Couchbase vs SQLite

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Couchbase

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102
SQLite

11.9K
9.3K
+ 1
528
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Couchbase vs SQLite: What are the differences?

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; SQLite: A software library that implements a self-contained, serverless, zero-configuration, transactional SQL database engine. SQLite is an embedded SQL database engine. Unlike most other SQL databases, SQLite does not have a separate server process. SQLite reads and writes directly to ordinary disk files. A complete SQL database with multiple tables, indices, triggers, and views, is contained in a single disk file.

Couchbase and SQLite can be primarily classified as "Databases" tools.

"Flexible data model, easy scalability, extremely fast" is the primary reason why developers consider Couchbase over the competitors, whereas "Lightweight" was stated as the key factor in picking SQLite.

Coderus, BrightMachine, and Infoshare are some of the popular companies that use SQLite, whereas Couchbase is used by RecordSetter, Musixmatch, and Crowdpark. SQLite has a broader approval, being mentioned in 313 company stacks & 470 developers stacks; compared to Couchbase, which is listed in 45 company stacks and 20 developer stacks.

Advice on Couchbase and SQLite
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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Dimelo Waterson
Needs advice
on
SQLite
PostgreSQL
and
MySQL

I need to add a DBMS to my stack, but I don't know which. I'm tempted to learn SQLite since it would be useful to me with its focus on local access without concurrency. However, doing so feels like I would be defeating the purpose of trying to expand my skill set since it seems like most enterprise applications have the opposite requirements.

To be able to apply what I learn to more projects, what should I try to learn? MySQL? PostgreSQL? Something else? Is there a comfortable middle ground between high applicability and ease of use?

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Replies (3)
Recommends
SQLite

You can easily start with SQlite. Really easy to startup since it doesn't require you to install any additional software since is self-contained. It has interfaces in almost any language and also GUIs. Start learning SQL basics and simpler data models and structures. There are many tutorials, also available in the official website. From there you will easily migrate to another database. MySQL could be next, sonce it's easier to learn at first and has more resources available. PostgreSQL is less widespread, more challenging and has the fewer resorces, but once you have some experience with MySQL is really easy to learn as well. All these technologies are really widespread and used accross the industry so you won't make a wrong decision with any of these.

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Stephen Badger | Vital Beats
Senior DevOps Engineer at Vital Beats · | 6 upvotes · 67.4K views

A question you might want to think about is "What kind of experience do I want to gain, by using a DBMS?". If your aim is to have experience with SQL and any related libraries and frameworks for your language of choice (python, I think?), then it kind of doesn't matter too much which you pick so much. As others have said, SQLite would offer you the ability to very easily get started, and would give you a reasonably standard (if a little basic) SQL dialect to work with.

If your aim is actually to have a bit of "operational" experience, in terms of things like what command line tools might be available as standard for the DBMS, understanding how the DBMS handles multiple databases, when to use multiple schemas vs multiple databases, some basic privilege management etc. Then I would recommend PostgreSQL. SQLite's simplicity actually avoids most of these experiences, which is not helpful to you if that is what you hope to learn. MySQL has a few "quirks" to how it manages things like multiple databases, which may lead you to making less good decisions if you tried to take your experience over to different DBMS, especially in bigger enterprise roles. PostgreSQL is kind of a happy middle ground here, with the ability to start PostgreSQL servers via docker or docker-compose making the actual day-to-day management pretty easy, while still giving you experience of the kinds of considerations I have listed above.

At Vital Beats we make use of PostgreSQL, largely because it offers us a happy balance between good management and backup of data, and good standard command line tools, which is essential for us where we are deploying our solutions within Kubernetes / docker, and so more graphical tools are not always appropriate for us. PostgreSQL is also pretty universally supported in terms of language libraries and frameworks, without having to make compromises on how we want to store and layout our data.

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Julien DeFrance
Principal Software Engineer at Tophatter · | 1 upvotes · 59.4K views
Recommends
MySQL

MySQL's very popular, easy to install, is also available as a managed service across most popular cloud offerings. The support/default tooling (such as MySQL Query Workbench) certainly is a little more baked than what you'll find for Postgres.

https://dev.mysql.com/downloads/workbench/

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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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Needs advice
on
Firebase
MySQL
and
SQLite

Hi everyone! I am a high school student, starting a massive project. I'm building a system for a boarding school to be better connected to their students and be more efficient with information. In the meantime, I am developing a website and an android app. What's the best datastore I can use? I need to be able to access student data on the app from the main database and send push notifications. Also feed updates. What's the best approach? What's the best tool I can use to deploy the website and the database? One for testing and prototyping, and an official one... Thanks in advance!!!!

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Replies (3)
Ahmed AlAskalany
Android Developer at Kitab Sawti · | 5 upvotes · 83K views
Recommends
Firebase

Firebase has Android, iOS, and Web SDKs; and a console where you can develop, manage, and monitor all the data and analytics from one place. Firebase real-time database is good for online presence and instant feed updates, while Firebase Firestone is good for user profile and other relational data records. Firebase has a UI SDK which makes it easy to interface with the resources in the project, and with tons of tutorials and starter projects it should be easy to quickly have a decent prototype to iterate upon. Since you said Massive, use their pricing calculator to figure if your expected scale will be covered by the free quota or if you go for the pay-as-you-go that the price is reasonable for your project.

Good luck with the project!

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Paul Whittemore
Developer and Owner at Appurist Software · | 4 upvotes · 83.1K views
Recommends
Firebase

It sounds like a server-client relationship (central database) and while SQLite is probably the simplest, note that its performance is probably the worst of the top 20 or so choices you have. It is different from Firebase and MySQL (and most other databases) in that it is embedded in the product, although it could be embedded in your server itself.

MySQL would require a separate MySQL db server, which means either two servers (one for MySQL, and one to provide your specific services to your client app) or both running on a single server machine. There are many alternatives in the same category as MySQL, and a choice of relational databases or document (NoSQL) databases. But architecturally, they are in the same category as MySQL, a separate db server that your application server would get its data from.

Firebase is different yet again, in that it is a service that is already hosted by a company, providing many integrated features such as authentication and storage of user account info. However it does take care of many of the concerns with running a server, such as performance, scalability and management. There are some negatives that you should be aware of though: any investment of time and coding with Firebase is pretty much non-portable, in that you are stuck with Firebase going forward. If you needed to switch to a different service, not only would it be a different API, but it would be a different architecture and much of your coding would need to be discarded. Second, it's owned and run by Google now, so you have a large corporation backing it, but that also means they could decide to discontinue it without any real effect on the Google bottom line. Also some folks would have concerns with storing data on Google servers. That said, I think if you are aware of these in advance, and especially if you are a high school student, that Firebase is a fairly easy winner here. The server is already set up for you, the documentation is very complete and rich, with lots of examples, and Google is not going away. The main concern would be if it really is massive, there could be a rising cost to the service. I suspect though that it is not massive, even if everyone in a school used it. The number of concurrent connections would not be huge (probably not even into the hundreds, even if there are thousands of users).

I'd go with Firebase even though you will need to learn their API, because you'll need to learn something one way or another. SQLite is a bit of a toy database, and MySQL is a real one but you (or someone) would need to manage that server on top of needing to develop the server and client app. With Firebase, much of the server already exists, including a professionally hosted database. There are tons of high-level features provided and initial cost is somewhere between very low and zero.

Part of this is dependent on what language you want to write this in. Javascript for a cross-platform client app (I'd use Vue.js + Vuetify for UI, and provide it as a web app and optionally wrap that with Electron for a desktop app, Apache Cordova for mobile). Server could be Javascript with an Express-based REST API on Node.js, talking to Firebase for services.

If you were a Java developer though, all this goes out the window and I'd recommend a simple Java server with Javalin for REST API, and embedded ObjectDB for database storage (combined into one server). ObjectDB is very very fast and can be separated out into a scalable server if this became truly massive. But you would probably never need to go that far.

All of this is a lot of work. I hope this isn't for something like an assignment. It is in the order of 6 months of work if you know what you're doing, all year if you're learning as you go.

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Michael Maraist
Chief Architect at Pixia Corp · | 2 upvotes · 82.6K views
Recommends
RocksDB

Don't think you can go wrong with MySQL or postgresql. python+postgres is VERY well supported stack and can do almost anything. Great visualization and administrative tools for both. There are some data-mismatch problems, however.. node.js/python with mongodb is a bit more modern and makes it trivial to "serialize" data with sprinklings of indexes. If you're using go-lang, then RocksDB is a great high-performance data-modeling base (it's not relational how-ever) It's more like a building-block for key-value store. But it's ACID so you CAN build relational systems on top. I've used LevelDB for other projects (Java/C) (similar architecture and works great on android - chrome uses it for it's metadata-storage). Rock/Level can achieve multi-million writes on cheap hardware thanks to it's trade-offs.

I'm very familiar with SQLite.. Personally my least favorite, but it's the most portable database format, and it does support ACID.. I have many gripes, but biggest issue is parallel access (you really need a single process/thread to own the data-model, then use IPC to communicate with your process/thread).. (same could be said for LevelDB, but that's so efficient, it's almost never an issue).

If your'e using Java, then JavaDB/DerbyDB/HSQLDB are EXCELLENT systems.. highly multi-threaded, good stand-alone tools. (embedded or TCP-connected). Perfect for unit-tests. Can use simple dumb portable formats (e.g. text-file containing only inserts) all the way to classic journaled binary B-tree formats to pure-in-memory. Java has a lot of overhead, so this is only really viable if you're already using Java in your project.

For high performance "memsql" is mysql API to a hybrid in-memory index + on-disk column-database (feels like classic SQL to you though). Falls into the mysql-swiss-army-knife tool-kit.

Similarly with in-memory there is "redis".. Absolutely a joy to work with. It too is a specialty swiss army knife. Steer clear of redis for primary data that you can't lose.. while redis does support persisting data, it isn't very efficient and will become the bottleneck. redis is great for micro-queue's, topics, stat-aggregators, message-repositories (password-management systems, where writes are rare so persistance is viable). Plus I love that redis uses a pure-text protocol so I can netcat or telnet directly into it and do stuff.

I've loved cloud-data-stores.. Amazon "DynamoDB" or Google BigTable are awesome!!! Cheap compared to normal hosting fees of an AWS EC2 instance.. You can play all day.. put a terabyte up, then blow it away.. pay for what you play with. It's a very very different data-model though.. They give you a very very few set of tricks that let you do complex data-modeling - and you have to be clever and have enough foresight to not block yourself into a hole (or have customer abuse expensive queries).

Then there's Cassandra/Hadoop (HBase). These are petabyte scale databases (technically so is Dynamo/BigTable). They're incredibly efficient at what they do. And they have a lot of plugins to do almost anything you need. I personally love these the best (and RocksDB/LevelDB are like their infant children offspring). You can run these on your laptop (unlike Amazon/Google engines above). But their discipline is very different than all the other's above.

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Decisions about Couchbase and SQLite

Backend:

  • Considering that our main app functionality involves data processing, we chose Python as the programming language because it offers many powerful math libraries for data-related tasks. We will use Flask for the server due to its good integration with Python. We will use a relational database because it has good performance and we are mostly dealing with CSV files that have a fixed structure. We originally chose SQLite, but after realizing the limitations of file-based databases, we decided to switch to PostgreSQL, which has better compatibility with our hosting service, Heroku.
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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 Couchbase
Pros of SQLite
  • 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
  • 160
    Lightweight
  • 134
    Portable
  • 121
    Simple
  • 80
    Sql
  • 28
    Preinstalled on iOS and Android
  • 2
    Tcl integration
  • 1
    Free
  • 1
    Telefon
  • 1
    Portable A database on my USB 'love it'

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Cons of Couchbase
Cons of SQLite
  • 3
    Terrible query language
  • 2
    Not for multi-process of multithreaded apps
  • 1
    Needs different binaries for each platform

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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.

What is SQLite?

SQLite is an embedded SQL database engine. Unlike most other SQL databases, SQLite does not have a separate server process. SQLite reads and writes directly to ordinary disk files. A complete SQL database with multiple tables, indices, triggers, and views, is contained in a single disk file.

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What companies use Couchbase?
What companies use SQLite?
See which teams inside your own company are using Couchbase or SQLite.
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What tools integrate with Couchbase?
What tools integrate with SQLite?

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What are some alternatives to Couchbase and SQLite?
MongoDB
MongoDB stores data in JSON-like documents that can vary in structure, offering a dynamic, flexible schema. MongoDB was also designed for high availability and scalability, with built-in replication and auto-sharding.
CouchDB
Apache CouchDB is a database that uses JSON for documents, JavaScript for MapReduce indexes, and regular HTTP for its API. CouchDB is a database that completely embraces the web. Store your data with JSON documents. Access your documents and query your indexes with your web browser, via HTTP. Index, combine, and transform your documents with JavaScript.
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.
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.
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.
See all alternatives