Flink CEP for Stream Processing

Introduction to Flink CEP

Flink CEP is a real-time processing framework that has the ability to process streaming data. Being founded by Data Artisans company and now developed under Apache License by Apache Flink Community, it has been marked as a true streaming model that does not take input data as batch or micro-batches. But what does one understand from the term ‘CEP’?

Complex Event Processing (CEP)

CEP is a library implemented on the top of Flink. It is of great use in the current era when data is considered to be as important as oil, which is always growing. Data is being continuously streamed from smart devices and it is a great deal to analyze it in real-time. CEP comes into play in such scenarios.

  • It solves the key problem in real-time processing of pattern detection of events in data streams.
  • It matches continuously incoming data against a suggested pattern. This helps in keeping the data that’s currently needed and discarding the non-relevant data. Inputs are matched immediately and the results are emitted straight away.
  • It helps you to detect patterns in a data stream, allowing you to get hold of only the important data.

CEP’s are written using Pattern API’s.

Use Cases of CEP

CEP is used in many cases and scenarios. Notably, it is used in:

  • Financial apps to check the trend in the stock market
  • Credit card fraud detection
  • RFID based tracking and monitoring systems (like detecting thefts in the warehouse)
  • Detecting network intrusion by specifying patterns of suspicious behavior

Why CEP?

CEP is of great use in today’s scenario of Data Analysis. Although the data is necessary, only a handful of it is of great importance at a given time. We can get this handful of data easily by using CEP in Apache Flink. CEP has the ability to achieve high throughput and low latency processing. It also has the ability to produce the results as soon as the input stream is available to it. It provides necessary real-time alerts and notifications on the detection of complex event patterns.

Anatomy of CEP

Below are certain guidelines to be kept in mind while operating Flink CEP.

  • Obtain an Execution Environment (Stream or Batch environments)
  • Load Data from the defined source (Like Apache Kafka Security)
  • Pattern Definition (To get the desired data from the incoming stream)
  • Pattern Detection (Getting of the desired data)
  • Creating of alert
  • Specify where to sink the results after computation (Kafka topics, Database, etc.)

Anatomy of Flink CEP

Quick Question for You

We are provided with locationID and Temperature. We’re supposed to generate an alert.

Situation: Where will the temperature of a given locationID be greater than that of the TEMPERATURE_THRESHOLD?

Implementation of Technology

We can solve the given problem statement by the following steps:

1. First, TEMPERATURE_THRESHOLD is provided.

2. Execution Environment for data stream is created.

3. A Kafka source to get the desired data is created.

4. The coming data is mapped accordingly so as to get the desired final result.

5. A pattern is declared that gets the temperature from incoming data and compared against the TEMPERATURE_THRESHOLD.

6. CEP is used to compare these both data.

7. Result stream is created that will generate an alert whenever the temperature of a given location would be greater than the temperature defined as TEMPERATURE_THRESHOLD.

8. Kafka sink is added for the alerts that will display the locationID with the temperature having temperature greater than that of the TEMPERATURE_THRESHOLD.


Clearly, we get the desired output by the elimination of data that has a temperature less than that of the THRESHOLD_TEMPERATURE. This example lets us know the location where the temperature is above the critical point. And this is how we can use Flink CEP for particular data analysis and pattern detection while streaming.

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