forked from bellwether/minerva
added test files
This commit is contained in:
Ari Brown
parent
9f8b67b98f
commit
e3055333b3
9 changed files with 313 additions and 17 deletions
124
test/dask_test.py
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124
test/dask_test.py
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import sys
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import minerva
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from minerva.timing import Timing
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import dask
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import dask.dataframe as dd
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import time
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#dask.config.set({'distributed.worker.multiprocessing-method': 'fork'})
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import dask.distributed
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from dask.distributed import Client
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m = minerva.Minerva("hay")
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print(f"connecting to {sys.argv[1]}")
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client = Client(sys.argv[1])
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manifest_files = ['s3://haystac-pmo-athena/results/0.32812059783186387/20231130_212053_00057_gqvh5_b311740c-02a5-42a5-9996-b17bdbf604ca',
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's3://haystac-pmo-athena/results/0.32812059783186387/20231130_212053_00057_gqvh5_1abe3b5f-ab17-401e-91e9-a5c4f3fa17b9',
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's3://haystac-pmo-athena/results/0.32812059783186387/20231130_212053_00057_gqvh5_c08442a6-74b8-4cf0-afc5-ab1a03f59eea',
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's3://haystac-pmo-athena/results/0.32812059783186387/20231130_212053_00057_gqvh5_399a2786-68b5-4c40-961e-476451885f7f',
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's3://haystac-pmo-athena/results/0.32812059783186387/20231130_212053_00057_gqvh5_8a699085-7d6b-426a-8a59-1ae4a6a183b9',
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's3://haystac-pmo-athena/results/0.32812059783186387/20231130_212053_00057_gqvh5_0ca91f4c-85cf-4c93-b039-4f307dde0a86',
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's3://haystac-pmo-athena/results/0.32812059783186387/20231130_212053_00057_gqvh5_bf38065d-fde8-45d3-87eb-3a56479f8b57',
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's3://haystac-pmo-athena/results/0.32812059783186387/20231130_212053_00057_gqvh5_6a0ebea5-9a23-4fb5-8547-d4bb82f6a821',
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's3://haystac-pmo-athena/results/0.32812059783186387/20231130_212053_00057_gqvh5_3464c3f6-fa5c-4d6b-bdf4-be387ff9dfda',
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's3://haystac-pmo-athena/results/0.32812059783186387/20231130_212053_00057_gqvh5_9088425e-0c87-49ef-a883-92c217a715bb',
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's3://haystac-pmo-athena/results/0.32812059783186387/20231130_212053_00057_gqvh5_0517e42b-089b-4819-b166-fa21489485a6',
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's3://haystac-pmo-athena/results/0.32812059783186387/20231130_212053_00057_gqvh5_0197ff95-8c3c-4121-8077-a499927bb097',
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's3://haystac-pmo-athena/results/0.32812059783186387/20231130_212053_00057_gqvh5_990579d4-0bae-4547-a630-e5a05341d264',
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's3://haystac-pmo-athena/results/0.32812059783186387/20231130_212053_00057_gqvh5_2c838980-65d6-4a3d-898d-bfcb63e361bd',
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's3://haystac-pmo-athena/results/0.32812059783186387/20231130_212053_00057_gqvh5_930df083-3c4c-45d6-963e-51e47d3a704c',
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's3://haystac-pmo-athena/results/0.32812059783186387/20231130_212053_00057_gqvh5_6739cc33-43e0-468d-a6d2-fa17161bbd5e',
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's3://haystac-pmo-athena/results/0.32812059783186387/20231130_212053_00057_gqvh5_685d81de-ffff-4629-bfb2-4bca3941474b',
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's3://haystac-pmo-athena/results/0.32812059783186387/20231130_212053_00057_gqvh5_41e3cb39-a917-460a-bd86-413df774806b',
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's3://haystac-pmo-athena/results/0.32812059783186387/20231130_212053_00057_gqvh5_ce533449-b14f-439e-92dc-69abdeda706f',
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's3://haystac-pmo-athena/results/0.32812059783186387/20231130_212053_00057_gqvh5_c68ff721-8b97-4abf-8573-ac3fd617af97',
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's3://haystac-pmo-athena/results/0.32812059783186387/20231130_212053_00057_gqvh5_04f13894-63d8-453c-ace7-87368511e94b',
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's3://haystac-pmo-athena/results/0.32812059783186387/20231130_212053_00057_gqvh5_0d906712-e469-419a-8bf7-508b0b79848d',
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's3://haystac-pmo-athena/results/0.32812059783186387/20231130_212053_00057_gqvh5_c514089d-c2eb-496a-a1a2-8501ecaf7d84',
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's3://haystac-pmo-athena/results/0.32812059783186387/20231130_212053_00057_gqvh5_fe04a9d7-56ad-4e99-bbfa-8adb398af971',
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's3://haystac-pmo-athena/results/0.32812059783186387/20231130_212053_00057_gqvh5_1ea10655-a063-468e-ad9e-0788eb15bd8d',
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's3://haystac-pmo-athena/results/0.32812059783186387/20231130_212053_00057_gqvh5_7146bbe6-1278-43d3-a9e3-1a9e6a776105',
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's3://haystac-pmo-athena/results/0.32812059783186387/20231130_212053_00057_gqvh5_add3ea7e-961f-4760-88b3-c839d4a344b3',
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's3://haystac-pmo-athena/results/0.32812059783186387/20231130_212053_00057_gqvh5_2a00d610-18bf-4611-8f58-f47bdd484504',
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's3://haystac-pmo-athena/results/0.32812059783186387/20231130_212053_00057_gqvh5_54ea410d-fa42-4ab5-bc2b-8179ad1cfb46',
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's3://haystac-pmo-athena/results/0.32812059783186387/20231130_212053_00057_gqvh5_07aded26-d650-4852-9d3f-732409db3fd7']
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#manifest_files = ["s3://haystac-pmo-athena/results/f236b9a3-8aa7-4e29-b4c7-1fee5981a0ad.csv"]
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def full_s3(obj):
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return "s3://" + obj.bucket_name + "/" + obj.key
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try:
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athena = m.athena("s3://haystac-pmo-athena/")
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records = 1_000_000_000
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# Iteratively load files and scatter them to the cluster
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with Timing("read parquet from athena"):
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# Delta Table
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#files = m.s3.ls("s3://haystac-archive-phase1.trial1/ta1.kitware/ta1/simulation/train/")
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#parqs = [full_s3(o) for o in files if o.key.endswith('.parquet')]
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# 20 partitions at a time, concat 20 at a time
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#df = dd.read_parquet(parqs[0:20])#.astype({"agent": "category",
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# # "timestamp": "datetime64[ns, UTC]"})
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#for i in range(20, len(parqs), 20):
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# new = dd.read_parquet(parqs[i:i + 20])#.astype({"agent": "category",
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# # "timestamp": "datetime64[ns, UTC]"})
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# df = df.concat(new)
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# 20 partitions at a time, concat all at once
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#df = dd.concat([dd.read_parquet(parqs[i:i + 20], engine='fastparquet') for i in range(0, len(parqs), 20)])
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# All partitions at once
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#df = dd.read_parquet(parqs, engine='fastparquet')
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# Single CSV file
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#query = athena.execute(f"select * from trajectories.kitware limit {records}", format="csv")
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#query.finish()
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#df = dd.read_csv(query.info_cache['ResultConfiguration']['OutputLocation'])
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#df = dd.read_csv(manifest_files[0])
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# Unload to Parquet files
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#query = athena.query(f"select * from trajectories.kitware limit {records}")
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#df = dd.read_parquet(query.manifest_files())
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df = dd.read_parquet(manifest_files, engine='fastparquet')
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with Timing("partitioning"):
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divisions = list(range(0, 10001))
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df = df.set_index('agent', divisions=divisions)
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with Timing("persisting"):
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dp = df.persist()
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print(dp)
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#import IPython
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#IPython.embed()
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with Timing("memory usage"):
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print(dp.memory_usage().compute())
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with Timing("count()"):
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print(dp.count().compute())
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with Timing("memory usage"):
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print(dp.memory_usage().compute())
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with Timing("mean latitude"):
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print(dp.groupby(dp.index).latitude.mean().compute())
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with Timing("count()"):
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print(dp.count().compute())
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#with Timing("mean longitude"):
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# print(dp.groupby(dp.index).longitude.mean().compute())
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finally:
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########## FIN #######################
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print("closing client")
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client.close()
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#print("end")
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#input()
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