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Posted to github@datafusion.apache.org by "dqkqd (via GitHub)" <gi...@apache.org> on 2025/09/26 15:32:53 UTC

[PR] fix: ignore `DataType::Null` in possible types during csv type inference [datafusion]

dqkqd opened a new pull request, #17796:
URL: https://github.com/apache/datafusion/pull/17796

   ## Which issue does this PR close?
   
   <!--
   We generally require a GitHub issue to be filed for all bug fixes and enhancements and this helps us generate change logs for our releases. You can link an issue to this PR using the GitHub syntax. For example `Closes #123` indicates that this PR will close issue #123.
   -->
   
   - Closes #17517.
   
   ## Rationale for this change
   
   <!--
    Why are you proposing this change? If this is already explained clearly in the issue then this section is not needed.
    Explaining clearly why changes are proposed helps reviewers understand your changes and offer better suggestions for fixes.  
   -->
   
   Datafusion cannot infer types correctly for CSV files where one of their chunks only contains NULLs.
   
   Example: Consider the file below
   ```
   a       // 1st chunk
   1       // 1st chunk: Int64
   2       // 1st chunk: Int64
   <null>  // 2nd chunk: null
   <null>  // 2nd chunk: null
   ```
   Because all the records in the second chunk are nulls, Datafusion sees the possible data types for `a` as `[Int64, Null]`, and thus infers the data type as `Utf8`.
   
   https://github.com/apache/datafusion/blob/691dd4727d43cf88e90ce5d38d1bcc6b2111ea47/datafusion/datasource-csv/src/file_format.rs#L610-L613
   
   ## What changes are included in this PR?
   
   <!--
   There is no need to duplicate the description in the issue here but it is sometimes worth providing a summary of the individual changes in this PR.
   -->
   
   Ignore `DataType::Null` when inferring data type from possible data types.
   
   
   ## Are these changes tested?
   
   <!--
   We typically require tests for all PRs in order to:
   1. Prevent the code from being accidentally broken by subsequent changes
   2. Serve as another way to document the expected behavior of the code
   
   If tests are not included in your PR, please explain why (for example, are they covered by existing tests)?
   -->
   
   Yes.
   
   ## Are there any user-facing changes?
   
   <!--
   If there are user-facing changes then we may require documentation to be updated before approving the PR.
   -->
   
   <!--
   If there are any breaking changes to public APIs, please add the `api change` label.
   -->
   
   No.


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Re: [PR] fix: ignore `DataType::Null` in possible types during csv type inference [datafusion]

Posted by "alamb (via GitHub)" <gi...@apache.org>.
alamb commented on code in PR #17796:
URL: https://github.com/apache/datafusion/pull/17796#discussion_r2388091218


##########
datafusion/core/src/datasource/file_format/csv.rs:
##########
@@ -470,6 +471,59 @@ mod tests {
         Ok(())
     }
 
+    #[tokio::test]
+    async fn test_infer_schema_stream_null_chunks() -> Result<()> {
+        let session_ctx = SessionContext::new();
+        let state = session_ctx.state();
+
+        // a stream where each line is read as a separate chunk,
+        // data type for each chunk is inferred separately.
+        // +----+-----+----+

Review Comment:
   thank you for these comments



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Re: [PR] fix: ignore `DataType::Null` in possible types during csv type inference [datafusion]

Posted by "alamb (via GitHub)" <gi...@apache.org>.
alamb commented on code in PR #17796:
URL: https://github.com/apache/datafusion/pull/17796#discussion_r2388103532


##########
datafusion/datasource-csv/src/file_format.rs:
##########
@@ -582,20 +582,25 @@ impl CsvFormat {
             }
         }
 
-        let schema = build_schema_helper(column_names, &column_type_possibilities);
+        let schema = build_schema_helper(column_names, column_type_possibilities);
         Ok((schema, total_records_read))
     }
 }
 
-fn build_schema_helper(names: Vec<String>, types: &[HashSet<DataType>]) -> Schema {
+fn build_schema_helper(names: Vec<String>, types: Vec<HashSet<DataType>>) -> Schema {
     let fields = names
         .into_iter()
         .zip(types)
-        .map(|(field_name, data_type_possibilities)| {
+        .map(|(field_name, mut data_type_possibilities)| {
             // ripped from arrow::csv::reader::infer_reader_schema_with_csv_options
             // determine data type based on possible types
             // if there are incompatible types, use DataType::Utf8
+
+            // ignore nulls, to avoid conflicting datatypes (e.g. [nulls, int]) being inferred as Utf8.
+            data_type_possibilities.remove(&DataType::Null);
+
             match data_type_possibilities.len() {
+                0 => Field::new(field_name, DataType::Null, true),

Review Comment:
   I agree this makes sense. Some context might help too:
   
   ```suggestion
                   // Return Null for columns with only nulls / empty files
                   // This allows schema merging to work when reading folders 
                   // such files along with normal files.
                   0 => Field::new(field_name, DataType::Null, true),
   ```



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Re: [PR] fix: ignore `DataType::Null` in possible types during csv type inference [datafusion]

Posted by "alamb (via GitHub)" <gi...@apache.org>.
alamb merged PR #17796:
URL: https://github.com/apache/datafusion/pull/17796


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Re: [PR] fix: ignore `DataType::Null` in possible types during csv type inference [datafusion]

Posted by "alamb (via GitHub)" <gi...@apache.org>.
alamb commented on code in PR #17796:
URL: https://github.com/apache/datafusion/pull/17796#discussion_r2388104054


##########
datafusion/sqllogictest/test_files/ddl.slt:
##########
@@ -707,9 +707,9 @@ CREATE EXTERNAL TABLE empty STORED AS CSV LOCATION '../core/tests/data/empty.csv
 query TTI
 select column_name, data_type, ordinal_position from information_schema.columns where table_name='empty';;

Review Comment:
   this makes sense to me



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Re: [PR] fix: ignore `DataType::Null` in possible types during csv type inference [datafusion]

Posted by "dqkqd (via GitHub)" <gi...@apache.org>.
dqkqd commented on code in PR #17796:
URL: https://github.com/apache/datafusion/pull/17796#discussion_r2383659583


##########
datafusion/datasource-csv/src/file_format.rs:
##########
@@ -593,15 +593,23 @@ fn build_schema_helper(names: Vec<String>, types: &[HashSet<DataType>]) -> Schem
         .zip(types)
         .map(|(field_name, data_type_possibilities)| {
             // ripped from arrow::csv::reader::infer_reader_schema_with_csv_options
-            // determine data type based on possible types
-            // if there are incompatible types, use DataType::Utf8
-            match data_type_possibilities.len() {
-                1 => Field::new(
+            // determine data type based on possible types, ignoring DataType::Null,

Review Comment:
   Thanks for pointing out. I simplified the code as suggested.
   
   These tests failed because there were columns with nulls only, after removing them, the match arm fell to `Utf8` case. But they asserted that nulls columns should have data type `DataType::Null`.
   



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Re: [PR] fix: ignore `DataType::Null` in possible types during csv type inference [datafusion]

Posted by "dqkqd (via GitHub)" <gi...@apache.org>.
dqkqd commented on code in PR #17796:
URL: https://github.com/apache/datafusion/pull/17796#discussion_r2383668162


##########
datafusion/core/src/datasource/file_format/csv.rs:
##########
@@ -470,6 +471,47 @@ mod tests {
         Ok(())
     }
 
+    #[tokio::test]
+    async fn test_infer_schema_stream_separated_chunks_with_nulls() -> Result<()> {

Review Comment:
   Datafusion infers data type from each chunk separately, then combines all the possible types.
   
   This test creates a [ChunkedStore](https://docs.rs/object_store/latest/object_store/chunked/struct.ChunkedStore.html), reading each line as a separated chunk (one of them contains only nulls),
   then ensure type inference shouldn't be skewed by null chunks.
   
   I should have commented and make the test clearer.



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Re: [PR] fix: ignore `DataType::Null` in possible types during csv type inference [datafusion]

Posted by "dqkqd (via GitHub)" <gi...@apache.org>.
dqkqd commented on code in PR #17796:
URL: https://github.com/apache/datafusion/pull/17796#discussion_r2383659583


##########
datafusion/datasource-csv/src/file_format.rs:
##########
@@ -593,15 +593,23 @@ fn build_schema_helper(names: Vec<String>, types: &[HashSet<DataType>]) -> Schem
         .zip(types)
         .map(|(field_name, data_type_possibilities)| {
             // ripped from arrow::csv::reader::infer_reader_schema_with_csv_options
-            // determine data type based on possible types
-            // if there are incompatible types, use DataType::Utf8
-            match data_type_possibilities.len() {
-                1 => Field::new(
+            // determine data type based on possible types, ignoring DataType::Null,

Review Comment:
   Thanks for pointing out. I simplify the code as suggested.
   
   These tests failed because there were columns with only `NULL`, after removing them, the match arm fell to `Utf8` case.
   



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Re: [PR] fix: ignore `DataType::Null` in possible types during csv type inference [datafusion]

Posted by "alamb (via GitHub)" <gi...@apache.org>.
alamb commented on code in PR #17796:
URL: https://github.com/apache/datafusion/pull/17796#discussion_r2383346810


##########
datafusion/datasource-csv/src/file_format.rs:
##########
@@ -593,15 +593,23 @@ fn build_schema_helper(names: Vec<String>, types: &[HashSet<DataType>]) -> Schem
         .zip(types)
         .map(|(field_name, data_type_possibilities)| {
             // ripped from arrow::csv::reader::infer_reader_schema_with_csv_options
-            // determine data type based on possible types
-            // if there are incompatible types, use DataType::Utf8
-            match data_type_possibilities.len() {
-                1 => Field::new(
+            // determine data type based on possible types, ignoring DataType::Null,

Review Comment:
   Thank you @dqkqd -- this looks like it would work well. I played around with it and I think we might be able to make this simpler and more efficient by passing in the HashTable and removing the the null. Something like this seemed to work locally 
   
   
   ```rust
   // changed signature to take Vec<HashSet<DataType>>.  ----v
   fn build_schema_helper(names: Vec<String>, types: Vec<HashSet<DataType>>) -> Schema {
       let fields = names
           .into_iter()
           .zip(types)
           .map(|(field_name, mut data_type_possibilities)| {
               // ripped from arrow::csv::reader::infer_reader_schema_with_csv_options
               // determine data type based on possible types
   
               // Remove Null (missing column) from possibilities
               data_type_possibilities.remove(&DataType::Null);  <--- changed this
   
               // if there are incompatible types, use DataType::Utf8
               match data_type_possibilities.len() {
   ...
   ```
   
   
   



##########
datafusion/core/src/datasource/file_format/csv.rs:
##########
@@ -470,6 +471,47 @@ mod tests {
         Ok(())
     }
 
+    #[tokio::test]
+    async fn test_infer_schema_stream_separated_chunks_with_nulls() -> Result<()> {

Review Comment:
   I don't understand how this test coves the new code. 
   
   However, I did verify that without this code change, this PR fails like this:
   
   ```
   thread 'datasource::file_format::csv::tests::test_infer_schema_stream_separated_chunks_with_nulls' panicked at datafusion/core/src/datasource/file_format/csv.rs:511:9:
   assertion `left == right` failed
     left: ["c1: Int64", "c2: Float64"]
    right: ["c1: Utf8", "c2: Utf8"]
   note: run with `RUST_BACKTRACE=1` environment variable to display a backtrace
   ```



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Re: [PR] fix: ignore `DataType::Null` in possible types during csv type inference [datafusion]

Posted by "alamb (via GitHub)" <gi...@apache.org>.
alamb commented on PR #17796:
URL: https://github.com/apache/datafusion/pull/17796#issuecomment-3357903838

   Thanks again @dqkqd  -- this is a very nice first PR


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Re: [PR] fix: ignore `DataType::Null` in possible types during csv type inference [datafusion]

Posted by "dqkqd (via GitHub)" <gi...@apache.org>.
dqkqd commented on PR #17796:
URL: https://github.com/apache/datafusion/pull/17796#issuecomment-3341017167

   The test failed. It ensures an empty table should have its columns infer as `Uft8`.
   DuckDB does the same so I think this is correct.
   ```bash
   D CREATE TABLE empty AS
     SELECT * FROM read_csv_auto('empty.csv');
   D select * from empty;
   ┌─────────┬─────────┬─────────┐
   │   c1    │   c2    │   c3    │
   │ varchar │ varchar │ varchar │
   ├─────────┴─────────┴─────────┤
   │           0 rows            │
   └─────────────────────────────┘
   ```
   
   When I check how DuckDB handles table with null columns, it infer those columns as `VARCHAR`.
   ```bash
   D CREATE TABLE has_nulls_column AS
     SELECT * FROM read_csv_auto('has_nulls_column.csv');
   D select * from has_nulls_column;
   ┌───────┬───────┬─────────┐
   │  c1   │  c2   │   c3    │
   │ int64 │ int64 │ varchar │
   ├───────┼───────┼─────────┤
   │     1 │     2 │ NULL    │
   │     3 │     4 │ NULL    │
   └───────┴───────┴─────────┘
   ```
   
   However, datafusion infers those as `Null`. I think we should change them to `Utf8`.
   ```bash
   > CREATE EXTERNAL TABLE has_nulls_column STORED AS CSV LOCATION 'has_nulls_column.csv' OPTIONS ('format.has_header' 'true');
   0 row(s) fetched.
   Elapsed 0.025 seconds
   
   > select column_name, data_type, ordinal_position from information_schema.columns where table_name='has_nulls_column';
   +-------------+-----------+------------------+
   | column_name | data_type | ordinal_position |
   +-------------+-----------+------------------+
   | c1          | Int64     | 0                |
   | c2          | Int64     | 1                |
   | c3          | Null      | 2                |
   +-------------+-----------+------------------+
   3 row(s) fetched.
   Elapsed 0.010 seconds.
   ```


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Re: [PR] fix: ignore `DataType::Null` in possible types during csv type inference [datafusion]

Posted by "alamb (via GitHub)" <gi...@apache.org>.
alamb commented on PR #17796:
URL: https://github.com/apache/datafusion/pull/17796#issuecomment-3347188832

   Sadly, I tried this branch with the reproducer in
   - https://github.com/apache/datafusion/issues/17516
   
   And it seems that CSV type inference is still not working correctly 😢  
   
   To be clear, I don't think anything needs to change in this PR, I was just hoping it also fixed something else, which it did not


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Re: [PR] fix: ignore `DataType::Null` in possible types during csv type inference [datafusion]

Posted by "alamb (via GitHub)" <gi...@apache.org>.
alamb commented on PR #17796:
URL: https://github.com/apache/datafusion/pull/17796#issuecomment-3357903377

   🦾  🚀 


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Re: [PR] fix: ignore `DataType::Null` in possible types during csv type inference [datafusion]

Posted by "dqkqd (via GitHub)" <gi...@apache.org>.
dqkqd commented on PR #17796:
URL: https://github.com/apache/datafusion/pull/17796#issuecomment-3346731891

   I've just realized that returning `Utf8` for columns with only nulls (or empty files) causes schema mismatch when reading folders containing those files along with normal files. 
   So maybe returning `DataType::Null` is a better choice. I'll revert the code and update testcase.
   
   ```
   > cat test_data/a.csv
   c1,c2,c3
   
   > cat test_data/b.csv
   c1,c2,c3
   1,1,1
   2,2,2
   ```
   
   This fails in `main`.
   ```
   DataFusion CLI v50.0.0
   > select * from 'test_data';
   Arrow error: Schema error: Fail to merge schema field 'c1' because the from data_type = Utf8 does not equal Int64
   ```


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Re: [PR] fix: ignore `DataType::Null` in possible types during csv type inference [datafusion]

Posted by "alamb (via GitHub)" <gi...@apache.org>.
alamb commented on code in PR #17796:
URL: https://github.com/apache/datafusion/pull/17796#discussion_r2383402440


##########
datafusion/datasource-csv/src/file_format.rs:
##########
@@ -593,15 +593,23 @@ fn build_schema_helper(names: Vec<String>, types: &[HashSet<DataType>]) -> Schem
         .zip(types)
         .map(|(field_name, data_type_possibilities)| {
             // ripped from arrow::csv::reader::infer_reader_schema_with_csv_options
-            // determine data type based on possible types
-            // if there are incompatible types, use DataType::Utf8
-            match data_type_possibilities.len() {
-                1 => Field::new(
+            // determine data type based on possible types, ignoring DataType::Null,

Review Comment:
   Though when I did this I found some test failures
   
   ```
   ---- datasource::file_format::csv::tests::infer_schema stdout ----
   
   thread 'datasource::file_format::csv::tests::infer_schema' panicked at datafusion/core/src/datasource/file_format/csv.rs:273:9:
   assertion `left == right` failed
     left: ["c1: Utf8", "c2: Int64", "c3: Int64", "c4: Int64", "c5: Int64", "c6: Int64", "c7: Int64", "c8: Int64", "c9: Int64", "c10: Utf8", "c11: Float64", "c12: Float64", "c13: Utf8", "c14: Null", "c15: Utf8"]
    right: ["c1: Utf8", "c2: Int64", "c3: Int64", "c4: Int64", "c5: Int64", "c6: Int64", "c7: Int64", "c8: Int64", "c9: Int64", "c10: Utf8", "c11: Float64", "c12: Float64", "c13: Utf8", "c14: Utf8", "c15: Utf8"]
   note: run with `RUST_BACKTRACE=1` environment variable to display a backtrace
   
   ---- datasource::file_format::csv::tests::infer_schema_with_null_regex stdout ----
   
   thread 'datasource::file_format::csv::tests::infer_schema_with_null_regex' panicked at datafusion/core/src/datasource/file_format/csv.rs:324:9:
   assertion `left == right` failed
     left: ["c1: Utf8", "c2: Int64", "c3: Int64", "c4: Int64", "c5: Int64", "c6: Int64", "c7: Int64", "c8: Int64", "c9: Int64", "c10: Utf8", "c11: Float64", "c12: Float64", "c13: Utf8", "c14: Null", "c15: Null"]
    right: ["c1: Utf8", "c2: Int64", "c3: Int64", "c4: Int64", "c5: Int64", "c6: Int64", "c7: Int64", "c8: Int64", "c9: Int64", "c10: Utf8", "c11: Float64", "c12: Float64", "c13: Utf8", "c14: Utf8", "c15: Utf8"]
   
   
   failures:
       datasource::file_format::csv::tests::infer_schema
       datasource::file_format::csv::tests::infer_schema_with_null_regex
   ```



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