No Data Transfer During Creation: --> Data transfer occurs only when an
> action is triggered.
> Distributed Processing: --> DataFrames are distributed for parallel
> execution, not stored entirely on the driver node.
> Lazy Evaluation Optimization: --> Delaying data transf
On Mon, Mar 18, 2024 at 1:16 PM Mich Talebzadeh
wrote:
>
> "I may need something like that for synthetic data for testing. Any way to
> do that ?"
>
> Have a look at this.
>
> https://github.com/joke2k/faker
>
No I was not actually referring to data that can be faked. I want data to
actually res
eyan Chakravarty wrote:
>
> On Fri, Mar 15, 2024 at 3:10 AM Mich Talebzadeh
> wrote:
>
>>
>> No Data Transfer During Creation: --> Data transfer occurs only when an
>> action is triggered.
>> Distributed Processing: --> DataFrames are distributed
On Fri, Mar 15, 2024 at 3:10 AM Mich Talebzadeh
wrote:
>
> No Data Transfer During Creation: --> Data transfer occurs only when an
> action is triggered.
> Distributed Processing: --> DataFrames are distributed for parallel
> execution, not stored entirely on the driver no
disk. look at
stages tab in UI (4040)
*In summary:*
No Data Transfer During Creation: --> Data transfer occurs only when an
action is triggered.
Distributed Processing: --> DataFrames are distributed for parallel
execution, not stored entirely on the driver node.
Lazy Evaluation Optimi
I am trying to understand Spark Architecture.
For Dataframes that are created from python objects ie. that are *created
in memory where are they stored ?*
Take following example:
from pyspark.sql import Rowimport datetime
courses = [
{
'course_id': 1,
m Verma
Sent: Monday, December 26, 2022 8:08 PM
To: Russell Jurney
Cc: Gurunandan ; user@spark.apache.org
Subject: EXT: Re: Check if shuffle is caused for repartitioned pyspark
dataframes
EXTERNAL: Report suspicious emails to Email Abuse.
I tried sorting the repartitioned dataframes on the pa
I tried sorting the repartitioned dataframes on the partition key before
saving them as parquet files, however, when I read those
repartitioned-sorted dataframes
and join them on the partition key, the spark plan still shows `Exchange
hashpartitioning` step, which I want to avoid
ut I can see it in both
> the experiments:
> 1. Using repartitioned dataframes
> 2. Using initial dataframes
>
> Does that mean that the repartitioned dataframes are not actually
> "co-partitioned"?
> If that's the case, I have two more questions:
>
> 1
Hi Gurunandan,
Thanks for the reply!
I do see the exchange operator in the SQL tab, but I can see it in both the
experiments:
1. Using repartitioned dataframes
2. Using initial dataframes
Does that mean that the repartitioned dataframes are not actually
"co-partitioned"?
If that
Hello folks,
I have a use case where I save two pyspark dataframes as parquet files and
then use them later to join with each other or with other tables and
perform multiple aggregations.
Since I know the column being used in the downstream joins and groupby, I
was hoping I could use co
is the reason you got the IOM and analysis exception.
my suggestion is you need checkpoint the dataframe when joined 200 dataframes.
so you can trancate the lineage. so the optimizer only analysis the 200
dataframe.
this will reduce the pressure of spark engine.
| |
Hollis
|
Replied
dataframes may apply and RDD
are used, but for UDF's I prefer SQL as well, but that may be a
personal idiosyncrasy. The Oreilly book on data algorithms using SPARK,
pyspark uses dataframes and RDD API's :)
Regards,
Gourav Sengupta
On Fri, Dec 24, 2021 at 6:11 PM Sean Owen wrote:
operators will be faster.
Sometimes you have to go outside SQL where necessary, like in UDFs or
complex aggregation logic. Then you can't use SQL.
On Fri, Dec 24, 2021 at 12:05 PM Gourav Sengupta
wrote:
> Hi,
>
> yeah I think that in practice you will always find that dataframes can
&
Hi,
yeah I think that in practice you will always find that dataframes can give
issues regarding a lot of things, and then you can argue. In the SPARK
conference, I think last year, it was shown that more than 92% or 95% use
the SPARK SQL API, if I am not mistaken.
I think that you can do the
Hi Sean and Gourav
Thanks for the suggestions. I thought that both the sql and dataframe apis are
wrappers around the same frame work? Ie. catalysts.
I tend to mix and match my code. Sometimes I find it easier to write using sql
some times dataframes. What is considered best practices?
Here
the same
>> ordering, and a sort/hash merge doesn't need to be done?
>>
>> Thanks
>> Andrew
>>
>> On Wed, May 12, 2021 at 11:07 AM Sean Owen wrote:
>> >
>> > Yeah I don't think that's going to work - you aren't guarantee
ed to be done?
>
> Thanks
> Andrew
>
> On Wed, May 12, 2021 at 11:07 AM Sean Owen wrote:
> >
> > Yeah I don't think that's going to work - you aren't guaranteed to get
> 1, 2, 3, etc. I think row_number() might be what you need to generate a
> join ID.
tent is explicitly
>> disclaimed. The author will in no case be liable for any monetary damages
>> arising from such loss, damage or destruction.
>>
>>
>>
>>
>> On Tue, 18 May 2021 at 16:39, kushagra deep
>> wrote:
>>
>>> The use cas
;
>
> On Tue, 18 May 2021 at 16:39, kushagra deep
> wrote:
>
>> The use case is to calculate PSI/CSI values . And yes the union is one to
>> one row as you showed.
>>
>> On Tue, May 18, 2021, 20:39 Mich Talebzadeh
>> wrote:
>>
>>>
>>>
Kushagra,
>>
>> A bit late on this but what is the business use case for this merge?
>>
>> You have two data frames each with one column and you want to UNION them
>> in a certain way but the correlation is not known. In other words this
>> UNION is as is?
>>
>>
shagra deep
> wrote:
>
>> Hi All,
>>
>> I have two dataframes
>>
>> df1
>>
>> amount_6m
>> 100
>> 200
>> 300
>> 400
>> 500
>>
>> And a second data df2 below
>>
>> amount_9m
>>
500
200 600
HTH
On Wed, 12 May 2021 at 13:51, kushagra deep
wrote:
> Hi All,
>
> I have two dataframes
>
> df1
>
> amount_6m
> 100
> 200
> 300
> 400
> 500
>
> And a second data df2 below
>
> amount_9m
> 500
> 600
d. In our initial implementation, we had
> a series of r one per rule. For N rules, we created N dataframes that had the
> rows that satisfied the rules. The we unioned the N data frames. Horrible
> performance that didn't scale with N. We reimplemented to add N Boolean
> c
ation, we had a
series of r one per rule. For N rules, we created N dataframes that had the
rows that satisfied the rules. The we unioned the N data frames. Horrible
performance that didn't scale with N. We reimplemented to add N Boolean
columns; one per rule; that indicated if the rule was sat
In our case, these UDFs are quite expensive and worked on in an
iterative manner, so being able to cache the two "sides" of the graphs
independently will speed up the development cycle. Otherwise, if you
modify foo() here, then you have to recompute bar and baz, even though
they're unchanged.
df.w
Why join here - just add two columns to the DataFrame directly?
On Mon, May 17, 2021 at 1:04 PM Andrew Melo wrote:
> Anyone have ideas about the below Q?
>
> It seems to me that given that "diamond" DAG, that spark could see
> that the rows haven't been shuffled/filtered, it could do some type o
drew
>
> On Wed, May 12, 2021 at 11:07 AM Sean Owen wrote:
> >
> > Yeah I don't think that's going to work - you aren't guaranteed to get 1,
> > 2, 3, etc. I think row_number() might be what you need to generate a join
> > ID.
> >
> > R
think row_number() might be what you need to generate a join ID.
>
> RDD has a .zip method, but (unless I'm forgetting!) DataFrame does not. You
> could .zip two RDDs you get from DataFrames and manually convert the Rows
> back to a single Row and back to DataFrame.
Yeah I don't think that's going to work - you aren't guaranteed to get 1,
2, 3, etc. I think row_number() might be what you need to generate a join
ID.
RDD has a .zip method, but (unless I'm forgetting!) DataFrame does not. You
could .zip two RDDs you get from DataFrames and
Thanks Raghvendra
Will the ids for corresponding columns be same always ? Since
monotonic_increasing_id() returns a number based on partitionId and the row
number of the partition ,will it be same for corresponding columns? Also
is it guaranteed that the two dataframes will be divided into
ot;), "inner").drop("id").show()
+-+-+
|amount_6m|amount_9m|
+-+-+
| 100| 500|
| 200| 600|
| 300| 700|
| 400| 800|
| 500| 900|
+-+-+
--
Raghavendra
On Wed, May 12, 2021 at 6:20 PM kushagra deep
w
Hi All,
I have two dataframes
df1
amount_6m
100
200
300
400
500
And a second data df2 below
amount_9m
500
600
700
800
900
The number of rows is same in both dataframes.
Can I merge the two dataframes to achieve below df
df3
amount_6m | amount_9m
100
I am creating a spark structured streaming job, where I need to find the
difference between two dataframes.
Dataframe 1 :
[1, item1, value1]
[2, item2, value2]
[3, item3, value3]
[4, item4, value4]
[5, item5, value5]
Dataframe 2:
[4, item4, value4]
[5, item5, value5]
New Dataframe with
incurring IO overhead on every microbatch.
From: Arti Pande
Date: Friday, November 13, 2020 at 2:19 PM
To: "Lalwani, Jayesh"
Cc: "user@spark.apache.org"
Subject: RE: [EXTERNAL] Refreshing Data in Spark Memory (DataFrames)
CAUTION: This email originated from outside of the
old
> computation, your results don’t change.
> There might be scenarios where you want to correct old reference data. In
> this case you update your reference table, and rerun your computation.
>
>
>
> Now, if you are talking about streaming applications, then it’s a
> diff
reference data. Spark reloads the dataframes
from batch sources at the beginning of every microbatch. As long as you are
reading the data from from a non-streaming source, it will get refreshed in
every microbatch. Alternatively, you can send updates to reference data through
a stream, and then merge
Hi
In the financial systems world, if some data is being updated too
frequently, and that data is to be used as reference data by a Spark job
that runs for 6/7 hours, most likely Spark job may read that data at the
beginning and keep it in memory as DataFrame and will keep running for
remaining 6/
Hello,
I need to filter one huge table using others huge tables.
I could not avoid sort operation using `WHERE IN` or `INNER JOIN`.
Can this be avoided?
As I'm ok with false positives maybe Bloom filter is an alternative.
I saw that Scala has a builtin dataframe function
(https://spark.apache.o
Hi Jorge,
Thank you. This union function is better alternative for my work.
Regards,
Tanveer Ahmad
From: Jorge Machado
Sent: Monday, May 25, 2020 3:56:04 PM
To: Tanveer Ahmad - EWI
Cc: Spark Group
Subject: Re: Arrow RecordBatches/Pandas Dataframes to (Arrow
Hey, from what I know you can try to Union them df.union(df2)
Not sure if this is what you need
> On 25. May 2020, at 13:53, Tanveer Ahmad - EWI wrote:
>
> Hi all,
>
> I need some help regarding Arrow RecordBatches/Pandas Dataframes to (Arrow
> enabled) Spark Dataframe c
Hi all,
I need some help regarding Arrow RecordBatches/Pandas Dataframes to (Arrow
enabled) Spark Dataframe conversions.
Here the example explains very well how to convert a single Pandas Dataframe to
Spark Dataframe [1].
But in my case, some external applications are generating Arrow
fle. Spark automatically caches when a data shuffle
happens.
Let me know if you get it to work.
Regards
On Mon, May 18, 2020 at 10:27 PM Mohit Durgapal
wrote:
> Dear All,
>
> I would like to know how, in spark 2.0, can I split a dataframe into two
> dataframes when I know the exact cou
Dear All,
I would like to know how, in spark 2.0, can I split a dataframe into two
dataframes when I know the exact counts the two dataframes should have. I
tried using limit but got quite weird results. Also, I am looking for exact
counts in child dfs, not the approximate % based split
Hi all,
I'm using ML Pipeline to construct a flow of transformation. I'm wondering
if it is possible to set multiple dataframes as the input of a transformer?
For example I need to join two dataframes together in a transformer, then
feed into the estimator for training. If not, is ther
Hi all,
I'm using ML Pipeline to construct a flow of transformation. I'm wondering
if it is possible to set multiple dataframes as the input of a transformer?
For example I need to join two dataframes together in a transformer, then
feed into the estimator for training. If not, is ther
Hi all!
Is it possible that Spark creates under certain circumstances duplicate
rows when doing multiple joins?
What I did:
buse.count
res0: Long = 20554365
buse.alias("buse").join(bdef.alias("bdef"), $"buse._c4"===$"bdef._c4").count
res1: Long = 20554365
buse.alias("buse").join(bdef.alia
> either materialize the Dataframe on HDFS (e.g. parquet or checkpoint)
I wonder if avro is a better candidate for this because it's row
oriented it should be faster to write/read for such a task. Never heard
about checkpoint.
Enrico Minack writes:
> It is not about very large or small, it is
It is not about very large or small, it is about how large your cluster
is w.r.t. your data. Caching is only useful if you have the respective
memory available across your executors. Otherwise you could either
materialize the Dataframe on HDFS (e.g. parquet or checkpoint) or indeed
have to do t
> .dropDuplicates() \ .cache() |
> Since df_actions is cached, you can count inserts and updates quickly
> with only that one join in df_actions:
Hi Enrico. I am wondering if this is ok for very large tables ? Is
caching faster than recomputing both insert/update ?
Thanks
Enrico Minack writes
Hi,
Thank you both for your suggestions! These have been eyeopeners for me.
Just to clarify, I need the counts for logging and auditing purposes
otherwise I would exclude the step. I should have also mentioned that
while I am processing around 30 GB of raw data, the individual outputs are
relat
Ashley,
I want to suggest a few optimizations. The problem might go away but at
least performance should improve.
The freeze problems could have many reasons, the Spark UI SQL pages and
stages detail pages would be useful. You can send them privately, if you
wish.
1. the repartition(1) shoul
Hi ashley,
Apologies reading this on my phone as work l laptop doesn't let me access
personal email.
Are you actually doing anything with the counts (printing to log, writing
to table?)
If you're not doing anything with them get rid of them and the caches
entirely.
If you do want to do somethin
Thanks David,
I did experiment with the .cache() keyword and have to admit I didn't see
any marked improvement on the sample that I was running, so yes I am a bit
apprehensive including it (not even sure why I actually left it in).
When you say "do the count as the final step", are you referring
Hi Ashley,
I'm not an expert but think this is because spark does lazy execution and
doesn't actually perform any actions until you do some kind of write, count
or other operation on the dataframe.
If you remove the count steps it will work out a more efficient execution
plan reducing the number
Hi,
I am currently working on an app using PySpark to produce an insert and
update daily delta capture, being outputted as Parquet. This is running on
a 8 core 32 GB Linux server in standalone mode (set to 6 worker cores of
2GB memory each) running Spark 2.4.3.
This is being achieved by reading
Dear Yeikel
I checked my code and it uses getOrCreate to create a SparkSession.
Therefore, I should be retrieving the same SparkSession instance everytime I
call that method.
Thanks for your reminding.
Best regard
--
Sent from: http://apache-spark-user-list.1001560.n3.nabble.com/
---
d
HDFS with given parameters. This function returns a pyspark dataframe and
the SparkContext it used.
With client's increasing demands, I need to merge data from multiple query.
I tested using "union" function to merge the pyspark dataframes returned by
different function calls d
ot; function to merge the pyspark dataframes returned by
different function calls directly and it worked. This surprised me that
pyspark dataframe can actually union dataframes from different SparkSession.
I am using pyspark 2.3.1 and Python 3.5.
I wonder if this is a good practice or I
ions
there.
On Sun, Oct 6, 2019 at 2:49 PM KhajaAsmath Mohammed
wrote:
>
> Hi,
>
> What is the best approach to loop through 3 dataframes in scala based on
> some keys instead of using collect.
>
> Thanks,
> Asmath
>
--
Twitter: https://twitter.com/holdenkarau
Books (L
Hi,
What is the best approach to loop through 3 dataframes in scala based on
some keys instead of using collect.
Thanks,
Asmath
Hi ,
I have a use case where I have to cogroup two streams using cogroup in
streaming. However when I do so I get an exception that “Cogrouping in
streaming is not supported in DataFrame/Dataset”. Please clarify.
Regards ,
Kushagra Deep
ts of the plan refer to static pieces of
>> data ..."* Could you elaborate a bit more on what does this static
>> piece of data refer to? Are you referring to the 10 records that had
>> already arrived at T1 and are now sitting as old static data in the
>> unbounded t
tatic pieces of
>> data ..."* Could you elaborate a bit more on what does this static
>> piece of data refer to? Are you referring to the 10 records that had
>> already arrived at T1 and are now sitting as old static data in the
>> unbounded table?
>>
>> Regard
t; ..."* Could you elaborate a bit more on what does this static piece of
> data refer to? Are you referring to the 10 records that had already arrived
> at T1 and are now sitting as old static data in the unbounded table?
>
> Regards
> Sheel
>
>
> On Thu, May 16, 2019 at 3
AM Russell Spitzer
wrote:
> Dataframes describe the calculation to be done, but the underlying
> implementation is an "Incremental Query". That is that the dataframe code
> is executed repeatedly with Catalyst adjusting the final execution plan on
> each run. Some parts of th
Dataframes describe the calculation to be done, but the underlying
implementation is an "Incremental Query". That is that the dataframe code
is executed repeatedly with Catalyst adjusting the final execution plan on
each run. Some parts of the plan refer to static pieces of data, other
Hi
Structured Streaming treats a stream as an unbounded table in the form of a
DataFrame. Continuously flowing data from the stream keeps getting added to
this DataFrame (which is the unbounded table) which warrants a change to
the DataFrame which violates the vary basic nature of a DataFrame since
am new to spark and want to start contributing to Apache spark to know more
> about it.
> I found this JIRA to have "Standardized Join Types for DataFrames", which I
> feel could be a good starter task for me. I wanted to confirm if this is a
> relevant/actionable task and i
Hi,
I am new to spark and want to start contributing to Apache spark to know
more about it.
I found this JIRA to have "Standardized Join Types for DataFrames", which I
feel could be a good starter task for me. I wanted to confirm if this is a
relevant/actionable task and if I can start
: What are the alternatives to nested DataFrames?
2 options I can think of:
1- Can you perform a union of dfs returned by elastic research queries. It
would still be distributed but I don't know if you will run out of how many
union operations you can perform at a time.
2- Can you used
m...@yeikel.com
> *Cc:* Shahab Yunus ; user
> *Subject:* Re: What are the alternatives to nested DataFrames?
>
>
>
> Could you join() the DFs on a common key?
>
>
>
> On Fri, Dec 28, 2018 at 18:35 wrote:
>
> Shabad , I am not sure what you are trying to say. Could
iginal DF and returns a new dataframe including all the
matching terms
From: Andrew Melo
Sent: Friday, December 28, 2018 8:48 PM
To: em...@yeikel.com
Cc: Shahab Yunus ; user
Subject: Re: What are the alternatives to nested DataFrames?
Could you join() the DFs on a common key?
tString(0)*
>
>
>
> * val qb = QueryBuilders.matchQuery("name",
> city).operator(Operator.AND)*
>
> * print(qb.toString)*
>
>
>
> * val dfs = sqlContext.esDF("cities/docs", qb.toString) // null
> pointer*
>
>
>
> * dfs.show()*
>
>
>
&g
uery("name", city).operator(Operator.AND)
print(qb.toString)
val dfs = sqlContext.esDF("cities/docs", qb.toString) // null pointer
dfs.show()
})
From: Shahab Yunus
Sent: Friday, December 28, 2018 12:34 PM
To: em...@yeikel.com
Cc: user
Sub
not support the nesting of
> DataFrames , but what are the options?
>
>
>
> I have the following scenario :
>
>
>
> dataFrame1 = List of Cities
>
>
>
> dataFrame2 = Created after searching in ElasticSearch for each city in
> dataFrame1
>
>
>
&g
Hi community ,
As shown in other answers online , Spark does not support the nesting of
DataFrames , but what are the options?
I have the following scenario :
dataFrame1 = List of Cities
dataFrame2 = Created after searching in ElasticSearch for each city in
dataFrame1
I
s added to a python array like
the_dfs.append(df.select(cols).toDF(*cols).cache())
the_dfs[len(the_dfs)].count()
The dataframes are finally combined using
df_all = reduce(DataFrame.union, the_dfs).cache()
df_all.count()
THE CURRENT STATE:
The proof of concept works on a smaller amount of data
e where P1 = key._1 and P2 = key._2")
> }
>
> Regards,
> Nirav
>
>
> On Wed, Aug 1, 2018 at 4:18 PM, Koert Kuipers wrote:
>
>> this works for dataframes with spark 2.3 by changing a global setting,
>> and will be configurable per write in 2.4
>&g
t;)
}
Regards,
Nirav
On Wed, Aug 1, 2018 at 4:18 PM, Koert Kuipers wrote:
> this works for dataframes with spark 2.3 by changing a global setting, and
> will be configurable per write in 2.4
> see:
> https://issues.apache.org/jira/browse/SPARK-20236
> https://issues.apache.org/ji
this works for dataframes with spark 2.3 by changing a global setting, and
will be configurable per write in 2.4
see:
https://issues.apache.org/jira/browse/SPARK-20236
https://issues.apache.org/jira/browse/SPARK-24860
On Wed, Aug 1, 2018 at 3:11 PM, Nirav Patel wrote:
> Hi Peay,
>
>
Hi Peay,
Have you find better solution yet? I am having same issue.
Following says it works with spark 2.1 onward but only when you use
sqlContext and not Dataframe
https://medium.com/@anuvrat/writing-into-dynamic-partitions-using-spark-2e2b818a007a
Thanks,
Nirav
On Mon, Oct 2, 2017 at 4:37 AM,
Hi guys,
I was wondering if there is a way to compress files using zstd. It seems
zstd compression can be used for shuffle data, is there a way to use it for
output data as well?
Thanks
Nikhil
d Streaming(SSS) and explode
>>> the data and flatten all data into single record using DataFrame joins
>>> and
>>> land into a relational database table(DB2).
>>>
>>> But we are getting the following error when we write into d
ng DataFrame joins and
>> land into a relational database table(DB2).
>>
>> But we are getting the following error when we write into db using JDBC.
>>
>> “org.apache.spark.sql.AnalysisException: Inner join between two streaming
>> DataFrames/Datasets
using DataFrame joins and
> land into a relational database table(DB2).
>
> But we are getting the following error when we write into db using JDBC.
>
> “org.apache.spark.sql.AnalysisException: Inner join between two streaming
> DataFrames/Datasets is not supported;”
>
>
Thanks for the quick response...I'm able to inner join the dataframes with
regular spark session. The issue is only with the spark streaming session.
BTW I'm using Spark 2.2.0 version...
--
Sent from: http://apache-spark-user-list.1001560.n3.
en we write into db using JDBC.
>
> “org.apache.spark.sql.AnalysisException: Inner join between two streaming
> DataFrames/Datasets is not supported;”
>
> Any help would be greatly appreciated.
>
> Thanks,
> Thomas Thomas
> Mastermind Solutions LLC.
>
>
>
>
table(DB2).
But we are getting the following error when we write into db using JDBC.
“org.apache.spark.sql.AnalysisException: Inner join between two streaming
DataFrames/Datasets is not supported;”
Any help would be greatly appreciated.
Thanks,
Thomas Thomas
Mastermind Solutions LLC.
--
Sent
I suppose that it's hardly possible that this issue is connected with
the string encoding, because
- "pr^?files.10056.10040" should be "profiles.10056.10040" and is
defined as constant in the source code
-
"profiles.total^@^@f2-a733-9304fda722ac^@^@^@^@profiles.10361.10005^@^@^@^@.total^@^@0075^@
Encoding issue of the data? Eg spark uses utf-8 , but source encoding is
different?
> On 28. Mar 2018, at 20:25, Sergey Zhemzhitsky wrote:
>
> Hello guys,
>
> I'm using Spark 2.2.0 and from time to time my job fails printing into
> the log the following errors
>
> scala.MatchError:
> profiles
Hello guys,
I'm using Spark 2.2.0 and from time to time my job fails printing into
the log the following errors
scala.MatchError:
profiles.total^@^@f2-a733-9304fda722ac^@^@^@^@profiles.10361.10005^@^@^@^@.total^@^@0075^@^@^@^@
scala.MatchError: pr^?files.10056.10040 (of class java.lang.String)
sc
ne B
df_t =
df.groupby("ID").agg(F.countDistinct("LABEL").alias("nL")).filter(F.col("nL")>1)
df = df.join(df_t.select("ID"),["ID"])
df_sw = df.groupby(["ID","kk"]).count().withColumnRenamed("count", "cnt1&
Hi Spark-users:
I have a dataframe "df_t" which was generated from other dataframes by
several transformations. And then I did something very simple, just
counting the rows, that is the following code:
(A)
df_t_1 = df_t.groupby(["Id","key"]).count().withColumnR
I have been interested in finding out why I am getting strange behavior
when running a certain spark job. The job will error out if I place an
action (A .show(1) method) either right after caching the DataFrame or
right before writing the dataframe back to hdfs. There is a very similar
post to Stac
You are using Spark Streaming Kafka package. The correct package name is "
org.apache.spark:spark-sql-kafka-0-10_2.11:2.2.0"
On Mon, Nov 20, 2017 at 4:15 PM, salemi wrote:
> Yes, we are using --packages
>
> $SPARK_HOME/bin/spark-submit --packages
> org.apache.spark:spark-streaming-kafka-0-10_2.1
Yes, we are using --packages
$SPARK_HOME/bin/spark-submit --packages
org.apache.spark:spark-streaming-kafka-0-10_2.11:2.2.0 --py-files shell.py
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On Mon, Nov 20, 2017 at 3:07 PM, salemi wrote:
> Hi All,
>
> we are trying to use DataFrames approach with Kafka 0.10 and PySpark 2.2.0.
> We followed the instruction on the wiki
> https://spark.apache.org/docs/latest/struc
Hi All,
we are trying to use DataFrames approach with Kafka 0.10 and PySpark 2.2.0.
We followed the instruction on the wiki
https://spark.apache.org/docs/latest/structured-streaming-kafka-integration.html.
We coded something similar to the code below using Python:
df = spark \
.read \
.format
Is union of 2 Structured streaming dataframes from different sources supported
in 2.2?
We have done a union of 2 streaming dataframes that are from the same source. I
wanted to know if multiple streams are supported or going to be supported in
the future
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