Spark Streaming和Storm对比

	Spark Streaming	Storm
Data sources	HDFS, HBase, Cassandra, Kafka	HDFS, Base, Cassandra, Kafka
Resource Manager	YARN, Mesos	YARN, Mesos
Latency	Few seconds	<1 second
Fault tolerance(every recourd processed)	Exactly once	At least once
Reliability	Imporoved reliability(Spark + YARN)	Guarantees on data loss(Storm + Kafka)

区别：

1.Latency

2.Fault tolerance

3.Reliability

Storm架构

Supervisor：从节点，物理机器

Worker: Supervisor的进程

executor: Worker的线程

Spark Streaming架构：

worker: 物理节点

executor: 进程

------------------------------

Data Source:
- SQL
- NOSQL
- Log Data
- Streaming Data


Ingestion:
- Flume
- Sqoop
- NFS
- Kafka


Processing:
- MapReduce
- Spark
- Storm
- Drill
- Mahout
- Ooize
- Hive
- Pig
- HBase
- Elasticsearch
- Slor


Visualization:
- WebTier
- Banana
- Kibana
- Data Warehouse


ELK:
- E: Elasticsearch
- L: Logstash
- K: Kibana


Alluxio(Tachyon)<br>


Real time Processing:
Storm/Trident/Spark Streaming/Samza/Flink<br>




Latency:<br>
Spark: Few Seconds<br>
Storm: <1 Seconds<br>


Fault tolerance:<br>
Spark: Exactly once<br>
Storm: at least once/at most once<br>
Trident:Exactly once<br>


Reliability:<br>
Spark: Improved reliability (cache)<br>
Storm: Guarantees no data loss<br>


Storm:<br>
- Niumus
- Zookeeper
- Supervisor
   Worker: 进程<br>
      Executor：线程<br>
      Task: Bolt/Spout<br>


Spark Streaming:<br>
- Cluster Manager<br>
   Mesos<br>
   Yarn<br>
- Executor
   Task<br>
   cache<br>


Spark Streaming: DStream<br>


```
val conf = new SparkConf().setAppName().setMaster("local")
val ssc = new StreamingContext(conf, Seconds(1))
val lines = ssc.socketTextStream("spark001", 9999)
val lines2 = ssc.textFileStream("hdfs://spark:9000/wordcount_dir");
```


Streaming Window：<br>
reduceByKeyAndWindow(f(), Durations.seconds(60), Durations.seconds(10))<br>
每隔10s计算前60s的RDD。<br>


Kafka + Spark Streaming + HBase<br>
```
val lines = KafkaUtils.createStream(
ssc,
"192.168.80.201:2181,192.168.80.202:2181,192.168.80.203:2181", //zookeeper
"wordcountGroup",
"topicThreadMap"
)
```


```
topics.add("topic1")
KafkaParms.put("meta.broker.list",
192.168.80.201:9092,192.168.80.202:9092,192.168.80.203:9092); //broker.list
val lines = KafkaUtils.createDirectStream(
   ssc,
   String.class //key类型
   String.class //value类型
   StringDecoder.class, //解码器
   StringDecode.class,
   KafkaParms,
   topics
) 
```


jar包提交时 --master 会覆盖代码中的.setMaster("local")<br>


hdfs dfs -copyFromLocal spakr.txt /wordcount_dir<br>