I am new to Scala, Spark and so struggling with a map function I am trying to create.
The map function on the Dataframe a Row (org.apache.spark.sql.Row)
I have been loosely following this article.
val rddWithExceptionHandling = filterValueDF.rdd.map { row: Row =>
val parsed = Try(from_avro(???, currentValueSchema.value, fromAvroOptions)) match {
case Success(parsedValue) => List(parsedValue, null)
case Failure(ex) => List(null, ex.toString)
}
Row.fromSeq(row.toSeq.toList ++ parsed)
}
The from_avro function wants to accept a Column (org.apache.spark.sql.Column), however I don't see a way in the docs to get a column from a Row.
I am fully open to the idea that I may be doing this whole thing wrong.
Ultimately my goal is to parse the bytes coming in from a Structure Stream.
Parsed records get written to a Delta Table A and the failed records to another Delta Table B
For context the source table looks as follows:
Edit - from_avro returning null on "bad record"
There have been a few comments saying that from_avro returns null if it fails to parse a "bad record". By default from_avro uses mode FAILFAST which will throw an exception if parsing fails. If one sets the mode to PERMISSIVE an object in the shape of the schema is returned but with all properties being null (also not particularly useful...). Link to the Apache Avro Data Source Guide - Spark 3.1.1 Documentation
Here is my original command:
val parsedDf = filterValueDF.select($"topic",
$"partition",
$"offset",
$"timestamp",
$"timestampType",
$"valueSchemaId",
from_avro($"fixedValue", currentValueSchema.value, fromAvroOptions).as('parsedValue))
If there are ANY bad rows the job is aborted with org.apache.spark.SparkException: Job aborted.
A snippet of the log of the exception:
Caused by: org.apache.spark.SparkException: Malformed records are detected in record parsing. Current parse Mode: FAILFAST. To process malformed records as null result, try setting the option 'mode' as 'PERMISSIVE'.
at org.apache.spark.sql.avro.AvroDataToCatalyst.nullSafeEval(AvroDataToCatalyst.scala:111)
at org.apache.spark.sql.catalyst.expressions.GeneratedClass$GeneratedIteratorForCodegenStage1.processNext(Unknown Source)
at org.apache.spark.sql.execution.BufferedRowIterator.hasNext(BufferedRowIterator.java:43)
at org.apache.spark.sql.execution.WholeStageCodegenExec$$anon$1.hasNext(WholeStageCodegenExec.scala:732)
at org.apache.spark.sql.execution.datasources.FileFormatWriter$.$anonfun$executeTask$2(FileFormatWriter.scala:291)
at org.apache.spark.util.Utils$.tryWithSafeFinallyAndFailureCallbacks(Utils.scala:1615)
at org.apache.spark.sql.execution.datasources.FileFormatWriter$.executeTask(FileFormatWriter.scala:300)
... 10 more
Suppressed: java.lang.NullPointerException
at shaded.databricks.org.apache.hadoop.fs.azure.NativeAzureFileSystem$NativeAzureFsOutputStream.write(NativeAzureFileSystem.java:1099)
at org.apache.hadoop.fs.FSDataOutputStream$PositionCache.write(FSDataOutputStream.java:58)
at java.io.DataOutputStream.write(DataOutputStream.java:107)
at org.apache.parquet.hadoop.util.HadoopPositionOutputStream.write(HadoopPositionOutputStream.java:50)
at shaded.parquet.org.apache.thrift.transport.TIOStreamTransport.write(TIOStreamTransport.java:145)
at shaded.parquet.org.apache.thrift.transport.TTransport.write(TTransport.java:107)
at shaded.parquet.org.apache.thrift.protocol.TCompactProtocol.writeByteDirect(TCompactProtocol.java:482)
at shaded.parquet.org.apache.thrift.protocol.TCompactProtocol.writeByteDirect(TCompactProtocol.java:489)
at shaded.parquet.org.apache.thrift.protocol.TCompactProtocol.writeFieldBeginInternal(TCompactProtocol.java:252)
at shaded.parquet.org.apache.thrift.protocol.TCompactProtocol.writeFieldBegin(TCompactProtocol.java:234)
at org.apache.parquet.format.InterningProtocol.writeFieldBegin(InterningProtocol.java:74)
at org.apache.parquet.format.FileMetaData$FileMetaDataStandardScheme.write(FileMetaData.java:1184)
at org.apache.parquet.format.FileMetaData$FileMetaDataStandardScheme.write(FileMetaData.java:1051)
at org.apache.parquet.format.FileMetaData.write(FileMetaData.java:949)
at org.apache.parquet.format.Util.write(Util.java:222)
at org.apache.parquet.format.Util.writeFileMetaData(Util.java:69)
at org.apache.parquet.hadoop.ParquetFileWriter.serializeFooter(ParquetFileWriter.java:757)
at org.apache.parquet.hadoop.ParquetFileWriter.end(ParquetFileWriter.java:750)
at org.apache.parquet.hadoop.InternalParquetRecordWriter.close(InternalParquetRecordWriter.java:135)
at org.apache.parquet.hadoop.ParquetRecordWriter.close(ParquetRecordWriter.java:165)
at org.apache.spark.sql.execution.datasources.parquet.ParquetOutputWriter.close(ParquetOutputWriter.scala:42)
at org.apache.spark.sql.execution.datasources.FileFormatDataWriter.releaseResources(FileFormatDataWriter.scala:58)
at org.apache.spark.sql.execution.datasources.FileFormatDataWriter.abort(FileFormatDataWriter.scala:84)
at org.apache.spark.sql.execution.datasources.FileFormatWriter$.$anonfun$executeTask$3(FileFormatWriter.scala:297)
at org.apache.spark.util.Utils$.tryWithSafeFinallyAndFailureCallbacks(Utils.scala:1626)
... 11 more
Caused by: java.lang.ArithmeticException: Unscaled value too large for precision
at org.apache.spark.sql.types.Decimal.set(Decimal.scala:83)
at org.apache.spark.sql.types.Decimal$.apply(Decimal.scala:577)
at org.apache.spark.sql.avro.AvroDeserializer.createDecimal(AvroDeserializer.scala:308)
at org.apache.spark.sql.avro.AvroDeserializer.$anonfun$newWriter$16(AvroDeserializer.scala:177)
at org.apache.spark.sql.avro.AvroDeserializer.$anonfun$newWriter$16$adapted(AvroDeserializer.scala:174)
at org.apache.spark.sql.avro.AvroDeserializer.$anonfun$getRecordWriter$1(AvroDeserializer.scala:336)
at org.apache.spark.sql.avro.AvroDeserializer.$anonfun$getRecordWriter$1$adapted(AvroDeserializer.scala:332)
at org.apache.spark.sql.avro.AvroDeserializer.$anonfun$getRecordWriter$2(AvroDeserializer.scala:354)
at org.apache.spark.sql.avro.AvroDeserializer.$anonfun$getRecordWriter$2$adapted(AvroDeserializer.scala:351)
at org.apache.spark.sql.avro.AvroDeserializer.$anonfun$converter$3(AvroDeserializer.scala:75)
at org.apache.spark.sql.avro.AvroDeserializer.deserialize(AvroDeserializer.scala:89)
at org.apache.spark.sql.avro.AvroDataToCatalyst.nullSafeEval(AvroDataToCatalyst.scala:101)
... 16 more
In order to get a specific column from the Row object you can use either row.get(i) or using the column name with row.getAs[T]("columnName"). Here you can check the details of the Row class.
Then your code would look as next:
val rddWithExceptionHandling = filterValueDF.rdd.map { row: Row =>
val binaryFixedValue = row.getSeq[Byte](6) // or row.getAs[Seq[Byte]]("fixedValue")
val parsed = Try(from_avro(binaryFixedValue, currentValueSchema.value, fromAvroOptions)) match {
case Success(parsedValue) => List(parsedValue, null)
case Failure(ex) => List(null, ex.toString)
}
Row.fromSeq(row.toSeq.toList ++ parsed)
}
Although in your case, you don't really need to go into the map function because then you have to work with primitive Scala types when from_avro works with the Dataframe API. This is the reason that you can't call from_avro directly from map since instances of Column class can be used only in combination with the Dataframe API, i.e: df.select($"c1"), here c1 is an instance of Column. In order to use from_avro, as you initially intended, just type:
filterValueDF.select(from_avro($"fixedValue", currentValueSchema))
As #mike already mentioned, if from_avro fails to parse the AVRO content will return null. Finally, if you want to separate succeeded rows from failed, you could do something like:
val includingFailuresDf = filterValueDF.select(
from_avro($"fixedValue", currentValueSchema) as "avro_res")
.withColumn("failed", $"avro_res".isNull)
val successDf = includingFailuresDf.where($"failed" === false)
val failedDf = includingFailuresDf.where($"failed" === true)
Please be aware that the code was not tested.
From what i understand you just need to fetch a column for a row . You can probably do that by getting a column value at specific index using row.get()
Related
We are currently facing a performance issue in sparksql written in scala language. Application flow is mentioned below.
Spark application reads a text file from input hdfs directory
Creates a data frame on top of the file using programmatically specifying schema. This dataframe will be an exact replication of the input file kept in memory. Will have around 18 columns in the dataframe
var eqpDF = sqlContext.createDataFrame(eqpRowRdd, eqpSchema)
Creates a filtered dataframe from the first data frame constructed in step 2. This dataframe will contain unique account numbers with the help of distinct keyword.
var distAccNrsDF = eqpDF.select("accountnumber").distinct().collect()
Using the two dataframes constructed in step 2 & 3, we will get all the records which belong to one account number and do some Json parsing logic on top of the filtered data.
var filtrEqpDF =
eqpDF.where("accountnumber='" + data.getString(0) + "'").collect()
Finally the json parsed data will be put into Hbase table
Here we are facing performance issues while calling the collect method on top of the data frames. Because collect will fetch all the data into a single node and then do the processing, thus losing the parallel processing benefit.
Also in real scenario there will be 10 billion records of data which we can expect. Hence collecting all those records in to driver node will might crash the program itself due to memory or disk space limitations.
I don't think the take method can be used in our case which will fetch limited number of records at a time. We have to get all the unique account numbers from the whole data and hence I am not sure whether take method, which takes
limited records at a time, will suit our requirements
Appreciate any help to avoid calling collect methods and have some other best practises to follow. Code snippets/suggestions/git links will be very helpful if anyone have had faced similar issues
Code snippet
val eqpSchemaString = "acoountnumber ....."
val eqpSchema = StructType(eqpSchemaString.split(" ").map(fieldName =>
StructField(fieldName, StringType, true)));
val eqpRdd = sc.textFile(inputPath)
val eqpRowRdd = eqpRdd.map(_.split(",")).map(eqpRow => Row(eqpRow(0).trim, eqpRow(1).trim, ....)
var eqpDF = sqlContext.createDataFrame(eqpRowRdd, eqpSchema);
var distAccNrsDF = eqpDF.select("accountnumber").distinct().collect()
distAccNrsDF.foreach { data =>
var filtrEqpDF = eqpDF.where("accountnumber='" + data.getString(0) + "'").collect()
var result = new JSONObject()
result.put("jsonSchemaVersion", "1.0")
val firstRowAcc = filtrEqpDF(0)
//Json parsing logic
{
.....
.....
}
}
The approach usually take in this kind of situation is:
Instead of collect, invoke foreachPartition: foreachPartition applies a function to each partition (represented by an Iterator[Row]) of the underlying DataFrame separately (the partition being the atomic unit of parallelism of Spark)
the function will open a connection to HBase (thus making it one per partition) and send all the contained values through this connection
This means the every executor opens a connection (which is not serializable but lives within the boundaries of the function, thus not needing to be sent across the network) and independently sends its contents to HBase, without any need to collect all data on the driver (or any one node, for that matter).
It looks like you are reading a CSV file, so probably something like the following will do the trick:
spark.read.csv(inputPath). // Using DataFrameReader but your way works too
foreachPartition { rows =>
val conn = ??? // Create HBase connection
for (row <- rows) { // Loop over the iterator
val data = parseJson(row) // Your parsing logic
??? // Use 'conn' to save 'data'
}
}
You can ignore collect in your code if you have large set of data.
Collect Return all the elements of the dataset as an array at the driver program. This is usually useful after a filter or other operation that returns a sufficiently small subset of the data.
Also this can cause the driver to run out of memory, though, because collect() fetches the entire RDD/DF to a single machine.
I have just edited your code, which should work for you.
var distAccNrsDF = eqpDF.select("accountnumber").distinct()
distAccNrsDF.foreach { data =>
var filtrEqpDF = eqpDF.where("accountnumber='" + data.getString(0) + "'")
var result = new JSONObject()
result.put("jsonSchemaVersion", "1.0")
val firstRowAcc = filtrEqpDF(0)
//Json parsing logic
{
.....
.....
}
}
I am new to Spark and HBase. I am working with the backups of a HBase table. These backups are in a S3 bucket. I am reading them via spark(scala) using newAPIHadoopFile like this:
conf.set("io.serializations", "org.apache.hadoop.io.serializer.WritableSerialization,org.apache.hadoop.hbase.mapreduce.ResultSerialization")
val data = sc.newAPIHadoopFile(path,classOf[SequenceFileInputFormat[ImmutableBytesWritable, Result]], classOf[ImmutableBytesWritable], classOf[Result], conf)
The table in question is called Emps. The schema of Emps is :
key: empid {COMPRESSION => 'gz' }
family: data
dob - date of birth of this employee.
e_info - avro structure for storing emp info.
e_dept- avro structure for storing info about dept.
family: extra - Extra Metadata {NAME => 'extra', BLOOMFILTER => 'ROW', VERSIONS => '1', IN_MEMORY => 'false', KEEP_DELETED_CELLS => 'FALSE', DATA_BLOCK_ENCODING => 'NONE', TTL => 'FOREVER', COMPRESSION => 'SNAPPY', MIN_VERSIONS => '0', BLOCKCACHE => 'true', BLOCKSIZE => '65536', REPLICATION_SCOPE => '0'}
e_region - emp region
e_status - some data about his achievements
.
.
some more meta data
The table has some columns that have simple string data in them, and some columns that has AVRO stuctures in them.
I am trying to read this data directly from the HBase backup files in the S3. I do not want to re-create this HBase table in my local machine as the table is very, very large.
This is how I am trying to read this:
data.keys.map{k=>(new String(k.get()))}.take(1)
res1: Array[String] = Array(111111111100011010102462)
data.values.map{ v =>{ for(cell <- v.rawCells()) yield{
val family = CellUtil.cloneFamily(cell);
val column = CellUtil.cloneQualifier(cell);
val value = CellUtil.cloneValue(cell);
new String(family) +"->"+ new String(column)+ "->"+ new String(value)
}
}
}.take(1)
res2: Array[Array[String]] = Array(Array(info->dob->01/01/1996, info->e_info->?ж�?�ո� ?�� ???̶�?�ո� ?�� ????, info->e_dept->?ж�??�ո� ?̶�??�ո� �ո� ??, extra->e_region-> CA, extra->e_status->, .....))
As expected I can see the simple string data correctly, but the AVRO data is garbage.
I tried reading the AVRO structures using GenericDatumReader:
data.values.map{ v =>{ for(cell <- v.rawCells()) yield{
val family = new String(CellUtil.cloneFamily(cell));
val column = new String(CellUtil.cloneQualifier(cell));
val value = CellUtil.cloneValue(cell);
if(column=="e_info"){
var schema_obj = new Schema.Parser
//schema_e_info contains the AVRO schema for e_info
var schema = schema_obj.parse(schema_e_info)
var READER2 = new GenericDatumReader[GenericRecord](schema)
var datum= READER2.read(null, DecoderFactory.defaultFactory.createBinaryDecoder(value,null))
var result=datum.get("type").toString()
family +"->"+column+ "->"+ new String(result) + "\n"
}
else
family +"->"+column+ "->"+ new String(value)+"\n"
}
}
}
But this is giving me the following error:
org.apache.spark.SparkException: Task not serializable
at org.apache.spark.util.ClosureCleaner$.ensureSerializable(ClosureCleaner.scala:298)
at org.apache.spark.util.ClosureCleaner$.org$apache$spark$util$ClosureCleaner$$clean(ClosureCleaner.scala:288)
at org.apache.spark.util.ClosureCleaner$.clean(ClosureCleaner.scala:108)
at org.apache.spark.SparkContext.clean(SparkContext.scala:2101)
at org.apache.spark.rdd.RDD$$anonfun$map$1.apply(RDD.scala:370)
at org.apache.spark.rdd.RDD$$anonfun$map$1.apply(RDD.scala:369)
at org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:151)
at org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:112)
at org.apache.spark.rdd.RDD.withScope(RDD.scala:362)
at org.apache.spark.rdd.RDD.map(RDD.scala:369)
... 74 elided
Caused by: java.io.NotSerializableException: org.apache.avro.Schema$RecordSchema
Serialization stack:
- object not serializable (class: org.apache.avro.Schema$RecordSchema, value: .....
So I want to ask:
Is there any way to make the non-serializable class RecordSchema work with the map function?
Is my approach right upto this point? I would be glad to know about better approaches to handle this kind of data.
I read that handling this in a Dataframe would be a lot easier. I tried to convert the Hadoop RDD so formed into a Dataframe, but again I am running blindly there.
As the exception says - the schema is non-serializable. Can you initialize it inside the mapper function? So that it doesn't need to get shipped from the driver to the executors.
Alternatively, you can also create a scala singleton object that contains the schema. You get one scala singleton initialized on each executor, so when you access any member from the singleton, it doesn't need to be serialized & sent across the network. This avoids the unnecessary overhead of re-creating the schema for each and every row in the data.
Just for the purpose of checking that you can read the data fine - you can also convert it to a byte array on the executors, collect it on the driver and do the deserialization (parsing the AVRO data) in the driver code. But this obviously won't scale, it's just to make sure that your data looks good and to avoid spark-related complications while you're writing your prototype code to extract the data.
I have following partitioned parquet data on hdfs written using spark:
year
|---Month
|----monthlydata.parquet
|----Day
|---dailydata.parquet
Now when I read df from year path, spark read dailydata.parquet. How can i read monthlydata from all partitions. I tried using setting option mergeSchema = true which gives error.
I would urge you stop doing the following:
year
|---Month
|----monthlydata.parquet
|----Day
|---dailydata.parquet
When you read from year/month/ or even just year/, you won't just get monthlydata.parquet, you'll also be getting dailydata.parquet. I can't speak much to the error you're getting (please post it), but my humble suggestion would be to separate the paths in HDFS since you're already duplicating the data:
dailies
|---year
|---Month
|----Day
|---dailydata.parquet
monthlies
|---year
|---Month
|----monthlydata.parquet
Is there a reason you were keeping them in the same directories?
However, if you insist on this structure, use something like this:
schema = "dailydata1"
val dfList = dates.map { case (month, day) =>
Try(sqlContext.read.parquet(s"/hdfs/table/month=$month/day=$day/$schema.parquet"))
}
val dfUnion = dfList.collect { case Success(v) => v }.reduce { (a, b) =>
a.unionAll(b)
}
Where you can toggle the schema between dailydata1, dailydata2, etc.
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.
This might be a very special case but after pounding on my head for a while I thought to get help from Stackoverflow community.
I am building an inverted index for large data set (One day worth of data from large system). The building of inverted index get executed as a map reduce job on Hadoop. Inverted index is build with the help of scala. Structure of the inverted index as follows: {key:"New", ProductID:[1,2,3,4,5,...]} these get written in to avro files.
During this process I run into Java Heap Size issue. I think the reason is that terms like "New" as I showed above contain large number of productId(s). I have a, rough idea where the issue could take place in my Scala code:
def toIndexedRecord(ids: List[Long], token: String): IndexRecord = {
val javaList = ids.map(l => l: java.lang.Long).asJava //need to convert from scala long to java long
new IndexRecord(token, javaList)
}
And this is how I use this method (It get used in many locations but same code structure and login get use)
val titles = textPipeDump.map(vf => (vf.itemId, normalizer.customNormalizer(vf.title + " " + vf.subTitle).trim))
.flatMap {
case (id, title) =>
val ss = title.split("\\s+")
ss.map(word => (word, List(id)))
}
.filter(f => f._2.nonEmpty)
.group
.sum
.map {
case (token, ids) =>
toIndexedRecord(ids, token)
}
textPipeDump is scalding MultipleTextLine field object
case class MultipleTextLineFiles(p : String*) extends FixedPathSource(p:_*) with TextLineScheme
I have a case class to split and grab the fields that I want from that text line and thats the object ss
Here is my stack trace:
Exception in thread "IPC Client (47) connection to /127.0.0.1:55977 from job_201306241658_232590" java.lang.OutOfMemoryError: Java heap space
at org.apache.hadoop.io.IOUtils.closeStream(IOUtils.java:226)
at org.apache.hadoop.ipc.Client$Connection.close(Client.java:903)
at org.apache.hadoop.ipc.Client$Connection.run(Client.java:800)
28079664 [main] ERROR cascading.flow.stream.TrapHandler - caught Throwable, no trap available, rethrowing
cascading.pipe.OperatorException: [WritableSequenceFile(h...][com.twitter.scalding.GroupBuilder$$anonfun$1.apply(GroupBuilder.scala:189)] operator Every failed executing operation: MRMAggregator[decl:'value']
at cascading.flow.stream.AggregatorEveryStage.receive(AggregatorEveryStage.java:136)
at cascading.flow.stream.AggregatorEveryStage.receive(AggregatorEveryStage.java:39)
at cascading.flow.stream.OpenReducingDuct.receive(OpenReducingDuct.java:49)
at cascading.flow.stream.OpenReducingDuct.receive(OpenReducingDuct.java:28)
at cascading.flow.hadoop.stream.HadoopGroupGate.run(HadoopGroupGate.java:90)
at cascading.flow.hadoop.FlowReducer.reduce(FlowReducer.java:133)
at org.apache.hadoop.mapred.ReduceTask.runOldReducer(ReduceTask.java:520)
at org.apache.hadoop.mapred.ReduceTask.run(ReduceTask.java:421)
at org.apache.hadoop.mapred.Child$4.run(Child.java:255)
at java.security.AccessController.doPrivileged(Native Method)
at javax.security.auth.Subject.doAs(Subject.java:396)
at org.apache.hadoop.security.UserGroupInformation.doAs(UserGroupInformation.java:1178)
at org.apache.hadoop.mapred.Child.main(Child.java:249)
Caused by: java.lang.OutOfMemoryError: Java heap space
at scala.collection.mutable.ListBuffer.$plus$eq(ListBuffer.scala:168)
at scala.collection.mutable.ListBuffer.$plus$eq(ListBuffer.scala:45)
at scala.collection.generic.Growable$$anonfun$$plus$plus$eq$1.apply(Growable.scala:48)
at scala.collection.generic.Growable$$anonfun$$plus$plus$eq$1.apply(Growable.scala:48)
at scala.collection.immutable.List.foreach(List.scala:318)
at scala.collection.generic.Growable$class.$plus$plus$eq(Growable.scala:48)
at scala.collection.mutable.ListBuffer.$plus$plus$eq(ListBuffer.scala:176)
at scala.collection.immutable.List.$colon$colon$colon(List.scala:127)
at scala.collection.immutable.List.$plus$plus(List.scala:193)
at com.twitter.algebird.ListMonoid.plus(Monoid.scala:86)
at com.twitter.algebird.ListMonoid.plus(Monoid.scala:84)
at com.twitter.scalding.KeyedList$$anonfun$sum$1.apply(TypedPipe.scala:264)
at com.twitter.scalding.MRMAggregator.aggregate(Operations.scala:279)
at cascading.flow.stream.AggregatorEveryStage.receive(AggregatorEveryStage.java:128)
... 12 more
When I execute the map reduce job for small data set I don't get the error. Which means that as the data increase number of items/product_ids that I index for words like New or old etc get bigger which cause the Heap Size to overflow.
So, the question is how can avoid java heap size overflow and accomplish this task.