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APACHE-SPARK QUESTIONS

Keeping data together in spark based on cassandra table partition key
Keeping data together in spark based on cassandra table partition key
I hope this helps . Repartition by Cassandra replica does not deterministically place keys. There is a ticket currently to change that. https://datastax-oss.atlassian.net/projects/SPARKC/issues/SPARKC-278
TAG : apache-spark
Date : November 28 2020, 09:01 AM , By : Stoyan Tsalev
Spark SQL: apply aggregate functions to a list of columns
Spark SQL: apply aggregate functions to a list of columns
this will help There are multiple ways of applying aggregate functions to multiple columns.GroupedData class provides a number of methods for the most common functions, including count, max, min, mean and sum, which can be used directly as follows:
TAG : apache-spark
Date : November 27 2020, 09:01 AM , By : Iseldore
How to list RDDs defined in Spark shell?
How to list RDDs defined in Spark shell?
wish helps you In both "spark-shell" or "pyspark" shells, I created many RDDs but I could not find any way through which I can list all the available RDDs in my current session of Spark Shell? , In Python you can simply try to filter globals by type:
TAG : apache-spark
Date : November 06 2020, 04:03 AM , By : Tuan Hoang
Does spark streaming write ahead log saves all received data to HDFS?
Does spark streaming write ahead log saves all received data to HDFS?
To fix this issue When you enable WAL, data is being serialized and saved into HDFS. Therefore, all your assumptions are right, HDFS file gets bigger. However, it gets cleaned up with a separate process. I haven't had my hands on an actual reference
TAG : apache-spark
Date : November 05 2020, 09:01 AM , By : Ihs
Pyspark: repartition vs partitionBy
Pyspark: repartition vs partitionBy
Any of those help repartition already exists in RDDs, and does not handle partitioning by key (or by any other criterion except Ordering). Now PairRDDs add the notion of keys and subsequently add another method that allows to partition by that key.So
TAG : apache-spark
Date : November 04 2020, 09:01 AM , By : Pedrox
Why do all data end up in one partition after reduceByKey?
Why do all data end up in one partition after reduceByKey?
wish helps you I will answer my own question, since I figured it out. My DateTimes were all without seconds and milliseconds since I wanted to group data belonging to the same minute. The hashCode() for Joda DateTimes which are one minute apart is a
TAG : apache-spark
Date : November 04 2020, 08:17 AM , By : Kanika Jain
How to control number of parquet files generated when using partitionBy
How to control number of parquet files generated when using partitionBy
Hope this helps As you noted correctly, spark.sql.shuffle.partitions only applies to shuffles and joins in SparkSQL.partitionBy in DataFrameWriter (you move from DataFrame to DataFrameWriter as soon as you call write) simply operates on the previous
TAG : apache-spark
Date : November 01 2020, 01:05 PM , By : Abdullah Jamal
collect RDD with buffer in pyspark
collect RDD with buffer in pyspark
it fixes the issue The best available option is to use RDD.toLocalIterator which collects only a single partition at the time. It creates a standard Python generator:
TAG : apache-spark
Date : October 31 2020, 10:01 AM , By : chenwei
SparkConf not reading spark-submit arguments
SparkConf not reading spark-submit arguments
wish help you to fix your issue Apparently, the order of the arguments matter. The last argument should be the name of the python script. So, the call should be
TAG : apache-spark
Date : October 31 2020, 10:01 AM , By : handler_mo
How to use long-lived expensive-to-instantiate utility services where executors run?
How to use long-lived expensive-to-instantiate utility services where executors run?
To fix the issue you can do It is possible to share objects at partition level:I've tried this : How to make Apache Spark mapPartition work correctly?
TAG : apache-spark
Date : October 29 2020, 10:01 AM , By : Lucas Vasconcelos
Pyspark shell outputs several numbers instead of the loading arrow
Pyspark shell outputs several numbers instead of the loading arrow
I wish this help you I am currently fitting a Support Vector Machine model for some training data with 104 boolean features, for so I use a SparseVector as features, e.g. (I show it as a DataFrame just for readability but it's in fact an RDD): , Try
TAG : apache-spark
Date : October 29 2020, 10:01 AM , By : livelovelve
PySpark: withColumn() with two conditions and three outcomes
PySpark: withColumn() with two conditions and three outcomes
seems to work fine There are a few efficient ways to implement this. Let's start with required imports:
TAG : apache-spark
Date : October 25 2020, 07:18 PM , By : BJ Cameron
Add columns on a Pyspark Dataframe
Add columns on a Pyspark Dataframe
I think the issue was by ths following , I have a Pyspark Dataframe with this structure: , I have a work around for this
TAG : apache-spark
Date : October 24 2020, 01:32 PM , By : Nawir Dastam
Apache Spark vs Apache Spark 2
Apache Spark vs Apache Spark 2
will be helpful for those in need Apache Spark 2.0.0 APIs have stayed largely similar to 1.X, Spark 2.0.0 does have API breaking changes
TAG : apache-spark
Date : October 22 2020, 01:58 PM , By : justin paul
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