Apache Flink vs Apache Spark Streaming comparison

"Easy to deploy and manage."

"What I appreciate best about Apache Flink is that it's open source and geared towards a distributed stream processing framework."

"Apache Flink's best feature is its data streaming tool."

"Another feature is how Flink handles its radiuses. It has something called the checkpointing concept. You're dealing with billions and billions of requests, so your system is going to fail in large storage systems. Flink handles this by using the concept of checkpointing and savepointing, where they write the aggregated state into some separate storage. So in case of failure, you can basically recall from that state and come back."

"The event processing function is the most useful or the most used function. The filter function and the mapping function are also very useful because we have a lot of data to transform. For example, we store a lot of information about a person, and when we want to retrieve this person's details, we need all the details. In the map function, we can actually map all persons based on their age group. That's why the mapping function is very useful. We can really get a lot of events, and then we keep on doing what we need to do."

"It is user-friendly and the reporting is good."

"This is truly a real-time solution."

"The top feature of Apache Flink is its low latency for fast, real-time data. Another great feature is the real-time indicators and alerts which make a big difference when it comes to data processing and analysis."

"Apache Spark's capabilities for machine learning are quite extensive and can be used in a low-code way."

"Apache Spark Streaming has features like checkpointing and Streaming API that are useful."

"The solution is better than average and some of the valuable features include efficiency and stability."

"Spark Streaming is critical, quite stable, full-featured, and scalable."

"The solution is very stable and reliable."

"Apache Spark Streaming's most valuable feature is near real-time analytics. The developers can build APIs easily for a code-steaming pipeline. The solutions have an ecosystem of integration with other stock services."

"The platform’s most valuable feature for processing real-time data is its ability to handle continuous data streams."

"It's the fastest solution on the market with low latency data on data transformations."

"In terms of stability with Flink, it is something that you have to deal with every time. Stability is the number one problem that we have seen with Flink, and it really depends on the kind of problem that you're trying to solve."

"Apache Flink is very powerful, but it can be challenging for beginners because it requires prior experience with similar tools and technologies, such as Kafka and batch processing."

"PyFlink is not as fully featured as Python itself, so there are some limitations to what you can do with it."

"There are more libraries that are missing and also maybe more capabilities for machine learning."

"One way to improve Flink would be to enhance integration between different ecosystems. For example, there could be more integration with other big data vendors and platforms similar in scope to how Apache Flink works with Cloudera. Apache Flink is a part of the same ecosystem as Cloudera, and for batch processing it's actually very useful but for real-time processing there could be more development with regards to the big data capabilities amongst the various ecosystems out there."

"In a future release, they could improve on making the error descriptions more clear."

"The TimeWindow feature is a bit tricky. The timing of the content and the windowing is a bit changed in 1.11. They have introduced watermarks. A watermark is basically associating every data with a timestamp. The timestamp could be anything, and we can provide the timestamp. So, whenever I receive a tweet, I can actually assign a timestamp, like what time did I get that tweet. The watermark helps us to uniquely identify the data. Watermarks are tricky if you use multiple events in the pipeline. For example, you have three resources from different locations, and you want to combine all those inputs and also perform some kind of logic. When you have more than one input screen and you want to collect all the information together, you have to apply TimeWindow all. That means that all the events from the upstream or from the up sources should be in that TimeWindow, and they were coming back. Internally, it is a batch of events that may be getting collected every five minutes or whatever timing is given. Sometimes, the use case for TimeWindow is a bit tricky. It depends on the application as well as on how people have given this TimeWindow. This kind of documentation is not updated. Even the test case documentation is a bit wrong. It doesn't work. Flink has updated the version of Apache Flink, but they have not updated the testing documentation. Therefore, I have to manually understand it. We have also been exploring failure handling. I was looking into changelogs for which they have posted the future plans and what are they going to deliver. We have two concerns regarding this, which have been noted down. I hope in the future that they will provide this functionality. Integration of Apache Flink with other metric services or failure handling data tools needs some kind of update or its in-depth knowledge is required in the documentation. We have a use case where we want to actually analyze or get analytics about how much data we process and how many failures we have. For that, we need to use Tomcat, which is an analytics tool for implementing counters. We can manage reports in the analyzer. This kind of integration is pretty much straightforward. They say that people must be well familiar with all the things before using this type of integration. They have given this complete file, which you can update, but it took some time. There is a learning curve with it, which consumed a lot of time. It is evolving to a newer version, but the documentation is not demonstrating that update. The documentation is not well incorporated. Hopefully, these things will get resolved now that they are implementing it. Failure is another area where it is a bit rigid or not that flexible. We never use this for scaling because complexity is very high in case of a failure. Processing and providing the scaled data back to Apache Flink is a bit challenging. They have this concept of offsetting, which could be simplified."

"There is a learning curve. It takes time to learn."

"In terms of improvement, the UI could be better."

"The cost and load-related optimizations are areas where the tool lacks and needs improvement."

"The initial setup is quite complex."

"Integrating event-level streaming capabilities could be beneficial."

"The debugging aspect could use some improvement."

"It was resource-intensive, even for small-scale applications."

"The solution itself could be easier to use."

"There could be an improvement in the area of the user configuration section, it should be less developer-focused and more business user-focused."

"Apache Flink is open source so we pay no licensing for the use of the software."

"This is an open-source platform that can be used free of charge."

"The solution is open-source, which is free."

"It's an open-source solution."

"It's an open source."

"I was using the open-source community version, which was self-hosted."

"People pay for Apache Spark Streaming as a service."

"Spark is an affordable solution, especially considering its open-source nature."

"On a scale from one to ten, where one is expensive, or not cost-effective, and ten is cheap, I rate the price a seven."