Cassandra vs Couchbase

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Cassandra

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3.1K
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486
Couchbase

377
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Cassandra vs Couchbase: What are the differences?

What is Cassandra? A partitioned row store. Rows are organized into tables with a required primary key. Partitioning means that Cassandra can distribute your data across multiple machines in an application-transparent matter. Cassandra will automatically repartition as machines are added and removed from the cluster. Row store means that like relational databases, Cassandra organizes data by rows and columns. The Cassandra Query Language (CQL) is a close relative of SQL.

What is Couchbase? Document-Oriented NoSQL Database. Developed as an alternative to traditionally inflexible SQL databases, the Couchbase NoSQL database is built on an open source foundation and architected to help developers solve real-world problems and meet high scalability demands.

Cassandra and Couchbase belong to "Databases" category of the tech stack.

"Distributed" is the top reason why over 96 developers like Cassandra, while over 13 developers mention "Flexible data model, easy scalability, extremely fast" as the leading cause for choosing Couchbase.

Cassandra is an open source tool with 5.27K GitHub stars and 2.35K GitHub forks. Here's a link to Cassandra's open source repository on GitHub.

According to the StackShare community, Cassandra has a broader approval, being mentioned in 342 company stacks & 239 developers stacks; compared to Couchbase, which is listed in 45 company stacks and 21 developer stacks.

Advice on Cassandra and Couchbase
Ilias Mentzelos
Software Engineer at Plum Fintech · | 9 upvotes · 36.1K views
Needs advice
on
MongoDB
and
Couchbase

Hey, we want to build a referral campaign mechanism that will probably contain millions of records within the next few years. We want fast read access based on IDs or some indexes, and isolation is crucial as some listeners will try to update the same document at the same time. What's your suggestion between Couchbase and MongoDB? Thanks!

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Replies (2)
Jon Clarke
Enterprise Account Exec at ScyllaDB · | 4 upvotes · 22.2K views
Recommends
Scylla
Couchbase

I am biased (work for Scylla) but it sounds like a KV/wide column would be better in this use case. Document/schema free/lite DBs data stores are easier to get up and running on but are not as scalable (generally) as NoSQL flavors that require a more rigid data model like ScyllaDB. If your data volumes are going to be 10s of TB and transactions per sec 10s of 1000s (or more), look at Scylla. We have something called lightweight transactions (LWT) that can get you consistency.

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Recommends
MongoDB

I have found MongoDB highly consistent and highly available. It suits your needs. We usually trade off partion tolerance fot this. Having said that, I am little biased in recommendation as I haven't had much experience with couchbase on production.

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Umair Iftikhar
Technical Architect at Vappar · | 3 upvotes · 120K views
Needs advice
on
TimescaleDB
Druid
and
Cassandra

Developing a solution that collects Telemetry Data from different devices, nearly 1000 devices minimum and maximum 12000. Each device is sending 2 packets in 1 second. This is time-series data, and this data definition and different reports are saved on PostgreSQL. Like Building information, maintenance records, etc. I want to know about the best solution. This data is required for Math and ML to run different algorithms. Also, data is raw without definitions and information stored in PostgreSQL. Initially, I went with TimescaleDB due to PostgreSQL support, but to increase in sites, I started facing many issues with timescale DB in terms of flexibility of storing data.

My major requirement is also the replication of the database for reporting and different purposes. You may also suggest other options other than Druid and Cassandra. But an open source solution is appreciated.

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Replies (1)
Recommends
MongoDB

Hi Umair, Did you try MongoDB. We are using MongoDB on a production environment and collecting data from devices like your scenario. We have a MongoDB cluster with three replicas. Data from devices are being written to the master node and real-time dashboard UI is using the secondary nodes for read operations. With this setup write operations are not affected by read operations too.

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Needs advice
on
MongoDB
and
Couchbase

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.

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Replies (3)
Petr Havlicek
Freelancer at havlicekpetr.cz · | 12 upvotes · 34.6K views
Recommends
MongoDB

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.

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Ivan Begtin
Director - NGO "Informational Culture" / Ambassador - OKFN Russia at Infoculture · | 7 upvotes · 34.6K views
Recommends
ArangoDB

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.

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OtkudznamDamir Radinović-Lukić
Recommends
Linux

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.

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Vinay Mehta
Needs advice
on
Scylla
and
Cassandra

The problem I have is - we need to process & change(update/insert) 55M Data every 2 min and this updated data to be available for Rest API for Filtering / Selection. Response time for Rest API should be less than 1 sec.

The most important factors for me are processing and storing time of 2 min. There need to be 2 views of Data One is for Selection & 2. Changed data.

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Replies (4)
Recommends
Scylla

Scylla can handle 1M/s events with a simple data model quite easily. The api to query is CQL, we have REST api but that's for control/monitoring

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Alex Peake
Recommends
Cassandra

Cassandra is quite capable of the task, in a highly available way, given appropriate scaling of the system. Remember that updates are only inserts, and that efficient retrieval is only by key (which can be a complex key). Talking of keys, make sure that the keys are well distributed.

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Recommends
Scylla

By 55M do you mean 55 million entity changes per 2 minutes? It is relatively high, means almost 460k per second. If I had to choose between Scylla or Cassandra, I would opt for Scylla as it is promising better performance for simple operations. However, maybe it would be worth to consider yet another alternative technology. Take into consideration required consistency, reliability and high availability and you may realize that there are more suitable once. Rest API should not be the main driver, because you can always develop the API yourself, if not supported by given technology.

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Pankaj Soni
Chief Technical Officer at Software Joint · | 2 upvotes · 51.4K views
Recommends
Cassandra

i love syclla for pet projects however it's license which is based on server model is an issue. thus i recommend cassandra

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Decisions about Cassandra and Couchbase
Gabriel Pa

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.

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 This was roughly 4 years ago at this point. We had been using an old iteration of memcache on Windows as the data cache per server for while and had for whatever reason opted to store our session data (to which the application is heavily dependent) in App Fabric. App Fabric had come to EOL and we needed to move away from it. As a quick search showed throughput capacity to be higher and overall features of Redis were better we initially implemented it. The number of changes required were minimal and we were able to migrate away to a more resilient system pretty quickly.
 We hit a snag in that the implementation of the Redis session handler at that point only took a single IP so we had to do use keepalived and HAProxy to display the application consistently between master and slave failovers. This caused issues on occasion of dropped connections to the backend service. We upgraded our client and could put both members into our config files and it stopped timing out. We were all happy in this and it was (for it's own part) a significant upgrade. Generally performance was better for all pages. 
 We found however, that our application was serializing all requests and locking on the thread via session lock for a great many things and this caused us and our users considerable headaches on occasion. We found that the implementation of the couchbase session handler gave us the option of ignoring session write locks and not be required to rewrite dozens of pages to handle the no session requirement as the application was working fine without the need to be threadsafe. Though the maximum throughput was not as good compared to Redis the application performance was considerably better as a result of the change. The multi-master write was a big benefit and the cross data center replication was a nice thing to have as that would allow our users to remain logged in even across DataCenter fail-over events (praying that never happened). Overall we used that for session handling and chose to use couchbase (in a 2nd cluster) to handle memcache requests as that gave us greater capacity to handle larger objects more efficiently. 
 We are, all these years later, looking to move into the newer features of couchbase to give us more and better use of this product that really has been the answer to a bunch of the growing pains we experienced. Since the decision performance has not been on wild rides and stability has never been better. So I sing the praises of couchbase to anyone that will listen.
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Gabriel Pa

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.

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Pros of Cassandra
Pros of Couchbase
  • 112
    Distributed
  • 93
    High performance
  • 80
    High availability
  • 74
    Easy scalability
  • 52
    Replication
  • 26
    Reliable
  • 26
    Multi datacenter deployments
  • 8
    OLTP
  • 7
    Schema optional
  • 6
    Open source
  • 2
    Workload separation (via MDC)
  • 18
    High performance
  • 17
    Flexible data model, easy scalability, extremely fast
  • 8
    Mobile app support
  • 6
    You can query it with Ansi-92 SQL
  • 5
    All nodes can be read/write
  • 4
    Local cache capability
  • 4
    Open source, community and enterprise editions
  • 4
    Both a key-value store and document (JSON) db
  • 4
    Equal nodes in cluster, allowing fast, flexible changes
  • 3
    Automatic configuration of sharding
  • 3
    SDKs in popular programming languages
  • 3
    Elasticsearch connector
  • 3
    Easy setup
  • 3
    Web based management, query and monitoring panel
  • 3
    Linearly scalable, useful to large number of tps
  • 3
    Easy cluster administration
  • 3
    Cross data center replication
  • 2
    NoSQL
  • 2
    DBaaS available
  • 2
    Map reduce views
  • 1
    FTS + SQL together
  • 1
    Buckets, Scopes, Collections & Documents

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Cons of Cassandra
Cons of Couchbase
  • 2
    Reliability of replication
  • 1
    Updates
  • 3
    Terrible query language

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- No public GitHub repository available -

What is Cassandra?

Partitioning means that Cassandra can distribute your data across multiple machines in an application-transparent matter. Cassandra will automatically repartition as machines are added and removed from the cluster. Row store means that like relational databases, Cassandra organizes data by rows and columns. The Cassandra Query Language (CQL) is a close relative of SQL.

What is Couchbase?

Developed as an alternative to traditionally inflexible SQL databases, the Couchbase NoSQL database is built on an open source foundation and architected to help developers solve real-world problems and meet high scalability demands.

Need advice about which tool to choose?Ask the StackShare community!

What companies use Cassandra?
What companies use Couchbase?
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What tools integrate with Couchbase?

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What are some alternatives to Cassandra and Couchbase?
HBase
Apache HBase is an open-source, distributed, versioned, column-oriented store modeled after Google' Bigtable: A Distributed Storage System for Structured Data by Chang et al. Just as Bigtable leverages the distributed data storage provided by the Google File System, HBase provides Bigtable-like capabilities on top of Apache Hadoop.
Hadoop
The Apache Hadoop software library is a framework that allows for the distributed processing of large data sets across clusters of computers using simple programming models. It is designed to scale up from single servers to thousands of machines, each offering local computation and storage.
Redis
Redis is an open source, BSD licensed, advanced key-value store. It is often referred to as a data structure server since keys can contain strings, hashes, lists, sets and sorted sets.
MySQL
The MySQL software delivers a very fast, multi-threaded, multi-user, and robust SQL (Structured Query Language) database server. MySQL Server is intended for mission-critical, heavy-load production systems as well as for embedding into mass-deployed software.
PostgreSQL
PostgreSQL is an advanced object-relational database management system that supports an extended subset of the SQL standard, including transactions, foreign keys, subqueries, triggers, user-defined types and functions.
See all alternatives