Dataflow requires minuemiun 4 minutes time for res...

askreddii · 02-04-2024 03:57 PM

Hi,

When we submit a new job in Dataflow, the service automatically allocates the necessary infrastructure, including worker instances, to execute the specific job for my project production runs. but we are facing issues with the dataflow provision time. it currently takes 4 minutes for the resource provision and 1 minute for the processing job. Not sure, how can we reduce the dataflow resource allocation time here ? I'm expecting the data processing job and worker nodes provision should be completed in 1 minute time. would it be possible in Google Dataflow?? thanks

Regards,

Reddy

ms4446

Achieving a provisioning time under one minute for Dataflow jobs is challenging due to the complexity of cloud resource allocation. However, by employing strategic optimizations, you can potentially reduce startup times. Here are refined strategies and key considerations:

1. Refine Job Configuration

Machine Types: Opt for smaller or custom machine types that match your job's requirements closely to streamline provisioning.
Dependencies and Startup Scripts: Simplify dependencies and ensure startup scripts are optimized for speed to decrease worker initialization times.

2. Implement Pre-Provisioning Techniques

Continuous Jobs: While Dataflow does not offer a dedicated "warm pool" feature, running continuous low-volume jobs or periodic dummy jobs can help maintain a pool of semi-warm workers, potentially reducing startup times for subsequent jobs.
Flexible Resource Scheduling (FlexRS): For batch jobs, FlexRS can utilize pre-provisioned resources for cost-effective provisioning, which may also contribute to reduced startup latency.

3. Optimize Pipeline Design

Simplify Transformations: Streamline your pipeline's structure to minimize the time Dataflow spends on parsing and validation.
Use Templates: Deploying Dataflow templates for repetitive jobs can skip some initial setup steps, leading to quicker starts.

4. Enhance Regional Resource Allocation

Ensure Data Locality: Positioning Dataflow jobs in the same region as your data sources and other Google Cloud services can significantly reduce network latency and improve provisioning efficiency.

5. Utilize Custom Worker Images

Pre-installed Dependencies: Crafting custom worker images with all necessary dependencies pre-installed can expedite worker startup, shaving off valuable seconds from the provisioning process.

6. Consider Alternative Google Cloud Services for Small Tasks

Cloud Functions and Cloud Run: For tasks that are very small or require rapid execution, Cloud Functions or Cloud Run might offer quicker startup times, though they come with their own set of limitations for data processing tasks.

7. Engage in Continuous Monitoring and Adaptation

Analyze and Adjust: Regularly review job performance metrics to identify bottlenecks and areas for optimization.
Collaborate with Google Cloud Support: Leverage insights from Google Cloud Support and stay informed about new features or optimizations that could improve provisioning times.

Important Considerations

Realistic Expectations: Given the inherent complexities of cloud resource provisioning, consistently achieving startup times under one minute for every job may not be feasible. Setting realistic expectations based on job size and complexity is crucial.
Cost vs. Speed Trade-offs: Faster provisioning can lead to higher costs, especially if maintaining a semi-warm environment. Carefully balance your need for speed against budget constraints.
Stay Informed: Cloud technologies evolve rapidly. Staying updated on Google Cloud's latest offerings and best practices is essential for optimizing Dataflow jobs over time.

askreddii

Hi,

Thank you for outlining the strategies to enhance Dataflow performance and minimize startup delays. After reviewing my project's framework, I find that recommendations 1, 2, and 5 align well with our needs. I'm eager to apply these insights and will share the outcomes and experiences here.

Please share if you have any implementation links for Utilize Custom Worker Images and Implement Pre-Provisioning Techniques, thanks in advance.

Regards,

Suresh

ms4446

Custom worker images allow you to pre-install dependencies and tailor the execution environment for your Dataflow workers, leading to faster startup times and improved efficiency.

Base Image Selection: Start with a Dataflow worker base image from Google Cloud's selection. These images come with the Dataflow SDK and essential dependencies.
Creating a Dockerfile: Extend your chosen base image by adding required dependencies. Here's an example for Dataflow templates using the python3-template-launcher-base:

FROM gcr.io/dataflow-templates-base/python3-template-launcher-base 

# Install custom dependencies
RUN pip install my-custom-package==1.0.0

Compatibility Note: Ensure the base image's Dataflow SDK version matches your code.
Building and Pushing the Image: Build your custom Docker image and push it to Google Container Registry (GCR):

docker build -t gcr.io/[YOUR_PROJECT_ID]/dataflow-worker:latest . 
docker push gcr.io/[YOUR_PROJECT_ID]/dataflow-worker:latest

Specifying the Custom Image in Your Dataflow Job: Use the --workerHarnessContainerImage parameter to use your custom image. Experiment with standard and custom machine types (--worker-machine-type) for optimal performance and cost.

II. Pre-Provisioning Techniques

Minimize worker startup delays with these strategies:

Continuous Low-Volume Job: Maintain a pool of workers by running a small Dataflow job continuously.
Periodic Warm-up Jobs: Use Cloud Scheduler to run short Dataflow jobs at intervals, keeping the environment ready.

III. Important Considerations

Costs: Continuous or periodic worker usage incurs costs. Weigh the benefits against your budget.
Workload Suitability: Pre-provisioning is best for unpredictable workloads requiring rapid scaling.

IV. Additional Considerations

Autoscaling: Combine these techniques with Dataflow's autoscaling for dynamic resource management.
Monitoring: Track provisioning times, costs, and worker usage with Google Cloud's monitoring tools.
Experimentation: Find the best setup for your needs through testing.

Resources

Google Cloud Dataflow Documentation: https://cloud.google.com/dataflow
Docker Documentation: https://docs.docker.com/
Google Container Registry: https://cloud.google.com/container-registry
Dataflow Pricing: https://cloud.google.com/dataflow/pricing

askreddii

Hello ms4446,

I tried 1 & 5 options with multiple parameters but I could not see any improvements in the worker nodes startup time. Taking a minimum of 4 minutes time if I tested with the least machine type configuration, disksize and custom image options . Referred multiple documents for dataflow resource provision time reduction settings but seems like a minimum of 4 minutes is required for resource allocation.

Sharing code & log screenshots for more information :

python3 -m apache_beam.examples.wordcount \
--region us-central1 \
--input gs://dataflow-samples/shakespeare/kinglear.txt \
--output gs://dataflow-apache-quickstart_dbt-analytics-engineer-403622/results/output \
--runner DataflowRunner \
--project dbt-analytics-engineer-403622 \
--temp_location gs://dataflow-apache-quickstart_dbt-analytics-engineer-403622/temp/ \
--workerHarnessContainerImage gcr.io/dbt-analytics-engineer-403622/dataflow_test_custom_image:latest \
--worker_machine_type n1-standard-1 \
--num_workers 1 \
--diskSizeGb=30 \
--additional-experiments=enable_streaming_engine,use_runner_v2 \
--autoscaling_algorithm THROUGHPUT_BASED

Btw am looking for batch-type solutions instead of streaming service to improve the dataflow resource provision time, I appreciate it if you could provide any helpful suggestions , thank you

Regards,

Suresh R

ms4446

Thanks for sharing the detailed code and logs! Please note the following:

Cloud worker provisioning always includes overheads like resource distribution, disk operations, and code loading. Sub-minute provisioning might not be consistently achievable due to these steps.

Data distribution across regions, input file sizes, and network conditions between GCS and compute instances can add delays.

The 4-MinuteConsistency might suggests a potential bottleneck in the provisioning process that optimizing images or pre-provisioning might not directly address.

Here are some debugging and tuning options you might take

Profiling:
- Cloud Logging: Add structured logging to measure time taken for tasks like file downloads and code distribution.
- GCP Stackdriver/Profiler: For granular breakdowns of execution, revealing potential optimization areas.
Input Data Optimization:
- Location: Match input data region (like gs://dataflow-samples/shakespeare/kinglear.txt in us-central1) with your Dataflow job's region.
- Splitting Larger Files: Pre-splitting large files facilitates parallel processing and might reduce overall startup time.

Experimentation:

Smaller Disk: Experiment with a smaller disk size (e.g., 10GB) to potentially minimize setup overheads.
Custom Machine Types (Cautiously):Explore slightly "over-provisioned" custom machine types for faster setup, but balance this against cost.

Batch-Specific Insights:

FlexRS Research: Investigate if its cost-focus indirectly benefits startup for batch jobs with careful scheduling.
Dataflow Templates: For repeatable jobs,templates can pre-process some steps,potentially leading to faster startup.

Additional Considerations:

Your image customization and warmup jobs are valid strategies for longer-running or frequent workflows.

Further Questions (To Aid in Assisting You):

Could you describe your typical input data size and the complexity of your Dataflow pipeline?
Have you profiled your job to identify specific bottlenecks?
Would you consider using Cloud Dataflow's FlexRS autoscaling feature (optimized for cost rather than speed)?
Are you open to using Dataflow templates for repeatable jobs to potentially improve startup?

askreddii

Thanks again for your prompt response. Pls find below inline comments for your queries.

Could you describe your typical input data size and the complexity of your Dataflow pipeline? - Very small , less than 1000 records , 1 MB-5MB
Have you profiled your job to identify specific bottlenecks? - Cleansed data, no data profiling or transformation required.
Would you consider using Cloud Dataflow's FlexRS autoscaling feature (optimized for cost rather than speed)? -- we are interested for resource quick provision instead of cost
Are you open to using Dataflow templates for repeatable jobs to potentially improve startup? - No, Dataflow templates are usually among data services on GCP. it will not support our custom or business requirements.
I tried again with above suggested options but no luck. would you mind if you can share any dataflow batch job with resource provision completed within 4 minutes screenshot?

I tried to capture resource provision time and execution time using Python code but I did not get the right direction to move forward on this issue, thank you.

log details :
python log.py Job submission started at: 2024-02-13 00:44:00.330710 Command Output (STDOUT): Error Output (STDERR):
INFO:apache_beam.internal.gcp.auth:Setting socket default timeout to 60 seconds.
INFO:apache_beam.internal.gcp.auth:socket default timeout is 60.0 seconds. WARNING:google.auth._default:No project ID could be determined.
Consider running `gcloud config set project` or setting the GOOGLE_CLOUD_PROJECT environment variable
INFO:apache_beam.runners.dataflow.dataflow_runner:Pipeline has additional dependencies to be installed in SDK worker container, consider using the SDK container image pre-building workflow to avoid repetitive installations.
Learn more on https://cloud.google.com/dataflow/docs/guides/using-custom-containers#prebuild
INFO:root:Using provided Python SDK container image: gcr.io/cloud-dataflow/v1beta3/beam_python3.9_sdk:2.53.0
INFO:root:Python SDK container image set to "gcr.io/cloud-dataflow/v1beta3/beam_python3.9_sdk:2.53.0" for Docker environment
INFO:apache_beam.runners.portability.fn_api_runner.translations:==================== <function pack_combiners at 0x7be15ad218b0> ==================== INFO:apache_beam.runners.portability.fn_api_runner.translations:==================== <function sort_stages at 0x7be15ad220d0> ====================
INFO:apache_beam.runners.dataflow.internal.apiclient:Starting GCS upload to gs://dataflow-apache-quickstart_dbt-analytics-engineer-403622/temp/beamapp-sureshreddyravada-0213004402-351769-8jbgnkn0.1707785042.351916/pickled_main_session...
INFO:apache_beam.runners.dataflow.internal.apiclient:Completed GCS upload to gs://dataflow-apache-quickstart_dbt-analytics-engineer-403622/temp/beamapp-sureshreddyravada-0213004402-351769-8jbgnkn0.1707785042.351916/pickled_main_session in 0 seconds.
INFO:apache_beam.runners.dataflow.internal.apiclient:Starting GCS upload to gs://dataflow-apache-quickstart_dbt-analytics-engineer-403622/temp/beamapp-sureshreddyravada-0213004402-351769-8jbgnkn0.1707785042.351916/pipeline.pb...
INFO:apache_beam.runners.dataflow.internal.apiclient:Completed GCS upload to gs://dataflow-apache-quickstart_dbt-analytics-engineer-403622/temp/beamapp-sureshreddyravada-0213004402-351769-8jbgnkn0.1707785042.351916/pipeline.pb in 0 seconds.
WARNING:apache_beam.options.pipeline_options:Unknown pipeline options received: --diskSizeGb,4,--usePublicIps,false,--additional-experiments,use_runner_v2. Ignore if flags are used for internal purposes.
WARNING:apache_beam.options.pipeline_options:Unknown pipeline options received: --diskSizeGb,4,--usePublicIps,false,--additional-experiments,use_runner_v2. Ignore if flags are used for internal purposes.
INFO:apache_beam.runners.dataflow.internal.apiclient:Create job: <Job clientRequestId: '20240213004402352822-2322' createTime: '2024-02-13T00:44:04.627360Z' currentStateTime: '1970-01-01T00:00:00Z' id: '2024-02-12_16_44_03-17537104544529152717' location: 'europe-west1' name: 'beamapp-sureshreddyravada-0213004402-351769-8jbgnkn0' projectId: 'dbt-analytics-engineer-403622' stageStates: [] startTime: '2024-02-13T00:44:04.627360Z' steps: [] tempFiles: [] type: TypeValueValuesEnum(JOB_TYPE_BATCH, 1)> INFO:apache_beam.runners.dataflow.internal.apiclient:Created job with id: [2024-02-12_16_44_03-17537104544529152717]
INFO:apache_beam.runners.dataflow.internal.apiclient:Submitted job: 2024-02-12_16_44_03-17537104544529152717
INFO:apache_beam.runners.dataflow.internal.apiclient:To access the Dataflow monitoring console, please navigate to https://console.cloud.google.com/dataflow/jobs/europe-west1/2024-02-12_16_44_03-17537104544529152717... INFO:apache_beam.runners.dataflow.dataflow_runner:Job 2024-02-12_16_44_03-17537104544529152717 is in state JOB_STATE_PENDING INFO:apache_beam.runners.dataflow.dataflow_runner:2024-02-13T00:44:07.800Z: JOB_MESSAGE_BASIC: Worker configuration: n1-standard-1 in us-central1.
INFO:apache_beam.runners.dataflow.dataflow_runner:2024-02-13T00:44:09.396Z: JOB_MESSAGE_BASIC: Executing operation Read/Read/Impulse+Read/Read/EmitSource+ref_AppliedPTransform_Read-Read-SDFBoundedSourceReader-ParDo-SDFBoundedSourceDoFn-_7/PairWithRestriction+ref_AppliedPTransform_Read-Read-SDFBoundedSourceReader-ParDo-SDFBoundedSourceDoFn-_7/SplitWithSizing INFO:apache_beam.runners.dataflow.dataflow_runner:2024-02-13T00:44:09.416Z: JOB_MESSAGE_BASIC: Executing operation Write/Write/WriteImpl/DoOnce/Impulse+Write/Write/WriteImpl/DoOnce/FlatMap(<lambda at core.py:3774>)+Write/Write/WriteImpl/DoOnce/Map(decode)+Write/Write/WriteImpl/InitializeWrite INFO:apache_beam.runners.dataflow.dataflow_runner:2024-02-13T00:44:09.446Z: JOB_MESSAGE_BASIC: Starting 1 workers in europe-west1-d... INFO:apache_beam.runners.dataflow.dataflow_runner:Job 2024-02-12_16_44_03-17537104544529152717 is in state JOB_STATE_RUNNING INFO:apache_beam.runners.dataflow.dataflow_runner:2024-02-13T00:47:34.438Z: JOB_MESSAGE_BASIC: All workers have finished the startup processes and began to receive work requests. INFO:apache_beam.runners.dataflow.dataflow_runner:2024-02-13T00:47:40.169Z: JOB_MESSAGE_BASIC: Finished operation Read/Read/Impulse+Read/Read/EmitSource+ref_AppliedPTransform_Read-Read-SDFBoundedSourceReader-ParDo-SDFBoundedSourceDoFn-_7/PairWithRestriction+ref_AppliedPTransform_Read-Read-SDFBoundedSourceReader-ParDo-SDFBoundedSourceDoFn-_7/SplitWithSizing INFO:apache_beam.runners.dataflow.dataflow_runner:2024-02-13T00:47:40.251Z: JOB_MESSAGE_BASIC: Executing operation GroupAndSum/GroupByKey/Create INFO:apache_beam.runners.dataflow.dataflow_runner:2024-02-13T00:47:41.064Z: JOB_MESSAGE_BASIC: Finished operation GroupAndSum/GroupByKey/Create INFO:apache_beam.runners.dataflow.dataflow_runner:2024-02-13T00:47:41.142Z: JOB_MESSAGE_BASIC: Executing operation ref_AppliedPTransform_Read-Read-SDFBoundedSourceReader-ParDo-SDFBoundedSourceDoFn-_7/ProcessElementAndRestrictionWithSizing+Split+PairWithOne+GroupAndSum/GroupByKey+GroupAndSum/Combine/Partial+GroupAndSum/GroupByKey/Write INFO:apache_beam.runners.dataflow.dataflow_runner:2024-02-13T00:47:41.283Z: JOB_MESSAGE_BASIC: Finished operation Write/Write/WriteImpl/DoOnce/Impulse+Write/Write/WriteImpl/DoOnce/FlatMap(<lambda at core.py:3774>)+Write/Write/WriteImpl/DoOnce/Map(decode)+Write/Write/WriteImpl/InitializeWrite INFO:apache_beam.runners.dataflow.dataflow_runner:2024-02-13T00:47:41.380Z: JOB_MESSAGE_BASIC: Executing operation Write/Write/WriteImpl/WriteBundles/View-python_side_input0 INFO:apache_beam.runners.dataflow.dataflow_runner:2024-02-13T00:47:41.396Z: JOB_MESSAGE_BASIC: Executing operation Write/Write/WriteImpl/FinalizeWrite/View-python_side_input0 INFO:apache_beam.runners.dataflow.dataflow_runner:2024-02-13T00:47:41.413Z: JOB_MESSAGE_BASIC: Executing operation Write/Write/WriteImpl/PreFinalize/View-python_side_input0 INFO:apache_beam.runners.dataflow.dataflow_runner:2024-02-13T00:47:41.495Z: JOB_MESSAGE_BASIC: Finished operation Write/Write/WriteImpl/WriteBundles/View-python_side_input0 INFO:apache_beam.runners.dataflow.dataflow_runner:2024-02-13T00:47:41.514Z: JOB_MESSAGE_BASIC: Finished operation Write/Write/WriteImpl/FinalizeWrite/View-python_side_input0 INFO:apache_beam.runners.dataflow.dataflow_runner:2024-02-13T00:47:41.546Z: JOB_MESSAGE_BASIC: Finished operation Write/Write/WriteImpl/PreFinalize/View-python_side_input0 INFO:apache_beam.runners.dataflow.dataflow_runner:2024-02-13T00:47:45.271Z: JOB_MESSAGE_BASIC: Finished operation ref_AppliedPTransform_Read-Read-SDFBoundedSourceReader-ParDo-SDFBoundedSourceDoFn-_7/ProcessElementAndRestrictionWithSizing+Split+PairWithOne+GroupAndSum/GroupByKey+GroupAndSum/Combine/Partial+GroupAndSum/GroupByKey/Write INFO:apache_beam.runners.dataflow.dataflow_runner:2024-02-13T00:47:45.307Z: JOB_MESSAGE_BASIC: Executing operation GroupAndSum/GroupByKey/Close INFO:apache_beam.runners.dataflow.dataflow_runner:2024-02-13T00:47:45.984Z: JOB_MESSAGE_BASIC: Finished operation GroupAndSum/GroupByKey/Close INFO:apache_beam.runners.dataflow.dataflow_runner:2024-02-13T00:47:46.019Z: JOB_MESSAGE_BASIC: Executing operation Write/Write/WriteImpl/GroupByKey/Create INFO:apache_beam.runners.dataflow.dataflow_runner:2024-02-13T00:47:46.116Z: JOB_MESSAGE_BASIC: Finished operation Write/Write/WriteImpl/GroupByKey/Create INFO:apache_beam.runners.dataflow.dataflow_runner:2024-02-13T00:47:46.191Z: JOB_MESSAGE_BASIC: Executing operation GroupAndSum/GroupByKey/Read+GroupAndSum/Combine+GroupAndSum/Combine/Extract+Format+Write/Write/WriteImpl/WindowInto(WindowIntoFn)+Write/Write/WriteImpl/WriteBundles+Write/Write/WriteImpl/Pair+Write/Write/WriteImpl/GroupByKey/Write INFO:apache_beam.runners.dataflow.dataflow_runner:2024-02-13T00:47:48.312Z: JOB_MESSAGE_BASIC: Finished operation GroupAndSum/GroupByKey/Read+GroupAndSum/Combine+GroupAndSum/Combine/Extract+Format+Write/Write/WriteImpl/WindowInto(WindowIntoFn)+Write/Write/WriteImpl/WriteBundles+Write/Write/WriteImpl/Pair+Write/Write/WriteImpl/GroupByKey/Write INFO:apache_beam.runners.dataflow.dataflow_runner:2024-02-13T00:47:48.354Z: JOB_MESSAGE_BASIC: Executing operation Write/Write/WriteImpl/GroupByKey/Close INFO:apache_beam.runners.dataflow.dataflow_runner:2024-02-13T00:47:48.390Z: JOB_MESSAGE_BASIC: Finished operation Write/Write/WriteImpl/GroupByKey/Close INFO:apache_beam.runners.dataflow.dataflow_runner:2024-02-13T00:47:48.431Z: JOB_MESSAGE_BASIC: Executing operation Write/Write/WriteImpl/GroupByKey/Read+Write/Write/WriteImpl/Extract INFO:apache_beam.runners.dataflow.dataflow_runner:2024-02-13T00:47:52.440Z: JOB_MESSAGE_BASIC: Finished operation Write/Write/WriteImpl/GroupByKey/Read+Write/Write/WriteImpl/Extract INFO:apache_beam.runners.dataflow.dataflow_runner:2024-02-13T00:47:52.513Z: JOB_MESSAGE_BASIC: Executing operation Write/Write/WriteImpl/FinalizeWrite/View-python_side_input1 INFO:apache_beam.runners.dataflow.dataflow_runner:2024-02-13T00:47:52.534Z: JOB_MESSAGE_BASIC: Executing operation Write/Write/WriteImpl/PreFinalize/View-python_side_input1 INFO:apache_beam.runners.dataflow.dataflow_runner:2024-02-13T00:47:52.631Z: JOB_MESSAGE_BASIC: Finished operation Write/Write/WriteImpl/FinalizeWrite/View-python_side_input1 INFO:apache_beam.runners.dataflow.dataflow_runner:2024-02-13T00:47:52.654Z: JOB_MESSAGE_BASIC: Finished operation Write/Write/WriteImpl/PreFinalize/View-python_side_input1 INFO:apache_beam.runners.dataflow.dataflow_runner:2024-02-13T00:47:52.732Z: JOB_MESSAGE_BASIC: Executing operation Write/Write/WriteImpl/PreFinalize INFO:apache_beam.runners.dataflow.dataflow_runner:2024-02-13T00:48:00.454Z: JOB_MESSAGE_BASIC: Finished operation Write/Write/WriteImpl/PreFinalize INFO:apache_beam.runners.dataflow.dataflow_runner:2024-02-13T00:48:00.526Z: JOB_MESSAGE_BASIC: Executing operation Write/Write/WriteImpl/FinalizeWrite/View-python_side_input2 INFO:apache_beam.runners.dataflow.dataflow_runner:2024-02-13T00:48:00.660Z: JOB_MESSAGE_BASIC: Finished operation Write/Write/WriteImpl/FinalizeWrite/View-python_side_input2 INFO:apache_beam.runners.dataflow.dataflow_runner:2024-02-13T00:48:00.743Z: JOB_MESSAGE_BASIC: Executing operation Write/Write/WriteImpl/FinalizeWrite INFO:apache_beam.runners.dataflow.dataflow_runner:2024-02-13T00:48:05.971Z: JOB_MESSAGE_BASIC: Finished operation Write/Write/WriteImpl/FinalizeWrite INFO:apache_beam.runners.dataflow.dataflow_runner:2024-02-13T00:48:06.625Z: JOB_MESSAGE_BASIC: Stopping worker pool... INFO:apache_beam.runners.dataflow.dataflow_runner:2024-02-13T00:48:48.797Z: JOB_MESSAGE_BASIC: Worker pool stopped. INFO:apache_beam.runners.dataflow.dataflow_runner:Job 2024-02-12_16_44_03-17537104544529152717 is in state JOB_STATE_DONE Dataflow job ID not found. Failed to capture Dataflow job ID.

askreddii

Hi

Hope you are doing good! I hope you have time to look at the logs shared with you. would you mind if you could share any dataflow batch job with resource provision completed within a 4-minute screenshot? I tried with a single record placed in the script, but still, provision takes 4 minutes for provision and 30 seconds for execution. I tried with multiple configurations & parameters but no luck, thank you.

regards,

Suresh R

ms4446

This situation suggests that the provisioning time you're experiencing might be closely tied to the inherent startup and initialization processes of Dataflow, which are not easily bypassed even with optimizations for small datasets and streamlined pipelines.

Regarding capturing resource provision time and execution time directly within Python code, Dataflow's startup process involves several stages, including resource allocation, worker initialization, and job setup, which are managed by the Dataflow service itself. Directly measuring these stages from client-side Python code can be challenging since much of the provisioning process occurs within Google Cloud's managed services, outside the direct control of client-side scripts.

However, you can use Cloud Monitoring to gain insights into job performance and resource provisioning times. Cloud Monitoring provides detailed metrics and logs for Google Cloud services, including Dataflow. By setting up custom metrics or exploring existing Dataflow metrics in Cloud Monitoring, you might be able to track the time taken for various stages of your Dataflow jobs more closely.

For your specific case, considering the small size of your input data and the simplicity of your pipeline, it seems the minimum provisioning time you're encountering might be a baseline for Dataflow's batch jobs under the current system configurations and optimizations available. If reducing this startup time is critical for your use case, you might want to consider alternative approaches or services for processing such small datasets, especially if real-time or near-real-time processing is not a strict requirement.

Dataflow requires minuemiun 4 minutes time for resources provision for processing small data jobs