Dev*_*xon 106 java sql json resultset
以下代码ResultSet
使用JSONArray
和将a转换为JSON字符串JSONObject
.
import org.json.JSONArray;
import org.json.JSONObject;
import org.json.JSONException;
import java.sql.SQLException;
import java.sql.ResultSet;
import java.sql.ResultSetMetaData;
public class ResultSetConverter {
public static JSONArray convert( ResultSet rs )
throws SQLException, JSONException
{
JSONArray json = new JSONArray();
ResultSetMetaData rsmd = rs.getMetaData();
while(rs.next()) {
int numColumns = rsmd.getColumnCount();
JSONObject obj = new JSONObject();
for (int i=1; i<numColumns+1; i++) {
String column_name = rsmd.getColumnName(i);
if(rsmd.getColumnType(i)==java.sql.Types.ARRAY){
obj.put(column_name, rs.getArray(column_name));
}
else if(rsmd.getColumnType(i)==java.sql.Types.BIGINT){
obj.put(column_name, rs.getInt(column_name));
}
else if(rsmd.getColumnType(i)==java.sql.Types.BOOLEAN){
obj.put(column_name, rs.getBoolean(column_name));
}
else if(rsmd.getColumnType(i)==java.sql.Types.BLOB){
obj.put(column_name, rs.getBlob(column_name));
}
else if(rsmd.getColumnType(i)==java.sql.Types.DOUBLE){
obj.put(column_name, rs.getDouble(column_name));
}
else if(rsmd.getColumnType(i)==java.sql.Types.FLOAT){
obj.put(column_name, rs.getFloat(column_name));
}
else if(rsmd.getColumnType(i)==java.sql.Types.INTEGER){
obj.put(column_name, rs.getInt(column_name));
}
else if(rsmd.getColumnType(i)==java.sql.Types.NVARCHAR){
obj.put(column_name, rs.getNString(column_name));
}
else if(rsmd.getColumnType(i)==java.sql.Types.VARCHAR){
obj.put(column_name, rs.getString(column_name));
}
else if(rsmd.getColumnType(i)==java.sql.Types.TINYINT){
obj.put(column_name, rs.getInt(column_name));
}
else if(rsmd.getColumnType(i)==java.sql.Types.SMALLINT){
obj.put(column_name, rs.getInt(column_name));
}
else if(rsmd.getColumnType(i)==java.sql.Types.DATE){
obj.put(column_name, rs.getDate(column_name));
}
else if(rsmd.getColumnType(i)==java.sql.Types.TIMESTAMP){
obj.put(column_name, rs.getTimestamp(column_name));
}
else{
obj.put(column_name, rs.getObject(column_name));
}
}
json.put(obj);
}
return json;
}
}
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Pla*_*lap 33
我认为有一种方法可以使用更少的内存(固定而非线性的数量取决于数据基数),但这意味着改变方法签名.实际上,我们可以在从ResultSet中获取Json数据时直接在输出流上打印Json数据:已经写入的数据将被垃圾收集,因为我们不需要将数据保存在内存中的数组.
我使用接受类型适配器的GSON.我写了一个类型适配器来将ResultSet转换为JsonArray,它看起来非常像你的代码.我正在等待"Gson 2.1:Targeted Dec 31,2011"发布,该版本将支持"支持用户定义的流式传输类型适配器".然后我将修改我的适配器作为流适配器.
正如所承诺的那样,我回来了但不是和Gson一起,而是和Jackson 2一起.抱歉迟到了(2年).
前言:使用较少内存的结果itsef的关键是在"服务器端"光标中.使用这种游标(也就是Java开发人员的结果集),当客户端继续读取时,DBMS会逐步向客户端(也称为驱动程序)发送数据.我认为Oracle游标默认是服务器端.对于MySQL> 5.0.2,在连接url paramenter中查找useCursorFetch .检查您最喜欢的DBMS.
1:因此,为了减少使用内存,我们必须:
JSONArray
)中,而是直接在输出行上写入每一行,其中输出行I表示输出流或编写器,或者包含输出流或编写器的json生成器.2:杰克逊文档说:
流式API性能最佳(开销最低,读/写速度最快;其他2种方法基于它)
3:我在你的代码中看到你使用getInt,getBoolean.getFloat ... ResultSet中没有的wasNull.我希望这会产生问题.
4:我使用数组来缓存think并避免每次迭代都调用getter.虽然不是开关/案例构造的粉丝,但我将它用于int
SQL Types
.
答案:尚未完全测试,它基于Jackson 2.2:
<dependency>
<groupId>com.fasterxml.jackson.core</groupId>
<artifactId>jackson-databind</artifactId>
<version>2.2.2</version>
</dependency>
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该ResultSetSerializer
对象指示Jackson如何序列化(将对象转换为JSON)ResultSet.它使用内部的Jackson Streaming API.这里的测试代码:
SimpleModule module = new SimpleModule();
module.addSerializer(new ResultSetSerializer());
ObjectMapper objectMapper = new ObjectMapper();
objectMapper.registerModule(module);
[ . . . do the query . . . ]
ResultSet resultset = statement.executeQuery(query);
// Use the DataBind Api here
ObjectNode objectNode = objectMapper.createObjectNode();
// put the resultset in a containing structure
objectNode.putPOJO("results", resultset);
// generate all
objectMapper.writeValue(stringWriter, objectNode);
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当然,还有ResultSetSerializer类的代码:
public class ResultSetSerializer extends JsonSerializer<ResultSet> {
public static class ResultSetSerializerException extends JsonProcessingException{
private static final long serialVersionUID = -914957626413580734L;
public ResultSetSerializerException(Throwable cause){
super(cause);
}
}
@Override
public Class<ResultSet> handledType() {
return ResultSet.class;
}
@Override
public void serialize(ResultSet rs, JsonGenerator jgen, SerializerProvider provider) throws IOException, JsonProcessingException {
try {
ResultSetMetaData rsmd = rs.getMetaData();
int numColumns = rsmd.getColumnCount();
String[] columnNames = new String[numColumns];
int[] columnTypes = new int[numColumns];
for (int i = 0; i < columnNames.length; i++) {
columnNames[i] = rsmd.getColumnLabel(i + 1);
columnTypes[i] = rsmd.getColumnType(i + 1);
}
jgen.writeStartArray();
while (rs.next()) {
boolean b;
long l;
double d;
jgen.writeStartObject();
for (int i = 0; i < columnNames.length; i++) {
jgen.writeFieldName(columnNames[i]);
switch (columnTypes[i]) {
case Types.INTEGER:
l = rs.getInt(i + 1);
if (rs.wasNull()) {
jgen.writeNull();
} else {
jgen.writeNumber(l);
}
break;
case Types.BIGINT:
l = rs.getLong(i + 1);
if (rs.wasNull()) {
jgen.writeNull();
} else {
jgen.writeNumber(l);
}
break;
case Types.DECIMAL:
case Types.NUMERIC:
jgen.writeNumber(rs.getBigDecimal(i + 1));
break;
case Types.FLOAT:
case Types.REAL:
case Types.DOUBLE:
d = rs.getDouble(i + 1);
if (rs.wasNull()) {
jgen.writeNull();
} else {
jgen.writeNumber(d);
}
break;
case Types.NVARCHAR:
case Types.VARCHAR:
case Types.LONGNVARCHAR:
case Types.LONGVARCHAR:
jgen.writeString(rs.getString(i + 1));
break;
case Types.BOOLEAN:
case Types.BIT:
b = rs.getBoolean(i + 1);
if (rs.wasNull()) {
jgen.writeNull();
} else {
jgen.writeBoolean(b);
}
break;
case Types.BINARY:
case Types.VARBINARY:
case Types.LONGVARBINARY:
jgen.writeBinary(rs.getBytes(i + 1));
break;
case Types.TINYINT:
case Types.SMALLINT:
l = rs.getShort(i + 1);
if (rs.wasNull()) {
jgen.writeNull();
} else {
jgen.writeNumber(l);
}
break;
case Types.DATE:
provider.defaultSerializeDateValue(rs.getDate(i + 1), jgen);
break;
case Types.TIMESTAMP:
provider.defaultSerializeDateValue(rs.getTime(i + 1), jgen);
break;
case Types.BLOB:
Blob blob = rs.getBlob(i);
provider.defaultSerializeValue(blob.getBinaryStream(), jgen);
blob.free();
break;
case Types.CLOB:
Clob clob = rs.getClob(i);
provider.defaultSerializeValue(clob.getCharacterStream(), jgen);
clob.free();
break;
case Types.ARRAY:
throw new RuntimeException("ResultSetSerializer not yet implemented for SQL type ARRAY");
case Types.STRUCT:
throw new RuntimeException("ResultSetSerializer not yet implemented for SQL type STRUCT");
case Types.DISTINCT:
throw new RuntimeException("ResultSetSerializer not yet implemented for SQL type DISTINCT");
case Types.REF:
throw new RuntimeException("ResultSetSerializer not yet implemented for SQL type REF");
case Types.JAVA_OBJECT:
default:
provider.defaultSerializeValue(rs.getObject(i + 1), jgen);
break;
}
}
jgen.writeEndObject();
}
jgen.writeEndArray();
} catch (SQLException e) {
throw new ResultSetSerializerException(e);
}
}
}
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ora*_*ecz 27
使这更快的两件事是:
将您的通话移至rsmd.getColumnCount()
while循环之外.列数不应在行之间变化.
对于每个列类型,您最终都会调用以下内容:
obj.put(column_name, rs.getInt(column_name));
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使用列索引检索列值会稍微快一些:
obj.put(column_name, rs.getInt(i));
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And*_*ite 23
JIT编译器可能会非常快,因为它只是分支和基本测试.你可以通过HashMap查找回调来使它更优雅,但我怀疑它会更快.至于记忆,这是非常苗条的.
不知何故,我怀疑这段代码实际上是内存或性能的关键瓶颈.你有什么理由尝试优化它吗?
Elh*_*aky 21
一个更简单的解决方案(基于相关代码):
JSONArray json = new JSONArray();
ResultSetMetaData rsmd = rs.getMetaData();
while(rs.next()) {
int numColumns = rsmd.getColumnCount();
JSONObject obj = new JSONObject();
for (int i=1; i<=numColumns; i++) {
String column_name = rsmd.getColumnName(i);
obj.put(column_name, rs.getObject(column_name));
}
json.put(obj);
}
return json;
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Luk*_*der 10
你可以使用jOOQ来完成这项工作.您不必使用jOOQ的所有功能来利用一些有用的JDBC扩展.在这种情况下,只需写:
String json = DSL.using(connection).fetch(resultSet).formatJSON();
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使用的相关API方法是:
DSLContext.fetch(ResultSet)
将JDBC ResultSet转换为jOOQ结果.Result.formatJSON()
将jOOQ结果格式化为JSON字符串.生成的格式将如下所示:
{"fields":[{"name":"field-1","type":"type-1"},
{"name":"field-2","type":"type-2"},
...,
{"name":"field-n","type":"type-n"}],
"records":[[value-1-1,value-1-2,...,value-1-n],
[value-2-1,value-2-2,...,value-2-n]]}
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您也可以轻松地创建自己的格式 Result.map(RecordMapper)
这基本上与您的代码相同,绕过JSON对象的生成,直接"流式传输"到StringBuilder
.不过,我会说两种情况下的性能开销应该可以忽略不计.
(免责声明:我为jOOQ背后的公司工作)
除了@Jim Cook提出的建议.另一个想法是使用开关而不是if-elses:
while(rs.next()) {
int numColumns = rsmd.getColumnCount();
JSONObject obj = new JSONObject();
for( int i=1; i<numColumns+1; i++) {
String column_name = rsmd.getColumnName(i);
switch( rsmd.getColumnType( i ) ) {
case java.sql.Types.ARRAY:
obj.put(column_name, rs.getArray(column_name)); break;
case java.sql.Types.BIGINT:
obj.put(column_name, rs.getInt(column_name)); break;
case java.sql.Types.BOOLEAN:
obj.put(column_name, rs.getBoolean(column_name)); break;
case java.sql.Types.BLOB:
obj.put(column_name, rs.getBlob(column_name)); break;
case java.sql.Types.DOUBLE:
obj.put(column_name, rs.getDouble(column_name)); break;
case java.sql.Types.FLOAT:
obj.put(column_name, rs.getFloat(column_name)); break;
case java.sql.Types.INTEGER:
obj.put(column_name, rs.getInt(column_name)); break;
case java.sql.Types.NVARCHAR:
obj.put(column_name, rs.getNString(column_name)); break;
case java.sql.Types.VARCHAR:
obj.put(column_name, rs.getString(column_name)); break;
case java.sql.Types.TINYINT:
obj.put(column_name, rs.getInt(column_name)); break;
case java.sql.Types.SMALLINT:
obj.put(column_name, rs.getInt(column_name)); break;
case java.sql.Types.DATE:
obj.put(column_name, rs.getDate(column_name)); break;
case java.sql.Types.TIMESTAMP:
obj.put(column_name, rs.getTimestamp(column_name)); break;
default:
obj.put(column_name, rs.getObject(column_name)); break;
}
}
json.put(obj);
}
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