rstudio/sparklyr

Javier Luraschi

@javierluraschi

I’ll try to take a look at it tomorrow or ask Yitao for help

Javier Luraschi

@javierluraschi

AH, yes, a fix was needed in the serializer. Here is the PR @dkincaid sparklyr/sparklyr#2442 — We will merge withing the next few hours after tests finish running. Excited to see how you get all this working in sparknlp !!!

Dave Kincaid

@dkincaid

Wow! That's great! Thank you so much. I'll check it out this weekend

Javier Luraschi

@javierluraschi

NP! Merged now. Try remotes::install_github("sparklyr/sparklyr")

Jeff P

@HikaGenji

hi, are sliding windows supported in sparklyr ? looking at this, seems they are not sparklyr/sparklyr#2231

2 replies

Rob Linger

@th3walkingdud3

Does anyone have any documentation or a set of repos for dealing with json objects from kafka? I can read from and write to kafka using the example code, but I have not had much luck with any type of processing.

Jeff P

@HikaGenji

@th3walkingdud3 you can use the spark SQL function from_json function to parse the json string into columns. You need to provide it with the json schema, you can find examples here https://stackoverflow.com/questions/50373104/spark-sql-from-json-documentation

13 replies

Jeff P

@HikaGenji

For how to use SQL in sparklyr, I personally like this resource: https://sparkfromr.com/constructing-sql-and-executing-it-with-spark.html

Rob Linger

@th3walkingdud3

I will check it out, thanks for the quick reply @HikaGenji

Rob Linger

@th3walkingdud3

Screen Shot 2020-05-05 at 9.23.06 AM.png

Rob Linger

@th3walkingdud3

I have not had any luck with deserializing data streaming from kafka using spark_read_kafka. Does anyone have any resources of code examples for this use case? The provided example on the sparklyr site reads and immediately writes back to kafka, which works fine, but I am needing some example code of processing incoming data prior to writing it back out to kafka.

Jeff P

@HikaGenji

https://therinspark.com/streaming.html section 12.3

Essentially you can treat the result of stream_read_kafka just like a dataframe with dplyr verbs

Kumar G

@abdkumar

@JakeRuss I'm trying to connect to remote cassandra using host, port, user name and password
conf <- spark_config()
conf[["spark.cassandra.connection.ssl.enabled"]] = TRUE
conf[["spark.cassandra.connection.host"]] = cassandra_host
conf[["spark.cassandra.connection.port"]] = cassandra_port
conf[["spark.cassandra.auth.username"]] = cassandra_username
conf[["spark.cassandra.auth.password"]] = cassandra_password
config[["sparklyr.defaultPackages"]] <- c("org.apache.hadoop:hadoop-aws:2.7.3", "datastax:spark-cassandra-connector:2.0.0-RC1-s_2.11")

sc <- spark_connect(master = "local", version = "2.2.0", spark_home = spark_path, config = conf)

df <- spark_read_source(
sc,
name = "emp",
source = "org.apache.spark.sql.cassandra",
options = list(keyspace = "temp", table = "category_distribution"),
memory = FALSE)

but this is not working. please suggest a solution

tarun9450

@tarun9450

Hi All, can you please help me with this error- "Failed while connecting to sparklyr to port (8880) for sessionid (26038): Gateway in localhost:8880 did not respond."

tarun9450

@tarun9450

library(sparklyr)
sc <- spark_connect(master = "local", spark_version = "2.4.5")

Error in force(code) :
Failed while connecting to sparklyr to port (8880) for sessionid (52016): Gateway in localhost:8880 did not respond.
Path: C:\Users\Tarun_Gupta2\AppData\Local\spark\spark-2.4.5-bin-hadoop2.7\bin\spark-submit2.cmd
Parameters: --class, sparklyr.Shell, "C:\Users\TarunGupta2\Documents\R\win-library\3.6\sparklyr\java\sparklyr-2.4-2.11.jar", 8880, 52016
Log: C:\Users\TARUN~1\AppData\Local\Temp\Rtmpw9ZV82\filea70487da97_spark.log

---- Output Log ----
/Java/jdk1.8.0_251\bin\java was unexpected at this time.

---- Error Log ----

Yitao Li

@yitao-li

@tarun9450 Did you have space in your JAVA_HOME environment variable?

(see https://stackoverflow.com/questions/17258231/java-jdk1-6-0-03-bin-was-unexpected-at-this-time/31049133)

Jeff P

@HikaGenji

Hi, is anybody available to discuss sparklyr/sparklyr#2534 ? A bit difficult to express in words and requires Kafka to replicate. This is blockinge from going further so I am curious to see if any of you has insights

Jeff P

@HikaGenji

@HikaGenji note to self: problem solved, see the ticket for more info

bdharang

@bdharang

Hi , Someone please help me to fix the below error ,I have another setup working which is hadoop 2 (EMR 5.x) , Now I am testing EMR 6 with new spark home that is /usr/lib/spark6/ , Just I compare with both setting everything looks good for me. Is there any specific setting I need to checlk

sc <- spark_connect(master = "yarn", spark_home = "/usr/lib/spark6", deploymode = "cluster", enableHiveSupport = TRUE)
Error in force(code) :
Failed while connecting to sparklyr to port (8880) for sessionid (32486): Gateway in localhost:8880 did not respond.
Path: /usr/lib/spark6/bin/spark-submit
Parameters: --class, sparklyr.Shell, '/opt/R/3.6.0/lib64/R/library/sparklyr/java/sparklyr-2.4-2.11.jar', 8880, 32486
Log: /tmp/RtmpijZOtA/filee69e18f188dc_spark.log

---- Output Log ----
Exception in thread "main" java.lang.NoSuchMethodError: scala.Predef$.refArrayOps([Ljava/lang/Object;)Lscala/collection/mutable/ArrayOps;
at sparklyr.Shell$.main(shell.scala:9)
at sparklyr.Shell.main(shell.scala)

Yitao Li

@yitao-li

@bdharang The error is caused by scala version incompatibility -- I still need to look into whether there is a reasonable way to fix it, but for now you can work around it by passing in "version = 3.0.0-preview" to force sparklyr to load jar files compiled with scala 2.12

Maher Daoud

@maherdaoud

Hi Guys, I'm now working on a project that we need to apply a huge number of mutation on the data, I'm using sparkler package with dplyr mutate function
The problem is when applying a huge number of the mutation using mutate I will have the following error
"Error: org.apache.spark.sql.AnalysisException: cannot resolve 'GOOGLE_CITY_DESC' given input columns:"
It seems there is a limitation for using mutate function
Any help plz?

Yitao Li

@yitao-li

@maherdaoud I'm not aware of any built-in limitation with dplyr.

^^ dplyr is just a R interface translating all your data manipulation verbs to Spark SQL, so, it has the same capabilities as Spark SQL itself. I suggest double-checking the schema of your result before the failed dplyr::mutate and see if the column names are what you expected

Maher Daoud

@maherdaoud

@yl790
https://stackoverflow.com/questions/62737545/dplyr-mutate-error-org-apache-spark-sql-analysisexception-cannot-resolve-nam
check this link, I add a reproducible example you can see the error

Yitao Li

@yitao-li

@maherdaoud wow that's really interesting. I didn't realize too many mutate calls would fail in this way. My guess is it might be because of the resulting SQL query becoming too long. Anyways I'll create a github issue for it.

Oh nevermind I saw the one you created already.

Maher Daoud

@maherdaoud

Any workaround suggestion?

Yitao Li

@yitao-li

I'll need to look into it. I think what happens is all dplyr::mutate calls are evaluated lazily so when you accumulate too many of them that are yet to be "materialized" in the backend, you end up with a long SQL query. But I'm not exactly sure whether SQL query being too long is the root cause of this problem it was just the guess off top of my head.

Maher Daoud

@maherdaoud

I think you are right, it seems each time we have mutate function, it creates a new subquery with a new name. after too many subqueries it can't generate more names for the last one

Yitao Li

@yitao-li

If I read correctly https://dplyr.tidyverse.org/reference/compute.html essentially says compute() forces the SQL query you have accumulated so far to be evaluated so that might help

Maher Daoud

@maherdaoud

let me try, I will back, and thanks for your great support bro :>

Maher Daoud

@maherdaoud

I tried both "compute" and "collapse" functions, it tasks a long time without finishing. I applied it on 60,000 rows of spark_dataframe

Yitao Li

@yitao-li

If it appears to be hanging forever, then I would also do the following:

<my_spark_dataframe> %>% dplyr::mutate(...) %>% ... %>% dplyr::show_query() and then sanity-check the query is OK and then try running the query directly to see how long that takes

Maher Daoud

@maherdaoud

okay

Maher Daoud

@maherdaoud

running the query directly using dbGetQuery?

Yitao Li

@yitao-li

Yes either that or just launch a spark-sql shell (from $SPARK_HOME/bin/spark-sql), copy-paste the query, and then run

Maher Daoud

@maherdaoud

Yes, but in fact, this will not help, as you know it takes too much time after adding "compute" or "collapse" functions.

I think I need to wait until solving this bug or finding a workaround :(

Yitao Li

@yitao-li

I was more suggesting doing it for debugging purposes: if running that query from the spark-sql shell directly returns result fast enough (i.e., we did that to bypass the R-layer entirely), then we can conclude slowness was caused by some bug in one of the R packages.

Maher Daoud

@maherdaoud

If we are talking about spark-sql, it's very fast using R-layer, Also I tried it directly using dbGetQuery it was fast

But we shouldn't forget the main mentioned issue

Yitao Li

@yitao-li

@maherdaoud Hey I just commented on sparklyr/sparklyr#2589 with some good news for you. Let me know what you think.

Thanks for raising this issue for sparklyr BTW. It's a really good catch! Even though at the end it appears to be not a sparklyr problem : D

Maher Daoud

@maherdaoud

What a great news, let me check and I will back to you with my feedback, again, many thanks for your great efforts

Niels Jespersen

@njesp

I just cannot get sparklyr to work on Windows in local mode. Not matter what I do, the only error message I see is "Error in spark_connect_gateway(gatewayAddress, gatewayPort, sessionId, : Gateway in localhost:8880 did not respond.". R 3.6.1, R 4.0.2, Spark 2.0.1, Spark 2.4.5, Java 8. Not filepaths involved (java, spark) contain spaces. Logfiles created, but empty. winutils.exe copied to where spark/hadoop wants it (at least the errormessage saying it misses winutils goes away). A Javaprocess is created. Any hints for further investigation? Has anyone made this work recently?

Yitao Li

@yitao-li

@njesp You can try the following to print spark-submit log to console to see what's failing:

library(sparklyr)
options(sparklyr.log.console = TRUE)
sc <- spark_connect(master = "local")

The spark-submit log usually ends up in a text file, but the path to that file is highly system-dependent and also could be influenced by your local config... so rather than spending time figuring out where it might be it's just easier to have options(sparklyr.log.console = TRUE) while trouble-shooting

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