Unable to write to Redshift using Spark 2.4.3

Question

I am running Spark 2.4.3 in local mode and am able to pull down files however I am unable to write them back to Redshift. I need to know the proper dependencies to do so.

I found that there have historically been issues with avro dependencies, however I cannot determine the proper dependencies for spark 2.4.3. I have tried all sorts of combinations but none allow me to write back to redshift.

spark = SparkSession.builder.master("local").appName("Test")\
    .config("spark.jars", 'RedshiftJDBC4-1.2.1.1001.jar,jets3t-0.9.0.jar,spark-avro_2.11-4.0.0.jar,hadoop-aws-2.7.4.jar')\
    .config("spark.jars.packages", 'com.databricks:spark-redshift_2.10:0.5.0,com.amazonaws:aws-java-sdk:1.10.34,org.apache.hadoop:hadoop-aws:2.7.4')\
    .config("driver.memory", '5g')\
    .getOrCreate()

...

fact_table.write \
    .format("com.databricks.spark.redshift") \
    .option("url", jdbcUrl) \
    .option("dbtable", "my_table") \
    .option("tempdir", tempDir) \
    .option('forward_spark_s3_credentials',True) \
    .mode("error") \
    .save()

I am receiving the following error:

: java.lang.AbstractMethodError: com.databricks.spark.redshift.DefaultSource.createRelation(Lorg/apache/spark/sql/SQLContext;Lorg/apache/spark/sql/SaveMode;Lscala/collection/immutable/Map;Lorg/apache/spark/sql/Dataset;)Lorg/apache/spark/sql/sources/BaseRelation;
    at org.apache.spark.sql.execution.datasources.SaveIntoDataSourceCommand.run(SaveIntoDataSourceCommand.scala:45)
    at org.apache.spark.sql.execution.command.ExecutedCommandExec.sideEffectResult$lzycompute(commands.scala:70)
    at org.apache.spark.sql.execution.command.ExecutedCommandExec.sideEffectResult(commands.scala:68)
    at org.apache.spark.sql.execution.command.ExecutedCommandExec.doExecute(commands.scala:86)
    at org.apache.spark.sql.execution.SparkPlan$$anonfun$execute$1.apply(SparkPlan.scala:131)
    at org.apache.spark.sql.execution.SparkPlan$$anonfun$execute$1.apply(SparkPlan.scala:127)
    at org.apache.spark.sql.execution.SparkPlan$$anonfun$executeQuery$1.apply(SparkPlan.scala:155)
    at org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:151)
    at org.apache.spark.sql.execution.SparkPlan.executeQuery(SparkPlan.scala:152)
    at org.apache.spark.sql.execution.SparkPlan.execute(SparkPlan.scala:127)
    at org.apache.spark.sql.execution.QueryExecution.toRdd$lzycompute(QueryExecution.scala:80)
    at org.apache.spark.sql.execution.QueryExecution.toRdd(QueryExecution.scala:80)
    at org.apache.spark.sql.DataFrameWriter$$anonfun$runCommand$1.apply(DataFrameWriter.scala:676)
    at org.apache.spark.sql.DataFrameWriter$$anonfun$runCommand$1.apply(DataFrameWriter.scala:676)
    at org.apache.spark.sql.execution.SQLExecution$$anonfun$withNewExecutionId$1.apply(SQLExecution.scala:78)
    at org.apache.spark.sql.execution.SQLExecution$.withSQLConfPropagated(SQLExecution.scala:125)
    at org.apache.spark.sql.execution.SQLExecution$.withNewExecutionId(SQLExecution.scala:73)
    at org.apache.spark.sql.DataFrameWriter.runCommand(DataFrameWriter.scala:676)
    at org.apache.spark.sql.DataFrameWriter.saveToV1Source(DataFrameWriter.scala:285)
    at org.apache.spark.sql.DataFrameWriter.save(DataFrameWriter.scala:271)
    at sun.reflect.NativeMethodAccessorImpl.invoke0(Native Method)
    at sun.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:62)
    at sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43)
    at java.lang.reflect.Method.invoke(Method.java:498)
    at py4j.reflection.MethodInvoker.invoke(MethodInvoker.java:244)
    at py4j.reflection.ReflectionEngine.invoke(ReflectionEngine.java:357)
    at py4j.Gateway.invoke(Gateway.java:282)
    at py4j.commands.AbstractCommand.invokeMethod(AbstractCommand.java:132)
    at py4j.commands.CallCommand.execute(CallCommand.java:79)
    at py4j.GatewayConnection.run(GatewayConnection.java:238)
    at java.lang.Thread.run(Thread.java:748)

Answer 1

As mentioned in comments, the open source databricks/spark-redshift is not maintained anymore.

But..

We've recently forked the project and upgraded to spark 2.4 - we called it spark_redshift_community in the spirit of community collaboration. Please feel free to try it out and report any issues that you might find.