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Couchbase vs Kafka: What are the differences?
Introduction
Couchbase and Kafka are both widely used technologies in the field of data management and processing. While they serve different purposes, there are key differences between the two. This Markdown code provides an overview of the differences between Couchbase and Kafka in a concise and structured format for website usage.
Data Storage and Management: Couchbase is a NoSQL database that offers flexible document-based data storage and management capabilities. It allows for schema-less data modeling and supports CRUD operations. On the other hand, Kafka is a distributed streaming platform that provides a fault-tolerant, high-throughput, and scalable system for handling real-time data streams. Kafka stores data as immutable streams of records, using a publish-subscribe model.
Data Processing Model: Couchbase primarily focuses on providing fast and efficient data retrieval, storage, and querying capabilities. It supports various querying mechanisms, including N1QL (SQL-like query language). Kafka, on the other hand, is designed for data streaming and processing. It enables real-time processing of data streams, allowing for data transformation, filtering, and analysis in a distributed manner.
Data Scalability and Replication: Couchbase offers built-in data replication and automatic data sharding for scalable and high-availability deployments. It uses a distributed architecture to ensure data durability and fault tolerance. Kafka, on the other hand, provides distributed messaging and storage capabilities, allowing for horizontally scalable deployments. It uses topic partitions and replication to achieve fault tolerance and provide scalable data processing.
Data Persistence and Durability: Couchbase stores data persistently on disk, providing durability for long-term data storage. It ensures durability through replica placement and replication across multiple nodes. Kafka, while it also persists data to disk, does not focus on long-term data storage. It primarily serves as a streaming platform, where data is stored temporarily for real-time processing and analysis.
Data Consistency and Concurrency: Couchbase offers strong consistency guarantees, ensuring that all clients accessing the data see the most recent update. It supports concurrent access and provides a flexible data consistency model. In contrast, Kafka provides eventual consistency. It allows for high-concurrency data streaming and processing while ensuring that data is eventually replicated to all subscribers.
Data Use Cases: Couchbase is suitable for a wide range of use cases, including high-performance web applications, caching layers, session stores, and real-time analytics. It provides a flexible data model and robust querying capabilities. Kafka, on the other hand, is ideal for building real-time streaming data pipelines, event-driven architectures, fault-tolerant messaging systems, and data processing applications. It excels in scenarios that require real-time data ingestion, processing, and analysis.
In summary, Couchbase is a NoSQL database that focuses on data storage, retrieval, and management, offering flexible data modeling and querying capabilities. Kafka, on the other hand, is a distributed streaming platform designed for real-time data streaming, processing, and analysis. Couchbase provides strong consistency, while Kafka prioritizes high-throughput data streaming and eventual consistency. Both technologies have their unique use cases and strengths in the data management and processing landscape.
We are going to develop a microservices-based application. It consists of AngularJS, ASP.NET Core, and MSSQL.
We have 3 types of microservices. Emailservice, Filemanagementservice, Filevalidationservice
I am a beginner in microservices. But I have read about RabbitMQ, but come to know that there are Redis and Kafka also in the market. So, I want to know which is best.
Kafka is an Enterprise Messaging Framework whereas Redis is an Enterprise Cache Broker, in-memory database and high performance database.Both are having their own advantages, but they are different in usage and implementation. Now if you are creating microservices check the user consumption volumes, its generating logs, scalability, systems to be integrated and so on. I feel for your scenario initially you can go with KAFKA bu as the throughput, consumption and other factors are scaling then gradually you can add Redis accordingly.
I first recommend that you choose Angular over AngularJS if you are starting something new. AngularJs is no longer getting enhancements, but perhaps you meant Angular. Regarding microservices, I recommend considering microservices when you have different development teams for each service that may want to use different programming languages and backend data stores. If it is all the same team, same code language, and same data store I would not use microservices. I might use a message queue, in which case RabbitMQ is a good one. But you may also be able to simply write your own in which you write a record in a table in MSSQL and one of your services reads the record from the table and processes it. The most challenging part of doing it yourself is writing a service that does a good job of reading the queue without reading the same message multiple times or missing a message; and that is where RabbitMQ can help.
We found that the CNCF landscape is a good advisor when working going into the cloud / microservices space: https://landscape.cncf.io/fullscreen=yes. When choosing a technology one important criteria to me is if it is cloud native or not. Neither Redis, RabbitMQ nor Kafka is cloud native. The try to adapt but will be replaced eventually with technologies that are cloud native.
We have gone with NATS and have never looked back. We haven't spend a single minute on server maintainance in the last year and the setup of a cluster is way too easy. With the new features NATS incorporates now (and the ones still on the roadmap) it is already and will be sooo much mure than Redis, RabbitMQ and Kafka are. It can replace service discovery, load balancing, global multiclusters and failover, etc, etc.
Your thought might be: But I don't need all of that! Well, at the same time it is much more leightweight than Redis, RabbitMQ and especially Kafka.
I think something is missing here and you should consider answering it to yourself. You are building a couple of services. Why are you considering event-sourcing architecture using Message Brokers such as the above? Won't a simple REST service based arch suffice? Read about CQRS and the problems it entails (state vs command impedance for example). Do you need Pub/Sub or Push/Pull? Is queuing of messages enough or would you need querying or filtering of messages before consumption? Also, someone would have to manage these brokers (unless using managed, cloud provider based solution), automate their deployment, someone would need to take care of backups, clustering if needed, disaster recovery, etc. I have a good past experience in terms of manageability/devops of the above options with Kafka and Redis, not so much with RabbitMQ. Both are very performant. But also note that Redis is not a pure message broker (at time of writing) but more of a general purpose in-memory key-value store. Kafka nowadays is much more than a distributed message broker. Long story short. In my taste, you should go with a minialistic approach and try to avoid either of them if you can, especially if your architecture does not fall nicely into event sourcing. If not I'd examine Kafka. If you need more capabilities than I'd consider Redis and use it for all sorts of other things such as a cache.
We Have thousands of .pdf docs generated from the same form but with lots of variability. We need to extract data from open text and more important - from tables inside the docs. The output of Couchbase/Mongo will be one row per document for backend processing. ADOBE renders the tables in an unusable form.
I prefer MongoDB due to own experience with migration of old archive of pdf and meta-data to a new “archive”. The biggest advantage is speed of filters output - a new archive is way faster and reliable then the old one - but also the the easy programming of MongoDB with many code snippets and examples available. I have no personal experience so far with Couchbase. From the architecture point of view both options are OK - go for the one you like.
I would like to suggest MongoDB or ArangoDB (can't choose both, so ArangoDB). MongoDB is more mature, but ArangoDB is more interesting if you will need to bring graph database ideas to solution. For example if some data or some documents are interlinked, then probably ArangoDB is a best solution.
To process tables we used Abbyy software stack. It's great on table extraction.
If you can select text with mouse drag in PDF. Use pdftotext it is fast! You can install it on server with command "apt-get install poppler-utils". Use it like "pdftotext -layout /path-to-your-file". In same folder it will make text file with line by line content. There is few classes on git stacks that you can use, also.
I am looking into IoT World Solution where we have MQTT Broker. This MQTT Broker Sits in one of the Data Center. We are doing a lot of Alert and Alarm related processing on that Data, Currently, we are looking into Solution which can do distributed persistence of log/alert primarily on remote Disk.
Our primary need is to use lightweight where operational complexity and maintenance costs can be significantly reduced. We want to do it on-premise so we are not considering cloud solutions.
We looked into the following alternatives:
Apache Kafka - Great choice but operation and maintenance wise very complex. Rabbit MQ - High availability is the issue, Apache Pulsar - Operational Complexity. NATS - Absence of persistence. Akka Streams - Big learning curve and operational streams.
So we are looking into a lightweight library that can do distributed persistence preferably with publisher and subscriber model. Preferable on JVM stack.
Kafka is best fit here. Below are the advantages with Kafka ACLs (Security), Schema (protobuf), Scale, Consumer driven and No single point of failure.
Operational complexity is manageable with open source monitoring tools.
Our backend application is sending some external messages to a third party application at the end of each backend (CRUD) API call (from UI) and these external messages take too much extra time (message building, processing, then sent to the third party and log success/failure), UI application has no concern to these extra third party messages.
So currently we are sending these third party messages by creating a new child thread at end of each REST API call so UI application doesn't wait for these extra third party API calls.
I want to integrate Apache Kafka for these extra third party API calls, so I can also retry on failover third party API calls in a queue(currently third party messages are sending from multiple threads at the same time which uses too much processing and resources) and logging, etc.
Question 1: Is this a use case of a message broker?
Question 2: If it is then Kafka vs RabitMQ which is the better?
RabbitMQ is great for queuing and retrying. You can send the requests to your backend which will further queue these requests in RabbitMQ (or Kafka, too). The consumer on the other end can take care of processing . For a detailed analysis, check this blog about choosing between Kafka and RabbitMQ.
Well, first off, it's good practice to do as little non-UI work on the foreground thread as possible, regardless of whether the requests take a long time. You don't want the UI thread blocked.
This sounds like a good use case for RabbitMQ. Primarily because you don't need each message processed by more than one consumer. If you wanted to process a single message more than once (say for different purposes), then Apache Kafka would be a much better fit as you can have multiple consumer groups consuming from the same topics independently.
Have your API publish messages containing the data necessary for the third-party request to a Rabbit queue and have consumers reading off there. If it fails, you can either retry immediately, or publish to a deadletter queue where you can reprocess them whenever you want (shovel them back into the regular queue).
In my opinion RabbitMQ fits better in your case because you don’t have order in queue. You can process your messages in any order. You don’t need to store the data what you sent. Kafka is a persistent storage like the blockchain. RabbitMQ is a message broker. Kafka is not a good solution for the system with confirmations of the messages delivery.
As far as I understand, Kafka is a like a persisted event state manager where you can plugin various source of data and transform/query them as event via a stream API. Regarding your use case I will consider using RabbitMQ if your intent is to implement service inter-communication kind of thing. RabbitMQ is a good choice for one-one publisher/subscriber (or consumer) and I think you can also have multiple consumers by configuring a fanout exchange. RabbitMQ provide also message retries, message cancellation, durable queue, message requeue, message ACK....
Hello! [Client sends live video frames -> Server computes and responds the result] Web clients send video frames from their webcam then on the back we need to run them through some algorithm and send the result back as a response. Since everything will need to work in a live mode, we want something fast and also suitable for our case (as everyone needs). Currently, we are considering RabbitMQ for the purpose, but recently I have noticed that there is Redis and Kafka too. Could you please help us choose among them or anything more suitable beyond these guys. I think something similar to our product would be people using their webcam to get Snapchat masks on their faces, and the calculated face points are responded on from the server, then the client-side draw the mask on the user's face. I hope this helps. Thank you!
For your use case, the tool that fits more is definitely Kafka. RabbitMQ was not invented to handle data streams, but messages. Plenty of them, of course, but individual messages. Redis is an in-memory database, which is what makes it so fast. Redis recently included features to handle data stream, but it cannot best Kafka on this, or at least not yet. Kafka is not also super fast, it also provides lots of features to help create software to handle those streams.
I've used all of them and Kafka is hard to set up and maintain. Mostly is a Java dinosaur that you can set up and. I've used it with Storm but that is another big dinosaur. Redis is mostly for caching. The queue mechanism is not very scalable for multiple processors. Depending on the speed you need to implement on the reliability I would use RabbitMQ. You can store the frames(if they are too big) somewhere else and just have a link to them. Moving data through any of these will increase cost of transportation. With Rabbit, you can always have multiple consumers and check for redundancy. Hope it clears out your thoughts!
For this kind of use case I would recommend either RabbitMQ or Kafka depending on the needs for scaling, redundancy and how you want to design it.
Kafka's true value comes into play when you need to distribute the streaming load over lot's of resources. If you were passing the video frames directly into the queue then you'd probably want to go with Kafka however if you can just pass a pointer to the frames then RabbitMQ should be fine and will be much simpler to run.
Bear in mind too that Kafka is a persistent log, not just a message bus so any data you feed into it is kept available until it expires (which is configurable). This can be useful if you have multiple clients reading from the queue with their own lifecycle but in your case it doesn't sound like that would be necessary. You could also use a RabbitMQ fanout exchange if you need that in the future.
After using couchbase for over 4 years, we migrated to MongoDB and that was the best decision ever! I'm very disappointed with Couchbase's technical performance. Even though we received enterprise support and were a listed Couchbase Partner, the experience was horrible. With every contact, the sales team was trying to get me on a $7k+ license for access to features all other open source NoSQL databases get for free.
Here's why you should not use Couchbase
Full-text search Queries The full-text search often returns a different number of results if you run the same query multiple types
N1QL queries Configuring the indexes correctly is next to impossible. It's poorly documented and nobody seems to know what to do, even the Couchbase support engineers have no clue what they are doing.
Community support I posted several problems on the forum and I never once received a useful answer
Enterprise support It's very expensive. $7k+. The team constantly tried to get me to buy even though the community edition wasn't working great
Autonomous Operator It's actually just a poorly configured Kubernetes role that no matter what I did, I couldn't get it to work. The support team was useless. Same lack of documentation. If you do get it to work, you need 6 servers at least to meet their minimum requirements.
Couchbase cloud Typical for Couchbase, the user experience is awful and I could never get it to work.
Minimum requirements
The minimum requirements in production are 6 servers. On AWS the calculated monthly cost would be ~$600
. We achieved better performance using a $16
MongoDB instance on the Mongo Atlas Cloud
writing queries is a nightmare While N1QL is similar to SQL and it's easier to write because of the familiarity, that isn't entirely true. The "smart index" that Couchbase advertises is not smart at all. Creating an index with 5 fields, and only using 4 of them won't result in Couchbase using the same index, so you have to create a new one.
Couchbase UI
The UI that comes with every database deployment is full of bugs, barely functional and the developer experience is poor. When I asked Couchbase about it, they basically said they don't care because real developers use SQL directly from code
Consumes too much RAM
Couchbase is shipped with a smaller Memcached instance to handle the in-memory cache. Memcached ends up using 8 GB of RAM for 5000 documents
! I'm not kidding! We had less than 5000 docs on a Couchbase instance and less than 20 indexes and RAM consumption was always over 8 GB
Memory allocations are useless I asked the Couchbase team a question: If a bucket has 1 GB allocated, what happens when I have more than 1GB stored? Does it overflow? Does it cache somewhere? Do I get an error? I always received the same answer: If you buy the Couchbase enterprise then we can guide you.
We implemented our first large scale EPR application from naologic.com using CouchDB .
Very fast, replication works great, doesn't consume much RAM, queries are blazing fast but we found a problem: the queries were very hard to write, it took a long time to figure out the API, we had to go and write our own @nodejs library to make it work properly.
It lost most of its support. Since then, we migrated to Couchbase and the learning curve was steep but all worth it. Memcached indexing out of the box, full text search works great.
Pros of Couchbase
- High performance18
- Flexible data model, easy scalability, extremely fast18
- Mobile app support9
- You can query it with Ansi-92 SQL7
- All nodes can be read/write6
- Equal nodes in cluster, allowing fast, flexible changes5
- Both a key-value store and document (JSON) db5
- Open source, community and enterprise editions5
- Automatic configuration of sharding4
- Local cache capability4
- Easy setup3
- Linearly scalable, useful to large number of tps3
- Easy cluster administration3
- Cross data center replication3
- SDKs in popular programming languages3
- Elasticsearch connector3
- Web based management, query and monitoring panel3
- Map reduce views2
- DBaaS available2
- NoSQL2
- Buckets, Scopes, Collections & Documents1
- FTS + SQL together1
Pros of Kafka
- High-throughput126
- Distributed119
- Scalable92
- High-Performance86
- Durable66
- Publish-Subscribe38
- Simple-to-use19
- Open source18
- Written in Scala and java. Runs on JVM12
- Message broker + Streaming system9
- KSQL4
- Avro schema integration4
- Robust4
- Suport Multiple clients3
- Extremely good parallelism constructs2
- Partioned, replayable log2
- Simple publisher / multi-subscriber model1
- Fun1
- Flexible1
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Cons of Couchbase
- Terrible query language3
Cons of Kafka
- Non-Java clients are second-class citizens32
- Needs Zookeeper29
- Operational difficulties9
- Terrible Packaging5