> For the complete documentation index, see [llms.txt](https://george-jen.gitbook.io/data-science-and-apache-spark/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://george-jen.gitbook.io/data-science-and-apache-spark/countbyvalueandwindow-windowlength-slideinterval-numtasks.md).

# countByValueAndWindow(windowLength, slideInterval, \[numTasks])

### countByValueAndWindow(windowLength, slideInterval, \[numTasks])

When called on a DStream of (K, V) pairs, returns a new DStream of (K, Long) pairs where the value of each key is its frequency within a sliding window. Like in reduceByKeyAndWindow, the number of reduce tasks is configurable through an optional argument.

Example:

```
import org.apache.spark._
import org.apache.spark.SparkContext._
import org.apache.spark.streaming._
import org.apache.spark.streaming.StreamingContext._
import org.apache.log4j.{Level, Logger}
import org.apache.spark.sql
 .{Row, SaveMode, SparkSession}
import org.apache.spark.sql.SQLContext

Logger.getLogger("org").setLevel(Level.ERROR)  
val spark = SparkSession
          .builder()
          .config("spark.master", "local[2]")
          .appName("streaming for book")
          .getOrCreate()

spark.sparkContext.setCheckpointDir("/tmp/")

import spark.implicits._
val sc=spark.sparkContext
val ssc = new StreamingContext(sc, Seconds(1))


val messages1 = ssc.textFileStream("/tmp/filestream1/")
val messages2 = ssc.textFileStream("/tmp/filestream2/")
val messages3 = messages1.union(messages2)
val messages=messages3.filter(_.nonEmpty)

val countMsg=messages
.countByValueAndWindow(Seconds(10), Seconds(5))

countMsg.print()

ssc.start()
ssc.awaitTermination()

/*
Input:

cat cccc.txt
1
2
3
4
5

cat dddd.txt
1
2
3
4
5

cat eeee.txt
1
2
3
4
5

cp cccc.txt /tmp/filestream1;cp dddd.txt /tmp/filestream2
cp eeee.txt /tmp/filestream1

Output:

-------------------------------------------
Time: 1583635409000 ms
-------------------------------------------

-------------------------------------------
Time: 1583635414000 ms
-------------------------------------------

-------------------------------------------
Time: 1583635419000 ms
-------------------------------------------

-------------------------------------------
Time: 1583635424000 ms
-------------------------------------------
(4,2)
(2,2)
(5,2)
(3,2)
(1,2)

-------------------------------------------
Time: 1583635429000 ms
-------------------------------------------
(4,2)
(2,2)
(5,2)
(3,2)
(1,2)

-------------------------------------------
Time: 1583635434000 ms
-------------------------------------------

-------------------------------------------
Time: 1583635439000 ms
-------------------------------------------
(4,1)
(2,1)
(5,1)
(3,1)
(1,1)

-------------------------------------------
Time: 1583635444000 ms
-------------------------------------------
(4,1)
(2,1)
(5,1)
(3,1)
(1,1)

It counts the value by the key, similar to reduceByKey









*/

```


---

# Agent Instructions
This documentation is published with GitBook. GitBook is the documentation platform designed so that both humans and AI agents can read, navigate, and reason over technical content effectively. Learn more at gitbook.com.

## Querying This Documentation
If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question.

Perform an HTTP GET request on the current page URL with the `ask` query parameter, and the optional `goal` query parameter:

```
GET https://george-jen.gitbook.io/data-science-and-apache-spark/countbyvalueandwindow-windowlength-slideinterval-numtasks.md?ask=<question>&goal=<endgoal>
```

`ask` is the immediate question: it should be specific, self-contained, and written in natural language.
`goal` is optional and describes the broader end goal you are ultimately trying to accomplish on behalf of the user. GitBook uses it to tailor the answer towards what is most useful for that goal.

The response will contain a direct answer to the question and relevant excerpts and sources from the documentation.

Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.
