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Posted to github@arrow.apache.org by "jiangzhx (via GitHub)" <gi...@apache.org> on 2023/06/12 04:14:48 UTC
[GitHub] [arrow-datafusion] jiangzhx opened a new pull request, #6642: Port tests in joins.rs to sqllogictes
jiangzhx opened a new pull request, #6642:
URL: https://github.com/apache/arrow-datafusion/pull/6642
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[GitHub] [arrow-datafusion] alamb commented on a diff in pull request #6642: Port tests in joins.rs to sqllogictes
Posted by "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
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[GitHub] [arrow-datafusion] alamb commented on a diff in pull request #6642: Port tests in joins.rs to sqllogictes
Posted by "alamb (via GitHub)" <gi...@apache.org>.
alamb commented on code in PR #6642:
URL: https://github.com/apache/arrow-datafusion/pull/6642#discussion_r1227413861
##########
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:
I had some time so I made a PR to do this in https://github.com/apache/arrow-datafusion/pull/6656
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[GitHub] [arrow-datafusion] alamb merged pull request #6642: Port tests in joins.rs to sqllogictes
Posted by "alamb (via GitHub)" <gi...@apache.org>.
alamb merged PR #6642:
URL: https://github.com/apache/arrow-datafusion/pull/6642
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