fix(script_mode): Use UTC timestamp for tar file attributes

[pingsutw]

Tracking issue

https://flyte-org.slack.com/archives/CP2HDHKE1/p1739795314554249

Why are the changes needed?

Failed to archive the file in the spark task

{"asctime": "2025-02-14 17:05:58,888", "name": "flytekit", "levelname": "ERROR", "message": "Trace:\n\n    Traceback (most recent call last):\n      File \"/databricks/python/lib/python3.10/site-packages/flytekit/bin/entrypoint.py\", line 179, in _dispatch_execute\n        outputs = task_def.dispatch_execute(ctx, idl_input_literals)\n      File \"/databricks/python/lib/python3.10/site-packages/flytekit/core/base_task.py\", line 728, in dispatch_execute\n        new_user_params = self.pre_execute(ctx.user_space_params)\n      File \"/databricks/python/lib/python3.10/site-packages/flytekitplugins/spark/task.py\", line 209, in pre_execute\n        shutil.make_archive(file_name, file_format, os.getcwd())\n      File \"/usr/lib/python3.10/shutil.py\", line 1124, in make_archive\n        filename = func(base_name, base_dir, **kwargs)\n      File \"/usr/lib/python3.10/shutil.py\", line 1009, in _make_zipfile\n        zf.write(path, arcname)\n      File \"/usr/lib/python3.10/zipfile.py\", line 1754, in write\n        zinfo = ZipInfo.from_file(filename, arcname,\n      File \"/usr/lib/python3.10/zipfile.py\", line 523, in from_file\n        zinfo = cls(arcname, date_time)\n      File \"/usr/lib/python3.10/zipfile.py\", line 366, in __init__\n        raise ValueError('ZIP does not support timestamps before 1980')\n    ValueError: ZIP does not support timestamps before 1980\n\nMessage:\n\n    ValueError: ZIP does not support timestamps before 1980"}
{"asctime": "2025-02-14 17:05:58,891", "name": "flytekit", "levelname": "ERROR", "message": "!! End Error Captured by Flyte !!"}

What changes were proposed in this pull request?

Set default timezone to UTC

How was this patch tested?

import datetime
import random
from operator import add
from flytekit import ImageSpec, Resources, task, workflow
import flytekit

from flytekitplugins.spark import Spark

new_flytekit = "git+https://github.com/flyteorg/flytekit.git@ddfae878eae76914c2199213c972b09d721ad6ce"
custom_image = ImageSpec(base_image="spark:3.5.3-python3", registry="ghcr.io/flyteorg", packages=[new_flytekit, "flytekitplugins-spark", "pyspark==3.5.2"], builder="default")


@task(
    task_config=Spark(
        spark_conf={
            "spark.driver.memory": "1000M",
            "spark.executor.memory": "1000M",
            "spark.executor.cores": "1",
            "spark.executor.instances": "2",
            "spark.driver.cores": "1",
            "spark.kubernetes.file.upload.path": "/opt/spark/work-dir",
            # "spark.jars": "https://storage.googleapis.com/hadoop-lib/gcs/gcs-connector-hadoop3-latest.jar",
            "spark.jars": "https://repo1.maven.org/maven2/org/apache/hadoop/hadoop-aws/3.2.2/hadoop-aws-3.2.2.jar,https://repo1.maven.org/maven2/com/amazonaws/aws-java-sdk-bundle/1.12.262/aws-java-sdk-bundle-1.12.262.jar"
        },
        # executor_path="/opt/micromamba/envs/runtime/bin/python",
        # applications_path="local:///opt/micromamba/envs/runtime/bin/entrypoint.py",
        executor_path="/usr/bin/python3",
        applications_path="local:///usr/bin/entrypoint.py"
    ),
    limits=Resources(mem="2000M"),
    container_image=ImageSpec(base_image="spark:3.5.2-python3", python_version="3.10", registry="ghcr.io/flyteorg", packages=["flytekitplugins-spark", "pyspark==3.5.2"], builder="envd"),
)
def hello_spark(partitions: int) -> float:
    session = flytekit.current_context().spark_session
    print("spark version", session.version)  # spark version 3.5.3
    print("Starting Spark with Partitions: {}".format(partitions))

    n = 1 * partitions
    sess = flytekit.current_context().spark_session
    count = sess.sparkContext.parallelize(range(1, n + 1), partitions).map(f).reduce(add)

    pi_val = 4.0 * count / n
    return pi_val


def f(_):
    x = random.random() * 2 - 1
    y = random.random() * 2 - 1
    return 1 if x**2 + y**2 <= 1 else 0


@task(
    cache_version="2",
    container_image=custom_image,
)
def print_every_time(value_to_print: float, date_triggered: datetime.datetime) -> int:
    print("My printed value: {} @ {}".format(value_to_print, date_triggered))
    return 1


@workflow
def my_spark(triggered_date: datetime.datetime = datetime.datetime.now()) -> float:
    """
    Using the workflow is still as any other workflow. As image is a property of the task, the workflow does not care
    about how the image is configured.
    """
    pi = hello_spark(partitions=1)
    print_every_time(value_to_print=pi, date_triggered=triggered_date)
    return pi

Setup process

Screenshots

Check all the applicable boxes

• I updated the documentation accordingly.
• All new and existing tests passed.
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Related PRs

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Docs link

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Summary by Bito

Fixed timestamp handling in script mode by implementing explicit UTC timezone setting for tar file attributes. This resolves ZIP file creation failures caused by timestamp inconsistencies. The solution ensures consistent handling of mtime attributes across different environments.

Unit tests added: False

Estimated effort to review (1-5, lower is better): 1

[flyte-bot]

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Review Details

• Files reviewed - 1 · Commit Range: ddfae87..ddfae87

  • flytekit/tools/script_mode.py
• Files skipped - 0
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Changelist by Bito

This pull request implements the following key changes.

Key Change	Files Impacted
Bug Fix - Fix Timestamp Handling in Script Mode	- script_mode.py - Added UTC timezone specification for tar file timestamp to prevent issues with ZIP file creation

[eapolinario]

Good catch. I hate timezone issues.