In today’s world, most businesses are dealing with an unprecedented flow of data. Due to the level of global connectivity we’ve achieved through internet and social media, companies are producing content at a rapid rate that’s simply incomparable to the past.
The data revolution has brought up two pivotal questions: where do we store this huge volume of data? And moreover, what are we supposed to get out of it?
After revolutions in technical advancements, we have landed on cloud computing, which represents a new era in data storage and data analytics, a solution to make more informed business decisions in real-time. Ever since the advent of data analytics, there are a lot of processes working to integrate data stored in different sources in different formats and prepare it for processing.
Flink
Apache Flink is a next-gen open-sourced, distributed data processing engine. Flink was built to reduce the latency of Hadoop MapReduce in fast data processing. Flink looks similar to Spark since it uses the same MapReduce concepts, but what really gives Flink the edge on Spark is its stream processing capabilities that manage to process rows and rows of data in real-time. This is not possible through Spark’s batch processing method.
Why Flink?
Spark, a batch processing model, allows only a portion of incoming data in a given time which is called “micro batching”. This issue can have a significant impact on certain use cases (finance) that cannot stand up to latencies. Since Flink is a stream processing framework, it supports event and out-of-order processing in the data stream. Apache Flink also has explicit memory management that can store the processed data which reduces execution time and improves performance.
Advantages of Flink
- Allows iterative processing to take place on the same model rather than having a cluster run them separately.
- Its performance can be further increased by tweaking it to re-process the data that has changed rather than the entire data set.
- Runtime supports the execution of iterative algorithms natively.
- Applications are fault-tolerant in the event of any hardware failure and support “exactly-once” processing semantics and “event-time” processing semantics.
- High throughput and low latency streaming engine.
Flink – Basic Architecture
The Flink engine is written in Java and Scala and its applications can be written in Scala, Java, Python, and R. It can be configured either on the Windows, Linux or Mac Operating Systems. Apache Flink includes two core APIs: a Datastream API for bounded or unbounded streams of data and a Dataset API for bounded data sets.
Apache Flink is a distributed system and can integrate with all common cluster managers like Hadoop, Apache, and Kubernetes but also can be set up to run as a stand-alone cluster. Flink is designed to work well with each of the above-mentioned resource managers and is achieved by resource manager-specific deployment models that allow Flink to interact with each resource manager idiomatically.
Having said that, I would like to shed some light on how we hosted Scala projects in the Flink environment at AVASOFT.
Steps to host Scala projects in the Flink environment
Scala is a scalable language whose predominant applications are concurrent programming, distributed applications and big data. It’s a tightly-coupled programming language that warns an IDE about errors during compile time (read: prevents a lot of run time errors!). As Scala’s popularity has grown, it’s being widely used to build web applications, APIs, parallel batch processing, and utilities/libraries, in addition to major apps.
Here’s how you can host Scala in Flink:
STEP 1: Choose the IDE (Integrated Development Environment) for the Scala Project. Scala compatible IDE’s are IntelliJ, Sublime Text, Atom, Eclipse, and VS Code
STEP 2: Build the project using sbt and Maven tools
(NOTE: Call the ExecutionEnvironment class to run the Scala program in the Flink environment)
STEP 3: Provide the following command in the command prompt to start the sbt Shell inside the project path
start -> sbt
STEP 4: Build the project using the following command and convert class files to jar files
sbt -> clean -> assembly
STEP 5: Run the project in the Flink environment using the following command
Flink run ->jar file
The emergence of BI has enabled the use of data like never before. There are a lot of channels through which we get data now and businesses always need ways to get valuable insight about that data. As a result, organizations have deployed robust analytics software that is capable of handling large volumes of raw data. However, this data has to be processed using Flink and platforms like it that can handle iterative processing. In our evolving digital landscape, using platforms like Flink for enterprise is critical to deal with huge volumes of data in an efficient and effective manner.
