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feat(functions): calendar field accessors, truncation, month arithmetic and zone conversions - #221
Registers and implements 21 more Spark datetime functions: the field
accessors hour, minute, second, dayofweek, dayofyear, weekofyear, weekday
and quarter; monthname and dayname (Spark 4.0+); unix_date; add_months,
last_day, next_day; trunc and date_trunc; months_between; make_date; and
from_utc_timestamp, to_utc_timestamp and convert_timezone.
Kernels sit in krishiv-kernels (datetime/calendar.rs, with DatePart,
extract_date/extract_timestamp and date_add_months reused for the
accessors and add_months); signatures in registry/defs.rs, bindings in
scalar/datetime.rs. Time zones go through TimeZoneRules: only offset zones
(UTC, +08:00) resolve. Any other zone id (a region such as Asia/Seoul, a
short id such as CET) is KRISHIV_UNSUPPORTED_FEATURE, and text no zone can
have is INVALID_TIMEZONE; no time-zone database was added. A timestamp_ntz
passed to date_trunc, months_between, from_utc_timestamp or
to_utc_timestamp is read in the session zone, as Spark's implicit cast
does, and convert_timezone reads a timestamp as the session zone's wall
clock.
Spark compatibility, checked against the function matrix recorded from
Spark 4.2 (both ANSI modes): every cell the engine can run (1,333 cells)
matches in rows, error class and parameters, except that the registry's
CAST_INVALID_INPUT has no ansiConfig parameter (a cast concern) and that
Spark shows months_between's default roundOff and convert_timezone's
inserted current_timezone() in its error texts. Statements of date.sql and
datetime-special.sql that use these functions match too. months_between
counts the time of day in whole seconds from the instant the local day
starts, which the matrix pins down. The analysis-level matrix manifest and
the type-coercion manifest are regenerated; the latter shrinks by seven
statements that now resolve.
Not registered, still KnownUnsupported: make_timestamp, try_make_timestamp
and the _ltz/_ntz forms (about 70% of their cells need TIME or intervals,
and make_timestamp's result type follows spark.sql.timestampType, which a
call's typing cannot see), timestampadd/timestampdiff and the unit forms of
dateadd/datediff (the parser lowers them to calls the registry has no
catalog row for), and every interval-typed function.
Not verified against a Spark run (no recorded cell reaches them): next_day
matches names case-insensitively without trimming; last_day and next_day
wrap in int at the ends of the DATE range; make_date past about 5.88
million years is ARITHMETIC_OVERFLOW in both modes and trunc of a date
whose first day leaves the range is DATETIME_OVERFLOW; DST gaps and
overlaps in date_trunc, months_between and the zone conversions follow
java.time and are tested on a synthetic zone, not compared with Spark.
… zone conversions
Registers and implements 21 more Spark datetime functions: the field
accessors hour, minute, second, dayofweek, dayofyear, weekofyear, weekday
and quarter; monthname and dayname (Spark 4.0+); unix_date; add_months,
last_day, next_day; trunc and date_trunc; months_between; make_date; and
from_utc_timestamp, to_utc_timestamp and convert_timezone.
Kernels sit in krishiv-kernels (datetime/calendar.rs, with DatePart,
extract_date/extract_timestamp and date_add_months reused for the
accessors and add_months); signatures in registry/defs.rs, bindings in
scalar/datetime.rs. Time zones go through TimeZoneRules: only offset zones
(UTC, +08:00) resolve. Any other zone id (a region such as Asia/Seoul, a
short id such as CET) is KRISHIV_UNSUPPORTED_FEATURE, and text no zone can
have is INVALID_TIMEZONE; no time-zone database was added. A timestamp_ntz
passed to date_trunc, months_between, from_utc_timestamp or
to_utc_timestamp is read in the session zone, as Spark's implicit cast
does, and convert_timezone reads a timestamp as the session zone's wall
clock.
Spark compatibility, checked against the function matrix recorded from
Spark 4.2 (both ANSI modes): every cell the engine can run (1,333 cells)
matches in rows, error class and parameters, except that the registry's
CAST_INVALID_INPUT has no ansiConfig parameter (a cast concern) and that
Spark shows months_between's default roundOff and convert_timezone's
inserted current_timezone() in its error texts. Statements of date.sql and
datetime-special.sql that use these functions match too. months_between
counts the time of day in whole seconds from the instant the local day
starts, which the matrix pins down. The analysis-level matrix manifest and
the type-coercion manifest are regenerated; the latter shrinks by seven
statements that now resolve.
Not registered, still KnownUnsupported: make_timestamp, try_make_timestamp
and the _ltz/_ntz forms (about 70% of their cells need TIME or intervals,
and make_timestamp's result type follows spark.sql.timestampType, which a
call's typing cannot see), timestampadd/timestampdiff and the unit forms of
dateadd/datediff (the parser lowers them to calls the registry has no
catalog row for), and every interval-typed function.
Not verified against a Spark run (no recorded cell reaches them): next_day
matches names case-insensitively without trimming; last_day and next_day
wrap in int at the ends of the DATE range; make_date past about 5.88
million years is ARITHMETIC_OVERFLOW in both modes and trunc of a date
whose first day leaves the range is DATETIME_OVERFLOW; DST gaps and
overlaps in date_trunc, months_between and the zone conversions follow
java.time and are tested on a synthetic zone, not compared with Spark.
Signed-off-by: gopal tathe <44522021+techgopal@users.noreply.github.com>
…e overflow, pin matrix skips
Zone text that no zone id can have (xx, foo, utc, etc/utc, out-of-range
offsets) is now INVALID_TIMEZONE; only plausible region ids (area/city
with an uppercase area), known short ids, and parseable-but-unreadable
offsets stay KRISHIV_UNSUPPORTED_FEATURE.
trunc, date_trunc, months_between and the zone conversions take an ANSI
flag: DATETIME_OVERFLOW with ANSI on, NULL with it off, matching the
existing next_day and make_date behavior. Literal zones use Zones::One
and tracked allocation; second/weekday conversions document their
invariants with debug assertions.
The session matrix pins the exact skip set and the two parameter
normalization counts, so a new unsupported classification fails instead
of passing silently. Kernel and function docs mark the int-wrap and
DST-gap/overlap rules as assumed, unverified against a Spark run.
Signed-off-by: gopal tathe <44522021+techgopal@users.noreply.github.com>
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Walkthrough
Adds calendar kernels and registers scalar functions for field extraction, date arithmetic, truncation, months-between, date construction, and time-zone conversion. The changes add ANSI-dependent error handling, session-zone conversions, and kernel, scalar, and session tests.
Adds kernels for calendar calculations, truncation, date construction, and time-zone conversion. Adds related error constructors, exports, and kernel tests.
Function signatures and registration crates/krishiv-functions/src/registry/defs.rs, crates/krishiv-functions/src/registry/tests.rs, crates/krishiv-functions/CLAUDE.md, crates/krishiv-testkit/tests/type_coercion.rs
Registers the datetime functions and defines their input coercions, result types, nullability, and Spark-version availability. Updates the resolving-query count assertion.
Adds scalar bindings, timestamp-type conversions, and time-zone argument resolution. Adds scalar tests for function results, nulls, input types, and errors.
KrishivAI/krishiv-engine#183: Defines fixed-offset parsing, timestamp literal interpretation, and session-zone handling used by the added time-zone functions.
KrishivAI/krishiv-engine#213: Validates registered functions against PySpark signatures or explicit exceptions, which connects to the new registry entries.
KrishivAI/krishiv-engine#161: Adds session time-zone configuration used when interpreting timestamp arguments and converting zones.
Merge Risk: Low · up to eec84
The new calendar functions are mostly sound. A few edge cases need fixing before or soon after merge: NULL timestamps paired with bad zone names raise errors, some valid one-word zone names get the wrong error class, a debug-build assertion can panic on NULL weekday rows, and legacy-mode overflow NULLs are declared non-nullable.
Security architecture risk: Low · up to eec84
The reviewed functions compute date and time values without introducing external access or additional privileges. Remaining uncertainty concerns unusual-input failure handling and result-type consistency, rather than a demonstrated security-boundary regression.
Retained concerns
No architecture-level concerns identified.
Security review details
Security Blast Radius
inferred — The demonstrated exposure is SQL-controlled value computation within the query process. The inspected new branches do not select credentials, services, files, tenants, or data stores from zone strings.
Trust Boundaries and Controls
observed — Both literal and column zone arguments pass through the same resolver. Timestamp coercion receives the session zone through explicit options rather than reading or modifying ambient process configuration.
Resilience and Maintainability Implications
observed — The existing executor converts Tokio task panics into structured internal errors. The distribution profile inherits release settings and retains unwinding. These controls counter a process-wide interpretation of the weekday debug-assertion question, but do not establish isolation for every direct library embedding.
Pre-merge checks: 3 · 2
Failed checks: 2 warnings
Check name
Status
Explanation
Resolution
Description check
The description gives a detailed technical summary and reports compatibility testing, but it omits the required Task/spec/ADRs section, test commands with result lines, and checklist responses.
Add the required Summary and Task/spec/ADRs sections, list the exact test commands and result lines, and complete the repository checklist.
Docstring Coverage
Docstring coverage is 41.30% which is insufficient. The required threshold is 80.00%. Docstring coverage is scoped to functions touched by this diff. Analyzed 138 functions across 11 files. (2 skipped…
Write docstrings for the functions missing them to satisfy the coverage threshold.
Passed checks: 3 passed
Check name
Status
Explanation
Title check
The title clearly summarizes the primary changes: calendar field accessors, truncation, month arithmetic, and time-zone conversions.
Linked Issues check
Check skipped because no linked issues were found for this pull request.
Out of Scope Changes check
Check skipped because no linked issues were found for this pull request.
Full details: Docstring Coverage
Explanation
Docstring coverage is 41.30% which is insufficient. The required threshold is 80.00%. Docstring coverage is scoped to functions touched by this diff. Analyzed 138 functions across 11 files. (2 skipped: 2 unsupported.)
Resolution
Write docstrings for the functions missing them to satisfy the coverage threshold.
Finishing Touches: 💡 1
⚔️ Resolve merge conflicts: 💡
Resolve merge conflict in branch feat/datetime-calendar
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Actionable comments posted: 4
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Inline comments:
Review comments at @crates/krishiv-functions/src/registry/defs.rs:
- Around line 178-180: Preserve the logical nullability returned by `typed` and
the shared registry rules; instead, update the physical execution schema for
`months_between`, `zoned`, and `convert_timezone` to allow NULL rows from legacy
overflow handling when ANSI mode is off. Ensure this schema adjustment does not
change Spark analysis nullability.
Review comments at @crates/krishiv-functions/src/scalar/datetime.rs:
- Around line 402-407: Remove the debug_assert! from the DAYOFWEEK_ISO weekday
binding so NULL slots cannot panic in debug builds; keep the existing
wrapping_sub(1) conversion unchanged.
- Around line 137-144: Update the timezone classification that uses
SHORT_ZONE_IDS so valid-looking, uppercase-leading region identifiers not in the
list are treated as unsupported rather than invalid. Preserve INVALID_TIMEZONE
for lowercase or otherwise malformed identifiers, including the existing
recorded matrix cases.
- Around line 269-288: Update column_zones to resolve zone names only for rows
the kernel will read. Pass row-liveness based on timestamp validity for
from_utc_timestamp and to_utc_timestamp, and on clock plus other-zone validity
for convert_timezone; preserve NULL outputs and avoid resolving zones on
inactive rows.
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System calls cannot be simulated consistently and are left out of the measure. The reported value understates the real cost. Switch to the Walltime instrument to measure them.
Add PySpark rows for the 20 datetime functions added to the registry
(add_months, date_trunc, dayname, dayofweek, dayofyear, from_utc_timestamp,
hour, last_day, make_date, minute, monthname, months_between, next_day,
quarter, second, to_utc_timestamp, trunc, unix_date, weekday, weekofyear),
regenerate the checked-in wrappers, and keep convert_timezone handwritten
because its first parameter is Optional without a default. Sync the
independent PySpark signature record and cover the new call shapes.
Signed-off-by: gopal tathe <44522021+techgopal@users.noreply.github.com>
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Registers and implements 21 more Spark datetime functions: the field
accessors hour, minute, second, dayofweek, dayofyear, weekofyear, weekday
and quarter; monthname and dayname (Spark 4.0+); unix_date; add_months,
last_day, next_day; trunc and date_trunc; months_between; make_date; and
from_utc_timestamp, to_utc_timestamp and convert_timezone.
Kernels sit in krishiv-kernels (datetime/calendar.rs, with DatePart,
extract_date/extract_timestamp and date_add_months reused for the
accessors and add_months); signatures in registry/defs.rs, bindings in
scalar/datetime.rs. Time zones go through TimeZoneRules: only offset zones
(UTC, +08:00) resolve. Any other zone id (a region such as Asia/Seoul, a
short id such as CET) is KRISHIV_UNSUPPORTED_FEATURE, and text no zone can
have is INVALID_TIMEZONE; no time-zone database was added. A timestamp_ntz
passed to date_trunc, months_between, from_utc_timestamp or
to_utc_timestamp is read in the session zone, as Spark's implicit cast
does, and convert_timezone reads a timestamp as the session zone's wall
clock.
Spark compatibility, checked against the function matrix recorded from
Spark 4.2 (both ANSI modes): every cell the engine can run (1,333 cells)
matches in rows, error class and parameters, except that the registry's
CAST_INVALID_INPUT has no ansiConfig parameter (a cast concern) and that
Spark shows months_between's default roundOff and convert_timezone's
inserted current_timezone() in its error texts. Statements of date.sql and
datetime-special.sql that use these functions match too. months_between
counts the time of day in whole seconds from the instant the local day
starts, which the matrix pins down. The analysis-level matrix manifest and
the type-coercion manifest are regenerated; the latter shrinks by seven
statements that now resolve.
Not registered, still KnownUnsupported: make_timestamp, try_make_timestamp
and the _ltz/_ntz forms (about 70% of their cells need TIME or intervals,
and make_timestamp's result type follows spark.sql.timestampType, which a
call's typing cannot see), timestampadd/timestampdiff and the unit forms of
dateadd/datediff (the parser lowers them to calls the registry has no
catalog row for), and every interval-typed function.
Not verified against a Spark run (no recorded cell reaches them): next_day
matches names case-insensitively without trimming; last_day and next_day
wrap in int at the ends of the DATE range; make_date past about 5.88
million years is ARITHMETIC_OVERFLOW in both modes and trunc of a date
whose first day leaves the range is DATETIME_OVERFLOW; DST gaps and
overlaps in date_trunc, months_between and the zone conversions follow
java.time and are tested on a synthetic zone, not compared with Spark.