Couchbase vs Oracle

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Couchbase

375
480
+ 1
102
Oracle

1.5K
1.3K
+ 1
107
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Couchbase vs Oracle: What are the differences?

Developers describe Couchbase as "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. On the other hand, Oracle is detailed as "An RDBMS that implements object-oriented features such as user-defined types, inheritance, and polymorphism". Oracle Database is an RDBMS. An RDBMS that implements object-oriented features such as user-defined types, inheritance, and polymorphism is called an object-relational database management system (ORDBMS). Oracle Database has extended the relational model to an object-relational model, making it possible to store complex business models in a relational database.

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

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

Netflix, ebay, and LinkedIn are some of the popular companies that use Oracle, whereas Couchbase is used by RecordSetter, Musixmatch, and Crowdpark. Oracle has a broader approval, being mentioned in 106 company stacks & 92 developers stacks; compared to Couchbase, which is listed in 45 company stacks and 21 developer stacks.

Advice on Couchbase and Oracle
Ilias Mentzelos
Software Engineer at Plum Fintech · | 9 upvotes · 33.2K 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 · 20.4K 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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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 · 30.7K 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 · 30.7K 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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Decisions about Couchbase and Oracle
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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Daniel Moya
Data Engineer at Dimensigon · | 4 upvotes · 156.5K views

We have chosen Tibero over Oracle because we want to offer a PL/SQL-as-a-Service that the users can deploy in any Cloud without concerns from our website at some standard cost. With Oracle Database, developers would have to worry about what they implement and the related costs of each feature but the licensing model from Tibero is just 1 price and we have all features included, so we don't have to worry and developers using our SQLaaS neither. PostgreSQL would be open source. We have chosen Tibero over Oracle because we want to offer a PL/SQL that you can deploy in any Cloud without concerns. PostgreSQL would be the open source option but we need to offer an SQLaaS with encryption and more enterprise features in the background and best value option we have found, it was Tibero Database for PL/SQL-based applications.

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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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We wanted a JSON datastore that could save the state of our bioinformatics visualizations without destructive normalization. As a leading NoSQL data storage technology, MongoDB has been a perfect fit for our needs. Plus it's open source, and has an enterprise SLA scale-out path, with support of hosted solutions like Atlas. Mongo has been an absolute champ. So much so that SQL and Oracle have begun shipping JSON column types as a new feature for their databases. And when Fast Healthcare Interoperability Resources (FHIR) announced support for JSON, we basically had our FHIR datalake technology.

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In the field of bioinformatics, we regularly work with hierarchical and unstructured document data. Unstructured text data from PDFs, image data from radiographs, phylogenetic trees and cladograms, network graphs, streaming ECG data... none of it fits into a traditional SQL database particularly well. As such, we prefer to use document oriented databases.

MongoDB is probably the oldest component in our stack besides Javascript, having been in it for over 5 years. At the time, we were looking for a technology that could simply cache our data visualization state (stored in JSON) in a database as-is without any destructive normalization. MongoDB was the perfect tool; and has been exceeding expectations ever since.

Trivia fact: some of the earliest electronic medical records (EMRs) used a document oriented database called MUMPS as early as the 1960s, prior to the invention of SQL. MUMPS is still in use today in systems like Epic and VistA, and stores upwards of 40% of all medical records at hospitals. So, we saw MongoDB as something as a 21st century version of the MUMPS database.

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Pros of Couchbase
Pros of Oracle
  • 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
  • 42
    Reliable
  • 31
    Enterprise
  • 15
    High Availability
  • 5
    Hard to maintain
  • 4
    Expensive
  • 4
    Maintainable
  • 3
    High complexity
  • 3
    Hard to use

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Cons of Couchbase
Cons of Oracle
  • 3
    Terrible query language
  • 13
    Expensive

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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 Oracle?

Oracle Database is an RDBMS. An RDBMS that implements object-oriented features such as user-defined types, inheritance, and polymorphism is called an object-relational database management system (ORDBMS). Oracle Database has extended the relational model to an object-relational model, making it possible to store complex business models in a relational database.

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

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What are some alternatives to Couchbase and Oracle?
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