High 10 Issues When Utilizing Apache Kafka
Apache Kafka is among the most popularly used open-source distributed occasion streaming platforms. Its use instances vary from enabling mission-critical apps to constructing and sustaining high-performance knowledge pipelines. If you happen to take into account whether or not to make use of Apache Kafka on your future initiatives, it’s best to know all in regards to the execs and cons of utilizing it. In as we speak’s article, we are going to follow the cons. Whereas Apache Kafka is a strong distributed messaging platform, it has some limitations. To color the image, now we have put collectively the highest 10 issues when utilizing Apache Kafka.
1. In Sync Reproduction Alerts
Kafka In Sync Reproduction Alert tells you that among the subjects are under-replicated. The information is just not being replicated to brokers. These alerts point out a doubtlessly major problem as a result of the chance of knowledge being misplaced turns into increased. It will probably occur totally unexpectedly, even if you happen to do nothing in your facet. It normally takes place when downlevel shoppers have an effect on the amount of knowledge.
A spike in knowledge quantity causes the Kafka dealer to again up message conversion. Nevertheless, the issue must be addressed as quickly as potential. Often, the questionable dealer must be mounted for the whole system to be operational once more.
2. Kafka Liveness Examine Issues and Automation
The Kafka liveness verify issues can rapidly happen if the host the place the liveness verify is operating can’t attain the host the place the dealer is operating. If this occurs, the dealer will carry on restarting. In the meantime, all of the downlevel shoppers received’t be capable to run their apps. It will probably turn out to be an actual nuance if you wish to automate a few of your duties on Kafka.
Why? As a result of you could allow liveness verify to streamline automation and guarantee that the dealer’s client-serving port is open. You may merely write a chunk of code to restart the dealer when the port will not be open. But when the dealer falls right into a dead-loop and retains restarting, your whole infrastructure is rendered ineffective. Is there a fast repair? Merely flip off the liveness verify.
3. New Brokers Can Influence the Efficiency
Staging a brand new cluster and putting in the dealer software program on Apache Kafka is easy. Including new brokers mustn’t trigger any issues, proper? Pushing a brand new Kafka dealer into manufacturing can doubtlessly influence the efficiency and trigger critical latency and lacking file issues.
The dealer can work correctly earlier than the partition reassign course of is accomplished. Devs normally overlook about it and use the default instructions from the documentation. Shifting hundreds of partitions to the staging cluster can take hours. And, till all of the partitions have been moved, its efficiency will undergo. This is the reason try to be cautious and have a plan while you need to add a brand new dealer to the infrastructure.
4. Questionable Lengthy-Time period Storage Resolution
In case you are working with giant units of knowledge, utilizing Apache Kafka to retailer it would trigger you many issues. The foremost drawback comes from Kafka storing redundant copies of knowledge. It will probably have an effect on the efficiency, however, extra importantly, it could actually considerably enhance your storage prices.
The very best answer can be to make use of Kafka just for storing knowledge for a quick interval and migrate knowledge to a relational or non-relational database, relying in your particular necessities.
5. Discovering Good Information Retention Settings
Whereas we’re discussing long-term storage answer issues, let’s level out one further situation associated to it. The downstream shoppers usually have fully unpredictable knowledge request patterns. This makes discovering the proper and most optimum knowledge retention settings considerably of an issue.
Kafka shops messages in subjects. This knowledge can take up important disk area in your brokers. To dump the info, you could set the retention interval or configurable measurement. If you happen to don’t tune the info retention settings appropriately, you danger both rendering knowledge ineffective or paying manner an excessive amount of for storage than it’s best to must within the first place.
6. Overly Complicated Information Transformations on-Fly
Utilizing Apache Kafka on large knowledge integration and migration initiatives can turn out to be too advanced. How come? Kafka was constructed to streamline delivering messages, and the platform excels at it. Nevertheless, you’ll run into some issues if you wish to rework knowledge on-fly.
Even with Kafka Stream API, you’ll have to spend days constructing advanced knowledge pipelines and managing the interplay between knowledge producers and knowledge shoppers. To not point out having to take care of and handle a system this advanced. There are different distributed messaging programs which might be significantly better for streamlining ETL jobs, reminiscent of Apache Pulsar.
7. Upscaling and Subject Rebalancing
The amount of your knowledge streams can go in each instructions. This is the reason it’s essential to decide on a distributed messaging platform straightforward to scale up and down. With Kafka, it is a drawback as a result of you could steadiness issues manually to scale back useful resource bottlenecks.
You’ll have to do it each time a serious change within the knowledge stream happens. And do it each through partition management balancing and Kafka reassign partition script. On the identical time, with stateless brokers, Apache Pulsar makes the scale-out course of considerably simpler.
8. MirrorMaker Doesn’t Replicate the Subject Offsets
MirrorMaker is certainly one of Kafka’s options that means that you can make copies of your clusters. This may be a terrific catastrophe restoration plan if it weren’t for one draw back. MirrorMaker doesn’t replicate the subject offsets between the clusters. You’ll have to create distinctive keys in messages to beat this drawback which might turn out to be a frightening job when you’re working at scale.
9. Not All Messaging Paradigms Are Included
Whereas Apache Kafka comes with many messaging paradigms, some are nonetheless lacking.This may flip into an actual drawback if you could prolong your infrastructure use case.It limits the Kafka functionality to help constructing advanced knowledge pipelines.
Two main messaging paradigms not supported in Kafka are point-to-point queues and request/reply queues
10. Altering Messages Reduces Efficiency
If you wish to use Apache Kafka to ship messages as they’re, you should have no points performance-wise. Nevertheless, the issue happens when you want to modify the messages earlier than you ship them.
Manipulating knowledge on the fly is feasible with Kafka, however the system it makes use of has some limits. It makes use of system calls to do it, and modifying messages makes the whole platform carry out considerably slower.
However, many giants throughout industries use Apache Kafka, together with Twitter, Netflix, and LinkedIn. These ten issues are fairly particular to Kafka, they usually would possibly have an effect on your implementation of the distributed messaging answer in a particular case. Be at liberty to verify Pandio if you wish to be taught extra about Apache Pulsar, a distributed messaging platform that outperforms Kafka in nearly each potential use case and is positioned for the ML workloads of the longer term.