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RustRepoTrans / Evaluate /projects /iceberg /python /dev /provision.py
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# Licensed to the Apache Software Foundation (ASF) under one
# or more contributor license agreements. See the NOTICE file
# distributed with this work for additional information
# regarding copyright ownership. The ASF licenses this file
# to you under the Apache License, Version 2.0 (the
# "License"); you may not use this file except in compliance
# with the License. You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing,
# software distributed under the License is distributed on an
# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
# KIND, either express or implied. See the License for the
# specific language governing permissions and limitations
# under the License.
from pyspark.sql import SparkSession
from pyspark.sql.functions import current_date, date_add, expr
from pyiceberg.catalog import load_catalog
from pyiceberg.schema import Schema
from pyiceberg.types import FixedType, NestedField, UUIDType
spark = SparkSession.builder.getOrCreate()
catalogs = {
'rest': load_catalog(
"rest",
**{
"type": "rest",
"uri": "http://rest:8181",
"s3.endpoint": "http://minio:9000",
"s3.access-key-id": "admin",
"s3.secret-access-key": "password",
},
),
'hive': load_catalog(
"hive",
**{
"type": "hive",
"uri": "http://hive:9083",
"s3.endpoint": "http://minio:9000",
"s3.access-key-id": "admin",
"s3.secret-access-key": "password",
},
),
}
for catalog_name, catalog in catalogs.items():
spark.sql(
f"""
CREATE DATABASE IF NOT EXISTS {catalog_name}.default;
"""
)
schema = Schema(
NestedField(field_id=1, name="uuid_col", field_type=UUIDType(), required=False),
NestedField(field_id=2, name="fixed_col", field_type=FixedType(25), required=False),
)
catalog.create_table(identifier="default.test_uuid_and_fixed_unpartitioned", schema=schema)
spark.sql(
f"""
INSERT INTO {catalog_name}.default.test_uuid_and_fixed_unpartitioned VALUES
('102cb62f-e6f8-4eb0-9973-d9b012ff0967', CAST('1234567890123456789012345' AS BINARY)),
('ec33e4b2-a834-4cc3-8c4a-a1d3bfc2f226', CAST('1231231231231231231231231' AS BINARY)),
('639cccce-c9d2-494a-a78c-278ab234f024', CAST('12345678901234567ass12345' AS BINARY)),
('c1b0d8e0-0b0e-4b1e-9b0a-0e0b0d0c0a0b', CAST('asdasasdads12312312312111' AS BINARY)),
('923dae77-83d6-47cd-b4b0-d383e64ee57e', CAST('qweeqwwqq1231231231231111' AS BINARY));
"""
)
spark.sql(
f"""
CREATE OR REPLACE TABLE {catalog_name}.default.test_null_nan
USING iceberg
AS SELECT
1 AS idx,
float('NaN') AS col_numeric
UNION ALL SELECT
2 AS idx,
null AS col_numeric
UNION ALL SELECT
3 AS idx,
1 AS col_numeric
"""
)
spark.sql(
f"""
CREATE OR REPLACE TABLE {catalog_name}.default.test_null_nan_rewritten
USING iceberg
AS SELECT * FROM default.test_null_nan
"""
)
spark.sql(
f"""
CREATE OR REPLACE TABLE {catalog_name}.default.test_limit as
SELECT * LATERAL VIEW explode(ARRAY(1, 2, 3, 4, 5, 6, 7, 8, 9, 10)) AS idx;
"""
)
spark.sql(
f"""
CREATE OR REPLACE TABLE {catalog_name}.default.test_positional_mor_deletes (
dt date,
number integer,
letter string
)
USING iceberg
TBLPROPERTIES (
'write.delete.mode'='merge-on-read',
'write.update.mode'='merge-on-read',
'write.merge.mode'='merge-on-read',
'format-version'='2'
);
"""
)
# Partitioning is not really needed, but there is a bug:
# https://github.com/apache/iceberg/pull/7685
spark.sql(f"ALTER TABLE {catalog_name}.default.test_positional_mor_deletes ADD PARTITION FIELD years(dt) AS dt_years")
spark.sql(
f"""
INSERT INTO {catalog_name}.default.test_positional_mor_deletes
VALUES
(CAST('2023-03-01' AS date), 1, 'a'),
(CAST('2023-03-02' AS date), 2, 'b'),
(CAST('2023-03-03' AS date), 3, 'c'),
(CAST('2023-03-04' AS date), 4, 'd'),
(CAST('2023-03-05' AS date), 5, 'e'),
(CAST('2023-03-06' AS date), 6, 'f'),
(CAST('2023-03-07' AS date), 7, 'g'),
(CAST('2023-03-08' AS date), 8, 'h'),
(CAST('2023-03-09' AS date), 9, 'i'),
(CAST('2023-03-10' AS date), 10, 'j'),
(CAST('2023-03-11' AS date), 11, 'k'),
(CAST('2023-03-12' AS date), 12, 'l');
"""
)
spark.sql(f"ALTER TABLE {catalog_name}.default.test_positional_mor_deletes CREATE TAG tag_12")
spark.sql(f"ALTER TABLE {catalog_name}.default.test_positional_mor_deletes CREATE BRANCH without_5")
spark.sql(f"DELETE FROM {catalog_name}.default.test_positional_mor_deletes.branch_without_5 WHERE number = 5")
spark.sql(f"DELETE FROM {catalog_name}.default.test_positional_mor_deletes WHERE number = 9")
spark.sql(
f"""
CREATE OR REPLACE TABLE {catalog_name}.default.test_positional_mor_double_deletes (
dt date,
number integer,
letter string
)
USING iceberg
TBLPROPERTIES (
'write.delete.mode'='merge-on-read',
'write.update.mode'='merge-on-read',
'write.merge.mode'='merge-on-read',
'format-version'='2'
);
"""
)
# Partitioning is not really needed, but there is a bug:
# https://github.com/apache/iceberg/pull/7685
spark.sql(f"ALTER TABLE {catalog_name}.default.test_positional_mor_double_deletes ADD PARTITION FIELD years(dt) AS dt_years")
spark.sql(
f"""
INSERT INTO {catalog_name}.default.test_positional_mor_double_deletes
VALUES
(CAST('2023-03-01' AS date), 1, 'a'),
(CAST('2023-03-02' AS date), 2, 'b'),
(CAST('2023-03-03' AS date), 3, 'c'),
(CAST('2023-03-04' AS date), 4, 'd'),
(CAST('2023-03-05' AS date), 5, 'e'),
(CAST('2023-03-06' AS date), 6, 'f'),
(CAST('2023-03-07' AS date), 7, 'g'),
(CAST('2023-03-08' AS date), 8, 'h'),
(CAST('2023-03-09' AS date), 9, 'i'),
(CAST('2023-03-10' AS date), 10, 'j'),
(CAST('2023-03-11' AS date), 11, 'k'),
(CAST('2023-03-12' AS date), 12, 'l');
"""
)
spark.sql(f"DELETE FROM {catalog_name}.default.test_positional_mor_double_deletes WHERE number = 9")
spark.sql(f"DELETE FROM {catalog_name}.default.test_positional_mor_double_deletes WHERE letter == 'f'")
all_types_dataframe = (
spark.range(0, 5, 1, 5)
.withColumnRenamed("id", "longCol")
.withColumn("intCol", expr("CAST(longCol AS INT)"))
.withColumn("floatCol", expr("CAST(longCol AS FLOAT)"))
.withColumn("doubleCol", expr("CAST(longCol AS DOUBLE)"))
.withColumn("dateCol", date_add(current_date(), 1))
.withColumn("timestampCol", expr("TO_TIMESTAMP(dateCol)"))
.withColumn("stringCol", expr("CAST(dateCol AS STRING)"))
.withColumn("booleanCol", expr("longCol > 5"))
.withColumn("binaryCol", expr("CAST(longCol AS BINARY)"))
.withColumn("byteCol", expr("CAST(longCol AS BYTE)"))
.withColumn("decimalCol", expr("CAST(longCol AS DECIMAL(10, 2))"))
.withColumn("shortCol", expr("CAST(longCol AS SHORT)"))
.withColumn("mapCol", expr("MAP(longCol, decimalCol)"))
.withColumn("arrayCol", expr("ARRAY(longCol)"))
.withColumn("structCol", expr("STRUCT(mapCol, arrayCol)"))
)
all_types_dataframe.writeTo(f"{catalog_name}.default.test_all_types").tableProperty("format-version", "2").partitionedBy(
"intCol"
).createOrReplace()
for table_name, partition in [
("test_partitioned_by_identity", "ts"),
("test_partitioned_by_years", "years(dt)"),
("test_partitioned_by_months", "months(dt)"),
("test_partitioned_by_days", "days(ts)"),
("test_partitioned_by_hours", "hours(ts)"),
("test_partitioned_by_truncate", "truncate(1, letter)"),
("test_partitioned_by_bucket", "bucket(16, number)"),
]:
spark.sql(
f"""
CREATE OR REPLACE TABLE {catalog_name}.default.{table_name} (
dt date,
ts timestamp,
number integer,
letter string
)
USING iceberg;
"""
)
spark.sql(f"ALTER TABLE {catalog_name}.default.{table_name} ADD PARTITION FIELD {partition}")
spark.sql(
f"""
INSERT INTO {catalog_name}.default.{table_name}
VALUES
(CAST('2022-03-01' AS date), CAST('2022-03-01 01:22:00' AS timestamp), 1, 'a'),
(CAST('2022-03-02' AS date), CAST('2022-03-02 02:22:00' AS timestamp), 2, 'b'),
(CAST('2022-03-03' AS date), CAST('2022-03-03 03:22:00' AS timestamp), 3, 'c'),
(CAST('2022-03-04' AS date), CAST('2022-03-04 04:22:00' AS timestamp), 4, 'd'),
(CAST('2023-03-05' AS date), CAST('2023-03-05 05:22:00' AS timestamp), 5, 'e'),
(CAST('2023-03-06' AS date), CAST('2023-03-06 06:22:00' AS timestamp), 6, 'f'),
(CAST('2023-03-07' AS date), CAST('2023-03-07 07:22:00' AS timestamp), 7, 'g'),
(CAST('2023-03-08' AS date), CAST('2023-03-08 08:22:00' AS timestamp), 8, 'h'),
(CAST('2023-03-09' AS date), CAST('2023-03-09 09:22:00' AS timestamp), 9, 'i'),
(CAST('2023-03-10' AS date), CAST('2023-03-10 10:22:00' AS timestamp), 10, 'j'),
(CAST('2023-03-11' AS date), CAST('2023-03-11 11:22:00' AS timestamp), 11, 'k'),
(CAST('2023-03-12' AS date), CAST('2023-03-12 12:22:00' AS timestamp), 12, 'l');
"""
)
# There is an issue with CREATE OR REPLACE
# https://github.com/apache/iceberg/issues/8756
spark.sql(f"DROP TABLE IF EXISTS {catalog_name}.default.test_table_version")
spark.sql(
f"""
CREATE TABLE {catalog_name}.default.test_table_version (
dt date,
number integer,
letter string
)
USING iceberg
TBLPROPERTIES (
'format-version'='1'
);
"""
)
spark.sql(
f"""
CREATE TABLE {catalog_name}.default.test_table_sanitized_character (
`letter/abc` string
)
USING iceberg
TBLPROPERTIES (
'format-version'='1'
);
"""
)
spark.sql(
f"""
INSERT INTO {catalog_name}.default.test_table_sanitized_character
VALUES
('123')
"""
)
spark.sql(
f"""
INSERT INTO {catalog_name}.default.test_table_sanitized_character
VALUES
('123')
"""
)
spark.sql(
f"""
CREATE TABLE {catalog_name}.default.test_table_add_column (
a string
)
USING iceberg
"""
)
spark.sql(f"INSERT INTO {catalog_name}.default.test_table_add_column VALUES ('1')")
spark.sql(f"ALTER TABLE {catalog_name}.default.test_table_add_column ADD COLUMN b string")
spark.sql(f"INSERT INTO {catalog_name}.default.test_table_add_column VALUES ('2', '2')")
spark.sql(
f"""
CREATE TABLE {catalog_name}.default.test_table_empty_list_and_map (
col_list array<int>,
col_map map<int, int>,
col_list_with_struct array<struct<test:int>>
)
USING iceberg
TBLPROPERTIES (
'format-version'='1'
);
"""
)
spark.sql(
f"""
INSERT INTO {catalog_name}.default.test_table_empty_list_and_map
VALUES (null, null, null),
(array(), map(), array(struct(1)))
"""
)