[jira] [Closed] (SPARK-18709) Automatic null conversion bug (instead of throwing error) when creating a Spark Datarame with incompatible types for fields.
[ https://issues.apache.org/jira/browse/SPARK-18709?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ] Amogh Param closed SPARK-18709. --- The fix is in 2.0.0. > Automatic null conversion bug (instead of throwing error) when creating a > Spark Datarame with incompatible types for fields. > > > Key: SPARK-18709 > URL: https://issues.apache.org/jira/browse/SPARK-18709 > Project: Spark > Issue Type: Bug > Components: SQL >Affects Versions: 1.6.2, 1.6.3 >Reporter: Amogh Param > Labels: bug > Fix For: 2.0.0 > > > When converting an RDD with a `float` type field to a spark dataframe with an > `IntegerType` / `LongType` schema field, spark 1.6.2 and 1.6.3 silently > convert the field values to nulls instead of throwing an error like `LongType > can not accept object ___ in type `. However, this seems to be > fixed in Spark 2.0.2. > The following example should make the problem clear: > {code} > from pyspark.sql.types import StructField, StructType, LongType, DoubleType > schema = StructType([ > StructField("0", LongType(), True), > StructField("1", DoubleType(), True), > ]) > data = [[1.0, 1.0], [nan, 2.0]] > spark_df = sqlContext.createDataFrame(sc.parallelize(data), schema) > spark_df.show() > {code} > Instead of throwing an error like: > {code} > LongType can not accept object 1.0 in type > {code} > Spark converts all the values in the first column to nulls > Running `spark_df.show()` gives: > {code} > ++---+ > | 0| 1| > ++---+ > |null|1.0| > |null|1.0| > ++---+ > {code} > For the purposes of my computation, I'm doing a `mapPartitions` on a spark > data frame, and for each partition, converting it into a pandas data frame, > doing a few computations on this pandas dataframe and the return value will > be a list of lists, which is converted to an RDD while being returned from > 'mapPartitions' (for all partitions). This RDD is then converted into a spark > dataframe similar to the example above, using > `sqlContext.createDataFrame(rdd, schema)`. The rdd has a column that should > be converted to a `LongType` in the spark data frame, but since it has > missing values, it is a `float` type. When spark tries to create the data > frame, it converts all the values in that column to nulls instead of throwing > an error that there is a type mismatch. -- This message was sent by Atlassian JIRA (v6.3.4#6332) - To unsubscribe, e-mail: issues-unsubscr...@spark.apache.org For additional commands, e-mail: issues-h...@spark.apache.org
[jira] [Commented] (SPARK-18709) Automatic null conversion bug (instead of throwing error) when creating a Spark Datarame with incompatible types for fields.
[ https://issues.apache.org/jira/browse/SPARK-18709?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel=15723236#comment-15723236 ] Amogh Param commented on SPARK-18709: - Thanks, I'll close the ticket. > Automatic null conversion bug (instead of throwing error) when creating a > Spark Datarame with incompatible types for fields. > > > Key: SPARK-18709 > URL: https://issues.apache.org/jira/browse/SPARK-18709 > Project: Spark > Issue Type: Bug > Components: SQL >Affects Versions: 1.6.2, 1.6.3 >Reporter: Amogh Param > Labels: bug > Fix For: 2.0.0 > > > When converting an RDD with a `float` type field to a spark dataframe with an > `IntegerType` / `LongType` schema field, spark 1.6.2 and 1.6.3 silently > convert the field values to nulls instead of throwing an error like `LongType > can not accept object ___ in type `. However, this seems to be > fixed in Spark 2.0.2. > The following example should make the problem clear: > {code} > from pyspark.sql.types import StructField, StructType, LongType, DoubleType > schema = StructType([ > StructField("0", LongType(), True), > StructField("1", DoubleType(), True), > ]) > data = [[1.0, 1.0], [nan, 2.0]] > spark_df = sqlContext.createDataFrame(sc.parallelize(data), schema) > spark_df.show() > {code} > Instead of throwing an error like: > {code} > LongType can not accept object 1.0 in type > {code} > Spark converts all the values in the first column to nulls > Running `spark_df.show()` gives: > {code} > ++---+ > | 0| 1| > ++---+ > |null|1.0| > |null|1.0| > ++---+ > {code} > For the purposes of my computation, I'm doing a `mapPartitions` on a spark > data frame, and for each partition, converting it into a pandas data frame, > doing a few computations on this pandas dataframe and the return value will > be a list of lists, which is converted to an RDD while being returned from > 'mapPartitions' (for all partitions). This RDD is then converted into a spark > dataframe similar to the example above, using > `sqlContext.createDataFrame(rdd, schema)`. The rdd has a column that should > be converted to a `LongType` in the spark data frame, but since it has > missing values, it is a `float` type. When spark tries to create the data > frame, it converts all the values in that column to nulls instead of throwing > an error that there is a type mismatch. -- This message was sent by Atlassian JIRA (v6.3.4#6332) - To unsubscribe, e-mail: issues-unsubscr...@spark.apache.org For additional commands, e-mail: issues-h...@spark.apache.org
[jira] [Comment Edited] (SPARK-18709) Automatic null conversion bug (instead of throwing error) when creating a Spark Datarame with incompatible types for fields.
[ https://issues.apache.org/jira/browse/SPARK-18709?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel=15723189#comment-15723189 ] Amogh Param edited comment on SPARK-18709 at 12/5/16 7:50 PM: -- [~dongjoon] Thanks for the fix. Just to clarify, does this mean that the fix will only be in 2.0.0 and not in 1.6.4 (assuming there will be a 1.6.4 release)? was (Author: amogh.91): [~dongjoon] Thanks for the fix. Just to clarify, does this mean that the fix will only be in 2.0.0 and not in 1.6.4 (assuming there will be a 1.6.4 update)? > Automatic null conversion bug (instead of throwing error) when creating a > Spark Datarame with incompatible types for fields. > > > Key: SPARK-18709 > URL: https://issues.apache.org/jira/browse/SPARK-18709 > Project: Spark > Issue Type: Bug > Components: SQL >Affects Versions: 1.6.2, 1.6.3 >Reporter: Amogh Param > Labels: bug > Fix For: 2.0.0 > > > When converting an RDD with a `float` type field to a spark dataframe with an > `IntegerType` / `LongType` schema field, spark 1.6.2 and 1.6.3 silently > convert the field values to nulls instead of throwing an error like `LongType > can not accept object ___ in type `. However, this seems to be > fixed in Spark 2.0.2. > The following example should make the problem clear: > {code} > from pyspark.sql.types import StructField, StructType, LongType, DoubleType > schema = StructType([ > StructField("0", LongType(), True), > StructField("1", DoubleType(), True), > ]) > data = [[1.0, 1.0], [nan, 2.0]] > spark_df = sqlContext.createDataFrame(sc.parallelize(data), schema) > spark_df.show() > {code} > Instead of throwing an error like: > {code} > LongType can not accept object 1.0 in type > {code} > Spark converts all the values in the first column to nulls > Running `spark_df.show()` gives: > {code} > ++---+ > | 0| 1| > ++---+ > |null|1.0| > |null|1.0| > ++---+ > {code} > For the purposes of my computation, I'm doing a `mapPartitions` on a spark > data frame, and for each partition, converting it into a pandas data frame, > doing a few computations on this pandas dataframe and the return value will > be a list of lists, which is converted to an RDD while being returned from > 'mapPartitions' (for all partitions). This RDD is then converted into a spark > dataframe similar to the example above, using > `sqlContext.createDataFrame(rdd, schema)`. The rdd has a column that should > be converted to a `LongType` in the spark data frame, but since it has > missing values, it is a `float` type. When spark tries to create the data > frame, it converts all the values in that column to nulls instead of throwing > an error that there is a type mismatch. -- This message was sent by Atlassian JIRA (v6.3.4#6332) - To unsubscribe, e-mail: issues-unsubscr...@spark.apache.org For additional commands, e-mail: issues-h...@spark.apache.org
[jira] [Commented] (SPARK-18709) Automatic null conversion bug (instead of throwing error) when creating a Spark Datarame with incompatible types for fields.
[ https://issues.apache.org/jira/browse/SPARK-18709?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel=15723189#comment-15723189 ] Amogh Param commented on SPARK-18709: - [~dongjoon] Thanks for the fix. Just to clarify, does this mean that the fix will only be in 2.0.0 and not in 1.6.4 (assuming there will be a 1.6.4 update)? > Automatic null conversion bug (instead of throwing error) when creating a > Spark Datarame with incompatible types for fields. > > > Key: SPARK-18709 > URL: https://issues.apache.org/jira/browse/SPARK-18709 > Project: Spark > Issue Type: Bug > Components: SQL >Affects Versions: 1.6.2, 1.6.3 >Reporter: Amogh Param > Labels: bug > Fix For: 2.0.0 > > > When converting an RDD with a `float` type field to a spark dataframe with an > `IntegerType` / `LongType` schema field, spark 1.6.2 and 1.6.3 silently > convert the field values to nulls instead of throwing an error like `LongType > can not accept object ___ in type `. However, this seems to be > fixed in Spark 2.0.2. > The following example should make the problem clear: > {code} > from pyspark.sql.types import StructField, StructType, LongType, DoubleType > schema = StructType([ > StructField("0", LongType(), True), > StructField("1", DoubleType(), True), > ]) > data = [[1.0, 1.0], [nan, 2.0]] > spark_df = sqlContext.createDataFrame(sc.parallelize(data), schema) > spark_df.show() > {code} > Instead of throwing an error like: > {code} > LongType can not accept object 1.0 in type > {code} > Spark converts all the values in the first column to nulls > Running `spark_df.show()` gives: > {code} > ++---+ > | 0| 1| > ++---+ > |null|1.0| > |null|1.0| > ++---+ > {code} > For the purposes of my computation, I'm doing a `mapPartitions` on a spark > data frame, and for each partition, converting it into a pandas data frame, > doing a few computations on this pandas dataframe and the return value will > be a list of lists, which is converted to an RDD while being returned from > 'mapPartitions' (for all partitions). This RDD is then converted into a spark > dataframe similar to the example above, using > `sqlContext.createDataFrame(rdd, schema)`. The rdd has a column that should > be converted to a `LongType` in the spark data frame, but since it has > missing values, it is a `float` type. When spark tries to create the data > frame, it converts all the values in that column to nulls instead of throwing > an error that there is a type mismatch. -- This message was sent by Atlassian JIRA (v6.3.4#6332) - To unsubscribe, e-mail: issues-unsubscr...@spark.apache.org For additional commands, e-mail: issues-h...@spark.apache.org
[jira] [Updated] (SPARK-18709) Automatic null conversion bug (instead of throwing error) when creating a Spark Datarame with incompatible types for fields.
[ https://issues.apache.org/jira/browse/SPARK-18709?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ] Amogh Param updated SPARK-18709: Summary: Automatic null conversion bug (instead of throwing error) when creating a Spark Datarame with incompatible types for fields. (was: Failure to throw error and automatic null conversion bug when creating a Spark Datarame with incompatible types for fields.) > Automatic null conversion bug (instead of throwing error) when creating a > Spark Datarame with incompatible types for fields. > > > Key: SPARK-18709 > URL: https://issues.apache.org/jira/browse/SPARK-18709 > Project: Spark > Issue Type: Bug > Components: Spark Core >Affects Versions: 1.6.2, 1.6.3 >Reporter: Amogh Param > Labels: bug > Fix For: 2.0.2 > > > When converting an RDD with a `float` type field to a spark dataframe with an > `IntegerType` / `LongType` schema field, spark 1.6.2 and 1.6.3 silently > convert the field values to nulls instead of throwing an error like `LongType > can not accept object ___ in type `. However, this seems to be > fixed in Spark 2.0.2. > The following example should make the problem clear: > {code} > from pyspark.sql.types import StructField, StructType, LongType, DoubleType > schema = StructType([ > StructField("0", LongType(), True), > StructField("1", DoubleType(), True), > ]) > data = [[1.0, 1.0], [nan, 2.0]] > spark_df = sqlContext.createDataFrame(sc.parallelize(data), schema) > spark_df.show() > {code} > Instead of throwing an error like: > {code} > LongType can not accept object 1.0 in type > {code} > Spark converts all the values in the first column to nulls > Running `spark_df.show()` gives: > {code} > ++---+ > | 0| 1| > ++---+ > |null|1.0| > |null|1.0| > ++---+ > {code} > For the purposes of my computation, I'm doing a `mapPartitions` on a spark > data frame, and for each partition, converting it into a pandas data frame, > doing a few computations on this pandas dataframe and the return value will > be a list of lists, which is converted to an RDD while being returned from > 'mapPartitions' (for all partitions). This RDD is then converted into a spark > dataframe similar to the example above, using > `sqlContext.createDataFrame(rdd, schema)`. The rdd has a column that should > be converted to a `LongType` in the spark data frame, but since it has > missing values, it is a `float` type. When spark tries to create the data > frame, it converts all the values in that column to nulls instead of throwing > an error that there is a type mismatch. -- This message was sent by Atlassian JIRA (v6.3.4#6332) - To unsubscribe, e-mail: issues-unsubscr...@spark.apache.org For additional commands, e-mail: issues-h...@spark.apache.org
[jira] [Created] (SPARK-18709) Failure to throw error and automatic null conversion bug when creating a Spark Datarame with incompatible types for fields.
Amogh Param created SPARK-18709: --- Summary: Failure to throw error and automatic null conversion bug when creating a Spark Datarame with incompatible types for fields. Key: SPARK-18709 URL: https://issues.apache.org/jira/browse/SPARK-18709 Project: Spark Issue Type: Bug Components: Spark Core Affects Versions: 1.6.3, 1.6.2 Reporter: Amogh Param Fix For: 2.0.2 When converting an RDD with a `float` type field to a spark dataframe with an `IntegerType` / `LongType` schema field, spark 1.6.2 and 1.6.3 silently convert the field values to nulls instead of throwing an error like `LongType can not accept object ___ in type `. However, this seems to be fixed in Spark 2.0.2. The following example should make the problem clear: {code} from pyspark.sql.types import StructField, StructType, LongType, DoubleType schema = StructType([ StructField("0", LongType(), True), StructField("1", DoubleType(), True), ]) data = [[1.0, 1.0], [nan, 2.0]] spark_df = sqlContext.createDataFrame(sc.parallelize(data), schema) spark_df.show() {code} Instead of throwing an error like: {code} LongType can not accept object 1.0 in type {code} Spark converts all the values in the first column to nulls Running `spark_df.show()` gives: {code} ++---+ | 0| 1| ++---+ |null|1.0| |null|1.0| ++---+ {code} For the purposes of my computation, I'm doing a `mapPartitions` on a spark data frame, and for each partition, converting it into a pandas data frame, doing a few computations on this pandas dataframe and the return value will be a list of lists, which is converted to an RDD while being returned from 'mapPartitions' (for all partitions). This RDD is then converted into a spark dataframe similar to the example above, using `sqlContext.createDataFrame(rdd, schema)`. The rdd has a column that should be converted to a `LongType` in the spark data frame, but since it has missing values, it is a `float` type. When spark tries to create the data frame, it converts all the values in that column to nulls instead of throwing an error that there is a type mismatch. -- This message was sent by Atlassian JIRA (v6.3.4#6332) - To unsubscribe, e-mail: issues-unsubscr...@spark.apache.org For additional commands, e-mail: issues-h...@spark.apache.org