32b1456b28
1. remove FE config `enable_array_type` 2. limit the nested depth of array in FE side. 3. Fix bug that when loading array from parquet, the decimal type is treated as bigint 4. Fix loading array from csv(vec-engine), handle null and "null" 5. Change the csv array loading behavior, if the array string format is invalid in csv, it will be converted to null. 6. Remove `check_array_format()`, because it's logic is wrong and meaningless 7. Add stream load csv test cases and more parquet broker load tests
91 lines
3.4 KiB
Groovy
91 lines
3.4 KiB
Groovy
// Licensed to the Apache Software Foundation (ASF) under one
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// or more contributor license agreements. See the NOTICE file
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// distributed with this work for additional information
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// regarding copyright ownership. The ASF licenses this file
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// to you under the Apache License, Version 2.0 (the
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// "License"); you may not use this file except in compliance
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// with the License. You may obtain a copy of the License at
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//
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// http://www.apache.org/licenses/LICENSE-2.0
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//
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// Unless required by applicable law or agreed to in writing,
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// software distributed under the License is distributed on an
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// "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
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// KIND, either express or implied. See the License for the
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// specific language governing permissions and limitations
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// under the License.
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suite("test_array_string_insert", "load") {
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// define a sql table
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def testTable = "tbl_test_array_string_insert"
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def create_test_table = {testTablex, enable_vectorized_flag ->
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if (enable_vectorized_flag) {
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sql """ set enable_vectorized_engine = true """
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} else {
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sql """ set enable_vectorized_engine = false """
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}
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def result1 = sql """
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CREATE TABLE IF NOT EXISTS ${testTable} (
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`k1` INT(11) NULL COMMENT "",
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`k2` ARRAY<CHAR(5)> NULL COMMENT "",
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`k3` ARRAY<CHAR(5)> NOT NULL COMMENT "",
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`k4` ARRAY<ARRAY<CHAR(5)>> NULL COMMENT ""
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) ENGINE=OLAP
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DUPLICATE KEY(`k1`)
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DISTRIBUTED BY HASH(`k1`) BUCKETS 1
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PROPERTIES (
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"replication_allocation" = "tag.location.default: 1",
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"storage_format" = "V2"
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)
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"""
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// DDL/DML return 1 row and 3 column, the only value is update row count
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assertTrue(result1.size() == 1)
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assertTrue(result1[0].size() == 1)
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assertTrue(result1[0][0] == 0, "Create table should update 0 rows")
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}
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def test_insert_array_string = { enable_vectorized_flag ->
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sql "DROP TABLE IF EXISTS ${testTable}"
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create_test_table.call(testTable, enable_vectorized_flag)
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sql "set enable_insert_strict = true"
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// ARRAY<char> too long
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test {
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sql "INSERT INTO ${testTable} VALUES (1, ['12345','123456'], [], NULL)"
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exception "Insert has filtered data in strict mode"
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}
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// NULL for NOT NULL column
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test {
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sql "INSERT INTO ${testTable} VALUES (2, ['12345','123'], NULL, NULL)"
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exception "Insert has filtered data in strict mode"
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}
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// ARRAY<ARRAY<char>> too long
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test {
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sql "INSERT INTO ${testTable} VALUES (3, NULL, ['4'], [['123456'],['222']])"
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exception "Insert has filtered data in strict mode"
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}
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// normal insert
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sql "INSERT INTO ${testTable} VALUES (4, ['12345','123'], ['4'], NULL)"
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sql "INSERT INTO ${testTable} VALUES (5, NULL, [NULL, '4'], NULL)"
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sql "INSERT INTO ${testTable} VALUES (6, NULL, ['4'], [['123'],['222']])"
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sql "INSERT INTO ${testTable} VALUES (7, NULL, ['4'], [['12345',NULL],['222']])"
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// select the table and check whether the data is correct
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qt_select "select * from ${testTable} order by k1"
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}
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// case1: enable_vectorized_flag = false
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test_insert_array_string(false);
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// case2: enable_vectorized_flag = true
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test_insert_array_string(true);
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}
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