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APACHE-SPARK QUESTIONS

What is lazy evaluation in functional programming language? And how it helps?
What is lazy evaluation in functional programming language? And how it helps?
I wish did fix the issue. It saves resources. You can for example have an infinite array (which wouldn't fit into memory) and you generate each of its values only when they are actually being read.Also check out http://perldesignpatterns.com/?LazyEva
TAG : apache-spark
Date : November 25 2020, 11:01 PM , By : Jaypee Ignacio
Spark Memory/worker issues & what is the correct spark configuration?
Spark Memory/worker issues & what is the correct spark configuration?
I wish this helpful for you As far as I know you should only start one Worker per Node:http://spark.apache.org/docs/latest/hardware-provisioning.html
TAG : apache-spark
Date : November 21 2020, 11:01 PM , By : Peter Fusco
How to turn off scientific notation in pyspark?
How to turn off scientific notation in pyspark?
Any of those help The easiest way is to cast double column to decimal, giving appropriate precision and scale:
TAG : apache-spark
Date : November 20 2020, 11:01 PM , By : groovybear
How to execute Spark programs with Dynamic Resource Allocation?
How to execute Spark programs with Dynamic Resource Allocation?
wish help you to fix your issue In Spark dynamic allocation spark.dynamicAllocation.enabled needs to be set to true because it's false by default. This requires spark.shuffle.service.enabled to be set to true, as spark application is running on YARN.
TAG : apache-spark
Date : November 15 2020, 11:01 PM , By : Alphadroid
scala.collection.mutable.WrappedArray$ofRef cannot be cast to Integer
scala.collection.mutable.WrappedArray$ofRef cannot be cast to Integer
This might help you Changing the datatype from Array[Int] to Seq[Int] in the function filterMapKeysWithSet seems to resolve the above issue.
TAG : apache-spark
Date : November 14 2020, 11:01 PM , By : Sayfur Rahman
Spark Streaming failed executor tasks
Spark Streaming failed executor tasks
it fixes the issue While you should be wary of failing tasks (they are frequently an indicator of an underlying memory issue), you need not worry about data loss. The stages have been marked as successfully completed, so the tasks that failed were in
TAG : apache-spark
Date : November 11 2020, 11:01 PM , By : Faaiz Nurji
Beginner Spark Dev tips
Beginner Spark Dev tips
this one helps. I think you should just download "Vanilla Spark". Then create a Maven Project in Eclipse. At the Pom File you should add the correct dependencys (Spark Core, Spark SQL, ...). Then export your Jar. You can start it then with the submit
TAG : apache-spark
Date : November 11 2020, 11:01 PM , By : Stuart Mitchell
Getting java.lang.RuntimeException: Unsupported data type NullType when turning a dataframe into permanent hive table
Getting java.lang.RuntimeException: Unsupported data type NullType when turning a dataframe into permanent hive table
Hope this helps The error you have Unsupported data type NullType indicates that one of the columns for the table you are saving has a NULL column. To workaround this issue, you can do a NULL check for the columns in your table and ensure that one of
TAG : apache-spark
Date : November 10 2020, 11:01 PM , By : Ned Armsby
Spark Streaming - Can an offline model be used against a data stream
Spark Streaming - Can an offline model be used against a data stream
Any of those help Does this mean that one can use a complex learning model like Random Forest model built in Spark for testing against streaming data in Spark Streaming program?Yes, you can train a model like Random Forest in batch mode and store the
TAG : apache-spark
Date : November 06 2020, 11:01 PM , By : preethi s
Reading files dynamically from HDFS from within spark transformation functions
Reading files dynamically from HDFS from within spark transformation functions
this will help You don't necessarily need service context to interact with HDFS. You can simply broadcast the hadoop configuration from master and use the broadcasted configuration value on executors to construct a hadoop.fs.FileSystem. Then the worl
TAG : apache-spark
Date : November 04 2020, 11:01 PM , By : Jozz
Most efficient way to merge timestamp column in spark dataframe
Most efficient way to merge timestamp column in spark dataframe
Hope that helps The functions you're looking for is coalesce. You can import it from pyspark.sql.functions:
TAG : apache-spark
Date : November 04 2020, 04:05 PM , By : infoheiko
Selecting columns not present in the dataframe
Selecting columns not present in the dataframe
This might help you Since you already make specific assumptions about the schema the best thing you can do is to define it explicitly with nullable optional fields and use it when importing data.Let's say you expect documents similar to:
TAG : apache-spark
Date : November 01 2020, 11:01 PM , By : Ollie
yarn executor launch wrong version of spark
yarn executor launch wrong version of spark
it fixes the issue Start by commenting out your .bashrc exports and removing them from environment - they are incompatible. PYTHONPATH uses spark 1.6 libs and SPARK_HOME points to spark 2.0.Then run examples by using absolute path to spark-submit on
TAG : apache-spark
Date : October 31 2020, 05:55 AM , By : keredin
Can you read/write directly to hard disk from a spark job?
Can you read/write directly to hard disk from a spark job?
Hope that helps Fundamentally, no, you cannot use spark's native writing APIs (e.g. df.write.parquet) to write to local filesystem files. When running in spark local mode (on your own computer, not a cluster), you will be reading/writing from your lo
TAG : apache-spark
Date : October 22 2020, 03:08 PM , By : Ellie Bibi
Kryo Serialization Issue with a collection in ProtoBuf field
Kryo Serialization Issue with a collection in ProtoBuf field
To fix the issue you can do In case anyone face this issue - I got it working using the method explained in my other post - How to set Unmodifiable collection serializer of Kryo in Spark code
TAG : apache-spark
Date : October 19 2020, 01:08 AM , By : mikel
pyspark - attempting to create new column based on the difference of two ArrayType columns
pyspark - attempting to create new column based on the difference of two ArrayType columns
hop of those help? I have a table like so: , You have to use UDF:
TAG : apache-spark
Date : October 17 2020, 03:08 PM , By : liumaoqin
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