a/notes/Spark-Streaming与流处理.md
+++ b/notes/Spark-Streaming与流处理.md
@@ -55,13 +55,13 @@ Spark Streaming是Spark的一个子模块，用于快速构建可扩展，高吞
 + 能够和Spark其他模块无缝集成，将流处理与批处理完美结合；
 + Spark Streaming可以从HDFS，Flume，Kafka，Twitter和ZeroMQ读取数据，也支持自定义数据源。
 
-<div align="center"> <img src="https://github.com/heibaiying/BigData-Notes/blob/master/pictures/spark-streaming-arch.png"/> </div>
+<div align="center"> <img width="600px" src="https://github.com/heibaiying/BigData-Notes/blob/master/pictures/spark-streaming-arch.png"/> </div>
 
 ### 2.2 DStream
 
 Spark Streaming提供称为离散流(DStream)的高级抽象，用于表示连续的数据流。 DStream可以从来自Kafka，Flume和Kinesis等数据源的输入数据流创建，也可以由其他DStream转化而来。**在内部，DStream表示为一系列RDD**。
 
-<div align="center"> <img src="https://github.com/heibaiying/BigData-Notes/blob/master/pictures/spark-streaming-flow.png"/> </div>
+<div align="center"> <img width="600px"   src="https://github.com/heibaiying/BigData-Notes/blob/master/pictures/spark-streaming-flow.png"/> </div>
 
 
 
@@ -75,4 +75,4 @@ storm和Flink都是真正意义上的流计算框架，但 Spark Streaming 只
 
 ## 参考资料
 
-[Spark Streaming Programming Guide](https://spark.apache.org/docs/latest/streaming-programming-guide.html)
\ No newline at end of file
+[Spark Streaming Programming Guide](https://spark.apache.org/docs/latest/streaming-programming-guide.html)