Hi Artemis,
Thanks for your input, to answer your questions:
> You may want to ask yourself why it is necessary to change the jar
packages during runtime.
I have a long running orchestrator process, which executes multiple spark
jobs, currently on a single VM/driver, some of those jobs might
requ
You can run Spark in local mode and not require any standalone master or
worker.
Are you sure you're not using local mode? are you sure the daemons aren't
running?
What is the Spark master you pass?
On Wed, Mar 9, 2022 at 7:35 PM wrote:
> What I tried to say is, I didn't start spark master/worke
What I tried to say is, I didn't start spark master/worker at all, for a
standalone deployment.
But I still can login into pyspark to run the job. I don't know why.
$ ps -efw|grep spark
$ netstat -ntlp
both the output above have no spark related info.
And this machine is managed by myself, I k
Okay, found the root cause. Our k8s image got some changes, including a
mess with some jars dependencies around com.fasterxml.jackson ...
Sorry for the inconvenience.
Some earlier log in the driver contained that info...
[2022-03-09 21:54:25,163] ({task-result-getter-3}
Logging.scala[logWarning
Full trace doesn't provide any further details. It looks like this:
Py4JJavaError: An error occurred while calling o337.showString. :
org.apache.spark.SparkException: Job aborted due to stage failure: Task 1
in stage 18.0 failed 4 times, most recent failure: Lost task 1.3 in stage
18.0 (TID 220) (
I am not sure what column/properties you are referring to. But the
event log in Spark deals with application level "events', not JVM-level
metrics. To retrieve the JVM metrics, you need to use the REST API
provided in Spark. Please see
https://spark.apache.org/docs/latest/monitoring.html for
Doesn't quite seem the same. What is the rest of the error -- why did the
class fail to initialize?
On Wed, Mar 9, 2022 at 10:08 AM Andreas Weise
wrote:
> Hi,
>
> When playing around with spark.dynamicAllocation.enabled I face the
> following error after the first round of executors have been ki
Hi,
When playing around with spark.dynamicAllocation.enabled I face the
following error after the first round of executors have been killed.
Py4JJavaError: An error occurred while calling o337.showString. :
org.apache.spark.SparkException: Job aborted due to stage failure: Task 1
in stage 18.0 fa
This is indeed a JVM issue, not a Spark issue. You may want to ask
yourself why it is necessary to change the jar packages during runtime.
Changing package doesn't mean to reload the classes. There is no way to
reload the same class unless you customize the classloader of Spark. I
also don't
To be specific:
1. Check the log files on both master and worker and see if any errors.
2. If you are not running your browser on the same machine and the
Spark cluster, please use the host's external IP instead of
localhost IP when launching the worker
Hope this helps...
-- ND
On 3/9/22
Hi,
I am trying to calculate CPU utilization of an Executor(JVM level CPU
usage) using Event log. Can someone please help me with this?
1) Which column/properties to select
2) the correct formula to derive cpu usage
Has anyone done anything similar to this?
We have many pipelines and those are
Sean,
I understand you might be sceptical about adding this functionality into
(py)spark, I'm curious:
* would error/warning on update in configuration that is currently
effectively impossible (requires restart of JVM) be reasonable?
* what do you think about the workaround in the issue?
Cheers - R
Unfortunately this opens a lot more questions and problems than it solves.
What if you take something off the classpath, for example? change a class?
On Wed, Mar 9, 2022 at 8:22 AM Rafał Wojdyła wrote:
> Thanks Sean,
> To be clear, if you prefer to change the label on this issue from bug to
> st
Thanks Sean,
To be clear, if you prefer to change the label on this issue from bug to
sth else, feel free to do so, no strong opinions on my end. What happens to
the classpath, whether spark uses some classloader magic, is probably an
implementation detail. That said, it's definitely not intuitive
Did it start successfully? What do you mean ports were not opened?
On Wed, Mar 9, 2022 at 3:02 AM wrote:
> Hello
>
> I have spark 3.2.0 deployed in localhost as the standalone mode.
> I even didn't run the start master and worker command:
>
> start-master.sh
> start-worker.sh spark://1
That isn't a bug - you can't change the classpath once the JVM is executing.
On Wed, Mar 9, 2022 at 7:11 AM Rafał Wojdyła wrote:
> Hi,
> My use case is that, I have a long running process (orchestrator) with
> multiple tasks, some tasks might require extra spark dependencies. It seems
> once the
Hi,
My use case is that, I have a long running process (orchestrator) with
multiple tasks, some tasks might require extra spark dependencies. It seems
once the spark context is started it's not possible to update
`spark.jars.packages`? I have reported an issue at
https://issues.apache.org/jira/brow
Hello
I have spark 3.2.0 deployed in localhost as the standalone mode.
I even didn't run the start master and worker command:
start-master.sh
start-worker.sh spark://127.0.0.1:7077
And the ports (such as 7077) were not opened there.
But I still can login into pyspark to run the jobs.
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