// Licensed to the LF AI & Data foundation 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.

package importutil

import (
	"context"
	"fmt"

	"github.com/cockroachdb/errors"

	"github.com/milvus-io/milvus-proto/go-api/schemapb"
	"github.com/milvus-io/milvus/internal/allocator"
	"github.com/milvus-io/milvus/internal/common"
	"github.com/milvus-io/milvus/internal/log"
	"github.com/milvus-io/milvus/internal/storage"
	"github.com/milvus-io/milvus/internal/util/timerecord"
	"github.com/milvus-io/milvus/internal/util/typeutil"
	"go.uber.org/zap"
)

type NumpyColumnReader struct {
	fieldName string             // name of the target column
	fieldID   storage.FieldID    // ID of the target column
	dataType  schemapb.DataType  // data type of the target column
	rowCount  int                // how many rows need to be read
	dimension int                // only for vector
	file      storage.FileReader // file to be read
	reader    *NumpyAdapter      // data reader
}

func closeReaders(columnReaders []*NumpyColumnReader) {
	for _, reader := range columnReaders {
		if reader.file != nil {
			err := reader.file.Close()
			if err != nil {
				log.Error("Numper parser: failed to close numpy file", zap.String("fileName", reader.fieldName+NumpyFileExt))
			}
		}
	}
}

type NumpyParser struct {
	ctx                context.Context            // for canceling parse process
	collectionSchema   *schemapb.CollectionSchema // collection schema
	rowIDAllocator     *allocator.IDAllocator     // autoid allocator
	shardNum           int32                      // sharding number of the collection
	blockSize          int64                      // maximum size of a read block(unit:byte)
	chunkManager       storage.ChunkManager       // storage interfaces to browse/read the files
	autoIDRange        []int64                    // auto-generated id range, for example: [1, 10, 20, 25] means id from 1 to 10 and 20 to 25
	callFlushFunc      ImportFlushFunc            // call back function to flush segment
	updateProgressFunc func(percent int64)        // update working progress percent value
}

// NewNumpyParser is helper function to create a NumpyParser
func NewNumpyParser(ctx context.Context,
	collectionSchema *schemapb.CollectionSchema,
	idAlloc *allocator.IDAllocator,
	shardNum int32,
	blockSize int64,
	chunkManager storage.ChunkManager,
	flushFunc ImportFlushFunc,
	updateProgressFunc func(percent int64)) (*NumpyParser, error) {
	if collectionSchema == nil {
		log.Error("Numper parser: collection schema is nil")
		return nil, errors.New("collection schema is nil")
	}

	if idAlloc == nil {
		log.Error("Numper parser: id allocator is nil")
		return nil, errors.New("id allocator is nil")
	}

	if chunkManager == nil {
		log.Error("Numper parser: chunk manager pointer is nil")
		return nil, errors.New("chunk manager pointer is nil")
	}

	if flushFunc == nil {
		log.Error("Numper parser: flush function is nil")
		return nil, errors.New("flush function is nil")
	}

	parser := &NumpyParser{
		ctx:                ctx,
		collectionSchema:   collectionSchema,
		rowIDAllocator:     idAlloc,
		shardNum:           shardNum,
		blockSize:          blockSize,
		chunkManager:       chunkManager,
		autoIDRange:        make([]int64, 0),
		callFlushFunc:      flushFunc,
		updateProgressFunc: updateProgressFunc,
	}

	return parser, nil
}

func (p *NumpyParser) IDRange() []int64 {
	return p.autoIDRange
}

// Parse is the function entry
func (p *NumpyParser) Parse(filePaths []string) error {
	// check redundant files for column-based import
	// if the field is primary key and autoid is false, the file is required
	// any redundant file is not allowed
	err := p.validateFileNames(filePaths)
	if err != nil {
		return err
	}

	// open files and verify file header
	readers, err := p.createReaders(filePaths)
	// make sure all the files are closed finially, must call this method before the function return
	defer closeReaders(readers)
	if err != nil {
		return err
	}

	// read all data from the numpy files
	err = p.consume(readers)
	if err != nil {
		return err
	}

	return nil
}

// validateFileNames is to check redundant file and missed file
func (p *NumpyParser) validateFileNames(filePaths []string) error {
	requiredFieldNames := make(map[string]interface{})
	for _, schema := range p.collectionSchema.Fields {
		if schema.GetIsPrimaryKey() {
			if !schema.GetAutoID() {
				requiredFieldNames[schema.GetName()] = nil
			}
		} else {
			requiredFieldNames[schema.GetName()] = nil
		}
	}

	// check redundant file
	fileNames := make(map[string]interface{})
	for _, filePath := range filePaths {
		name, _ := GetFileNameAndExt(filePath)
		fileNames[name] = nil
		_, ok := requiredFieldNames[name]
		if !ok {
			log.Error("Numpy parser: the file has no corresponding field in collection", zap.String("fieldName", name))
			return fmt.Errorf("the file '%s' has no corresponding field in collection", filePath)
		}
	}

	// check missed file
	for name := range requiredFieldNames {
		_, ok := fileNames[name]
		if !ok {
			log.Error("Numpy parser: there is no file corresponding to field", zap.String("fieldName", name))
			return fmt.Errorf("there is no file corresponding to field '%s'", name)
		}
	}

	return nil
}

// createReaders open the files and verify file header
func (p *NumpyParser) createReaders(filePaths []string) ([]*NumpyColumnReader, error) {
	readers := make([]*NumpyColumnReader, 0)

	for _, filePath := range filePaths {
		fileName, _ := GetFileNameAndExt(filePath)

		// check existence of the target field
		var schema *schemapb.FieldSchema
		for i := 0; i < len(p.collectionSchema.Fields); i++ {
			tmpSchema := p.collectionSchema.Fields[i]
			if tmpSchema.GetName() == fileName {
				schema = tmpSchema
				break
			}
		}

		if schema == nil {
			log.Error("Numpy parser: the field is not found in collection schema", zap.String("fileName", fileName))
			return nil, fmt.Errorf("the field name '%s' is not found in collection schema", fileName)
		}

		file, err := p.chunkManager.Reader(p.ctx, filePath)
		if err != nil {
			log.Error("Numpy parser: failed to read the file", zap.String("filePath", filePath), zap.Error(err))
			return nil, fmt.Errorf("failed to read the file '%s', error: %s", filePath, err.Error())
		}

		adapter, err := NewNumpyAdapter(file)
		if err != nil {
			log.Error("Numpy parser: failed to read the file header", zap.String("filePath", filePath), zap.Error(err))
			return nil, fmt.Errorf("failed to read the file header '%s', error: %s", filePath, err.Error())
		}

		if file == nil || adapter == nil {
			log.Error("Numpy parser: failed to open file", zap.String("filePath", filePath))
			return nil, fmt.Errorf("failed to open file '%s'", filePath)
		}

		dim, _ := getFieldDimension(schema)
		columnReader := &NumpyColumnReader{
			fieldName: schema.GetName(),
			fieldID:   schema.GetFieldID(),
			dataType:  schema.GetDataType(),
			dimension: dim,
			file:      file,
			reader:    adapter,
		}

		// the validation method only check the file header information
		err = p.validateHeader(columnReader)
		if err != nil {
			return nil, err
		}
		readers = append(readers, columnReader)
	}

	// row count of each file should be equal
	if len(readers) > 0 {
		firstReader := readers[0]
		rowCount := firstReader.rowCount
		for i := 1; i < len(readers); i++ {
			compareReader := readers[i]
			if rowCount != compareReader.rowCount {
				log.Error("Numpy parser: the row count of files are not equal",
					zap.String("firstFile", firstReader.fieldName), zap.Int("firstRowCount", firstReader.rowCount),
					zap.String("compareFile", compareReader.fieldName), zap.Int("compareRowCount", compareReader.rowCount))
				return nil, fmt.Errorf("the row count(%d) of file '%s.npy' is not equal to row count(%d) of file '%s.npy'",
					firstReader.rowCount, firstReader.fieldName, compareReader.rowCount, compareReader.fieldName)
			}
		}
	}

	return readers, nil
}

// validateHeader is to verify numpy file header, file header information should match field's schema
func (p *NumpyParser) validateHeader(columnReader *NumpyColumnReader) error {
	if columnReader == nil || columnReader.reader == nil {
		log.Error("Numpy parser: numpy reader is nil")
		return errors.New("numpy adapter is nil")
	}

	elementType := columnReader.reader.GetType()
	shape := columnReader.reader.GetShape()
	columnReader.rowCount = shape[0]

	// 1. field data type should be consist to numpy data type
	// 2. vector field dimension should be consist to numpy shape
	if schemapb.DataType_FloatVector == columnReader.dataType {
		// float32/float64 numpy file can be used for float vector file, 2 reasons:
		// 1. for float vector, we support float32 and float64 numpy file because python float value is 64 bit
		// 2. for float64 numpy file, the performance is worse than float32 numpy file
		if elementType != schemapb.DataType_Float && elementType != schemapb.DataType_Double {
			log.Error("Numpy parser: illegal data type of numpy file for float vector field", zap.Any("dataType", elementType),
				zap.String("fieldName", columnReader.fieldName))
			return fmt.Errorf("illegal data type %s of numpy file for float vector field '%s'", getTypeName(elementType),
				columnReader.fieldName)
		}

		// vector field, the shape should be 2
		if len(shape) != 2 {
			log.Error("Numpy parser: illegal shape of numpy file for float vector field, shape should be 2", zap.Int("shape", len(shape)),
				zap.String("fieldName", columnReader.fieldName))
			return fmt.Errorf("illegal shape %d of numpy file for float vector field '%s', shape should be 2", shape,
				columnReader.fieldName)
		}

		if shape[1] != columnReader.dimension {
			log.Error("Numpy parser: illegal dimension of numpy file for float vector field", zap.String("fieldName", columnReader.fieldName),
				zap.Int("numpyDimension", shape[1]), zap.Int("fieldDimension", columnReader.dimension))
			return fmt.Errorf("illegal dimension %d of numpy file for float vector field '%s', dimension should be %d",
				shape[1], columnReader.fieldName, columnReader.dimension)
		}
	} else if schemapb.DataType_BinaryVector == columnReader.dataType {
		if elementType != schemapb.DataType_BinaryVector {
			log.Error("Numpy parser: illegal data type of numpy file for binary vector field", zap.Any("dataType", elementType),
				zap.String("fieldName", columnReader.fieldName))
			return fmt.Errorf("illegal data type %s of numpy file for binary vector field '%s'", getTypeName(elementType),
				columnReader.fieldName)
		}

		// vector field, the shape should be 2
		if len(shape) != 2 {
			log.Error("Numpy parser: illegal shape of numpy file for binary vector field, shape should be 2", zap.Int("shape", len(shape)),
				zap.String("fieldName", columnReader.fieldName))
			return fmt.Errorf("illegal shape %d of numpy file for binary vector field '%s', shape should be 2", shape,
				columnReader.fieldName)
		}

		if shape[1] != columnReader.dimension/8 {
			log.Error("Numpy parser: illegal dimension of numpy file for float vector field", zap.String("fieldName", columnReader.fieldName),
				zap.Int("numpyDimension", shape[1]*8), zap.Int("fieldDimension", columnReader.dimension))
			return fmt.Errorf("illegal dimension %d of numpy file for binary vector field '%s', dimension should be %d",
				shape[1]*8, columnReader.fieldName, columnReader.dimension)
		}
	} else {
		if elementType != columnReader.dataType {
			log.Error("Numpy parser: illegal data type of numpy file for scalar field", zap.Any("numpyDataType", elementType),
				zap.String("fieldName", columnReader.fieldName), zap.Any("fieldDataType", columnReader.dataType))
			return fmt.Errorf("illegal data type %s of numpy file for scalar field '%s' with type %s",
				getTypeName(elementType), columnReader.fieldName, getTypeName(columnReader.dataType))
		}

		// scalar field, the shape should be 1
		if len(shape) != 1 {
			log.Error("Numpy parser: illegal shape of numpy file for scalar field, shape should be 1", zap.Int("shape", len(shape)),
				zap.String("fieldName", columnReader.fieldName))
			return fmt.Errorf("illegal shape %d of numpy file for scalar field '%s', shape should be 1", shape, columnReader.fieldName)
		}
	}

	return nil
}

// calcRowCountPerBlock calculates a proper value for a batch row count to read file
func (p *NumpyParser) calcRowCountPerBlock() (int64, error) {
	sizePerRecord, err := typeutil.EstimateSizePerRecord(p.collectionSchema)
	if err != nil {
		log.Error("Numpy parser: failed to estimate size of each row", zap.Error(err))
		return 0, fmt.Errorf("failed to estimate size of each row: %s", err.Error())
	}

	if sizePerRecord <= 0 {
		log.Error("Numpy parser: failed to estimate size of each row, the collection schema might be empty")
		return 0, fmt.Errorf("failed to estimate size of each row: the collection schema might be empty")
	}

	// the sizePerRecord is estimate value, if the schema contains varchar field, the value is not accurate
	// we will read data block by block, by default, each block size is 16MB
	// rowCountPerBlock is the estimated row count for a block
	rowCountPerBlock := p.blockSize / int64(sizePerRecord)
	if rowCountPerBlock <= 0 {
		rowCountPerBlock = 1 // make sure the value is positive
	}

	log.Info("Numper parser: calculate row count per block to read file", zap.Int64("rowCountPerBlock", rowCountPerBlock),
		zap.Int64("blockSize", p.blockSize), zap.Int("sizePerRecord", sizePerRecord))
	return rowCountPerBlock, nil
}

// consume method reads numpy data section into a storage.FieldData
// please note it will require a large memory block(the memory size is almost equal to numpy file size)
func (p *NumpyParser) consume(columnReaders []*NumpyColumnReader) error {
	rowCountPerBlock, err := p.calcRowCountPerBlock()
	if err != nil {
		return err
	}

	updateProgress := func(readRowCount int) {
		if p.updateProgressFunc != nil && len(columnReaders) != 0 && columnReaders[0].rowCount > 0 {
			percent := (readRowCount * ProgressValueForPersist) / columnReaders[0].rowCount
			log.Debug("Numper parser: working progress", zap.Int("readRowCount", readRowCount),
				zap.Int("totalRowCount", columnReaders[0].rowCount), zap.Int("percent", percent))
			p.updateProgressFunc(int64(percent))
		}
	}

	// prepare shards
	shards := make([]map[storage.FieldID]storage.FieldData, 0, p.shardNum)
	for i := 0; i < int(p.shardNum); i++ {
		segmentData := initSegmentData(p.collectionSchema)
		if segmentData == nil {
			log.Error("Numper parser: failed to initialize FieldData list")
			return fmt.Errorf("failed to initialize FieldData list")
		}
		shards = append(shards, segmentData)
	}
	tr := timerecord.NewTimeRecorder("consume performance")
	defer tr.Elapse("end")
	// read data from files, batch by batch
	totalRead := 0
	for {
		readRowCount := 0
		segmentData := make(map[storage.FieldID]storage.FieldData)
		for _, reader := range columnReaders {
			fieldData, err := p.readData(reader, int(rowCountPerBlock))
			if err != nil {
				return err
			}

			if readRowCount == 0 {
				readRowCount = fieldData.RowNum()
			} else if readRowCount != fieldData.RowNum() {
				log.Error("Numpy parser: data block's row count mismatch", zap.Int("firstBlockRowCount", readRowCount),
					zap.Int("thisBlockRowCount", fieldData.RowNum()), zap.Int64("rowCountPerBlock", rowCountPerBlock))
				return fmt.Errorf("data block's row count mismatch: %d vs %d", readRowCount, fieldData.RowNum())
			}

			segmentData[reader.fieldID] = fieldData
		}

		// nothing to read
		if readRowCount == 0 {
			break
		}
		totalRead += readRowCount
		updateProgress(totalRead)
		tr.Record("readData")
		// split data to shards
		err = p.splitFieldsData(segmentData, shards)
		if err != nil {
			return err
		}
		tr.Record("splitFieldsData")
		// when the estimated size is close to blockSize, save to binlog
		err = tryFlushBlocks(p.ctx, shards, p.collectionSchema, p.callFlushFunc, p.blockSize, MaxTotalSizeInMemory, false)
		if err != nil {
			return err
		}
		tr.Record("tryFlushBlocks")
	}

	// force flush at the end
	return tryFlushBlocks(p.ctx, shards, p.collectionSchema, p.callFlushFunc, p.blockSize, MaxTotalSizeInMemory, true)
}

// readData method reads numpy data section into a storage.FieldData
func (p *NumpyParser) readData(columnReader *NumpyColumnReader, rowCount int) (storage.FieldData, error) {
	switch columnReader.dataType {
	case schemapb.DataType_Bool:
		data, err := columnReader.reader.ReadBool(rowCount)
		if err != nil {
			log.Error("Numpy parser: failed to read bool array", zap.Error(err))
			return nil, fmt.Errorf("failed to read bool array: %s", err.Error())
		}

		return &storage.BoolFieldData{
			Data: data,
		}, nil
	case schemapb.DataType_Int8:
		data, err := columnReader.reader.ReadInt8(rowCount)
		if err != nil {
			log.Error("Numpy parser: failed to read int8 array", zap.Error(err))
			return nil, fmt.Errorf("failed to read int8 array: %s", err.Error())
		}

		return &storage.Int8FieldData{
			Data: data,
		}, nil
	case schemapb.DataType_Int16:
		data, err := columnReader.reader.ReadInt16(rowCount)
		if err != nil {
			log.Error("Numpy parser: failed to int16 array", zap.Error(err))
			return nil, fmt.Errorf("failed to read int16 array: %s", err.Error())
		}

		return &storage.Int16FieldData{
			Data: data,
		}, nil
	case schemapb.DataType_Int32:
		data, err := columnReader.reader.ReadInt32(rowCount)
		if err != nil {
			log.Error("Numpy parser: failed to read int32 array", zap.Error(err))
			return nil, fmt.Errorf("failed to read int32 array: %s", err.Error())
		}

		return &storage.Int32FieldData{
			Data: data,
		}, nil
	case schemapb.DataType_Int64:
		data, err := columnReader.reader.ReadInt64(rowCount)
		if err != nil {
			log.Error("Numpy parser: failed to read int64 array", zap.Error(err))
			return nil, fmt.Errorf("failed to read int64 array: %s", err.Error())
		}

		return &storage.Int64FieldData{
			Data: data,
		}, nil
	case schemapb.DataType_Float:
		data, err := columnReader.reader.ReadFloat32(rowCount)
		if err != nil {
			log.Error("Numpy parser: failed to read float array", zap.Error(err))
			return nil, fmt.Errorf("failed to read float array: %s", err.Error())
		}

		return &storage.FloatFieldData{
			Data: data,
		}, nil
	case schemapb.DataType_Double:
		data, err := columnReader.reader.ReadFloat64(rowCount)
		if err != nil {
			log.Error("Numpy parser: failed to read double array", zap.Error(err))
			return nil, fmt.Errorf("failed to read double array: %s", err.Error())
		}

		return &storage.DoubleFieldData{
			Data: data,
		}, nil
	case schemapb.DataType_VarChar:
		data, err := columnReader.reader.ReadString(rowCount)
		if err != nil {
			log.Error("Numpy parser: failed to read varchar array", zap.Error(err))
			return nil, fmt.Errorf("failed to read varchar array: %s", err.Error())
		}

		return &storage.StringFieldData{
			Data: data,
		}, nil
	case schemapb.DataType_BinaryVector:
		data, err := columnReader.reader.ReadUint8(rowCount * (columnReader.dimension / 8))
		if err != nil {
			log.Error("Numpy parser: failed to read binary vector array", zap.Error(err))
			return nil, fmt.Errorf("failed to read binary vector array: %s", err.Error())
		}

		return &storage.BinaryVectorFieldData{
			Data: data,
			Dim:  columnReader.dimension,
		}, nil
	case schemapb.DataType_FloatVector:
		// float32/float64 numpy file can be used for float vector file, 2 reasons:
		// 1. for float vector, we support float32 and float64 numpy file because python float value is 64 bit
		// 2. for float64 numpy file, the performance is worse than float32 numpy file
		elementType := columnReader.reader.GetType()

		var data []float32
		var err error
		if elementType == schemapb.DataType_Float {
			data, err = columnReader.reader.ReadFloat32(rowCount * columnReader.dimension)
			if err != nil {
				log.Error("Numpy parser: failed to read float vector array", zap.Error(err))
				return nil, fmt.Errorf("failed to read float vector array: %s", err.Error())
			}
		} else if elementType == schemapb.DataType_Double {
			data = make([]float32, 0, columnReader.rowCount)
			data64, err := columnReader.reader.ReadFloat64(rowCount * columnReader.dimension)
			if err != nil {
				log.Error("Numpy parser: failed to read float vector array", zap.Error(err))
				return nil, fmt.Errorf("failed to read float vector array: %s", err.Error())
			}

			for _, f64 := range data64 {
				data = append(data, float32(f64))
			}
		}

		return &storage.FloatVectorFieldData{
			Data: data,
			Dim:  columnReader.dimension,
		}, nil
	default:
		log.Error("Numpy parser: unsupported data type of field", zap.Any("dataType", columnReader.dataType),
			zap.String("fieldName", columnReader.fieldName))
		return nil, fmt.Errorf("unsupported data type %s of field '%s'", getTypeName(columnReader.dataType),
			columnReader.fieldName)
	}
}

// appendFunc defines the methods to append data to storage.FieldData
func (p *NumpyParser) appendFunc(schema *schemapb.FieldSchema) func(src storage.FieldData, n int, target storage.FieldData) error {
	switch schema.DataType {
	case schemapb.DataType_Bool:
		return func(src storage.FieldData, n int, target storage.FieldData) error {
			arr := target.(*storage.BoolFieldData)
			arr.Data = append(arr.Data, src.GetRow(n).(bool))
			return nil
		}
	case schemapb.DataType_Float:
		return func(src storage.FieldData, n int, target storage.FieldData) error {
			arr := target.(*storage.FloatFieldData)
			arr.Data = append(arr.Data, src.GetRow(n).(float32))
			return nil
		}
	case schemapb.DataType_Double:
		return func(src storage.FieldData, n int, target storage.FieldData) error {
			arr := target.(*storage.DoubleFieldData)
			arr.Data = append(arr.Data, src.GetRow(n).(float64))
			return nil
		}
	case schemapb.DataType_Int8:
		return func(src storage.FieldData, n int, target storage.FieldData) error {
			arr := target.(*storage.Int8FieldData)
			arr.Data = append(arr.Data, src.GetRow(n).(int8))
			return nil
		}
	case schemapb.DataType_Int16:
		return func(src storage.FieldData, n int, target storage.FieldData) error {
			arr := target.(*storage.Int16FieldData)
			arr.Data = append(arr.Data, src.GetRow(n).(int16))
			return nil
		}
	case schemapb.DataType_Int32:
		return func(src storage.FieldData, n int, target storage.FieldData) error {
			arr := target.(*storage.Int32FieldData)
			arr.Data = append(arr.Data, src.GetRow(n).(int32))
			return nil
		}
	case schemapb.DataType_Int64:
		return func(src storage.FieldData, n int, target storage.FieldData) error {
			arr := target.(*storage.Int64FieldData)
			arr.Data = append(arr.Data, src.GetRow(n).(int64))
			return nil
		}
	case schemapb.DataType_BinaryVector:
		return func(src storage.FieldData, n int, target storage.FieldData) error {
			arr := target.(*storage.BinaryVectorFieldData)
			arr.Data = append(arr.Data, src.GetRow(n).([]byte)...)
			return nil
		}
	case schemapb.DataType_FloatVector:
		return func(src storage.FieldData, n int, target storage.FieldData) error {
			arr := target.(*storage.FloatVectorFieldData)
			arr.Data = append(arr.Data, src.GetRow(n).([]float32)...)
			return nil
		}
	case schemapb.DataType_String, schemapb.DataType_VarChar:
		return func(src storage.FieldData, n int, target storage.FieldData) error {
			arr := target.(*storage.StringFieldData)
			arr.Data = append(arr.Data, src.GetRow(n).(string))
			return nil
		}
	default:
		return nil
	}
}

func (p *NumpyParser) prepareAppendFunctions() (map[string]func(src storage.FieldData, n int, target storage.FieldData) error, error) {
	appendFunctions := make(map[string]func(src storage.FieldData, n int, target storage.FieldData) error)
	for i := 0; i < len(p.collectionSchema.Fields); i++ {
		schema := p.collectionSchema.Fields[i]
		appendFuncErr := p.appendFunc(schema)
		if appendFuncErr == nil {
			log.Error("Numpy parser: unsupported field data type")
			return nil, fmt.Errorf("unsupported field data type: %d", schema.GetDataType())
		}
		appendFunctions[schema.GetName()] = appendFuncErr
	}
	return appendFunctions, nil
}

// checkRowCount checks existence of each field, and returns the primary key schema
// check row count, all fields row count must be equal
func (p *NumpyParser) checkRowCount(fieldsData map[storage.FieldID]storage.FieldData) (int, *schemapb.FieldSchema, error) {
	rowCount := 0
	rowCounter := make(map[string]int)
	var primaryKey *schemapb.FieldSchema
	for i := 0; i < len(p.collectionSchema.Fields); i++ {
		schema := p.collectionSchema.Fields[i]
		if schema.GetIsPrimaryKey() {
			primaryKey = schema
		}

		if !schema.GetAutoID() {
			v, ok := fieldsData[schema.GetFieldID()]
			if !ok {
				log.Error("Numpy parser: field not provided", zap.String("fieldName", schema.GetName()))
				return 0, nil, fmt.Errorf("field '%s' not provided", schema.GetName())
			}
			rowCounter[schema.GetName()] = v.RowNum()
			if v.RowNum() > rowCount {
				rowCount = v.RowNum()
			}
		}
	}
	if primaryKey == nil {
		log.Error("Numpy parser: primary key field is not found")
		return 0, nil, fmt.Errorf("primary key field is not found")
	}

	for name, count := range rowCounter {
		if count != rowCount {
			log.Error("Numpy parser: field row count is not equal to other fields row count", zap.String("fieldName", name),
				zap.Int("rowCount", count), zap.Int("otherRowCount", rowCount))
			return 0, nil, fmt.Errorf("field '%s' row count %d is not equal to other fields row count: %d", name, count, rowCount)
		}
	}
	// log.Info("Numpy parser: try to split a block with row count", zap.Int("rowCount", rowCount))

	return rowCount, primaryKey, nil
}

// splitFieldsData is to split the in-memory data(parsed from column-based files) into shards
func (p *NumpyParser) splitFieldsData(fieldsData map[storage.FieldID]storage.FieldData, shards []map[storage.FieldID]storage.FieldData) error {
	if len(fieldsData) == 0 {
		log.Error("Numpy parser: fields data to split is empty")
		return fmt.Errorf("fields data to split is empty")
	}

	if len(shards) != int(p.shardNum) {
		log.Error("Numpy parser: block count is not equal to collection shard number", zap.Int("shardsLen", len(shards)),
			zap.Int32("shardNum", p.shardNum))
		return fmt.Errorf("block count %d is not equal to collection shard number %d", len(shards), p.shardNum)
	}

	rowCount, primaryKey, err := p.checkRowCount(fieldsData)
	if err != nil {
		return err
	}

	// generate auto id for primary key and rowid field
	rowIDBegin, rowIDEnd, err := p.rowIDAllocator.Alloc(uint32(rowCount))
	if err != nil {
		log.Error("Numpy parser: failed to alloc row ID", zap.Int("rowCount", rowCount), zap.Error(err))
		return fmt.Errorf("failed to alloc %d rows ID, error: %w", rowCount, err)
	}

	rowIDField, ok := fieldsData[common.RowIDField]
	if !ok {
		rowIDField = &storage.Int64FieldData{
			Data: make([]int64, 0),
		}
		fieldsData[common.RowIDField] = rowIDField
	}
	rowIDFieldArr := rowIDField.(*storage.Int64FieldData)
	for i := rowIDBegin; i < rowIDEnd; i++ {
		rowIDFieldArr.Data = append(rowIDFieldArr.Data, i)
	}

	// reset the primary keys, as we know, only int64 pk can be auto-generated
	if primaryKey.GetAutoID() {
		log.Info("Numpy parser: generating auto-id", zap.Int("rowCount", rowCount), zap.Int64("rowIDBegin", rowIDBegin))
		if primaryKey.GetDataType() != schemapb.DataType_Int64 {
			log.Error("Numpy parser: primary key field is auto-generated but the field type is not int64")
			return fmt.Errorf("primary key field is auto-generated but the field type is not int64")
		}

		primaryDataArr := &storage.Int64FieldData{
			Data: make([]int64, 0, rowCount),
		}
		for i := rowIDBegin; i < rowIDEnd; i++ {
			primaryDataArr.Data = append(primaryDataArr.Data, i)
		}

		fieldsData[primaryKey.GetFieldID()] = primaryDataArr
		p.autoIDRange = append(p.autoIDRange, rowIDBegin, rowIDEnd)
	}

	// if the primary key is not auto-gernerate and user doesn't provide, return error
	primaryData, ok := fieldsData[primaryKey.GetFieldID()]
	if !ok || primaryData.RowNum() <= 0 {
		log.Error("Numpy parser: primary key field is not provided", zap.String("keyName", primaryKey.GetName()))
		return fmt.Errorf("primary key '%s' field data is not provided", primaryKey.GetName())
	}

	// prepare append functions
	appendFunctions, err := p.prepareAppendFunctions()
	if err != nil {
		return err
	}

	// split data into shards
	for i := 0; i < rowCount; i++ {
		// hash to a shard number

		pk := primaryData.GetRow(i)
		shard, err := pkToShard(pk, uint32(p.shardNum))
		if err != nil {
			return err
		}

		// set rowID field
		rowIDField := shards[shard][common.RowIDField].(*storage.Int64FieldData)
		rowIDField.Data = append(rowIDField.Data, rowIDFieldArr.GetRow(i).(int64))

		// append row to shard
		for k := 0; k < len(p.collectionSchema.Fields); k++ {
			schema := p.collectionSchema.Fields[k]
			srcData := fieldsData[schema.GetFieldID()]
			targetData := shards[shard][schema.GetFieldID()]
			if srcData == nil || targetData == nil {
				log.Error("Numpy parser: cannot append data since source or target field data is nil",
					zap.String("FieldName", schema.GetName()),
					zap.Bool("sourceNil", srcData == nil), zap.Bool("targetNil", targetData == nil))
				return fmt.Errorf("cannot append data for field '%s' since source or target field data is nil",
					primaryKey.GetName())
			}
			appendFunc := appendFunctions[schema.GetName()]
			err := appendFunc(srcData, i, targetData)
			if err != nil {
				return err
			}
		}
	}

	return nil
}