Thanks Jörn,
I tried to super simplify my project so I can focus on the plumbing and I will
add the existing code & library later. So, as of now, the project will not have
a lot of meaning but will allow me to understand the job.
my call is:
String filename = "src/test/resources/simple.json";
SparkSession spark =
SparkSession.builder().appName("X-parse").master("local").getOrCreate();
Dataset<Row> df = spark.read().format("x.CharCounterDataSource")
.option("char", "a") // count the number of 'a'
.load(filename); // local file (line 40 in the stacks below)
df.show();
Ideally, this should display something like:
+--+
| a|
+--+
|45|
+--+
Things gets trickier when I try to work on x.CharCounterDataSource:
I looked at 2 ways to do it:
1) one based on FileFormat:
public class CharCounterDataSource implements FileFormat {
@Override
public Function1<PartitionedFile, Iterator<InternalRow>>
buildReader(SparkSession arg0, StructType arg1,
StructType arg2, StructType arg3, Seq<Filter> arg4,
Map<String, String> arg5, Configuration arg6) {
// TODO Auto-generated method stub
return null;
}
@Override
public Function1<PartitionedFile, Iterator<InternalRow>>
buildReaderWithPartitionValues(SparkSession arg0,
StructType arg1, StructType arg2, StructType arg3,
Seq<Filter> arg4, Map<String, String> arg5,
Configuration arg6) {
// TODO Auto-generated method stub
return null;
}
@Override
public Option<StructType> inferSchema(SparkSession arg0, Map<String,
String> arg1, Seq<FileStatus> arg2) {
// TODO Auto-generated method stub
return null;
}
@Override
public boolean isSplitable(SparkSession arg0, Map<String, String> arg1,
Path arg2) {
// TODO Auto-generated method stub
return false;
}
@Override
public OutputWriterFactory prepareWrite(SparkSession arg0, Job arg1,
Map<String, String> arg2, StructType arg3) {
// TODO Auto-generated method stub
return null;
}
@Override
public boolean supportBatch(SparkSession arg0, StructType arg1) {
// TODO Auto-generated method stub
return false;
}
}
I know it is an empty class (generated by Eclipse) and I am not expecting much
out of it.
Running it says:
java.lang.NullPointerException
at
org.apache.spark.sql.execution.datasources.DataSource.org$apache$spark$sql$execution$datasources$DataSource$$getOrInferFileFormatSchema(DataSource.scala:188)
at
org.apache.spark.sql.execution.datasources.DataSource.resolveRelation(DataSource.scala:387)
at org.apache.spark.sql.DataFrameReader.load(DataFrameReader.scala:152)
at org.apache.spark.sql.DataFrameReader.load(DataFrameReader.scala:135)
at
x.spark.datasource.counter.CharCounterDataSourceTest.test(CharCounterDataSourceTest.java:40)
Nothing surprising...
2) One based on RelationProvider:
public class CharCounterDataSource implements RelationProvider {
@Override
public BaseRelation createRelation(SQLContext arg0, Map<String, String>
arg1) {
// TODO Auto-generated method stub
return null;
}
}
which fails too...
java.lang.NullPointerException
at
org.apache.spark.sql.execution.datasources.LogicalRelation.<init>(LogicalRelation.scala:40)
at
org.apache.spark.sql.SparkSession.baseRelationToDataFrame(SparkSession.scala:389)
at org.apache.spark.sql.DataFrameReader.load(DataFrameReader.scala:146)
at org.apache.spark.sql.DataFrameReader.load(DataFrameReader.scala:135)
at x.CharCounterDataSourceTest.test(CharCounterDataSourceTest.java:40)
Don't get me wrong - I understand it fails - but what I need is "just one hint"
to continue building the glue ;-)...
(Un)fortunately, we cannot use Scala...
jg
> On Mar 22, 2017, at 4:00 PM, Jörn Franke <jornfra...@gmail.com> wrote:
>
> I think you can develop a Spark data source in Java, but you are right most
> use for the glue Spark even if they have a Java library (this is what I did
> for the project I open sourced). Coming back to your question, it is a little
> bit difficult to assess the exact issue without the code.
> You could also try to first have a very simple Scala data source that works
> and then translate it to Java and do the test there. You could then also post
> the code here without disclosing confidential stuff.
> Or you try directly in Java a data source that returns always a row with one
> column containing a String. I fear in any case you need to import some Scala
> classes in Java and/or have some wrappers in Scala.
> If you use fileformat that you need at least spark 2.0.
>
> On 22 Mar 2017, at 20:27, Jean Georges Perrin <j...@jgp.net
> <mailto:j...@jgp.net>> wrote:
>
>>
>> Hi,
>>
>> I am trying to build a custom file data source for Spark, in Java. I have
>> found numerous examples in Scala (including the CSV and XML data sources
>> from Databricks), but I cannot bring Scala in this project. We also already
>> have the parser itself written in Java, I just need to build the "glue"
>> between the parser and Spark.
>>
>> This is how I'd like to call it:
>>
>> String filename = "src/test/resources/simple.x";
>>
>> SparkSession spark =
>> SparkSession.builder().appName("X-parse").master("local").getOrCreate();
>>
>> Dataset<Row> df = spark.read().format("x.RandomDataSource")
>> .option("metadataTag", "schema") // hint to find schema
>> .option("dataTag", "data") // hint to find data
>> .load(filename); // local file
>> So far, I tried is implement x.RandomDataSource:
>>
>> • Based on FileFormat, which makes the most sense, but I do not have a
>> clue on how to build buildReader()...
>> • Based on RelationProvider, but same here...
>>
>> It seems that in both case, the call is made to the right class, but I get
>> into NPE because I do not provide much. Any hint or example would be greatly
>> appreciated!
>>
>> Thanks
>>
>> jg
