Note that both HashingTF and CountVectorizer are usually used for creating
TF-IDF normalized vectors. The definition (
https://en.wikipedia.org/wiki/Tf%E2%80%93idf#Definition) of term frequency
in TF-IDF is actually the "number of times the term occurs in the document".
So it's perhaps a bit of a
Based on your code, here is simpler test case on Spark 2.0
case class my (x: Int)
val rdd = sc.parallelize(0.until(1), 1000).map { x => my(x) }
val df1 = spark.createDataFrame(rdd)
val df2 = df1.limit(1)
df1.map { r => r.getAs[Int](0) }.first
df2.map { r => r.getAs[Int](0) }.first // Much slow
Thank you for your prompt response and great examples Sun Rui but I am
still confused about one thing. Do you see any particular reason to not
to merge subsequent limits? Following case
(limit n (map f (limit m ds)))
could be optimized to:
(map f (limit n (limit m ds)))
and further to
Widening to dev@spark
On Mon, Aug 1, 2016 at 4:21 PM, Noorul Islam K M wrote:
>
> Hi all,
>
> I was trying to test --supervise flag of spark-submit.
>
> The documentation [1] says that, the flag helps in restarting your
> application automatically if it exited with non-zero exit code.
>
> I am lo
Spark does optimise subsequent limits, for example:
scala> df1.limit(3).limit(1).explain
== Physical Plan ==
CollectLimit 1
+- *SerializeFromObject [assertnotnull(input[0, $line14.$read$$iw$$iw$my,
true], top level non-flat input object).x AS x#2]
+- Scan ExternalRDDScan[obj#1]
However, limit
Dear Spark developers,
Could you suggest how to perform pattern matching on the type of the graph edge
in the following scenario. I need to perform some math by means of
aggregateMessages on the graph edges if edges are Double. Here is the code:
def my[VD: ClassTag, ED: ClassTag] (graph: Graph[V
It seems like the += operator is missing from the new accumulator API,
although the docs still make reference to it. Anyone know if it was
intentionally not put in? I'm happy to do a PR for it or update the docs
to just use the add() method, just want to check if there was some reason
first.
Bry
I believe it was intentional with the idea that it would be more unified
between Java and Scala APIs. If your talking about the javadoc mention in
https://github.com/apache/spark/pull/14466/files - I believe the += is
meant to refer to what the internal implementation of the add function can
be for
Hi guys,
I wonder if anyone working on SQL based authorization already or not.
This is something we needed badly right now and we tried to embedded a
Hive frontend in front of SparkSQL to achieve this but it's not quite a
elegant solution. If SparkSQL has a way to do it or anyone already
work
There was SPARK-12008 which was closed.
Not sure if there is active JIRA in this regard.
On Tue, Aug 2, 2016 at 6:40 PM, 马晓宇 wrote:
> Hi guys,
>
> I wonder if anyone working on SQL based authorization already or not.
>
> This is something we needed badly right now and we tried to embedded a
> H
Hi All,
I am trying to run a spark job using yarn, and i specify --executor-cores
value as 20.
But when i go check the "nodes of the cluster" page in
http://hostname:8088/cluster/nodes then i see 4 containers getting created
on each of the node in cluster.
But can only see 1 vcore getting assigne
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