稀疏列是对 Null 值采用优化的存储方式的普通列。稀疏列减少了 Null 值的空间需求,但代价是检索非 Null 值的开销增加。当至少能够节省 20% 到 40% 的空间时,才应考虑使用稀疏列。
当您连接到 SQL Server 2008 或更高版本的服务器时,SQL Server JDBC Driver 3.0 支持稀疏列。可以使用 SQLServerDatabaseMetaData.getColumns、SQLServerDatabaseMetaData.getFunctionColumns 或 SQLServerDatabaseMetaData.getProcedureColumns 确定哪个列是稀疏列以及哪个列是列集列。
列集是返回非类型化 XML 形式的所有稀疏列的计算列。当表中有很多列、大于 1024 或分别对这些稀疏列进行操作很烦琐时,应考虑使用列集。列集最多可以包含 30,000 个列。
示例
说明
此示例说明如何检测列集。它还显示如何分析列集的 XML 输出,以便从稀疏列获取数据。
所列的第一个代码部分是您应该对服务器运行的 Transact-SQL。
所列的第二个代码部分是 Java 源代码。在编译应用程序之前,应更改连接字符串中服务器的名称。
代码
use AdventureWorks
CREATE TABLE ColdCalling
(
ID int IDENTITY(1,1) PRIMARY KEY,
[Date] date,
[Time] time,
PositiveFirstName nvarchar(50) SPARSE,
PositiveLastName nvarchar(50) SPARSE,
SpecialPurposeColumns XML COLUMN_SET FOR ALL_SPARSE_COLUMNS
);
GO
INSERT ColdCalling ([Date], [Time])
VALUES ('10-13-09','07:05:24')
GO
INSERT ColdCalling ([Date], [Time], PositiveFirstName, PositiveLastName)
VALUES ('07-20-09','05:00:24', 'AA', 'B')
GO
INSERT ColdCalling ([Date], [Time], PositiveFirstName, PositiveLastName)
VALUES ('07-20-09','05:15:00', 'CC', 'DD')
GO代码
import java.sql.*;
import javax.xml.parsers.DocumentBuilder;
import javax.xml.parsers.DocumentBuilderFactory;
import org.xml.sax.InputSource;
import java.io.StringReader;
import org.w3c.dom.Document;
import org.w3c.dom.Node;
import org.w3c.dom.NodeList;
public class SparseColumns {
public static void main(String args[]) {
final String connectionUrl = "jdbc:sqlserver://my_server;databaseName=AdventureWorks;integratedSecurity=true;";
Connection conn = null;
Statement stmt = null;
ResultSet rs = null;
try {
conn = DriverManager.getConnection(connectionUrl);
stmt = conn.createStatement();
// Determine the column set column
String columnSetColName = null;
String strCmd = "SELECT name FROM sys.columns WHERE object_id=(SELECT OBJECT_ID('ColdCalling')) AND is_column_set = 1";
rs = stmt.executeQuery(strCmd);
if (rs.next()) {
columnSetColName = rs.getString(1);
System.out.println(columnSetColName + " is the column set column!");
}
rs.close();
rs = null;
strCmd = "SELECT * FROM ColdCalling";
rs = stmt.executeQuery(strCmd);
// Iterate through the result set
ResultSetMetaData rsmd = rs.getMetaData();
DocumentBuilderFactory dbf = DocumentBuilderFactory.newInstance();
DocumentBuilder db = dbf.newDocumentBuilder();
InputSource is = new InputSource();
while (rs.next()) {
// Iterate through the columns
for (int i = 1; i <= rsmd.getColumnCount(); ++i) {
String name = rsmd.getColumnName(i);
String value = rs.getString(i);
// If this is the column set column
if (name.equalsIgnoreCase(columnSetColName)) {
System.out.println(name);
// Instead of printing the raw XML, parse it
if (value != null) {
// Add artificial root node "sparse" to ensure XML is well formed
String xml = "<sparse>" + value + "</sparse>";
is.setCharacterStream(new StringReader(xml));
Document doc = db.parse(is);
// Extract the NodeList from the artificial root node that was added
NodeList list = doc.getChildNodes();
// This is the <sparse> node
Node root = list.item(0);
// These are the xml column nodes
NodeList sparseColumnList = root.getChildNodes();
// Iterate through the XML document
for (int n = 0; n < sparseColumnList.getLength(); ++n) {
Node sparseColumnNode = sparseColumnList.item(n);
String columnName = sparseColumnNode.getNodeName();
// Note that the column value is not in the sparseColumNode, it is the value of the first child of it
Node sparseColumnValueNode = sparseColumnNode.getFirstChild();
String columnValue = sparseColumnValueNode.getNodeValue();
System.out.println("\t" + columnName + "\t: " + columnValue);
}
}
} else { // Just print the name + value of non-sparse columns
System.out.println(name + "\t: " + value);
}
}
System.out.println();//New line between rows
}
} catch (Exception e) {
e.printStackTrace();
} finally {
if (rs != null) {
try {
rs.close();
} catch (Exception e) {
e.printStackTrace();
}
}
if (stmt != null) {
try {
stmt.close();
} catch (Exception e) {
e.printStackTrace();
}
}
if (conn != null) {
try {
conn.close();
} catch (Exception e) {
e.printStackTrace();
}
}
}
}
}