[GitHub] [arrow-datafusion] alamb commented on a diff in pull request #6642: Port tests in joins.rs to sqllogictes

["alamb (via GitHub)" <gi...@apache.org>]

alamb commented on code in PR #6642:
URL: https://github.com/apache/arrow-datafusion/pull/6642#discussion_r1226634082


##########
datafusion/core/tests/sqllogictests/src/setup.rs:
##########
@@ -212,3 +220,311 @@ fn register_nan_table(ctx: &SessionContext) {
     .unwrap();
     ctx.register_batch("test_float", data).unwrap();
 }
+
+pub async fn register_timestamps_table(ctx: &SessionContext) {
+    let batch = make_timestamps();
+    let schema = batch.schema();
+    let partitions = vec![vec![batch]];
+
+    ctx.register_table(
+        "test_timestamps_table",
+        Arc::new(MemTable::try_new(schema, partitions).unwrap()),
+    )
+    .unwrap();
+}
+
+/// Return  record batch with all of the supported timestamp types
+/// values
+///
+/// Columns are named:
+/// "nanos" --> TimestampNanosecondArray
+/// "micros" --> TimestampMicrosecondArray
+/// "millis" --> TimestampMillisecondArray
+/// "secs" --> TimestampSecondArray
+/// "names" --> StringArray
+pub fn make_timestamps() -> RecordBatch {
+    let ts_strings = vec![
+        Some("2018-11-13T17:11:10.011375885995"),
+        Some("2011-12-13T11:13:10.12345"),
+        None,
+        Some("2021-1-1T05:11:10.432"),
+    ];
+
+    let ts_nanos = ts_strings
+        .into_iter()
+        .map(|t| {
+            t.map(|t| {
+                t.parse::<chrono::NaiveDateTime>()
+                    .unwrap()
+                    .timestamp_nanos()
+            })
+        })
+        .collect::<Vec<_>>();
+
+    let ts_micros = ts_nanos
+        .iter()
+        .map(|t| t.as_ref().map(|ts_nanos| ts_nanos / 1000))
+        .collect::<Vec<_>>();
+
+    let ts_millis = ts_nanos
+        .iter()
+        .map(|t| t.as_ref().map(|ts_nanos| ts_nanos / 1000000))
+        .collect::<Vec<_>>();
+
+    let ts_secs = ts_nanos
+        .iter()
+        .map(|t| t.as_ref().map(|ts_nanos| ts_nanos / 1000000000))
+        .collect::<Vec<_>>();
+
+    let names = ts_nanos
+        .iter()
+        .enumerate()
+        .map(|(i, _)| format!("Row {i}"))
+        .collect::<Vec<_>>();
+
+    let arr_nanos = TimestampNanosecondArray::from(ts_nanos);
+    let arr_micros = TimestampMicrosecondArray::from(ts_micros);
+    let arr_millis = TimestampMillisecondArray::from(ts_millis);
+    let arr_secs = TimestampSecondArray::from(ts_secs);
+
+    let names = names.iter().map(|s| s.as_str()).collect::<Vec<_>>();
+    let arr_names = StringArray::from(names);
+
+    let schema = Schema::new(vec![
+        Field::new("nanos", arr_nanos.data_type().clone(), true),
+        Field::new("micros", arr_micros.data_type().clone(), true),
+        Field::new("millis", arr_millis.data_type().clone(), true),
+        Field::new("secs", arr_secs.data_type().clone(), true),
+        Field::new("name", arr_names.data_type().clone(), true),
+    ]);
+    let schema = Arc::new(schema);
+
+    RecordBatch::try_new(
+        schema,
+        vec![
+            Arc::new(arr_nanos),
+            Arc::new(arr_micros),
+            Arc::new(arr_millis),
+            Arc::new(arr_secs),
+            Arc::new(arr_names),
+        ],
+    )
+    .unwrap()
+}
+
+/// Generate a partitioned CSV file and register it with an execution context
+pub async fn register_partition_table(test_ctx: &mut TestContext) {
+    test_ctx.enable_testdir();
+    let partition_count = 1;
+    let file_extension = "csv";
+    let schema = Arc::new(Schema::new(vec![
+        Field::new("c1", DataType::UInt32, false),
+        Field::new("c2", DataType::UInt64, false),
+        Field::new("c3", DataType::Boolean, false),
+    ]));
+    // generate a partitioned file
+    for partition in 0..partition_count {
+        let filename = format!("partition-{partition}.{file_extension}");
+        let file_path = test_ctx.testdir_path().join(filename);
+        let mut file = File::create(file_path).unwrap();
+
+        // generate some data
+        for i in 0..=10 {
+            let data = format!("{},{},{}\n", partition, i, i % 2 == 0);
+            file.write_all(data.as_bytes()).unwrap()
+        }
+    }
+
+    // register csv file with the execution context
+    test_ctx
+        .ctx
+        .register_csv(
+            "test_partition_table",
+            test_ctx.testdir_path().to_str().unwrap(),
+            CsvReadOptions::new().schema(&schema),
+        )
+        .await
+        .unwrap();
+}
+
+pub async fn register_hashjoin_datatype_table(ctx: &SessionContext) {

Review Comment:
   likewise we could port this to .sql as well



##########
datafusion/core/tests/sqllogictests/src/setup.rs:
##########
@@ -212,3 +220,311 @@ fn register_nan_table(ctx: &SessionContext) {
     .unwrap();
     ctx.register_batch("test_float", data).unwrap();
 }
+
+pub async fn register_timestamps_table(ctx: &SessionContext) {

Review Comment:
   I think this is code we can potentially port to sql (using `arrow-typeof`), but we can also handle that as a follow on PR too



-- 
This is an automated message from the Apache Git Service.
To respond to the message, please log on to GitHub and use the
URL above to go to the specific comment.

To unsubscribe, e-mail: github-unsubscribe@arrow.apache.org

For queries about this service, please contact Infrastructure at:
users@infra.apache.org