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-rw-r--r--docs/ml-advanced.md4
-rw-r--r--docs/streaming-custom-receivers.md4
-rw-r--r--docs/streaming-flume-integration.md2
-rw-r--r--docs/streaming-kafka-0-8-integration.md12
-rw-r--r--docs/streaming-programming-guide.md22
-rw-r--r--docs/structured-streaming-programming-guide.md12
6 files changed, 28 insertions, 28 deletions
diff --git a/docs/ml-advanced.md b/docs/ml-advanced.md
index f5804fdeee..12a03d3c91 100644
--- a/docs/ml-advanced.md
+++ b/docs/ml-advanced.md
@@ -49,7 +49,7 @@ MLlib L-BFGS solver calls the corresponding implementation in [breeze](https://g
## Normal equation solver for weighted least squares
-MLlib implements normal equation solver for [weighted least squares](https://en.wikipedia.org/wiki/Least_squares#Weighted_least_squares) by [WeightedLeastSquares](https://github.com/apache/spark/blob/master/mllib/src/main/scala/org/apache/spark/ml/optim/WeightedLeastSquares.scala).
+MLlib implements normal equation solver for [weighted least squares](https://en.wikipedia.org/wiki/Least_squares#Weighted_least_squares) by [WeightedLeastSquares]({{site.SPARK_GITHUB_URL}}/blob/v{{site.SPARK_VERSION_SHORT}}/mllib/src/main/scala/org/apache/spark/ml/optim/WeightedLeastSquares.scala).
Given $n$ weighted observations $(w_i, a_i, b_i)$:
@@ -73,7 +73,7 @@ In order to make the normal equation approach efficient, WeightedLeastSquares re
## Iteratively reweighted least squares (IRLS)
-MLlib implements [iteratively reweighted least squares (IRLS)](https://en.wikipedia.org/wiki/Iteratively_reweighted_least_squares) by [IterativelyReweightedLeastSquares](https://github.com/apache/spark/blob/master/mllib/src/main/scala/org/apache/spark/ml/optim/IterativelyReweightedLeastSquares.scala).
+MLlib implements [iteratively reweighted least squares (IRLS)](https://en.wikipedia.org/wiki/Iteratively_reweighted_least_squares) by [IterativelyReweightedLeastSquares]({{site.SPARK_GITHUB_URL}}/blob/v{{site.SPARK_VERSION_SHORT}}/mllib/src/main/scala/org/apache/spark/ml/optim/IterativelyReweightedLeastSquares.scala).
It can be used to find the maximum likelihood estimates of a generalized linear model (GLM), find M-estimator in robust regression and other optimization problems.
Refer to [Iteratively Reweighted Least Squares for Maximum Likelihood Estimation, and some Robust and Resistant Alternatives](http://www.jstor.org/stable/2345503) for more information.
diff --git a/docs/streaming-custom-receivers.md b/docs/streaming-custom-receivers.md
index fae5901e8d..117996db9d 100644
--- a/docs/streaming-custom-receivers.md
+++ b/docs/streaming-custom-receivers.md
@@ -181,7 +181,7 @@ val words = lines.flatMap(_.split(" "))
...
{% endhighlight %}
-The full source code is in the example [CustomReceiver.scala](https://github.com/apache/spark/blob/master/examples/src/main/scala/org/apache/spark/examples/streaming/CustomReceiver.scala).
+The full source code is in the example [CustomReceiver.scala]({{site.SPARK_GITHUB_URL}}/blob/v{{site.SPARK_VERSION_SHORT}}/examples/src/main/scala/org/apache/spark/examples/streaming/CustomReceiver.scala).
</div>
<div data-lang="java" markdown="1">
@@ -193,7 +193,7 @@ JavaDStream<String> words = lines.flatMap(new FlatMapFunction<String, String>()
...
{% endhighlight %}
-The full source code is in the example [JavaCustomReceiver.java](https://github.com/apache/spark/blob/master/examples/src/main/java/org/apache/spark/examples/streaming/JavaCustomReceiver.java).
+The full source code is in the example [JavaCustomReceiver.java]({{site.SPARK_GITHUB_URL}}/blob/v{{site.SPARK_VERSION_SHORT}}/examples/src/main/java/org/apache/spark/examples/streaming/JavaCustomReceiver.java).
</div>
</div>
diff --git a/docs/streaming-flume-integration.md b/docs/streaming-flume-integration.md
index 8eeeee75db..767e1f9402 100644
--- a/docs/streaming-flume-integration.md
+++ b/docs/streaming-flume-integration.md
@@ -63,7 +63,7 @@ configuring Flume agents.
By default, the Python API will decode Flume event body as UTF8 encoded strings. You can specify your custom decoding function to decode the body byte arrays in Flume events to any arbitrary data type.
See the [API docs](api/python/pyspark.streaming.html#pyspark.streaming.flume.FlumeUtils)
- and the [example]({{site.SPARK_GITHUB_URL}}/blob/master/examples/src/main/python/streaming/flume_wordcount.py).
+ and the [example]({{site.SPARK_GITHUB_URL}}/blob/v{{site.SPARK_VERSION_SHORT}}/examples/src/main/python/streaming/flume_wordcount.py).
</div>
</div>
diff --git a/docs/streaming-kafka-0-8-integration.md b/docs/streaming-kafka-0-8-integration.md
index da4a845fe2..f8f7b95cf7 100644
--- a/docs/streaming-kafka-0-8-integration.md
+++ b/docs/streaming-kafka-0-8-integration.md
@@ -29,7 +29,7 @@ Next, we discuss how to use this approach in your streaming application.
[ZK quorum], [consumer group id], [per-topic number of Kafka partitions to consume])
You can also specify the key and value classes and their corresponding decoder classes using variations of `createStream`. See the [API docs](api/scala/index.html#org.apache.spark.streaming.kafka.KafkaUtils$)
- and the [example]({{site.SPARK_GITHUB_URL}}/blob/master/examples/src/main/scala/org/apache/spark/examples/streaming/KafkaWordCount.scala).
+ and the [example]({{site.SPARK_GITHUB_URL}}/blob/v{{site.SPARK_VERSION_SHORT}}/examples/src/main/scala/org/apache/spark/examples/streaming/KafkaWordCount.scala).
</div>
<div data-lang="java" markdown="1">
import org.apache.spark.streaming.kafka.*;
@@ -39,7 +39,7 @@ Next, we discuss how to use this approach in your streaming application.
[ZK quorum], [consumer group id], [per-topic number of Kafka partitions to consume]);
You can also specify the key and value classes and their corresponding decoder classes using variations of `createStream`. See the [API docs](api/java/index.html?org/apache/spark/streaming/kafka/KafkaUtils.html)
- and the [example]({{site.SPARK_GITHUB_URL}}/blob/master/examples/src/main/java/org/apache/spark/examples/streaming/JavaKafkaWordCount.java).
+ and the [example]({{site.SPARK_GITHUB_URL}}/blob/v{{site.SPARK_VERSION_SHORT}}/examples/src/main/java/org/apache/spark/examples/streaming/JavaKafkaWordCount.java).
</div>
<div data-lang="python" markdown="1">
@@ -49,7 +49,7 @@ Next, we discuss how to use this approach in your streaming application.
[ZK quorum], [consumer group id], [per-topic number of Kafka partitions to consume])
By default, the Python API will decode Kafka data as UTF8 encoded strings. You can specify your custom decoding function to decode the byte arrays in Kafka records to any arbitrary data type. See the [API docs](api/python/pyspark.streaming.html#pyspark.streaming.kafka.KafkaUtils)
- and the [example]({{site.SPARK_GITHUB_URL}}/blob/master/examples/src/main/python/streaming/kafka_wordcount.py).
+ and the [example]({{site.SPARK_GITHUB_URL}}/blob/v{{site.SPARK_VERSION_SHORT}}/examples/src/main/python/streaming/kafka_wordcount.py).
</div>
</div>
@@ -106,7 +106,7 @@ Next, we discuss how to use this approach in your streaming application.
You can also pass a `messageHandler` to `createDirectStream` to access `MessageAndMetadata` that contains metadata about the current message and transform it to any desired type.
See the [API docs](api/scala/index.html#org.apache.spark.streaming.kafka.KafkaUtils$)
- and the [example]({{site.SPARK_GITHUB_URL}}/blob/master/examples/src/main/scala/org/apache/spark/examples/streaming/DirectKafkaWordCount.scala).
+ and the [example]({{site.SPARK_GITHUB_URL}}/blob/v{{site.SPARK_VERSION_SHORT}}/examples/src/main/scala/org/apache/spark/examples/streaming/DirectKafkaWordCount.scala).
</div>
<div data-lang="java" markdown="1">
import org.apache.spark.streaming.kafka.*;
@@ -118,7 +118,7 @@ Next, we discuss how to use this approach in your streaming application.
You can also pass a `messageHandler` to `createDirectStream` to access `MessageAndMetadata` that contains metadata about the current message and transform it to any desired type.
See the [API docs](api/java/index.html?org/apache/spark/streaming/kafka/KafkaUtils.html)
- and the [example]({{site.SPARK_GITHUB_URL}}/blob/master/examples/src/main/java/org/apache/spark/examples/streaming/JavaDirectKafkaWordCount.java).
+ and the [example]({{site.SPARK_GITHUB_URL}}/blob/v{{site.SPARK_VERSION_SHORT}}/examples/src/main/java/org/apache/spark/examples/streaming/JavaDirectKafkaWordCount.java).
</div>
<div data-lang="python" markdown="1">
@@ -127,7 +127,7 @@ Next, we discuss how to use this approach in your streaming application.
You can also pass a `messageHandler` to `createDirectStream` to access `KafkaMessageAndMetadata` that contains metadata about the current message and transform it to any desired type.
By default, the Python API will decode Kafka data as UTF8 encoded strings. You can specify your custom decoding function to decode the byte arrays in Kafka records to any arbitrary data type. See the [API docs](api/python/pyspark.streaming.html#pyspark.streaming.kafka.KafkaUtils)
- and the [example]({{site.SPARK_GITHUB_URL}}/blob/master/examples/src/main/python/streaming/direct_kafka_wordcount.py).
+ and the [example]({{site.SPARK_GITHUB_URL}}/blob/v{{site.SPARK_VERSION_SHORT}}/examples/src/main/python/streaming/direct_kafka_wordcount.py).
</div>
</div>
diff --git a/docs/streaming-programming-guide.md b/docs/streaming-programming-guide.md
index aef62ea900..df94e9533e 100644
--- a/docs/streaming-programming-guide.md
+++ b/docs/streaming-programming-guide.md
@@ -126,7 +126,7 @@ ssc.awaitTermination() // Wait for the computation to terminate
{% endhighlight %}
The complete code can be found in the Spark Streaming example
-[NetworkWordCount]({{site.SPARK_GITHUB_URL}}/blob/master/examples/src/main/scala/org/apache/spark/examples/streaming/NetworkWordCount.scala).
+[NetworkWordCount]({{site.SPARK_GITHUB_URL}}/blob/v{{site.SPARK_VERSION_SHORT}}/examples/src/main/scala/org/apache/spark/examples/streaming/NetworkWordCount.scala).
<br>
</div>
@@ -216,7 +216,7 @@ jssc.awaitTermination(); // Wait for the computation to terminate
{% endhighlight %}
The complete code can be found in the Spark Streaming example
-[JavaNetworkWordCount]({{site.SPARK_GITHUB_URL}}/blob/master/examples/src/main/java/org/apache/spark/examples/streaming/JavaNetworkWordCount.java).
+[JavaNetworkWordCount]({{site.SPARK_GITHUB_URL}}/blob/v{{site.SPARK_VERSION_SHORT}}/examples/src/main/java/org/apache/spark/examples/streaming/JavaNetworkWordCount.java).
<br>
</div>
@@ -277,7 +277,7 @@ ssc.awaitTermination() # Wait for the computation to terminate
{% endhighlight %}
The complete code can be found in the Spark Streaming example
-[NetworkWordCount]({{site.SPARK_GITHUB_URL}}/blob/master/examples/src/main/python/streaming/network_wordcount.py).
+[NetworkWordCount]({{site.SPARK_GITHUB_URL}}/blob/v{{site.SPARK_VERSION_SHORT}}/examples/src/main/python/streaming/network_wordcount.py).
<br>
</div>
@@ -854,7 +854,7 @@ JavaPairDStream<String, Integer> runningCounts = pairs.updateStateByKey(updateFu
The update function will be called for each word, with `newValues` having a sequence of 1's (from
the `(word, 1)` pairs) and the `runningCount` having the previous count. For the complete
Java code, take a look at the example
-[JavaStatefulNetworkWordCount.java]({{site.SPARK_GITHUB_URL}}/blob/master/examples/src/main/java/org/apache/spark/examples/streaming
+[JavaStatefulNetworkWordCount.java]({{site.SPARK_GITHUB_URL}}/blob/v{{site.SPARK_VERSION_SHORT}}/examples/src/main/java/org/apache/spark/examples/streaming
/JavaStatefulNetworkWordCount.java).
</div>
@@ -877,7 +877,7 @@ runningCounts = pairs.updateStateByKey(updateFunction)
The update function will be called for each word, with `newValues` having a sequence of 1's (from
the `(word, 1)` pairs) and the `runningCount` having the previous count. For the complete
Python code, take a look at the example
-[stateful_network_wordcount.py]({{site.SPARK_GITHUB_URL}}/blob/master/examples/src/main/python/streaming/stateful_network_wordcount.py).
+[stateful_network_wordcount.py]({{site.SPARK_GITHUB_URL}}/blob/v{{site.SPARK_VERSION_SHORT}}/examples/src/main/python/streaming/stateful_network_wordcount.py).
</div>
</div>
@@ -1428,7 +1428,7 @@ wordCounts.foreachRDD { (rdd: RDD[(String, Int)], time: Time) =>
{% endhighlight %}
-See the full [source code]({{site.SPARK_GITHUB_URL}}/blob/master/examples/src/main/scala/org/apache/spark/examples/streaming/RecoverableNetworkWordCount.scala).
+See the full [source code]({{site.SPARK_GITHUB_URL}}/blob/v{{site.SPARK_VERSION_SHORT}}/examples/src/main/scala/org/apache/spark/examples/streaming/RecoverableNetworkWordCount.scala).
</div>
<div data-lang="java" markdown="1">
{% highlight java %}
@@ -1491,7 +1491,7 @@ wordCounts.foreachRDD(new Function2<JavaPairRDD<String, Integer>, Time, Void>()
{% endhighlight %}
-See the full [source code]({{site.SPARK_GITHUB_URL}}/blob/master/examples/src/main/java/org/apache/spark/examples/streaming/JavaRecoverableNetworkWordCount.java).
+See the full [source code]({{site.SPARK_GITHUB_URL}}/blob/v{{site.SPARK_VERSION_SHORT}}/examples/src/main/java/org/apache/spark/examples/streaming/JavaRecoverableNetworkWordCount.java).
</div>
<div data-lang="python" markdown="1">
{% highlight python %}
@@ -1526,7 +1526,7 @@ wordCounts.foreachRDD(echo)
{% endhighlight %}
-See the full [source code]({{site.SPARK_GITHUB_URL}}/blob/master/examples/src/main/python/streaming/recoverable_network_wordcount.py).
+See the full [source code]({{site.SPARK_GITHUB_URL}}/blob/v{{site.SPARK_VERSION_SHORT}}/examples/src/main/python/streaming/recoverable_network_wordcount.py).
</div>
</div>
@@ -1564,7 +1564,7 @@ words.foreachRDD { rdd =>
{% endhighlight %}
-See the full [source code]({{site.SPARK_GITHUB_URL}}/blob/master/examples/src/main/scala/org/apache/spark/examples/streaming/SqlNetworkWordCount.scala).
+See the full [source code]({{site.SPARK_GITHUB_URL}}/blob/v{{site.SPARK_VERSION_SHORT}}/examples/src/main/scala/org/apache/spark/examples/streaming/SqlNetworkWordCount.scala).
</div>
<div data-lang="java" markdown="1">
{% highlight java %}
@@ -1619,7 +1619,7 @@ words.foreachRDD(
);
{% endhighlight %}
-See the full [source code]({{site.SPARK_GITHUB_URL}}/blob/master/examples/src/main/java/org/apache/spark/examples/streaming/JavaSqlNetworkWordCount.java).
+See the full [source code]({{site.SPARK_GITHUB_URL}}/blob/v{{site.SPARK_VERSION_SHORT}}/examples/src/main/java/org/apache/spark/examples/streaming/JavaSqlNetworkWordCount.java).
</div>
<div data-lang="python" markdown="1">
{% highlight python %}
@@ -1661,7 +1661,7 @@ def process(time, rdd):
words.foreachRDD(process)
{% endhighlight %}
-See the full [source code]({{site.SPARK_GITHUB_URL}}/blob/master/examples/src/main/python/streaming/sql_network_wordcount.py).
+See the full [source code]({{site.SPARK_GITHUB_URL}}/blob/v{{site.SPARK_VERSION_SHORT}}/examples/src/main/python/streaming/sql_network_wordcount.py).
</div>
</div>
diff --git a/docs/structured-streaming-programming-guide.md b/docs/structured-streaming-programming-guide.md
index 99d50e51e2..e2c881bf4a 100644
--- a/docs/structured-streaming-programming-guide.md
+++ b/docs/structured-streaming-programming-guide.md
@@ -14,9 +14,9 @@ Structured Streaming is a scalable and fault-tolerant stream processing engine b
# Quick Example
Let’s say you want to maintain a running word count of text data received from a data server listening on a TCP socket. Let’s see how you can express this using Structured Streaming. You can see the full code in
-[Scala]({{site.SPARK_GITHUB_URL}}/blob/master/examples/src/main/scala/org/apache/spark/examples/sql/streaming/StructuredNetworkWordCount.scala)/
-[Java]({{site.SPARK_GITHUB_URL}}/blob/master/examples/src/main/java/org/apache/spark/examples/sql/streaming/JavaStructuredNetworkWordCount.java)/
-[Python]({{site.SPARK_GITHUB_URL}}/blob/master/examples/src/main/python/sql/streaming/structured_network_wordcount.py). And if you
+[Scala]({{site.SPARK_GITHUB_URL}}/blob/v{{site.SPARK_VERSION_SHORT}}/examples/src/main/scala/org/apache/spark/examples/sql/streaming/StructuredNetworkWordCount.scala)/
+[Java]({{site.SPARK_GITHUB_URL}}/blob/v{{site.SPARK_VERSION_SHORT}}/examples/src/main/java/org/apache/spark/examples/sql/streaming/JavaStructuredNetworkWordCount.java)/
+[Python]({{site.SPARK_GITHUB_URL}}/blob/v{{site.SPARK_VERSION_SHORT}}/examples/src/main/python/sql/streaming/structured_network_wordcount.py). And if you
[download Spark](http://spark.apache.org/downloads.html), you can directly run the example. In any case, let’s walk through the example step-by-step and understand how it works. First, we have to import the necessary classes and create a local SparkSession, the starting point of all functionalities related to Spark.
<div class="codetabs">
@@ -618,9 +618,9 @@ The result tables would look something like the following.
![Window Operations](img/structured-streaming-window.png)
Since this windowing is similar to grouping, in code, you can use `groupBy()` and `window()` operations to express windowed aggregations. You can see the full code for the below examples in
-[Scala]({{site.SPARK_GITHUB_URL}}/blob/master/examples/src/main/scala/org/apache/spark/examples/sql/streaming/StructuredNetworkWordCountWindowed.scala)/
-[Java]({{site.SPARK_GITHUB_URL}}/blob/master/examples/src/main/java/org/apache/spark/examples/sql/streaming/JavaStructuredNetworkWordCountWindowed.java)/
-[Python]({{site.SPARK_GITHUB_URL}}/blob/master/examples/src/main/python/sql/streaming/structured_network_wordcount_windowed.py).
+[Scala]({{site.SPARK_GITHUB_URL}}/blob/v{{site.SPARK_VERSION_SHORT}}/examples/src/main/scala/org/apache/spark/examples/sql/streaming/StructuredNetworkWordCountWindowed.scala)/
+[Java]({{site.SPARK_GITHUB_URL}}/blob/v{{site.SPARK_VERSION_SHORT}}/examples/src/main/java/org/apache/spark/examples/sql/streaming/JavaStructuredNetworkWordCountWindowed.java)/
+[Python]({{site.SPARK_GITHUB_URL}}/blob/v{{site.SPARK_VERSION_SHORT}}/examples/src/main/python/sql/streaming/structured_network_wordcount_windowed.py).
<div class="codetabs">
<div data-lang="scala" markdown="1">
generated by cgit v1.2.3 (git 2.39.1) at 2024-07-03 17:51:52 +0000