Get the max value for each key in a Spark RDD
7
What is the best way to return the max row (value) associated with each unique key in a spark RDD?
I'm using python and I've tried Math max, mapping and reducing by keys and aggregates. Is there an efficient way to do this? Possibly an UDF?
I have in RDD format:
[(v, 3),
(v, 1),
(v, 1),
(w, 7),
(w, 1),
(x, 3),
(y, 1),
(y, 1),
(y, 2),
(y, 3)]
And I need to return:
[(v, 3),
(w, 7),
(x, 3),
(y, 3)]
Ties can return the first value or random.
python apache-spark pyspark rdd
edited Mar 14 '17 at 12:21
SiHa
3,36161733
asked May 4 '16 at 0:17
captainKirk104captainKirk104
4725
add a comment |
7
What is the best way to return the max row (value) associated with each unique key in a spark RDD?
I'm using python and I've tried Math max, mapping and reducing by keys and aggregates. Is there an efficient way to do this? Possibly an UDF?
I have in RDD format:
[(v, 3),
(v, 1),
(v, 1),
(w, 7),
(w, 1),
(x, 3),
(y, 1),
(y, 1),
(y, 2),
(y, 3)]
And I need to return:
[(v, 3),
(w, 7),
(x, 3),
(y, 3)]
Ties can return the first value or random.
python apache-spark pyspark rdd
edited Mar 14 '17 at 12:21
SiHa
3,36161733
asked May 4 '16 at 0:17
captainKirk104captainKirk104
4725
add a comment |
7
7
7
3
What is the best way to return the max row (value) associated with each unique key in a spark RDD?
I'm using python and I've tried Math max, mapping and reducing by keys and aggregates. Is there an efficient way to do this? Possibly an UDF?
I have in RDD format:
[(v, 3),
(v, 1),
(v, 1),
(w, 7),
(w, 1),
(x, 3),
(y, 1),
(y, 1),
(y, 2),
(y, 3)]
And I need to return:
[(v, 3),
(w, 7),
(x, 3),
(y, 3)]
Ties can return the first value or random.
python apache-spark pyspark rdd
edited Mar 14 '17 at 12:21
SiHa
3,36161733
asked May 4 '16 at 0:17
captainKirk104captainKirk104
4725
What is the best way to return the max row (value) associated with each unique key in a spark RDD?
I'm using python and I've tried Math max, mapping and reducing by keys and aggregates. Is there an efficient way to do this? Possibly an UDF?
I have in RDD format:
[(v, 3),
(v, 1),
(v, 1),
(w, 7),
(w, 1),
(x, 3),
(y, 1),
(y, 1),
(y, 2),
(y, 3)]
And I need to return:
[(v, 3),
(w, 7),
(x, 3),
(y, 3)]
Ties can return the first value or random.
python apache-spark pyspark rdd
python apache-spark pyspark rdd
edited Mar 14 '17 at 12:21
SiHa
3,36161733
asked May 4 '16 at 0:17
captainKirk104captainKirk104
4725
edited Mar 14 '17 at 12:21
SiHa
3,36161733
asked May 4 '16 at 0:17
captainKirk104captainKirk104
4725
edited Mar 14 '17 at 12:21
SiHa
3,36161733
edited Mar 14 '17 at 12:21
SiHa
3,36161733
edited Mar 14 '17 at 12:21
SiHa
3,36161733
3,36161733
asked May 4 '16 at 0:17
captainKirk104captainKirk104
4725
asked May 4 '16 at 0:17
captainKirk104captainKirk104
4725
asked May 4 '16 at 0:17
captainKirk104captainKirk104
4725
4725
add a comment |
add a comment |
1 Answer
1
active
oldest
votes
15
Actually you have a PairRDD. One of the best ways to do it is with reduceByKey:
(Scala)
val grouped = rdd.reduceByKey(math.max(_, _))
(Python)
grouped = rdd.reduceByKey(max)
(Java 7)
JavaPairRDD<String, Integer> grouped = new JavaPairRDD(rdd).reduceByKey(
new Function2<Integer, Integer, Integer>() {
public Integer call(Integer v1, Integer v2) {
return Math.max(v1, v2);
}
});
(Java 8)
JavaPairRDD<String, Integer> grouped = new JavaPairRDD(rdd).reduceByKey(
(v1, v2) -> Math.max(v1, v2)
);
API doc for reduceByKey:
- Scala
- Python
- Java
answered May 4 '16 at 0:29
Daniel de PaulaDaniel de Paula
9,76954360
can you give a way to do this in Java as well? I am using java and looking for exactly the same thing
– tsar2512
Jan 24 '17 at 22:47
@tsar2512 With Java 8, this might work:new JavaPairRDD(rdd).reduceByKey((v1, v2) -> Math.max(v1, v2));
– Daniel de Paula
Jan 25 '17 at 9:12
thanks for the response, unfortunately, I am using Java 7 - it does not allow lambda functions. One typically has to write anonymous functions. Could you let me know what would be the solution in Java 7? I suspext a simple comparator function should work!
– tsar2512
Jan 25 '17 at 9:55
Additionally. What we are getting is the max of values which belong to each key. Is that correct?
– tsar2512
Jan 25 '17 at 9:56
@tsar2512, yes, the resulting RDD will contain a single entry for each key, containing a pair (key, maxValue). I updated the answer with versions for Java 7 and Java 8, but I haven't tested them, so please let me know if it works.
– Daniel de Paula
Jan 25 '17 at 10:10
add a comment |
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1 Answer
1
active
oldest
votes
1 Answer
1
active
oldest
votes
active
oldest
votes
active
oldest
votes
15
Actually you have a PairRDD. One of the best ways to do it is with reduceByKey:
(Scala)
val grouped = rdd.reduceByKey(math.max(_, _))
(Python)
grouped = rdd.reduceByKey(max)
(Java 7)
JavaPairRDD<String, Integer> grouped = new JavaPairRDD(rdd).reduceByKey(
new Function2<Integer, Integer, Integer>() {
public Integer call(Integer v1, Integer v2) {
return Math.max(v1, v2);
}
});
(Java 8)
JavaPairRDD<String, Integer> grouped = new JavaPairRDD(rdd).reduceByKey(
(v1, v2) -> Math.max(v1, v2)
);
API doc for reduceByKey:
- Scala
- Python
- Java
answered May 4 '16 at 0:29
Daniel de PaulaDaniel de Paula
9,76954360
can you give a way to do this in Java as well? I am using java and looking for exactly the same thing
– tsar2512
Jan 24 '17 at 22:47
@tsar2512 With Java 8, this might work:new JavaPairRDD(rdd).reduceByKey((v1, v2) -> Math.max(v1, v2));
– Daniel de Paula
Jan 25 '17 at 9:12
thanks for the response, unfortunately, I am using Java 7 - it does not allow lambda functions. One typically has to write anonymous functions. Could you let me know what would be the solution in Java 7? I suspext a simple comparator function should work!
– tsar2512
Jan 25 '17 at 9:55
Additionally. What we are getting is the max of values which belong to each key. Is that correct?
– tsar2512
Jan 25 '17 at 9:56
@tsar2512, yes, the resulting RDD will contain a single entry for each key, containing a pair (key, maxValue). I updated the answer with versions for Java 7 and Java 8, but I haven't tested them, so please let me know if it works.
– Daniel de Paula
Jan 25 '17 at 10:10
add a comment |
15
Actually you have a PairRDD. One of the best ways to do it is with reduceByKey:
(Scala)
val grouped = rdd.reduceByKey(math.max(_, _))
(Python)
grouped = rdd.reduceByKey(max)
(Java 7)
JavaPairRDD<String, Integer> grouped = new JavaPairRDD(rdd).reduceByKey(
new Function2<Integer, Integer, Integer>() {
public Integer call(Integer v1, Integer v2) {
return Math.max(v1, v2);
}
});
(Java 8)
JavaPairRDD<String, Integer> grouped = new JavaPairRDD(rdd).reduceByKey(
(v1, v2) -> Math.max(v1, v2)
);
API doc for reduceByKey:
- Scala
- Python
- Java
answered May 4 '16 at 0:29
Daniel de PaulaDaniel de Paula
9,76954360
can you give a way to do this in Java as well? I am using java and looking for exactly the same thing
– tsar2512
Jan 24 '17 at 22:47
@tsar2512 With Java 8, this might work:new JavaPairRDD(rdd).reduceByKey((v1, v2) -> Math.max(v1, v2));
– Daniel de Paula
Jan 25 '17 at 9:12
thanks for the response, unfortunately, I am using Java 7 - it does not allow lambda functions. One typically has to write anonymous functions. Could you let me know what would be the solution in Java 7? I suspext a simple comparator function should work!
– tsar2512
Jan 25 '17 at 9:55
Additionally. What we are getting is the max of values which belong to each key. Is that correct?
– tsar2512
Jan 25 '17 at 9:56
@tsar2512, yes, the resulting RDD will contain a single entry for each key, containing a pair (key, maxValue). I updated the answer with versions for Java 7 and Java 8, but I haven't tested them, so please let me know if it works.
– Daniel de Paula
Jan 25 '17 at 10:10
add a comment |
15
15
15
Actually you have a PairRDD. One of the best ways to do it is with reduceByKey:
(Scala)
val grouped = rdd.reduceByKey(math.max(_, _))
(Python)
grouped = rdd.reduceByKey(max)
(Java 7)
JavaPairRDD<String, Integer> grouped = new JavaPairRDD(rdd).reduceByKey(
new Function2<Integer, Integer, Integer>() {
public Integer call(Integer v1, Integer v2) {
return Math.max(v1, v2);
}
});
(Java 8)
JavaPairRDD<String, Integer> grouped = new JavaPairRDD(rdd).reduceByKey(
(v1, v2) -> Math.max(v1, v2)
);
API doc for reduceByKey:
- Scala
- Python
- Java
answered May 4 '16 at 0:29
Daniel de PaulaDaniel de Paula
9,76954360
Actually you have a PairRDD. One of the best ways to do it is with reduceByKey:
(Scala)
val grouped = rdd.reduceByKey(math.max(_, _))
(Python)
grouped = rdd.reduceByKey(max)
(Java 7)
JavaPairRDD<String, Integer> grouped = new JavaPairRDD(rdd).reduceByKey(
new Function2<Integer, Integer, Integer>() {
public Integer call(Integer v1, Integer v2) {
return Math.max(v1, v2);
}
});
(Java 8)
JavaPairRDD<String, Integer> grouped = new JavaPairRDD(rdd).reduceByKey(
(v1, v2) -> Math.max(v1, v2)
);
API doc for reduceByKey:
- Scala
- Python
- Java
answered May 4 '16 at 0:29
Daniel de PaulaDaniel de Paula
9,76954360
edited Jan 25 '17 at 10:07
answered May 4 '16 at 0:29
Daniel de PaulaDaniel de Paula
9,76954360
answered May 4 '16 at 0:29
Daniel de PaulaDaniel de Paula
9,76954360
answered May 4 '16 at 0:29
Daniel de PaulaDaniel de Paula
9,76954360
9,76954360
can you give a way to do this in Java as well? I am using java and looking for exactly the same thing
– tsar2512
Jan 24 '17 at 22:47
@tsar2512 With Java 8, this might work:new JavaPairRDD(rdd).reduceByKey((v1, v2) -> Math.max(v1, v2));
– Daniel de Paula
Jan 25 '17 at 9:12
thanks for the response, unfortunately, I am using Java 7 - it does not allow lambda functions. One typically has to write anonymous functions. Could you let me know what would be the solution in Java 7? I suspext a simple comparator function should work!
– tsar2512
Jan 25 '17 at 9:55
Additionally. What we are getting is the max of values which belong to each key. Is that correct?
– tsar2512
Jan 25 '17 at 9:56
@tsar2512, yes, the resulting RDD will contain a single entry for each key, containing a pair (key, maxValue). I updated the answer with versions for Java 7 and Java 8, but I haven't tested them, so please let me know if it works.
– Daniel de Paula
Jan 25 '17 at 10:10
add a comment |
can you give a way to do this in Java as well? I am using java and looking for exactly the same thing
– tsar2512
Jan 24 '17 at 22:47
@tsar2512 With Java 8, this might work:new JavaPairRDD(rdd).reduceByKey((v1, v2) -> Math.max(v1, v2));
– Daniel de Paula
Jan 25 '17 at 9:12
thanks for the response, unfortunately, I am using Java 7 - it does not allow lambda functions. One typically has to write anonymous functions. Could you let me know what would be the solution in Java 7? I suspext a simple comparator function should work!
– tsar2512
Jan 25 '17 at 9:55
Additionally. What we are getting is the max of values which belong to each key. Is that correct?
– tsar2512
Jan 25 '17 at 9:56
@tsar2512, yes, the resulting RDD will contain a single entry for each key, containing a pair (key, maxValue). I updated the answer with versions for Java 7 and Java 8, but I haven't tested them, so please let me know if it works.
– Daniel de Paula
Jan 25 '17 at 10:10
can you give a way to do this in Java as well? I am using java and looking for exactly the same thing
– tsar2512
Jan 24 '17 at 22:47
can you give a way to do this in Java as well? I am using java and looking for exactly the same thing
– tsar2512
Jan 24 '17 at 22:47
@tsar2512 With Java 8, this might work:
new JavaPairRDD(rdd).reduceByKey((v1, v2) -> Math.max(v1, v2));– Daniel de Paula
Jan 25 '17 at 9:12
@tsar2512 With Java 8, this might work:
new JavaPairRDD(rdd).reduceByKey((v1, v2) -> Math.max(v1, v2));– Daniel de Paula
Jan 25 '17 at 9:12
thanks for the response, unfortunately, I am using Java 7 - it does not allow lambda functions. One typically has to write anonymous functions. Could you let me know what would be the solution in Java 7? I suspext a simple comparator function should work!
– tsar2512
Jan 25 '17 at 9:55
thanks for the response, unfortunately, I am using Java 7 - it does not allow lambda functions. One typically has to write anonymous functions. Could you let me know what would be the solution in Java 7? I suspext a simple comparator function should work!
– tsar2512
Jan 25 '17 at 9:55
Additionally. What we are getting is the max of values which belong to each key. Is that correct?
– tsar2512
Jan 25 '17 at 9:56
Additionally. What we are getting is the max of values which belong to each key. Is that correct?
– tsar2512
Jan 25 '17 at 9:56
@tsar2512, yes, the resulting RDD will contain a single entry for each key, containing a pair (key, maxValue). I updated the answer with versions for Java 7 and Java 8, but I haven't tested them, so please let me know if it works.
– Daniel de Paula
Jan 25 '17 at 10:10
@tsar2512, yes, the resulting RDD will contain a single entry for each key, containing a pair (key, maxValue). I updated the answer with versions for Java 7 and Java 8, but I haven't tested them, so please let me know if it works.
– Daniel de Paula
Jan 25 '17 at 10:10
add a comment |
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