Merge branch 'main' into convert-lead-lag-udwf

README.md

@@ -44,8 +44,9 @@
 DataFusion is an extensible query engine written in [Rust] that
 uses [Apache Arrow] as its in-memory format.
 
-The DataFusion libraries in this repository are used to build data-centric system software. DataFusion also provides the
-following subprojects, which are packaged versions of DataFusion intended for end users.
+This crate provides libraries and binaries for developers building fast and
+feature rich database and analytic systems, customized to particular workloads.
+See [use cases] for examples. The following related subprojects target end users:
 
 - [DataFusion Python](https://github.com/apache/datafusion-python/) offers a Python interface for SQL and DataFrame
   queries.
@@ -54,13 +55,10 @@ following subprojects, which are packaged versions of DataFusion intended for en
 - [DataFusion Comet](https://github.com/apache/datafusion-comet/) is an accelerator for Apache Spark based on
   DataFusion.
 
-The target audience for the DataFusion crates in this repository are
-developers building fast and feature rich database and analytic systems,
-customized to particular workloads. See [use cases] for examples.
-
-DataFusion offers [SQL] and [`Dataframe`] APIs,
-excellent [performance], built-in support for CSV, Parquet, JSON, and Avro,
-extensive customization, and a great community.
+"Out of the box,"
+DataFusion offers [SQL] and [`Dataframe`] APIs, excellent [performance],
+built-in support for CSV, Parquet, JSON, and Avro, extensive customization, and
+a great community.
 
 DataFusion features a full query planner, a columnar, streaming, multi-threaded,
 vectorized execution engine, and partitioned data sources. You can

datafusion/common/src/test_util.rs

@@ -279,8 +279,88 @@ pub fn get_data_dir(
     }
 }
 
+#[macro_export]
+macro_rules! create_array {
+    (Boolean, $values: expr) => {
+        std::sync::Arc::new(arrow::array::BooleanArray::from($values))
+    };
+    (Int8, $values: expr) => {
+        std::sync::Arc::new(arrow::array::Int8Array::from($values))
+    };
+    (Int16, $values: expr) => {
+        std::sync::Arc::new(arrow::array::Int16Array::from($values))
+    };
+    (Int32, $values: expr) => {
+        std::sync::Arc::new(arrow::array::Int32Array::from($values))
+    };
+    (Int64, $values: expr) => {
+        std::sync::Arc::new(arrow::array::Int64Array::from($values))
+    };
+    (UInt8, $values: expr) => {
+        std::sync::Arc::new(arrow::array::UInt8Array::from($values))
+    };
+    (UInt16, $values: expr) => {
+        std::sync::Arc::new(arrow::array::UInt16Array::from($values))
+    };
+    (UInt32, $values: expr) => {
+        std::sync::Arc::new(arrow::array::UInt32Array::from($values))
+    };
+    (UInt64, $values: expr) => {
+        std::sync::Arc::new(arrow::array::UInt64Array::from($values))
+    };
+    (Float16, $values: expr) => {
+        std::sync::Arc::new(arrow::array::Float16Array::from($values))
+    };
+    (Float32, $values: expr) => {
+        std::sync::Arc::new(arrow::array::Float32Array::from($values))
+    };
+    (Float64, $values: expr) => {
+        std::sync::Arc::new(arrow::array::Float64Array::from($values))
+    };
+    (Utf8, $values: expr) => {
+        std::sync::Arc::new(arrow::array::StringArray::from($values))
+    };
+}
+
+/// Creates a record batch from literal slice of values, suitable for rapid
+/// testing and development.
+///
+/// Example:
+/// ```
+/// use datafusion_common::{record_batch, create_array};
+/// let batch = record_batch!(
+///     ("a", Int32, vec![1, 2, 3]),
+///     ("b", Float64, vec![Some(4.0), None, Some(5.0)]),
+///     ("c", Utf8, vec!["alpha", "beta", "gamma"])
+/// );
+/// ```
+#[macro_export]
+macro_rules! record_batch {
+    ($(($name: expr, $type: ident, $values: expr)),*) => {
+        {
+            let schema = std::sync::Arc::new(arrow_schema::Schema::new(vec![
+                $(
+                    arrow_schema::Field::new($name, arrow_schema::DataType::$type, true),
+                )*
+            ]));
+
+            let batch = arrow_array::RecordBatch::try_new(
+                schema,
+                vec![$(
+                    create_array!($type, $values),
+                )*]
+            );
+
+            batch
+        }
+    }
+}
+
 #[cfg(test)]
 mod tests {
+    use crate::cast::{as_float64_array, as_int32_array, as_string_array};
+    use crate::error::Result;
+
     use super::*;
     use std::env;
 
@@ -333,4 +413,44 @@ mod tests {
         let res = parquet_test_data();
         assert!(PathBuf::from(res).is_dir());
     }
+
+    #[test]
+    fn test_create_record_batch() -> Result<()> {
+        use arrow_array::Array;
+
+        let batch = record_batch!(
+            ("a", Int32, vec![1, 2, 3, 4]),
+            ("b", Float64, vec![Some(4.0), None, Some(5.0), None]),
+            ("c", Utf8, vec!["alpha", "beta", "gamma", "delta"])
+        )?;
+
+        assert_eq!(3, batch.num_columns());
+        assert_eq!(4, batch.num_rows());
+
+        let values: Vec<_> = as_int32_array(batch.column(0))?
+            .values()
+            .iter()
+            .map(|v| v.to_owned())
+            .collect();
+        assert_eq!(values, vec![1, 2, 3, 4]);
+
+        let values: Vec<_> = as_float64_array(batch.column(1))?
+            .values()
+            .iter()
+            .map(|v| v.to_owned())
+            .collect();
+        assert_eq!(values, vec![4.0, 0.0, 5.0, 0.0]);
+
+        let nulls: Vec<_> = as_float64_array(batch.column(1))?
+            .nulls()
+            .unwrap()
+            .iter()
+            .collect();
+        assert_eq!(nulls, vec![true, false, true, false]);
+
+        let values: Vec<_> = as_string_array(batch.column(2))?.iter().flatten().collect();
+        assert_eq!(values, vec!["alpha", "beta", "gamma", "delta"]);
+
+        Ok(())
+    }
 }

datafusion/core/src/bin/print_functions_docs.rs

@@ -20,10 +20,16 @@ use datafusion_expr::{
     aggregate_doc_sections, scalar_doc_sections, window_doc_sections, AggregateUDF,
     DocSection, Documentation, ScalarUDF, WindowUDF,
 };
+use hashbrown::HashSet;
 use itertools::Itertools;
 use std::env::args;
 use std::fmt::Write as _;
 
+/// Print documentation for all functions of a given type to stdout
+///
+/// Usage: `cargo run --bin print_functions_docs -- <type>`
+///
+/// Called from `dev/update_function_docs.sh`
 fn main() {
     let args: Vec<String> = args().collect();
 
@@ -83,9 +89,12 @@ fn print_docs(
 ) -> String {
     let mut docs = "".to_string();
 
+    // Ensure that all providers have documentation
+    let mut providers_with_no_docs = HashSet::new();
+
     // doc sections only includes sections that have 'include' == true
     for doc_section in doc_sections {
-        // make sure there is a function that is in this doc section
+        // make sure there is at least one function that is in this doc section
         if !&providers.iter().any(|f| {
             if let Some(documentation) = f.get_documentation() {
                 documentation.doc_section == doc_section
@@ -96,19 +105,21 @@ fn print_docs(
             continue;
         }
 
+        // filter out functions that are not in this doc section
         let providers: Vec<&Box<dyn DocProvider>> = providers
             .iter()
             .filter(|&f| {
                 if let Some(documentation) = f.get_documentation() {
                     documentation.doc_section == doc_section
                 } else {
+                    providers_with_no_docs.insert(f.get_name());
                     false
                 }
             })
             .collect::<Vec<_>>();
 
         // write out section header
-        let _ = writeln!(docs, "## {} ", doc_section.label);
+        let _ = writeln!(docs, "\n## {} \n", doc_section.label);
 
         if let Some(description) = doc_section.description {
             let _ = writeln!(docs, "{description}");
@@ -202,9 +213,19 @@ fn print_docs(
         }
     }
 
+    // If there are any functions that do not have documentation, print them out
+    // eventually make this an error: https://github.com/apache/datafusion/issues/12872
+    if !providers_with_no_docs.is_empty() {
+        eprintln!("INFO: The following functions do not have documentation:");
+        for f in providers_with_no_docs {
+            eprintln!("  - {f}");
+        }
+    }
+
     docs
 }
 
+/// Trait for accessing name / aliases / documentation for differnet functions
 trait DocProvider {
     fn get_name(&self) -> String;
     fn get_aliases(&self) -> Vec<String>;

datafusion/core/src/catalog_common/listing_schema.rs

@@ -136,6 +136,7 @@ impl ListingSchemaProvider {
                             file_type: self.format.clone(),
                             table_partition_cols: vec![],
                             if_not_exists: false,
+                            temporary: false,
                             definition: None,
                             order_exprs: vec![],
                             unbounded: false,

datafusion/core/src/datasource/listing_table_factory.rs

@@ -197,6 +197,7 @@ mod tests {
             schema: Arc::new(DFSchema::empty()),
             table_partition_cols: vec![],
             if_not_exists: false,
+            temporary: false,
             definition: None,
             order_exprs: vec![],
             unbounded: false,
@@ -236,6 +237,7 @@ mod tests {
             schema: Arc::new(DFSchema::empty()),
             table_partition_cols: vec![],
             if_not_exists: false,
+            temporary: false,
             definition: None,
             order_exprs: vec![],
             unbounded: false,

datafusion/core/src/datasource/memory.rs

@@ -37,14 +37,14 @@ use crate::physical_planner::create_physical_sort_exprs;
 
 use arrow::datatypes::SchemaRef;
 use arrow::record_batch::RecordBatch;
+use datafusion_catalog::Session;
 use datafusion_common::{not_impl_err, plan_err, Constraints, DFSchema, SchemaExt};
 use datafusion_execution::TaskContext;
 use datafusion_expr::dml::InsertOp;
+use datafusion_expr::SortExpr;
 use datafusion_physical_plan::metrics::MetricsSet;
 
 use async_trait::async_trait;
-use datafusion_catalog::Session;
-use datafusion_expr::SortExpr;
 use futures::StreamExt;
 use log::debug;
 use parking_lot::Mutex;
@@ -241,7 +241,7 @@ impl TableProvider for MemTable {
                     )
                 })
                 .collect::<Result<Vec<_>>>()?;
-            exec = exec.with_sort_information(file_sort_order);
+            exec = exec.try_with_sort_information(file_sort_order)?;
         }
 
         Ok(Arc::new(exec))

datafusion/core/src/execution/context/mod.rs

@@ -738,6 +738,11 @@ impl SessionContext {
         cmd: &CreateExternalTable,
     ) -> Result<DataFrame> {
         let exist = self.table_exist(cmd.name.clone())?;
+
+        if cmd.temporary {
+            return not_impl_err!("Temporary tables not supported");
+        }
+
         if exist {
             match cmd.if_not_exists {
                 true => return self.return_empty_dataframe(),
@@ -761,10 +766,16 @@ impl SessionContext {
             or_replace,
             constraints,
             column_defaults,
+            temporary,
         } = cmd;
 
         let input = Arc::unwrap_or_clone(input);
         let input = self.state().optimize(&input)?;
+
+        if temporary {
+            return not_impl_err!("Temporary tables not supported");
+        }
+
         let table = self.table(name.clone()).await;
         match (if_not_exists, or_replace, table) {
             (true, false, Ok(_)) => self.return_empty_dataframe(),
@@ -813,10 +824,15 @@ impl SessionContext {
             input,
             or_replace,
             definition,
+            temporary,
         } = cmd;
 
         let view = self.table(name.clone()).await;
 
+        if temporary {
+            return not_impl_err!("Temporary views not supported");
+        }
+
         match (or_replace, view) {
             (true, Ok(_)) => {
                 self.deregister_table(name.clone())?;