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Merged
alamb merged 10 commits into from
Nov 30, 2023
Merged

Double type argument for to_timestamp function #8159

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c964a2f
feat: test queries for to_timestamp(float) WIP
spaydar Nov 3, 2023
34c72b9
feat: Float64 input for to_timestamp
spaydar Nov 14, 2023
9256de4
cargo fmt
spaydar Nov 14, 2023
e489e93
clippy
spaydar Nov 14, 2023
c784ca9
docs: double input type for to_timestamp
spaydar Nov 14, 2023
61fbfda
resolve merge conflicts
spaydar Nov 14, 2023
d5c908c
feat: cast floats to timestamp
spaydar Nov 29, 2023
340688f
style: cargo fmt
spaydar Nov 29, 2023
7fdb7e4
resolve merge conflict
spaydar Nov 29, 2023
55ffc5c
fix: float64 cast for timestamp nanos only
spaydar Nov 29, 2023
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1 change: 1 addition & 0 deletions datafusion/expr/src/built_in_function.rs
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Expand Up@@ -1012,6 +1012,7 @@ impl BuiltinScalarFunction {
1,
vec![
Int64,
Float64,
Timestamp(Nanosecond, None),
Timestamp(Microsecond, None),
Timestamp(Millisecond, None),
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5 changes: 5 additions & 0 deletions datafusion/physical-expr/src/datetime_expressions.rs
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Expand Up@@ -971,6 +971,11 @@ pub fn to_timestamp_invoke(args: &[ColumnarValue]) -> Result<ColumnarValue> {
&DataType::Timestamp(TimeUnit::Nanosecond, None),
None,
),
DataType::Float64 => cast_column(
&args[0],
&DataType::Timestamp(TimeUnit::Nanosecond, None),
None,
),
DataType::Timestamp(_, None) => cast_column(
&args[0],
&DataType::Timestamp(TimeUnit::Nanosecond, None),
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20 changes: 18 additions & 2 deletions datafusion/physical-expr/src/expressions/cast.rs
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Expand Up@@ -176,7 +176,20 @@ pub fn cast_column(
kernels::cast::cast_with_options(array, cast_type, &cast_options)?,
)),
ColumnarValue::Scalar(scalar) => {
let scalar_array = scalar.to_array()?;
let scalar_array = if cast_type
== &DataType::Timestamp(arrow_schema::TimeUnit::Nanosecond, None)
{
if let ScalarValue::Float64(Some(float_ts)) = scalar {
ScalarValue::Int64(
Some((float_ts * 1_000_000_000_f64).trunc() as i64),

@compheadcompheadNov 29, 2023

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I think here complexity can be reduced. too many conditions

@spaydarspaydarNov 29, 2023

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If we are allowing this type cast to be pushed down to the arrow crate, then I can add this logic to kernel::compute::cast_with_options or the appropriate fn

)
.to_array()?
} else {
scalar.to_array()?
}
} else {
scalar.to_array()?
};
let cast_array = kernels::cast::cast_with_options(
&scalar_array,
cast_type,
Expand All@@ -201,7 +214,10 @@ pub fn cast_with_options(
let expr_type = expr.data_type(input_schema)?;
if expr_type == cast_type {
Ok(expr.clone())
} else if can_cast_types(&expr_type, &cast_type) {
} else if can_cast_types(&expr_type, &cast_type)

@compheadcompheadNov 29, 2023

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I think we need to have a followup PR in arrow-rs, I'll do it

@spaydarspaydarNov 29, 2023

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Yes, if this can be pushed down to the arrow crate, the complexity in datafusion would be reduced. I wasn't sure if doing so was appropriate

@compheadcompheadNov 29, 2023

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I just checked arrow-rs so such cast is supported https://github.com/apache/arrow-rs/blob/master/arrow-cast/src/cast.rs#L224

@spaydarspaydarNov 30, 2023

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can_cast_types was returning false in my testing yesterday for Float64 -> Timestamp(Nanosecond, None), seemingly because the line you linked has not been released yet. The type check was changed from is_integer to is_numericonly a few days ago, whereas the last arrow-rs release was 3 weeks ago.

Should we wait until the next arrow-rs release so I can leverage this change?

@compheadcompheadNov 30, 2023

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I'm okay to let it go, because this is important piece. I'll create a followup issue to move to arrow-rs cast and small other refactoring. Thanks @spaydar for your work

|| (expr_type == DataType::Float64
&& cast_type == DataType::Timestamp(arrow_schema::TimeUnit::Nanosecond, None))
{
Ok(Arc::new(CastExpr::new(expr, cast_type, cast_options)))
} else {
not_impl_err!("Unsupported CAST from {expr_type:?} to {cast_type:?}")
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29 changes: 29 additions & 0 deletions datafusion/sqllogictest/test_files/timestamps.slt
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Expand Up@@ -291,6 +291,35 @@ SELECT COUNT(*) FROM ts_data_secs where ts > to_timestamp_seconds('2020-09-08T12
----
2


# to_timestamp float inputs

query PPP
SELECT to_timestamp(1.1) as c1, cast(1.1 as timestamp) as c2, 1.1::timestamp as c3;
----
1970-01-01T00:00:01.100 1970-01-01T00:00:01.100 1970-01-01T00:00:01.100

query PPP
SELECT to_timestamp(-1.1) as c1, cast(-1.1 as timestamp) as c2, (-1.1)::timestamp as c3;
----
1969-12-31T23:59:58.900 1969-12-31T23:59:58.900 1969-12-31T23:59:58.900

query PPP
SELECT to_timestamp(0.0) as c1, cast(0.0 as timestamp) as c2, 0.0::timestamp as c3;
----
1970-01-01T00:00:00 1970-01-01T00:00:00 1970-01-01T00:00:00

query PPP
SELECT to_timestamp(1.23456789) as c1, cast(1.23456789 as timestamp) as c2, 1.23456789::timestamp as c3;
----
1970-01-01T00:00:01.234567890 1970-01-01T00:00:01.234567890 1970-01-01T00:00:01.234567890

query PPP
SELECT to_timestamp(123456789.123456789) as c1, cast(123456789.123456789 as timestamp) as c2, 123456789.123456789::timestamp as c3;
----
1973-11-29T21:33:09.123456784 1973-11-29T21:33:09.123456784 1973-11-29T21:33:09.123456784


# from_unixtime

# 1599566400 is '2020-09-08T12:00:00+00:00'
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4 changes: 2 additions & 2 deletions docs/source/user-guide/sql/scalar_functions.md
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Expand Up@@ -1442,9 +1442,9 @@ extract(field FROM source)
### `to_timestamp`

Converts a value to a timestamp (`YYYY-MM-DDT00:00:00Z`).
Supports strings, integer, and unsigned integer types as input.
Supports strings, integer, unsigned integer, and double types as input.
Strings are parsed as RFC3339 (e.g. '2023-07-20T05:44:00')
Integers and unsigned integers are interpreted as seconds since the unix epoch (`1970-01-01T00:00:00Z`)
Integers, unsigned integers, and doubles are interpreted as seconds since the unix epoch (`1970-01-01T00:00:00Z`)
return the corresponding timestamp.

```
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