Basic Understanding of Stateful Data Streaming Supported by Apache Flink
A data expert and DZone Core member discusses the concepts of stateful data and data streaming, and how Apache Flink helps data scientists work with them.
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The technologies related to the Apache Flink big data processing platform are enhancing its maturity in order to efficiently execute data streaming, which is becoming a major focal point for businesses as it allows them to quickly make decisions.
By leveraging data streaming applications, we can process/analyze the continuous flow of data without storing the data (i.e. the data stays in motion) to undercover any discrepancies, issues, errors, behavioral patterns, etc. that can help you make informed, data-driven decisions.
Data coming from multiple sources in an infinite succession with the same pattern is called a data stream. Analyzing and acting on this data using continuous queries is a processes known as stream processing. A couple of built-in operations provided by stream processing engines can be leveraged to ingest, transform, and output data.
Operations or computations can be stateless or stateful. Stateless computations do not maintain/depend on any event. Data scientists consider every event individually and then apply computations over these events in order to produce an output based on them. For example, when a clickstream (i.e. clicks on products in an e-commerce site) passes through a streaming program, it raises an alarm if the number of clicks on a specific product/item reaches over 10,000 within an hour.
Stateful operations maintain state and get updated based on every input. In order to produce an output, the last input and the current value of the state will be utilized. Ideally, the output is based on the accumulation of multiple events/inputs during a given period. Here, if we compare the previous clickstream example, an alarm can be raised by the application if there are alarmingly few clicks within half an hour. Stateful computation is surrounded by lots of challenges like concurrent updates, maintaining parallelism, etc.
Apache Flink was developed to overcome those challenges. The ‘checkpoint’ feature in Flink confirms that the correct state of events is retrieved, even after a program is interrupted while processing streaming data. A consistent checkpoint of a stateful streaming application is a copy of the state of each of its operators at a point when all operators have processed exactly the same input.
Flink allows you to use distributed storage mechanism where the state can be persisted, like HDFS. In many cases, Flink can partition the state by a key and manage the state of each partition independently.
’Savepoint’ or versioning state is another feature provided by Flink. It works exactly the same as ‘checkpoint,’ but has to be triggered manually by the user. Operators, namely KeyBy, as well as a stateful map can be used programmatically to better understand how Flink periodically takes consistent checkpoints to protect a streaming application from failure.
Published at DZone with permission of Gautam Goswami. See the original article here.
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