我正在阅读一个大小为30MB的json文件,该文件用于创建列族和键值。然后创建put对象,将rowkey和值插入其中。创建此类put对象的列表并调用table.batch()并传递此列表。当我的arraylist大小为50000时,我称之为此。然后清除列表并调用下一批。然而,处理最终有800000个条目的while文件需要300秒。我也累了,但它更慢。我正在使用hbase 1.1。我从Kafka那里得到了json。如有任何改进性能的建议,我们将不胜感激。我查了那么多论坛,但没什么帮助。如果你想看的话,我会和你分享代码。
当做
拉格哈文德拉
public static void processData(String jsonData)
{
if (jsonData == null || jsonData.isEmpty())
{
System.out.println("JSON data is null or empty. Nothing to process");
return;
}
long startTime = System.currentTimeMillis();
Table table = null;
try
{
table = HBaseConfigUtil.getInstance().getConnection().getTable(TableName.valueOf("MYTABLE"));
}
catch (IOException e1)
{
System.out.println(e1);
}
Put processData = null;
List<Put> bulkData = new ArrayList<Put>();
try
{
//Read the json and generate the model into a class
//ProcessExecutions is List<ProcessExecution>
ProcessExecutions peData = JsonToColumnData.gson.fromJson(jsonData, ProcessExecutions.class);
if (peData != null)
{
//Read the data and pass it to Hbase
for (ProcessExecution pe : peData.processExecutions)
{
//Class Header stores some header information
Header headerData = pe.getHeader();
String rowKey = headerData.getRowKey();
processData = new Put(Bytes.toBytes(JsonToColumnData.rowKey));
processData.addColumn(Bytes.toBytes("Data"),
Bytes.toBytes("Time"),
Bytes.toBytes("value"));
//Add to list
bulkData.add(processData);
if (bulkData.size() >= 50000) //hardcoded for demo
{
long tmpTime = System.currentTimeMillis();
Object[] results = null;
table.batch(bulkData, results);
bulkData.clear();
System.gc();
}
} //end for
//Complete the remaining write operation
if (bulkData.size() > 0)
{
Object[] results = null;
table.batch(bulkData, results);
bulkData.clear();
//Try to free memory
System.gc();
}
}
catch (Exception e)
{
System.out.println(e);
e.printStackTrace();
}
finally
{
try
{
table.close();
}
catch (IOException e)
{
System.out.println("Error closing table " + e);
e.printStackTrace();
}
}
}
//This function is added here to show the connection
/*public Connection getConnection()
{
try
{
if (this.connection == null)
{
ExecutorService executor = Executors.newFixedThreadPool(HBaseConfigUtil.THREADCOUNT);
this.connection = ConnectionFactory.createConnection(this.getHBaseConfiguration(), executor);
}
}
catch (IOException e)
{
e.printStackTrace();
System.out.println("Error in getting connection " + e.getMessage());
}
return this.connection;
}*/
1条答案
按热度按时间4ngedf3f1#
我也遇到过同样的情况,我需要解析5gbjson并插入hbase表…您可以尝试下面的方法(应该可以),在我的例子中,这对于批量处理100000条记录来说非常快。
有关增加缓冲区大小的更多详细信息,请在不同的上下文中检查我的答案以增加缓冲区大小,请参阅https://hbase.apache.org/apidocs/org/apache/hadoop/hbase/client/table.html