![]() Test it using the following code: df = spark. Voila, your SPARK should be able to connect to Redshift. Rest all of the jars are already available in SPARK 3.1.2 with their latest versions. In computing, extract, transform, load (ETL) is a three-phase process where data is extracted, transformed (cleaned, sanitized, scrubbed) and loaded into an. You need to set the tcpKeepAlive time to 1 min or less while getting the connection to redshift cluster. your java code might be setting this policy. Out of the above, copy only the ones highlighted in blue to your $SPARK_HOME/jars. check if the redshift server have a workload management policy that is timing out queries after 10 minutes. So, all we need to do is to add the Redshift dependencies here.ĭownload the dependencies from here : Official Redshift JDBC Driver Pageĭownload from the option: “JDBC 4.2–compatible driver version 2.0 and AWS SDK driver–dependent libraries.” While running a Spark program, Spark looks into the jars directory in $SPARK_HOME for all the dependencies. ![]() With the above two fundamentals in mind, lets look into where does spark look into when its looking for classes.
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