Data streaming reduces the need for expensive data storage
If an application needs to analyze large amounts of data from different sources, the data is often first stored in a database. From this database, the analysis program is supplied with the necessary data. As a result, of course, the performance suffers and at the same time the necessary investments for the analysis increase, since mass storage in the form of databases is, of course, expensive and appropriate storage space is necessary.
When using data streams or stream processing, no data storage and prior processing of the data is necessary. Tools such as Apache Flink process the incoming data in real time. All data to be analyzed is already analyzed when it is created, without having to be stored in a complicated and expensive way first. Tools like Apache Flink help to receive and forward data streams. Analyses take place and the solution ensures that the data for the analysis program is available efficiently and fault-tolerant. The most important features of Apache Flink are:
- A runtime environment that supports very high throughput and low event latency at the same time
- Support for event time and out-of-order processing in the DataStream API, based on the Dataflow model
- Various time semantics (event time, processing time)
- Fault tolerance with processing guarantee
- Natural back-pressure in streaming programs
- Libraries for graph processing (batch), machine learning (batch) and complex event processing (streaming)
- Built-in support for iterative programs (BSP) in the DataSet API (batch)
- Custom memory management for switching between in-memory and out-of-core data processing algorithms
- Compatibility Layers for Apache Hadoop MapReduce
- Integration with YARN, HDFS, HBase and other components of the Apache Hadoop ecosystem
High throughput and low latency are important
Data throughput plays an important role in the analysis of the data. This has to cope with the amount of data that is sent, for example, by the IoT sensors. At the same time, the latency must be low so that this data can also be processed effectively and quickly.
Normally, applications like Apache Flink never work alone. Such applications receive data from sources, process this data, and then send it to other applications. This means that Flink not only has to receive and process data quickly, but can also forward the data at the speed that the target application can effectively use the prepared data. For this purpose, Apache Flink can store the analyzed data and streams in file systems. Among other things, HDFS or S3 are used here. It can also be stored in conventional databases, for example Apache Cassandra or ElasticSearch
Apache Flink enables very fast processing of large amounts of data and is also able to perform state-oriented calculations in this area. The tool is also exactly in the processing. This combination of performance, speed and accuracy makes Apache Flink ideal for use in environments where unlimited data streams are to be analyzed quickly and reliably. For example, a streaming example looks like this:
case class WordWithCount(word: String, count: Long)val text = env.socketTextStream(host, port, 'n')val windowCounts = text.flatMap { w => w.split("s") } .map { w => WordWithCount(w, 1) } .keyBy("word") .window(TumblingProcessingTimeWindow.of(Time.seconds(5))) .sum("count")windowCounts.print()
Apache Flink is highly scalable
At the same time, Apache is also highly scalable and can process the incoming data on a large number of cluster nodes. This in turn enables collaboration with other processing solutions such as Hadoop, YARN or Apache Mesos. When operating in a cluster, Flink can be used to ensure that the analysis can take place with high availability.
Further strengths are the easy integration into existing systems. The REST API, which can control applications, also helps in this. In addition, there are other APIs with which other frameworks and a variety of applications can be connected. In addition, there are almost all known operations for processing data. This flexibility makes it possible, for example, to save the status of each incoming event and to store timers. At the time of triggering the timer, Flink can call the state of the event and correlate it with other events for calculations.
In addition, Apache Flink also provides an API for accessing tables and SQL support for queries. These queries can also be run on the sources. This allows data to be read from a limited number of data, but also from complete data streams. Other APIs also enable the processing of more complex data and patterns in events.
