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On 1/29/19, 3:11 PM, "Grant" wrote:
We could have a SQL syntax checker using the existing parser logic,
Once it detects the SQL expression with the DSL type "griffin-dsl", it
could take the following steps
1. attempt to delegate the execution of the rule to "spark-s
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On 1/21/19, 6:04 AM, "GitBox" wrote:
asfgit merged pull request #20: Replace images/project.jpg
URL:
https://na01.safelinks.protection.outlook.com/?url=https%3A%2F%2Fgithub.com%2Fapache%2Fgriffin-site%2Fpull%2F20&data=02%7C01%7Cbardia.afshin%40changehealthcare.com%7C6cf420
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On 1/3/19, 6:48 PM, "Zhen Li" wrote:
Hi Lionel,
Excuse me, but I didn’t see the QR code in mail attachment.
I am a software engineer in a e-commerce company and our big data platform
need a data quality system recently.
Many thanks.
Zhen
> 在 20
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On 12/6/18, 4:22 AM, "guoyuepeng" wrote:
Github user guoyuepeng commented on the issue:
https://na01.safelinks.protection.outlook.com/?url=https%3A%2F%2Fgithub.com%2Fapache%2Fgriffin%2Fpull%2F466&data=02%7C01%7Cbardia.afshin%40changehealthcare.com%7C3c8f92693f3b46e3fe4
I’m writing this email to reach out to the community to demisty the py-files
parameter when working with spark-submit and python projects.
Currently I have a project, say
Src/
* Main.py
* Modules/module1.py
When I zip up the src directory and submit it to spark via emr add step , the
Using pyspark cli on spark 2.1.1 I’m getting out of memory issues when running
the udf function on a recordset count of 10 with a mapping of the same value
(arbirtrary for testing purposes). This is on amazon EMR release label 5.6.0
with the following hardware specs
m4.4xlarge
32 vCPU, 64 GiB m
I’m running a process where I load the original data, remove some column and
write out the columns remaining into a output file. Spark is putting in hex 00
into some of the columns and this is causing issues when importing into
RedShift.
What’s the most efficient way to resolve this?
Starting long running jobs with upstarts on linux (spark-submit) is super slow.
I can see only a small percentage of the CPU is being utilized and applying
nice –n 20 to the process doesn’t seem to do anything. Anyone dealt with long
running processes / jobs on Spark and has any best practices t
Kicking off the process from ~ directory makes the message go away. I guess the
metastore_db created is relative to path of where it’s executed.
FIX: kick off from ~ directory
./spark-2.1.0-bin-hadoop2.7/bin/pysark
From: "Afshin, Bardia"
Date: Wednesday, April 26, 2017 at 9:47 AM
pache.spark.sql.hive.HiveSessionState':"
>>>
ubuntu@:~/spark-2.1.0-bin-hadoop2.7$ ps aux | grep spark
ubuntu 2796 0.0 0.0 10460 932 pts/0S+ 16:44 0:00 grep
--color=auto spark
From: Jacek Laskowski
Date: Wednesday, April 26, 2017 at 12:51 AM
To: "Afshin, Bardia
I’m having issues when I fire up pyspark on a fresh install.
When I submit the same process via spark-submit it works.
Here’s a dump of the trace:
at
org.apache.spark.sql.SparkSession.sessionState(SparkSession.scala:109)
at sun.reflect.NativeMethodAccessorImpl.invoke0(Nati
I wanted to reach out to the community to get a understanding of what everyones
experience is in regardst to maximizing performance as in decreasing load time
on loading multiple large datasets to RedShift.
Two approaches:
1. Spark writes file to S3, RedShift COPY INTO from S3 bucket.
2.
Hi there,
I have a process that downloads thousands of files from s3 bucket, removes a
set of columns from it, and upload it to s3.
S3 is currently not the bottleneck, having a Single Master Node Spark instance
is the bottleneck. One approach is to distribute the files on multiple Spark
Maste
Is there a API available to do this via SparkSession?
Sent from my iPhone
On Apr 24, 2017, at 6:20 AM, Devender Yadav
mailto:devender.ya...@impetus.co.in>> wrote:
Thanks Hemanth for a quick reply.
From: Hemanth Gudela
mailto:hemanth.gud...@qvantel.com>>
Sent:
I’m ingesting a CSV with hundreds of columns and the original CSV file it’self
doesn’t have any header. I do have a separate file that is just the headers, is
there a way to tell Spark API this information when loading the CSV file? Or do
I have to do some preprocesisng before doing so?
Thanks,
Hello community.
I’m considering consuming s3 objects via Hadoop via s3a protocol. The main
purpose of this is to utilize Spark to access s3, and it seems like the only
formal protocol / integration for doing so is Hadoop. The process that I am
implementing is rather formal and straight forward
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