Discover the Thrill of Football Landesliga Tirol Austria
Immerse yourself in the dynamic world of football with our comprehensive coverage of the Landesliga Tirol Austria. Our platform offers the latest updates on matches, expert betting predictions, and in-depth analysis to keep you at the forefront of this exciting league. Whether you're a seasoned fan or new to the game, our content is designed to provide valuable insights and enhance your experience. Stay ahead with our daily updates and expert opinions.
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Understanding the Landesliga Tirol Austria
The Landesliga Tirol is a crucial part of Austria's football structure, serving as a stepping stone for teams aspiring to reach higher leagues. It offers a competitive environment where clubs showcase their talent and ambition. Here’s what makes it unique:
- Rich History: The league has a storied past, with numerous clubs contributing to its vibrant culture.
- Diverse Teams: Featuring a mix of established clubs and emerging talents, each match promises excitement and unpredictability.
- Promotion Opportunities: Success in the league can lead to promotion to higher tiers, providing teams with significant incentives to perform.
Daily Match Highlights
Stay updated with our daily match highlights that capture the essence of each game. From thrilling goals to strategic plays, our coverage brings you closer to the action. Here’s what you can expect:
- Match Summaries: Comprehensive overviews of each game, highlighting key moments and performances.
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In-Depth Team Analysis
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Prominent Clubs in Landesliga Tirol
The Landesliga Tirol is home to several prominent clubs that have made significant impacts both locally and nationally. Here are some notable teams:
- Kufstein FC: Known for their strong defense and tactical discipline, Kufstein FC consistently competes at the top of the league.
- Innsbruck II: As an affiliate team of one of Austria’s biggest clubs, Innsbruck II brings youthful energy and potential to the league.
- Tirol Raiders: With a focus on developing young talent, Tirol Raiders are often seen as dark horses in close matches.
The Role of Youth Development
Youth development plays a pivotal role in shaping the future of football in Landesliga Tirol. Many clubs invest heavily in nurturing young talent, providing them with opportunities to shine at higher levels. Here’s why youth development is crucial:
- Fostering New Talent: Clubs focus on identifying and developing promising young players who can make significant contributions.
- Sustainable Growth: Investing in youth ensures long-term success and stability for clubs.
- National Representation: Many players from the league go on to represent Austria at national levels, showcasing their skills on larger platforms.
Fan Engagement and Community Support
JiangbinFu/BigDataLab<|file_sep|>/MapReduce/README.md
# MapReduce
## Spark
### Scala
#### Hadoop Spark WordCount
#### Spark Streaming WordCount
#### Spark SQL WordCount
#### Spark Streaming SQL WordCount
#### Spark MLlib K-Means
#### Spark MLLib Logistic Regression
### Python
#### Spark Streaming Python WordCount
## Hive
### Hive SQL WordCount
### Hive UDF WordCount
## Flink
### Scala
#### Flink Scala WordCount
#### Flink Scala Streaming WordCount
#### Flink SQL WordCount
#### Flink SQL Streaming WordCount
<|repo_name|>JiangbinFu/BigDataLab<|file_sep|>/MapReduce/Spark/src/main/scala/com/spark/mllib/kmeans/KMeans.scala
package com.spark.mllib.kmeans
import org.apache.spark.{SparkConf, SparkContext}
import org.apache.spark.mllib.clustering.KMeans
import org.apache.spark.mllib.linalg.Vectors
/**
* Created by jiangbinfu on 2017/5/26.
*/
object KMeans {
def main(args: Array[String]) {
val conf = new SparkConf().setAppName("KMeans").setMaster("local[2]")
val sc = new SparkContext(conf)
val data = sc.textFile("data/kmeans.txt")
val parsedData = data.map(s => Vectors.dense(s.split(' ').map(_.toDouble))).cache()
val numClusters = parsedData.first().size
val numIterations = 20
val clusters = KMeans.train(parsedData, numClusters, numIterations)
val WSSSE = clusters.computeCost(parsedData)
println(s"Within Set Sum of Squared Errors = $WSSSE")
clusters.clusterCenters.foreach(println)
sc.stop()
}
}
<|file_sep|># DataStream Library
The DataStream API is part of Apache Storm's core library.
## API Overview
java
package org.apache.storm;
package org.apache.storm.generated;
package org.apache.storm.generated.bolt;
package org.apache.storm.generated.spout;
package org.apache.storm.generated.component;
package org.apache.storm.generated.config;
package org.apache.storm.generated.spoutconfig;
package org.apache.storm.generated.boltconfig;
package org.apache.storm.generated.tuple;
package org.apache.storm.generated.task;
## Introduction
The Storm API is designed around two concepts: **topology** (a graph) and **stream** (a flow). A topology is a graph that defines how streams are processed.
A stream is an ordered sequence of tuples (a tuple is just a list of objects). Each tuple flows from one stream component (spout or bolt) to another.
A spout is responsible for emitting tuples into a topology. A bolt receives tuples from zero or more inputs streams, processes them, optionally emits new tuples into zero or more output streams.
Storm provides APIs for creating spouts and bolts using Java (or any JVM language), as well as for configuring them.
A topology can be thought as an acyclic directed graph whose nodes are spouts or bolts (components), whose edges are streams.
Each spout or bolt has one or more inputs streams (inbound edges) that bring data into it; it also has one or more outputs streams (outbound edges) that take data out.
In addition to processing tuples within components, Storm provides a mechanism for components communicate using custom messages.
The following diagram illustrates how messages flow through a topology:
1. Messages flow through streams between components.
2. Storm uses custom messages when components need to communicate using Storm's messaging infrastructure.
3. Each component communicates asynchronously with other components using either messages or streams.
4. All communications are guaranteed at-least-once delivery semantics.
### Topology Construction
The process for building a topology involves three steps:
1. Creating spouts.
2. Creating bolts.
3. Connecting them together.
Creating spouts:
java
// Create two spouts
SpoutConfig wordSpoutConfig = new SpoutConfig("localhost", "words");
wordSpoutConfig.setNumTasks(1);
// Specify that we want this spout's output fields partitioned by key
wordSpoutConfig.setFieldNames(new String[]{"word"});
wordSpoutConfig.setBroadcastHost("localhost");
wordSpoutConfig.setOutputFieldsDeclarer(new Fields("word"));
SpoutConfig staticWordSpoutConfig = new SpoutConfig("localhost", "staticwords");
staticWordSpoutConfig.setNumTasks(1);
staticWordSpoutConfig.setFieldNames(new String[]{"word"});
staticWordSpoutConfig.setBroadcastHost("localhost");
staticWordSpoutConfig.setOutputFieldsDeclarer(new Fields("word"));
// Create two instances of our spouts
TridentStateFactory stateFactory = new MemoryMapStateFactory();
TridentTupleSchemeFactory schemeFactory = new TridentTupleSchemeFactory();
TridentSpout wordTridentSpout = new TridentSpout(wordSpoutConfig,
stateFactory,
schemeFactory);
TridentSpout staticWordTridentSpout = new TridentSpout(staticWordSpoutConfig,
stateFactory,
schemeFactory);
Creating bolts:
java
// Create two instances of our bolts
TridentBolt wordCounterBolt = new TridentBolt();
TridentBolt wordSplitterBolt = new TridentBolt();
Connecting them together:
java
// Create topology builder object
TopologyBuilder builder = new TopologyBuilder();
// Add spouts
builder.setSpout("words", wordTridentSpout);
builder.setSpout("staticwords", staticWordTridentSpout);
// Add bolts
builder.setBolt("split", wordSplitterBolt).shuffleGrouping("words");
builder.setBolt("count", wordCounterBolt).fieldsGrouping(
"split",
new Fields("word"));
builder.setBolt("count", wordCounterBolt).fieldsGrouping(
"staticwords",
new Fields("word"));
### Submitting Topologies
Once you've created your topology builder object, you can submit it using StormSubmitter:
java
String topologyName = "mytopology";
Properties stormConf = new Properties();
stormConf.put(StormSubmitter.JOB_NAME_CONF_KEY,topologyName);
stormConf.put(Config.TOPOLOGY_WORKERS_CONF_KEY,"2");
stormConf.put(Config.TOPOLOGY_RECEIVER_BUFFER_SIZE_CONF_KEY,"100");
StormSubmitter.submitTopology(topologyName,builder.createJavaTopology(),stormConf);
## Concepts
### Tuples
A tuple is just an ordered list of objects. For example: `["hello","world"]`. This means that if you want access element `0`, you should use `tuple.get(0)` rather than `tuple.getByField("0")`.
### Streams
A stream is an ordered sequence of tuples flowing from one component (spout or bolt) into another.
Streams are configured using **StreamGroupings** which define how tuples will be routed between components:
* **shuffle grouping**: randomly route tuples from an upstream component into all tasks for this component.
* **fields grouping**: route tuples based on a field value - all tuples with same field value will go into same task.
* **all grouping**: send all tuples from upstream component into every task for this component.
* **global grouping**: send all tuples from upstream component into only one task for this component.
* **direct grouping**: directly route tuples from upstream component into specified task IDs.
* **localOrShuffle grouping**: if tuple has affinity for local task then send it there otherwise shuffle it randomly into any task.
* **none grouping**: don't send any tuples from upstream component into this component.
### Spouts & Bolts
The Storm API provides interfaces for creating spouts (`ISpout`) and bolts (`IBasicBolt`). These interfaces are implemented by extending classes (`BaseRichSpout` / `BaseBasicBolt`). When implementing these classes you need override certain methods which define how your spouts/bolts behave.
## Example Code
### Hello World Topology
The following code shows how easy it is create simple topology which reads lines from stdin then emits them back out again:
java
import backtype.storm.Config;
import backtype.storm.LocalCluster;
import backtype.storm.topology.TopologyBuilder;
import backtype.storm.tuple.Fields;
public class HelloWorldTopology {
public static void main(String[] args) throws Exception {
Config conf=new Config();
conf.setDebug(true);
LocalCluster cluster=new LocalCluster();
try {
cluster.submitTopology(
"hello-world",
conf,new TopologyBuilder()
.setNumWorkers(1)
.setSpout(
"read-input",
new ReadInput(),
1)
.setBolt(
"write-output",
new WriteOutput(),
1)
.shuffleGrouping(
"read-input","write-output"));
Thread.sleep(10000);
} finally {
cluster.shutdown();
}
}
}
class ReadInput extends BaseRichSpout {
private SpoutingExecutor executor;
public void open(Map conf,
TopologyContext context,
SpoutOutputCollector collector) {
this.collector=collector;
executor=new SpoutingExecutor(this);
}
public void nextTuple() {
executor.run();
}
public void declareOutputFields(OutputFieldsDeclarer declarer) {
}
class SpoutingExecutor implements Runnable {
private final ReadInput outerInstance;
public SpoutingExecutor(ReadInput outerInstance) {
this.outerInstance=outerInstance;
this.outerInstance.collector=collector;
executor=new Executors.newSingleThreadExecutor();
executor.submit(this);
System.out.println("Started executor thread.");
return ;
}
public void run() {
try {
while (!Thread.interrupted()) {
BufferedReader reader=new BufferedReader(new InputStreamReader(System.in));
String line=reader.readLine();
if(line!=null)collector.emit(new Values(line));
}
} catch(Exception e) {System.out.println(e);}
}
}
}
class WriteOutput extends BaseBasicBolt {
public void execute(Tuple input,TupleOutputCollector collector) throws Exception {
System.out.println(input.getString(0));
collector.emit(input,new Values(input.getString(0)));
collector.ack(input);
}
public void declareOutputFields(OutputFieldsDeclarer declarer) {
declarer.declare(new Fields("line"));
}
}
## Troubleshooting & Debugging
When debugging Storm topologies there are several things you can do:
1. Run your topology locally using LocalCluster instead submitting it directly onto cluster - this will help find problems early before deploying onto real cluster.
2. Set `conf.setDebug(true)` when submitting topology - this will cause Storm logs much more verbose output which includes every message passing between components along with timings etcetera.<|file_sep|># MapReduce
## Hadoop
### Java
Hadoop MapReduce是Apache Hadoop的核心，它提供了一个编程框架，让用户可以对大规模数据进行分布式计算。MapReduce包括两个阶段：map阶段和reduce阶段。在map阶段，用户编写的Mapper类负责将输入数据切分成key/value对，并传递给reduce阶段；在reduce阶段，用户编写的Reducer类负责对相同key的value进行聚合处理，并输出最终结果。
Hadoop MapReduce的核心类有三个：Job、Configuration和JobClient。Job用于描述一个MapReduce作业的各种属性，包括输入和输出路径、Mapper和Reducer类、配置参数等。Configuration用于管理作业的配置参数。JobClient用于提交作业到集群中执行。
下面是一个简单的Hadoop MapReduce应用程序，它统计每个单词在文本中出现的次数：
java
public class WordCount {
public static class TokenizerMapper extends Mapper{
private final static IntWritable one=new IntWritable(1);
private Text word=new Text();
public void map(Object key,
Text value,
Context context)
throws IOException,
InterruptedException {
StringTokenizer itr=new StringTokenizer(value.toString());
while(itr.hasMoreTokens()){
word.set(itr.nextToken());
context.write(word,one);
}
}
}
public static class IntSumReducer extends Reducer{
private IntWritable result=new IntWritable();
public void
