Spark-based-Ripley-K-Functions | visual analytics framework for Distributed Ripley

by ZPGuiGroupWhu JavaScript Version: Current License: No License

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kandi X-RAY | Spark-based-Ripley-K-Functions Summary

Spark-based-Ripley-K-Functions is a JavaScript library typically used in Big Data, Spark applications. Spark-based-Ripley-K-Functions has no bugs, it has no vulnerabilities and it has low support. You can download it from GitHub.

A visual analytics framework for Distributed Ripley's K functions based on Apache Spark, including Space K function, Space-Time K function, Local K function and Cross K function. This framework consists of three parts, i.e., front-end web visualization components, Web service API component and back-end Spark-based algorithm packages.

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Spark-based-Ripley-K-Functions has a low active ecosystem.
It has 21 star(s) with 3 fork(s). There are 1 watchers for this library.
It had no major release in the last 12 months.
Spark-based-Ripley-K-Functions has no issues reported. There are 10 open pull requests and 0 closed requests.
It has a neutral sentiment in the developer community.
The latest version of Spark-based-Ripley-K-Functions is current.

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quality kandi Quality

Spark-based-Ripley-K-Functions has no bugs reported.

This Library - Quality

Best in #JavaScript

Average in #JavaScript

This Library - Quality

Best in #JavaScript

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security Security

Spark-based-Ripley-K-Functions has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

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This Library - Security

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license License

Spark-based-Ripley-K-Functions does not have a standard license declared.
Check the repository for any license declaration and review the terms closely.
Without a license, all rights are reserved, and you cannot use the library in your applications.

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build Reuse

Spark-based-Ripley-K-Functions releases are not available. You will need to build from source code and install.

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Top functions reviewed by kandi - BETA

kandi has reviewed Spark-based-Ripley-K-Functions and discovered the below as its top functions. This is intended to give you an instant insight into Spark-based-Ripley-K-Functions implemented functionality, and help decide if they suit your requirements.

D3 time format function
Computes the clip - line interceptor .
initializer for brush event
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Make a clipping clip circle .
Computes a clip - point clipping method for the clipping point .
Returns D3 locale - number formatting function .
Divide a scale function
Resample stream
Calculates the quadtree of the given data

Spark-based-Ripley-K-Functions Key Features

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Community Discussions

Trending Discussions on Big Data

How to group unassociated content
Using Spark window with more than one partition when there is no obvious partitioning column
What is the best way to store +3 millions records in Firestore?
spark-shell throws java.lang.reflect.InvocationTargetException on running
For function over multiple rows (i+1)?
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handling million of rows for lookup operation using python
split function does not return any observations with large dataset

Trending Discussions on Big Data

QUESTION

How to group unassociated content

Asked 2022-Apr-15 at 12:43

I have a hive table that records user behavior

like this

userid	behavior	timestamp	url
1	view	1650022601	url1
1	click	1650022602	url2
1	click	1650022614	url3
1	view	1650022617	url4
1	click	1650022622	url5
1	view	1650022626	url7
2	view	1650022628	url8
2	view	1650022631	url9

About 400GB is added to the table every day.

I want to order by timestamp asc, then one 'view' is in a group between another 'view' like this table, the first 3 lines belong to a same group , then subtract the timestamps, like 1650022614 - 1650022601 as the view time.

How to do this?

i try lag and lead function, or scala like this

        val pairRDD: RDD[(Int, String)] = record.map(x => {
            if (StringUtil.isDateString(x.split("\\s+")(0))) {
                partition = partition + 1
                (partition, x)
            } else {
                (partition, x)
            }
        })

or java like this

        LongAccumulator part = spark.sparkContext().longAccumulator("part");

        JavaPairRDD<Long, Row> pairRDD = spark.sql(sql).coalesce(1).javaRDD().mapToPair((PairFunction<Row, Long, Row>) row -> {
            if (row.getAs("event") == "pageview") {
                part.add(1L);
            }
        return new Tuple2<>(part.value(), row);
        });

but when a dataset is very large, this code just stupid.

save me plz

ANSWER

Answered 2022-Apr-15 at 12:43

If you use dataframe, you can build partition by using window that sum a column whose value is 1 when you change partition and 0 if you don't change partition.

You can transform a RDD to a dataframe with sparkSession.createDataframe() method as explained in this answer

Back to your problem. In you case, you change partition every time column behavior is equal to "view". So we can start with this condition:

import org.apache.spark.sql.functions.col

val df1 = df.withColumn("is_view", (col("behavior") === "view").cast("integer"))

You get the following dataframe:

+------+--------+----------+----+-------+
|userid|behavior|timestamp |url |is_view|
+------+--------+----------+----+-------+
|1     |view    |1650022601|url1|1      |
|1     |click   |1650022602|url2|0      |
|1     |click   |1650022614|url3|0      |
|1     |view    |1650022617|url4|1      |
|1     |click   |1650022622|url5|0      |
|1     |view    |1650022626|url7|1      |
|2     |view    |1650022628|url8|1      |
|2     |view    |1650022631|url9|1      |
+------+--------+----------+----+-------+

Then you use a window ordered by timestamp to sum over the is_view column:

import org.apache.spark.sql.expressions.Window
import org.apache.spark.sql.functions.sum

val df2 = df1.withColumn("partition", sum("is_view").over(Window.partitionBy("userid").orderBy("timestamp")))

Which get you the following dataframe:

+------+--------+----------+----+-------+---------+
|userid|behavior|timestamp |url |is_view|partition|
+------+--------+----------+----+-------+---------+
|1     |view    |1650022601|url1|1      |1        |
|1     |click   |1650022602|url2|0      |1        |
|1     |click   |1650022614|url3|0      |1        |
|1     |view    |1650022617|url4|1      |2        |
|1     |click   |1650022622|url5|0      |2        |
|1     |view    |1650022626|url7|1      |3        |
|2     |view    |1650022628|url8|1      |1        |
|2     |view    |1650022631|url9|1      |2        |
+------+--------+----------+----+-------+---------+

Then, you just have to aggregate per userid and partition:

import org.apache.spark.sql.functions.{max, min}

val result = df2.groupBy("userid", "partition")
  .agg((max("timestamp") - min("timestamp")).as("duration"))

And you get the following results:

+------+---------+--------+
|userid|partition|duration|
+------+---------+--------+
|1     |1        |13      |
|1     |2        |5       |
|1     |3        |0       |
|2     |1        |0       |
|2     |2        |0       |
+------+---------+--------+

The complete scala code:

import org.apache.spark.sql.expressions.Window
import org.apache.spark.sql.functions.{col, max, min, sum}

val result = df
  .withColumn("is_view", (col("behavior") === "view").cast("integer"))
  .withColumn("partition", sum("is_view").over(Window.partitionBy("userid").orderBy("timestamp")))
  .groupBy("userid", "partition")
  .agg((max("timestamp") - min("timestamp")).as("duration"))

Source https://stackoverflow.com/questions/71883786

QUESTION

Using Spark window with more than one partition when there is no obvious partitioning column

Asked 2022-Apr-10 at 20:21

Here is the scenario. Assuming I have the following table:

identifier	line
51169081604	2
00034886044	22
51168939455	52

The challenge is to, for every single column line, select the next biggest column line, which I have accomplished by the following SQL:

SELECT i1.line,i1.identifier, 
MAX(i1.line) OVER (
    ORDER BY i1.line ROWS BETWEEN 1 PRECEDING AND 1 FOLLOWING
)AS parent
FROM global_temp.documentIdentifiers i1

The challenge is partially solved alright, the problem is, when I execute this code on Spark, the performance is terrible. The warning message is very clear about it:

No Partition Defined for Window operation! Moving all data to a single partition, this can cause serious performance degradation.

Partitioning by any of the two fields does not work, it breaks the result, of course, as every created partition is not aware of the other lines.

Does anyone have any clue on how can I " select the next biggest column line" without performance issues?

Thanks

ANSWER

Answered 2022-Apr-10 at 20:21

Using your "next" approach AND assuming the data is generated in ascending line order, the following does work in parallel, but if actually faster you can tell me; I do not know your volume of data. In any event you cannot solve just with SQL (%sql).

Here goes:

import org.apache.spark.sql.functions._
import org.apache.spark.sql.expressions.Window
import spark.implicits._

case class X(identifier: Long, line: Long) // Too hard to explain, just gets around issues with df --> rdd --> df.

// Gen some more data.
val df = Seq(
 (1000000, 23), (1200, 56), (1201, 58), (1202, 60),
 (8200, 63), (890000, 67), (990000, 99), (33000, 123),
 (33001, 124), (33002, 126), (33009, 132), (33019, 133),
 (33029, 134), (33039, 135), (800, 201), (1800, 999),
 (1801, 1999), (1802, 2999), (1800444, 9999)
 ).toDF("identifier", "line")

// Add partition so as to be able to apply parallelism - except for upper boundary record.
val df2 = df.as[X]
            .rdd
            .mapPartitionsWithIndex((index, iter) => {
                iter.map(x => (index, x ))   
             }).mapValues(v => (v.identifier, v.line)).map(x => (x._1, x._2._1, x._2._2))
            .toDF("part", "identifier", "line")

// Process per partition.
@transient val w = org.apache.spark.sql.expressions.Window.partitionBy("part").orderBy("line")  
val df3 = df2.withColumn("next", lead("line", 1, null).over(w))

// Process upper boundary.
val df4 = df3.filter(df3("part") =!= 0).groupBy("part").agg(min("line").as("nxt")).toDF("pt", "nxt")
val df5 = df3.join(df4, (df3("part") === df4("pt") - 1), "outer" )
val df6 = df5.withColumn("next", when(col("next").isNull, col("nxt")).otherwise(col("next"))).select("identifier", "line", "next")

// Display. Sort accordingly.
df6.show(false)

returns:

+----------+----+----+
|identifier|line|next|
+----------+----+----+
|1000000   |23  |56  |
|1200      |56  |58  |
|1201      |58  |60  |
|1202      |60  |63  |
|8200      |63  |67  |
|890000    |67  |99  |
|990000    |99  |123 |
|33000     |123 |124 |
|33001     |124 |126 |
|33002     |126 |132 |
|33009     |132 |133 |
|33019     |133 |134 |
|33029     |134 |135 |
|33039     |135 |201 |
|800       |201 |999 |
|1800      |999 |1999|
|1801      |1999|2999|
|1802      |2999|9999|
|1800444   |9999|null|
+----------+----+----+

You can add additional sorting etc. Relies on narrow transformation when adding partition index. How you load may be an issue. Caching not considered.

If data is not ordered as stated above, a range partitioning needs to occur first.

Source https://stackoverflow.com/questions/71803991

QUESTION

What is the best way to store +3 millions records in Firestore?

Asked 2022-Apr-09 at 13:18

I want to store +3 millions records in my Firestore database and I would like to know what is the best way, practice, to do that.

In fact, I want to store every prices of 30 cryptos every 15 minutes since 01/01/2020.

For example:

ETH price at 01/01/2020 at 00h00 = xxx
ETH price at 01/01/2020 at 00h15 = xxx
ETH price at 01/01/2020 at 00h30 = xxx
...
ETH price at 09/04/2022 at 14h15 = xxx

and this, for 30 cryptos (or more).

So, 120 prices per day multiplied by 829 days multiplied by 30 cryptos ~= 3M records

I thought of saving this like this:

[Collection of Crypto] [Document of crypto] [Collection of dates] [Document of hour] [Price]

I don't know if this is the right way, that's why I come here :)

Of course, the goal of this database will be to retrieve ALL the historical prices of a currency that I would have selected. This will allow me to make statistics etc later.

Thanks for your help

ANSWER

Answered 2022-Apr-09 at 13:18

For the current structure, instead of creating a document every 15 minutes you can just create a "prices" document and store an array of format { time: "00:00", price: 100 } which will cost only 1 read to fetch prices of a given currency on a day instead of 96.

Alternatively, you can create a single collection "prices" and create a document everyday for each currency. A document in this collection can look like this:

{
  name: "BTC",
  date: "2022/04/09", // or Firestore timestamp
  prices: [
    { time: "00:05", price: 12.345 },
    { time: "00:10", price: 6.345 },
    { time: "00:15", price: 68.586 },
  ]
}

With this structure as well you can query rates a particular coin in a given date range. An example for this query:

const qSnap = await getDocs(
  query(
    collection(db, "prices"),
    where("name", "==", "BTC"),
    where("time", ">=", startDateTimestamp),
    where("time", "<", endDateTimestamp)
  )
);

Source https://stackoverflow.com/questions/71808107

QUESTION

spark-shell throws java.lang.reflect.InvocationTargetException on running

Asked 2022-Apr-01 at 19:53

When I execute run-example SparkPi, for example, it works perfectly, but when I run spark-shell, it throws these exceptions:

WARNING: An illegal reflective access operation has occurred
WARNING: Illegal reflective access by org.apache.spark.unsafe.Platform (file:/C:/big_data/spark-3.2.0-bin-hadoop3.2-scala2.13/jars/spark-unsafe_2.13-3.2.0.jar) to constructor java.nio.DirectByteBuffer(long,int)
WARNING: Please consider reporting this to the maintainers of org.apache.spark.unsafe.Platform
WARNING: Use --illegal-access=warn to enable warnings of further illegal reflective access operations
WARNING: All illegal access operations will be denied in a future release
Using Spark's default log4j profile: org/apache/spark/log4j-defaults.properties
Setting default log level to "WARN".
To adjust logging level use sc.setLogLevel(newLevel). For SparkR, use setLogLevel(newLevel).
Welcome to
      ____              __
     / __/__  ___ _____/ /__
    _\ \/ _ \/ _ `/ __/  '_/
   /___/ .__/\_,_/_/ /_/\_\   version 3.2.0
      /_/

Using Scala version 2.13.5 (OpenJDK 64-Bit Server VM, Java 11.0.9.1)
Type in expressions to have them evaluated.
Type :help for more information.
21/12/11 19:28:36 ERROR SparkContext: Error initializing SparkContext.
java.lang.reflect.InvocationTargetException
        at java.base/jdk.internal.reflect.NativeConstructorAccessorImpl.newInstance0(Native Method)
        at java.base/jdk.internal.reflect.NativeConstructorAccessorImpl.newInstance(NativeConstructorAccessorImpl.java:62)
        at java.base/jdk.internal.reflect.DelegatingConstructorAccessorImpl.newInstance(DelegatingConstructorAccessorImpl.java:45)
        at java.base/java.lang.reflect.Constructor.newInstance(Constructor.java:490)
        at org.apache.spark.executor.Executor.addReplClassLoaderIfNeeded(Executor.scala:909)
        at org.apache.spark.executor.Executor.<init>(Executor.scala:160)
        at org.apache.spark.scheduler.local.LocalEndpoint.<init>(LocalSchedulerBackend.scala:64)
        at org.apache.spark.scheduler.local.LocalSchedulerBackend.start(LocalSchedulerBackend.scala:132)
        at org.apache.spark.scheduler.TaskSchedulerImpl.start(TaskSchedulerImpl.scala:220)
        at org.apache.spark.SparkContext.<init>(SparkContext.scala:581)
        at org.apache.spark.SparkContext$.getOrCreate(SparkContext.scala:2690)
        at org.apache.spark.sql.SparkSession$Builder.$anonfun$getOrCreate$2(SparkSession.scala:949)
        at scala.Option.getOrElse(Option.scala:201)
        at org.apache.spark.sql.SparkSession$Builder.getOrCreate(SparkSession.scala:943)
        at org.apache.spark.repl.Main$.createSparkSession(Main.scala:114)
        at $line3.$read$$iw.<init>(<console>:5)
        at $line3.$read.<init>(<console>:4)
        at $line3.$read$.<clinit>(<console>)
        at $line3.$eval$.$print$lzycompute(<synthetic>:6)
        at $line3.$eval$.$print(<synthetic>:5)
        at $line3.$eval.$print(<synthetic>)
        at java.base/jdk.internal.reflect.NativeMethodAccessorImpl.invoke0(Native Method)
        at java.base/jdk.internal.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:62)
        at java.base/jdk.internal.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43)
        at java.base/java.lang.reflect.Method.invoke(Method.java:566)
        at scala.tools.nsc.interpreter.IMain$ReadEvalPrint.call(IMain.scala:670)
        at scala.tools.nsc.interpreter.IMain$Request.loadAndRun(IMain.scala:1006)
        at scala.tools.nsc.interpreter.IMain.$anonfun$doInterpret$1(IMain.scala:506)
        at scala.reflect.internal.util.ScalaClassLoader.asContext(ScalaClassLoader.scala:36)
        at scala.reflect.internal.util.ScalaClassLoader.asContext$(ScalaClassLoader.scala:116)
        at scala.reflect.internal.util.AbstractFileClassLoader.asContext(AbstractFileClassLoader.scala:43)
        at scala.tools.nsc.interpreter.IMain.loadAndRunReq$1(IMain.scala:505)
        at scala.tools.nsc.interpreter.IMain.$anonfun$doInterpret$3(IMain.scala:519)
        at scala.tools.nsc.interpreter.IMain.doInterpret(IMain.scala:519)
        at scala.tools.nsc.interpreter.IMain.interpret(IMain.scala:503)
        at scala.tools.nsc.interpreter.IMain.interpret(IMain.scala:501)
        at scala.tools.nsc.interpreter.IMain.$anonfun$quietRun$1(IMain.scala:216)
        at scala.tools.nsc.interpreter.shell.ReplReporterImpl.withoutPrintingResults(Reporter.scala:64)
        at scala.tools.nsc.interpreter.IMain.quietRun(IMain.scala:216)
        at scala.tools.nsc.interpreter.shell.ILoop.$anonfun$interpretPreamble$1(ILoop.scala:924)
        at scala.collection.immutable.List.foreach(List.scala:333)
        at scala.tools.nsc.interpreter.shell.ILoop.interpretPreamble(ILoop.scala:924)
        at scala.tools.nsc.interpreter.shell.ILoop.$anonfun$run$3(ILoop.scala:963)
        at scala.runtime.java8.JFunction0$mcV$sp.apply(JFunction0$mcV$sp.scala:18)
        at scala.tools.nsc.interpreter.shell.ILoop.echoOff(ILoop.scala:90)
        at scala.tools.nsc.interpreter.shell.ILoop.$anonfun$run$2(ILoop.scala:963)
        at scala.runtime.java8.JFunction0$mcV$sp.apply(JFunction0$mcV$sp.scala:18)
        at scala.tools.nsc.interpreter.IMain.withSuppressedSettings(IMain.scala:1406)
        at scala.tools.nsc.interpreter.shell.ILoop.$anonfun$run$1(ILoop.scala:954)
        at scala.runtime.java8.JFunction0$mcV$sp.apply(JFunction0$mcV$sp.scala:18)
        at scala.tools.nsc.interpreter.shell.ReplReporterImpl.withoutPrintingResults(Reporter.scala:64)
        at scala.tools.nsc.interpreter.shell.ILoop.run(ILoop.scala:954)
        at org.apache.spark.repl.Main$.doMain(Main.scala:84)
        at org.apache.spark.repl.Main$.main(Main.scala:59)
        at org.apache.spark.repl.Main.main(Main.scala)
        at java.base/jdk.internal.reflect.NativeMethodAccessorImpl.invoke0(Native Method)
        at java.base/jdk.internal.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:62)
        at java.base/jdk.internal.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43)
        at java.base/java.lang.reflect.Method.invoke(Method.java:566)
        at org.apache.spark.deploy.JavaMainApplication.start(SparkApplication.scala:52)
        at org.apache.spark.deploy.SparkSubmit.org$apache$spark$deploy$SparkSubmit$$runMain(SparkSubmit.scala:955)
        at org.apache.spark.deploy.SparkSubmit.doRunMain$1(SparkSubmit.scala:180)
        at org.apache.spark.deploy.SparkSubmit.submit(SparkSubmit.scala:203)
        at org.apache.spark.deploy.SparkSubmit.doSubmit(SparkSubmit.scala:90)
        at org.apache.spark.deploy.SparkSubmit$$anon$2.doSubmit(SparkSubmit.scala:1043)
        at org.apache.spark.deploy.SparkSubmit$.main(SparkSubmit.scala:1052)
        at org.apache.spark.deploy.SparkSubmit.main(SparkSubmit.scala)
Caused by: java.net.URISyntaxException: Illegal character in path at index 42: spark://DESKTOP-JO73CF4.mshome.net:2103/C:\classes
        at java.base/java.net.URI$Parser.fail(URI.java:2913)
        at java.base/java.net.URI$Parser.checkChars(URI.java:3084)
        at java.base/java.net.URI$Parser.parseHierarchical(URI.java:3166)
        at java.base/java.net.URI$Parser.parse(URI.java:3114)
        at java.base/java.net.URI.<init>(URI.java:600)
        at org.apache.spark.repl.ExecutorClassLoader.<init>(ExecutorClassLoader.scala:57)
        ... 67 more
21/12/11 19:28:36 ERROR Utils: Uncaught exception in thread main
java.lang.NullPointerException
        at org.apache.spark.scheduler.local.LocalSchedulerBackend.org$apache$spark$scheduler$local$LocalSchedulerBackend$$stop(LocalSchedulerBackend.scala:173)
        at org.apache.spark.scheduler.local.LocalSchedulerBackend.stop(LocalSchedulerBackend.scala:144)
        at org.apache.spark.scheduler.TaskSchedulerImpl.stop(TaskSchedulerImpl.scala:927)
        at org.apache.spark.scheduler.DAGScheduler.stop(DAGScheduler.scala:2516)
        at org.apache.spark.SparkContext.$anonfun$stop$12(SparkContext.scala:2086)
        at org.apache.spark.util.Utils$.tryLogNonFatalError(Utils.scala:1442)
        at org.apache.spark.SparkContext.stop(SparkContext.scala:2086)
        at org.apache.spark.SparkContext.<init>(SparkContext.scala:677)
        at org.apache.spark.SparkContext$.getOrCreate(SparkContext.scala:2690)
        at org.apache.spark.sql.SparkSession$Builder.$anonfun$getOrCreate$2(SparkSession.scala:949)
        at scala.Option.getOrElse(Option.scala:201)
        at org.apache.spark.sql.SparkSession$Builder.getOrCreate(SparkSession.scala:943)
        at org.apache.spark.repl.Main$.createSparkSession(Main.scala:114)
        at $line3.$read$$iw.<init>(<console>:5)
        at $line3.$read.<init>(<console>:4)
        at $line3.$read$.<clinit>(<console>)
        at $line3.$eval$.$print$lzycompute(<synthetic>:6)
        at $line3.$eval$.$print(<synthetic>:5)
        at $line3.$eval.$print(<synthetic>)
        at java.base/jdk.internal.reflect.NativeMethodAccessorImpl.invoke0(Native Method)
        at java.base/jdk.internal.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:62)
        at java.base/jdk.internal.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43)
        at java.base/java.lang.reflect.Method.invoke(Method.java:566)
        at scala.tools.nsc.interpreter.IMain$ReadEvalPrint.call(IMain.scala:670)
        at scala.tools.nsc.interpreter.IMain$Request.loadAndRun(IMain.scala:1006)
        at scala.tools.nsc.interpreter.IMain.$anonfun$doInterpret$1(IMain.scala:506)
        at scala.reflect.internal.util.ScalaClassLoader.asContext(ScalaClassLoader.scala:36)
        at scala.reflect.internal.util.ScalaClassLoader.asContext$(ScalaClassLoader.scala:116)
        at scala.reflect.internal.util.AbstractFileClassLoader.asContext(AbstractFileClassLoader.scala:43)
        at scala.tools.nsc.interpreter.IMain.loadAndRunReq$1(IMain.scala:505)
        at scala.tools.nsc.interpreter.IMain.$anonfun$doInterpret$3(IMain.scala:519)
        at scala.tools.nsc.interpreter.IMain.doInterpret(IMain.scala:519)
        at scala.tools.nsc.interpreter.IMain.interpret(IMain.scala:503)
        at scala.tools.nsc.interpreter.IMain.interpret(IMain.scala:501)
        at scala.tools.nsc.interpreter.IMain.$anonfun$quietRun$1(IMain.scala:216)
        at scala.tools.nsc.interpreter.shell.ReplReporterImpl.withoutPrintingResults(Reporter.scala:64)
        at scala.tools.nsc.interpreter.IMain.quietRun(IMain.scala:216)
        at scala.tools.nsc.interpreter.shell.ILoop.$anonfun$interpretPreamble$1(ILoop.scala:924)
        at scala.collection.immutable.List.foreach(List.scala:333)
        at scala.tools.nsc.interpreter.shell.ILoop.interpretPreamble(ILoop.scala:924)
        at scala.tools.nsc.interpreter.shell.ILoop.$anonfun$run$3(ILoop.scala:963)
        at scala.runtime.java8.JFunction0$mcV$sp.apply(JFunction0$mcV$sp.scala:18)
        at scala.tools.nsc.interpreter.shell.ILoop.echoOff(ILoop.scala:90)
        at scala.tools.nsc.interpreter.shell.ILoop.$anonfun$run$2(ILoop.scala:963)
        at scala.runtime.java8.JFunction0$mcV$sp.apply(JFunction0$mcV$sp.scala:18)
        at scala.tools.nsc.interpreter.IMain.withSuppressedSettings(IMain.scala:1406)
        at scala.tools.nsc.interpreter.shell.ILoop.$anonfun$run$1(ILoop.scala:954)
        at scala.runtime.java8.JFunction0$mcV$sp.apply(JFunction0$mcV$sp.scala:18)
        at scala.tools.nsc.interpreter.shell.ReplReporterImpl.withoutPrintingResults(Reporter.scala:64)
        at scala.tools.nsc.interpreter.shell.ILoop.run(ILoop.scala:954)
        at org.apache.spark.repl.Main$.doMain(Main.scala:84)
        at org.apache.spark.repl.Main$.main(Main.scala:59)
        at org.apache.spark.repl.Main.main(Main.scala)
        at java.base/jdk.internal.reflect.NativeMethodAccessorImpl.invoke0(Native Method)
        at java.base/jdk.internal.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:62)
        at java.base/jdk.internal.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43)
        at java.base/java.lang.reflect.Method.invoke(Method.java:566)
        at org.apache.spark.deploy.JavaMainApplication.start(SparkApplication.scala:52)
        at org.apache.spark.deploy.SparkSubmit.org$apache$spark$deploy$SparkSubmit$$runMain(SparkSubmit.scala:955)
        at org.apache.spark.deploy.SparkSubmit.doRunMain$1(SparkSubmit.scala:180)
        at org.apache.spark.deploy.SparkSubmit.submit(SparkSubmit.scala:203)
        at org.apache.spark.deploy.SparkSubmit.doSubmit(SparkSubmit.scala:90)
        at org.apache.spark.deploy.SparkSubmit$$anon$2.doSubmit(SparkSubmit.scala:1043)
        at org.apache.spark.deploy.SparkSubmit$.main(SparkSubmit.scala:1052)
        at org.apache.spark.deploy.SparkSubmit.main(SparkSubmit.scala)
21/12/11 19:28:36 WARN MetricsSystem: Stopping a MetricsSystem that is not running
21/12/11 19:28:36 ERROR Main: Failed to initialize Spark session.
java.lang.reflect.InvocationTargetException
        at java.base/jdk.internal.reflect.NativeConstructorAccessorImpl.newInstance0(Native Method)
        at java.base/jdk.internal.reflect.NativeConstructorAccessorImpl.newInstance(NativeConstructorAccessorImpl.java:62)
        at java.base/jdk.internal.reflect.DelegatingConstructorAccessorImpl.newInstance(DelegatingConstructorAccessorImpl.java:45)
        at java.base/java.lang.reflect.Constructor.newInstance(Constructor.java:490)
        at org.apache.spark.executor.Executor.addReplClassLoaderIfNeeded(Executor.scala:909)
        at org.apache.spark.executor.Executor.<init>(Executor.scala:160)
        at org.apache.spark.scheduler.local.LocalEndpoint.<init>(LocalSchedulerBackend.scala:64)
        at org.apache.spark.scheduler.local.LocalSchedulerBackend.start(LocalSchedulerBackend.scala:132)
        at org.apache.spark.scheduler.TaskSchedulerImpl.start(TaskSchedulerImpl.scala:220)
        at org.apache.spark.SparkContext.<init>(SparkContext.scala:581)
        at org.apache.spark.SparkContext$.getOrCreate(SparkContext.scala:2690)
        at org.apache.spark.sql.SparkSession$Builder.$anonfun$getOrCreate$2(SparkSession.scala:949)
        at scala.Option.getOrElse(Option.scala:201)
        at org.apache.spark.sql.SparkSession$Builder.getOrCreate(SparkSession.scala:943)
        at org.apache.spark.repl.Main$.createSparkSession(Main.scala:114)
        at $line3.$read$$iw.<init>(<console>:5)
        at $line3.$read.<init>(<console>:4)
        at $line3.$read$.<clinit>(<console>)
        at $line3.$eval$.$print$lzycompute(<synthetic>:6)
        at $line3.$eval$.$print(<synthetic>:5)
        at $line3.$eval.$print(<synthetic>)
        at java.base/jdk.internal.reflect.NativeMethodAccessorImpl.invoke0(Native Method)
        at java.base/jdk.internal.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:62)
        at java.base/jdk.internal.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43)
        at java.base/java.lang.reflect.Method.invoke(Method.java:566)
        at scala.tools.nsc.interpreter.IMain$ReadEvalPrint.call(IMain.scala:670)
        at scala.tools.nsc.interpreter.IMain$Request.loadAndRun(IMain.scala:1006)
        at scala.tools.nsc.interpreter.IMain.$anonfun$doInterpret$1(IMain.scala:506)
        at scala.reflect.internal.util.ScalaClassLoader.asContext(ScalaClassLoader.scala:36)
        at scala.reflect.internal.util.ScalaClassLoader.asContext$(ScalaClassLoader.scala:116)
        at scala.reflect.internal.util.AbstractFileClassLoader.asContext(AbstractFileClassLoader.scala:43)
        at scala.tools.nsc.interpreter.IMain.loadAndRunReq$1(IMain.scala:505)
        at scala.tools.nsc.interpreter.IMain.$anonfun$doInterpret$3(IMain.scala:519)
        at scala.tools.nsc.interpreter.IMain.doInterpret(IMain.scala:519)
        at scala.tools.nsc.interpreter.IMain.interpret(IMain.scala:503)
        at scala.tools.nsc.interpreter.IMain.interpret(IMain.scala:501)
        at scala.tools.nsc.interpreter.IMain.$anonfun$quietRun$1(IMain.scala:216)
        at scala.tools.nsc.interpreter.shell.ReplReporterImpl.withoutPrintingResults(Reporter.scala:64)
        at scala.tools.nsc.interpreter.IMain.quietRun(IMain.scala:216)
        at scala.tools.nsc.interpreter.shell.ILoop.$anonfun$interpretPreamble$1(ILoop.scala:924)
        at scala.collection.immutable.List.foreach(List.scala:333)
        at scala.tools.nsc.interpreter.shell.ILoop.interpretPreamble(ILoop.scala:924)
        at scala.tools.nsc.interpreter.shell.ILoop.$anonfun$run$3(ILoop.scala:963)
        at scala.runtime.java8.JFunction0$mcV$sp.apply(JFunction0$mcV$sp.scala:18)
        at scala.tools.nsc.interpreter.shell.ILoop.echoOff(ILoop.scala:90)
        at scala.tools.nsc.interpreter.shell.ILoop.$anonfun$run$2(ILoop.scala:963)
        at scala.runtime.java8.JFunction0$mcV$sp.apply(JFunction0$mcV$sp.scala:18)
        at scala.tools.nsc.interpreter.IMain.withSuppressedSettings(IMain.scala:1406)
        at scala.tools.nsc.interpreter.shell.ILoop.$anonfun$run$1(ILoop.scala:954)
        at scala.runtime.java8.JFunction0$mcV$sp.apply(JFunction0$mcV$sp.scala:18)
        at scala.tools.nsc.interpreter.shell.ReplReporterImpl.withoutPrintingResults(Reporter.scala:64)
        at scala.tools.nsc.interpreter.shell.ILoop.run(ILoop.scala:954)
        at org.apache.spark.repl.Main$.doMain(Main.scala:84)
        at org.apache.spark.repl.Main$.main(Main.scala:59)
        at org.apache.spark.repl.Main.main(Main.scala)
        at java.base/jdk.internal.reflect.NativeMethodAccessorImpl.invoke0(Native Method)
        at java.base/jdk.internal.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:62)
        at java.base/jdk.internal.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43)
        at java.base/java.lang.reflect.Method.invoke(Method.java:566)
        at org.apache.spark.deploy.JavaMainApplication.start(SparkApplication.scala:52)
        at org.apache.spark.deploy.SparkSubmit.org$apache$spark$deploy$SparkSubmit$$runMain(SparkSubmit.scala:955)
        at org.apache.spark.deploy.SparkSubmit.doRunMain$1(SparkSubmit.scala:180)
        at org.apache.spark.deploy.SparkSubmit.submit(SparkSubmit.scala:203)
        at org.apache.spark.deploy.SparkSubmit.doSubmit(SparkSubmit.scala:90)
        at org.apache.spark.deploy.SparkSubmit$$anon$2.doSubmit(SparkSubmit.scala:1043)
        at org.apache.spark.deploy.SparkSubmit$.main(SparkSubmit.scala:1052)
        at org.apache.spark.deploy.SparkSubmit.main(SparkSubmit.scala)
Caused by: java.net.URISyntaxException: Illegal character in path at index 42: spark://DESKTOP-JO73CF4.mshome.net:2103/C:\classes
        at java.base/java.net.URI$Parser.fail(URI.java:2913)
        at java.base/java.net.URI$Parser.checkChars(URI.java:3084)
        at java.base/java.net.URI$Parser.parseHierarchical(URI.java:3166)
        at java.base/java.net.URI$Parser.parse(URI.java:3114)
        at java.base/java.net.URI.<init>(URI.java:600)
        at org.apache.spark.repl.ExecutorClassLoader.<init>(ExecutorClassLoader.scala:57)
        ... 67 more
21/12/11 19:28:36 ERROR Utils: Uncaught exception in thread shutdown-hook-0
java.lang.ExceptionInInitializerError
        at org.apache.spark.executor.Executor.stop(Executor.scala:333)
        at org.apache.spark.executor.Executor.$anonfun$stopHookReference$1(Executor.scala:76)
        at org.apache.spark.util.SparkShutdownHook.run(ShutdownHookManager.scala:214)
        at org.apache.spark.util.SparkShutdownHookManager.$anonfun$runAll$2(ShutdownHookManager.scala:188)
        at scala.runtime.java8.JFunction0$mcV$sp.apply(JFunction0$mcV$sp.scala:18)
        at org.apache.spark.util.Utils$.logUncaughtExceptions(Utils.scala:2019)
        at org.apache.spark.util.SparkShutdownHookManager.$anonfun$runAll$1(ShutdownHookManager.scala:188)
        at scala.runtime.java8.JFunction0$mcV$sp.apply(JFunction0$mcV$sp.scala:18)
        at scala.util.Try$.apply(Try.scala:210)
        at org.apache.spark.util.SparkShutdownHookManager.runAll(ShutdownHookManager.scala:188)
        at org.apache.spark.util.SparkShutdownHookManager$$anon$2.run(ShutdownHookManager.scala:178)
        at java.base/java.util.concurrent.Executors$RunnableAdapter.call(Executors.java:515)
        at java.base/java.util.concurrent.FutureTask.run(FutureTask.java:264)
        at java.base/java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1128)
        at java.base/java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:628)
        at java.base/java.lang.Thread.run(Thread.java:829)
Caused by: java.lang.NullPointerException
        at org.apache.spark.shuffle.ShuffleBlockPusher$.<clinit>(ShuffleBlockPusher.scala:465)
        ... 16 more
21/12/11 19:28:36 WARN ShutdownHookManager: ShutdownHook '' failed, java.util.concurrent.ExecutionException: java.lang.ExceptionInInitializerError
java.util.concurrent.ExecutionException: java.lang.ExceptionInInitializerError
        at java.base/java.util.concurrent.FutureTask.report(FutureTask.java:122)
        at java.base/java.util.concurrent.FutureTask.get(FutureTask.java:205)
        at org.apache.hadoop.util.ShutdownHookManager.executeShutdown(ShutdownHookManager.java:124)
        at org.apache.hadoop.util.ShutdownHookManager$1.run(ShutdownHookManager.java:95)
Caused by: java.lang.ExceptionInInitializerError
        at org.apache.spark.executor.Executor.stop(Executor.scala:333)
        at org.apache.spark.executor.Executor.$anonfun$stopHookReference$1(Executor.scala:76)
        at org.apache.spark.util.SparkShutdownHook.run(ShutdownHookManager.scala:214)
        at org.apache.spark.util.SparkShutdownHookManager.$anonfun$runAll$2(ShutdownHookManager.scala:188)
        at scala.runtime.java8.JFunction0$mcV$sp.apply(JFunction0$mcV$sp.scala:18)
        at org.apache.spark.util.Utils$.logUncaughtExceptions(Utils.scala:2019)
        at org.apache.spark.util.SparkShutdownHookManager.$anonfun$runAll$1(ShutdownHookManager.scala:188)
        at scala.runtime.java8.JFunction0$mcV$sp.apply(JFunction0$mcV$sp.scala:18)
        at scala.util.Try$.apply(Try.scala:210)
        at org.apache.spark.util.SparkShutdownHookManager.runAll(ShutdownHookManager.scala:188)
        at org.apache.spark.util.SparkShutdownHookManager$$anon$2.run(ShutdownHookManager.scala:178)
        at java.base/java.util.concurrent.Executors$RunnableAdapter.call(Executors.java:515)
        at java.base/java.util.concurrent.FutureTask.run(FutureTask.java:264)
        at java.base/java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1128)
        at java.base/java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:628)
        at java.base/java.lang.Thread.run(Thread.java:829)
Caused by: java.lang.NullPointerException
        at org.apache.spark.shuffle.ShuffleBlockPusher$.<clinit>(ShuffleBlockPusher.scala:465)
        ... 16 more

As I can see it caused by Illegal character in path at index 42: spark://DESKTOP-JO73CF4.mshome.net:2103/C:\classes, but I don't understand what does it mean exactly and how to deal with that

How can I solve this problem?

I use Spark 3.2.0 Pre-built for Apache Hadoop 3.3 and later (Scala 2.13)

JAVA_HOME, HADOOP_HOME, SPARK_HOME path variables are set.

ANSWER

Answered 2022-Jan-07 at 15:11

i face the same problem, i think Spark 3.2 is the problem itself

switched to Spark 3.1.2, it works fine

Source https://stackoverflow.com/questions/70317481

QUESTION

For function over multiple rows (i+1)?

Asked 2022-Mar-30 at 08:31

New to R, my apologies if there is an easy answer that I don't know of.

I have a dataframe with 127.124 observations and 5 variables

Head(SortedDF)

       number Retention.time..min. Charge      m.z Group
102864   6947             12.58028      5 375.0021 Pro
68971   60641             23.36693      2 375.1373 Pro
75001  104156             24.54187      3 375.1540 Pro
87435  146322             22.69630      3 375.1540 Pro
82658   88256             22.32042      3 375.1541 Pro
113553  97971             14.54600      3 375.1566 Pro
...

I want to compare every row with the row underneath it (so basically rownumber vs rownumber +1) and see if they match. After reading the For and if-else functions, I came up with this code:

for (i in 1:dim(SortedDF)) 
  if(abs(m.z[i]-m.z[i+1])<0.01 | abs(Retention.time..min.[i]-Retention.time..min.[i+1])<1 | (Charge[i]=Charge[i+1]) | Group[i]!=Group[i+1]) 
    print("Match")
  else
    print("No match")

However, this code does not work as it only prints out the first function function [1], and I'm not sure if i+1 is a thing. Is there any way to solve this not using i+1?

ANSWER

Answered 2022-Mar-30 at 08:31

library(tidyverse)

data <- tibble(x = c(1, 1, 2), y = "a")
data
#> # A tibble: 3 × 2
#>       x y    
#>   <dbl> <chr>
#> 1     1 a    
#> 2     1 a    
#> 3     2 a

same_rows <-
  data %>%
  # consider all columns
  unite(col = "all") %>%
  transmute(same_as_next_row = all == lead(all))

data %>%
  bind_cols(same_rows)
#> # A tibble: 3 × 3
#>       x y     same_as_next_row
#>   <dbl> <chr> <lgl>           
#> 1     1 a     TRUE            
#> 2     1 a     FALSE           
#> 3     2 a     NA

^{Created on 2022-03-30 by the reprex package (v2.0.0)}

library(tidyverse)

data <- tibble::tribble(
  ~id, ~number, ~Retention.time..min., ~Charge, ~m.z, ~Group,
  102864, 6947, 12.58028, 5, 375.0021, "Pro",
  68971, 60641, 23.36693, 2, 375.1373, "Pro",
  75001, 104156, 24.54187, 3, 375.1540, "Pro",
  87435, 146322, 22.69630, 3, 375.1540, "Pro",
  82658, 88256, 22.32042, 3, 375.1541, "Pro",
  113553, 97971, 14.54600, 3, 375.1566, "Pro"
)

data %>%
  mutate(
    matches_with_next_row = (abs(m.z - lead(m.z)) < 0.01) |
      (abs(Retention.time..min. - lead(Retention.time..min.)) < 1)
  )
#> # A tibble: 6 × 7
#>       id number Retention.time..min. Charge   m.z Group matches_with_next_row
#>    <dbl>  <dbl>                <dbl>  <dbl> <dbl> <chr> <lgl>                
#> 1 102864   6947                 12.6      5  375. Pro   FALSE                
#> 2  68971  60641                 23.4      2  375. Pro   FALSE                
#> 3  75001 104156                 24.5      3  375. Pro   TRUE                 
#> 4  87435 146322                 22.7      3  375. Pro   TRUE                 
#> 5  82658  88256                 22.3      3  375. Pro   TRUE                 
#> 6 113553  97971                 14.5      3  375. Pro   NA

^{Created on 2022-03-30 by the reprex package (v2.0.0)}

Source https://stackoverflow.com/questions/71673259

QUESTION

Filling up shuffle buffer (this may take a while)

Asked 2022-Mar-28 at 20:44

I have a dataset that includes video frames partially 1000 real videos and 1000 deep fake videos. each video after preprocessing phase converted to the 300 frames in other worlds I have a dataset with 300000 images with Real(0) label and 300000 images with Fake(1) label. I want to train MesoNet with this data. I used costum DataGenerator class to handle train, validation, test data with 0.8,0.1,0.1 ratios but when I run the project show this message:

Filling up shuffle buffer (this may take a while):

What can I do to solve this problem?

You can see the DataGenerator class below.

class DataGenerator(keras.utils.Sequence):
'Generates data for Keras'
def __init__(self, df, labels, batch_size =32, img_size = (224,224),
             n_classes = 2, shuffle=True):
    'Initialization'
    self.batch_size = batch_size
    self.labels = labels
    self.df = df
    self.img_size = img_size
    self.n_classes = n_classes
    self.shuffle = shuffle
    self.batch_labels = []
    self.batch_names = []
    self.on_epoch_end()

def __len__(self):
    'Denotes the number of batches per epoch'
    return int(np.floor(len(self.df) / self.batch_size))

def __getitem__(self, index):
    
    batch_index = self.indexes[index * self.batch_size : (index + 1) * self.batch_size]
    frame_paths = self.df.iloc[batch_index]["framePath"].values
    frame_label = self.df.iloc[batch_index]["label"].values

    imgs = [cv2.imread(frame) for frame in frame_paths]
    imgs = [cv2.cvtColor(img, cv2.COLOR_BGR2RGB) for img in imgs]
    imgs = [
             cv2.resize(img, self.img_size) for img in imgs if img.shape != self.img_size
             ]
    batch_imgs = np.asarray(imgs)
    labels = list(map(int, frame_label))
    y = np.array(labels)
    self.batch_labels.extend(labels)
    self.batch_names.extend([str(frame).split("\\")[-1] for frame in frame_paths])

    return (
        batch_imgs,y  
    )

def on_epoch_end(self):
    'Updates indexes after each epoch'
    self.indexes = np.arange(len(self.df))
    if self.shuffle == True:
        np.random.shuffle(self.indexes)

ANSWER

Answered 2021-Nov-10 at 14:23

Note that this is not an error, but a log message: https://github.com/tensorflow/tensorflow/blob/42b5da6659a75bfac77fa81e7242ddb5be1a576a/tensorflow/core/kernels/data/shuffle_dataset_op.cc#L138

It seems you may be choosing too large a dataset if it's taking too long: https://github.com/tensorflow/tensorflow/issues/30646

You can address this by lowering your buffer size: https://support.huawei.com/enterprise/en/doc/EDOC1100164821/2610406b/what-do-i-do-if-training-times-out-due-to-too-many-dataset-shuffle-operations

Source https://stackoverflow.com/questions/69914867

QUESTION

Designing Twitter Search - How to sort large datasets?

Asked 2022-Mar-24 at 17:25

I'm reading an article about how to design a Twitter Search. The basic idea is to map tweets based on their ids to servers where each server has the mapping

English word -> A set of tweetIds having this word

Now if we want to find all the tweets that have some word all we need is to query all servers and aggregate the results. The article casually suggests that we can also sort the results by some parameter like "popularity" but isn't that a heavy task, especially if the word is an hot word?

What is done in practice in such search systems?

Maybe some tradeoff are being used?

Thanks!

ANSWER

Answered 2022-Mar-24 at 17:25

First of all, there are two types of indexes: local and global.

A local index is stored on the same computer as tweet data. For example, you may have 10 shards and each of these shards will have its own index; like word "car" -> sorted list of tweet ids.

When search is run we will have to send the query to every server. As we don't know where the most popular tweets are. That query will ask every server to return their top results. All of these results will be collected on the same box - the one executing the user request - and that process will pick top 10 of of entire population.

Since all results are already sorted in the index itself, it is a O(1) operation to pick top 10 results from all lists - as we will be doing simple heap/watermarking on set number of tweets.

Second nice property, we can do pagination - the next query will be also sent to every box with additional data - give me top 10, with popularity below X, where X is the popularity of last tweet returned to customer.

Global index is a different beast - it does not live on the same boxes as data (it could, but does not have to). In that case, when we search for a keyword, we know exactly where to look for. And the index itself is also sorted, hence it is fast to get top 10 most popular results (or get pagination).

Since the global index returns only tweet Ids and not tweet itself, we will have to lookup tweets for every id - this is called N+1 problem - 1 query to get a list of ids and then one query for every id. There are several ways to solve this - caching and data duplication are by far most common approaches.

Source https://stackoverflow.com/questions/71588238

test_id	value
T0003	1.0
T0003	2.0
T0003	3.0
T0003	4.0

test_id	value
T0001	1.0
T0001	2.0
T0001	3.0
T0001	4.0

QUESTION

handling million of rows for lookup operation using python

Asked 2022-Mar-19 at 11:27

I am new to data handling . I need to create python program to search a record from a samplefile1 in samplefile2. i am able to achieve it but for each record out of 200 rows in samplefile1 is looped over 200 rows in samplefile2 , it took 180 seconds complete execution time.

I am looking for something to be more time efficient so that i can do this task in minimum time .

My actual Dataset size is : 9million -> samplefile1 and 9million --> samplefile2.

Here is my code using Pandas.

sample1file1 rows:

number='7777777777' subscriber-id="7777777777" rrid=0 NAPTR {order=10 preference=50 flags="U"service="sip+e2u"regexp="!^(.*)$!sip:+7777777777@ims.mnc001.mcc470.3gppnetwork.org;user=phone!"replacement=[]};
number='7777777778' subscriber-id="7777777778" rrid=0 NAPTR {order=10 preference=50 flags="U"service="sip+e2u"regexp="!^(.*)$!sip:+7777777778@ims.mnc001.mcc470.3gppnetwork.org;user=phone!"replacement=[]};
number='7777777779' subscriber-id="7777777779" rrid=0 NAPTR {order=10 preference=50 flags="U"service="sip+e2u"regexp="!^(.*)$!sip:+7777777779@ims.mnc001.mcc470.3gppnetwork.org;user=phone!"replacement=[]};
.........100 rows

samplefile2 rows

number='7777777777' subscriber-id="7777777777" rrid=0 NAPTR {order=10 preference=50 flags="U"service="sip+e2u"regexp="!^(.*)$!sip:+7777777777@ims.mnc001.mcc470.3gppnetwork.org;user=phone!"replacement=[]};
number='7777777778' subscriber-id="7777777778" rrid=0 NAPTR {order=10 preference=50 flags="U"service="sip+e2u"regexp="!^(.*)$!sip:+7777777778@ims.mnc001.mcc470.3gppnetwork.org;user=phone!"replacement=[]};
number='7777777769' subscriber-id="7777777779" rrid=0 NAPTR {order=10 preference=50 flags="U"service="sip+e2u"regexp="!^(.*)$!sip:+7777777779@ims.mnc001.mcc470.3gppnetwork.org;user=phone!"replacement=[]};
........100 rows

import time
import pandas as pd

def timeit(func):
    """
    Decorator for measuring function's running time.
    """
    def measure_time(*args, **kw):
        start_time = time.time()
        result = func(*args, **kw)
        print("Processing time of %s(): %.2f seconds."
              % (func.__qualname__, time.time() - start_time))
        return result

    return measure_time

@timeit
def func():
    df = pd.read_csv("sample_2.txt", names=["A1"], skiprows=0, sep=';')
    df.drop(df.filter(regex="Unname"),axis=1, inplace=True)
    finaldatafile1=df.fillna("TrackRow")
    
    df1=pd.read_csv("sample_1.txt",names=["A1"],skiprows=0,sep=';')
    df1.drop(df.filter(regex="Unname"),axis=1, inplace=True)
    finaldatafile2=df1.fillna("TrackRow")
    indexdf=df.index
    indexdf1=df1.index
    ##### for loop for string to be matched (small datasets#######
    for i in range(0,len(indexdf)-1):
        lookup_value=finaldatafile1.iloc[[i]].to_string()
       # print(lookup_value)
    ######### for loop for lookup dataset( large dataset #########
        for j in range(0,len(indexdf1)-1):
            match_value=finaldatafile2.iloc[[j]].to_string()
            if i is j:
                print (f"Its a match on lookup table position {j} and for string {lookup_value}")
            else:
                print("no match found in complete dataset")
if __name__ == "__main__":
      func()

ANSWER

Answered 2022-Mar-19 at 11:27

I don't think using Pandas is helping here as you are just comparing whole lines. An alternative approach would be to load the first file as a set of lines. Then enumerate over the lines in the second file testing if it is in the set. This will be much faster:

@timeit
def func():
    with open('sample_1.txt') as f_sample1:
        data1 = set(f_sample1.read().splitlines())
    
    with open('sample_2.txt') as f_sample2:
        data2 = f_sample2.read().splitlines()
        
    for index, entry in enumerate(data2):
        if entry in data1:
            print(f"It's a match on lookup table position {index} and for string\n{entry}")
        else:
            print("no match found in complete dataset")

Source https://stackoverflow.com/questions/71526523

QUESTION

split function does not return any observations with large dataset

Asked 2022-Mar-12 at 22:29

I have a dataframe like this:

seqnames       pos     strand    nucleotide     count
    id1         12        +          A            13
    id1         13        +          C            25
    id2         24        +          G            10
    id2         25        +          T            25
    id2         26        +          A            10
    id3         10        +          C            5

But it has more than 100,000 rows in total, seqnames has 3138 levels. I would like to split it into lists of dataframes according to seqnames, so I used split function:

data_list <- split(data,data$seqnames)

But it only returns something like this:

List of 3138
 $ id1:'data.frame':    0 obs. of  6 variables:
  ..$ seqnames  : Factor w/ 3138 levels "id1","id2",..: 
  ..$ pos       : int(0) 
  ..$ strand    : Factor w/ 3 levels "+","-","*": 
  ..$ nucleotide: Factor w/ 8 levels "A","C","G","T",..: 
  ..$ count     : int(0) 
  ..$ sample_id : chr(0) 
 $ id2:'data.frame':    0 obs. of  6 variables:
  ..$ seqnames  : Factor w/ 3138 levels "id1","id2",..: 
  ..$ pos       : int(0) 
  ..$ strand    : Factor w/ 3 levels "+","-","*": 
  ..$ nucleotide: Factor w/ 8 levels "A","C","G","T",..: 
  ..$ count     : int(0) 
  ..$ sample_id : chr(0)

I can't figure out why it is like this because I have used it on a made up dataframe with all numbers (of course, not as many rows as this one) and it works. How can I solve this problem?

ANSWER

Answered 2022-Mar-12 at 22:29

It is just that there are many unused levels as the column 'seqnames' is a factor. With split, there is an option to drop (drop = TRUE - by default it is FALSE) to remove those list elements. Otherwise, they will return as data.frame with 0 rows. If we want those elements to be replaced by NULL, then find those elements where the number of rows (nrow) are 0 and assign it to NULL

data_list <- split(data,data$seqnames)
> str(data_list)
List of 5
 $ id1:'data.frame':    2 obs. of  5 variables:
  ..$ seqnames  : Factor w/ 5 levels "id1","id2","id3",..: 1 1
  ..$ pos       : int [1:2] 12 13
  ..$ strand    : chr [1:2] "+" "+"
  ..$ nucleotide: chr [1:2] "A" "C"
  ..$ count     : int [1:2] 13 25
 $ id2:'data.frame':    3 obs. of  5 variables:
  ..$ seqnames  : Factor w/ 5 levels "id1","id2","id3",..: 2 2 2
  ..$ pos       : int [1:3] 24 25 26
  ..$ strand    : chr [1:3] "+" "+" "+"
  ..$ nucleotide: chr [1:3] "G" "T" "A"
  ..$ count     : int [1:3] 10 25 10
 $ id3:'data.frame':    1 obs. of  5 variables:
  ..$ seqnames  : Factor w/ 5 levels "id1","id2","id3",..: 3
  ..$ pos       : int 10
  ..$ strand    : chr "+"
  ..$ nucleotide: chr "C"
  ..$ count     : int 5
 $ id4:'data.frame':    0 obs. of  5 variables:
  ..$ seqnames  : Factor w/ 5 levels "id1","id2","id3",..: 
  ..$ pos       : int(0) 
  ..$ strand    : chr(0) 
  ..$ nucleotide: chr(0) 
  ..$ count     : int(0) 
 $ id5:'data.frame':    0 obs. of  5 variables:
  ..$ seqnames  : Factor w/ 5 levels "id1","id2","id3",..: 
  ..$ pos       : int(0) 
  ..$ strand    : chr(0) 
  ..$ nucleotide: chr(0) 
  ..$ count     : int(0)

Doing the assignment to NULL

data_list[sapply(data_list, nrow) == 0] <- list(NULL)

-check again

> str(data_list)
List of 5
 $ id1:'data.frame':    2 obs. of  5 variables:
  ..$ seqnames  : Factor w/ 5 levels "id1","id2","id3",..: 1 1
  ..$ pos       : int [1:2] 12 13
  ..$ strand    : chr [1:2] "+" "+"
  ..$ nucleotide: chr [1:2] "A" "C"
  ..$ count     : int [1:2] 13 25
 $ id2:'data.frame':    3 obs. of  5 variables:
  ..$ seqnames  : Factor w/ 5 levels "id1","id2","id3",..: 2 2 2
  ..$ pos       : int [1:3] 24 25 26
  ..$ strand    : chr [1:3] "+" "+" "+"
  ..$ nucleotide: chr [1:3] "G" "T" "A"
  ..$ count     : int [1:3] 10 25 10
 $ id3:'data.frame':    1 obs. of  5 variables:
  ..$ seqnames  : Factor w/ 5 levels "id1","id2","id3",..: 3
  ..$ pos       : int 10
  ..$ strand    : chr "+"
  ..$ nucleotide: chr "C"
  ..$ count     : int 5
 $ id4: NULL
 $ id5: NULL

data

data <- structure(list(seqnames = structure(c(1L, 1L, 2L, 2L, 2L, 
3L), .Label = c("id1", 
"id2", "id3", "id4", "id5"), class = "factor"), pos = c(12L, 
13L, 24L, 25L, 26L, 10L), strand = c("+", "+", "+", "+", "+", 
"+"), nucleotide = c("A", "C", "G", "T", "A", "C"), count = c(13L, 
25L, 10L, 25L, 10L, 5L)), row.names = c(NA, -6L), class = "data.frame")

Source https://stackoverflow.com/questions/71453084

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