Use foreachBatch or foreach methods:
https://spark.apache.org/docs/latest/structured-streaming-programming-guide.html#using-foreach-and-foreachbatch
On Wed, 10 Jan 2024, 17:42 PRASHANT L, wrote:
> Hi
> I have a use case where I need to process json payloads coming from Kafka
> using structured
This command only defines a new DataFrame, in order to see some results you
need to do something like merged_spark_data.show() on a new line.
Regarding the error I think it's typical error that you get when you run
Spark on Windows OS. You can suppress it using Winutils tool (Google it or
ChatGPT
Perhaps that parquet file is corrupted or got that is in that folder?
To check, try to read that file with pandas or other tools to see if you
can read without Spark.
On Wed, 5 Jul 2023, 07:25 elango vaidyanathan, wrote:
>
> Hi team,
>
> Any updates on this below issue
>
> On Mon, 3 Jul 2023 at
Hey AN-TRUONG
I have got some articles about this subject that should help.
E.g.
https://khalidmammadov.github.io/spark/spark_internals_rdd.html
Also check other Spark Internals on web.
Regards
Khalid
On Fri, 31 Mar 2023, 16:29 AN-TRUONG Tran Phan,
wrote:
> Thank you for your information,
>
I am not k8s expert but I think you got permission issue. Try 777 as an
example to see if it works.
On Mon, 13 Feb 2023, 21:42 karan alang, wrote:
> Hello All,
>
> I'm trying to run a simple application on GKE (Kubernetes), and it is
> failing:
> Note : I have spark(bitnami spark chart)
Hi
I believe there is a feature in Spark specifically for this purpose. You
can create a new spark session and set those configs.
Note that it's not the same as creating a separate driver processes with
separate sessions, here you will still have the same SparkContext that
works as a backend for
There is a feature in SparkContext to set localProperties
(setLocalProperty) where you can set your Request ID and then using
SparkListener instance read that ID with Job ID using onJobStart event.
Hope this helps.
On Tue, 27 Dec 2022, 13:04 Dhruv Toshniwal,
wrote:
> TL;Dr -
>
Hi Rajat
There were a lot of changes between those versions and the only possible
option to assess impact to do your testings unfortunately.
Most probably you will have to do some changes to your codebase.
Regards
Khalid
On Thu, 1 Sept 2022, 11:45 rajat kumar, wrote:
> Hello Members,
>
> We
Pool.map() missing 1 required positional argument: 'iterable'
>
> So any hints on what to change? :)
>
> Spark has the pandas on spark API, and that is realy great. I prefer
> pandas on spark API and pyspark over pandas.
>
> tor. 21. jul. 2022 kl. 09:18 skrev Khalid Mammadov &l
One quick observation is that you allocate all your local CPUs to Spark
then execute that app with 10 Threads i.e 10 spark apps and so you will
need 160cores in total as each will need 16CPUs IMHO. Wouldn't that create
CPU bottleneck?
Also on the side note, why you need Spark if you use that on
One way is to split->explode->pivot
These are column and Dataframe methods.
Here are quick examples from web:
https://www.google.com/amp/s/sparkbyexamples.com/spark/spark-split-dataframe-column-into-multiple-columns/amp/
Your scala program does not use any Spark API hence faster that others. If
you write the same code in pure Python I think it will be even faster than
Scala program, especially taking into account these 2 programs runs on a
single VM.
Regarding Dataframe and RDD I would suggest to use Dataframes
I don't know actual implementation: But, to me it's still necessary as
each worker reads data separately and reduces to get local distinct these
will then need to be shuffled to find actual distinct.
On Sun, 23 Jan 2022, 17:39 ashok34...@yahoo.com.INVALID,
wrote:
> Hello,
>
> I know some
I think, you will get 1 partition as you have only one Executor/Worker
(I.e. your local machine, a node). But your tasks (smallest unit of work
item in Spark framework) will be processed in parallel on your 4 core. As
Spark runs one task per core.
You can also force to repartition it if you want
Hi Mitch
IMO, it's done to provide most flexibility. So, some users can have
limited/restricted version of the image or with some additional software
that they use on the executors that is used during processing.
So, in your case you only need to provide the first one since the other two
configs
Hi Anil
You dont need to download and Install Spark.
It's enough to add pyspark to PyCharm as a package for your environment and
start developing and testing locally. The thing is PySpark includes local
Spark that is installed as part of pip install.
When it comes to your particular issue. I
Hi Mich
I think you need to check your code.
If code does not use PySpark API effectively you may get this. I.e. if you
use pure Python/pandas api rather than Pyspark i.e.
transform->transform->action. e.g df.select(..).withColumn(...)...count()
Hope this helps to put you on right direction.
if I can’t
answer then someone else may have. I am CCing and you can reply all.
Hope these all helps.
Regards
Khalid
Sent from my iPad
> On 25 Jul 2021, at 10:50, Dinakar Chennubotla
> wrote:
>
> Hi Khalid Mammadov,
>
> I am now, reworking from scratch i.e. on How to
.
https://github.com/khalidmammadov/spark_docker
On Sat, Jul 24, 2021 at 4:07 PM Dinakar Chennubotla <
chennu.bigd...@gmail.com> wrote:
> Hi Khalid Mammadov,
>
> I tried the which says distributed mode Spark installation. But when run
> below command it says " deployment mode =
Databricks autoscaling works..). I am not sure k8s TBH, perhaps it's
handled this more gracefully
On Sat, Jul 24, 2021 at 3:38 PM Dinakar Chennubotla <
chennu.bigd...@gmail.com> wrote:
> Hi Khalid Mammadov,
>
> Thank you for your response,
> Yes, I did, I built standalone ap
Hi,
Have you checked out docs?
https://spark.apache.org/docs/latest/spark-standalone.html
Thanks,
Khalid
On Sat, Jul 24, 2021 at 1:45 PM Dinakar Chennubotla <
chennu.bigd...@gmail.com> wrote:
> Hi All,
>
> I am Dinakar, Hadoop admin,
> could someone help me here,
>
> 1. I have a DEV-POC task
Hi David
If you need alternative way to do it you can use below:
df.select(expr("stack(2, 1,2,3)"))
Or
df.withColumn('stacked', expr("stack(2, 1,2,3)"))
Thanks
Khalid
On Mon, 14 Jun 2021, 10:14 , wrote:
> I noticed that the stack SQL function
>
ed. So also wanted to ask if this is
feasible and if yes do we need to send some special jars to executors
so that it can execute udfs on the dataframe.
On Wed, 31 Mar 2021 at 3:37 AM, Khalid Mammadov
mailto:khalidmammad...@gmail.com>> wrote:
Hi Aditya,
I think you orig
Hi Aditya,
I think you original question was as how to convert a DataFrame from
Spark session created on Java/Scala to a DataFrame on a Spark session
created from Python(PySpark).
So, as I have answered on your SO question:
There is a missing call to *entry_point* before calling getDf()
the same goes for
this BigDecimal case. So I personally would go with Scala types and compiler
should do the rest for you.
Cheers,
Khalid Mammadov
> On 18 Feb 2021, at 09:39, Ivan Petrov wrote:
>
>
> I'm fine with both. So does it make sense to use java.math.BigDecimal
> everywher
Hi,
I am not sure if you were writing pseudo-code or real one but there were
few issues in the sql.
I have reproduced you example in the Spark REPL and all worked as
expected and result is the one you need
Please see below full code:
## *Spark 3.0.0*
>>> a = spark.read.csv("tab1",
Correct. Also as explained in the book LearningSpark2.0 by Databiricks:
Unified Analytics
While the notion of unification is not unique to Spark, it is a core component
of its design philosophy and evolution. In November 2016, the Association for
Computing Machinery (ACM) recognized Apache
Perhaps you can use Global Temp Views?
https://spark.apache.org/docs/latest/api/python/pyspark.sql.html#pyspark.sql.DataFrame.createGlobalTempView
On 24/09/2020 14:52, Gang Li wrote:
Hi all,
There are three jobs, among which the first rdd is the same. Can the first
rdd be calculated once,
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