I'm seeing the same behavior in Spark 2.0.1. Does anybody have an
explanation?
Thanks!
Kaspar
bmiller1 wrote
> Hi All,
>
> I've recently noticed some caching behavior which I did not understand
> and may or may not have indicated a bug. In short, the web UI seemed
> to indicate that some block
Hi,
I have multiple hive configurations(hive-site.xml) and because of that only
I am not able to add any hive configuration in spark *conf* directory. I
want to add this configuration file at start of any *spark-submit* or
*spark-shell*. This conf file is huge so *--conf* is not a option for me.
I would say Java, since it will be somewhat similar to Scala. Now, this
assumes that you have some app already written in Scala. If you don't, then
pick the language that you feel most comfortable with.
Thank you,
Irving Duran
On Feb 9, 2017, at 11:59 PM, nancy henry wrote:
Hi All,
Is it b
Hi all,
I'm struggling (Spark / Scala newbie) to create a DataFrame from a C* table
but also create a DataFrame from column with json .
e.g. From C* table
| id | jsonData |
==
| 1 | {"a": "123", "b": "xyz" } |
+--+---+
| 2 |
Hi All,
Is it better to Use Java or Python on Scala for Spark coding..
Mainly My work is with getting file data which is in csv format and I have
to do some rule checking and rule aggrgeation
and put the final filtered data back to oracle so that real time apps can
use it..
the spark version is 2.1.0
--发件人：方孝健(玄弟)
发送时间：2017年2月10日(星期五) 12:35收件人：spark-dev
; spark-user 主 题：Driver hung and
happend out of memory while writing to console progress bar
[Stage 172:==>
[Stage 172:==> (10328 + 93) / 16144]
[Stage 172:==> (10329 + 93) / 16144]
[Stage 172:==> (10330 + 93) / 16144]
[Stage 172:==>
Dear spark users,
>From this site https://spark.apache.org/docs/latest/tuning.html where it
offers recommendation on setting the level of parallelism
Clusters will not be fully utilized unless you set the level of parallelism
> for each operation high enough. Spark automatically sets the number o
Pinging again on this topic.
Is there an easy way to select TopN in a RelationalGroupedDataset?
Basically in the below example dataSet.groupBy("Column1").agg(udaf("Column2",
"Column3") returns a RelationalGroupedDataset. One way to address the data
skew would be to reduce the data per key (Column1
Hi,
Yes, that's ForeachWriter.
Yes, it works with element by element. You're looking for mapPartition
and ForeachWriter has partitionId that you could use to implement a
similar thing.
Pozdrawiam,
Jacek Laskowski
https://medium.com/@jaceklaskowski/
Mastering Apache Spark 2.0 https://bit.ly/
> by specifying a larger heap size than default on each worker node.
I don't follow. Which heap? Are you specifying a large heap size on the
executors? If so, do you mean you somehow launch the shuffle service when
you launch executors? Or something else?
On Wed, Feb 8, 2017 at 5:50 PM, Sun R
Probably something like this.
dataset
.filter { userData =>
val dateThreshold = lookupThreshold(record)// look up the
threshold date based on the record details
userData.date > dateThreshold // compare
}
.groupBy()
.count()
This would probabl
Hi,
I was wondering on how foreachRDD would run.
Specifically, let's say I do something like (nothing real, just for
understanding):
var df = ???
var counter = 0
dstream.foreachRDD {
rdd: RDD[Long] => {
val df2 = rdd.toDF(...)
df = df.union(df2)
counter += 1
if (counter
Error in the highlighted line. Code, error and pom.xml included below
code :
final Session session = connector.openSession();
final PreparedStatement prepared = session.prepare("INSERT INTO
spark_test5.messages JSON?");
JavaStreamingContext ssc = new JavaStreamingContext(s
If you have to get the data into parquet format for other reasons then I
think count() on the parquet should be better. If it just the count you need
using database sending dbTable = (select count(*) from ) might be
quicker, t will avoid unnecessary data transfer from the database to spar
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