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Amazon DynamoDB vs Couchbase: What are the differences?
Introduction
Amazon DynamoDB and Couchbase are both popular NoSQL databases used for storing and retrieving large volumes of data. While they share some similarities, there are several key differences between these two databases.
Data Model: One of the main differences between DynamoDB and Couchbase is their data model. DynamoDB uses a key-value data model, where each item is identified by a unique primary key. In contrast, Couchbase uses a document data model, where data is stored as JSON documents with a unique key. This difference in data models allows for different querying and data manipulation capabilities.
Scalability: DynamoDB is known for its seamless scalability. It provides automatic horizontal scaling through built-in sharding, allowing users to handle high volumes of traffic without any manual intervention. On the other hand, Couchbase also offers scalability but requires manual partitioning and distribution of data across nodes. This manual process can be time-consuming and complex.
Consistency Model: DynamoDB offers eventual consistency by default, meaning that changes made to the data may not be immediately reflected in all copies of the data. However, it also provides strong consistency as an option for specific read operations. In contrast, Couchbase offers strong consistency by default, ensuring that all copies of data are immediately consistent. Users can choose to relax consistency requirements if needed.
Querying Capabilities: DynamoDB relies on secondary indexes for querying data. It supports basic query operations but lacks advanced querying capabilities such as joins and aggregations. In contrast, Couchbase provides a powerful query language called N1QL (pronounced nickel) that supports SQL-like queries with joins, aggregations, and advanced filtering capabilities.
Data Distribution: DynamoDB automatically replicates data across multiple Availability Zones to ensure high availability and durability. It also provides global tables for distributing data across multiple regions. Couchbase, on the other hand, allows users to manually configure data replication and distribution across nodes and clusters, providing more control over data placement.
Integration and Ecosystem: DynamoDB is tightly integrated with other AWS services, making it a preferred choice for users already using AWS infrastructure. It also has a mature ecosystem of SDKs, tools, and libraries. Couchbase, on the other hand, has its own ecosystem and can be used in a variety of environments, including hybrid and multi-cloud setups. It offers integrations with various technologies and frameworks.
In summary, DynamoDB and Couchbase differ in their data models, scalability approaches, consistency models, querying capabilities, data distribution methods, and integration ecosystems. The choice between these databases depends on specific requirements, such as the nature of the data, workload patterns, and existing infrastructure.
We are building a social media app, where users will post images, like their post, and make friends based on their interest. We are currently using Cloud Firestore and Firebase Realtime Database. We are looking for another database like Amazon DynamoDB; how much this decision can be efficient in terms of pricing and overhead?
Hi, Akash,
I wouldn't make this decision without lots more information. Cloud Firestore has a much richer metamodel (document-oriented) than Dynamo (key-value), and Dynamo seems to be particularly restrictive. That is why it is so fast. There are many needs in most applications to get lightning access to the members of a set, one set at a time. Dynamo DB is a great choice. But, social media applications generally need to be able to make long traverses across a graph. While you can make almost any metamodel act like another one, with your own custom layers on top of it, or just by writing a lot more code, it's a long way around to do that with simple key-value sets. It's hard enough to traverse across networks of collections in a document-oriented database. So, if you are moving, I think a graph-oriented database like Amazon Neptune, or, if you might want built-in reasoning, Allegro or Ontotext, would take the least programming, which is where the most cost and bugs can be avoided. Also, managed systems are also less costly in terms of people's time and system errors. It's easier to measure the costs of managed systems, so they are often seen as more costly.
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.
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 Amazon DynamoDB
- Predictable performance and cost62
- Scalable56
- Native JSON Support35
- AWS Free Tier21
- Fast7
- No sql3
- To store data3
- Serverless2
- No Stored procedures is GOOD2
- ORM with DynamoDBMapper1
- Elastic Scalability using on-demand mode1
- Elastic Scalability using autoscaling1
- DynamoDB Stream1
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
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Cons of Amazon DynamoDB
- Only sequential access for paginate data4
- Scaling1
- Document Limit Size1
Cons of Couchbase
- Terrible query language3
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