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Couchbase vs H2 Database: What are the differences?
Introduction:
Couchbase and H2 Database are both popular choices for storing and managing data in applications. While both databases have their strengths, they also have key differences that make them suitable for different use cases.
Data Modeling and Query Language: One key difference between Couchbase and H2 Database lies in their data modeling and query language. Couchbase is a NoSQL database that uses a flexible schema-less model, making it well-suited for dynamic and unstructured data. On the other hand, H2 Database is a relational database that follows a strict schema, making it ideal for applications that require complex relationships between different entities.
Scalability and Performance: Another important distinction between Couchbase and H2 Database is their scalability and performance capabilities. Couchbase is designed for high availability and scalability, using features like sharding and replication to handle large volumes of data and high traffic loads. In contrast, H2 Database is more limited in its scalability options and may not perform as well under heavy loads or with extremely large datasets.
Consistency and ACID Compliance: When it comes to data consistency and ACID compliance, H2 Database offers strong support for transactions and ensures data integrity through features like lock-based synchronization. In comparison, Couchbase prioritizes high availability and partition tolerance over strict consistency, which can lead to eventual consistency in certain scenarios.
Deployment and Setup Complexity: The deployment and setup process for Couchbase and H2 Database also differ significantly. Couchbase is designed to be easily deployable in distributed and cloud environments, with built-in support for auto-scaling and data rebalancing. In contrast, H2 Database is more traditional in its deployment model, often requiring manual configuration and management of servers.
Storage and Indexing Mechanisms: The way data is stored and indexed in Couchbase and H2 Database varies as well. Couchbase uses a memory-first architecture with disk persistence for durability, offering efficient indexing through its built-in secondary indexes and global secondary indexes. H2 Database, on the other hand, relies on disk storage by default and allows for index creation on specific columns to optimize query performance.
Community and Ecosystem: The ecosystem and community around Couchbase and H2 Database are also worth considering. Couchbase has a strong community backing and an active development team that continuously enhances the platform with new features and improvements. H2 Database, while popular in Java applications, may have a smaller community and may not have the same level of support for advanced features or integrations.
In Summary, Couchbase and H2 Database differ in terms of data modeling, scalability, consistency, deployment complexity, storage mechanisms, and community support, making them suitable for distinct use cases based on specific requirements.
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.
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 H2 Database
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Cons of Couchbase
- Terrible query language3