I'm trying to combine 3 files present in HDFS through scala. All the 3 files are present in the HDFS location srcPath as mentioned in the code below.
Created a function as below:
def mergeFiles(conf: Configuration, fs: FileSystem, srcPath: Path, dstPath: String, finalFileName: String): Unit {
val localfs = FileSystem.getLocal(conf)
val status = fs.listStatus(srcPath)
status.foreach(x =>
FileUtil.copyMerge(fs, x.getPath, localfs, new Path(dstPath.toString), false, conf, null)
}
I tried executing this, no result, no error, also no file gets created even.
I verified that I'm passing all the arguments properly.
Any clues?
The second argument of copyMerge is a directory, not an individual file.
This should work:
FileUtil.copyMerge(fs, srcPath, localfs, new Path(dstPath.toString), false, conf, null)
Usually reading the source code is the best way to debug such issues.
FileUtil#copyMerge method has been removed. See details for the major change:
https://issues.apache.org/jira/browse/HADOOP-12967
https://issues.apache.org/jira/browse/HADOOP-11392
You can use getmerge
Usage: hadoop fs -getmerge [-nl]
Related
Currently I have a configuration file like this:
project {
inputs {
baseFile {
paths = ["project/src/test/resources/inputs/parquet1/date=2020-11-01/"]
type = parquet
applyConversions = false
}
}
}
And I want to change the date "2020-11-01" to another one during run time. I read I need a new config object since it's immutable, I'm trying this but I'm not quite sure how to edit paths since it's a list and not a String and it definitely needs to be a list or else it's going to say I haven't configured a path for the parquet.
val newConfig = config.withValue("project.inputs.baseFile.paths"(0),
ConfigValueFactory.fromAnyRef("project/src/test/resources/inputs/parquet1/date=2020-10-01/"))
But I'm getting a:
Error com.typesafe.config.ConfigException$BadPath: path parameter: Invalid path 'project.inputs.baseFile.': path has a leading, trailing, or two adjacent period '.' (use quoted "" empty string if you want an empty element)
What's the correct way to set the new path?
One option you have, is to override the entire array:
import scala.collection.JavaConverters._
val mergedConfig = config.withValue("project.inputs.baseFile.paths",
ConfigValueFactory.fromAnyRef(Seq("project/src/test/resources/inputs/parquet1/date=2020-10-01/").asJava))
But a more elegant way to do this (IMHO), is to create a new config, and to use the existing as a fallback.
For example, we can create a new config:
val newJsonString = """project {
|inputs {
|baseFile {
| paths = ["project/src/test/resources/inputs/parquet1/date=2020-10-01/"]
|}}}""".stripMargin
val newConfig = ConfigFactory.parseString(newJsonString)
And now to merge them:
val mergedConfig = newConfig.withFallback(config)
The output of:
println(mergedConfig.getList("project.inputs.baseFile.paths"))
println(mergedConfig.getString("project.inputs.baseFile.type"))
is:
SimpleConfigList(["project/src/test/resources/inputs/parquet1/date=2020-10-01/"])
parquet
As expected.
You can read more about Merging config trees. Code run at Scastie.
I didn't find any way to replace one element of the array with withValue.
I am using Scala and Apache Flink to build an ETL that reads all the files under a directory in my local file system periodically and write the result of processing each file in a single output file under another directory.
So an example of this is would be:
/dir/to/input/files/file1
/dir/to/intput/files/fil2
/dir/to/input/files/file3
and the output of the ETL would be exactly:
/dir/to/output/files/file1
/dir/to/output/files/file2
/dir/to/output/files/file3
I have tried various approaches including reducing the parallel processing to one when writing to the dataSink but I still can't achieve the required result.
This is my current code:
val path = "/path/to/input/files/"
val format = new TextInputFormat(new Path(path))
val socketStream = env.readFile(format, path, FileProcessingMode.PROCESS_CONTINUOUSLY, 10)
val wordsStream = socketStream.flatMap(value => value.split(",")).map(value => WordWithCount(value,1))
val keyValuePair = wordsStream.keyBy(_.word)
val countPair = keyValuePair.sum("count")
countPair.print()
countPair.writeAsText("/path/to/output/directory/"+
DateTime.now().getHourOfDay.toString
+
DateTime.now().getMinuteOfHour.toString
+
DateTime.now().getSecondOfMinute.toString
, FileSystem.WriteMode.NO_OVERWRITE)
// The first write method I trid:
val sink = new BucketingSink[WordWithCount]("/path/to/output/directory/")
sink.setBucketer(new DateTimeBucketer[WordWithCount]("yyyy-MM-dd--HHmm"))
// The second write method I trid:
val sink3 = new BucketingSink[WordWithCount]("/path/to/output/directory/")
sink3.setUseTruncate(false)
sink3.setBucketer(new DateTimeBucketer("yyyy-MM-dd--HHmm"))
sink3.setWriter(new StringWriter[WordWithCount])
sink3.setBatchSize(3)
sink3.setPendingPrefix("file-")
sink3.setPendingSuffix(".txt")
Both writing methods fail in producing the wanted result.
Can some with experience with Apache Flink guide me to the write approach please.
I solved this issue importing the next dependencies to run on local machine:
hadoop-aws-2.7.3.jar
aws-java-sdk-s3-1.11.183.jar
aws-java-sdk-core-1.11.183.jar
aws-java-sdk-kms-1.11.183.jar
jackson-annotations-2.6.7.jar
jackson-core-2.6.7.jar
jackson-databind-2.6.7.jar
joda-time-2.8.1.jar
httpcore-4.4.4.jar
httpclient-4.5.3.jar
You can review it on :
https://ci.apache.org/projects/flink/flink-docs-stable/ops/deployment/aws.html
Section "Provide S3 FileSystem Dependency"
I have a Vagrant image with Spark Notebook, Spark, Accumulo 1.6, and Hadoop all running. From notebook, I can manually create a Scanner and pull test data from a table I created using one of the Accumulo examples:
val instanceNameS = "accumulo"
val zooServersS = "localhost:2181"
val instance: Instance = new ZooKeeperInstance(instanceNameS, zooServersS)
val connector: Connector = instance.getConnector( "root", new PasswordToken("password"))
val auths = new Authorizations("exampleVis")
val scanner = connector.createScanner("batchtest1", auths)
scanner.setRange(new Range("row_0000000000", "row_0000000010"))
for(entry: Entry[Key, Value] <- scanner) {
println(entry.getKey + " is " + entry.getValue)
}
will give the first ten rows of table data.
When I try to create the RDD thusly:
val rdd2 =
sparkContext.newAPIHadoopRDD (
new Configuration(),
classOf[org.apache.accumulo.core.client.mapreduce.AccumuloInputFormat],
classOf[org.apache.accumulo.core.data.Key],
classOf[org.apache.accumulo.core.data.Value]
)
I get an RDD returned to me that I can't do much with due to the following error:
java.io.IOException: Input info has not been set. at
org.apache.accumulo.core.client.mapreduce.lib.impl.InputConfigurator.validateOptions(InputConfigurator.java:630)
at
org.apache.accumulo.core.client.mapreduce.AbstractInputFormat.validateOptions(AbstractInputFormat.java:343)
at
org.apache.accumulo.core.client.mapreduce.AbstractInputFormat.getSplits(AbstractInputFormat.java:538)
at
org.apache.spark.rdd.NewHadoopRDD.getPartitions(NewHadoopRDD.scala:98)
at
org.apache.spark.rdd.RDD$$anonfun$partitions$2.apply(RDD.scala:222)
at
org.apache.spark.rdd.RDD$$anonfun$partitions$2.apply(RDD.scala:220)
at scala.Option.getOrElse(Option.scala:120) at
org.apache.spark.rdd.RDD.partitions(RDD.scala:220) at
org.apache.spark.SparkContext.runJob(SparkContext.scala:1367) at
org.apache.spark.rdd.RDD.count(RDD.scala:927)
This totally makes sense in light of the fact that I haven't specified any parameters as to which table to connect with, what the auths are, etc.
So my question is: What do I need to do from here to get those first ten rows of table data into my RDD?
update one
Still doesn't work, but I did discover a few things. Turns out there are two nearly identical packages,
org.apache.accumulo.core.client.mapreduce
&
org.apache.accumulo.core.client.mapred
both have nearly identical members, except for the fact that some of the method signatures are different. not sure why both exist as there's no deprecation notice that I could see. I attempted to implement Sietse's answer with no joy. Below is what I did, and the responses:
import org.apache.hadoop.mapred.JobConf
import org.apache.hadoop.conf.Configuration
val jobConf = new JobConf(new Configuration)
import org.apache.hadoop.mapred.JobConf import
org.apache.hadoop.conf.Configuration jobConf:
org.apache.hadoop.mapred.JobConf = Configuration: core-default.xml,
core-site.xml, mapred-default.xml, mapred-site.xml, yarn-default.xml,
yarn-site.xml
Configuration: core-default.xml, core-site.xml, mapred-default.xml,
mapred-site.xml, yarn-default.xml, yarn-site.xml
AbstractInputFormat.setConnectorInfo(jobConf,
"root",
new PasswordToken("password")
AbstractInputFormat.setScanAuthorizations(jobConf, auths)
AbstractInputFormat.setZooKeeperInstance(jobConf, new ClientConfiguration)
val rdd2 =
sparkContext.hadoopRDD (
jobConf,
classOf[org.apache.accumulo.core.client.mapred.AccumuloInputFormat],
classOf[org.apache.accumulo.core.data.Key],
classOf[org.apache.accumulo.core.data.Value],
1
)
rdd2: org.apache.spark.rdd.RDD[(org.apache.accumulo.core.data.Key,
org.apache.accumulo.core.data.Value)] = HadoopRDD[1] at hadoopRDD at
:62
rdd2.first
java.io.IOException: Input info has not been set. at
org.apache.accumulo.core.client.mapreduce.lib.impl.InputConfigurator.validateOptions(InputConfigurator.java:630)
at
org.apache.accumulo.core.client.mapred.AbstractInputFormat.validateOptions(AbstractInputFormat.java:308)
at
org.apache.accumulo.core.client.mapred.AbstractInputFormat.getSplits(AbstractInputFormat.java:505)
at org.apache.spark.rdd.HadoopRDD.getPartitions(HadoopRDD.scala:201)
at
org.apache.spark.rdd.RDD$$anonfun$partitions$2.apply(RDD.scala:222)
at
org.apache.spark.rdd.RDD$$anonfun$partitions$2.apply(RDD.scala:220)
at scala.Option.getOrElse(Option.scala:120) at
org.apache.spark.rdd.RDD.partitions(RDD.scala:220) at
org.apache.spark.rdd.RDD.take(RDD.scala:1077) at
org.apache.spark.rdd.RDD.first(RDD.scala:1110) at
$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC.(:64)
at
$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC.(:69)
at...
* edit 2 *
re: Holden's answer - still no joy:
AbstractInputFormat.setConnectorInfo(jobConf,
"root",
new PasswordToken("password")
AbstractInputFormat.setScanAuthorizations(jobConf, auths)
AbstractInputFormat.setZooKeeperInstance(jobConf, new ClientConfiguration)
InputFormatBase.setInputTableName(jobConf, "batchtest1")
val rddX = sparkContext.newAPIHadoopRDD(
jobConf,
classOf[org.apache.accumulo.core.client.mapreduce.AccumuloInputFormat],
classOf[org.apache.accumulo.core.data.Key],
classOf[org.apache.accumulo.core.data.Value]
)
rddX: org.apache.spark.rdd.RDD[(org.apache.accumulo.core.data.Key,
org.apache.accumulo.core.data.Value)] = NewHadoopRDD[0] at
newAPIHadoopRDD at :58
Out[15]: NewHadoopRDD[0] at newAPIHadoopRDD at :58
rddX.first
java.io.IOException: Input info has not been set. at
org.apache.accumulo.core.client.mapreduce.lib.impl.InputConfigurator.validateOptions(InputConfigurator.java:630)
at
org.apache.accumulo.core.client.mapreduce.AbstractInputFormat.validateOptions(AbstractInputFormat.java:343)
at
org.apache.accumulo.core.client.mapreduce.AbstractInputFormat.getSplits(AbstractInputFormat.java:538)
at
org.apache.spark.rdd.NewHadoopRDD.getPartitions(NewHadoopRDD.scala:98)
at
org.apache.spark.rdd.RDD$$anonfun$partitions$2.apply(RDD.scala:222)
at
org.apache.spark.rdd.RDD$$anonfun$partitions$2.apply(RDD.scala:220)
at scala.Option.getOrElse(Option.scala:120) at
org.apache.spark.rdd.RDD.partitions(RDD.scala:220) at
org.apache.spark.rdd.RDD.take(RDD.scala:1077) at
org.apache.spark.rdd.RDD.first(RDD.scala:1110) at
$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC.(:61)
at
edit 3 -- progress!
i was able to figure out why the 'input INFO not set' error was occurring. the eagle-eyed among you will no doubt see the following code is missing a closing '('
AbstractInputFormat.setConnectorInfo(jobConf, "root", new PasswordToken("password")
as I'm doing this in spark-notebook, I'd been clicking the execute button and moving on because I wasn't seeing an error. what I forgot was that notebook is going to do what spark-shell will do when you leave off a closing ')' -- it will wait forever for you to add it. so the error was the result of the 'setConnectorInfo' method never getting executed.
unfortunately, I'm still unable to shove the accumulo table data into an RDD that's useable to me. when I execute
rddX.count
I get back
res15: Long = 10000
which is the correct response - there are 10,000 rows of data in the table I pointed to. however, when I try to grab the first element of data thusly:
rddX.first
I get the following error:
org.apache.spark.SparkException: Job aborted due to stage failure:
Task 0.0 in stage 0.0 (TID 0) had a not serializable result:
org.apache.accumulo.core.data.Key
any thoughts on where to go from here?
edit 4 -- success!
the accepted answer + comments are 90% of the way there - except for the fact that the accumulo key/value need to be cast into something serializable. i got this working by invoking the .toString() method on both. i'll try to post something soon that's complete working code incase anyone else runs into the same issue.
Generally with custom Hadoop InputFormats, the information is specified using a JobConf. As #Sietse pointed out there are some static methods on the AccumuloInputFormat that you can use to configure the JobConf. In this case I think what you would want to do is:
val jobConf = new JobConf() // Create a job conf
// Configure the job conf with our accumulo properties
AccumuloInputFormat.setConnectorInfo(jobConf, principal, token)
AccumuloInputFormat.setScanAuthorizations(jobConf, authorizations)
val clientConfig = new ClientConfiguration().withInstance(instanceName).withZkHosts(zooKeepers)
AccumuloInputFormat.setZooKeeperInstance(jobConf, clientConfig)
AccumuloInputFormat.setInputTableName(jobConf, tableName)
// Create an RDD using the jobConf
val rdd2 = sc.newAPIHadoopRDD(jobConf,
classOf[org.apache.accumulo.core.client.mapreduce.AccumuloInputFormat],
classOf[org.apache.accumulo.core.data.Key],
classOf[org.apache.accumulo.core.data.Value]
)
Note: After digging into the code, it seems the the is configured property is set based in part on the class which is called (makes sense to avoid conflicts with other packages potentially), so when we go and get it back in the concrete class later it fails to find the is configured flag. The solution to this is to not use the Abstract classes. see https://github.com/apache/accumulo/blob/bf102d0711103e903afa0589500f5796ad51c366/core/src/main/java/org/apache/accumulo/core/client/mapreduce/lib/impl/ConfiguratorBase.java#L127 for the implementation details). If you can't call this method on the concrete implementation with spark-notebook probably using spark-shell or a regularly built application is the easiest solution.
It looks like those parameters have to be set through static methods : http://accumulo.apache.org/1.6/apidocs/org/apache/accumulo/core/client/mapred/AccumuloInputFormat.html. So try setting the non-optional parameters and run again. It should work.
I have 20 million files in S3 spanning roughly 8000 days.
The files are organized by timestamps in UTC, like this: s3://mybucket/path/txt/YYYY/MM/DD/filename.txt.gz. Each file is UTF-8 text containing between 0 (empty) and 100KB of text (95th percentile, although there are a few files that are up to several MBs).
Using Spark and Scala (I'm new to both and want to learn), I would like to save "daily bundles" (8000 of them), each containing whatever number of files were found for that day. Ideally I would like to store the original filenames as well as their content. The output should reside in S3 as well and be compressed, in some format that is suitable for input in further Spark steps and experiments.
One idea was to store bundles as a bunch of JSON objects (one per line and '\n'-separated), e.g.
{id:"doc0001", meta:{x:"blah", y:"foo", ...}, content:"some long string here"}
{id:"doc0002", meta:{x:"foo", y:"bar", ...}, content: "another long string"}
Alternatively, I could try the Hadoop SequenceFile, but again I'm not sure how to set that up elegantly.
Using the Spark shell for example, I saw that it was very easy to read the files, for example:
val textFile = sc.textFile("s3n://mybucket/path/txt/1996/04/09/*.txt.gz")
// or even
val textFile = sc.textFile("s3n://mybucket/path/txt/*/*/*/*.txt.gz")
// which will take for ever
But how do I "intercept" the reader to provide the file name?
Or perhaps I should get an RDD of all the files, split by day, and in a reduce step write out K=filename, V=fileContent?
You can use this
First You can get a Buffer/List of S3 Paths :
import scala.collection.JavaConverters._
import java.util.ArrayList
import com.amazonaws.services.s3.AmazonS3Client
import com.amazonaws.services.s3.model.ObjectListing
import com.amazonaws.services.s3.model.S3ObjectSummary
import com.amazonaws.services.s3.model.ListObjectsRequest
def listFiles(s3_bucket:String, base_prefix : String) = {
var files = new ArrayList[String]
//S3 Client and List Object Request
var s3Client = new AmazonS3Client();
var objectListing: ObjectListing = null;
var listObjectsRequest = new ListObjectsRequest();
//Your S3 Bucket
listObjectsRequest.setBucketName(s3_bucket)
//Your Folder path or Prefix
listObjectsRequest.setPrefix(base_prefix)
//Adding s3:// to the paths and adding to a list
do {
objectListing = s3Client.listObjects(listObjectsRequest);
for (objectSummary <- objectListing.getObjectSummaries().asScala) {
files.add("s3://" + s3_bucket + "/" + objectSummary.getKey());
}
listObjectsRequest.setMarker(objectListing.getNextMarker());
} while (objectListing.isTruncated());
//Removing Base Directory Name
files.remove(0)
//Creating a Scala List for same
files.asScala
}
Now Pass this List object to the following piece of code, note : sc is an object of SQLContext
var df: DataFrame = null;
for (file <- files) {
val fileDf= sc.textFile(file)
if (df!= null) {
df= df.unionAll(fileDf)
} else {
df= fileDf
}
}
Now you got a final Unified RDD i.e. df
Optional, And You can also repartition it in a single BigRDD
val files = sc.textFile(filename, 1).repartition(1)
Repartitioning always works :D
have you tried something along the lines of sc.wholeTextFiles?
It creates an RDD where the key is the filename and the value is the byte array of the whole file. You can then map this so the key is the file date, and then groupByKey?
http://spark.apache.org/docs/latest/programming-guide.html
At your scale, elegant solution would be a stretch.
I would recommend against using sc.textFile("s3n://mybucket/path/txt/*/*/*/*.txt.gz") as it takes forever. What you can do is use AWS DistCp or something similar to move files into HDFS. Once its in HDFS, spark is quite fast in ingesting the information in whatever way suits you.
Note that most of these processes require some sort of file list so you'll need to generate that somehow. for 20 mil files, this creation of file list will be a bottle neck. I'd recommend creating a file that get appended with the file path, every-time a file gets uploaded to s3.
Same for output, put into hdfs and then move to s3 (although direct copy might be equally efficient).
I'm working on an SBT plugin where I'd like to zip up a directory. This is possible due to the following method in IO:
def zip(sources: Traversable[(File,String)], outputZip: File): Unit
After tinkering with this method, it seems that simply passing it a directory and expecting the resulting zip file to have the same file & folder structure is wrong.. Passing a directory (empty or otherwise) results in the following:
[error]...:zipper: java.util.zip.ZipException: ZIP file must have at least one entry
Therefore, it appears that the way to get use the zip method is by stepping through the directory and adding each file individually to the Traversable object.
Assuming my understanding is correct, this strikes me as very odd - vey rarely do users need to cherry-pick what is to be added to an archive.
Any thoughts on this?
It seems like you can use this to compose a zip with files from multiple places. I can see the use of that in a build system.
A bit late to the party, but this should do what you need:
val parentFolder: File = ???
val folderName: String = ???
val src: File = parentFolder / folderName
val tgt: File = parentFolder / s"$folderName.zip"
IO.zip(allSubpaths(src), tgt)
Here is some code for zipping directories using sbt's IO class:
IO.withTemporaryDirectory(base => {
val dirToZip = new File(base, "lib")
IO.createDirectory(dirToZip)
IO.write(dirToZip / "test1", "test")
IO.write(dirToZip / "test2", "test")
val zip: File = base / ("test.zip")
IO.zip(allSubpaths(dirToZip), zip)
val out: File = base / "out"
IO.createDirectory(out)
IO.unzip(zip,out) mustEqual(Set(out /"test1", out / "test2"))
IO.delete((out ** "*").get)
//Create a zip containing this lib directory but under a different directory in the zip
val finder: PathFinder = dirToZip ** "*" --- dirToZip //Remove dirToZip as you can't rebase a directory to itself
IO.zip(finder x rebase(dirToZip, "newlib"), base / "rebased.zip")
IO.createDirectory(out)
IO.unzip(base / "rebased.zip",out) mustEqual(Set(out /"newlib"/"test1", out / "newlib"/ "test2"))
})
See the docs
http://www.scala-sbt.org/0.12.2/docs/Detailed-Topics/Mapping-Files.html
http://www.scala-sbt.org/0.12.3/docs/Detailed-Topics/Paths.html
for tips on creating the Traversable object to pass to IO.zip