SZT-bigdata | Shenzhen Metro Big Data Passenger Flow Analysis System🚇🚄🌟
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QUESTION
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 url9About 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 pairRDD = spark.sql(sql).coalesce(1).javaRDD().mapToPair((PairFunction) 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 12:43If 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"))
QUESTION
Here is the scenario. Assuming I have the following table:
identifier line 51169081604 2 00034886044 22 51168939455 52The 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 20:21Using 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.
QUESTION
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 13:18For 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)
)
);
QUESTION
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.(Executor.scala:160)
at org.apache.spark.scheduler.local.LocalEndpoint.(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.(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.(:5)
at $line3.$read.(:4)
at $line3.$read$.()
at $line3.$eval$.$print$lzycompute(:6)
at $line3.$eval$.$print(:5)
at $line3.$eval.$print()
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$$anon$2.doSubmit(SparkSubmit.scala:1043)
at org.apache.spark.deploy.SparkSubmit$.main(SparkSubmit.scala:1052)
at org.apache.spark.deploy.SparkSubmit.main(SparkSubmit.scala)
Caused by: java.net.URISyntaxException: Illegal character in path at index 42: spark://DESKTOP-JO73CF4.mshome.net:2103/C:\classes
at java.base/java.net.URI$Parser.fail(URI.java:2913)
at java.base/java.net.URI$Parser.checkChars(URI.java:3084)
at java.base/java.net.URI$Parser.parseHierarchical(URI.java:3166)
at java.base/java.net.URI$Parser.parse(URI.java:3114)
at java.base/java.net.URI.(URI.java:600)
at org.apache.spark.repl.ExecutorClassLoader.(ExecutorClassLoader.scala:57)
... 67 more
21/12/11 19:28:36 ERROR Utils: Uncaught exception in thread main
java.lang.NullPointerException
at org.apache.spark.scheduler.local.LocalSchedulerBackend.org$apache$spark$scheduler$local$LocalSchedulerBackend$$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.(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.(:5)
at $line3.$read.(:4)
at $line3.$read$.()
at $line3.$eval$.$print$lzycompute(:6)
at $line3.$eval$.$print(:5)
at $line3.$eval.$print()
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$$anon$2.doSubmit(SparkSubmit.scala:1043)
at org.apache.spark.deploy.SparkSubmit$.main(SparkSubmit.scala:1052)
at org.apache.spark.deploy.SparkSubmit.main(SparkSubmit.scala)
21/12/11 19:28:36 WARN MetricsSystem: Stopping a MetricsSystem that is not running
21/12/11 19:28:36 ERROR Main: Failed to initialize Spark session.
java.lang.reflect.InvocationTargetException
at java.base/jdk.internal.reflect.NativeConstructorAccessorImpl.newInstance0(Native Method)
at java.base/jdk.internal.reflect.NativeConstructorAccessorImpl.newInstance(NativeConstructorAccessorImpl.java:62)
at java.base/jdk.internal.reflect.DelegatingConstructorAccessorImpl.newInstance(DelegatingConstructorAccessorImpl.java:45)
at java.base/java.lang.reflect.Constructor.newInstance(Constructor.java:490)
at org.apache.spark.executor.Executor.addReplClassLoaderIfNeeded(Executor.scala:909)
at org.apache.spark.executor.Executor.(Executor.scala:160)
at org.apache.spark.scheduler.local.LocalEndpoint.(LocalSchedulerBackend.scala:64)
at org.apache.spark.scheduler.local.LocalSchedulerBackend.start(LocalSchedulerBackend.scala:132)
at org.apache.spark.scheduler.TaskSchedulerImpl.start(TaskSchedulerImpl.scala:220)
at org.apache.spark.SparkContext.(SparkContext.scala:581)
at org.apache.spark.SparkContext$.getOrCreate(SparkContext.scala:2690)
at org.apache.spark.sql.SparkSession$Builder.$anonfun$getOrCreate$2(SparkSession.scala:949)
at scala.Option.getOrElse(Option.scala:201)
at org.apache.spark.sql.SparkSession$Builder.getOrCreate(SparkSession.scala:943)
at org.apache.spark.repl.Main$.createSparkSession(Main.scala:114)
at $line3.$read$$iw.(:5)
at $line3.$read.(:4)
at $line3.$read$.()
at $line3.$eval$.$print$lzycompute(:6)
at $line3.$eval$.$print(:5)
at $line3.$eval.$print()
at java.base/jdk.internal.reflect.NativeMethodAccessorImpl.invoke0(Native Method)
at java.base/jdk.internal.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:62)
at java.base/jdk.internal.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43)
at java.base/java.lang.reflect.Method.invoke(Method.java:566)
at scala.tools.nsc.interpreter.IMain$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.(URI.java:600)
at org.apache.spark.repl.ExecutorClassLoader.(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$$anon$2.run(ShutdownHookManager.scala:178)
at java.base/java.util.concurrent.Executors$RunnableAdapter.call(Executors.java:515)
at java.base/java.util.concurrent.FutureTask.run(FutureTask.java:264)
at java.base/java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1128)
at java.base/java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:628)
at java.base/java.lang.Thread.run(Thread.java:829)
Caused by: java.lang.NullPointerException
at org.apache.spark.shuffle.ShuffleBlockPusher$.(ShuffleBlockPusher.scala:465)
... 16 more
21/12/11 19:28:36 WARN ShutdownHookManager: ShutdownHook '' failed, java.util.concurrent.ExecutionException: java.lang.ExceptionInInitializerError
java.util.concurrent.ExecutionException: java.lang.ExceptionInInitializerError
at java.base/java.util.concurrent.FutureTask.report(FutureTask.java:122)
at java.base/java.util.concurrent.FutureTask.get(FutureTask.java:205)
at org.apache.hadoop.util.ShutdownHookManager.executeShutdown(ShutdownHookManager.java:124)
at org.apache.hadoop.util.ShutdownHookManager$1.run(ShutdownHookManager.java:95)
Caused by: java.lang.ExceptionInInitializerError
at org.apache.spark.executor.Executor.stop(Executor.scala:333)
at org.apache.spark.executor.Executor.$anonfun$stopHookReference$1(Executor.scala:76)
at org.apache.spark.util.SparkShutdownHook.run(ShutdownHookManager.scala:214)
at org.apache.spark.util.SparkShutdownHookManager.$anonfun$runAll$2(ShutdownHookManager.scala:188)
at scala.runtime.java8.JFunction0$mcV$sp.apply(JFunction0$mcV$sp.scala:18)
at org.apache.spark.util.Utils$.logUncaughtExceptions(Utils.scala:2019)
at org.apache.spark.util.SparkShutdownHookManager.$anonfun$runAll$1(ShutdownHookManager.scala:188)
at scala.runtime.java8.JFunction0$mcV$sp.apply(JFunction0$mcV$sp.scala:18)
at scala.util.Try$.apply(Try.scala:210)
at org.apache.spark.util.SparkShutdownHookManager.runAll(ShutdownHookManager.scala:188)
at org.apache.spark.util.SparkShutdownHookManager$$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$Worker.run(ThreadPoolExecutor.java:628)
at java.base/java.lang.Thread.run(Thread.java:829)
Caused by: java.lang.NullPointerException
at org.apache.spark.shuffle.ShuffleBlockPusher$.(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 15:11i face the same problem, i think Spark 3.2 is the problem itself
switched to Spark 3.1.2, it works fine
QUESTION
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 08:31library(tidyverse)
data <- tibble(x = c(1, 1, 2), y = "a")
data
#> # A tibble: 3 × 2
#> x y
#>
#> 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
#>
#> 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
#>
#> 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)
QUESTION
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 14:23Note 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
QUESTION
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 17:25First 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.
QUESTION
I'm trying to optimise my query for when an internal customer only want to return one result *(and it's associated nested dataset). My aim is to reduce the query process size.
However, it appears to be the exact same value regardless of whether I'm querying for 1 record (with unnested 48,000 length array) or the whole dataset (10,000 records with unnest total 514,048,748 in total length of arrays)!
So my table results for one record query:
SELECT test_id, value
FROM , unnest(TimeSeries)timeseries
WHERE test_id= "T0003" and SignalName = "Distance";
looks like this:
test_id value T0003 1.0 T0003 2.0 T0003 3.0 T0003 4.0(48000 rows)
This will continue until value (Distance) = 48000m (48000 rows) for 1 record: WHERE == 'T0003.
Total process was 3.84GB
For whole table (~10,000 records):
SELECT test_id, value
FROM , unnest(TimeSeries)timeseries
WHERE SignalName = "Distance";
looks like this:
test_id value T0001 1.0 T0001 2.0 T0001 3.0 T0001 4.0(514,048,748 rows)
Total process was 3.84GB
Why are the process size the same for both queries and how can I optimise this for singular row extractions?
ANSWER
Answered 2022-Mar-24 at 11:45This is happening because there is still need for a full table scan to find all the test IDs that are equal to the specified one.
It is not clear from your example which columns are part of the timeseries record. In case test_id is not one of them, I would suggest to cluster the table on the test_id column. By clustering, the data will be automatically organized according to the contents of the test_id column.
So, when you query with a filter on that column a full scan won't be needed to find all values.
Read more about clustered tables here.
QUESTION
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 11:27I 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")
QUESTION
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 22:29It 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 <- 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")
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