package example.avro;
import java.io.File;
import java.io.IOException;
import org.apache.avro.file.DataFileReader;
import org.apache.avro.file.DataFileWriter;
import org.apache.avro.io.DatumReader;
import org.apache.avro.io.DatumWriter;
import org.apache.avro.specific.SpecificDatumReader;
import org.apache.avro.specific.SpecificDatumWriter;
public class Test {
public static void main(String[] args) throws IOException {
code();
decode();
}
public static void code() throws IOException{
User user1 = new User();
user1.setName("Alyssa");
user1.setFavoriteNumber(256);
// Leave favorite color null
// Alternate constructor
User user2 = new User("Ben", 7, "red");
// Construct via builder
User user3 = User.newBuilder().setName("Charlie").setFavoriteColor("blue")
.setFavoriteNumber(null).build();
User user4 = new User("Jimmy", 7, "yellow");
// Serialize user1 and user2 to disk
File file = new File("users.avro");
DatumWriter userDatumWriter = new SpecificDatumWriter(
User.class);
DataFileWriter dataFileWriter = new DataFileWriter(
userDatumWriter);
dataFileWriter.create(user1.getSchema(), new File("users.avro"));
dataFileWriter.append(user1);
dataFileWriter.append(user2);
dataFileWriter.append(user3);
dataFileWriter.append(user4);
dataFileWriter.close();
}
public static void decode() throws IOException{
// Deserialize Users from disk
DatumReader userDatumReader = new SpecificDatumReader(User.class);
File file = new File("users.avro");
DataFileReader dataFileReader = new DataFileReader(file , userDatumReader);
User user = null;
while (dataFileReader.hasNext()) {
// Reuse user object by passing it to next(). This saves us from
// allocating and garbage collecting many objects for files with
// many items.
user = dataFileReader.next(user);
System.out.println(user);
}
}
}
昨晚和朋友聊天,喝了点咖啡,由于我经常喝茶,很长时间没喝咖啡了,所以失眠了,于是起床读JVM规范,读完后在朋友圈发了一条信息:
JVM Run-Time Data Areas:The Java Virtual Machine defines various run-time data areas that are used during execution of a program. So
Spark SQL supports most commonly used features of HiveQL. However, different HiveQL statements are executed in different manners:
1. DDL statements (e.g. CREATE TABLE, DROP TABLE, etc.)
nginx在运行过程中是否稳定,是否有异常退出过?这里总结几项平时会用到的小技巧。
1. 在error.log中查看是否有signal项,如果有,看看signal是多少。
比如,这是一个异常退出的情况:
$grep signal error.log
2012/12/24 16:39:56 [alert] 13661#0: worker process 13666 exited on s
方法一:常用方法 关闭XML验证
工具栏:windows => preferences => xml => xml files => validation => Indicate when no grammar is specified:选择Ignore即可。
方法二:(个人推荐)
添加 内容如下
<?xml version=
<!DOCTYPE html PUBLIC "-//W3C//DTD XHTML 1.0 Transitional//EN" "http://www.w3.org/TR/xhtml1/DTD/xhtml1-transitional.dtd"><html xmlns="http://www.w3.org/1999/xhtml&quo
最主要的是使用到了一个jquery的插件jquery.media.js,使用这个插件就很容易实现了。
核心代码
<!DOCTYPE html PUBLIC "-//W3C//DTD XHTML 1.0 Transitional//EN" "http://www.w3.org/TR/xhtml1/DTD/xhtml1-transitional.