I have to read in files from vendors, that can get potentially pretty big (multiple GB). These files may have multiple header and footer rows I want to strip off.
Reading the file in is easy:
val rawData = spark.read
.format("csv")
.option("delimiter","|")
.option("mode","PERMISSIVE")
.schema(schema)
.load("/path/to/file.csv")
I can add a simple row number using monotonically_increasing_id:
val withRN = rawData.withColumn("aIndex",monotonically_increasing_id())
That seems to work fine.
I can easily use that to strip off header rows:
val noHeader = withRN.filter($"aIndex".geq(2))
but how can I strip off footer rows?
I was thinking about getting the max of the index column, and using that as a filter, but I can't make that work.
val MaxRN = withRN.agg(max($"aIndex")).first.toString
val noFooter = noHeader.filter($"aIndex".leq(MaxRN))
That returns no rows, because MaxRN is a string.
If I try to convert it to a long, that fails:
noHeader.filter($"aIndex".leq(MaxRN.toLong))
java.lang.NumberFormatException: For input string: "[100000]"
How can I use that max value in a filter?
Is trying to use monotonically_increasing_id like this even a viable approach? Is it really deterministic?
This happens because first will return a Row. To access the first element of the row you must do:
val MaxRN = withRN.agg(max($"aIndex")).first.getLong(0)
By converting the row to string you will get [100000] and of course this is not a valid Long that's why the casting is failing.
I am trying to filter the good and bad rows by counting the number of delimiters in a TSV.gz file and write to separate files in HDFS
I ran the below commands in spark-shell
Spark Version: 1.6.3
val file = sc.textFile("/abc/abc.tsv.gz")
val data = file.map(line => line.split("\t"))
var good = data.filter(a => a.size == 995)
val bad = data.filter(a => a.size < 995)
When I checked the first record the value could be seen in the spark shell
good.first()
But when I try to write to an output file I am seeing the below records,
good.saveAsTextFile(good.tsv)
Output in HDFS (top 2 rows):
[Ljava.lang.String;#1287b635
[Ljava.lang.String;#2ef89922
Could ypu please let me know on how to get the required output file in HDFS
Thanks.!
Your final RDD is type of org.apache.spark.rdd.RDD[Array[String]]. Which leads to writing objects instead of string values in the write operation.
You should convert the array of strings to tab separated string values again before saving. Just try;
good.map(item => item.mkString("\t")).saveAsTextFile("goodFile.tsv")
Elaborated scenario -> HDFS directory which is "fed" with new log data of multiple types of bank accounts activity.
Each row represents a random activity type, and each row (String) contains the text "ActivityType=<TheTypeHere>".
In Spark-Scala, what's the best approach to read the input file/s in the HDFS directory and output multiple HDFS files, where each ActivityType is written to its own new file?
Adapted first answer to the statement:
The location of the "key" string is random within the parent String,
the only thing that is guaranteed is that it contains that sub-string,
in this case "ActivityType" followed by some val.
The question is really about this. Here goes:
// SO Question
val rdd = sc.textFile("/FileStore/tables/activitySO.txt")
val rdd2 = rdd.map(x => (x.slice (x.indexOfSlice("ActivityType=<")+14, x.indexOfSlice(">", (x.indexOfSlice("ActivityType=<")+14))), x))
val df = rdd2.toDF("K", "V")
df.write.partitionBy("K").text("SO_QUESTION2")
Input is:
ActivityType=<ACT_001>,34,56,67,89,90
3,4,4,ActivityType=<ACT_002>,A,1,2
ABC,ActivityType=<ACT_0033>
DEF,ActivityType=<ACT_0033>
Output is 3 files whereby the key is e.g. not ActivityType=, but rather ACT_001, etc. The key data is not stripped, it is still there in the String. You can modify that if you want as well as output location and format.
You can use MultipleOutputFormat for this.Convert rdd into key value pairs such that ActivityType is the key.Spark will create different files for different keys.You can decide based on the key where to place the files and what their names will be.
You can do something like this using RDDs whereby I assume you have variable length files and then converting to DFs:
val rdd = sc.textFile("/FileStore/tables/activity.txt")
val rdd2 = rdd.map(_.split(","))
.keyBy(_(0))
val rdd3 = rdd2.map(x => (x._1, x._2.mkString(",")))
val df = rdd3.toDF("K", "V")
//df.show(false)
df.write.partitionBy("K").text("SO_QUESTION")
Input is:
ActivityType=<ACT_001>,34,56,67,89,90
ActivityType=<ACT_002>,A,1,2
ActivityType=<ACT_003>,ABC
I get then as output 3 files, in this case 1 for each record. A bit hard to show as did it in Databricks.
You can adjust your output format and location, etc. partitionBy is the key here.
I have a file of RDD of Strings in the format :
78656|twitterId:14|Hi|2010-05-19
I want to convert this RDD[String] to a JSON file in the format :
"78656","twitterId:14","Hi","2010-05-19"
I am currently using the code :
tweetTable.toJSON.saveAsTextFile("Gaga")
to convert it to a JSON File but the data is in the format :
"_1":"78656|twitterId:14|Hi|2010-05-19"
Can someone please help me with this?
Based on what you have in the question, you can do something like this:
value <- "78656|twitterId:14|Hi|2010-05-19"
valueList <- strsplit(value, split ="\\|")
library("jsonlite")
res <- toJSON(valueList)
I'm trying to extract data from an RDD[string] into another RDD[string]
the RDD contains data similar to this :
17.808 15.749 6.649 -0.548 15.9994
I need to multiply 4th and 5th fields of each row and store them into a different RDD[string].
I can use the following code to pull out one field
ansRDD = rawRDD(._split(" ")(4)).(_.toFloat)
rawRDD contains the string.
But I need to pull out both the fields into a single RDD as
-0.548 15.9994
so that I can simply do
answer = ansRDD.foreach(case(a,b) => a*b)
You could use:
rawRDD.map(_.split(' ').view(4, 6).map(_.toFloat).reduce(_*_).toString)
You could define ansRDD as:
ansRDD = rawRD.map(item => {val comps=item.split(" "); (comps(3),comps(4)})