Any one?
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Nirav,
Spark does not create a duplicate column when you use the below join
expression, as an array of column(s) like below but that requires the column
name to be same in both the data frames.
Example: df1.join(df2, [‘a’])
Thanks.
Vamshi Talla
On Jul 6, 2018, at 4:47 PM, Gokula Krishnan D
Hi Ravi,
RDDs are always immutable, so you cannot change them, instead you create new
ones by transforming one. Repartition is a transformation, so it lazily
evaluated, hence computed only when you call an action on it.
Thanks.
Vamshi Talla
On Jul 8, 2018, at 12:26 PM,
When you run on Yarn, you don’t even need to start a spark cluster (spark
master and slaves). Yarn receives a job and then allocate resources for the
application master and then its workers.
Check the resources available in the node section of the resource manager UI
(and is your node actually
Hi,
Can anyone clarify how repartition works please ?
* I have a DataFrame df which has only one partition:
// Returns 1
df.rdd.getNumPartitions
* I repartitioned it by passing "3" and assigned it a new DataFrame
newdf
val newdf = df.repartition(3)
*
@yohann sorry I am assuming you meant application master if so I believe
spark is the one that provides application master. Is there anyway to look
for how much resources are being requested and how much yarn is allowed to
provide? I would assume this is a common case if so I am not sure why these
yarn.scheduler.capacity.maximum-am-resource-percent by default is set to
0.1 and I tried changing it to 1.0 and still no luck. same problem
persists. The master here is yarn and I just trying to spawn spark-shell
--master yarn --deploy-mode client and run a simple world count so I am not
sure why
Following the logs from the resource manager:
2018-07-08 07:23:23,382 WARN
org.apache.hadoop.yarn.server.resourcemanager.scheduler.capacity.LeafQueue:
maximum-am-resource-percent is insufficient to start a single application in
queue, it is likely set too low. skipping enforcement to allow at
>From Stackoverflow:
from pyspark.sql.types import StructType
from pyspark.sql.types import StructField
from pyspark.sql.types import StringType
sc = SparkContext(conf=SparkConf())
spark = SparkSession(sc) # Need to use SparkSession(sc) to
createDataFrame
schema = StructType([
Are you able to run a simple Map Reduce job on yarn without any issues?
If you have any issues: I had this problem on Mac. Use CSRUTIL in Mac, to
disable it. Then add a softlink
sudo ln –s /usr/bin/java/bin/java
The new versions of Mac from EL Captain does not allow softlinks in
Hi Dimitris,
Could you explain your use case in a bit more details?
What you are asking for, if I understand you correctly, is not the advised
way to go about.
If you're running analytics and expect their output to be a Dataframe with
the specified columns, then you should compose your queries
Hi,
It's on local mac book pro machine that has 16GB RAM 512GB disk and 8 vCpu!
I am not running any code since I can't even spawn spark-shell with yarn as
master as described in my previous email. I just want to run simple word
count using yarn as master.
Thanks!
Below is the resource manager
You running on emr? You checked the emr logs?
Was in similar situation where job was stuck in accepted and then it
died..turned out to be an issue w. My code when running g with huge
data.perhaps try to reduce gradually the load til it works and then start
from there?
Not a huge help but I
Hi All,
I am trying to run a simple word count using YARN as a cluster manager. I
am currently using Spark 2.3.1 and Apache hadoop 2.7.3. When I spawn
spark-shell like below it gets stuck in ACCEPTED stated forever.
./bin/spark-shell --master yarn --deploy-mode client
I set my
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