Thanks Tathagata Das
Currently in my use case I am planning to collect() all the data in the
driver and publish it into KairosDB something like this
I am not worried about the size of the data. If I am doing something simple
like this do I still need to bother about the un-stability of the API ?
I'm writing a Spark Streaming application where the input data is put into
an S3 bucket in small batches (using Database Migration Service - DMS). The
Spark application is the only consumer. I'm considering two possible
architectures:
Have Spark Streaming watch an S3 prefix and pick up new objects
This is interface is actually unstable. The v2 of DataSource APIs is being
designed right now which will be public and stable in a release or two. So
unfortunately there is no stable interface right now that I can officially
recommend.
That said, you could always use the ForeachWriter interface (s
We are using Spark 2.3 and would want to know if it is recommended to create
a custom KairoDBSink by implementing the StreamSinkProvider ?
The interface is marked experimental and in-stable ?
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Try using `TaskContext`:
import org.apache.spark.TaskContext
val partitionId = TaskContext.getPartitionId()
On Mon, Jun 25, 2018 at 11:17 AM Lalwani, Jayesh
wrote:
>
> We are trying to add a column to a Dataframe with some data that is seeded by
> some random data. We want to be able to control
We are trying to add a column to a Dataframe with some data that is seeded by
some random data. We want to be able to control the seed, so multiple runs of
the same transformation generate the same output. We also want to generate
different random numbers for each partition
This is easy to do w
Hi Guys,
Pyspark is not picking up correct python version on azure hdinsight
property setup in spark2-env
PYSPARK_PYTHON=${PYSPARK3_PYTHON:-/usr/bin/anaconda/envs/py35/bin/python3}
export
PYSPARK_DRIVER_PYTHON=${PYSPARK3_PYTHON:-/usr/bin/anaconda/envs/py35/bin/python3}
Thanks
What I did:
I have two datasets I need to join. One of the datasets does not change so
I bucket it once and save in a table. It looks something like:
spark.table("profiles").bucketBy(500, "uid").saveAsTable("profiles_bkt").
Now I have another dataset that I bucket "online":
spark.sql(".").c
hi,all
i'm using spark-2.0.0 on hdp 2.5.0 to build a spark-sql app,below is the
spark-submit configuration:
spark-submit\
--class "FASTMDTFlow" \
--master yarn \
--deploy-mode client \
--driver-memory 12g \
--num-executors 110\
--executor-memory 8g \
--executor-cores 3 \
--conf "sp
Issue:
Not able to broadcast or place the files locally in the Spark worker nodes
from Spark application in Cluster deploy mode.Spark job always throws
FileNotFoundException.
Issue Description:
We are trying to access Kafka Cluster which is configured with SSL for
encryption from Spark Streami
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