File size: 5,013 Bytes
2b06d1d | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 | from unittest.mock import patch
import pyspark
from datasets.packaged_modules.spark.spark import (
Spark,
SparkExamplesIterable,
_generate_iterable_examples,
)
from ..utils import (
require_dill_gt_0_3_2,
require_not_windows,
)
def _get_expected_row_ids_and_row_dicts_for_partition_order(df, partition_order):
expected_row_ids_and_row_dicts = []
for part_id in partition_order:
partition = df.where(f"SPARK_PARTITION_ID() = {part_id}").collect()
for row_idx, row in enumerate(partition):
expected_row_ids_and_row_dicts.append((f"{part_id}_{row_idx}", row.asDict()))
return expected_row_ids_and_row_dicts
@require_not_windows
@require_dill_gt_0_3_2
def test_repartition_df_if_needed():
spark = pyspark.sql.SparkSession.builder.master("local[*]").appName("pyspark").getOrCreate()
df = spark.range(100).repartition(1)
spark_builder = Spark(df)
# The id ints will be converted to Pyarrow int64s, so each row will be 8 bytes. Setting a max_shard_size of 16 means
# that each partition can hold 2 rows.
spark_builder._repartition_df_if_needed(max_shard_size=16)
# Given that the dataframe has 100 rows and each partition has 2 rows, we expect 50 partitions.
assert spark_builder.df.rdd.getNumPartitions() == 50
@require_not_windows
@require_dill_gt_0_3_2
def test_generate_iterable_examples():
spark = pyspark.sql.SparkSession.builder.master("local[*]").appName("pyspark").getOrCreate()
df = spark.range(10).repartition(2)
partition_order = [1, 0]
generate_fn = _generate_iterable_examples(df, partition_order) # Reverse the partitions.
expected_row_ids_and_row_dicts = _get_expected_row_ids_and_row_dicts_for_partition_order(df, partition_order)
for i, (row_id, row_dict) in enumerate(generate_fn()):
expected_row_id, expected_row_dict = expected_row_ids_and_row_dicts[i]
assert row_id == expected_row_id
assert row_dict == expected_row_dict
@require_not_windows
@require_dill_gt_0_3_2
def test_spark_examples_iterable():
spark = pyspark.sql.SparkSession.builder.master("local[*]").appName("pyspark").getOrCreate()
df = spark.range(10).repartition(1)
it = SparkExamplesIterable(df)
assert it.n_shards == 1
for i, (row_id, row_dict) in enumerate(it):
assert row_id == f"0_{i}"
assert row_dict == {"id": i}
@require_not_windows
@require_dill_gt_0_3_2
def test_spark_examples_iterable_shuffle():
spark = pyspark.sql.SparkSession.builder.master("local[*]").appName("pyspark").getOrCreate()
df = spark.range(30).repartition(3)
# Mock the generator so that shuffle reverses the partition indices.
with patch("numpy.random.Generator") as generator_mock:
generator_mock.shuffle.side_effect = lambda x: x.reverse()
expected_row_ids_and_row_dicts = _get_expected_row_ids_and_row_dicts_for_partition_order(df, [2, 1, 0])
shuffled_it = SparkExamplesIterable(df).shuffle_data_sources(generator_mock)
assert shuffled_it.n_shards == 3
for i, (row_id, row_dict) in enumerate(shuffled_it):
expected_row_id, expected_row_dict = expected_row_ids_and_row_dicts[i]
assert row_id == expected_row_id
assert row_dict == expected_row_dict
@require_not_windows
@require_dill_gt_0_3_2
def test_spark_examples_iterable_shard():
spark = pyspark.sql.SparkSession.builder.master("local[*]").appName("pyspark").getOrCreate()
df = spark.range(20).repartition(4)
# Partitions 0 and 2
shard_it_1 = SparkExamplesIterable(df).shard_data_sources(worker_id=0, num_workers=2)
assert shard_it_1.n_shards == 2
expected_row_ids_and_row_dicts_1 = _get_expected_row_ids_and_row_dicts_for_partition_order(df, [0, 2])
for i, (row_id, row_dict) in enumerate(shard_it_1):
expected_row_id, expected_row_dict = expected_row_ids_and_row_dicts_1[i]
assert row_id == expected_row_id
assert row_dict == expected_row_dict
# Partitions 1 and 3
shard_it_2 = SparkExamplesIterable(df).shard_data_sources(worker_id=1, num_workers=2)
assert shard_it_2.n_shards == 2
expected_row_ids_and_row_dicts_2 = _get_expected_row_ids_and_row_dicts_for_partition_order(df, [1, 3])
for i, (row_id, row_dict) in enumerate(shard_it_2):
expected_row_id, expected_row_dict = expected_row_ids_and_row_dicts_2[i]
assert row_id == expected_row_id
assert row_dict == expected_row_dict
@require_not_windows
@require_dill_gt_0_3_2
def test_repartition_df_if_needed_max_num_df_rows():
spark = pyspark.sql.SparkSession.builder.master("local[*]").appName("pyspark").getOrCreate()
df = spark.range(100).repartition(1)
spark_builder = Spark(df)
# Choose a small max_shard_size for maximum partitioning.
spark_builder._repartition_df_if_needed(max_shard_size=1)
# The new number of partitions should not be greater than the number of rows.
assert spark_builder.df.rdd.getNumPartitions() == 100
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