HeartSaVioR commented on code in PR #389:
URL: https://github.com/apache/spark-website/pull/389#discussion_r875607350
##########
site/streaming/index.html:
##########
@@ -125,135 +125,98 @@
<div class="row mt-4">
<div class="col-12 col-md-9">
<div class="jumbotron">
- <b>Spark streaming</b> makes it easy to build scalable fault-tolerant
streaming
- applications.
+ <a href="/docs/latest/structured-streaming-programming-guide.html">Spark
Structured Streaming</a> makes it easy to build streaming applications and
pipelines with the same and familiar Spark APIs.
</div>
<div class="row row-padded">
<div class="col-md-7 col-sm-7">
- <h2>Ease of use</h2>
- <p class="lead">
- Build applications through high-level operators.
- </p>
+ <h2>Ease to use</h2>
<p>
- Spark Streaming brings Apache Spark's
- <a
href="/docs/latest/streaming-programming-guide.html">language-integrated API</a>
- to stream processing, letting you write streaming jobs the same way you
write batch jobs.
- It supports Java, Scala and Python.
+ Spark Structured Streaming abstracts away complex streaming concepts
such as incremental processing, checkpointing, and watermarks
+ so that you can build streaming applications and pipelines without
learning any new concepts or tools.
</p>
</div>
<div class="col-md-5 col-sm-5 col-padded-top col-center">
<div style="margin-top: 15px; text-align: left; display: inline-block;">
<div class="code">
- TwitterUtils.createStream(...)<br />
- .<span class="sparkop">filter</span>(<span
class="closure">_.getText.contains("Spark")</span>)<br />
- .<span
class="sparkop">countByWindow</span>(Seconds(5))
+ spark<br />
+ .<span class="sparkop">readStream</span><br />
+ .<span class="sparkop">select</span>(<span
class="closure">cast("string").alias("jsonData")</span>)<br />
Review Comment:
Just fixed it; I used the simple batch query below for the verification:
```
import org.apache.spark.sql.functions._
import org.apache.spark.sql.types._
val jsonSchema = new StructType().add("hello", StringType).add("year",
IntegerType)
val df = spark.range(1)
.select(lit("{'hello': 'world', 'year':
2022}").alias("value")).select($"value".cast("string").alias("jsonData"))
.select(from_json($"jsonData", jsonSchema).alias("payload"))
df.show()
```
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