Add workflow for reading CSV from s3, cleaning, saving to Parquet #738
There are no files selected for viewing
This file contains hidden or bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
This file contains hidden or bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
This file contains hidden or bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,142 @@ | ||
| import os | ||
| import uuid | ||
|
|
||
| import coiled | ||
| import dask.dataframe as dd | ||
| import pytest | ||
| from distributed import Client, LocalCluster, wait | ||
|
|
||
|
|
||
| @pytest.fixture(scope="module") | ||
| def from_csv_to_parquet_cluster( | ||
| dask_env_variables, | ||
| cluster_kwargs, | ||
| github_cluster_tags, | ||
| ): | ||
| with coiled.Cluster( | ||
| f"from-csv-to-parquet-{uuid.uuid4().hex[:8]}", | ||
| environ=dask_env_variables, | ||
| tags=github_cluster_tags, | ||
| **cluster_kwargs["from_csv_to_parquet_cluster"], | ||
| ) as cluster: | ||
| yield cluster | ||
|
|
||
|
|
||
| @pytest.fixture | ||
| def from_csv_to_parquet_client( | ||
| from_csv_to_parquet_cluster, | ||
| cluster_kwargs, | ||
| upload_cluster_dump, | ||
| benchmark_all, | ||
| ): | ||
| n_workers = cluster_kwargs["from_csv_to_parquet_cluster"]["n_workers"] | ||
| with Client(from_csv_to_parquet_cluster) as client: | ||
| from_csv_to_parquet_cluster.scale(n_workers) | ||
| client.wait_for_workers(n_workers) | ||
| client.restart() | ||
| with upload_cluster_dump(client), benchmark_all(client): | ||
| yield client | ||
|
|
||
|
|
||
| COLUMNSV1 = { | ||
| "GlobalEventID": "Int64", | ||
| "Day": "Int64", | ||
| "MonthYear": "Int64", | ||
| "Year": "Int64", | ||
| "FractionDate": "Float64", | ||
| "Actor1Code": "string[pyarrow]", | ||
| "Actor1Name": "string[pyarrow]", | ||
| "Actor1CountryCode": "string[pyarrow]", | ||
| "Actor1KnownGroupCode": "string[pyarrow]", | ||
| "Actor1EthnicCode": "string[pyarrow]", | ||
| "Actor1Religion1Code": "string[pyarrow]", | ||
| "Actor1Religion2Code": "string[pyarrow]", | ||
| "Actor1Type1Code": "string[pyarrow]", | ||
| "Actor1Type2Code": "string[pyarrow]", | ||
| "Actor1Type3Code": "string[pyarrow]", | ||
| "Actor2Code": "string[pyarrow]", | ||
| "Actor2Name": "string[pyarrow]", | ||
| "Actor2CountryCode": "string[pyarrow]", | ||
| "Actor2KnownGroupCode": "string[pyarrow]", | ||
| "Actor2EthnicCode": "string[pyarrow]", | ||
| "Actor2Religion1Code": "string[pyarrow]", | ||
| "Actor2Religion2Code": "string[pyarrow]", | ||
| "Actor2Type1Code": "string[pyarrow]", | ||
| "Actor2Type2Code": "string[pyarrow]", | ||
| "Actor2Type3Code": "string[pyarrow]", | ||
| "IsRootEvent": "Int64", | ||
| "EventCode": "string[pyarrow]", | ||
| "EventBaseCode": "string[pyarrow]", | ||
| "EventRootCode": "string[pyarrow]", | ||
| "QuadClass": "Int64", | ||
| "GoldsteinScale": "Float64", | ||
| "NumMentions": "Int64", | ||
| "NumSources": "Int64", | ||
| "NumArticles": "Int64", | ||
| "AvgTone": "Float64", | ||
| "Actor1Geo_Type": "Int64", | ||
| "Actor1Geo_Fullname": "string[pyarrow]", | ||
| "Actor1Geo_CountryCode": "string[pyarrow]", | ||
| "Actor1Geo_ADM1Code": "string[pyarrow]", | ||
| "Actor1Geo_Lat": "Float64", | ||
| "Actor1Geo_Long": "Float64", | ||
| "Actor1Geo_FeatureID": "string[pyarrow]", | ||
| "Actor2Geo_Type": "Int64", | ||
| "Actor2Geo_Fullname": "string[pyarrow]", | ||
| "Actor2Geo_CountryCode": "string[pyarrow]", | ||
| "Actor2Geo_ADM1Code": "string[pyarrow]", | ||
| "Actor2Geo_Lat": "Float64", | ||
| "Actor2Geo_Long": "Float64", | ||
| "Actor2Geo_FeatureID": "string[pyarrow]", | ||
| "ActionGeo_Type": "Int64", | ||
| "ActionGeo_Fullname": "string[pyarrow]", | ||
| "ActionGeo_CountryCode": "string[pyarrow]", | ||
| "ActionGeo_ADM1Code": "string[pyarrow]", | ||
| "ActionGeo_Lat": "Float64", | ||
| "ActionGeo_Long": "Float64", | ||
| "ActionGeo_FeatureID": "string[pyarrow]", | ||
| "DATEADDED": "Int64", | ||
| "SOURCEURL": "string[pyarrow]", | ||
| } | ||
|
|
||
|
|
||
| def test_from_csv_to_parquet(from_csv_to_parquet_client, s3_factory, s3_url): | ||
| s3 = s3_factory(anon=True) | ||
|
There was a problem hiding this comment. Just checking -- is There was a problem hiding this comment. Not sure, I'll give it a test There was a problem hiding this comment. Looks like it is necessary |
||
|
|
||
| bad_files = [ | ||
| "gdelt-open-data/events/20161004.export.csv", | ||
| "gdelt-open-data/events/20170106.export.csv", | ||
| "gdelt-open-data/events/20170422.export.csv", | ||
| "gdelt-open-data/events/20170802.export.csv", | ||
| "gdelt-open-data/events/20170920.export.csv", | ||
| "gdelt-open-data/events/20171021.export.csv", | ||
| "gdelt-open-data/events/20180415.export.csv", | ||
| "gdelt-open-data/events/20180416.export.csv", | ||
| "gdelt-open-data/events/20180613.export.csv", | ||
| "gdelt-open-data/events/20180806.export.csv", | ||
| "gdelt-open-data/events/20190217.export.csv", | ||
| "gdelt-open-data/events/20190613.export.csv", | ||
| ] | ||
| files = s3.ls("s3://gdelt-open-data/events/")[120:] | ||
| files = [f"s3://{f}" for f in files if f not in bad_files] | ||
|
|
||
| df = dd.read_csv( | ||
| files, | ||
| names=COLUMNSV1.keys(), | ||
| sep="\t", | ||
| dtype=COLUMNSV1, | ||
| storage_options=s3.storage_options, | ||
| on_bad_lines="skip", | ||
| ) | ||
|
|
||
| df = df.map_partitions( | ||
| lambda xdf: xdf.drop_duplicates(subset=["SOURCEURL"], keep="first") | ||
| ) | ||
| df["national_paper"] = df.SOURCEURL.str.contains( | ||
| "washingtonpost|nytimes", regex=True | ||
| ) | ||
| df = df[df["national_paper"]] | ||
|
|
||
| output = s3_url + "/from-csv-to-parquet/" | ||
|
|
||
| df.to_parquet(output) | ||
Add this suggestion to a batch that can be applied as a single commit.
This suggestion is invalid because no changes were made to the code.
Suggestions cannot be applied while the pull request is closed.
Suggestions cannot be applied while viewing a subset of changes.
Only one suggestion per line can be applied in a batch.
Add this suggestion to a batch that can be applied as a single commit.
Applying suggestions on deleted lines is not supported.
You must change the existing code in this line in order to create a valid suggestion.
Outdated suggestions cannot be applied.
This suggestion has been applied or marked resolved.
Suggestions cannot be applied from pending reviews.
Suggestions cannot be applied on multi-line comments.
Suggestions cannot be applied while the pull request is queued to merge.
Suggestion cannot be applied right now. Please check back later.
Uh oh!
There was an error while loading. Please reload this page.