Merge branch 'main' into aalam-SNOW-1418523-make-internal-session-var…

…iables-thread-safe

CHANGELOG.md

@@ -2,6 +2,20 @@
 
 ## 1.23.0 (TBD)
 
+### Snowpark pandas API Updates
+
+#### Improvements
+
+- Improved `to_pandas` to persist the original timezone offset for TIMESTAMP_TZ type.
+
+#### New Features
+
+- Added support for `TimedeltaIndex.mean` method.
+
+
+## 1.22.1 (2024-09-11)
+This is a re-release of 1.22.0. Please refer to the 1.22.0 release notes for detailed release content.
+
 
 ## 1.22.0 (2024-09-10)
 
@@ -109,6 +123,7 @@
 - Added support for string indexing with `Timedelta` objects.
 - Added support for `Series.dt.total_seconds` method.
 - Added support for `DataFrame.apply(axis=0)`.
+- Added support for `Series.dt.tz_convert` and `Series.dt.tz_localize`.
 
 #### Improvements
 
@@ -121,6 +136,7 @@
 - Raise a warning whenever MultiIndex values are pulled in locally.
 - Improve warning message for `pd.read_snowflake` include the creation reason when temp table creation is triggered.
 - Improve performance for `DataFrame.set_index`, or setting `DataFrame.index` or `Series.index` by avoiding checks require eager evaluation. As a consequence, when the new index that does not match the current `Series`/`DataFrame` object length, a `ValueError` is no longer raised. Instead, when the `Series`/`DataFrame` object is longer than the provided index, the `Series`/`DataFrame`'s new index is filled with `NaN` values for the "extra" elements. Otherwise, the extra values in the provided index are ignored.
+- Properly raise `NotImplementedError` when ambiguous/nonexistent are non-string in `ceil`/`floor`/`round`.
 
 #### Bug Fixes
 

docs/source/modin/series.rst

@@ -279,6 +279,8 @@ Series
     Series.dt.seconds
     Series.dt.microseconds
     Series.dt.nanoseconds
+    Series.dt.tz_convert
+    Series.dt.tz_localize
 
 
 .. rubric:: String accessor methods

docs/source/modin/supported/series_dt_supported.rst

@@ -80,9 +80,10 @@ the method in the left column.
 +-----------------------------+---------------------------------+----------------------------------------------------+
 | ``to_pydatetime``           | N                               |                                                    |
 +-----------------------------+---------------------------------+----------------------------------------------------+
-| ``tz_localize``             | N                               |                                                    |
+| ``tz_localize``             | P                               | ``N`` if `ambiguous` or `nonexistent` are set to a |
+|                             |                                 | non-default value.                                 |
 +-----------------------------+---------------------------------+----------------------------------------------------+
-| ``tz_convert``              | N                               |                                                    |
+| ``tz_convert``              | Y                               |                                                    |
 +-----------------------------+---------------------------------+----------------------------------------------------+
 | ``normalize``               | Y                               |                                                    |
 +-----------------------------+---------------------------------+----------------------------------------------------+

docs/source/modin/supported/timedelta_index_supported.rst

@@ -44,7 +44,7 @@ Methods
 +-----------------------------+---------------------------------+----------------------------------+-------------------------------------------+
 | ``ceil``                    | Y                               |                                  |                                           |
 +-----------------------------+---------------------------------+----------------------------------+-------------------------------------------+
-| ``mean``                    | N                               |                                  |                                           |
+| ``mean``                    | Y                               |                                  |                                           |
 +-----------------------------+---------------------------------+----------------------------------+-------------------------------------------+
 | ``total_seconds``           | Y                               |                                  |                                           |
 +-----------------------------+---------------------------------+----------------------------------+-------------------------------------------+

recipe/meta.yaml

@@ -1,5 +1,5 @@
 {% set name = "snowflake-snowpark-python" %}
-{% set version = "1.22.0" %}
+{% set version = "1.22.1" %}
 
 package:
   name: {{ name|lower }}

src/snowflake/snowpark/_internal/compiler/plan_compiler.py

@@ -3,21 +3,31 @@
 #
 
 import copy
+import time
 from typing import Dict, List
 
+from snowflake.snowpark._internal.analyzer.query_plan_analysis_utils import (
+    get_complexity_score,
+)
 from snowflake.snowpark._internal.analyzer.snowflake_plan import (
     PlanQueryType,
     Query,
     SnowflakePlan,
 )
 from snowflake.snowpark._internal.analyzer.snowflake_plan_node import LogicalPlan
 from snowflake.snowpark._internal.compiler.large_query_breakdown import (
+    COMPLEXITY_SCORE_LOWER_BOUND,
+    COMPLEXITY_SCORE_UPPER_BOUND,
     LargeQueryBreakdown,
 )
 from snowflake.snowpark._internal.compiler.repeated_subquery_elimination import (
     RepeatedSubqueryElimination,
 )
+from snowflake.snowpark._internal.compiler.telemetry_constants import (
+    CompilationStageTelemetryField,
+)
 from snowflake.snowpark._internal.compiler.utils import create_query_generator
+from snowflake.snowpark._internal.telemetry import TelemetryField
 from snowflake.snowpark.mock._connection import MockServerConnection
 
 
@@ -68,24 +78,74 @@ def compile(self) -> Dict[PlanQueryType, List[Query]]:
         if self.should_start_query_compilation():
             # preparation for compilation
             # 1. make a copy of the original plan
+            start_time = time.time()
+            complexity_score_before_compilation = get_complexity_score(
+                self._plan.cumulative_node_complexity
+            )
             logical_plans: List[LogicalPlan] = [copy.deepcopy(self._plan)]
+            deep_copy_end_time = time.time()
+
             # 2. create a code generator with the original plan
             query_generator = create_query_generator(self._plan)
 
-            # apply each optimizations if needed
+            # 3. apply each optimizations if needed
+            # CTE optimization
+            cte_start_time = time.time()
             if self._plan.session.cte_optimization_enabled:
                 repeated_subquery_eliminator = RepeatedSubqueryElimination(
                     logical_plans, query_generator
                 )
                 logical_plans = repeated_subquery_eliminator.apply()
+
+            cte_end_time = time.time()
+            complexity_scores_after_cte = [
+                get_complexity_score(logical_plan.cumulative_node_complexity)
+                for logical_plan in logical_plans
+            ]
+
+            # Large query breakdown
             if self._plan.session.large_query_breakdown_enabled:
                 large_query_breakdown = LargeQueryBreakdown(
                     self._plan.session, query_generator, logical_plans
                 )
                 logical_plans = large_query_breakdown.apply()
 
-            # do a final pass of code generation
-            return query_generator.generate_queries(logical_plans)
+            large_query_breakdown_end_time = time.time()
+            complexity_scores_after_large_query_breakdown = [
+                get_complexity_score(logical_plan.cumulative_node_complexity)
+                for logical_plan in logical_plans
+            ]
+
+            # 4. do a final pass of code generation
+            queries = query_generator.generate_queries(logical_plans)
+
+            # log telemetry data
+            deep_copy_time = deep_copy_end_time - start_time
+            cte_time = cte_end_time - cte_start_time
+            large_query_breakdown_time = large_query_breakdown_end_time - cte_end_time
+            total_time = time.time() - start_time
+            session = self._plan.session
+            summary_value = {
+                TelemetryField.CTE_OPTIMIZATION_ENABLED.value: session.cte_optimization_enabled,
+                TelemetryField.LARGE_QUERY_BREAKDOWN_ENABLED.value: session.large_query_breakdown_enabled,
+                CompilationStageTelemetryField.COMPLEXITY_SCORE_BOUNDS.value: (
+                    COMPLEXITY_SCORE_LOWER_BOUND,
+                    COMPLEXITY_SCORE_UPPER_BOUND,
+                ),
+                CompilationStageTelemetryField.TIME_TAKEN_FOR_COMPILATION.value: total_time,
+                CompilationStageTelemetryField.TIME_TAKEN_FOR_DEEP_COPY_PLAN.value: deep_copy_time,
+                CompilationStageTelemetryField.TIME_TAKEN_FOR_CTE_OPTIMIZATION.value: cte_time,
+                CompilationStageTelemetryField.TIME_TAKEN_FOR_LARGE_QUERY_BREAKDOWN.value: large_query_breakdown_time,
+                CompilationStageTelemetryField.COMPLEXITY_SCORE_BEFORE_COMPILATION.value: complexity_score_before_compilation,
+                CompilationStageTelemetryField.COMPLEXITY_SCORE_AFTER_CTE_OPTIMIZATION.value: complexity_scores_after_cte,
+                CompilationStageTelemetryField.COMPLEXITY_SCORE_AFTER_LARGE_QUERY_BREAKDOWN.value: complexity_scores_after_large_query_breakdown,
+            }
+            session._conn._telemetry_client.send_query_compilation_summary_telemetry(
+                session_id=session.session_id,
+                plan_uuid=self._plan.uuid,
+                compilation_stage_summary=summary_value,
+            )
+            return queries
         else:
             final_plan = self._plan
             if self._plan.session.cte_optimization_enabled:

src/snowflake/snowpark/_internal/compiler/telemetry_constants.py

@@ -6,10 +6,25 @@
 
 
 class CompilationStageTelemetryField(Enum):
+    # types
     TYPE_LARGE_QUERY_BREAKDOWN_OPTIMIZATION_SKIPPED = (
         "snowpark_large_query_breakdown_optimization_skipped"
     )
+    TYPE_COMPILATION_STAGE_STATISTICS = "snowpark_compilation_stage_statistics"
+
+    # keys
     KEY_REASON = "reason"
+    PLAN_UUID = "plan_uuid"
+    TIME_TAKEN_FOR_COMPILATION = "time_taken_for_compilation_sec"
+    TIME_TAKEN_FOR_DEEP_COPY_PLAN = "time_taken_for_deep_copy_plan_sec"
+    TIME_TAKEN_FOR_CTE_OPTIMIZATION = "time_taken_for_cte_optimization_sec"
+    TIME_TAKEN_FOR_LARGE_QUERY_BREAKDOWN = "time_taken_for_large_query_breakdown_sec"
+    COMPLEXITY_SCORE_BOUNDS = "complexity_score_bounds"
+    COMPLEXITY_SCORE_BEFORE_COMPILATION = "complexity_score_before_compilation"
+    COMPLEXITY_SCORE_AFTER_CTE_OPTIMIZATION = "complexity_score_after_cte_optimization"
+    COMPLEXITY_SCORE_AFTER_LARGE_QUERY_BREAKDOWN = (
+        "complexity_score_after_large_query_breakdown"
+    )
 
 
 class SkipLargeQueryBreakdownCategory(Enum):

src/snowflake/snowpark/_internal/telemetry.py

@@ -168,6 +168,11 @@ def wrap(*args, **kwargs):
         ]._session.sql_simplifier_enabled
         try:
             api_calls[0][TelemetryField.QUERY_PLAN_HEIGHT.value] = plan.plan_height
+            # The uuid for df._select_statement can be different from df._plan. Since plan
+            # can take both values, we cannot use plan.uuid. We always use df._plan.uuid
+            # to track the queries.
+            uuid = args[0]._plan.uuid
+            api_calls[0][CompilationStageTelemetryField.PLAN_UUID.value] = uuid
             api_calls[0][
                 TelemetryField.QUERY_PLAN_NUM_DUPLICATE_NODES.value
             ] = plan.num_duplicate_nodes
@@ -428,6 +433,24 @@ def send_large_query_breakdown_telemetry(
         }
         self.send(message)
 
+    def send_query_compilation_summary_telemetry(
+        self,
+        session_id: int,
+        plan_uuid: str,
+        compilation_stage_summary: Dict[str, Any],
+    ) -> None:
+        message = {
+            **self._create_basic_telemetry_data(
+                CompilationStageTelemetryField.TYPE_COMPILATION_STAGE_STATISTICS.value
+            ),
+            TelemetryField.KEY_DATA.value: {
+                TelemetryField.SESSION_ID.value: session_id,
+                CompilationStageTelemetryField.PLAN_UUID.value: plan_uuid,
+                **compilation_stage_summary,
+            },
+        }
+        self.send(message)
+
     def send_large_query_optimization_skipped_telemetry(
         self, session_id: int, reason: str
     ) -> None:

src/snowflake/snowpark/modin/pandas/general.py

@@ -1047,7 +1047,7 @@ def unique(values) -> np.ndarray:
 
     >>> pd.unique([pd.Timestamp('2016-01-01', tz='US/Eastern')
     ...            for _ in range(3)])
-    array([Timestamp('2015-12-31 21:00:00-0800', tz='America/Los_Angeles')],
+    array([Timestamp('2016-01-01 00:00:00-0500', tz='UTC-05:00')],
           dtype=object)
 
     >>> pd.unique([("a", "b"), ("b", "a"), ("a", "c"), ("b", "a")])
@@ -1750,35 +1750,35 @@ def to_datetime(
     DatetimeIndex(['2018-10-26 12:00:00', '2018-10-26 13:00:15'], dtype='datetime64[ns]', freq=None)
 
     >>> pd.to_datetime(['2018-10-26 12:00:00 -0500', '2018-10-26 13:00:00 -0500'])
-    DatetimeIndex(['2018-10-26 10:00:00-07:00', '2018-10-26 11:00:00-07:00'], dtype='datetime64[ns, America/Los_Angeles]', freq=None)
+    DatetimeIndex(['2018-10-26 12:00:00-05:00', '2018-10-26 13:00:00-05:00'], dtype='datetime64[ns, UTC-05:00]', freq=None)
 
     - Use right format to convert to timezone-aware type (Note that when call Snowpark
       pandas API to_pandas() the timezone-aware output will always be converted to session timezone):
 
     >>> pd.to_datetime(['2018-10-26 12:00:00 -0500', '2018-10-26 13:00:00 -0500'], format="%Y-%m-%d %H:%M:%S %z")
-    DatetimeIndex(['2018-10-26 10:00:00-07:00', '2018-10-26 11:00:00-07:00'], dtype='datetime64[ns, America/Los_Angeles]', freq=None)
+    DatetimeIndex(['2018-10-26 12:00:00-05:00', '2018-10-26 13:00:00-05:00'], dtype='datetime64[ns, UTC-05:00]', freq=None)
 
     - Timezone-aware inputs *with mixed time offsets* (for example
       issued from a timezone with daylight savings, such as Europe/Paris):
 
     >>> pd.to_datetime(['2020-10-25 02:00:00 +0200', '2020-10-25 04:00:00 +0100'])
-    DatetimeIndex(['2020-10-24 17:00:00-07:00', '2020-10-24 20:00:00-07:00'], dtype='datetime64[ns, America/Los_Angeles]', freq=None)
+    DatetimeIndex([2020-10-25 02:00:00+02:00, 2020-10-25 04:00:00+01:00], dtype='object', freq=None)
 
     >>> pd.to_datetime(['2020-10-25 02:00:00 +0200', '2020-10-25 04:00:00 +0100'], format="%Y-%m-%d %H:%M:%S %z")
-    DatetimeIndex(['2020-10-24 17:00:00-07:00', '2020-10-24 20:00:00-07:00'], dtype='datetime64[ns, America/Los_Angeles]', freq=None)
+    DatetimeIndex([2020-10-25 02:00:00+02:00, 2020-10-25 04:00:00+01:00], dtype='object', freq=None)
 
     Setting ``utc=True`` makes sure always convert to timezone-aware outputs:
 
     - Timezone-naive inputs are *localized* based on the session timezone
 
     >>> pd.to_datetime(['2018-10-26 12:00', '2018-10-26 13:00'], utc=True)
-    DatetimeIndex(['2018-10-26 05:00:00-07:00', '2018-10-26 06:00:00-07:00'], dtype='datetime64[ns, America/Los_Angeles]', freq=None)
+    DatetimeIndex(['2018-10-26 12:00:00+00:00', '2018-10-26 13:00:00+00:00'], dtype='datetime64[ns, UTC]', freq=None)
 
     - Timezone-aware inputs are *converted* to session timezone
 
     >>> pd.to_datetime(['2018-10-26 12:00:00 -0530', '2018-10-26 12:00:00 -0500'],
     ...                utc=True)
-    DatetimeIndex(['2018-10-26 10:30:00-07:00', '2018-10-26 10:00:00-07:00'], dtype='datetime64[ns, America/Los_Angeles]', freq=None)
+    DatetimeIndex(['2018-10-26 17:30:00+00:00', '2018-10-26 17:00:00+00:00'], dtype='datetime64[ns, UTC]', freq=None)
     """
     # TODO: SNOW-1063345: Modin upgrade - modin.pandas functions in general.py
     raise_if_native_pandas_objects(arg)