mirror of
https://github.com/ClickHouse/ClickHouse.git
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47dfeaa487
Change-Id: I2677dc20fc515bbbe91f54154fc4c081f164758e
212 lines
8.0 KiB
Bash
Executable File
212 lines
8.0 KiB
Bash
Executable File
#!/usr/bin/env bash
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# Tags: no-fasttest
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CURDIR=$(cd "$(dirname "${BASH_SOURCE[0]}")" && pwd)
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# shellcheck source=../shell_config.sh
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. "$CURDIR"/../shell_config.sh
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PAR_PATH="$CURDIR"/data_parquet/native_parquet_reader.parquet
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# the content of parquet file can be generated by following codes
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# <<EndOfCodes
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#
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# #include <Columns/ColumnNullable.h>
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# #include <Columns/ColumnString.h>
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# #include <Common/tests/gtest_global_register.h>
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# #include <Core/Block.h>
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# #include <DataTypes/DataTypeDateTime64.h>
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# #include <DataTypes/DataTypeLowCardinality.h>
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# #include <DataTypes/DataTypeNullable.h>
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# #include <DataTypes/DataTypeString.h>
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# #include <DataTypes/DataTypesDecimal.h>
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# #include <IO/WriteBufferFromFile.h>
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# #include <Processors/Formats/IOutputFormat.h>
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# #include <Processors/Formats/Impl/Parquet/ParquetRecordReader.h>
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#
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# namespace
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# {
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#
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# using namespace DB;
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#
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# const UInt32 ROW_NUM = 2000;
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# const UInt32 MIN_STRING_LEN = 3;
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# const UInt32 MAX_STRING_LEN = 5;
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#
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# const UInt32 PLAIN_ENCODING_CARDINALITY = ROW_NUM * 2;
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# const UInt32 MIX_ENCODING_CARDINALITY = 800;
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# const UInt32 DICT_ENCODING_CARDINALITY = 20;
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#
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# UInt16 nextNum()
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# {
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# static UInt16 idx = 0;
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# static UInt16 nums[] = {0, 21845, 43690};
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# static size_t nums_len = sizeof(nums) / sizeof(nums[0]);
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# return nums[(idx++) % nums_len]++;
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# }
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#
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# template <typename NumericDataType>
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# void generateValues(MutableColumnPtr & col, size_t num)
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# {
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# using FieldType = typename NumericDataType::FieldType;
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#
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# const size_t next_num_bytes = sizeof(nextNum());
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# char bytewise_val[sizeof(FieldType)];
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#
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# while (col->size() < num)
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# {
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# for (auto bytes = 0; bytes < sizeof(FieldType); bytes += next_num_bytes)
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# {
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# auto tmp = nextNum();
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# memcpy(bytewise_val + bytes, &tmp, std::min(next_num_bytes, sizeof(FieldType) - bytes));
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# }
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# if (is_decimal<FieldType>)
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# {
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# // clean highest 3 bits, make sure the result doest not exceed the limits of the decimal type
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# if (bytewise_val[sizeof(FieldType) - 1] > 0)
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# bytewise_val[sizeof(FieldType) - 1] &= 0x0f;
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# else
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# bytewise_val[sizeof(FieldType) - 1] |= 0xf0;
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# }
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# FieldType val;
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# memcpy(&val, &bytewise_val, sizeof(FieldType));
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# col->insert(val);
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# }
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# }
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#
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# template <>
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# void generateValues<DataTypeString>(MutableColumnPtr & col, size_t num)
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# {
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# std::string str;
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# while (col->size() < num)
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# {
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# auto len = MIN_STRING_LEN + nextNum() % (MAX_STRING_LEN - MIN_STRING_LEN);
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# str.clear();
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# for (size_t i = 0; i < len; i++)
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# {
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# str.push_back('a' + nextNum() % ('z' - 'a'));
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# }
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# col->insert(str);
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# }
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# }
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#
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# template <typename DataType>
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# ColumnWithTypeAndName generateColumn(
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# std::shared_ptr<DataType> ch_type,
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# size_t cardinality,
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# const std::string & col_name,
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# const std::set<size_t> & null_indice)
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# {
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# DataTypePtr col_type = ch_type;
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# if (!null_indice.empty())
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# {
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# col_type = std::make_shared<DataTypeNullable>(ch_type);
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# }
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#
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# auto values = ch_type->createColumn();
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# values->reserve(cardinality);
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# generateValues<DataType>(values, cardinality);
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#
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# auto col = col_type->createColumn();
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# col->reserve(ROW_NUM);
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# for (size_t i = 0; i < ROW_NUM; i++)
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# {
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# if (!null_indice.empty() && null_indice.contains(i))
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# {
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# col->insert(Null());
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# }
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# else
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# {
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# col->insert(values->operator[](nextNum() % cardinality));
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# }
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# }
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# return {std::move(col), col_type, col_name};
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# }
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#
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# Block generateBlock()
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# {
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# ColumnsWithTypeAndName cols;
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#
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# // test Int32 type
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# std::set<size_t> null_indice{512, 1001, 211, 392, 553, 1725};
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# // Nullability is expressed by definition level, and encoded by bit packed with smallest group size of 8
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# // when null value appeared. Here we make a big bit packed group with more than 1000 values.
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# for (size_t i = 0; i < 170; i++)
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# {
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# null_indice.emplace(622 + i * 6);
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# }
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# cols.emplace_back(generateColumn(
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# std::make_shared<DataTypeInt32>(), PLAIN_ENCODING_CARDINALITY, "plain_encoding_i32", null_indice));
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# null_indice = {917, 482, 283, 580, 1926, 1667, 1971};
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# cols.emplace_back(generateColumn(
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# std::make_shared<DataTypeInt32>(), DICT_ENCODING_CARDINALITY, "dict_encoding_i32", null_indice));
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#
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# // test string type
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# null_indice = {818, 928, 1958, 1141, 1553, 1407, 690, 1769};
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# cols.emplace_back(generateColumn(
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# std::make_shared<DataTypeString>(), PLAIN_ENCODING_CARDINALITY, "plain_encoding_str", null_indice));
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# null_indice = {1441, 1747, 216, 1209, 89, 52, 536, 625};
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# cols.emplace_back(generateColumn(
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# std::make_shared<DataTypeString>(), MIX_ENCODING_CARDINALITY, "mix_encoding_str", null_indice));
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# null_indice = {1478, 1862, 894, 1314, 1844, 243, 869, 551};
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# cols.emplace_back(generateColumn(
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# std::make_shared<DataTypeString>(), DICT_ENCODING_CARDINALITY, "dict_encoding_str", null_indice));
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#
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# // test DateTime64 type
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# auto dt_type = std::make_shared<DataTypeDateTime64>(ParquetRecordReader::default_datetime64_scale);
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# null_indice = {1078, 112, 1981, 795, 371, 1176, 1526, 11};
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# cols.emplace_back(generateColumn(dt_type, PLAIN_ENCODING_CARDINALITY, "plain_encoding_dt64", null_indice));
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# null_indice = {1734, 1153, 1893, 1205, 644, 1670, 1482, 1479};
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# cols.emplace_back(generateColumn(dt_type, DICT_ENCODING_CARDINALITY, "dict_encoding_dt64", null_indice));
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#
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# // test Decimal128 type
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# auto d128_type = std::make_shared<DataTypeDecimal128>(DecimalUtils::max_precision<Decimal128>, 3);
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# null_indice = {852, 1448, 1569, 896, 1866, 1655, 100, 418};
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# cols.emplace_back(generateColumn(d128_type, PLAIN_ENCODING_CARDINALITY, "plain_encoding_decimal128", null_indice));
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#
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# return {cols};
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# }
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#
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# void dumpBlock(const Block & block)
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# {
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# WriteBufferFromFile output_buf("/tmp/ut-out.csv");
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# auto out = getContext().context->getOutputFormat("CSVWithNames", output_buf, block);
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# out->write(block);
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# out->finalize();
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# std::cerr << block.dumpStructure() << std::endl << std::endl;
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# }
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#
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# }
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#
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# EndOfCodes
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#
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# How to generate the parquet file:
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# 1. Use above C++ codes.
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# Put above codes in src/Common/tests/gtest_main.cpp, add following two inlines in main function:
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# tryRegisterFormats();
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# dumpBlock(generateBlock());
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# 2. Genetate /tmp/ut-out.csv.
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# After compiled, run any test, such as "./src/unit_tests_dbms --gtest_filter=IColumn.dumpStructure",
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# 3. Generate the parquet file by following spark sql
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# create temporary view tv using csv options('path' '/tmp/ut-out.csv', 'header' 'true', 'nullValue' '\\N');
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# insert overwrite directory "/tmp/test-parquet" using Parquet
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# options('parquet.dictionary.page.size' '500')
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# select /*+ COALESCE(1) */ cast(plain_encoding_i32 as int), cast(dict_encoding_i32 as int),
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# plain_encoding_str, mix_encoding_str, dict_encoding_str,
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# cast(plain_encoding_dt64 as timestamp), cast(dict_encoding_dt64 as timestamp),
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# cast(plain_encoding_decimal128 as decimal(38, 3))
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# from tv;
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#
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CH_SCHEMA="\
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plain_encoding_i32 Nullable(Int32), \
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dict_encoding_i32 Nullable(Int32), \
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plain_encoding_str Nullable(String), \
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mix_encoding_str Nullable(String), \
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dict_encoding_str LowCardinality(Nullable(String)), \
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plain_encoding_dt64 Nullable(DateTime64(9, \\'UTC\\')), \
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dict_encoding_dt64 Nullable(DateTime64(9, \\'UTC\\')), \
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plain_encoding_decimal128 Nullable(Decimal(38, 3))"
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QUERY="SELECT * from file('$PAR_PATH', 'Parquet', '$CH_SCHEMA')"
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# there may be more than on group in parquet files, unstable results may generated by multithreads
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$CLICKHOUSE_LOCAL --multiquery --max_threads 1 --max_parsing_threads 1 --input_format_parquet_use_native_reader true --query "$QUERY"
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