ed3ff470ce
This is part of the array type support and has not been fully completed. The following functions are implemented 1. fe array type support and implementation of array function, support array syntax analysis and planning 2. Support import array type data through insert into 3. Support select array type data 4. Only the array type is supported on the value lie of the duplicate table this pr merge some code from #4655 #4650 #4644 #4643 #4623 #2979
561 lines
19 KiB
Python
Executable File
561 lines
19 KiB
Python
Executable File
#!/usr/bin/env python
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# encoding: utf-8
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# 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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"""
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# This script will generate the implementation of the simple vector functions for the BE.
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# These include:
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# - Arithmetic functions
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# - Binary functions
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# - Cast functions
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#
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# The script outputs (run: 'src/common/function/gen_vector_functions.py')
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# - header and implemention for above functions:
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# - src/gen_cpp/opcode/vector_functions.[h/cc]
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# - python file that contains the metadata for those functions:
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# - src/gen_cpp/generated_vector_functions.py
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"""
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import string
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import os
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filter_binary_op = string.Template("\
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bool VectorComputeFunctions::${fn_signature}(\n\
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Expr* expr, VectorizedRowBatch* batch) {\n\
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int n = batch->size();\n\
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if (0 == n) {\n\
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return false;\n\
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}\n\
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int* sel = batch->selected();\n\
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Expr* op1 = expr->children()[0];\n\
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Expr* op2 = expr->children()[1];\n\
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batch->add_column(expr->output_column(), expr->type());\n\
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if (expr->is_constant()) {\n\
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${native_type1}* val1 = reinterpret_cast<${native_type1}*>(op1->get_value(NULL));\n\
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${native_type2}* val2 = reinterpret_cast<${native_type2}*>(op2->get_value(NULL));\n\
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if (val1 == NULL || val2 == NULL) return false;\n\
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if (!(*val1 ${native_op} *val2)) batch->set_size(0);\n\
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} else if (op1->is_constant()) {\n\
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${native_type1}* value = reinterpret_cast<${native_type1}*>(op1->get_value(NULL));\n\
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if (NULL == value || !op2->evaluate(batch)) return false;\n\
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${native_type1}* vector1\n\
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= reinterpret_cast<${native_type1}*>(batch->column(op2->output_column())->col_data());\n\
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\n\
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int new_size = 0;\n\
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if (batch->selected_in_use()) {\n\
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for (int j = 0; j != n; ++j) {\n\
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int i = sel[j];\n\
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if (*value ${native_op} vector1[i]) {\n\
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sel[new_size++] = i;\n\
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}\n\
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}\n\
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batch->set_size(new_size);\n\
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} else {\n\
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for (int i = 0; i != n; ++i) {\n\
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if (*value ${native_op} vector1[i]) {\n\
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sel[new_size++] = i;\n\
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}\n\
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}\n\
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\n\
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if (new_size < n) {\n\
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batch->set_size(new_size);\n\
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batch->set_selected_in_use(true);\n\
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}\n\
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}\n\
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} else if (op2->is_constant()) {\n\
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${native_type2}* value = reinterpret_cast<${native_type2}*>(op2->get_value(NULL));\n\
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if (NULL == value || !op1->evaluate(batch)) return false;\n\
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${native_type1}* vector1\n\
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= reinterpret_cast<${native_type1}*>(batch->column(op1->output_column())->col_data());\n\
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\n\
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int new_size = 0;\n\
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if (batch->selected_in_use()) {\n\
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for (int j = 0; j != n; ++j) {\n\
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int i = sel[j];\n\
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if (vector1[i] ${native_op} *value) {\n\
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sel[new_size++] = i;\n\
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}\n\
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}\n\
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batch->set_size(new_size);\n\
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} else {\n\
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for (int i = 0; i != n; ++i) {\n\
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if (vector1[i] ${native_op} *value) {\n\
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sel[new_size++] = i;\n\
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}\n\
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}\n\
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\n\
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if (new_size < n) {\n\
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batch->set_size(new_size);\n\
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batch->set_selected_in_use(true);\n\
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}\n\
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}\n\
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} else {\n\
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if (!op1->evaluate(batch) || !op2->evaluate(batch)) return false;\n\
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${native_type1}* vector1\n\
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= reinterpret_cast<${native_type1}*>(batch->column(op1->output_column())->col_data());\n\
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${native_type2}* vector2\n\
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= reinterpret_cast<${native_type2}*>(batch->column(op2->output_column())->col_data());\n\
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\n\
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int new_size = 0;\n\
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if (batch->selected_in_use()) {\n\
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for (int j = 0; j != n; ++j) {\n\
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int i = sel[j];\n\
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if (vector1[i] ${native_op} vector2[i]) {\n\
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sel[new_size++] = i;\n\
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}\n\
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}\n\
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batch->set_size(new_size);\n\
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} else {\n\
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for (int i = 0; i != n; ++i) {\n\
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if (vector1[i] ${native_op} vector2[i]) {\n\
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sel[new_size++] = i;\n\
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}\n\
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}\n\
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if (new_size < n) {\n\
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batch->set_size(new_size);\n\
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batch->set_selected_in_use(true);\n\
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}\n\
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}\n\
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}\n\
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return true;\n\
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}\n\n")
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filter_in_op = string.Template("\
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bool VectorComputeFunctions::${fn_signature}(\n\
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Expr* expr, VectorizedRowBatch* batch) {\n\
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int n = batch->size();\n\
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if (0 == n) {\n\
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return true;\n\
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}\n\
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batch->add_column(expr->output_column(), expr->type());\n\
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int* sel = batch->selected();\n\
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int num_children = expr->get_num_children();\n\
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Expr* op1 = expr->children()[0];\n\
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InPredicate *in_pred = static_cast<InPredicate*>(expr);\n\
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\n\
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if (op1->is_constant()) {\n\
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void* value = op1->get_value(NULL);\n\
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if (!in_pred->hybird_set()->find(value)) {\n\
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batch->set_size(0);\n\
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return true;\n\
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}\n\
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\n\
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if (num_children > 1) {\n\
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${native_type1}* v = reinterpret_cast<${native_type1}*>(value);\n\
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${native_type1}* vectors[num_children];\n\
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for (int i = 1; i < num_children; ++i) {\n\
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if (expr->get_child(i)->evaluate(batch)) return false;\n\
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vectors[i] = reinterpret_cast<${native_type1}*>(batch->column(expr->get_child(i)->output_column())->col_data());\n\
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}\n\
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\n\
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int new_size = 0;\n\
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if (batch->selected_in_use()) {\n\
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for (int j = 0; j != n; ++j) {\n\
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int i = sel[j];\n\
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for (int k = 1; k < num_children; ++k) {\n\
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if (*v == vectors[k][i]) {\n\
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sel[new_size++] = i;\n\
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break;\n\
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}\n\
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}\n\
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}\n\
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batch->set_size(new_size);\n\
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} else {\n\
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for (int i = 0; i != n; ++i) {\n\
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for (int k = 1; k < num_children; ++k) {\n\
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if (*v == vectors[k][i]) {\n\
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sel[new_size++] = i;\n\
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break;\n\
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}\n\
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}\n\
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}\n\
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\n\
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if (new_size < n) {\n\
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batch->set_size(new_size);\n\
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batch->set_selected_in_use(true);\n\
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}\n\
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}\n\
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}\n\
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} else {\n\
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int c1 = op1->evaluate(batch);\n\
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DCHECK(c1 >= 0);\n\
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${native_type1}* vector1 \n\
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=reinterpret_cast<${native_type1}*>(batch->column(op1->output_column())->col_data());\n\
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if (0 != in_pred->hybird_set()->size()) {\n\
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int new_size = 0;\n\
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if (batch->selected_in_use()) {\n\
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for (int j = 0; j != n; ++j) {\n\
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int i = sel[j];\n\
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if (in_pred->hybird_set()->find(&vector1[i])) {\n\
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sel[new_size++] = i;\n\
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}\n\
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}\n\
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batch->set_size(new_size);\n\
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} else {\n\
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for (int i = 0; i != n; ++i) {\n\
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if (in_pred->hybird_set()->find(&vector1[i])) {\n\
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sel[new_size++] = i;\n\
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}\n\
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}\n\
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\n\
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if (new_size < n) {\n\
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batch->set_size(new_size);\n\
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batch->set_selected_in_use(true);\n\
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}\n\
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}\n\
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}\n\
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\n\
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if (num_children > 1) {\n\
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${native_type1}* vectors[num_children];\n\
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for (int i = 1; i < num_children; ++i) {\n\
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if (!expr->get_child(i)->evaluate(batch)) return false;\n\
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vectors[i] = reinterpret_cast<${native_type1}*>(batch->column(expr->get_child(i)->output_column())->col_data());\n\
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}\n\
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\n\
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int new_size = 0;\n\
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if (batch->selected_in_use()) {\n\
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for (int j = 0; j != n; ++j) {\n\
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int i = sel[j];\n\
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for (int k = 1; k < num_children; ++k) {\n\
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if (vector1[i] == vectors[k][i]) {\n\
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sel[new_size++] = i;\n\
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break;\n\
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}\n\
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}\n\
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}\n\
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batch->set_size(new_size);\n\
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} else {\n\
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for (int i = 0; i != n; ++i) {\n\
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for (int k = 1; k < num_children; ++k) {\n\
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if (vector1[i] == vectors[k][i]) {\n\
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sel[new_size++] = i;\n\
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break;\n\
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}\n\
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}\n\
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}\n\
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\n\
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if (new_size < n) {\n\
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batch->set_size(new_size);\n\
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batch->set_selected_in_use(true);\n\
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}\n\
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}\n\
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}\n\
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}\n\
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return true;\n\
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}\n\n")
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python_template = string.Template("\
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['${fn_name}', '${return_type}', [${args}], 'VectorComputeFunctions::${fn_signature}', []], \n")
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# Mapping of function to template
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templates = {
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'Filter_Eq': filter_binary_op,
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'Filter_Ne': filter_binary_op,
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'Filter_Gt': filter_binary_op,
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'Filter_Lt': filter_binary_op,
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'Filter_Ge': filter_binary_op,
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'Filter_Le': filter_binary_op,
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'Filter_In': filter_in_op,
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}
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# Some aggregate types that are useful for defining functions
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types = {
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'BOOLEAN': ['BOOLEAN'],
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'TINYINT': ['TINYINT'],
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'SMALLINT': ['SMALLINT'],
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'INT': ['INT'],
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'BIGINT': ['BIGINT'],
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'LARGEINT': ['LARGEINT'],
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'FLOAT': ['FLOAT'],
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'DOUBLE': ['DOUBLE'],
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'STRING': ['VARCHAR'],
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'DATE': ['DATE'],
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'DATETIME': ['DATETIME'],
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'DECIMALV2': ['DECIMALV2'],
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'NATIVE_INT_TYPES': ['TINYINT', 'SMALLINT', 'INT', 'BIGINT'],
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'INT_TYPES': ['TINYINT', 'SMALLINT', 'INT', 'BIGINT', 'LARGEINT'],
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'FLOAT_TYPES': ['FLOAT', 'DOUBLE'],
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'NUMERIC_TYPES': ['TINYINT', 'SMALLINT', 'INT', 'BIGINT', 'FLOAT', 'DOUBLE'],
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'NATIVE_TYPES': ['BOOLEAN', 'TINYINT', 'SMALLINT', 'INT', 'BIGINT', 'FLOAT', 'DOUBLE'],
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'STRCAST_TYPES': ['BOOLEAN', 'SMALLINT', 'INT', 'BIGINT', 'FLOAT', 'DOUBLE'],
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'ALL_TYPES': ['BOOLEAN', 'TINYINT', 'SMALLINT', 'INT', 'BIGINT', 'LARGEINT', 'FLOAT',\
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'DOUBLE', 'VARCHAR', 'DATETIME', 'DECIMALV2'],
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'MAX_TYPES': ['BIGINT', 'LARGEINT', 'DOUBLE', 'DECIMALV2'],
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}
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# Operation, [ReturnType], [[Args1], [Args2], ... [ArgsN]]
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functions = [
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# BinaryPredicates
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['Filter_Eq', ['BOOLEAN'], [['ALL_TYPES'], ['ALL_TYPES']]],
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['Filter_Ne', ['BOOLEAN'], [['ALL_TYPES'], ['ALL_TYPES']]],
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['Filter_Gt', ['BOOLEAN'], [['ALL_TYPES'], ['ALL_TYPES']]],
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['Filter_Lt', ['BOOLEAN'], [['ALL_TYPES'], ['ALL_TYPES']]],
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['Filter_Ge', ['BOOLEAN'], [['ALL_TYPES'], ['ALL_TYPES']]],
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['Filter_Le', ['BOOLEAN'], [['ALL_TYPES'], ['ALL_TYPES']]],
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# InPredicates
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['Filter_In', ['BOOLEAN'], [['ALL_TYPES']]],
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]
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native_types = {
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'BOOLEAN': 'bool',
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'TINYINT': 'char',
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'SMALLINT': 'short',
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'INT': 'int',
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'BIGINT': 'long',
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'LARGEINT': '__int128',
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'FLOAT': 'float',
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'DOUBLE': 'double',
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'VARCHAR': 'StringValue',
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'DATE': 'DateTimeValue',
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'DATETIME': 'DateTimeValue',
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'DECIMALV2': 'DecimalV2Value',
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}
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# Portable type used in the function implementation
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implemented_types = {
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'BOOLEAN': 'bool',
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'TINYINT': 'int8_t',
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'SMALLINT': 'int16_t',
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'INT': 'int32_t',
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'BIGINT': 'int64_t',
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'LARGEINT': '__int128',
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'FLOAT': 'float',
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'DOUBLE': 'double',
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'VARCHAR': 'StringValue',
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'DATE': 'DateTimeValue',
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'DATETIME': 'DateTimeValue',
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'DECIMALV2': 'DecimalV2Value',
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}
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native_ops = {
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'Filter_Eq': '==',
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'Filter_Ne': '!=',
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'Filter_Gt': '>',
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'Filter_Lt': '<',
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'Filter_Ge': '>=',
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'Filter_Le': '<=',
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'Eq': '==',
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'Ne': '!=',
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'Gt': '>',
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'Lt': '<',
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'Ge': '>=',
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'Le': '<=',
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'BITAND': '&',
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'BITNOT': '~',
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'BITOR': '|',
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'BITXOR': '^',
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'DIVIDE': '/',
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'EQ': '==',
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'GT': '>',
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'GE': '>=',
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'INT_DIVIDE': '/',
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'SUBTRACT': '-',
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'MOD': '%',
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'MULTIPLY': '*',
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'LT': '<',
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'LE': '<=',
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'NE': '!=',
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'ADD': '+',
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}
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native_funcs = {
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'EQ': 'Eq',
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'LE': 'Le',
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'LT': 'Lt',
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'NE': 'Ne',
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'GE': 'Ge',
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'GT': 'Gt',
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}
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cc_preamble = '\
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// This is a generated file, DO NOT EDIT IT.\n\
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// To add new functions, see impala/common/function-registry/gen_vector_functions.py\n\
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\n\
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#include "gen_cpp/opcode/vector-functions.h"\n\
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#include "exprs/case_expr.h"\n\
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#include "exprs/expr.h"\n\
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#include "exprs/in_predicate.h"\n\
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#include "runtime/string_value.hpp"\n\
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#include "runtime/vectorized_row_batch.h"\n\
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#include "util/string_parser.hpp"\n\
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#include <boost/lexical_cast.hpp>\n\
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\n\
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using namespace boost;\n\
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using namespace std;\n\
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\n\
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namespace doris { \n\
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\n'
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cc_epilogue = '\
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}\n'
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h_preamble = '\
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// This is a generated file, DO NOT EDIT IT.\n\
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// To add new functions, see impala/common/function-registry/gen_vector_functions.py\n\
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\n\
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#ifndef DORIS_OPCODE_VECTOR_FUNCTIONS_H\n\
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#define DORIS_OPCODE_VECTOR_FUNCTIONS_H\n\
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\n\
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namespace doris {\n\
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class Expr;\n\
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class OpcodeRegistry;\n\
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class VectorizedRowBatch;\n\
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\n\
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class VectorComputeFunctions {\n\
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public:\n'
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h_epilogue = '\
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};\n\
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\n\
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}\n\
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\n\
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#endif\n'
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python_preamble = '\
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#!/usr/bin/env python\n\
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# Licensed to the Apache Software Foundation (ASF) under one \n\
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# or more contributor license agreements. See the NOTICE file \n\
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# distributed with this work for additional information \n\
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# regarding copyright ownership. The ASF licenses this file \n\
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# to you under the Apache License, Version 2.0 (the \n\
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# "License"); you may not use this file except in compliance \n\
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# with the License. You may obtain a copy of the License at \n\
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# \n\
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# http://www.apache.org/licenses/LICENSE-2.0\n\
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# \n\
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# Unless required by applicable law or agreed to in writing, software\n\
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# distributed under the License is distributed on an "AS IS" BASIS,\n\
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n\
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# See the License for the specific language governing permissions and\n\
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# limitations under the License.\n\
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\n\
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# This is a generated file, DO NOT EDIT IT.\n\
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# To add new functions, see impala/common/function-registry/gen_opcodes.py\n\
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\n\
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functions = [\n'
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python_epilogue = ']'
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header_template = string.Template("\
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static bool ${fn_signature}(\n\
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Expr* e, VectorizedRowBatch* batch);\n")
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BE_PATH = "../gen_cpp/opcode/"
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if not os.path.exists(BE_PATH):
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os.makedirs(BE_PATH)
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def initialize_sub(op, return_type, arg_types):
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"""
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|
Expand the signature data for template substitution. Returns
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|
a dictionary with all the entries for all the templates used in this script
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|
"""
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|
sub = {}
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sub["fn_name"] = op
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|
sub["fn_signature"] = op
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|
sub["return_type"] = return_type
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|
sub["args"] = ""
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|
if op in native_ops:
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|
sub["native_op"] = native_ops[op]
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|
for idx in range(0, len(arg_types)):
|
|
arg = arg_types[idx]
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|
sub["fn_signature"] += "_" + native_types[arg]
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|
sub["native_type" + repr(idx + 1)] = implemented_types[arg]
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|
sub["args"] += "'" + arg + "', "
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|
return sub
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|
|
|
if __name__ == "__main__":
|
|
h_file = open(BE_PATH + 'vector-functions.h', 'w')
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|
cc_file = open(BE_PATH + 'vector-functions.cc', 'w')
|
|
python_file = open('generated_vector_functions.py', 'w')
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|
h_file.write(h_preamble)
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|
cc_file.write(cc_preamble)
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|
python_file.write(python_preamble)
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|
|
|
# Generate functions and headers
|
|
for func_data in functions:
|
|
op = func_data[0]
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|
# If a specific template has been specified, use that one.
|
|
if len(func_data) >= 4:
|
|
template = func_data[3]
|
|
else:
|
|
# Skip functions with no template (shouldn't be auto-generated)
|
|
if not op in templates:
|
|
continue
|
|
template = templates[op]
|
|
|
|
# Expand all arguments
|
|
return_types = []
|
|
for ret in func_data[1]:
|
|
for t in types[ret]:
|
|
return_types.append(t)
|
|
signatures = []
|
|
for args in func_data[2]:
|
|
expanded_arg = []
|
|
for arg in args:
|
|
for t in types[arg]:
|
|
expanded_arg.append(t)
|
|
signatures.append(expanded_arg)
|
|
|
|
# Put arguments into substitution structure
|
|
num_functions = 0
|
|
for args in signatures:
|
|
num_functions = max(num_functions, len(args))
|
|
num_functions = max(num_functions, len(return_types))
|
|
num_args = len(signatures)
|
|
|
|
# Validate the input is correct
|
|
if len(return_types) != 1 and len(return_types) != num_functions:
|
|
print("Invalid Declaration: " + func_data)
|
|
sys.exit(1)
|
|
|
|
for args in signatures:
|
|
if len(args) != 1 and len(args) != num_functions:
|
|
print("Invalid Declaration: " + func_data)
|
|
sys.exit(1)
|
|
|
|
# Iterate over every function signature to generate
|
|
for i in range(0, num_functions):
|
|
if len(return_types) == 1:
|
|
return_type = return_types[0]
|
|
else:
|
|
return_type = return_types[i]
|
|
|
|
arg_types = []
|
|
for j in range(0, num_args):
|
|
if len(signatures[j]) == 1:
|
|
arg_types.append(signatures[j][0])
|
|
else:
|
|
arg_types.append(signatures[j][i])
|
|
|
|
# At this point, 'return_type' is a single type and 'arg_types'
|
|
# is a list of single types
|
|
sub = initialize_sub(op, return_type, arg_types)
|
|
|
|
h_file.write(header_template.substitute(sub))
|
|
cc_file.write(template.substitute(sub))
|
|
python_file.write(python_template.substitute(sub))
|
|
|
|
h_file.write(h_epilogue)
|
|
cc_file.write(cc_epilogue)
|
|
python_file.write(python_epilogue)
|
|
h_file.close()
|
|
cc_file.close()
|
|
python_file.close()
|