Hello Dongwon Kim!
Thanks you for sharing these numbers with us.
I have gone through your implementation and there are two things you could
try:
1)
I see that you sort Hadoop's Text data type with Flink. I think this may be
less efficient than if you sort String, or a Flink specific data type.
For efficient byte operations on managed memory, Flink needs to understand
the binary representation of the data type. Flink understands that for
"String" and many other types, but not for "Text".
There are two things you can do:
- First, try what happens if you map the Hadoop Text type to a Java
String (only for the tera key).
- Second, you can try what happens if you wrap the Hadoop Text type in a
Flink type that supports optimized binary sorting. I have pasted code for
that at the bottom of this email.
2)
You can see if it helps performance if you enable object re-use in Flink.
You can do this on the ExecutionEnvironment via
"env.getConfig().enableObjectReuse()". Then Flink tries to use the same
objects repeatedly, in case they are mutable.
Can you try these options out and see how they affect Flink's runtime?
Greetings,
Stephan
---------------------------------------------------------
Code for optimized sortable (Java):
public final class OptimizedText implements NormalizableKey<OptimizedText >
{
private final Text text;
public OptimizedText () {
this.text = new Text();
}
public OptimizedText (Text from) {
this.text = from;
}
public Text getText() {
return text;
}
@Override
public int getMaxNormalizedKeyLen() {
return 10;
}
@Override
public void copyNormalizedKey(MemorySegment memory, int offset, int len) {
memory.put(offset, text.getBytes(), 0, Math.min(text.getLength(),
Math.min(10, len)));
}
@Override
public void write(DataOutputView out) throws IOException {
text.write(out);
}
@Override
public void read(DataInputView in) throws IOException {
text.readFields(in);
}
@Override
public int compareTo(OptimizedText o) {
return this.text.compareTo(o.text);
}
}
---------------------------------------------------------
Converting Text to OptimizedText (Java code)
map(new MapFunction<Tuple2<Text, Text>, Tuple2<OptimizedText, Text>>() {
@Override
public Tuple2<OptimizedText, Text> map(Tuple2<Text, Text> value) {
return new Tuple2<OptimizedText, Text>(new OptimizedText(value.f0),
value.f1);
}
})
On Thu, Jul 2, 2015 at 6:47 PM, Dongwon Kim <eastcirc...@postech.ac.kr>
wrote:
> Hello,
>
> I'd like to share my code for TeraSort on Flink and Spark which uses
> the same range partitioner as Hadoop TeraSort:
> https://github.com/eastcirclek/terasort
>
> I also write a short report on it:
>
> http://eastcirclek.blogspot.kr/2015/06/terasort-for-spark-and-flink-with-range.html
> In the blog post, I make a simple performance comparison between
> Flink, Spark, Tez, and MapReduce.
>
> I hope it will be helpful to you guys!
> Thanks.
>
> Dongwon Kim
> Postdoctoral Researcher @ Postech
>
