[jira] [Updated] (SPARK-37604) The option emptyValueInRead(in CSVOptions) is suggested to be designed as that any fields matching this string will be set as empty values "" when reading
[
https://issues.apache.org/jira/browse/SPARK-37604?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]
Wei Guo updated SPARK-37604:
Description:
The csv data format is imported from databricks
[spark-csv|https://github.com/databricks/spark-csv] by issue SPARK-12833 with
PR [10766|https://github.com/apache/spark/pull/10766] .
{*}For the nullValue option{*}, according to features described in spark-csv
readme file, it's designed as:
{noformat}
When reading files:
nullValue: specifies a string that indicates a null value, any fields matching
this string will be set as nulls in the DataFrame
When writing files:
nullValue: specifies a string that indicates a null value, nulls in the
DataFrame will be written as this string.
{noformat}
For example, when writing:
{code:scala}
Seq(("Tesla", null:String)).toDF("make", "comment").write.option("nullValue",
"NULL").csv(path){code}
The saved csv file is shown as:
{noformat}
Tesla,NULL
{noformat}
When reading:
{code:scala}
spark.read.option("nullValue", "NULL").csv(path).show()
{code}
The parsed dataframe is shown as:
||make||comment||
|Tesla|null|
We can find that null columns in dataframe can be saved as "NULL" strings in
csv files and {color:#00875a}*"NULL" strings in csv files can be parsed as null
columns*{color} in dataframe. That is:
{noformat}
When writing, convert null(in dataframe) to nullValue(in csv)
When reading, convert nullValue or nothing(in csv) to null(in dataframe)
{noformat}
But actually, the option nullValue in depended component univocity's
{*}_CommonSettings_{*}, is designed as that:
{noformat}
when reading, if the parser does not read any character from the input, the
nullValue is used instead of an empty string.
when writing, if the writer has a null object to write to the output, the
nullValue is used instead of an empty string.{noformat}
{*}There is a difference when reading{*}. In univocity, nothing content will be
convert to nullValue strings. But In Spark, we finally convert nothing content
or nullValue strings to null in *_UnivocityParser_ _nullSafeDatum_* method:
{code:java}
private def nullSafeDatum(
datum: String,
name: String,
nullable: Boolean,
options: CSVOptions)(converter: ValueConverter): Any = {
if (datum == options.nullValue || datum == null) {
if (!nullable) {
throw QueryExecutionErrors.foundNullValueForNotNullableFieldError(name)
}
null
} else {
converter.apply(datum)
}
} {code}
>From now, we start to talk about emptyValue.
{*}For the emptyValue option{*}, we add a emptyValueInRead option for reading
and a emptyValueInWrite option for writing. I found that Spark keeps the same
behaviors for emptyValue with univocity, that is:
{noformat}
When reading, if the parser does not read any character from the input, and the
input is within quotes, the empty is used instead of an empty string.
When writing, if the writer has an empty String to write to the output, the
emptyValue is used instead of an empty string.{noformat}
For example, when writing:
{code:scala}
Seq(("Tesla", "")).toDF("make", "comment").write.option("emptyValue",
"EMPTY").csv(path){code}
The saved csv file is shown as:
{noformat}
Tesla,EMPTY {noformat}
When reading:
{code:scala}
spark.read.option("emptyValue", "EMPTY").csv(path).show()
{code}
The parsed dataframe is shown as:
||make||comment||
|Tesla|EMPTY|
We can find that empty columns in dataframe can be saved as "EMPTY" strings in
csv files, *{color:#de350b}but "EMPTY" strings in csv files can not be parsed
as empty columns{color}* in dataframe. That is:
{noformat}
When writing, convert "" empty(in dataframe) to emptyValue(in csv)
When reading, convert "\"\"" quoted empty strings to emptyValue(in dataframe)
{noformat}
There is an obvious difference between nullValue and emptyValue in read
handling. For nullValue, we will convert nothing or nullValue strings to null
in dataframe, but for emptyValue, we just try to convert "\"\""(quoted empty
strings) to emptyValue strings rather than to convert both "\"\""(quoted empty
strings) and emptyValue strings to ""(empty) in dataframe.
I think it's better that if we keep the similar behavior(try to recover
emptyValue in csv files to "") for emptyValue as nullValue when reading. So, I
suggest that the emptyValueInRead(in CSVOptions) should be designed as that
any fields matching this string will be set as empty values "" when reading.
was:
The csv data format is imported from databricks
[spark-csv|https://github.com/databricks/spark-csv] by issue SPARK-12833 with
PR [10766|https://github.com/apache/spark/pull/10766] .
{*}For the nullValue option{*}, according to features described in spark-csv
readme file, it's designed as:
{noformat}
When reading files:
nullValue: specifies a string that indicates a null value, any fields matching
this string will be set as nulls in the DataFrame
When writing files:
nullValue: specif
[jira] [Updated] (SPARK-37604) The option emptyValueInRead(in CSVOptions) is suggested to be designed as that any fields matching this string will be set as empty values "" when reading
[
https://issues.apache.org/jira/browse/SPARK-37604?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]
Wei Guo updated SPARK-37604:
Description:
The csv data format is imported from databricks
[spark-csv|https://github.com/databricks/spark-csv] by issue SPARK-12833 with
PR [10766|https://github.com/apache/spark/pull/10766] .
{*}For the nullValue option{*}, according to features described in spark-csv
readme file, it's designed as:
{noformat}
When reading files:
nullValue: specifies a string that indicates a null value, any fields matching
this string will be set as nulls in the DataFrame
When writing files:
nullValue: specifies a string that indicates a null value, nulls in the
DataFrame will be written as this string.
{noformat}
For example, when writing:
{code:scala}
Seq(("Tesla", null:String)).toDF("make", "comment").write.option("nullValue",
"NULL").csv(path){code}
The saved csv file is shown as:
{noformat}
Tesla,NULL
{noformat}
When reading:
{code:java}
spark.read.option("nullValue", "NULL").csv(path).show()
{code}
The parsed dataframe is shown as:
||make||comment||
|Tesla|null|
We can find that null columns in dataframe can be saved as "NULL" strings in
csv files and {color:#00875a}*"NULL" strings in csv files can be parsed as null
columns*{color} in dataframe. That is:
{noformat}
When writing, convert null(in dataframe) to nullValue(in csv)
When reading, convert nullValue or nothing(in csv) to null(in dataframe)
{noformat}
But actually, the option nullValue in depended component univocity's
{*}_CommonSettings_{*}, is designed as that:
{noformat}
when reading, if the parser does not read any character from the input, the
nullValue is used instead of an empty string.
when writing, if the writer has a null object to write to the output, the
nullValue is used instead of an empty string.{noformat}
{*}There is a difference when reading{*}. In univocity, nothing content will be
convert to nullValue strings. But In Spark, we finally convert nothing content
or nullValue strings to null in *_UnivocityParser_ _nullSafeDatum_* method:
{code:java}
private def nullSafeDatum(
datum: String,
name: String,
nullable: Boolean,
options: CSVOptions)(converter: ValueConverter): Any = {
if (datum == options.nullValue || datum == null) {
if (!nullable) {
throw QueryExecutionErrors.foundNullValueForNotNullableFieldError(name)
}
null
} else {
converter.apply(datum)
}
} {code}
>From now, we start to talk about emptyValue.
{*}For the emptyValue option{*}, we add a emptyValueInRead option for reading
and a emptyValueInWrite option for writing. I found that Spark keeps the same
behaviors for emptyValue with univocity, that is:
{noformat}
When reading, if the parser does not read any character from the input, and the
input is within quotes, the empty is used instead of an empty string.
When writing, if the writer has an empty String to write to the output, the
emptyValue is used instead of an empty string.{noformat}
For example, when writing:
{code:scala}
Seq(("Tesla", "")).toDF("make", "comment").write.option("emptyValue",
"EMPTY").csv(path){code}
The saved csv file is shown as:
{noformat}
Tesla,EMPTY {noformat}
When reading:
{code:scala}
spark.read.option("emptyValue", "EMPTY").csv(path).show()
{code}
The parsed dataframe is shown as:
||make||comment||
|Tesla|EMPTY|
We can find that empty columns in dataframe can be saved as "EMPTY" strings in
csv files, *{color:#de350b}but "EMPTY" strings in csv files can not be parsed
as empty columns{color}* in dataframe. That is:
{noformat}
When writing, convert "" empty(in dataframe) to emptyValue(in csv)
When reading, convert "\"\"" quoted empty strings to emptyValue(in dataframe)
{noformat}
There is obvious difference between nullValue and emptyValue in read handling.
For nullValue, we try to convert nothing or nullValue strings to null in
dataframe, but for emptyValue, we just try to convert "\"\""(quoted empty
strings) to emptyValue rather than to convert both "\"\""(quoted empty strings)
and emptyValue strings to ""(empty) in dataframe.
I think it's better that we keep the similar behavior(try to recover emptyValue
to "") for emptyValue as nullValue when reading, so I suggest that the
emptyValueInRead(in CSVOptions) should be designed as that any fields matching
this string will be set as empty values "" when reading.
was:
The csv data format is imported from databricks
[spark-csv|https://github.com/databricks/spark-csv] by issue SPARK-12833 with
PR [10766|https://github.com/apache/spark/pull/10766] .
{*}For the nullValue option{*}, according to features described in spark-csv
readme file, it's designed as:
{noformat}
When reading files:
nullValue: specifies a string that indicates a null value, any fields matching
this string will be set as nulls in the DataFrame
When writing files:
nullValue: specifies a string that indicates
[jira] [Updated] (SPARK-37604) The option emptyValueInRead(in CSVOptions) is suggested to be designed as that any fields matching this string will be set as empty values "" when reading
[
https://issues.apache.org/jira/browse/SPARK-37604?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]
Wei Guo updated SPARK-37604:
Description:
The csv data format is imported from databricks
[spark-csv|https://github.com/databricks/spark-csv] by issue SPARK-12833 with
PR [10766|https://github.com/apache/spark/pull/10766] .
{*}For the nullValue option{*}, according to features described in spark-csv
readme file, it's designed as:
{noformat}
When reading files:
nullValue: specifies a string that indicates a null value, any fields matching
this string will be set as nulls in the DataFrame
When writing files:
nullValue: specifies a string that indicates a null value, nulls in the
DataFrame will be written as this string.
{noformat}
For example, when writing:
{code:scala}
Seq(("Tesla", null:String)).toDF("make", "comment").write.option("nullValue",
"NULL").csv(path){code}
The saved csv file is shown as:
{noformat}
Tesla,NULL
{noformat}
When reading:
{code:java}
spark.read.option("nullValue", "NULL").csv(path).show()
{code}
The parsed dataframe is shown as:
||make||comment||
|Tesla|null|
We can find that null columns in dataframe can be saved as "NULL" strings in
csv files and {color:#00875a}*"NULL" strings in csv files can be parsed as null
columns*{color} in dataframe. That is:
{noformat}
When writing, convert null(in dataframe) to nullValue(in csv)
When reading, convert nullValue or nothing(in csv) to null(in dataframe)
{noformat}
But actually, the option nullValue in depended component univocity's
{*}_CommonSettings_{*}, is designed as that:
{noformat}
when reading, if the parser does not read any character from the input, the
nullValue is used instead of an empty string.
when writing, if the writer has a null object to write to the output, the
nullValue is used instead of an empty string.{noformat}
{*}There is a difference when reading{*}. In univocity, nothing content will be
convert to nullValue strings. But In Spark, we finally convert nothing content
or nullValue strings to null in *_UnivocityParser_ _nullSafeDatum_* method:
{code:java}
private def nullSafeDatum(
datum: String,
name: String,
nullable: Boolean,
options: CSVOptions)(converter: ValueConverter): Any = {
if (datum == options.nullValue || datum == null) {
if (!nullable) {
throw QueryExecutionErrors.foundNullValueForNotNullableFieldError(name)
}
null
} else {
converter.apply(datum)
}
} {code}
>From now, we start to talk about emptyValue.
{*}For the emptyValue option{*}, we add a emptyValueInRead option for reading
and a emptyValueInWrite option for writing. I found that both Spark keeps the
same behaviors for emptyValue with univocity.
{noformat}
When reading, if the parser does not read any character from the input, and the
input is within quotes, the empty is used instead of an empty string.
When writing, if the writer has an empty String to write to the output, the
emptyValue is used instead of an empty string.{noformat}
For example, when writing:
{code:scala}
Seq(("Tesla",
{code}
{color:#910091}""{color}
{code:scala}
)).toDF("make", "comment").write.option("emptyValue", "EMPTY").csv(path){code}
The saved csv file is shown as:
{noformat}
Tesla,EMPTY {noformat}
When reading:
{code:java}
spark.read.option("emptyValue", "EMPTY").csv(path).show()
{code}
The parsed dataframe is shown as:
||make||comment||
|Tesla|EMPTY|
We can find that empty columns in dataframe can be saved as "EMPTY" strings in
csv files, *{color:#de350b}but "EMPTY" strings in csv files can not be parsed
as empty columns{color}* in dataframe. That is:
{noformat}
When writing, convert "" empty(in dataframe) to emptyValue(in csv)
When reading, convert "\"\"" quoted empty strings to emptyValue(in dataframe)
{noformat}
There is obvious difference between nullValue and emptyValue in read handling.
For nullValue, we try to convert nothing or nullValue strings to null in
dataframe, but for emptyValue, we just try to convert "\"\""(quoted empty
strings) to emptyValue rather than to convert both "\"\""(quoted empty strings)
and emptyValue strings to ""(empty) in dataframe.
I think it's better that we keep the similar behavior(try to recover emptyValue
to "") for emptyValue as nullValue when reading, so I suggest that the
emptyValueInRead(in CSVOptions) should be designed as that any fields matching
this string will be set as empty values "" when reading.
was:
The csv data format is imported from databricks
[spark-csv|https://github.com/databricks/spark-csv] by issue SPARK-12833 with
PR [10766|https://github.com/apache/spark/pull/10766] .
{*}For the nullValue option{*}, according to features described in spark-csv
readme file, it's designed as:
{noformat}
When reading files:
nullValue: specifies a string that indicates a null value, any fields matching
this string will be set as nulls in the DataFrame
When writing files:
nullVa
[jira] [Updated] (SPARK-37604) The option emptyValueInRead(in CSVOptions) is suggested to be designed as that any fields matching this string will be set as empty values "" when reading
[
https://issues.apache.org/jira/browse/SPARK-37604?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]
Wei Guo updated SPARK-37604:
Description:
The csv data format is imported from databricks
[spark-csv|https://github.com/databricks/spark-csv] by issue SPARK-12833 with
PR [10766|https://github.com/apache/spark/pull/10766] .
{*}For the nullValue option{*}, according to features described in spark-csv
readme file, it's designed as:
{noformat}
When reading files:
nullValue: specifies a string that indicates a null value, any fields matching
this string will be set as nulls in the DataFrame
When writing files:
nullValue: specifies a string that indicates a null value, nulls in the
DataFrame will be written as this string.
{noformat}
For example, when writing:
{code:scala}
Seq(("Tesla", null:String)).toDF("make", "comment").write.option("nullValue",
"NULL").csv(path){code}
The saved csv file is shown as:
{noformat}
Tesla,NULL
{noformat}
When reading:
{code:java}
spark.read.option("nullValue", "NULL").csv(path).show()
{code}
The parsed dataframe is shown as:
||make||comment||
|Tesla|null|
We can find that null columns in dataframe can be saved as "NULL" strings in
csv files and {color:#00875a}*"NULL" strings in csv files can be parsed as null
columns*{color} in dataframe. That is:
{noformat}
When writing, convert null(in dataframe) to nullValue(in csv)
When reading, convert nullValue or nothing(in csv) to null(in dataframe)
{noformat}
But actually, the option nullValue in depended component univocity's
{*}_CommonSettings_{*}, is designed as that:
{noformat}
when reading, if the parser does not read any character from the input, the
nullValue is used instead of an empty string.
when writing, if the writer has a null object to write to the output, the
nullValue is used instead of an empty string.{noformat}
{*}There is a difference when reading{*}. In univocity, nothing content will be
convert to nullValue strings. But In Spark, we finally convert nothing content
or nullValue strings to null in *_UnivocityParser_ _nullSafeDatum_* method:
{code:java}
private def nullSafeDatum(
datum: String,
name: String,
nullable: Boolean,
options: CSVOptions)(converter: ValueConverter): Any = {
if (datum == options.nullValue || datum == null) {
if (!nullable) {
throw QueryExecutionErrors.foundNullValueForNotNullableFieldError(name)
}
null
} else {
converter.apply(datum)
}
} {code}
{*}For the emptyValue option{*}, we add a emptyValueInRead option for reading
and a emptyValueInWrite option for writing. I found that both Spark keeps the
same behaviors for emptyValue with univocity.
{noformat}
When reading, if the parser does not read any character from the input, and the
input is within quotes, the empty is used instead of an empty string.
When writing, if the writer has an empty String to write to the output, the
emptyValue is used instead of an empty string.{noformat}
For example, when writing:
{code:scala}
Seq(("Tesla",
{code}
{color:#910091}""{color}
{code:scala}
)).toDF("make", "comment").write.option("emptyValue", "EMPTY").csv(path){code}
The saved csv file is shown as:
{noformat}
Tesla,EMPTY {noformat}
When reading:
{code:java}
spark.read.option("emptyValue", "EMPTY").csv(path).show()
{code}
The parsed dataframe is shown as:
||make||comment||
|Tesla|EMPTY|
We can find that empty columns in dataframe can be saved as "EMPTY" strings in
csv files, *{color:#de350b}but "EMPTY" strings in csv files can not be parsed
as empty columns{color}* in dataframe. That is:
{noformat}
When writing, convert "" empty(in dataframe) to emptyValue(in csv)
When reading, convert "\"\"" quoted empty strings to emptyValue(in dataframe)
{noformat}
There is obvious difference between nullValue and emptyValue in read handling.
For nullValue, we try to convert nothing or nullValue strings to null in
dataframe, but for emptyValue, we just try to convert "\"\""(quoted empty
strings) to emptyValue rather than to convert both "\"\""(quoted empty strings)
and emptyValue strings to ""(empty) in dataframe.
I think it's better that we keep the similar behavior(try to recover emptyValue
to "") for emptyValue as nullValue when reading, so I suggest that the
emptyValueInRead(in CSVOptions) should be designed as that any fields matching
this string will be set as empty values "" when reading.
was:
The csv data format is imported from databricks
[spark-csv|https://github.com/databricks/spark-csv] by issue SPARK-12833 with
PR [10766|https://github.com/apache/spark/pull/10766] .
{*}For the nullValue option{*}, according to features described in spark-csv
readme file, it's designed as:
{noformat}
When reading files:
nullValue: specifies a string that indicates a null value, any fields matching
this string will be set as nulls in the DataFrame
When writing files:
nullValue: specifies a string that indicates a null v
[jira] [Updated] (SPARK-37604) The option emptyValueInRead(in CSVOptions) is suggested to be designed as that any fields matching this string will be set as empty values "" when reading
[
https://issues.apache.org/jira/browse/SPARK-37604?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]
Wei Guo updated SPARK-37604:
Description:
The csv data format is imported from databricks
[spark-csv|https://github.com/databricks/spark-csv] by issue SPARK-12833 with
PR [10766|https://github.com/apache/spark/pull/10766] .
{*}For the nullValue option{*}, according to features described in spark-csv
readme file, it's designed as:
{noformat}
When reading files:
nullValue: specifies a string that indicates a null value, any fields matching
this string will be set as nulls in the DataFrame
When writing files:
nullValue: specifies a string that indicates a null value, nulls in the
DataFrame will be written as this string.
{noformat}
For example, when writing:
{code:scala}
Seq(("Tesla", null:String)).toDF("make", "comment").write.option("nullValue",
"NULL").csv(path){code}
The saved csv file is shown as:
{noformat}
Tesla,NULL
{noformat}
When reading:
{code:java}
spark.read.option("nullValue", "NULL").csv(path).show()
{code}
The parsed dataframe is shown as:
||make||comment||
|Tesla|null|
We can find that null columns in dataframe can be saved as "NULL" strings in
csv files and {color:#00875a}*"NULL" strings in csv files can be parsed as null
columns*{color} in dataframe. That is:
{noformat}
When writing, convert null(in dataframe) to nullValue(in csv)
When reading, convert nullValue or nothing(in csv) to null(in dataframe)
{noformat}
But actually, the option nullValue in depended component univocity's
{*}_CommonSettings_{*}, is designed as that:
{noformat}
when reading, if the parser does not read any character from the input, the
nullValue is used instead of an empty string.
when writing, if the writer has a null object to write to the output, the
nullValue is used instead of an empty string.{noformat}
{*}There is a difference when reading{*}. In univocity, nothing content would
be convert to nullValue strings. But In Spark, we finally convert nothing
content or nullValue strings to null in *_UnivocityParser_ _nullSafeDatum_*
method:
{code:java}
private def nullSafeDatum(
datum: String,
name: String,
nullable: Boolean,
options: CSVOptions)(converter: ValueConverter): Any = {
if (datum == options.nullValue || datum == null) {
if (!nullable) {
throw QueryExecutionErrors.foundNullValueForNotNullableFieldError(name)
}
null
} else {
converter.apply(datum)
}
} {code}
{*}For the emptyValue option{*}, we add a emptyValueInRead option for reading
and a emptyValueInWrite option for writing. I found that both Spark keeps the
same behaviors for emptyValue with univocity.
{noformat}
When reading, if the parser does not read any character from the input, and the
input is within quotes, the empty is used instead of an empty string.
When writing, if the writer has an empty String to write to the output, the
emptyValue is used instead of an empty string.{noformat}
For example, when writing:
{code:scala}
Seq(("Tesla",
{code}
{color:#910091}""{color}
{code:scala}
)).toDF("make", "comment").write.option("emptyValue", "EMPTY").csv(path){code}
The saved csv file is shown as:
{noformat}
Tesla,EMPTY {noformat}
When reading:
{code:java}
spark.read.option("emptyValue", "EMPTY").csv(path).show()
{code}
The parsed dataframe is shown as:
||make||comment||
|Tesla|EMPTY|
We can find that empty columns in dataframe can be saved as "EMPTY" strings in
csv files, *{color:#de350b}but "EMPTY" strings in csv files can not be parsed
as empty columns{color}* in dataframe. That is:
{noformat}
When writing, convert "" empty(in dataframe) to emptyValue(in csv)
When reading, convert "\"\"" quoted empty strings to emptyValue(in dataframe)
{noformat}
There is obvious difference between nullValue and emptyValue in read handling.
For nullValue, we try to convert nothing or nullValue strings to null in
dataframe, but for emptyValue, we just try to convert "\"\""(quoted empty
strings) to emptyValue rather than to convert both "\"\""(quoted empty strings)
and emptyValue strings to ""(empty) in dataframe.
I think it's better that we keep the similar behavior(try to recover emptyValue
to "") for emptyValue as nullValue when reading, so I suggest that the
emptyValueInRead(in CSVOptions) should be designed as that any fields matching
this string will be set as empty values "" when reading.
was:
The csv data format is imported from databricks
[spark-csv|https://github.com/databricks/spark-csv] by issue SPARK-12833 with
PR [10766|https://github.com/apache/spark/pull/10766] .
{*}For the nullValue option{*}, according to features described in spark-csv
readme file, it's designed as:
{noformat}
When reading files:
nullValue: specifies a string that indicates a null value, any fields matching
this string will be set as nulls in the DataFrame
When writing files:
nullValue: specifies a string that
[jira] [Updated] (SPARK-37604) The option emptyValueInRead(in CSVOptions) is suggested to be designed as that any fields matching this string will be set as empty values "" when reading
[
https://issues.apache.org/jira/browse/SPARK-37604?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]
Wei Guo updated SPARK-37604:
Description:
The csv data format is imported from databricks
[spark-csv|https://github.com/databricks/spark-csv] by issue SPARK-12833 with
PR [10766|https://github.com/apache/spark/pull/10766] .
{*}For the nullValue option{*}, according to features described in spark-csv
readme file, it's designed as:
{noformat}
When reading files:
nullValue: specifies a string that indicates a null value, any fields matching
this string will be set as nulls in the DataFrame
When writing files:
nullValue: specifies a string that indicates a null value, nulls in the
DataFrame will be written as this string.
{noformat}
For example, when writing:
{code:scala}
Seq(("Tesla", null:String)).toDF("make", "comment").write.option("nullValue",
"NULL").csv(path){code}
The saved csv file is shown as:
{noformat}
Tesla,NULL
{noformat}
When reading:
{code:java}
spark.read.option("nullValue", "NULL").csv(path).show()
{code}
The parsed dataframe is shown as:
||make||comment||
|Tesla|null|
We can find that null columns in dataframe can be saved as "NULL" strings in
csv files and {color:#00875a}*"NULL" strings in csv files can be parsed as null
columns*{color} in dataframe. That is:
{noformat}
When writing, convert null(in dataframe) to nullValue(in csv)
When reading, convert nullValue or nothing(in csv) to null(in dataframe)
{noformat}
But actually, the option nullValue in depended component univocity's
{*}_CommonSettings_{*}, is designed as that:
{noformat}
when reading, if the parser does not read any character from the input, the
nullValue is used instead of an empty string.
when writing, if the writer has a null object to write to the output, the
nullValue is used instead of an empty string.{noformat}
There is a difference when reading. In univocity, nothing content would be
convert to nullValue strings. But In Spark, we finally convert nothing content
or nullValue strings to null in *_UnivocityParser_ _nullSafeDatum_* method:
{code:java}
private def nullSafeDatum(
datum: String,
name: String,
nullable: Boolean,
options: CSVOptions)(converter: ValueConverter): Any = {
if (datum == options.nullValue || datum == null) {
if (!nullable) {
throw QueryExecutionErrors.foundNullValueForNotNullableFieldError(name)
}
null
} else {
converter.apply(datum)
}
} {code}
{*}For the emptyValue option{*}, we add a emptyValueInRead option for reading
and a emptyValueInWrite option for writing. I found that both Spark keeps the
same behaviors for emptyValue with univocity.
{noformat}
When reading, if the parser does not read any character from the input, and the
input is within quotes, the empty is used instead of an empty string.
When writing, if the writer has an empty String to write to the output, the
emptyValue is used instead of an empty string.{noformat}
For example, when writing:
{code:scala}
Seq(("Tesla",
{code}
{color:#910091}""{color}
{code:scala}
)).toDF("make", "comment").write.option("emptyValue", "EMPTY").csv(path){code}
The saved csv file is shown as:
{noformat}
Tesla,EMPTY {noformat}
When reading:
{code:java}
spark.read.option("emptyValue", "EMPTY").csv(path).show()
{code}
The parsed dataframe is shown as:
||make||comment||
|Tesla|EMPTY|
We can find that empty columns in dataframe can be saved as "EMPTY" strings in
csv files, *{color:#de350b}but "EMPTY" strings in csv files can not be parsed
as empty columns{color}* in dataframe. That is:
{noformat}
When writing, convert "" empty(in dataframe) to emptyValue(in csv)
When reading, convert "\"\"" quoted empty strings to emptyValue(in dataframe)
{noformat}
There is obvious difference between nullValue and emptyValue in read handling.
For nullValue, we try to convert nothing or nullValue strings to null in
dataframe, but for emptyValue, we just try to convert "\"\""(quoted empty
strings) to emptyValue rather than to convert both "\"\""(quoted empty strings)
and emptyValue strings to ""(empty) in dataframe.
I think it's better that we keep the similar behavior(try to recover emptyValue
to "") for emptyValue as nullValue when reading, so I suggest that the
emptyValueInRead(in CSVOptions) should be designed as that any fields matching
this string will be set as empty values "" when reading.
was:
The csv data format is imported from databricks
[spark-csv|https://github.com/databricks/spark-csv] by issue SPARK-12833 with
PR [10766|https://github.com/apache/spark/pull/10766] .
{*}For the nullValue option{*}, according to features described in spark-csv
readme file, it's designed as:
{noformat}
When reading files:
nullValue: specifies a string that indicates a null value, any fields matching
this string will be set as nulls in the DataFrame
When writing files:
nullValue: specifies a string that indica
[jira] [Updated] (SPARK-37604) The option emptyValueInRead(in CSVOptions) is suggested to be designed as that any fields matching this string will be set as empty values "" when reading
[
https://issues.apache.org/jira/browse/SPARK-37604?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]
Wei Guo updated SPARK-37604:
Description:
The csv data format is imported from databricks
[spark-csv|https://github.com/databricks/spark-csv] by issue SPARK-12833 with
PR [10766|https://github.com/apache/spark/pull/10766] .
{*}For the nullValue option{*}, according to features described in spark-csv
readme file, it's designed as:
{noformat}
When reading files:
nullValue: specifies a string that indicates a null value, any fields matching
this string will be set as nulls in the DataFrame
When writing files:
nullValue: specifies a string that indicates a null value, nulls in the
DataFrame will be written as this string.
{noformat}
For example, when writing:
{code:scala}
Seq(("Tesla", null:String)).toDF("make", "comment").write.option("nullValue",
"NULL").csv(path){code}
The saved csv file is shown as:
{noformat}
Tesla,NULL
{noformat}
When reading:
{code:java}
spark.read.option("nullValue", "NULL").csv(path).show()
{code}
The parsed dataframe is shown as:
||make||comment||
|Tesla|null|
We can find that null columns in dataframe can be saved as "NULL" strings in
csv files and *{color:#de350b}"NULL" strings in csv files can be parsed as null
columns{color}* in dataframe. That is:
{noformat}
When writing, convert null(in dataframe) to nullValue(in csv)
When reading, convert nullValue or nothing(in csv) to null(in dataframe)
{noformat}
But actually, the option nullValue in depended component univocity's
{*}_CommonSettings_{*}, is designed as that:
{noformat}
when reading, if the parser does not read any character from the input, the
nullValue is used instead of an empty string.
when writing, if the writer has a null object to write to the output, the
nullValue is used instead of an empty string.{noformat}
There is a difference when reading. In univocity, nothing content would be
convert to nullValue strings. But In Spark, we finally convert nothing content
or nullValue strings to null in *_UnivocityParser_ _nullSafeDatum_* method:
{code:java}
private def nullSafeDatum(
datum: String,
name: String,
nullable: Boolean,
options: CSVOptions)(converter: ValueConverter): Any = {
if (datum == options.nullValue || datum == null) {
if (!nullable) {
throw QueryExecutionErrors.foundNullValueForNotNullableFieldError(name)
}
null
} else {
converter.apply(datum)
}
} {code}
{*}For the emptyValue option{*}, we add a emptyValueInRead option for reading
and a emptyValueInWrite option for writing. I found that both Spark keeps the
same behaviors for emptyValue with univocity.
{noformat}
When reading, if the parser does not read any character from the input, and the
input is within quotes, the empty is used instead of an empty string.
When writing, if the writer has an empty String to write to the output, the
emptyValue is used instead of an empty string.{noformat}
For example, when writing:
{code:scala}
Seq(("Tesla",
{code}
{color:#910091}""{color}
{code:scala}
)).toDF("make", "comment").write.option("emptyValue", "EMPTY").csv(path){code}
The saved csv file is shown as:
{noformat}
Tesla,EMPTY {noformat}
When reading:
{code:java}
spark.read.option("emptyValue", "EMPTY").csv(path).show()
{code}
The parsed dataframe is shown as:
||make||comment||
|Tesla|EMPTY|
We can find that empty columns in dataframe can be saved as "EMPTY" strings in
csv files, *{color:#de350b}but "EMPTY" strings in csv files can not be parsed
as empty columns{color}* in dataframe. That is:
{noformat}
When writing, convert "" empty(in dataframe) to emptyValue(in csv)
When reading, convert "\"\"" quoted empty strings to emptyValue(in dataframe)
{noformat}
There is obvious difference between nullValue and emptyValue in read handling.
For nullValue, we try to convert nothing or nullValue strings to null in
dataframe, but for emptyValue, we just try to convert "\"\""(quoted empty
strings) to emptyValue rather than to convert both "\"\""(quoted empty strings)
and emptyValue strings to ""(empty) in dataframe.
I think it's better that we keep the similar behavior(try to recover emptyValue
to "") for emptyValue as nullValue when reading, so I suggest that the
emptyValueInRead(in CSVOptions) should be designed as that any fields matching
this string will be set as empty values "" when reading.
was:
The csv data format is imported from databricks
[spark-csv|https://github.com/databricks/spark-csv] by issue SPARK-12833 with
PR [10766|https://github.com/apache/spark/pull/10766] .
{*}For the nullValue option{*}, according to features described in spark-csv
readme file, it's designed as:
{noformat}
When reading files:
nullValue: specifies a string that indicates a null value, any fields matching
this string will be set as nulls in the DataFrame
When writing files:
nullValue: specifies a string that indica
[jira] [Updated] (SPARK-37604) The option emptyValueInRead(in CSVOptions) is suggested to be designed as that any fields matching this string will be set as empty values "" when reading
[
https://issues.apache.org/jira/browse/SPARK-37604?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]
Wei Guo updated SPARK-37604:
Description:
The csv data format is imported from databricks
[spark-csv|https://github.com/databricks/spark-csv] by issue SPARK-12833 with
PR [10766|https://github.com/apache/spark/pull/10766] .
{*}For the nullValue option{*}, according to features described in spark-csv
readme file, it's designed as:
{noformat}
When reading files:
nullValue: specifies a string that indicates a null value, any fields matching
this string will be set as nulls in the DataFrame
When writing files:
nullValue: specifies a string that indicates a null value, nulls in the
DataFrame will be written as this string.
{noformat}
For example, when writing:
{code:scala}
Seq(("Tesla", null:String)).toDF("make", "comment").write.option("nullValue",
"NULL").csv(path){code}
The saved csv file is shown as:
{noformat}
Tesla,NULL
{noformat}
When reading:
{code:java}
spark.read.option("nullValue", "NULL").csv(path).show()
{code}
The parsed dataframe is shown as:
||make||comment||
|tesla|null|
We can find that null columns in dataframe can be saved as "NULL" strings in
csv files and *{color:#de350b}"NULL" strings in csv files can be parsed as null
columns{color}* in dataframe. That is:
{noformat}
When writing, convert null(in dataframe) to nullValue(in csv)
When reading, convert nullValue or nothing(in csv) to null(in dataframe)
{noformat}
But actually, the option nullValue in depended component univocity's
{*}_CommonSettings_{*}, is designed as that:
{noformat}
when reading, if the parser does not read any character from the input, the
nullValue is used instead of an empty string.
when writing, if the writer has a null object to write to the output, the
nullValue is used instead of an empty string.{noformat}
There is a difference when reading. In univocity, nothing content would be
convert to nullValue strings. But In Spark, we finally convert nothing content
or nullValue strings to null in *_UnivocityParser_ _nullSafeDatum_* method:
{code:java}
private def nullSafeDatum(
datum: String,
name: String,
nullable: Boolean,
options: CSVOptions)(converter: ValueConverter): Any = {
if (datum == options.nullValue || datum == null) {
if (!nullable) {
throw QueryExecutionErrors.foundNullValueForNotNullableFieldError(name)
}
null
} else {
converter.apply(datum)
}
} {code}
{*}For the emptyValue option{*}, we add a emptyValueInRead option for reading
and a emptyValueInWrite option for writing. I found that both Spark keeps the
same behaviors for emptyValue with univocity.
{noformat}
When reading, if the parser does not read any character from the input, and the
input is within quotes, the empty is used instead of an empty string.
When writing, if the writer has an empty String to write to the output, the
emptyValue is used instead of an empty string.{noformat}
For example, when writing:
{code:scala}
Seq(("Tesla",
{code}
{color:#910091}""{color}
{code:scala}
)).toDF("make", "comment").write.option("emptyValue", "EMPTY").csv(path){code}
The saved csv file is shown as:
{noformat}
Tesla,EMPTY {noformat}
When reading:
{code:java}
spark.read.option("emptyValue", "EMPTY").csv(path).show()
{code}
The parsed dataframe is shown as:
||make||comment||
|tesla|EMPTY|
We can find that empty columns in dataframe can be saved as "EMPTY" strings in
csv files, *{color:#de350b}but "EMPTY" strings in csv files can not be parsed
as empty columns{color}* in dataframe. That is:
{noformat}
When writing, convert "" empty(in dataframe) to emptyValue(in csv)
When reading, convert "\"\"" quoted empty strings to emptyValue(in dataframe)
{noformat}
There is obvious difference between nullValue and emptyValue in read handling.
For nullValue, we try to convert nothing or nullValue strings to null in
dataframe, but for emptyValue, we just try to convert "\"\""(quoted empty
strings) to emptyValue rather than to convert both "\"\""(quoted empty strings)
and emptyValue strings to ""(empty) in dataframe.
I think it's better that we keep the similar behavior(try to recover emptyValue
to "") for emptyValue as nullValue when reading, so I suggest that the
emptyValueInRead(in CSVOptions) should be designed as that any fields matching
this string will be set as empty values "" when reading.
was:
The csv data format is imported from databricks
[spark-csv|https://github.com/databricks/spark-csv] by issue SPARK-12833 with
PR [10766|https://github.com/apache/spark/pull/10766] .
{*}For the nullValue option{*}, according to features description in spark-csv
readme file, it's designed as:
{noformat}
When reading files:
nullValue: specifies a string that indicates a null value, any fields matching
this string will be set as nulls in the DataFrame
When writing files:
nullValue: specifies a string that indi
[jira] [Updated] (SPARK-37604) The option emptyValueInRead(in CSVOptions) is suggested to be designed as that any fields matching this string will be set as empty values "" when reading
[
https://issues.apache.org/jira/browse/SPARK-37604?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]
Wei Guo updated SPARK-37604:
Description:
The csv data format is imported from databricks
[spark-csv|https://github.com/databricks/spark-csv] by issue SPARK-12833 with
PR [10766|https://github.com/apache/spark/pull/10766] .
{*}For the nullValue option{*}, according to the features description in
spark-csv readme file, it's designed as:
{noformat}
When reading files:
nullValue: specifies a string that indicates a null value, any fields matching
this string will be set as nulls in the DataFrame
When writing files:
nullValue: specifies a string that indicates a null value, nulls in the
DataFrame will be written as this string.
{noformat}
For example, when writing:
{code:scala}
Seq(("Tesla", null:String)).toDF("make", "comment").write.option("nullValue",
"NULL").csv(path){code}
The saved csv file is shown as:
{noformat}
Tesla,NULL
{noformat}
When reading:
{code:java}
spark.read.option("nullValue", "NULL").csv(path).show()
{code}
The parsed dataframe is shown as:
||make||comment||
|tesla|null|
We can find that null columns in dataframe can be saved as "NULL" strings in
csv files and *{color:#de350b}"NULL" strings in csv files can be parsed as null
columns{color}* in dataframe. That is:
{noformat}
When writing, convert null(in dataframe) to nullValue(in csv)
When reading, convert nullValue or nothing(in csv) to null(in dataframe)
{noformat}
But actually, the option nullValue in depended component univocity's
{*}_CommonSettings_{*}, is designed as that:
{noformat}
when reading, if the parser does not read any character from the input, the
nullValue is used instead of an empty string.
when writing, if the writer has a null object to write to the output, the
nullValue is used instead of an empty string.{noformat}
There is a difference when reading. In univocity, nothing content would be
convert to nullValue strings. But In Spark, we finally convert nothing content
or nullValue strings to null in *_UnivocityParser_ _nullSafeDatum_* method:
{code:java}
private def nullSafeDatum(
datum: String,
name: String,
nullable: Boolean,
options: CSVOptions)(converter: ValueConverter): Any = {
if (datum == options.nullValue || datum == null) {
if (!nullable) {
throw QueryExecutionErrors.foundNullValueForNotNullableFieldError(name)
}
null
} else {
converter.apply(datum)
}
} {code}
{*}For the emptyValue option{*}, we add a emptyValueInRead option for reading
and a emptyValueInWrite option for writing. I found that both Spark keeps the
same behaviors for emptyValue with univocity.
{noformat}
When reading, if the parser does not read any character from the input, and the
input is within quotes, the empty is used instead of an empty string.
When writing, if the writer has an empty String to write to the output, the
emptyValue is used instead of an empty string.{noformat}
For example, when writing:
{code:scala}
Seq(("Tesla",
{code}
{color:#910091}""{color}
{code:scala}
)).toDF("make", "comment").write.option("emptyValue", "EMPTY").csv(path){code}
The saved csv file is shown as:
{noformat}
Tesla,EMPTY {noformat}
When reading:
{code:java}
spark.read.option("emptyValue", "EMPTY").csv(path).show()
{code}
The parsed dataframe is shown as:
||make||comment||
|tesla|EMPTY|
We can find that empty columns in dataframe can be saved as "EMPTY" strings in
csv files, *{color:#de350b}but "EMPTY" strings in csv files can not be parsed
as empty columns{color}* in dataframe. That is:
{noformat}
When writing, convert "" empty(in dataframe) to emptyValue(in csv)
When reading, convert "\"\"" quoted empty strings to emptyValue(in dataframe)
{noformat}
There is obvious difference between nullValue and emptyValue in read handling.
For nullValue, we try to convert nothing or nullValue strings to null in
dataframe, but for emptyValue, we just try to convert "\"\""(quoted empty
strings) to emptyValue rather than to convert both "\"\""(quoted empty strings)
and emptyValue strings to ""(empty) in dataframe.
I think it's better that we keep the similar behavior(try to recover emptyValue
to "") for emptyValue as nullValue when reading, so I suggest that the
emptyValueInRead(in CSVOptions) should be designed as that any fields matching
this string will be set as empty values "" when reading.
was:
The csv data format is imported from databricks
[spark-csv|https://github.com/databricks/spark-csv] by issue SPARK-12833 with
PR [10766|https://github.com/apache/spark/pull/10766] .
{*}For the nullValue option{*}, according to the features description in
spark-csv readme file, it is designed as:
{noformat}
When reading files:
nullValue: specifies a string that indicates a null value, any fields matching
this string will be set as nulls in the DataFrame
When writing files:
nullValue: specifies a strin
[jira] [Updated] (SPARK-37604) The option emptyValueInRead(in CSVOptions) is suggested to be designed as that any fields matching this string will be set as empty values "" when reading
[
https://issues.apache.org/jira/browse/SPARK-37604?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]
Wei Guo updated SPARK-37604:
Description:
The csv data format is imported from databricks
[spark-csv|https://github.com/databricks/spark-csv] by issue SPARK-12833 with
PR [10766|https://github.com/apache/spark/pull/10766] .
{*}For the nullValue option{*}, according to features description in spark-csv
readme file, it's designed as:
{noformat}
When reading files:
nullValue: specifies a string that indicates a null value, any fields matching
this string will be set as nulls in the DataFrame
When writing files:
nullValue: specifies a string that indicates a null value, nulls in the
DataFrame will be written as this string.
{noformat}
For example, when writing:
{code:scala}
Seq(("Tesla", null:String)).toDF("make", "comment").write.option("nullValue",
"NULL").csv(path){code}
The saved csv file is shown as:
{noformat}
Tesla,NULL
{noformat}
When reading:
{code:java}
spark.read.option("nullValue", "NULL").csv(path).show()
{code}
The parsed dataframe is shown as:
||make||comment||
|tesla|null|
We can find that null columns in dataframe can be saved as "NULL" strings in
csv files and *{color:#de350b}"NULL" strings in csv files can be parsed as null
columns{color}* in dataframe. That is:
{noformat}
When writing, convert null(in dataframe) to nullValue(in csv)
When reading, convert nullValue or nothing(in csv) to null(in dataframe)
{noformat}
But actually, the option nullValue in depended component univocity's
{*}_CommonSettings_{*}, is designed as that:
{noformat}
when reading, if the parser does not read any character from the input, the
nullValue is used instead of an empty string.
when writing, if the writer has a null object to write to the output, the
nullValue is used instead of an empty string.{noformat}
There is a difference when reading. In univocity, nothing content would be
convert to nullValue strings. But In Spark, we finally convert nothing content
or nullValue strings to null in *_UnivocityParser_ _nullSafeDatum_* method:
{code:java}
private def nullSafeDatum(
datum: String,
name: String,
nullable: Boolean,
options: CSVOptions)(converter: ValueConverter): Any = {
if (datum == options.nullValue || datum == null) {
if (!nullable) {
throw QueryExecutionErrors.foundNullValueForNotNullableFieldError(name)
}
null
} else {
converter.apply(datum)
}
} {code}
{*}For the emptyValue option{*}, we add a emptyValueInRead option for reading
and a emptyValueInWrite option for writing. I found that both Spark keeps the
same behaviors for emptyValue with univocity.
{noformat}
When reading, if the parser does not read any character from the input, and the
input is within quotes, the empty is used instead of an empty string.
When writing, if the writer has an empty String to write to the output, the
emptyValue is used instead of an empty string.{noformat}
For example, when writing:
{code:scala}
Seq(("Tesla",
{code}
{color:#910091}""{color}
{code:scala}
)).toDF("make", "comment").write.option("emptyValue", "EMPTY").csv(path){code}
The saved csv file is shown as:
{noformat}
Tesla,EMPTY {noformat}
When reading:
{code:java}
spark.read.option("emptyValue", "EMPTY").csv(path).show()
{code}
The parsed dataframe is shown as:
||make||comment||
|tesla|EMPTY|
We can find that empty columns in dataframe can be saved as "EMPTY" strings in
csv files, *{color:#de350b}but "EMPTY" strings in csv files can not be parsed
as empty columns{color}* in dataframe. That is:
{noformat}
When writing, convert "" empty(in dataframe) to emptyValue(in csv)
When reading, convert "\"\"" quoted empty strings to emptyValue(in dataframe)
{noformat}
There is obvious difference between nullValue and emptyValue in read handling.
For nullValue, we try to convert nothing or nullValue strings to null in
dataframe, but for emptyValue, we just try to convert "\"\""(quoted empty
strings) to emptyValue rather than to convert both "\"\""(quoted empty strings)
and emptyValue strings to ""(empty) in dataframe.
I think it's better that we keep the similar behavior(try to recover emptyValue
to "") for emptyValue as nullValue when reading, so I suggest that the
emptyValueInRead(in CSVOptions) should be designed as that any fields matching
this string will be set as empty values "" when reading.
was:
The csv data format is imported from databricks
[spark-csv|https://github.com/databricks/spark-csv] by issue SPARK-12833 with
PR [10766|https://github.com/apache/spark/pull/10766] .
{*}For the nullValue option{*}, according to the features description in
spark-csv readme file, it's designed as:
{noformat}
When reading files:
nullValue: specifies a string that indicates a null value, any fields matching
this string will be set as nulls in the DataFrame
When writing files:
nullValue: specifies a string tha
[jira] [Updated] (SPARK-37604) The option emptyValueInRead(in CSVOptions) is suggested to be designed as that any fields matching this string will be set as empty values "" when reading
[
https://issues.apache.org/jira/browse/SPARK-37604?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]
Wei Guo updated SPARK-37604:
Description:
The csv data format is imported from databricks
[spark-csv|https://github.com/databricks/spark-csv] by issue SPARK-12833 and PR
[10766|https://github.com/apache/spark/pull/10766] .
{*}For the nullValue option{*}, according to the features description in
spark-csv readme file, it is designed as:
{noformat}
When reading files:
nullValue: specifies a string that indicates a null value, any fields matching
this string will be set as nulls in the DataFrame
When writing files:
nullValue: specifies a string that indicates a null value, nulls in the
DataFrame will be written as this string.
{noformat}
For example, when writing:
{code:scala}
Seq(("Tesla", null:String)).toDF("make", "comment").write.option("nullValue",
"NULL").csv(path){code}
The saved csv file is shown as:
{noformat}
Tesla,NULL
{noformat}
When reading:
{code:java}
spark.read.option("nullValue", "NULL").csv(path).show()
{code}
The parsed dataframe is shown as:
||make||comment||
|tesla|null|
We can find that null columns in dataframe can be saved as "NULL" strings in
csv files and *{color:#de350b}"NULL" strings in csv files can be parsed as null
columns{color}* in dataframe. That is:
{noformat}
When writing, convert null(in dataframe) to nullValue(in csv)
When reading, convert nullValue or nothing(in csv) to null(in dataframe)
{noformat}
But actually, the option nullValue in depended component univocity's
{*}_CommonSettings_{*}, is designed as that:
{noformat}
when reading, if the parser does not read any character from the input, the
nullValue is used instead of an empty string.
when writing, if the writer has a null object to write to the output, the
nullValue is used instead of an empty string.{noformat}
There is a difference when reading. In univocity, nothing content would be
convert to nullValue strings. But In Spark, we finally convert nothing content
or nullValue strings to null in *_UnivocityParser_ _nullSafeDatum_* method:
{code:java}
private def nullSafeDatum(
datum: String,
name: String,
nullable: Boolean,
options: CSVOptions)(converter: ValueConverter): Any = {
if (datum == options.nullValue || datum == null) {
if (!nullable) {
throw QueryExecutionErrors.foundNullValueForNotNullableFieldError(name)
}
null
} else {
converter.apply(datum)
}
} {code}
{*}For the emptyValue option{*}, we add a emptyValueInRead option for reading
and a emptyValueInWrite option for writing. I found that both Spark keeps the
same behaviors for emptyValue with univocity.
{noformat}
When reading, if the parser does not read any character from the input, and the
input is within quotes, the empty is used instead of an empty string.
When writing, if the writer has an empty String to write to the output, the
emptyValue is used instead of an empty string.{noformat}
For example, when writing:
{code:scala}
Seq(("Tesla",
{code}
{color:#910091}""{color}
{code:scala}
)).toDF("make", "comment").write.option("emptyValue", "EMPTY").csv(path){code}
The saved csv file is shown as:
{noformat}
Tesla,EMPTY {noformat}
When reading:
{code:java}
spark.read.option("emptyValue", "EMPTY").csv(path).show()
{code}
The parsed dataframe is shown as:
||make||comment||
|tesla|EMPTY|
We can find that empty columns in dataframe can be saved as "EMPTY" strings in
csv files, *{color:#de350b}but "EMPTY" strings in csv files can not be parsed
as empty columns{color}* in dataframe. That is:
{noformat}
When writing, convert "" empty(in dataframe) to emptyValue(in csv)
When reading, convert "\"\"" quoted empty strings to emptyValue(in dataframe)
{noformat}
There is obvious difference between nullValue and emptyValue in read handling.
For nullValue, we try to convert nothing or nullValue strings to null in
dataframe, but for emptyValue, we just try to convert "\"\""(quoted empty
strings) to emptyValue rather than to convert both "\"\""(quoted empty strings)
and emptyValue strings to ""(empty) in dataframe.
I think it's better that we keep the similar behavior(try to recover emptyValue
to "") for emptyValue as nullValue when reading, so I suggest that the
emptyValueInRead(in CSVOptions) should be designed as that any fields matching
this string will be set as empty values "" when reading.
was:
The csv data format is imported from databricks
[spark-csv|https://github.com/databricks/spark-csv] by issue SPARK-12833 and PR
[10766|https://github.com/apache/spark/pull/10766] .
{*}For the nullValue option{*}, according to the features description in
spark-csv readme file, it is designed as:
{noformat}
When reading files:
nullValue: specifies a string that indicates a null value, any fields matching
this string will be set as nulls in the DataFrame
When writing files:
nullValue: specifies a string
[jira] [Updated] (SPARK-37604) The option emptyValueInRead(in CSVOptions) is suggested to be designed as that any fields matching this string will be set as empty values "" when reading
[
https://issues.apache.org/jira/browse/SPARK-37604?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]
Wei Guo updated SPARK-37604:
Description:
The csv data format is imported from databricks
[spark-csv|https://github.com/databricks/spark-csv] by issue SPARK-12833 with
PR [10766|https://github.com/apache/spark/pull/10766] .
{*}For the nullValue option{*}, according to the features description in
spark-csv readme file, it is designed as:
{noformat}
When reading files:
nullValue: specifies a string that indicates a null value, any fields matching
this string will be set as nulls in the DataFrame
When writing files:
nullValue: specifies a string that indicates a null value, nulls in the
DataFrame will be written as this string.
{noformat}
For example, when writing:
{code:scala}
Seq(("Tesla", null:String)).toDF("make", "comment").write.option("nullValue",
"NULL").csv(path){code}
The saved csv file is shown as:
{noformat}
Tesla,NULL
{noformat}
When reading:
{code:java}
spark.read.option("nullValue", "NULL").csv(path).show()
{code}
The parsed dataframe is shown as:
||make||comment||
|tesla|null|
We can find that null columns in dataframe can be saved as "NULL" strings in
csv files and *{color:#de350b}"NULL" strings in csv files can be parsed as null
columns{color}* in dataframe. That is:
{noformat}
When writing, convert null(in dataframe) to nullValue(in csv)
When reading, convert nullValue or nothing(in csv) to null(in dataframe)
{noformat}
But actually, the option nullValue in depended component univocity's
{*}_CommonSettings_{*}, is designed as that:
{noformat}
when reading, if the parser does not read any character from the input, the
nullValue is used instead of an empty string.
when writing, if the writer has a null object to write to the output, the
nullValue is used instead of an empty string.{noformat}
There is a difference when reading. In univocity, nothing content would be
convert to nullValue strings. But In Spark, we finally convert nothing content
or nullValue strings to null in *_UnivocityParser_ _nullSafeDatum_* method:
{code:java}
private def nullSafeDatum(
datum: String,
name: String,
nullable: Boolean,
options: CSVOptions)(converter: ValueConverter): Any = {
if (datum == options.nullValue || datum == null) {
if (!nullable) {
throw QueryExecutionErrors.foundNullValueForNotNullableFieldError(name)
}
null
} else {
converter.apply(datum)
}
} {code}
{*}For the emptyValue option{*}, we add a emptyValueInRead option for reading
and a emptyValueInWrite option for writing. I found that both Spark keeps the
same behaviors for emptyValue with univocity.
{noformat}
When reading, if the parser does not read any character from the input, and the
input is within quotes, the empty is used instead of an empty string.
When writing, if the writer has an empty String to write to the output, the
emptyValue is used instead of an empty string.{noformat}
For example, when writing:
{code:scala}
Seq(("Tesla",
{code}
{color:#910091}""{color}
{code:scala}
)).toDF("make", "comment").write.option("emptyValue", "EMPTY").csv(path){code}
The saved csv file is shown as:
{noformat}
Tesla,EMPTY {noformat}
When reading:
{code:java}
spark.read.option("emptyValue", "EMPTY").csv(path).show()
{code}
The parsed dataframe is shown as:
||make||comment||
|tesla|EMPTY|
We can find that empty columns in dataframe can be saved as "EMPTY" strings in
csv files, *{color:#de350b}but "EMPTY" strings in csv files can not be parsed
as empty columns{color}* in dataframe. That is:
{noformat}
When writing, convert "" empty(in dataframe) to emptyValue(in csv)
When reading, convert "\"\"" quoted empty strings to emptyValue(in dataframe)
{noformat}
There is obvious difference between nullValue and emptyValue in read handling.
For nullValue, we try to convert nothing or nullValue strings to null in
dataframe, but for emptyValue, we just try to convert "\"\""(quoted empty
strings) to emptyValue rather than to convert both "\"\""(quoted empty strings)
and emptyValue strings to ""(empty) in dataframe.
I think it's better that we keep the similar behavior(try to recover emptyValue
to "") for emptyValue as nullValue when reading, so I suggest that the
emptyValueInRead(in CSVOptions) should be designed as that any fields matching
this string will be set as empty values "" when reading.
was:
The csv data format is imported from databricks
[spark-csv|https://github.com/databricks/spark-csv] by issue SPARK-12833 and PR
[10766|https://github.com/apache/spark/pull/10766] .
{*}For the nullValue option{*}, according to the features description in
spark-csv readme file, it is designed as:
{noformat}
When reading files:
nullValue: specifies a string that indicates a null value, any fields matching
this string will be set as nulls in the DataFrame
When writing files:
nullValue: specifies a strin
[jira] [Updated] (SPARK-37604) The option emptyValueInRead(in CSVOptions) is suggested to be designed as that any fields matching this string will be set as empty values "" when reading
[
https://issues.apache.org/jira/browse/SPARK-37604?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]
Wei Guo updated SPARK-37604:
Description:
The csv data format is imported from databricks
[spark-csv|https://github.com/databricks/spark-csv] by issue SPARK-12833 and PR
[10766|https://github.com/apache/spark/pull/10766] .
{*}For the nullValue option{*}, according to the features description in
spark-csv readme file, it is designed as:
{noformat}
When reading files:
nullValue: specifies a string that indicates a null value, any fields matching
this string will be set as nulls in the DataFrame
When writing files:
nullValue: specifies a string that indicates a null value, nulls in the
DataFrame will be written as this string.
{noformat}
For example, when writing:
{code:scala}
Seq(("Tesla", null:String)).toDF("make", "comment").write.option("nullValue",
"NULL").csv(path){code}
The saved csv file is shown as:
{noformat}
Tesla,NULL
{noformat}
When reading:
{code:java}
spark.read.option("nullValue", "NULL").csv(path).show()
{code}
The parsed dataframe is shown as:
||make||comment||
|tesla|null|
We can find that null columns in dataframe can be saved as "NULL" strings in
csv files and *{color:#de350b}"NULL" strings in csv files can be parsed as null
columns{color}* in dataframe. That is:
{noformat}
When writing, convert null(in dataframe) to nullValue(in csv)
When reading, convert nullValue or nothing(in csv) to null(in dataframe)
{noformat}
But actually, the option nullValue in depended component univocity's
{*}_CommonSettings_{*}, is designed as that:
{noformat}
when reading, if the parser does not read any character from the input, the
nullValue is used instead of an empty string.
when writing, if the writer has a null object to write to the output, the
nullValue is used instead of an empty string.{noformat}
There is a difference when reading. In univocity, nothing content would be
convert to nullValue strings. But In Spark, we finally convert nothing content
or nullValue strings to null in *_UnivocityParser_ _nullSafeDatum_* method:
{code:java}
private def nullSafeDatum(
datum: String,
name: String,
nullable: Boolean,
options: CSVOptions)(converter: ValueConverter): Any = {
if (datum == options.nullValue || datum == null) {
if (!nullable) {
throw QueryExecutionErrors.foundNullValueForNotNullableFieldError(name)
}
null
} else {
converter.apply(datum)
}
} {code}
{*}For the emptyValue option{*}, we add a emptyValueInRead option for reading
and a emptyValueInWrite option for writing. I found that both Spark keeps the
same behaviors for emptyValue with univocity.
{noformat}
When reading, if the parser does not read any character from the input, and the
input is within quotes, the empty is used instead of an empty string.
When writing, if the writer has an empty String to write to the output, the
emptyValue is used instead of an empty string.{noformat}
For example, when writing:
{code:scala}
Seq(("Tesla",
{code}
{color:#910091}""{color}
{code:scala}
)).toDF("make", "comment").write.option("emptyValue", "EMPTY").csv(path){code}
The saved csv file is shown as:
{noformat}
Tesla,EMPTY {noformat}
When reading:
{code:java}
spark.read.option("emptyValue", "EMPTY").csv(path).show()
{code}
The parsed dataframe is shown as:
||make||comment||
|tesla|EMPTY|
We can find that empty columns in dataframe can be saved as "EMPTY" strings in
csv files, *{color:#de350b}but "EMPTY" strings in csv files can not be parsed
as empty columns{color}* in dataframe. That is:
{noformat}
When writing, convert "" empty(in dataframe) to emptyValue(in csv)
When reading, convert "\"\"" quoted empty strings to emptyValue(in dataframe)
{noformat}
There is obvious difference between nullValue and emptyValue in read handling.
For nullValue, we try to convert nothing or nullValue strings to null in
dataframe, but for emptyValue, we just try to convert "\"\""(quoted empty
strings) to emptyValue rather than to convert both "\"\""(quoted empty strings)
and emptyValue strings to ""(empty) in dataframe.
I think it's better that we keep the similar behavior(try to recover emptyValue
to "") for emptyValue as nullValue when reading, so
was:
The csv data format is imported from databricks
[spark-csv|https://github.com/databricks/spark-csv] by issue SPARK-12833 and PR
[10766|https://github.com/apache/spark/pull/10766] .
{*}For the nullValue option{*}, according to the features description in
spark-csv readme file, it is designed as:
{noformat}
When reading files:
nullValue: specifies a string that indicates a null value, any fields matching
this string will be set as nulls in the DataFrame
When writing files:
nullValue: specifies a string that indicates a null value, nulls in the
DataFrame will be written as this string.
{noformat}
For example, when writing:
{code:scala}
Seq(("Tesla", null:S
[jira] [Updated] (SPARK-37604) The option emptyValueInRead(in CSVOptions) is suggested to be designed as that any fields matching this string will be set as empty values "" when reading
[
https://issues.apache.org/jira/browse/SPARK-37604?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]
Wei Guo updated SPARK-37604:
Description:
The csv data format is imported from databricks
[spark-csv|https://github.com/databricks/spark-csv] by issue SPARK-12833 and PR
[10766|https://github.com/apache/spark/pull/10766] .
{*}For the nullValue option{*}, according to the features description in
spark-csv readme file, it is designed as:
{noformat}
When reading files:
nullValue: specifies a string that indicates a null value, any fields matching
this string will be set as nulls in the DataFrame
When writing files:
nullValue: specifies a string that indicates a null value, nulls in the
DataFrame will be written as this string.
{noformat}
For example, when writing:
{code:scala}
Seq(("Tesla", null:String)).toDF("make", "comment").write.option("nullValue",
"NULL").csv(path){code}
The saved csv file is shown as:
{noformat}
Tesla,NULL
{noformat}
When reading:
{code:java}
spark.read.option("nullValue", "NULL").csv(path).show()
{code}
The parsed dataframe is shown as:
||make||comment||
|tesla|null|
We can find that null columns in dataframe can be saved as "NULL" strings in
csv files and *{color:#de350b}"NULL" strings in csv files can be parsed as null
columns{color}* in dataframe. That is:
{noformat}
When writing, convert null(in dataframe) to nullValue(in csv)
When reading, convert nullValue or nothing(in csv) to null(in dataframe)
{noformat}
But actually, the option nullValue in depended component univocity's
{*}_CommonSettings_{*}, is designed as that:
{noformat}
when reading, if the parser does not read any character from the input, the
nullValue is used instead of an empty string.
when writing, if the writer has a null object to write to the output, the
nullValue is used instead of an empty string.{noformat}
There is a difference when reading. In univocity, nothing content would be
convert to nullValue strings. But In Spark, we finally convert nothing content
or nullValue strings to null in *_UnivocityParser_ _nullSafeDatum_* method:
{code:java}
private def nullSafeDatum(
datum: String,
name: String,
nullable: Boolean,
options: CSVOptions)(converter: ValueConverter): Any = {
if (datum == options.nullValue || datum == null) {
if (!nullable) {
throw QueryExecutionErrors.foundNullValueForNotNullableFieldError(name)
}
null
} else {
converter.apply(datum)
}
} {code}
{*}For the emptyValue option{*}, we add a emptyValueInRead option for reading
and a emptyValueInWrite option for writing. I found that both Spark keeps the
same behaviors for emptyValue with univocity.
{noformat}
When reading, if the parser does not read any character from the input, and the
input is within quotes, the empty is used instead of an empty string.
When writing, if the writer has an empty String to write to the output, the
emptyValue is used instead of an empty string.{noformat}
For example, when writing:
{code:scala}
Seq(("Tesla",
{code}
{color:#910091}""{color}
{code:scala}
)).toDF("make", "comment").write.option("emptyValue", "EMPTY").csv(path){code}
The saved csv file is shown as:
{noformat}
Tesla,EMPTY {noformat}
When reading:
{code:java}
spark.read.option("emptyValue", "EMPTY").csv(path).show()
{code}
The parsed dataframe is shown as:
||make||comment||
|tesla|EMPTY|
We can find that empty columns in dataframe can be saved as "EMPTY" strings in
csv files, *{color:#de350b}but "EMPTY" strings in csv files can not be parsed
as empty columns{color}* in dataframe. That is:
{noformat}
When writing, convert "" empty(in dataframe) to emptyValue(in csv)
When reading, convert "\"\"" quoted empty strings to emptyValue(in dataframe)
{noformat}
There is obvious difference between nullValue and emptyValue in read handling.
For nullValue, we try to convert nothing or nullValue strings to null in
dataframe, but for emptyValue, we just try to convert "\"\""(quoted empty
strings) to emptyValue rather than to convert both "\"\""(quoted empty strings)
and emptyValue strings to ""(empty) in dataframe.
I think it's better that we keep the similar behavior for emptyValue as
nullValue when reading.
was:
The csv data format is imported from databricks
[spark-csv|https://github.com/databricks/spark-csv] by issue SPARK-12833 and PR
[10766|https://github.com/apache/spark/pull/10766] .
{*}For the nullValue option{*}, according to the features description in
spark-csv readme file, it is designed as:
{noformat}
When reading files:
nullValue: specifies a string that indicates a null value, any fields matching
this string will be set as nulls in the DataFrame
When writing files:
nullValue: specifies a string that indicates a null value, nulls in the
DataFrame will be written as this string.
{noformat}
For example, when writing:
{code:scala}
Seq(("Tesla", null:String)).toDF("make", "comment").write
[jira] [Updated] (SPARK-37604) The option emptyValueInRead(in CSVOptions) is suggested to be designed as that any fields matching this string will be set as empty values "" when reading
[
https://issues.apache.org/jira/browse/SPARK-37604?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]
Wei Guo updated SPARK-37604:
Description:
The csv data format is imported from databricks
[spark-csv|https://github.com/databricks/spark-csv] by issue SPARK-12833 and PR
[10766|https://github.com/apache/spark/pull/10766] .
{*}For the nullValue option{*}, according to the features description in
spark-csv readme file, it is designed as:
{noformat}
When reading files:
nullValue: specifies a string that indicates a null value, any fields matching
this string will be set as nulls in the DataFrame
When writing files:
nullValue: specifies a string that indicates a null value, nulls in the
DataFrame will be written as this string.
{noformat}
For example, when writing:
{code:scala}
Seq(("Tesla", null:String)).toDF("make", "comment").write.option("nullValue",
"NULL").csv(path){code}
The saved csv file is shown as:
{noformat}
Tesla,NULL
{noformat}
When reading:
{code:java}
spark.read.option("nullValue", "NULL").csv(path).show()
{code}
The parsed dataframe is shown as:
||make||comment||
|tesla|null|
We can find that null columns in dataframe can be saved as "NULL" strings in
csv files and *{color:#de350b}"NULL" strings in csv files can be parsed as null
columns{color}* in dataframe. That is:
{noformat}
When writing, convert null(in dataframe) to nullValue(in csv)
When reading, convert nullValue or nothing(in csv) to null(in dataframe)
{noformat}
But actually, the option nullValue in depended component univocity's
{*}_CommonSettings_{*}, is designed as that:
{noformat}
when reading, if the parser does not read any character from the input, the
nullValue is used instead of an empty string.
when writing, if the writer has a null object to write to the output, the
nullValue is used instead of an empty string.{noformat}
There is a difference when reading. In univocity, nothing content would be
convert to nullValue strings. But In Spark, we finally convert nothing content
or nullValue strings to null in *_UnivocityParser_ _nullSafeDatum_* method:
{code:java}
private def nullSafeDatum(
datum: String,
name: String,
nullable: Boolean,
options: CSVOptions)(converter: ValueConverter): Any = {
if (datum == options.nullValue || datum == null) {
if (!nullable) {
throw QueryExecutionErrors.foundNullValueForNotNullableFieldError(name)
}
null
} else {
converter.apply(datum)
}
} {code}
{*}For the emptyValue option{*}, we add a emptyValueInRead option for reading
and a emptyValueInWrite option for writing. I found that both Spark keeps the
same behaviors for emptyValue with univocity.
{noformat}
When reading, if the parser does not read any character from the input, and the
input is within quotes, the empty is used instead of an empty string.
When writing, if the writer has an empty String to write to the output, the
emptyValue is used instead of an empty string.{noformat}
For example, when writing:
{code:scala}
Seq(("Tesla",
{code}
{color:#910091}""{color}
{code:scala}
)).toDF("make", "comment").write.option("emptyValue", "EMPTY").csv(path){code}
The saved csv file is shown as:
{noformat}
Tesla,EMPTY {noformat}
When reading:
{code:java}
spark.read.option("emptyValue", "EMPTY").csv(path).show()
{code}
The parsed dataframe is shown as:
||make||comment||
|tesla|EMPTY|
We can find that empty columns in dataframe can be saved as "EMPTY" strings in
csv files, *{color:#de350b}but "EMPTY" strings in csv files can not be parsed
as empty columns{color}* in dataframe. That is:
{noformat}
When writing, convert "" empty(in dataframe) to emptyValue(in csv)
When reading, convert "\"\"" quoted empty strings to emptyValue(in dataframe)
{noformat}
There is obvious difference between nullValue and emptyValue in read handling.
For nullValue, we try to convert nothing or nullValue strings to null in
dataframe, but for emptyValue, we just try to convert "\"\""(quoted empty
strings) to emptyValue rather than to convert both "\"\""(quoted empty strings)
and emptyValue strings to ""(empty) in dataframe.
I think
was:
The csv data format is imported from databricks
[spark-csv|https://github.com/databricks/spark-csv] by issue SPARK-12833 and PR
[10766|https://github.com/apache/spark/pull/10766] .
{*}For the nullValue option{*}, according to the features description in
spark-csv readme file, it is designed as:
{noformat}
When reading files:
nullValue: specifies a string that indicates a null value, any fields matching
this string will be set as nulls in the DataFrame
When writing files:
nullValue: specifies a string that indicates a null value, nulls in the
DataFrame will be written as this string.
{noformat}
For example, when writing:
{code:scala}
Seq(("Tesla", null:String)).toDF("make", "comment").write.option("nullValue",
"NULL").csv(path){code}
The saved csv file is shown as:
{noformat}
[jira] [Updated] (SPARK-37604) The option emptyValueInRead(in CSVOptions) is suggested to be designed as that any fields matching this string will be set as empty values "" when reading
[
https://issues.apache.org/jira/browse/SPARK-37604?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]
Wei Guo updated SPARK-37604:
Description:
The csv data format is imported from databricks
[spark-csv|https://github.com/databricks/spark-csv] by issue SPARK-12833 and PR
[10766|https://github.com/apache/spark/pull/10766] .
{*}For the nullValue option{*}, according to the features description in
spark-csv readme file, it is designed as:
{noformat}
When reading files:
nullValue: specifies a string that indicates a null value, any fields matching
this string will be set as nulls in the DataFrame
When writing files:
nullValue: specifies a string that indicates a null value, nulls in the
DataFrame will be written as this string.
{noformat}
For example, when writing:
{code:scala}
Seq(("Tesla", null:String)).toDF("make", "comment").write.option("nullValue",
"NULL").csv(path){code}
The saved csv file is shown as:
{noformat}
Tesla,NULL
{noformat}
When reading:
{code:java}
spark.read.option("nullValue", "NULL").csv(path).show()
{code}
The parsed dataframe is shown as:
||make||comment||
|tesla|null|
We can find that null columns in dataframe can be saved as NULL strings in csv
files and NULL strings in csv files can be parsed as columns of null values in
dataframe. That is:
{noformat}
When writing, convert null(in dataframe) to nullValue(in csv)
When reading, convert nullValue or nothing(in csv) to null(in dataframe)
{noformat}
But actually, the option nullValue in depended component univocity's
{*}_CommonSettings_{*}, is designed as that:
{noformat}
when reading, if the parser does not read any character from the input, the
nullValue is used instead of an empty string.
when writing, if the writer has a null object to write to the output, the
nullValue is used instead of an empty string.{noformat}
There is a difference when reading. In univocity, nothing content would be
convert to nullValue strings. But In Spark, we finally convert nothing content
or nullValue strings to null in *_UnivocityParser_ _nullSafeDatum_* method:
{code:java}
private def nullSafeDatum(
datum: String,
name: String,
nullable: Boolean,
options: CSVOptions)(converter: ValueConverter): Any = {
if (datum == options.nullValue || datum == null) {
if (!nullable) {
throw QueryExecutionErrors.foundNullValueForNotNullableFieldError(name)
}
null
} else {
converter.apply(datum)
}
} {code}
{*}For the emptyValue option{*}, we add a emptyValueInRead option for reading
and a emptyValueInWrite option for writing. I found that both Spark keeps the
same behaviors for emptyValue with univocity.
{noformat}
When reading, if the parser does not read any character from the input, and the
input is within quotes, the empty is used instead of an empty string.
When writing, if the writer has an empty String to write to the output, the
emptyValue is used instead of an empty string.{noformat}
For example, when writing:
{code:scala}
Seq(("Tesla", null:String)).toDF("make", "comment").write.option("nullValue",
"NULL").csv(path){code}
The saved csv file is shown as:
{noformat}
Tesla,NULL
{noformat}
When reading:
{code:java}
spark.read.option("nullValue", "NULL").csv(path).show()
{code}
The parsed dataframe is shown as:
||make||comment||
|tesla|null|
We can find that null columns in dataframe can be saved as NULL strings in csv
files and NULL strings in csv files can be parsed as columns of null values in
dataframe. That is:
{noformat}
When writing, convert null(in dataframe) to nullValue(in csv)
When reading, convert nullValue or nothing(in csv) to null(in dataframe)
{noformat}
Since Spark 2.4, for empty strings, there are emptyValueInRead for reading and
emptyValueInWrite for writing that can be set in CSVOptions:
{code:scala}
// For writing, convert: ""(dataframe) => emptyValueInWrite(csv)
// For reading, convert: "" (csv) => emptyValueInRead(dataframe){code}
I think the read handling is not suitable, we can not convert "" or
`{color:#172b4d}emptyValueInWrite`{color} values as ""(real empty strings) but
get {color:#172b4d}emptyValueInRead's setting value actually{color}, it
supposed to be as flows:
{code:scala}
// For reading, convert: "" or emptyValueInRead (csv) => ""(dataframe){code}
{color:#de350b}*We can not recovery it to the original DataFrame.*{color}
was:
The csv data format is imported from databricks
[spark-csv|https://github.com/databricks/spark-csv] by issue SPARK-12833 and PR
[10766|https://github.com/apache/spark/pull/10766] .
For the nullValue option, according to the features description in spark-csv
readme file, it is designed as:
{noformat}
When reading files:
nullValue: specifies a string that indicates a null value, any fields matching
this string will be set as nulls in the DataFrame
When writing files:
nullValue: specifies a string that indicates a null value, nulls in the
Dat
[jira] [Updated] (SPARK-37604) The option emptyValueInRead(in CSVOptions) is suggested to be designed as that any fields matching this string will be set as empty values "" when reading
[
https://issues.apache.org/jira/browse/SPARK-37604?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]
Wei Guo updated SPARK-37604:
Description:
The csv data format is imported from databricks
[spark-csv|https://github.com/databricks/spark-csv] by issue SPARK-12833 and PR
[10766|https://github.com/apache/spark/pull/10766] .
For the nullValue option, according to the features description in spark-csv
readme file, it is designed as:
{noformat}
When reading files:
nullValue: specifies a string that indicates a null value, any fields matching
this string will be set as nulls in the DataFrame
When writing files:
nullValue: specifies a string that indicates a null value, nulls in the
DataFrame will be written as this string.
{noformat}
For example, when writing:
{code:scala}
Seq(("Tesla", null:String)).toDF("make", "comment").write.option("nullValue",
"NULL").csv(path){code}
The saved csv file is shown as:
{noformat}
Tesla,NULL
{noformat}
When reading:
{code:java}
spark.read.option("nullValue", "NULL").csv(path).show()
{code}
The parsed dataframe is shown as:
||make||comment||
|tesla|null|
We can find that null columns in dataframe can be saved as NULL strings in csv
files and NULL strings in csv files can be parsed as columns of null values in
dataframe. That is:
{noformat}
When writing, convert null(in dataframe) to nullValue(in csv)
When reading, convert nullValue or nothing(in csv) to null(in dataframe)
{noformat}
But actually, the option nullValue in depended component univocity's
{*}_CommonSettings_{*}, is designed as that:
{noformat}
when reading, if the parser does not read any character from the input, the
nullValue is used instead of an empty string
when writing, if the writer has a null object to write to the output, the
nullValue is used instead of an empty string{noformat}
There is a difference when reading. In univocity, nothing would be convert to
nullValue strings. But In Spark, we finally convert nothing or nullValue
strings to null in *_UnivocityParser_ _nullSafeDatum_* method:
{code:java}
private def nullSafeDatum(
datum: String,
name: String,
nullable: Boolean,
options: CSVOptions)(converter: ValueConverter): Any = {
if (datum == options.nullValue || datum == null) {
if (!nullable) {
throw QueryExecutionErrors.foundNullValueForNotNullableFieldError(name)
}
null
} else {
converter.apply(datum)
}
} {code}
For the emptyValue option, we add a emptyValueInRead option for reading and a
emptyValueInWrite option for writing.
{noformat}
*no* further _formatting_ is done here{noformat}
For example, a column has empty strings, if emptyValueInWrite is set to
"EMPTY" string.
{code:scala}
Seq(("Tesla", "")).toDF("make", "comment").write.option("emptyValue",
"EMPTY").csv(path){code}
The saved csv file is shown as:
{noformat}
Tesla,EMPTY {noformat}
and if we read this csv file with emptyValue(emptyValueInRead) set to "EMPTY"
string.
{code:java}
spark.read.option("emptyValue", "EMPTY").csv(path).show()
{code}
we actually get the DataFrame which data is shown as:
||make||comment||
|tesla|EMPTY|
but the DataFrame which data should be shown as below as expected:
||make||comment||
|tesla| |
I found that Spark keeps the same behavior with the depended component
univocity.
Since Spark 2.4, for empty strings, there are emptyValueInRead for reading and
emptyValueInWrite for writing that can be set in CSVOptions:
{code:scala}
// For writing, convert: ""(dataframe) => emptyValueInWrite(csv)
// For reading, convert: "" (csv) => emptyValueInRead(dataframe){code}
I think the read handling is not suitable, we can not convert "" or
`{color:#172b4d}emptyValueInWrite`{color} values as ""(real empty strings) but
get {color:#172b4d}emptyValueInRead's setting value actually{color}, it
supposed to be as flows:
{code:scala}
// For reading, convert: "" or emptyValueInRead (csv) => ""(dataframe){code}
{color:#de350b}*We can not recovery it to the original DataFrame.*{color}
was:
The csv data format is imported from databricks
[spark-csv|https://github.com/databricks/spark-csv] by issue SPARK-12833 and PR
[10766|https://github.com/apache/spark/pull/10766] .
For the nullValue option, according to the features description in spark-csv
readme file, it is designed as:
{noformat}
When reading files:
nullValue: specifies a string that indicates a null value, any fields matching
this string will be set as nulls in the DataFrame
When writing files:
nullValue: specifies a string that indicates a null value, nulls in the
DataFrame will be written as this string.
{noformat}
For example, when writing:
{code:scala}
Seq(("Tesla", null:String)).toDF("make", "comment").write.option("nullValue",
"NULL").csv(path){code}
The saved csv file is shown as:
{noformat}
Tesla,NULL
{noformat}
When reading:
{code:java}
spark.read.option("nullValue", "NULL").csv(path).show()
{code}
The parsed dataframe is
[jira] [Updated] (SPARK-37604) The option emptyValueInRead(in CSVOptions) is suggested to be designed as that any fields matching this string will be set as empty values "" when reading
[
https://issues.apache.org/jira/browse/SPARK-37604?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]
Wei Guo updated SPARK-37604:
Description:
The csv data format is imported from databricks
[spark-csv|https://github.com/databricks/spark-csv] by issue SPARK-12833 and PR
[10766|https://github.com/apache/spark/pull/10766] .
For the nullValue option, according to the features description in spark-csv
readme file, it is designed as:
{noformat}
When reading files:
nullValue: specifies a string that indicates a null value, any fields matching
this string will be set as nulls in the DataFrame
When writing files:
nullValue: specifies a string that indicates a null value, nulls in the
DataFrame will be written as this string.
{noformat}
For example, when writing:
{code:scala}
Seq(("Tesla", null:String)).toDF("make", "comment").write.option("nullValue",
"NULL").csv(path){code}
The saved csv file is shown as:
{noformat}
Tesla,NULL
{noformat}
When reading:
{code:java}
spark.read.option("nullValue", "NULL").csv(path).show()
{code}
The parsed dataframe is shown as:
||make||comment||
|tesla|null|
We can find that null columns in dataframe can be saved as NULL strings in csv
files and NULL strings in csv files can be parsed as columns of null values in
dataframe. That is:
{noformat}
When writing, convert null(in dataframe) to nullValue(in csv)
When reading, convert nullValue or nothing(in csv) to null(in dataframe)
{noformat}
But actually, the option nullValue in component univocity's
{*}_CommonSettings_{*}, is designed as that:
{noformat}
when reading, if the parser does not read any character from the input, the
nullValue is used instead of an empty string
when writing, if the writer has a null object to write to the output, the
nullValue is used instead of an empty string{noformat}
There is a difference when reading. In univocity, nothing would be convert to
nullValue strings. But In Spark, we finally convert nothing or nullValue
strings to null in *_UnivocityParser_ _nullSafeDatum_* method:
{code:java}
private def nullSafeDatum(
datum: String,
name: String,
nullable: Boolean,
options: CSVOptions)(converter: ValueConverter): Any = {
if (datum == options.nullValue || datum == null) {
if (!nullable) {
throw QueryExecutionErrors.foundNullValueForNotNullableFieldError(name)
}
null
} else {
converter.apply(datum)
}
} {code}
For the emptyValue option, we add a emptyValueInRead option for reading and a
emptyValueInWrite option for writing.
I found that
Since Spark 2.4, for empty strings, there are emptyValueInRead for reading and
emptyValueInWrite for writing that can be set in CSVOptions:
{code:scala}
// For writing, convert: ""(dataframe) => emptyValueInWrite(csv)
// For reading, convert: "" (csv) => emptyValueInRead(dataframe){code}
I think the read handling is not suitable, we can not convert "" or
`{color:#172b4d}emptyValueInWrite`{color} values as ""(real empty strings) but
get {color:#172b4d}emptyValueInRead's setting value actually{color}, it
supposed to be as flows:
{code:scala}
// For reading, convert: "" or emptyValueInRead (csv) => ""(dataframe){code}
For example, a column has empty strings, if emptyValueInWrite is set to
"EMPTY" string.
{code:scala}
Seq(("Tesla", "")).toDF("make", "comment").write.option("emptyValue",
"EMPTY").csv(path){code}
The saved csv file is shown as:
{noformat}
Tesla,EMPTY {noformat}
and if we read this csv file with emptyValue(emptyValueInRead) set to "EMPTY"
string.
{code:java}
spark.read.option("emptyValue", "EMPTY").csv(path).show()
{code}
we actually get the DataFrame which data is shown as:
||make||comment||
|tesla|EMPTY|
but the DataFrame which data should be shown as below as expected:
||make||comment||
|tesla| |
{color:#de350b}*We can not recovery it to the original DataFrame.*{color}
was:
The csv data format is imported from databricks
[spark-csv|https://github.com/databricks/spark-csv] by issue SPARK-12833 and PR
[10766|https://github.com/apache/spark/pull/10766] .
For the nullValue option, according to the features description in spark-csv
readme file, it is designed as:
{noformat}
When reading files:
nullValue: specifies a string that indicates a null value, any fields matching
this string will be set as nulls in the DataFrame
When writing files:
nullValue: specifies a string that indicates a null value, nulls in the
DataFrame will be written as this string.
{noformat}
For example, when writing:
{code:scala}
Seq(("Tesla", null:String)).toDF("make", "comment").write.option("nullValue",
"NULL").csv(path){code}
The saved csv file is shown as:
{noformat}
Tesla,NULL
{noformat}
When reading:
{code:java}
spark.read.option("nullValue", "NULL").csv(path).show()
{code}
The parsed dataframe is shown as:
||make||comment||
|tesla|null|
We can find that null columns in dataframe can be saved as NULL strings in csv
files and NULL st
[jira] [Updated] (SPARK-37604) The option emptyValueInRead(in CSVOptions) is suggested to be designed as that any fields matching this string will be set as empty values "" when reading
[
https://issues.apache.org/jira/browse/SPARK-37604?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]
Wei Guo updated SPARK-37604:
Description:
The csv data format is imported from databricks
[spark-csv|https://github.com/databricks/spark-csv] by issue SPARK-12833 and PR
[10766|https://github.com/apache/spark/pull/10766] .
For the nullValue option, according to the features description in spark-csv
readme file, it is designed as:
{noformat}
When reading files:
nullValue: specifies a string that indicates a null value, any fields matching
this string will be set as nulls in the DataFrame
When writing files:
nullValue: specifies a string that indicates a null value, nulls in the
DataFrame will be written as this string.
{noformat}
For example, when writing:
{code:scala}
Seq(("Tesla", null:String)).toDF("make", "comment").write.option("nullValue",
"NULL").csv(path){code}
The saved csv file is shown as:
{noformat}
Tesla,NULL
{noformat}
When reading:
{code:java}
spark.read.option("nullValue", "NULL").csv(path).show()
{code}
The parsed dataframe is shown as:
||make||comment||
|tesla|null|
We can find that null columns in dataframe can be saved as NULL strings in csv
files and NULL strings in csv files can be parsed as columns of null values in
dataframe. That is:
{noformat}
When writing, convert null(in dataframe) to nullValue(in csv)
When reading, convert nullValue or nothing(in csv) to null(in dataframe)
{noformat}
But actually, the option nullValue in component univocity's
{*}_CommonSettings_{*}, is designed as that:
{noformat}
when reading, if the parser does not read any character from the input, the
nullValue is used instead of an empty string
when writing, if the writer has a null object to write to the output, the
nullValue is used instead of an empty string{noformat}
There is a difference when reading. In univocity, nothing would be convert to
nullValue strings. But In Spark, we finally convert nothing or nullValue
strings to null in *_UnivocityParser_ _nullSafeDatum_* method:
{code:java}
private def nullSafeDatum(
datum: String,
name: String,
nullable: Boolean,
options: CSVOptions)(converter: ValueConverter): Any = {
if (datum == options.nullValue || datum == null) {
if (!nullable) {
throw QueryExecutionErrors.foundNullValueForNotNullableFieldError(name)
}
null
} else {
converter.apply(datum)
}
} {code}
Since Spark 2.4, for empty strings, there are emptyValueInRead for reading and
emptyValueInWrite for writing that can be set in CSVOptions:
{code:scala}
// For writing, convert: ""(dataframe) => emptyValueInWrite(csv)
// For reading, convert: "" (csv) => emptyValueInRead(dataframe){code}
I think the read handling is not suitable, we can not convert "" or
`{color:#172b4d}emptyValueInWrite`{color} values as ""(real empty strings) but
get {color:#172b4d}emptyValueInRead's setting value actually{color}, it
supposed to be as flows:
{code:scala}
// For reading, convert: "" or emptyValueInRead (csv) => ""(dataframe){code}
For example, a column has empty strings, if emptyValueInWrite is set to
"EMPTY" string.
{code:scala}
Seq(("Tesla", "")).toDF("make", "comment").write.option("emptyValue",
"EMPTY").csv(path){code}
The saved csv file is shown as:
{noformat}
Tesla,EMPTY {noformat}
and if we read this csv file with emptyValue(emptyValueInRead) set to "EMPTY"
string.
{code:java}
spark.read.option("emptyValue", "EMPTY").csv(path).show()
{code}
we actually get the DataFrame which data is shown as:
||make||comment||
|tesla|EMPTY|
but the DataFrame which data should be shown as below as expected:
||make||comment||
|tesla| |
{color:#de350b}*We can not recovery it to the original DataFrame.*{color}
was:
The csv data format is imported from databricks
[spark-csv|https://github.com/databricks/spark-csv] by issue SPARK-12833 and PR
[10766|https://github.com/apache/spark/pull/10766] .
According to the features description in spark-csv readme file, the nullValue
option is designed as:
{noformat}
When reading files:
nullValue: specifies a string that indicates a null value, any fields matching
this string will be set as nulls in the DataFrame
When writing files:
nullValue: specifies a string that indicates a null value, nulls in the
DataFrame will be written as this string.
{noformat}
For example, when writing:
{code:scala}
Seq(("Tesla", null:String)).toDF("make", "comment").write.option("nullValue",
"NULL").csv(path){code}
The saved csv file is shown as:
{noformat}
Tesla,NULL
{noformat}
When reading:
{code:java}
spark.read.option("nullValue", "NULL").csv(path).show()
{code}
The parsed dataframe is shown as:
||make||comment||
|tesla|null|
We can find that null columns in dataframe can be saved as NULL strings in csv
files and NULL strings in csv files can be parsed as columns of null values in
dataframe. That is:
{noformat}
When writing, convert null(in dataframe) to n
[jira] [Updated] (SPARK-37604) The option emptyValueInRead(in CSVOptions) is suggested to be designed as that any fields matching this string will be set as empty values "" when reading
[
https://issues.apache.org/jira/browse/SPARK-37604?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]
Wei Guo updated SPARK-37604:
Description:
The csv data format is imported from databricks
[spark-csv|https://github.com/databricks/spark-csv] by issue SPARK-12833 and PR
[10766|https://github.com/apache/spark/pull/10766] .
According to the features description in spark-csv readme file, the nullValue
option is designed as:
{noformat}
When reading files:
nullValue: specifies a string that indicates a null value, any fields matching
this string will be set as nulls in the DataFrame
When writing files:
nullValue: specifies a string that indicates a null value, nulls in the
DataFrame will be written as this string.
{noformat}
For example, when writing:
{code:scala}
Seq(("Tesla", null:String)).toDF("make", "comment").write.option("nullValue",
"NULL").csv(path){code}
The saved csv file is shown as:
{noformat}
Tesla,NULL
{noformat}
When reading:
{code:java}
spark.read.option("nullValue", "NULL").csv(path).show()
{code}
The parsed dataframe is shown as:
||make||comment||
|tesla|null|
We can find that null columns in dataframe can be saved as NULL strings in csv
files and NULL strings in csv files can be parsed as columns of null values in
dataframe. That is:
{noformat}
When writing, convert null(in dataframe) to nullValue(in csv)
When reading, convert nullValue or nothing(in csv) to null(in dataframe)
{noformat}
But actually, the option nullValue in component univocity's
{*}_CommonSettings_{*}, is designed as that:
{noformat}
when reading, if the parser does not read any character from the input, the
nullValue is used instead of an empty string
when writing, if the writer has a null object to write to the output, the
nullValue is used instead of an empty string{noformat}
There is a difference when reading. In univocity, nothing would be convert to
nullValue strings. But In Spark, we finally convert nothing or nullValue
strings to null in *_UnivocityParser_ _nullSafeDatum_* method:
{code:java}
private def nullSafeDatum(
datum: String,
name: String,
nullable: Boolean,
options: CSVOptions)(converter: ValueConverter): Any = {
if (datum == options.nullValue || datum == null) {
if (!nullable) {
throw QueryExecutionErrors.foundNullValueForNotNullableFieldError(name)
}
null
} else {
converter.apply(datum)
}
} {code}
Since Spark 2.4, for empty strings, there are emptyValueInRead for reading and
emptyValueInWrite for writing that can be set in CSVOptions:
{code:scala}
// For writing, convert: ""(dataframe) => emptyValueInWrite(csv)
// For reading, convert: "" (csv) => emptyValueInRead(dataframe){code}
I think the read handling is not suitable, we can not convert "" or
`{color:#172b4d}emptyValueInWrite`{color} values as ""(real empty strings) but
get {color:#172b4d}emptyValueInRead's setting value actually{color}, it
supposed to be as flows:
{code:scala}
// For reading, convert: "" or emptyValueInRead (csv) => ""(dataframe){code}
For example, a column has empty strings, if emptyValueInWrite is set to
"EMPTY" string.
{code:scala}
Seq(("Tesla", "")).toDF("make", "comment").write.option("emptyValue",
"EMPTY").csv(path){code}
The saved csv file is shown as:
{noformat}
Tesla,EMPTY {noformat}
and if we read this csv file with emptyValue(emptyValueInRead) set to "EMPTY"
string.
{code:java}
spark.read.option("emptyValue", "EMPTY").csv(path).show()
{code}
we actually get the DataFrame which data is shown as:
||make||comment||
|tesla|EMPTY|
but the DataFrame which data should be shown as below as expected:
||make||comment||
|tesla| |
{color:#de350b}*We can not recovery it to the original DataFrame.*{color}
was:
The csv data format is imported from databricks
[spark-csv|https://github.com/databricks/spark-csv] by issue
[SPARK-12833|https://issues.apache.org/jira/browse/SPARK-12833] and PR
[10766|https://github.com/apache/spark/pull/10766] .
According to databricks spark-csv's features description in readme file, the
nullValue option is designed as:
{noformat}
When reading files the API accepts several options:
nullValue: specifies a string that indicates a null value, any fields matching
this string will be set as nulls in the DataFrame
When writing files the API accepts several options:
nullValue: specifies a string that indicates a null value, nulls in the
DataFrame will be written as this string.
{noformat}
For null values, the parameter nullValue can be set when reading or writing in
CSVOptions:
{code:scala}
// For writing, convert: null(dataframe) => nullValue(csv)
// For reading, convert: nullValue or ,,(csv) => null(dataframe)
{code}
For example, a column has null values, if nullValue is set to "null" string.
{code:scala}
Seq(("Tesla", null.asInstanceOf[String])).toDF("make",
"comment").write.option("nullValue", "NULL").csv(path){code}
The saved csv file is shown as:
{
[jira] [Updated] (SPARK-37604) The option emptyValueInRead(in CSVOptions) is suggested to be designed as that any fields matching this string will be set as empty values "" when reading
[
https://issues.apache.org/jira/browse/SPARK-37604?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]
Wei Guo updated SPARK-37604:
Description:
The csv data format is imported from databricks
[spark-csv|https://github.com/databricks/spark-csv] by issue
[SPARK-12833|https://issues.apache.org/jira/browse/SPARK-12833] and PR
[10766|https://github.com/apache/spark/pull/10766] .
According to databricks spark-csv's features description in readme file, the
nullValue option is designed as:
{noformat}
When reading files the API accepts several options:
nullValue: specifies a string that indicates a null value, any fields matching
this string will be set as nulls in the DataFrame
When writing files the API accepts several options:
nullValue: specifies a string that indicates a null value, nulls in the
DataFrame will be written as this string.
{noformat}
For null values, the parameter nullValue can be set when reading or writing in
CSVOptions:
{code:scala}
// For writing, convert: null(dataframe) => nullValue(csv)
// For reading, convert: nullValue or ,,(csv) => null(dataframe)
{code}
For example, a column has null values, if nullValue is set to "null" string.
{code:scala}
Seq(("Tesla", null.asInstanceOf[String])).toDF("make",
"comment").write.option("nullValue", "NULL").csv(path){code}
The saved csv file is shown as:
{noformat}
Tesla,NULL
{noformat}
and if we read this csv file with nullValue set to "null" string.
{code:java}
spark.read.option("nullValue", "NULL").csv(path).show()
{code}
we can get the DataFrame which data is same with the original shown as:
||make||comment||
|tesla|null|
{color:#57d9a3}*We can succeed to recovery it to the original DataFrame.*{color}
Since Spark 2.4, for empty strings, there are emptyValueInRead for reading and
emptyValueInWrite for writing that can be set in CSVOptions:
{code:scala}
// For writing, convert: ""(dataframe) => emptyValueInWrite(csv)
// For reading, convert: "" (csv) => emptyValueInRead(dataframe){code}
I think the read handling is not suitable, we can not convert "" or
`{color:#172b4d}emptyValueInWrite`{color} values as ""(real empty strings) but
get {color:#172b4d}emptyValueInRead's setting value actually{color}, it
supposed to be as flows:
{code:scala}
// For reading, convert: "" or emptyValueInRead (csv) => ""(dataframe){code}
For example, a column has empty strings, if emptyValueInWrite is set to
"EMPTY" string.
{code:scala}
Seq(("Tesla", "")).toDF("make", "comment").write.option("emptyValue",
"EMPTY").csv(path){code}
The saved csv file is shown as:
{noformat}
Tesla,EMPTY {noformat}
and if we read this csv file with emptyValue(emptyValueInRead) set to "EMPTY"
string.
{code:java}
spark.read.option("emptyValue", "EMPTY").csv(path).show()
{code}
we actually get the DataFrame which data is shown as:
||make||comment||
|tesla|EMPTY|
but the DataFrame which data should be shown as below as expected:
||make||comment||
|tesla| |
{color:#de350b}*We can not recovery it to the original DataFrame.*{color}
was:
The csv data format is imported from databricks
[spark-csv|https://github.com/databricks/spark-csv] by issue
[SPARK-12833|https://issues.apache.org/jira/browse/SPARK-12833] and PR
[10766|https://github.com/apache/spark/pull/10766] .
In databricks spark-csv,
For null values, the parameter nullValue can be set when reading or writing in
CSVOptions:
{code:scala}
// For writing, convert: null(dataframe) => nullValue(csv)
// For reading, convert: nullValue or ,,(csv) => null(dataframe)
{code}
For example, a column has null values, if nullValue is set to "null" string.
{code:scala}
Seq(("Tesla", null.asInstanceOf[String])).toDF("make",
"comment").write.option("nullValue", "NULL").csv(path){code}
The saved csv file is shown as:
{noformat}
Tesla,NULL
{noformat}
and if we read this csv file with nullValue set to "null" string.
{code:java}
spark.read.option("nullValue", "NULL").csv(path).show()
{code}
we can get the DataFrame which data is same with the original shown as:
||make||comment||
|tesla|null|
{color:#57d9a3}*We can succeed to recovery it to the original DataFrame.*{color}
Since Spark 2.4, for empty strings, there are emptyValueInRead for reading and
emptyValueInWrite for writing that can be set in CSVOptions:
{code:scala}
// For writing, convert: ""(dataframe) => emptyValueInWrite(csv)
// For reading, convert: "" (csv) => emptyValueInRead(dataframe){code}
I think the read handling is not suitable, we can not convert "" or
`{color:#172b4d}emptyValueInWrite`{color} values as ""(real empty strings) but
get {color:#172b4d}emptyValueInRead's setting value actually{color}, it
supposed to be as flows:
{code:scala}
// For reading, convert: "" or emptyValueInRead (csv) => ""(dataframe){code}
For example, a column has empty strings, if emptyValueInWrite is set to
"EMPTY" string.
{code:scala}
Seq(("Tesla", "")).toDF("make", "comment").write.option("emptyValue"
[jira] [Updated] (SPARK-37604) The option emptyValueInRead(in CSVOptions) is suggested to be designed as that any fields matching this string will be set as empty values "" when reading
[
https://issues.apache.org/jira/browse/SPARK-37604?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]
Wei Guo updated SPARK-37604:
Summary: The option emptyValueInRead(in CSVOptions) is suggested to be
designed as that any fields matching this string will be set as empty values ""
when reading (was: The parameter emptyValueInRead(in CSVOptions) is suggested
to be designed as that any fields matching this string will be set as empty
values "" when reading)
> The option emptyValueInRead(in CSVOptions) is suggested to be designed as
> that any fields matching this string will be set as empty values "" when
> reading
> --
>
> Key: SPARK-37604
> URL: https://issues.apache.org/jira/browse/SPARK-37604
> Project: Spark
> Issue Type: Bug
> Components: SQL
>Affects Versions: 2.4.0, 3.2.0
>Reporter: Wei Guo
>Priority: Major
>
> The csv data format is imported from databricks
> [spark-csv|https://github.com/databricks/spark-csv] by issue
> [SPARK-12833|https://issues.apache.org/jira/browse/SPARK-12833] and PR
> [10766|https://github.com/apache/spark/pull/10766] .
> In databricks spark-csv,
> For null values, the parameter nullValue can be set when reading or writing
> in CSVOptions:
> {code:scala}
> // For writing, convert: null(dataframe) => nullValue(csv)
> // For reading, convert: nullValue or ,,(csv) => null(dataframe)
> {code}
> For example, a column has null values, if nullValue is set to "null" string.
> {code:scala}
> Seq(("Tesla", null.asInstanceOf[String])).toDF("make",
> "comment").write.option("nullValue", "NULL").csv(path){code}
> The saved csv file is shown as:
> {noformat}
> Tesla,NULL
> {noformat}
> and if we read this csv file with nullValue set to "null" string.
> {code:java}
> spark.read.option("nullValue", "NULL").csv(path).show()
> {code}
> we can get the DataFrame which data is same with the original shown as:
> ||make||comment||
> |tesla|null|
> {color:#57d9a3}*We can succeed to recovery it to the original
> DataFrame.*{color}
>
> Since Spark 2.4, for empty strings, there are emptyValueInRead for reading
> and emptyValueInWrite for writing that can be set in CSVOptions:
> {code:scala}
> // For writing, convert: ""(dataframe) => emptyValueInWrite(csv)
> // For reading, convert: "" (csv) => emptyValueInRead(dataframe){code}
> I think the read handling is not suitable, we can not convert "" or
> `{color:#172b4d}emptyValueInWrite`{color} values as ""(real empty strings)
> but get {color:#172b4d}emptyValueInRead's setting value actually{color}, it
> supposed to be as flows:
> {code:scala}
> // For reading, convert: "" or emptyValueInRead (csv) => ""(dataframe){code}
> For example, a column has empty strings, if emptyValueInWrite is set to
> "EMPTY" string.
> {code:scala}
> Seq(("Tesla", "")).toDF("make", "comment").write.option("emptyValue",
> "EMPTY").csv(path){code}
> The saved csv file is shown as:
> {noformat}
> Tesla,EMPTY {noformat}
> and if we read this csv file with emptyValue(emptyValueInRead) set to "EMPTY"
> string.
> {code:java}
> spark.read.option("emptyValue", "EMPTY").csv(path).show()
> {code}
> we actually get the DataFrame which data is shown as:
> ||make||comment||
> |tesla|EMPTY|
> but the DataFrame which data should be shown as below as expected:
> ||make||comment||
> |tesla| |
> {color:#de350b}*We can not recovery it to the original DataFrame.*{color}
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