How can I achieve this requirement using Rx Java

How can I achieve this requirement using Rx Java

By : Lightbot
Date : November 22 2020, 04:01 AM
Hope this helps You can use distinctUntilChanged() to suppress events that don't change the state. Filter out the normal events using filter().
Use the switchMap() operator to create a new subscription when the state changes. When the state is "critical", use the interval() operator to wait out the 15 seconds. If the state changes in that 15 seconds, switchMap() will unsubscribe and re-subscribe to a new observable.
code :
  .filter( state -> state != State.Normal )
  .switchMap( state -> {
                   if (state == State.Critical) {
                     return Observable.interval(0, 15, TimeUnit.SECONDS) // Note 1
                        .map(v -> State.Critical); // Note 2
                   return Observable.just( State.Noncritical );
  .subscribe( ... );

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how to achieve the project requirement

how to achieve the project requirement

By : user2261742
Date : March 29 2020, 07:55 AM
I think the issue was by ths following , I have a method Modify() shown below , If I understand correctly I'd just add the lines
code :
if(!RuntimeUp || m_reconnectInProgress) 
    return false;
how to achieve this in java ? requirement given below

how to achieve this in java ? requirement given below

By : Lycheng
Date : March 29 2020, 07:55 AM
To fix the issue you can do Creating an array of List seems a little weird to me, not that you can't do it, it just seems counter intuitive to me...
Instead, I'd create a List of Lists, something like...
code :
List<List<Integer>> nodes = new ArrayList<List<Integer>>(10);
nodes.add(new ArrayList<Integer>(10));
List<Integer> listOfIntegers = nodes.get(0);
What regex would achieve this requirement?

What regex would achieve this requirement?

By : Mikhail Maryakhin
Date : March 29 2020, 07:55 AM
seems to work fine What would be the regex to achieve this (can't seem to quite get it): , On Mac following awk should do the job:
code :
awk -F'CoronaViewer\[[0-9]+\] *' '/CoronaViewer\[[0-9]+\]/{print $2}' file
<Warning>: userdata: 0x1b5df900   10  nil began
<Warning>: userdata: 0x1b5df900   10  nil ended/cancelled
<Warning>: userdata: 0x1b544ff0   10  nil began
<Warning>: userdata: 0x1b544ff0   10  nil ended/cancelled
<Warning>: userdata: 0x1b549270   10  nil began
<Warning>: userdata: 0x1b549270   10  nil ended/cancelled
<Warning>: userdata: 0x1b33ab50   10  nil began
<Warning>: userdata: 0x1b33ab50   10  nil ended/cancelled
grep -oP "CoronaViewer\[\d+\] \K<Warning>:.*" file
how to use achieve below requirement using spark RDD

how to use achieve below requirement using spark RDD

By : jdavid
Date : March 29 2020, 07:55 AM
help you fix your problem Assuming you have a CSV file. So , first create dataframe using following steps.
code :
import org.apache.spark.sql.types._
import org.apache.spark.Row

    val members = sc.textFile("member").Map(lines => lines.split(",")).map(a => Row(a(0),a(1)))

val rddStruct = new StructType(Array(StructField("name", StringType, nullable = true),StructField("depart", StringType, nullable = true)))

val df = sqlContext.createDataFrame(members,rddStruct)
val df2 = df.groupBy("Name").agg(collect_set("Depart").as("Depart"))
| Name|   Depart|
|lmn|      [6]|
|def|   [2, 4]|
|abc|[1, 2, 3]|
|xyz|   [1, 2]|
val df3 = df2.withColumn("Count", size(df2("Depart")))
| Name|   Depart|Count|
|lmn|      [6]|    1|
|def|   [2, 4]|    2|
|abc|[1, 2, 3]|    3|
|xyz|   [1, 2]|    2|
val df4 = df3.orderBy(desc("Count"))
| Name|   Depart|Count|
|abc|[1, 2, 3]|    3|
|def|   [2, 4]|    2|
|xyz|   [1, 2]|    2|
|lmn|      [6]|    1|
How to use conditional statements or loops to achieve my requirement?

How to use conditional statements or loops to achieve my requirement?

By : user2790061
Date : March 29 2020, 07:55 AM
wish helps you I am new to python coding. Kindly, help me to achieve my requirement. , You can use the or | operator:
code :
In [11]: b >= 10
array([[ True, False, False, False],
       [False,  True, False,  True],
       [False,  True,  True, False]])

In [12]: a | (b >= 10)
array([[1, 0, 0, 1],
       [0, 1, 1, 1],
       [1, 1, 1, 1]])
In [13]: np.bitwise_or(a, b >= 10)
array([[1, 0, 0, 1],
       [0, 1, 1, 1],
       [1, 1, 1, 1]])
a, b = np.array(a), np.array(b)
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