hi Hemalatha,
you can use the time windows, it looks likee
df.groupby(windows('timestamp', '20 seconds', '10 seconds'))
---Original---
From: "Saisai Shao"
Date: 2017/3/1 09:39:58
To: "Hemalatha A";
Cc: "spark users";
Subject: Re: How to use ManualClock with Spark streaming
I don't think usin
SQLTransformer is a good solution if all operators are combined with SQL.
By the way,
if you like to get hands dirty,
writing a Transformer in scala is not hard,
and multiple output columns is valid in such case.
On Fri, Mar 17, 2017 at 9:10 PM, Yanbo Liang wrote:
> Hi Adrian,
>
> Did you tr
Hi, jinhong.
Do you use `setRegParam`, which is 0.0 by default ?
Both elasticNetParam and regParam are required if regularization is need.
val regParamL1 = $(elasticNetParam) * $(regParam)
val regParamL2 = (1.0 - $(elasticNetParam)) * $(regParam)
On Mon, Mar 20, 2017 at 6:31 PM, Yanbo Liang
Hello everybody,
I configured a simple standalone cluster with few machines and I am trying
to submit a very simple job just to test the cluster.
my laptop is the client and one of the workers. my server contains the
master and the second worker.
If I submit my job just executing the scala code
Hi Sam
A great way to contribute to Spark is to help answer user questions on the
user@spark.apache.org mailing list or on StackOverflow.
2017-03-20 11:50 GMT+08:00 Nick Pentreath :
> If you have experience and interest in Python then PySpark is a good area
> to look into.
>
> Yes, adding things
This issue on stackoverflow maybe help
https://stackoverflow.com/questions/42641573/why-does-memory-usage-of-spark-worker-increases-with-time/42642233#42642233
--
View this message in context:
http://apache-spark-user-list.1001560.n3.nabble.com/spark-streaming-exectors-memory-increasing-and-ex
Closing the loop on this --
It appears we were just hitting some other problem related to S3A/S3,
likely that the temporary directory used by the S3A Hadoop file system
implementation for buffering data during upload either was full or had the
wrong permissions.
On Thu, Mar 16, 2017 at 6:03 PM
I have a Spark job that processes incremental data and partitions it by
customer id. Some customers have very little data, and I have another job
that takes a previous period's data and combines it. However, the job runs
serially and I'd basically like to run the function on every partition
simulta
You want spark.streaming.kafka.maxRatePerPartition for the direct stream.
On Sat, Mar 18, 2017 at 3:37 PM, Mal Edwin wrote:
>
> Hi,
> You can enable backpressure to handle this.
>
> spark.streaming.backpressure.enabled
> spark.streaming.receiver.maxRate
>
> Thanks,
> Edwin
>
> On Mar 18, 2017, 12
Do you want to get sparse model that most of the coefficients are zeros? If
yes, using L1 regularization leads to sparsity. But the
LogisticRegressionModel coefficients vector's size is still equal with the
number of features, you can get the non-zero elements manually. Actually,
it would be a spar
foreachPartition is an action but run on each worker, which means you won't
see anything on driver.
mapPartitions is a transformation which is lazy and won't do anything until
an action.
it depends on the specific use case which is better. To output sth(like a
print in single machine) you could r
Exactly.
On Sat, Mar 11, 2017 at 1:35 PM, Dongjin Lee wrote:
> Hello Chetan,
>
> Could you post some code? If I understood correctly, you are trying to
> save JSON like:
>
> {
> "first_name": "Dongjin",
> "last_name: null
> }
>
> not in omitted form, like:
>
> {
> "first_name": "Dongjin"
>
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