最近着手处理大批量数据的任务。
现状是这样的，一个数据采集程序承载大批量数据的存储和检索。后期可能需要对大批量数据进行统计。
数据分布情况
13个点定时生成采集结果到4个文件（小文件生成周期是5分钟）
名称 大小（b） gather_1_2014-02-27-14-50-0.txt 568497 gather_1_2014-02-27-14-50-1.txt 568665 gather_1_2014-02-27-14-50-2.txt 568172 gather_1_2014-02-27-14-50-3.txt 568275
同步使用shell脚本对四个文件入到sybase_iq库的一张表tab_tmp_2014_2_27中.
每天数据量大概是3亿条，所以小文件的总量大概是3G。小文件数量大，单表容量大执行复合主键查询，由原来2s延时变成了，5~10分钟。
针对上述情况需要对目前的储存结构进行优化。
才是看了下相关系统 catior使用的是环状数据库，存储相关的数据优点方便生成MRTG图，缺点不利于数据统计。后来引入elasticsearch来对大数据检索进行优化。
测试平台
cpu: AMD Opteron(tm) Processor 6136 64bit 2.4GHz * 32 内存: 64G 硬盘：1.5T 操作系统：Red Hat Enterprise Linux Server release 6.4 (Santiago)
读取文件的目录结构：
[test@test001 data]$ ls 0 1 2 3
简单测试代码：
public class FileReader
{
private File file;
private String splitCharactor;
private Map<String, Class<?>> colNames;
private static final Logger LOG = Logger.getLogger(FileReader.class);
/**
* @param path
* 文件路径
* @param fileName
* 文件名
* @param splitCharactor
* 拆分字符
* @param colNames
* 主键名称
*/
public FileReader(File file, String splitCharactor, Map<String, Class<?>> colNames)
{
this.file = file;
this.splitCharactor = splitCharactor;
this.colNames = colNames;
}
/**
* 读取文件
*
* @return
* @throws Exception
*/
public List<Map<String, Object>> readFile() throws Exception
{
List<Map<String, Object>> list = new ArrayList<Map<String, Object>>();
if (!file.isFile())
{
throw new Exception("File not exists." + file.getName());
}
LineIterator lineIterator = null;
try
{
lineIterator = FileUtils.lineIterator(file, "UTF-8");
while (lineIterator.hasNext())
{
String line = lineIterator.next();
String[] values = line.split(splitCharactor);
if (colNames.size() != values.length)
{
continue;
}
Map<String, Object> map = new HashMap<String, Object>();
Iterator<Entry<String, Class<?>>> iterator = colNames.entrySet()
.iterator();
int count = 0;
while (iterator.hasNext())
{
Entry<String, Class<?>> entry = iterator.next();
Object value = values[count];
if (!String.class.equals(entry.getValue()))
{
value = entry.getValue().getMethod("valueOf", String.class)
.invoke(null, value);
}
map.put(entry.getKey(), value);
count++;
}
list.add(map);
}
}
catch (IOException e)
{
LOG.error("File reading line error." + e.toString(), e);
}
finally
{
LineIterator.closeQuietly(lineIterator);
}
return list;
}
}
public class StreamIntoEs
{
public static class ChildThread extends Thread
{
int number;
public ChildThread(int number)
{
this.number = number;
}
@Override
public void run()
{
Settings settings = ImmutableSettings.settingsBuilder()
.put("client.transport.sniff", true)
.put("client.transport.ping_timeout", 100)
.put("cluster.name", "elasticsearch").build();
TransportClient client = new TransportClient(settings)
.addTransportAddress(new InetSocketTransportAddress("192.168.32.228",
9300));
File dir = new File("/export/home/es/data/" + number);
LinkedHashMap<String, Class<?>> colNames = new LinkedHashMap<String, Class<?>>();
colNames.put("aa", Long.class);
colNames.put("bb", String.class);
colNames.put("cc", String.class);
colNames.put("dd", Integer.class);
colNames.put("ee", Long.class);
colNames.put("ff", Long.class);
colNames.put("hh", Long.class);
int count = 0;
long startTime = System.currentTimeMillis();
for (File file : dir.listFiles())
{
int currentCount = 0;
long startCurrentTime = System.currentTimeMillis();
FileReader reader = new FileReader(file, "\\$", colNames);
BulkResponse resp = null;
BulkRequestBuilder bulkRequest = client.prepareBulk();
try
{
List<Map<String, Object>> results = reader.readFile();
for (Map<String, Object> col : results)
{
bulkRequest.add(client.prepareIndex("flux", "fluxdata")
.setSource(JSON.toJSONString(col)).setId(col.get("getway")+"##"+col.get("port_info")+"##"+col.get("device_id")+"##"+col.get("collecttime")));
count++;
currentCount++;
}
resp = bulkRequest.execute().actionGet();
}
catch (Exception e)
{
// TODO Auto-generated catch block
e.printStackTrace();
}
long endCurrentTime = System.currentTimeMillis();
System.out.println("[thread-" + number + "-]per count:" + currentCount);
System.out.println("[thread-" + number + "-]per time:"
+ (endCurrentTime - startCurrentTime));
System.out.println("[thread-" + number + "-]per count/s:"
+ (float) currentCount / (endCurrentTime - startCurrentTime)
* 1000);
System.out.println("[thread-" + number + "-]per count/s:"
+ resp.toString());
}
long endTime = System.currentTimeMillis();
System.out.println("[thread-" + number + "-]total count:" + count);
System.out.println("[thread-" + number + "-]total time:"
+ (endTime - startTime));
System.out.println("[thread-" + number + "-]total count/s:" + (float) count
/ (endTime - startTime) * 1000);
// IndexRequest request =
// = client.index(request);
}
}
public static void main(String args[])
{
for (int i = 0; i < 4; i++)
{
ChildThread childThread = new ChildThread(i);
childThread.start();
}
}
}
起了4个线程来做入库,每个文件解析完成进行一次批处理。
初始化脚本：
curl -XDELETE 'http://192.168.32.228:9200/twitter/'
curl -XPUT 'http://192.168.32.228:9200/twitter/' -d '
{
"index" :{
"number_of_shards" : 5,
"number_of_replicas ": 0,
"index.refresh_interval": "-1",
"index.translog.flush_threshold_ops": "100000"
}
}'
curl -XPUT 'http://192.168.32.228:9200/twiter/twiterdata/_mapping' -d '
{
"twiterdata": {
"aa" : {"type" : "long", "index" : "not_analyzed"},
"bb" : {"type" : "String", "index" : "not_analyzed"},
"cc" : {"type" : "String", "index" : "not_analyzed"},
"dd" : {"type" : "integer", "index" : "not_analyzed"},
"ee" : {"type" : "long", "index" : "no"},
"ff" : {"type" : "long", "index" : "no"},
"gg" : {"type" : "long", "index" : "no"},
"hh" : {"type" : "long", "index" : "no"},
"ii" : {"type" : "long", "index" : "no"},
"jj" : {"type" : "long", "index" : "no"},
"kk" : {"type" : "long", "index" : "no"},
}
}
执行效率参考：
不开启refresh_interval [test@test001 bin]$ more StreamIntoEs.out|grep total [thread-2-]total count:1199411 [thread-2-]total time:1223718 [thread-2-]total count/s:980.1368 [thread-1-]total count:1447214 [thread-1-]total time:1393528 [thread-1-]total count/s:1038.5253 [thread-0-]total count:1508043 [thread-0-]total time:1430167 [thread-0-]total count/s:1054.4524 [thread-3-]total count:1650576 [thread-3-]total time:1471103 [thread-3-]total count/s:1121.9989 4195.1134 开启refresh_interval [test@test001 bin]$ more StreamIntoEs.out |grep total [thread-2-]total count:1199411 [thread-2-]total time:996111 [thread-2-]total count/s:1204.0938 [thread-1-]total count:1447214 [thread-1-]total time:1163207 [thread-1-]total count/s:1244.1586 [thread-0-]total count:1508043 [thread-0-]total time:1202682 [thread-0-]total count/s:1253.9 [thread-3-]total count:1650576 [thread-3-]total time:1236239 [thread-3-]total count/s:1335.1593 5037.3117 开启refresh_interval 字段类型转换 [test@test001 bin]$ more StreamIntoEs.out |grep total [thread-2-]total count:1199411 [thread-2-]total time:1065229 [thread-2-]total count/s:1125.9653 [thread-1-]total count:1447214 [thread-1-]total time:1218342 [thread-1-]total count/s:1187.8552 [thread-0-]total count:1508043 [thread-0-]total time:1230474 [thread-0-]total count/s:1225.5789 [thread-3-]total count:1650576 [thread-3-]total time:1274027 [thread-3-]total count/s:1295.5581 4834.9575 开启refresh_interval 字段类型转换 设置id [thread-2-]total count:1199411 [thread-2-]total time:912251 [thread-2-]total count/s:1314.7817 [thread-1-]total count:1447214 [thread-1-]total time:1067117 [thread-1-]total count/s:1356.1906 [thread-0-]total count:1508043 [thread-0-]total time:1090577 [thread-0-]total count/s:1382.7937 [thread-3-]total count:1650576 [thread-3-]total time:1128490 [thread-3-]total count/s:1462.6412 5516.4072
580M的数据平均用时大概是20分钟。索引文件大约为1.76G
相关测试结果可以参考这里：
elasticsearch 性能测试