4. 使用sql查詢excel內容
1. 簡介
我們在前面的文章中提到了calcite支持csv和json文件的數據源適配, 其實就是將文件解析成表然后以文件夾為schema, 然后將生成的schema注冊到RootSehema(RootSchema是所有數據源schema的parent,多個不同數據源schema可以掛在同一個RootSchema下)下, 最終使用calcite的特性進行sql的解析查詢返回.
但其實我們的數據文件一般使用excel進行存儲,流轉, 但很可惜, calcite本身沒有excel的適配器, 但其實我們可以模仿calcite-file, 自己搞一個calcite-file-excel, 也可以熟悉calcite的工作原理.
2. 實現思路
因為excel有sheet的概念, 所以可以將一個excel解析成schema, 每個sheet解析成table, 實現步驟如下:
- 實現
SchemaFactory重寫create方法: schema工廠 用于創建schema - 繼承
AbstractSchema: schema描述類 用于解析excel, 創建table(解析sheet) - 繼承
AbstractTable, ScannableTable: table描述類 提供字段信息和數據內容等(解析sheet data)
3. Excel樣例
excel有兩個sheet頁, 分別是user_info 和 role_info如下:


ok, 萬事具備.
4. Maven
<dependency>
<groupId>org.apache.poi</groupId>
<artifactId>poi-ooxml</artifactId>
<version>5.2.3</version>
</dependency>
<dependency>
<groupId>org.apache.poi</groupId>
<artifactId>poi</artifactId>
<version>5.2.3</version>
</dependency>
<dependency>
<groupId>org.apache.calcite</groupId>
<artifactId>calcite-core</artifactId>
<version>1.37.0</version>
</dependency>
5. 核心代碼
5.1 SchemaFactory
package com.ldx.calcite.excel;
import com.google.common.collect.Lists;
import org.apache.calcite.schema.Schema;
import org.apache.calcite.schema.SchemaFactory;
import org.apache.calcite.schema.SchemaPlus;
import org.apache.commons.lang3.ObjectUtils;
import org.apache.commons.lang3.StringUtils;
import java.io.File;
import java.util.List;
import java.util.Map;
/**
* schema factory
*/
public class ExcelSchemaFactory implements SchemaFactory {
public final static ExcelSchemaFactory INSTANCE = new ExcelSchemaFactory();
private ExcelSchemaFactory(){}
@Override
public Schema create(SchemaPlus parentSchema, String name, Map<String, Object> operand) {
final Object filePath = operand.get("filePath");
if (ObjectUtils.isEmpty(filePath)) {
throw new NullPointerException("can not find excel file");
}
return this.create(filePath.toString());
}
public Schema create(String excelFilePath) {
if (StringUtils.isBlank(excelFilePath)) {
throw new NullPointerException("can not find excel file");
}
return this.create(new File(excelFilePath));
}
public Schema create(File excelFile) {
if (ObjectUtils.isEmpty(excelFile) || !excelFile.exists()) {
throw new NullPointerException("can not find excel file");
}
if (!excelFile.isFile() || !isExcelFile(excelFile)) {
throw new RuntimeException("can not find excel file: " + excelFile.getAbsolutePath());
}
return new ExcelSchema(excelFile);
}
protected List<String> supportedFileSuffix() {
return Lists.newArrayList("xls", "xlsx");
}
private boolean isExcelFile(File excelFile) {
if (ObjectUtils.isEmpty(excelFile)) {
return false;
}
final String name = excelFile.getName();
return StringUtils.endsWithAny(name, this.supportedFileSuffix().toArray(new String[0]));
}
}
schema中有多個重載的create方法用于方便的創建schema, 最終將excel file 交給ExcelSchema創建一個schema對象
5.2 Schema
package com.ldx.calcite.excel;
import org.apache.calcite.schema.Table;
import org.apache.calcite.schema.impl.AbstractSchema;
import org.apache.commons.lang3.ObjectUtils;
import org.apache.poi.ss.usermodel.Sheet;
import org.apache.poi.ss.usermodel.Workbook;
import org.apache.poi.ss.usermodel.WorkbookFactory;
import org.testng.collections.Maps;
import java.io.File;
import java.util.Iterator;
import java.util.Map;
/**
* schema
*/
public class ExcelSchema extends AbstractSchema {
private final File excelFile;
private Map<String, Table> tableMap;
public ExcelSchema(File excelFile) {
this.excelFile = excelFile;
}
@Override
protected Map<String, Table> getTableMap() {
if (ObjectUtils.isEmpty(tableMap)) {
tableMap = createTableMap();
}
return tableMap;
}
private Map<String, Table> createTableMap() {
final Map<String, Table> result = Maps.newHashMap();
try (Workbook workbook = WorkbookFactory.create(excelFile)) {
final Iterator<Sheet> sheetIterator = workbook.sheetIterator();
while (sheetIterator.hasNext()) {
final Sheet sheet = sheetIterator.next();
final ExcelScannableTable excelScannableTable = new ExcelScannableTable(sheet, null);
result.put(sheet.getSheetName(), excelScannableTable);
}
}
catch (Exception ignored) {}
return result;
}
}
schema類讀取Excel file, 并循環讀取sheet, 將每個sheet解析成ExcelScannableTable并存儲
5.3 Table
package com.ldx.calcite.excel;
import com.google.common.collect.Lists;
import com.ldx.calcite.excel.enums.JavaFileTypeEnum;
import org.apache.calcite.DataContext;
import org.apache.calcite.adapter.java.JavaTypeFactory;
import org.apache.calcite.linq4j.Enumerable;
import org.apache.calcite.linq4j.Linq4j;
import org.apache.calcite.rel.type.RelDataType;
import org.apache.calcite.rel.type.RelDataTypeFactory;
import org.apache.calcite.rel.type.RelProtoDataType;
import org.apache.calcite.schema.ScannableTable;
import org.apache.calcite.schema.impl.AbstractTable;
import org.apache.calcite.sql.type.SqlTypeName;
import org.apache.calcite.util.Pair;
import org.apache.commons.lang3.ObjectUtils;
import org.apache.poi.ss.usermodel.Cell;
import org.apache.poi.ss.usermodel.Row;
import org.apache.poi.ss.usermodel.Sheet;
import org.checkerframework.checker.nullness.qual.Nullable;
import java.util.List;
/**
* table
*/
public class ExcelScannableTable extends AbstractTable implements ScannableTable {
private final RelProtoDataType protoRowType;
private final Sheet sheet;
private RelDataType rowType;
private List<JavaFileTypeEnum> fieldTypes;
private List<Object[]> rowDataList;
public ExcelScannableTable(Sheet sheet, RelProtoDataType protoRowType) {
this.protoRowType = protoRowType;
this.sheet = sheet;
}
@Override
public Enumerable<@Nullable Object[]> scan(DataContext root) {
JavaTypeFactory typeFactory = root.getTypeFactory();
final List<JavaFileTypeEnum> fieldTypes = this.getFieldTypes(typeFactory);
if (rowDataList == null) {
rowDataList = readExcelData(sheet, fieldTypes);
}
return Linq4j.asEnumerable(rowDataList);
}
@Override
public RelDataType getRowType(RelDataTypeFactory typeFactory) {
if (ObjectUtils.isNotEmpty(protoRowType)) {
return protoRowType.apply(typeFactory);
}
if (ObjectUtils.isEmpty(rowType)) {
rowType = deduceRowType((JavaTypeFactory) typeFactory, sheet, null);
}
return rowType;
}
public List<JavaFileTypeEnum> getFieldTypes(RelDataTypeFactory typeFactory) {
if (fieldTypes == null) {
fieldTypes = Lists.newArrayList();
deduceRowType((JavaTypeFactory) typeFactory, sheet, fieldTypes);
}
return fieldTypes;
}
private List<Object[]> readExcelData(Sheet sheet, List<JavaFileTypeEnum> fieldTypes) {
List<Object[]> rowDataList = Lists.newArrayList();
for (int rowIndex = 1; rowIndex <= sheet.getLastRowNum(); rowIndex++) {
Row row = sheet.getRow(rowIndex);
Object[] rowData = new Object[fieldTypes.size()];
for (int i = 0; i < row.getLastCellNum(); i++) {
final JavaFileTypeEnum javaFileTypeEnum = fieldTypes.get(i);
Cell cell = row.getCell(i, Row.MissingCellPolicy.CREATE_NULL_AS_BLANK);
final Object cellValue = javaFileTypeEnum.getCellValue(cell);
rowData[i] = cellValue;
}
rowDataList.add(rowData);
}
return rowDataList;
}
public static RelDataType deduceRowType(JavaTypeFactory typeFactory, Sheet sheet, List<JavaFileTypeEnum> fieldTypes) {
final List<String> names = Lists.newArrayList();
final List<RelDataType> types = Lists.newArrayList();
if (sheet != null) {
Row headerRow = sheet.getRow(0);
if (headerRow != null) {
for (int i = 0; i < headerRow.getLastCellNum(); i++) {
Cell cell = headerRow.getCell(i, Row.MissingCellPolicy.CREATE_NULL_AS_BLANK);
String[] columnInfo = cell
.getStringCellValue()
.split(":");
String columnName = columnInfo[0].trim();
String columnType = null;
if (columnInfo.length == 2) {
columnType = columnInfo[1].trim();
}
final JavaFileTypeEnum javaFileType = JavaFileTypeEnum
.of(columnType)
.orElse(JavaFileTypeEnum.UNKNOWN);
final RelDataType sqlType = typeFactory.createSqlType(javaFileType.getSqlTypeName());
names.add(columnName);
types.add(sqlType);
if (fieldTypes != null) {
fieldTypes.add(javaFileType);
}
}
}
}
if (names.isEmpty()) {
names.add("line");
types.add(typeFactory.createSqlType(SqlTypeName.VARCHAR));
}
return typeFactory.createStructType(Pair.zip(names, types));
}
}
table類中其中有兩個比較關鍵的方法
-
scan: 掃描表內容, 我們這里將sheet頁面的數據內容解析存儲最后交給calcite -
getRowType: 獲取字段信息, 我們這里默認使用第一條記錄作為表頭(row[0]) 并解析為字段信息, 字段規則跟csv一樣name:string, 冒號前面的是字段key, 冒號后面的是字段類型, 如果未指定字段類型, 則解析為UNKNOWN, 后續JavaFileTypeEnum會進行類型推斷, 最終在結果處理時calcite也會進行推斷 -
deduceRowType: 推斷字段類型, 方法中使用JavaFileTypeEnum枚舉類對java type & sql type & 字段值轉化處理方法 進行管理
5.4 ColumnTypeEnum
package com.ldx.calcite.excel.enums;
import lombok.Getter;
import lombok.extern.slf4j.Slf4j;
import org.apache.calcite.avatica.util.DateTimeUtils;
import org.apache.calcite.sql.type.SqlTypeName;
import org.apache.commons.lang3.ObjectUtils;
import org.apache.commons.lang3.StringUtils;
import org.apache.commons.lang3.time.FastDateFormat;
import org.apache.poi.ss.usermodel.Cell;
import org.apache.poi.ss.usermodel.DateUtil;
import org.apache.poi.ss.util.CellUtil;
import java.text.ParseException;
import java.text.SimpleDateFormat;
import java.util.Arrays;
import java.util.Date;
import java.util.Optional;
import java.util.TimeZone;
import java.util.function.Function;
/**
* type converter
*/
@Slf4j
@Getter
public enum JavaFileTypeEnum {
STRING("string", SqlTypeName.VARCHAR, Cell::getStringCellValue),
BOOLEAN("boolean", SqlTypeName.BOOLEAN, Cell::getBooleanCellValue),
BYTE("byte", SqlTypeName.TINYINT, Cell::getStringCellValue),
CHAR("char", SqlTypeName.CHAR, Cell::getStringCellValue),
SHORT("short", SqlTypeName.SMALLINT, Cell::getNumericCellValue),
INT("int", SqlTypeName.INTEGER, cell -> (Double.valueOf(cell.getNumericCellValue()).intValue())),
LONG("long", SqlTypeName.BIGINT, cell -> (Double.valueOf(cell.getNumericCellValue()).longValue())),
FLOAT("float", SqlTypeName.REAL, Cell::getNumericCellValue),
DOUBLE("double", SqlTypeName.DOUBLE, Cell::getNumericCellValue),
DATE("date", SqlTypeName.DATE, getValueWithDate()),
TIMESTAMP("timestamp", SqlTypeName.TIMESTAMP, getValueWithTimestamp()),
TIME("time", SqlTypeName.TIME, getValueWithTime()),
UNKNOWN("unknown", SqlTypeName.UNKNOWN, getValueWithUnknown()),;
// cell type
private final String typeName;
// sql type
private final SqlTypeName sqlTypeName;
// value convert func
private final Function<Cell, Object> cellValueFunc;
private static final FastDateFormat TIME_FORMAT_DATE;
private static final FastDateFormat TIME_FORMAT_TIME;
private static final FastDateFormat TIME_FORMAT_TIMESTAMP;
static {
final TimeZone gmt = TimeZone.getTimeZone("GMT");
TIME_FORMAT_DATE = FastDateFormat.getInstance("yyyy-MM-dd", gmt);
TIME_FORMAT_TIME = FastDateFormat.getInstance("HH:mm:ss", gmt);
TIME_FORMAT_TIMESTAMP = FastDateFormat.getInstance("yyyy-MM-dd HH:mm:ss", gmt);
}
JavaFileTypeEnum(String typeName, SqlTypeName sqlTypeName, Function<Cell, Object> cellValueFunc) {
this.typeName = typeName;
this.sqlTypeName = sqlTypeName;
this.cellValueFunc = cellValueFunc;
}
public static Optional<JavaFileTypeEnum> of(String typeName) {
return Arrays
.stream(values())
.filter(type -> StringUtils.equalsIgnoreCase(typeName, type.getTypeName()))
.findFirst();
}
public static SqlTypeName findSqlTypeName(String typeName) {
final Optional<JavaFileTypeEnum> javaFileTypeOptional = of(typeName);
if (javaFileTypeOptional.isPresent()) {
return javaFileTypeOptional
.get()
.getSqlTypeName();
}
return SqlTypeName.UNKNOWN;
}
public Object getCellValue(Cell cell) {
return cellValueFunc.apply(cell);
}
public static Function<Cell, Object> getValueWithUnknown() {
return cell -> {
if (ObjectUtils.isEmpty(cell)) {
return null;
}
switch (cell.getCellType()) {
case STRING:
return cell.getStringCellValue();
case NUMERIC:
if (DateUtil.isCellDateFormatted(cell)) {
// 如果是日期類型,返回日期對象
return cell.getDateCellValue();
}
else {
// 否則返回數值
return cell.getNumericCellValue();
}
case BOOLEAN:
return cell.getBooleanCellValue();
case FORMULA:
// 對于公式單元格,先計算公式結果,再獲取其值
try {
return cell.getNumericCellValue();
}
catch (Exception e) {
try {
return cell.getStringCellValue();
}
catch (Exception ex) {
log.error("parse unknown data error, cellRowIndex:{}, cellColumnIndex:{}", cell.getRowIndex(), cell.getColumnIndex(), e);
return null;
}
}
case BLANK:
return "";
default:
return null;
}
};
}
public static Function<Cell, Object> getValueWithDate() {
return cell -> {
Date date = cell.getDateCellValue();
if(ObjectUtils.isEmpty(date)) {
return null;
}
try {
final String formated = new SimpleDateFormat("yyyy-MM-dd").format(date);
Date newDate = TIME_FORMAT_DATE.parse(formated);
return (int) (newDate.getTime() / DateTimeUtils.MILLIS_PER_DAY);
}
catch (ParseException e) {
log.error("parse date error, date:{}", date, e);
}
return null;
};
}
public static Function<Cell, Object> getValueWithTimestamp() {
return cell -> {
Date date = cell.getDateCellValue();
if(ObjectUtils.isEmpty(date)) {
return null;
}
try {
final String formated = new SimpleDateFormat("yyyy-MM-dd HH:mm:ss").format(date);
Date newDate = TIME_FORMAT_TIMESTAMP.parse(formated);
return (int) newDate.getTime();
}
catch (ParseException e) {
log.error("parse timestamp error, date:{}", date, e);
}
return null;
};
}
public static Function<Cell, Object> getValueWithTime() {
return cell -> {
Date date = cell.getDateCellValue();
if(ObjectUtils.isEmpty(date)) {
return null;
}
try {
final String formated = new SimpleDateFormat("HH:mm:ss").format(date);
Date newDate = TIME_FORMAT_TIME.parse(formated);
return newDate.getTime();
}
catch (ParseException e) {
log.error("parse time error, date:{}", date, e);
}
return null;
};
}
}
該枚舉類主要管理了java type& sql type & cell value convert func, 方便統一管理類型映射及單元格內容提取時的轉換方法(這里借用了java8 function函數特性)
注: 這里的日期轉換只能這樣寫, 即使用GMT的時區(抄的
calcite-file), 要不然輸出的日期時間一直有時差...
6. 測試查詢
package com.ldx.calcite;
import com.ldx.calcite.excel.ExcelSchemaFactory;
import lombok.SneakyThrows;
import lombok.extern.slf4j.Slf4j;
import org.apache.calcite.config.CalciteConnectionProperty;
import org.apache.calcite.jdbc.CalciteConnection;
import org.apache.calcite.schema.Schema;
import org.apache.calcite.schema.SchemaPlus;
import org.apache.calcite.util.Sources;
import org.junit.jupiter.api.AfterAll;
import org.junit.jupiter.api.BeforeAll;
import org.junit.jupiter.api.Test;
import org.testng.collections.Maps;
import java.net.URL;
import java.sql.Connection;
import java.sql.DriverManager;
import java.sql.ResultSet;
import java.sql.ResultSetMetaData;
import java.sql.SQLException;
import java.sql.Statement;
import java.util.Map;
import java.util.Properties;
@Slf4j
public class CalciteExcelTest {
private static Connection connection;
private static SchemaPlus rootSchema;
private static CalciteConnection calciteConnection;
@BeforeAll
@SneakyThrows
public static void beforeAll() {
Properties info = new Properties();
// 不區分sql大小寫
info.setProperty(CalciteConnectionProperty.CASE_SENSITIVE.camelName(), "false");
// 創建Calcite連接
connection = DriverManager.getConnection("jdbc:calcite:", info);
calciteConnection = connection.unwrap(CalciteConnection.class);
// 構建RootSchema,在Calcite中,RootSchema是所有數據源schema的parent,多個不同數據源schema可以掛在同一個RootSchema下
rootSchema = calciteConnection.getRootSchema();
}
@Test
@SneakyThrows
public void test_execute_query() {
final Schema schema = ExcelSchemaFactory.INSTANCE.create(resourcePath("file/test.xlsx"));
rootSchema.add("test", schema);
// 設置默認的schema
calciteConnection.setSchema("test");
final Statement statement = calciteConnection.createStatement();
ResultSet resultSet = statement.executeQuery("SELECT * FROM user_info");
printResultSet(resultSet);
System.out.println("=========");
ResultSet resultSet2 = statement.executeQuery("SELECT * FROM test.user_info where id > 110 and birthday > '2003-01-01'");
printResultSet(resultSet2);
System.out.println("=========");
ResultSet resultSet3 = statement.executeQuery("SELECT * FROM test.user_info ui inner join test.role_info ri on ui.role_id = ri.id");
printResultSet(resultSet3);
}
@AfterAll
@SneakyThrows
public static void closeResource() {
connection.close();
}
private static String resourcePath(String path) {
final URL url = CalciteExcelTest.class.getResource("/" + path);
return Sources.of(url).file().getAbsolutePath();
}
public static void printResultSet(ResultSet resultSet) throws SQLException {
// 獲取 ResultSet 元數據
ResultSetMetaData metaData = resultSet.getMetaData();
// 獲取列數
int columnCount = metaData.getColumnCount();
log.info("Number of columns: {}",columnCount);
// 遍歷 ResultSet 并打印結果
while (resultSet.next()) {
final Map<String, String> item = Maps.newHashMap();
// 遍歷每一列并打印
for (int i = 1; i <= columnCount; i++) {
String columnName = metaData.getColumnName(i);
String columnValue = resultSet.getString(i);
item.put(columnName, columnValue);
}
log.info(item.toString());
}
}
}
測試結果如下:


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