二，使用Storm实现实时大数据分析实例：用storm来监测车辆速度是否超过80km/h
Bolt的实现
Spout的输出结果将给予Bolt进行更深一步的处理。经过对用例的思考，我们的topology中需要如Figure 3中的两个Bolt。
Figure 3：Spout到Bolt的数据流程。
ThresholdCalculatorBolt
Spout将tuple发出，由ThresholdCalculatorBolt接收并进行临界值处理。在这里，它将接收好几项输入进行检查；分别是：
临界值检查
临界值栏数检查（拆分成字段的数目）
临界值数据类型（拆分后字段的类型）
临界值出现的频数
临界值时间段检查
Listing Four中的类，定义用来保存这些值。
Listing Four:ThresholdInfo类
public class ThresholdInfo implementsSerializable
{
private String action;
private String rule;
private Object thresholdValue;
private int thresholdColNumber;
private Integer timeWindow;
private int frequencyOfOccurence;
}
基于字段中提供的值，临界值检查将被Listing Five中的execute（）方法执行。代码大部分的功能是解析和接收值的检测。
Listing Five：临界值检测代码段
public void execute(Tuple tuple, BasicOutputCollector collector)
{
if(tuple!=null)
{
List inputTupleList = (List) tuple.getValues();
int thresholdColNum = thresholdInfo.getThresholdColNumber();
Object thresholdValue = thresholdInfo.getThresholdValue();
String thresholdDataType = tupleInfo.getFieldList().get(thresholdColNum-1).getColumnType();
Integer timeWindow = thresholdInfo.getTimeWindow();
int frequency = thresholdInfo.getFrequencyOfOccurence();
if(thresholdDataType.equalsIgnoreCase("string"))
{
String valueToCheck = inputTupleList.get(thresholdColNum-1).toString();
String frequencyChkOp = thresholdInfo.getAction();
if(timeWindow!=null)
{
long curTime = System.currentTimeMillis();
long diffInMinutes = (curTime-startTime)/(1000);
if(diffInMinutes>=timeWindow)
{
if(frequencyChkOp.equals("=="))
{
if(valueToCheck.equalsIgnoreCase(thresholdValue.toString()))
{
count.incrementAndGet();
if(count.get() > frequency)
splitAndEmit(inputTupleList,collector);
}
}
else if(frequencyChkOp.equals("!="))
{
if(!valueToCheck.equalsIgnoreCase(thresholdValue.toString()))
{
count.incrementAndGet();
if(count.get() > frequency)
splitAndEmit(inputTupleList,collector);
}
}
else System.out.println("Operator not supported");
}
}
else
{
if(frequencyChkOp.equals("=="))
{
if(valueToCheck.equalsIgnoreCase(thresholdValue.toString()))
{
count.incrementAndGet();
if(count.get() > frequency)
splitAndEmit(inputTupleList,collector);
}
}
else if(frequencyChkOp.equals("!="))
{
if(!valueToCheck.equalsIgnoreCase(thresholdValue.toString()))
{
count.incrementAndGet();
if(count.get() > frequency)
splitAndEmit(inputTupleList,collector);
}
}
}
}
else if(thresholdDataType.equalsIgnoreCase("int") || thresholdDataType.equalsIgnoreCase("double") || thresholdDataType.equalsIgnoreCase("float") || thresholdDataType.equalsIgnoreCase("long") || thresholdDataType.equalsIgnoreCase("short"))
{
String frequencyChkOp = thresholdInfo.getAction();
if(timeWindow!=null)
{
long valueToCheck = Long.parseLong(inputTupleList.get(thresholdColNum-1).toString());
long curTime = System.currentTimeMillis();
long diffInMinutes = (curTime-startTime)/(1000);
System.out.println("Difference in minutes="+diffInMinutes);
if(diffInMinutes>=timeWindow)
{
if(frequencyChkOp.equals("<"))
{
if(valueToCheck < Double.parseDouble(thresholdValue.toString()))
{
count.incrementAndGet();
if(count.get() > frequency)
splitAndEmit(inputTupleList,collector);
}
}
else if(frequencyChkOp.equals(">"))
{
if(valueToCheck > Double.parseDouble(thresholdValue.toString()))
{
count.incrementAndGet();
if(count.get() > frequency)
splitAndEmit(inputTupleList,collector);
}
}
else if(frequencyChkOp.equals("=="))
{
if(valueToCheck == Double.parseDouble(thresholdValue.toString()))
{
count.incrementAndGet();
if(count.get() > frequency)
splitAndEmit(inputTupleList,collector);
}
}
else if(frequencyChkOp.equals("!="))
{
. . .
}
}
}
else
splitAndEmit(null,collector);
}
else
{
System.err.println("Emitting null in bolt");
splitAndEmit(null,collector);
}
}
经由Bolt发送的的tuple将会传递到下一个对应的Bolt，在我们的用例中是DBWriterBolt。
DBWriterBolt
经过处理的tuple必须被持久化以便于触发tigger或者更深层次的使用。DBWiterBolt做了这个持久化的工作并把tuple存入了数据库。表的建立由prepare（）函数完成，这也将是topology调用的第一个方法。方法的编码如Listing Six所示。
Listing Six：建表编码。
public void prepare( Map StormConf, TopologyContext context )
{
try
{
Class.forName(dbClass);
}
catch (ClassNotFoundException e)
{
System.out.println("Driver not found");
e.printStackTrace();
}
try
{
connection driverManager.getConnection(
"jdbc:mysql://"+databaseIP+":"+databasePort+"/"+databaseName, userName, pwd);
connection.prepareStatement("DROP TABLE IF EXISTS "+tableName).execute();
StringBuilder createQuery = new StringBuilder(
"CREATE TABLE IF NOT EXISTS "+tableName+"(");
for(Field fields : tupleInfo.getFieldList())
{
if(fields.getColumnType().equalsIgnoreCase("String"))
createQuery.append(fields.getColumnName()+" VARCHAR(500),");
else
createQuery.append(fields.getColumnName()+" "+fields.getColumnType()+",");
}
createQuery.append("thresholdTimeStamp timestamp)");
connection.prepareStatement(createQuery.toString()).execute();
// Insert Query
StringBuilder insertQuery = new StringBuilder("INSERT INTO "+tableName+"(");
String tempCreateQuery = new String();
for(Field fields : tupleInfo.getFieldList())
{
insertQuery.append(fields.getColumnName()+",");
}
insertQuery.append("thresholdTimeStamp").append(") values (");
for(Field fields : tupleInfo.getFieldList())
{
insertQuery.append("?,");
}
insertQuery.append("?)");
prepStatement = connection.prepareStatement(insertQuery.toString());
}
catch (SQLException e)
{
e.printStackTrace();
}
}
数据分批次的插入数据库。插入的逻辑由Listting Seven中的execute（）方法提供。大部分的编码都是用来实现可能存在不同类型输入的解析。
Listing Seven：数据插入的代码部分。
public void execute(Tuple tuple, BasicOutputCollector collector)
{
batchExecuted=false;
if(tuple!=null)
{
List inputTupleList = (List) tuple.getValues();
int dbIndex=0;
for(int i=0;i
{
Field field = tupleInfo.getFieldList().get(i);
try {
dbIndex = i+1;
if(field.getColumnType().equalsIgnoreCase("String"))
prepStatement.setString(dbIndex, inputTupleList.get(i).toString());
else if(field.getColumnType().equalsIgnoreCase("int"))
prepStatement.setInt(dbIndex,
Integer.parseInt(inputTupleList.get(i).toString()));
else if(field.getColumnType().equalsIgnoreCase("long"))
prepStatement.setLong(dbIndex,
Long.parseLong(inputTupleList.get(i).toString()));
else if(field.getColumnType().equalsIgnoreCase("float"))
prepStatement.setFloat(dbIndex,
Float.parseFloat(inputTupleList.get(i).toString()));
else if(field.getColumnType().equalsIgnoreCase("double"))
prepStatement.setDouble(dbIndex,
Double.parseDouble(inputTupleList.get(i).toString()));
else if(field.getColumnType().equalsIgnoreCase("short"))
prepStatement.setShort(dbIndex,
Short.parseShort(inputTupleList.get(i).toString()));
else if(field.getColumnType().equalsIgnoreCase("boolean"))
prepStatement.setBoolean(dbIndex,
Boolean.parseBoolean(inputTupleList.get(i).toString()));
else if(field.getColumnType().equalsIgnoreCase("byte"))
prepStatement.setByte(dbIndex,
Byte.parseByte(inputTupleList.get(i).toString()));
else if(field.getColumnType().equalsIgnoreCase("Date"))
{
Date dateToAdd=null;
if (!(inputTupleList.get(i) instanceof Date))
{
DateFormat df = new SimpleDateFormat("yyyy-MM-dd hh:mm:ss");
try
{
dateToAdd = df.parse(inputTupleList.get(i).toString());
}
catch (ParseException e)
{
System.err.println("Data type not valid");
}
}
else
{
dateToAdd = (Date)inputTupleList.get(i);
java.sql.Date sqlDate = new java.sql.Date(dateToAdd.getTime());
prepStatement.setDate(dbIndex, sqlDate);
}
}
catch (SQLException e)
{
e.printStackTrace();
}
}
Date now = new Date();
try
{
prepStatement.setTimestamp(dbIndex+1, new java.sql.Timestamp(now.getTime()));
prepStatement.addBatch();
counter.incrementAndGet();
if (counter.get()== batchSize)
executeBatch();
}
catch (SQLException e1)
{
e1.printStackTrace();
}
}
else
{
long curTime = System.currentTimeMillis();
long diffInSeconds = (curTime-startTime)/(60*1000);
if(counter.get()batchTimeWindowInSeconds)
{
try {
executeBatch();
startTime = System.currentTimeMillis();
}
catch (SQLException e) {
e.printStackTrace();
}
}
}
}
public void executeBatch() throws SQLException
{
batchExecuted=true;
prepStatement.executeBatch();
counter = new AtomicInteger(0);
}
一旦Spout和Bolt准备就绪（等待被执行），topology生成器将会建立topology并准备执行。下面就来看一下执行步骤。
在本地集群上运行和测试topology
通过TopologyBuilder建立topology。
使用Storm Submitter，将topology递交给集群。以topology的名字、配置和topology的对象作为参数。
提交topology。
Listing Eight：建立和执行topology。
public class StormMain
{
public static void main(String[] args) throws AlreadyAliveException,
InvalidTopologyException,
InterruptedException
{
ParallelFileSpout parallelFileSpout = new ParallelFileSpout();
ThresholdBolt thresholdBolt = new ThresholdBolt();
DBWriterBolt dbWriterBolt = new DBWriterBolt();
TopologyBuilder builder = new TopologyBuilder();
builder.setSpout("spout", parallelFileSpout, 1);
builder.setBolt("thresholdBolt", thresholdBolt,1).shuffleGrouping("spout");
builder.setBolt("dbWriterBolt",dbWriterBolt,1).shuffleGrouping("thresholdBolt");
if(this.argsMain!=null && this.argsMain.length > 0)
{
conf.setNumWorkers(1);
StormSubmitter.submitTopology(
this.argsMain[0], conf, builder.createTopology());
}
else
{
Config conf = new Config();
conf.setDebug(true);
conf.setMaxTaskParallelism(3);
LocalCluster cluster = new LocalCluster();
cluster.submitTopology(
"Threshold_Test", conf, builder.createTopology());
}
}
}
topology被建立后将被提交到本地集群。一旦topology被提交，除非被取缔或者集群关闭，它将一直保持运行不需要做任何的修改。这也是Storm的另一大特色之一。
这个简单的例子体现了当你掌握了topology、spout和bolt的概念，将可以轻松的使用Storm进行实时处理。如果你既想处理大数据又不想遍历Hadoop的话，不难发现使用Storm将是个很好的选择。
根据本文，我们基于storm开发了深圳市实时交通路况系统，源码已经在github上开源：
https://github.com/whughchen/RealTimeTraffic
欢迎关注并给出改进意见~
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