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https://issues.apache.org/jira/browse/SPARK-40637?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=17731318#comment-17731318
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ASF GitHub Bot commented on SPARK-40637:
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User 'Hisoka-X' has created a pull request for this issue:
https://github.com/apache/spark/pull/41531
> Spark-shell can correctly encode BINARY type but Spark-sql cannot
> -----------------------------------------------------------------
>
> Key: SPARK-40637
> URL: https://issues.apache.org/jira/browse/SPARK-40637
> Project: Spark
> Issue Type: Improvement
> Components: SQL
> Affects Versions: 3.2.1, 3.4.0
> Reporter: xsys
> Priority: Minor
> Attachments: image-2022-10-18-12-15-05-576.png
>
>
> h3. Describe the bug
> When we store a BINARY value (e.g. {{BigInt("1").toByteArray)}} /
> {{{}X'01'{}}}) either via {{spark-shell or spark-sql, and then read it from
> Spark-shell, it}} outputs {{{}[01]{}}}. However, it does not encode correctly
> when querying it via {{{}spark-sql{}}}.
> i.e.,
> Insert via spark-shell, read via spark-shell: display correctly
> Insert via spark-shell, read via spark-sql: does not display correctly
> Insert via spark-sql, read via spark-sql: does not display correctly
> Insert via spark-sql, read via spark-shell: display correctly
> 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> import org.apache.spark.sql.Row
> scala> import org.apache.spark.sql.types._
> 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.write.mode("overwrite").format("orc").saveAsTable("binary_vals_shell")
> scala> spark.sql("select * from binary_vals_shell;").show(false)
> +----+
> |c1 |
> +----+
> |[01]|
> +----+{code}
> Then using {{spark-sql}} to (1) query what is inserted via spark-shell to the
> binary_vals_shell table, and then (2) insert the value via spark-sql to the
> binary_vals_sql table (we use tee to redirect the log to a file)
> {code:java}
> $SPARK_HOME/bin/spark-sql | tee sql.log{code}
> Execute the following, we only get an empty output in the terminal (but a
> garbage character in the log file):
> {code:java}
> spark-sql> select * from binary_vals_shell; -- query what is inserted via
> spark-shell;
> spark-sql> create table binary_vals_sql(c1 BINARY) stored as ORC;
> spark-sql> insert into binary_vals_sql select X'01'; -- try to insert
> directly in spark-sql;
> spark-sql> select * from binary_vals_sql;
> Time taken: 0.077 seconds, Fetched 1 row(s)
> {code}
> From the log file, we find it shows as a garbage character. (We never
> encountered this garbage character in logs of other data types)
> h3. !image-2022-10-18-12-15-05-576.png!
> We then return to spark-shell again and run the following:
> {code:java}
> scala> spark.sql("select * from binary_vals_sql;").show(false)
> +----+
>
> |c1 |
> +----+
> |[01]|
> +----+{code}
> The binary value does not display correctly via spark-sql, it still displays
> correctly via spark-shell.
> 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.
>
> h3. Additional context
> We also tried Avro and Parquet and encountered the same issue. We believe
> this is format-independent.
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