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48 lines
1.9 KiB
Markdown
48 lines
1.9 KiB
Markdown
# Running on DataFlow
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The pipeline runs as is on GCP DataFlow. The following documents how I deployed to my personal GCP account but the approach may vary depending on project/account in GCP.
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## Prerequisites
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### Cloud Storage
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- A Cloud Storage bucket with the following structure:
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```
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./input
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./output
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./tmp
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```
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- Place the input files into the `./input` directory in the bucket.
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### VPC
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To get around public IP quotas I created a VPC in the `europe-west1` region that has `Private Google Access` turned to `ON`.
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## Command
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!!! tip
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We need to choose a `worker_machine_type` with sufficient memory to run the pipeline. As the pipeline uses a mapping table, and DataFlow autoscales on CPU and not memory usage, we need a machine with more ram than usual to ensure sufficient memory when running on one worker. For `pp-2020.csv` the type `n1-highmem-2` with 2vCPU and 13GB of ram was chosen and completed successfully in ~10 minutes using only 1 worker.
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Assuming the `pp-2020.csv` file has been placed in the `./input` directory in the bucket you can run a command similar to:
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```bash
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python -m analyse_properties.main \
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--runner DataflowRunner \
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--project street-group \
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--region europe-west1 \
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--input gs://street-group-technical-test-dmot-euw1/input/pp-2020.csv \
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--output gs://street-group-technical-test-dmot-euw1/output/pp-2020 \
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--temp_location gs://street-group-technical-test-dmot-euw1/tmp \
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--subnetwork=https://www.googleapis.com/compute/v1/projects/street-group/regions/europe-west1/subnetworks/europe-west-1-dataflow \
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--no_use_public_ips \
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--worker_machine_type=n1-highmem-2
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```
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The output file from this pipeline is publically available and can be downloaded [here](https://storage.googleapis.com/street-group-technical-test-dmot-euw1/output/pp-2020-00000-of-00001.json).
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The job graph for this pipeline is displayed below:
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