Scala is a programming language that has its roots in the Java programming language. It is a statically typed, functional programming language with object-oriented features. Scala has gained a lot of popularity in recent years due to its scalability, which makes it an attractive choice for big data applications.CLICK HERE To solve more code-related solutions you face every day.
I got the answer from different question which i raised before. This query is working fine select * from your_data where key[0] between 202006 and 202009 and NOT (key[0]=”202009″ and …
Apparently, you can use regex pattern for consuming the data from different kafka topics. Lets say, you have topic names like “topic-ingestion1”, “topic-ingestion2” – then you can create a regex …
Use cases for using multiple queries for Spark Structured streaming Read More »
You have to use explode in order to generate a line for each element in data. import org.apache.spark.sql.functions.explode ds .withColumn(“data”, explode($”data”)) .filter($”data.stat” === $”max_stat”) .drop($”max_stat”) .show() Output: +———+——-+ | data| …
compare the struct field to another column spark Read More »
“have a daily spark batch job which generate bunch of data” I guess you are providing the answer yourself. If you have a batch dataframe which runs once (compared to …
Using subgraph worked : This function should be used when you want to filter out of a graph either edges or vertices. Here is the code I used for this …
This is because sparks pushes the filter/predicate, i.e. spark optimizes the query in such a way that the filter is applied before the “projection”. The same occures with select instead …
Your code is correct and instances also. Here you have 1_000_001 n and for each n you call: map(n.toString) = s”number $n” map(key) = value is just syntactic sugar for …
Too many instances of DefaultEntry in Scala mutable.Map Read More »
You can try this: sqlContext.sql(“set id_value = 3”) sqlContext.sql(“select * from country where id = ${id_value}”).show() CLICK HERE to find out more related problems solutions.
You can use the rand() function from pyspark.sql.functions for generating a random number for each of the distinct account_id and create train and test dataframes based on this random number. …
How to test/train split by column value rather than by row in pyspark Read More »
You can try this: sqlContext.sql(“set id_value = 3”) sqlContext.sql(“select * from country where id = ${id_value}”).show() CLICK HERE to find out more related problems solutions.
