Spark 3 ( Scala 2.12) integration with HBase or Phoenix

Clone Git Repo -

git clone https://github.com/dinesh028/hbase-connectors.git

As of December 2022, the hbase-connectors releases in maven central are only available for Scala 2.11 and cannot be used with Spark 3.x

The connector has to be compiled from source for Spark 3.x, see also HBASE-25326 Allow hbase-connector to be used with Apache Spark 3.0

Build as in this example (customize HBase, Spark and Hadoop versions, as needed):

mvn -Dspark.version=3.3.1 -Dscala.version=2.12.15 -Dscala.binary.version=2.12 -Dhbase.version=2.4.15 -Dhadoop-three.version=3.3.2 -DskipTests clean package

Use Jar with Spark -

spark-shell --jars ~/hbase-connectors/spark/hbase-spark/target/hbase-spark*.jar

References -

Similarly, do build Phoenix connector or use Cloudera Repo to download Spark3 Jar @ https://repository.cloudera.com/service/rest/repository/browse/cloudera-repos/org/apache/phoenix/phoenix5-spark3-shaded/

Comments

Hive Parse JSON with Array Columns and Explode it in to Multiple rows.

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org.apache.spark.sql.AnalysisException: Cannot overwrite a path that is also being read from.;

Caused by: org.apache.spark.sql.AnalysisException: Cannot overwrite a path that is also being read from.; at org.apache.spark.sql.execution.command.DDLUtils$.verifyNotReadPath(ddl.scala:906) at org.apache.spark.sql.execution.datasources.DataSourceAnalysis$$anonfun$apply$1.applyOrElse(DataSourceStrategy.scala:192) at org.apache.spark.sql.execution.datasources.DataSourceAnalysis$$anonfun$apply$1.applyOrElse(DataSourceStrategy.scala:134) at org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$2.apply(TreeNode.scala:267) at org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$2.apply(TreeNode.scala:267) at org.apache.spark.sql.catalyst.trees.CurrentOrigin$.withOrigin(TreeNode.scala:70) at org.apache.spark.sql.catalyst.trees.TreeNode.transformDown(TreeNode.scala:266) at org.apache.spark.sql.catalyst.trees.TreeNode.transform(TreeNode.scala:256) at org.apache.spark.sql.execution.datasources.DataSourceAnalysis.apply(DataSourceStrategy.scala:134) at org.apache.spark.sql.execution.datasource

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One may face following error while copying data from one cluster to other, using Distcp Command: hadoop distcp -i {src} {tgt} Error: org.apache.hadoop.toolsCopyListing$DulicateFileException: File would cause duplicates. Ideally there can't be same file names. So, what might be happening in your case is you trying to copy partitioned table from one cluster to other. And, 2 different named partitions have same file name. Your solution is to correct Source path {src} in your command, such that you provide path uptil partitioned sub directory, not the file. For ex - Refer below : /a/partcol=1/file1.txt /a/partcol=2/file1.txt If you use {src} as "/a/*/*" then you will get the error "File would cause duplicates." But, if you use {src} as "/a" then you will not get error in copying.

Caused by: java.lang.UnsupportedOperationException: org.apache.parquet.column.values.dictionary.PlainValuesDictionary$PlainIntegerDictionary

Exception - Caused by: java.lang.UnsupportedOperationException: org.apache.parquet.column.values.dictionary.PlainValuesDictionary$PlainIntegerDictionary at org.apache.parquet.column.Dictionary.decodeToBinary(Dictionary.java:44) at org.apache.spark.sql.execution.vectorized.ColumnVector.getUTF8String(ColumnVector.java:645) at org.apache.spark.sql.catalyst.expressions.GeneratedClass$GeneratedIterator.processNext(Unknown Source) Analysis - This might occur because of data type mismatch between Hive Table & written Parquet file. Solution - Correct the data type to match between Hive Table & Parquet

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