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Added many new table feature reference tables #45

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merged 2 commits into from
Jan 31, 2024
Merged

Added many new table feature reference tables #45

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c0f13bf
Added many new table feature reference tables
hntd187 Jan 30, 2024
1999345
Merge branch 'master' into table_features
hntd187 Jan 30, 2024
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168 changes: 152 additions & 16 deletions dat/generated_tables.py
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Original file line number Diff line number Diff line change
Expand Up @@ -3,12 +3,13 @@
from datetime import date, datetime, timedelta
from decimal import Decimal
from pathlib import Path
from typing import Callable, List, Tuple
from typing import Callable, Tuple, List
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A linter make this change?


import pyspark.sql
import pyspark.sql.types as types
from delta.tables import DeltaTable
from pyspark.sql import SparkSession
from pyspark.sql.functions import col, lit, now

from dat.models import TableVersionMetadata, TestCaseInfo
from dat.spark_builder import get_spark_session
Expand All @@ -30,7 +31,7 @@ def get_version_metadata(case: TestCaseInfo) -> TableVersionMetadata:


def save_expected(case: TestCaseInfo, as_latest=False) -> None:
'''Save the specified version of a Delta Table as a Parquet file.'''
"""Save the specified version of a Delta Table as a Parquet file."""
spark = get_spark_session()
df = spark.read.format('delta').load(case.delta_root)

Expand Down Expand Up @@ -270,7 +271,7 @@ def create_nested_types(case: TestCaseInfo, spark: SparkSession):
[types.StructField(
'float64', types.DoubleType()),
types.StructField(
'bool', types.BooleanType()), ])),
'bool', types.BooleanType()), ])),
types.StructField(
'array', types.ArrayType(
types.ShortType())),
Expand Down Expand Up @@ -311,6 +312,7 @@ def get_sample_data(
description='Table with a checkpoint',
)
def create_with_checkpoint(case: TestCaseInfo, spark: SparkSession):
spark.conf.set('spark.databricks.delta.legacy.allowAmbiguousPathsInCreateTable', 'true')
df = get_sample_data(spark)

(DeltaTable.create(spark)
Expand Down Expand Up @@ -371,12 +373,12 @@ def create_no_replay(case: TestCaseInfo, spark: SparkSession):
def create_stats_as_struct(case: TestCaseInfo, spark: SparkSession):
df = get_sample_data(spark)
(DeltaTable.create(spark)
.location(str(Path(case.delta_root).absolute()))
.addColumns(df.schema)
.property('delta.checkpointInterval', '2')
.property('delta.checkpoint.writeStatsAsStruct', 'true')
.property('delta.checkpoint.writeStatsAsJson', 'false')
.execute())
.location(str(Path(case.delta_root).absolute()))
.addColumns(df.schema)
.property('delta.checkpointInterval', '2')
.property('delta.checkpoint.writeStatsAsStruct', 'true')
.property('delta.checkpoint.writeStatsAsJson', 'false')
.execute())

for i in range(3):
df = get_sample_data(spark, seed=i, nrows=5)
Expand All @@ -391,15 +393,149 @@ def create_stats_as_struct(case: TestCaseInfo, spark: SparkSession):
def create_no_stats(case: TestCaseInfo, spark: SparkSession):
df = get_sample_data(spark)
(DeltaTable.create(spark)
.location(str(Path(case.delta_root).absolute()))
.addColumns(df.schema)
.property('delta.checkpointInterval', '2')
.property('delta.checkpoint.writeStatsAsStruct', 'false')
.property('delta.checkpoint.writeStatsAsJson', 'false')
.property('delta.dataSkippingNumIndexedCols', '0')
.execute())
.location(str(Path(case.delta_root).absolute()))
.addColumns(df.schema)
.property('delta.checkpointInterval', '2')
.property('delta.checkpoint.writeStatsAsStruct', 'false')
.property('delta.checkpoint.writeStatsAsJson', 'false')
.property('delta.dataSkippingNumIndexedCols', '0')
.execute())

for i in range(3):
df = get_sample_data(spark, seed=i, nrows=5)
df.repartition(1).write.format('delta').mode(
'overwrite').save(case.delta_root)


@reference_table(
name='deletion_vectors',
description='Table with deletion vectors',
)
def create_deletion_vectors(case: TestCaseInfo, spark: SparkSession):
df = get_sample_data(spark)

delta_path = str(Path(case.delta_root).absolute())
delta_table: DeltaTable = (DeltaTable.create(spark)
.location(delta_path)
.addColumns(df.schema)
.property('delta.enableDeletionVectors', 'true')
.execute())

df.repartition(1).write.format('delta').mode('append').save(case.delta_root)

delta_table.delete(col("letter") == "a")
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Maybe append some more rows here so the tests are a little more reliable?



@reference_table(
name='check_constraints',
description='Table with a check constraint'
)
def check_constraint_table(case: TestCaseInfo, spark: SparkSession):
df = get_sample_data(spark)
delta_path = str(Path(case.delta_root).absolute())
(DeltaTable.create(spark)
.location(delta_path)
.addColumns(df.schema)
.property('delta.enableDeletionVectors', 'true')
.execute())

df.repartition(1).write.format('delta').mode('append').save(case.delta_root)
spark.sql(f"ALTER TABLE delta.`{delta_path}` ADD CONSTRAINT const1 CHECK (int > 0);")
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Oh my, this syntax.

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this is the only way to add a constraint :-) delta-rs though has it part of the builder methods :-D



@reference_table(
name='cdf',
description='Table with cdf turned on',
)
def create_change_data_feed(case: TestCaseInfo, spark: SparkSession):
df = get_sample_data(spark)
delta_path = str(Path(case.delta_root).absolute())
delta_table: DeltaTable = (DeltaTable.create(spark)
.location(delta_path)
.addColumns(df.schema)
.property('delta.enableChangeDataFeed', 'true')
.execute())

df.repartition(1).write.format('delta').mode('append').save(case.delta_root)

delta_table.update(
condition=col("letter") == "c",
set={"letter": lit("a")}
)
delta_table.delete(col("letter") == "a")


@reference_table(
name='generated_columns',
description='Table with a generated column',
)
def create_generated_columns(case: TestCaseInfo, spark: SparkSession):
df = get_sample_data(spark)
delta_path = str(Path(case.delta_root).absolute())
(DeltaTable.create(spark)
.location(delta_path)
.addColumns(df.schema)
.addColumn("creation", types.DateType(), generatedAlwaysAs="CAST(now() AS DATE)")
.execute())

df.repartition(1).write.format('delta').mode('append').save(case.delta_root)


@reference_table(
name='column_mapping',
description='Table with column mapping turned on',
)
def create_column_mapping(case: TestCaseInfo, spark: SparkSession):
df = get_sample_data(spark)
delta_path = str(Path(case.delta_root).absolute())
(DeltaTable.create(spark)
.location(delta_path)
.addColumns(df.schema)
.property('delta.columnMapping.mode', 'name')
.execute())

df.repartition(1).write.format('delta').mode('append').save(case.delta_root)
spark.sql(f"ALTER TABLE delta.`{delta_path}` RENAME COLUMN int TO new_int;")
(df.withColumnRenamed('int', 'new_int')
.repartition(1)
.write
.format('delta')
.mode('append')
.save(case.delta_root))


@reference_table(
name='timestamp_ntz',
description='Table with not timezone timestamps in it',
)
def create_timestamp_ntz(case: TestCaseInfo, spark: SparkSession):
df = get_sample_data(spark)
delta_path = str(Path(case.delta_root).absolute())
delta_table: DeltaTable = (DeltaTable.create(spark)
.location(delta_path)
.addColumns(df.schema)
.addColumn("timestampNTZ", types.TimestampNTZType())
.execute())
delta_table.upgradeTableProtocol(3, 7)
(df.withColumn("timestampNTZ", now().cast(types.TimestampNTZType()))
.repartition(1)
.write
.format('delta')
.mode('append')
.save(case.delta_root))


@reference_table(
name='iceberg_compat_v1',
description='Table with Iceberg compatability v1 turned on',
)
def create_iceberg_compat_v1(case: TestCaseInfo, spark: SparkSession):
df = get_sample_data(spark)
delta_path = str(Path(case.delta_root).absolute())
delta_table: DeltaTable = (DeltaTable.create(spark)
.location(delta_path)
.addColumns(df.schema)
.property('delta.enableIcebergCompatV1', 'true')
.execute())
delta_table.upgradeTableProtocol(3, 7)
df.repartition(1).write.format('delta').mode('append').save(case.delta_root)
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