[ https://issues.apache.org/jira/browse/SPARK-40637?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ]
xsys updated SPARK-40637: ------------------------- Description: h3. Describe the bug Storing a BINARY value (e.g. {{BigInt("1").toByteArray)}} / {{{}X'01'{}}}) via {{spark-shell}} outputs {{{}[01]{}}}. However, it does not encode correctly if the value is inserted into a BINARY column of a table via {{{}spark-sql{}}}. h3. To Reproduce On Spark 3.2.1 (commit {{{}4f25b3f712{}}}), using {{{}spark-shell{}}}: {code:java} $SPARK_HOME/bin/spark-shell{code} Execute the following: {code:java} scala> val rdd = sc.parallelize(Seq(Row(BigInt("1").toByteArray))) rdd: org.apache.spark.rdd.RDD[org.apache.spark.sql.Row] = ParallelCollectionRDD[356] at parallelize at <console>:28 scala> val schema = new StructType().add(StructField("c1", BinaryType, true)) schema: org.apache.spark.sql.types.StructType = StructType(StructField(c1,BinaryType,true)) scala> val df = spark.createDataFrame(rdd, schema) df: org.apache.spark.sql.DataFrame = [c1: binary] scala> df.show(false) +----+ |c1 | +----+ |[01]| +----+ {code} Using {{{}spark-sql{}}}: {code:java} $SPARK_HOME/bin/spark-sql{code} Execute the following, we only get an empty output: {code:java} spark-sql> create table binary_vals(c1 BINARY) stored as ORC; spark-sql> insert into binary_vals select X'01'; spark-sql> select * from binary_vals; Time taken: 0.077 seconds, Fetched 1 row(s) {code} h3. Expected behavior We expect the two Spark interfaces ({{{}spark-sql{}}} & {{{}spark-shell{}}}) to behave consistently for the same data type ({{{}BINARY{}}}) & input ({{{}BigInt("1").toByteArray){}}} / {{{}X'01'{}}}) combination. was: h3. Describe the bug Storing a BINARY value (e.g. {{BigInt("1").toByteArray)}} / {{{}X'01'{}}}) via {{spark-shell}} outputs {{{}[01]{}}}. However, it does not encode correctly if the value is inserted into a BINARY column of a table via {{{}spark-sql{}}}. h3. To Reproduce On Spark 3.2.1 (commit {{{}4f25b3f712{}}}), using {{{}spark-shell{}}}: {code:java} $SPARK_HOME/bin/spark-shell{code} Execute the following: {code:java} scala> val rdd = sc.parallelize(Seq(Row(BigInt("1").toByteArray))) rdd: org.apache.spark.rdd.RDD[org.apache.spark.sql.Row] = ParallelCollectionRDD[356] at parallelize at <console>:28 scala> val schema = new StructType().add(StructField("c1", BinaryType, true)) schema: org.apache.spark.sql.types.StructType = StructType(StructField(c1,BinaryType,true)) scala> val df = spark.createDataFrame(rdd, schema) df: org.apache.spark.sql.DataFrame = [c1: binary] scala> df.show(false) +----+ |c1 | +----+ |[01]| +----+ {code} Using {{{}spark-sql{}}}: {code:java} $SPARK_HOME/bin/spark-sql{code} Execute the following, we only get an empty output: {code:java} spark-sql> create table binary_vals(c1 BINARY) stored as ORC; spark-sql> insert into binary_vals select X'01'; spark-sql> select * from binary_vals; Time taken: 0.077 seconds, Fetched 1 row(s) {code} h3. Expected behavior We expect the two Spark interfaces ({{{}spark-sql{}}} & {{{}spark-shell{}}}) to behave consistently for the same data type ({{{}BINARY{}}}) & input ({{{}BigInt("1").toByteArray){}}} / {{{}X'01'{}}}) combination. > DataFrame can correctly encode BINARY type but SparkSQL cannot > -------------------------------------------------------------- > > Key: SPARK-40637 > URL: https://issues.apache.org/jira/browse/SPARK-40637 > Project: Spark > Issue Type: Bug > Components: SQL > Affects Versions: 3.2.1 > Reporter: xsys > Priority: Major > > h3. Describe the bug > Storing a BINARY value (e.g. {{BigInt("1").toByteArray)}} / {{{}X'01'{}}}) > via {{spark-shell}} outputs {{{}[01]{}}}. However, it does not encode > correctly if the value is inserted into a BINARY column of a table via > {{{}spark-sql{}}}. > h3. To Reproduce > On Spark 3.2.1 (commit {{{}4f25b3f712{}}}), using {{{}spark-shell{}}}: > {code:java} > $SPARK_HOME/bin/spark-shell{code} > Execute the following: > {code:java} > scala> val rdd = sc.parallelize(Seq(Row(BigInt("1").toByteArray))) > rdd: org.apache.spark.rdd.RDD[org.apache.spark.sql.Row] = > ParallelCollectionRDD[356] at parallelize at <console>:28 > scala> val schema = new StructType().add(StructField("c1", BinaryType, true)) > schema: org.apache.spark.sql.types.StructType = > StructType(StructField(c1,BinaryType,true)) > scala> val df = spark.createDataFrame(rdd, schema) > df: org.apache.spark.sql.DataFrame = [c1: binary] > scala> df.show(false) > +----+ > |c1 | > +----+ > |[01]| > +----+ > {code} > > Using {{{}spark-sql{}}}: > {code:java} > $SPARK_HOME/bin/spark-sql{code} > Execute the following, we only get an empty output: > {code:java} > spark-sql> create table binary_vals(c1 BINARY) stored as ORC; > spark-sql> insert into binary_vals select X'01'; > spark-sql> select * from binary_vals; > Time taken: 0.077 seconds, Fetched 1 row(s) > {code} > h3. Expected behavior > We expect the two Spark interfaces ({{{}spark-sql{}}} & {{{}spark-shell{}}}) > to behave consistently for the same data type ({{{}BINARY{}}}) & input > ({{{}BigInt("1").toByteArray){}}} / {{{}X'01'{}}}) combination. -- This message was sent by Atlassian Jira (v8.20.10#820010) --------------------------------------------------------------------- To unsubscribe, e-mail: issues-unsubscr...@spark.apache.org For additional commands, e-mail: issues-h...@spark.apache.org