I’m trying to use Apache Spark/Ignite integration in Azure Databricks. I install the org.apache.ignite:ignite-spark-2.4:2.9.0 maven library using the Databricks UI. And I have an error while accessing my ignite cahces: Here the AbstractApplicationContext is compiled with ReflectionUtils of different spring version. I see the spring-core-4.3.26.RELEASE.jar is installed in the /dbfs/FileStore/jars/maven/org/springframework during the org.apache.ignite:ignite-spark-2.4:2.9.0 installation and there are no other spring
Tag: apache-spark
How to get corresponding quarter of previous year in Scala
I have a date string with me in the format – “20202” [“yyyyQ”]. Is there a way to get the corresponding quarter of previous year ? ex- for 20202 , it should be 20192 Answer An alternative to the other answers is using my lib Time4J and its class CalendarQuarter. Example: Two main advantages of this solution are: Calendar quarters
Read data saved by spark redis using Java
I using spark-redis to save Dataset to Redis. Then I read this data by using Spring data redis: This object I save to redis: Save object by using spark-redis: Repository: I can’t read this data have been saved in Redis by using Spring data redis because structure data saved by spark-redis and spring data redis not same (I checked value
How to resolve (java.lang.ClassNotFoundException: com.mongodb.spark.sql.DefaultSource.DefaultSource) in pyspark i’m using pycharm
With Pycharm I’m getting this error: java.lang.ClassNotFoundException: com.mongodb.spark.sql.DefaultSource.DefaultSource How can I resolve this issue? I tried: I also tried setting the classpath of the jars also .bash_profile: I had many jars in my_jars but still didn’t get it to work. I keep getting the same error. Answer Provide comma separated jarfiles instead of directory path in spark.jars Alternatively you can
Apache spark and scala, error while executing queries
I am working with a dataset whose sample is as follows: I have executed the following commands successfully: I am getting following error: java.lang.RuntimeException: Error while encoding: java.lang.RuntimeException: java.lang.Character is not a valid external type for schema of string I am getting the same error when executing any query against the data. Can you please have a look and provide
CSV file from HDFS to Oracle BLOB using Spark
I’m working on Java app that uses Spark 2.3.1 to load data from Oracle to HDFS and vice versa. I want to create CSV file in HDFS and then load it to Oracle (12.2) BLOB. The code.. I’m new to Spark.. so any ideas please how to convert JavaRDD to BufferedInputStream, or get rid of mess above and put Dataset
spark-submit error: Invalid maximum heap size: -Xmx4g –jars, but enough of memory on the system
I am running a spark job: And the command gives an error: Invalid maximum heap size: -Xmx4g –jars Error: Could not create the Java Virtual Machine. Error: A fatal exception has occurred. Program will exit. I checked memory: So, it seems to be fine. I checked java: Then I checked in Chrome whether spark is running at ai-grisnodedev1:7077 and it does
ClassNotFoundException: Failed to find data source: bigquery
I’m trying to load data from Google BigQuery into Spark running on Google Dataproc (I’m using Java). I tried to follow instructions on here: https://cloud.google.com/dataproc/docs/tutorials/bigquery-connector-spark-example I get the error: “ClassNotFoundException: Failed to find data source: bigquery.” My pom.xml looks like this: After adding the dependency to my pom.xml it was downloading a lot to build the .jar, so I think
Spark – Transforming Complex Data Types
Goal The goal I want to achieve is to read a CSV file (OK) encode it to Dataset<Person>, where Person object has a nested object Address[]. (Throws an exception) The Person CSV file In a file called person.csv, there is the following data describing some persons: The first line is the schema and address is a nested structure. Data classes
version conflict, current version [2] is different than the one provided [1]
I have a Kafka topic and a Spark application. The Spark application gets data from Kafka topic, pre aggregates it and stores it in Elastic Search. Sounds simple, right? Everything works fine as expected, but the minute I set “spark.cores” property something other than 1, I start getting After researching a bit, I think the error is because multiple cores