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CouchDB vs RocksDB: What are the differences?
Introduction
CouchDB and RocksDB are two database systems that serve different purposes with distinct features. Let's explore the key differences between the two:
Data Model: CouchDB is a document-oriented NoSQL database, which means it stores and retrieves data in JSON-like documents. It supports flexible schemas, allowing documents with varying structures. On the other hand, RocksDB is a key-value store where data is stored as an ordered set of key-value pairs. It doesn't have a concept of documents or schemas.
Scalability and Performance: CouchDB is designed for distributed computing and offers a built-in replication mechanism for data synchronization between multiple CouchDB instances. It handles large datasets well and scales horizontally by adding more nodes. However, RocksDB is optimized for high-performance on a single machine with lower latency. It excels in read and write operations on large datasets with low disk space usage.
Storage and Persistence: CouchDB stores data on disk in append-only files, ensuring data durability and allowing easy replication. It follows the MVCC (Multi-Version Concurrency Control) model to handle concurrent writes. On the other hand, RocksDB utilizes LSM (Log-Structured Merge) tree technology for storage, which efficiently manages both memory and disk space. It uses in-memory structures for better write performance and flushes data to disk periodically.
Querying and Indexing: CouchDB provides a flexible querying approach with MapReduce views and built-in search capabilities using Lucene. It allows for complex queries and full-text search. In contrast, RocksDB primarily focuses on fast key-value lookup and lacks built-in querying capabilities. Advanced indexing or querying features need to be implemented outside of RocksDB.
Use Cases: CouchDB is suitable for use cases that require highly distributed data and offline capability, such as mobile or web applications. It is often used in scenarios where data needs to be synchronized across devices or clients. On the other hand, RocksDB is ideal for applications that demand high performance and low latency, where instant data access and efficient storage are critical.
Community and Ecosystem: CouchDB has a dedicated and globally distributed community with active development and support. It is a mature project with a rich set of features and integrations. RocksDB, which is a part of Facebook's open-source RocksDB project, has a relatively smaller community but still actively maintained. It has a growing ecosystem and is well-suited for integration within larger systems.
In summary, CouchDB provides a flexible document-oriented database for distributed scenarios, whereas RocksDB is optimized for high-performance, single-machine storage and retrieval of key-value data.
I am researching different querying solutions to handle ~1 trillion records of data (in the realm of a petabyte). The data is mostly textual. I have identified a few options: Milvus, HBase, RocksDB, and Elasticsearch. I was wondering if there is a good way to compare the performance of these options (or if anyone has already done something like this). I want to be able to compare the speed of ingesting and querying textual data from these tools. Does anyone have information on this or know where I can find some? Thanks in advance!
You've probably come to a decision already but for those reading...here are some resources we put together to help people learn more about Milvus and other databases https://zilliz.com/comparison and https://github.com/zilliztech/VectorDBBench. I don't think they include RocksDB or HBase yet (you could could recommend on GitHub) but hopefully they help answer your Elastic Search questions.
I’m newbie I was developing a pouchdb and couchdb app cause if the sync. Lots of learning very little code available. I dropped the project cause it consumed my life. Yeats later I’m back into it. I researched other db and came across rethinkdb and mongo for the subscription features. With socketio I should be able to create and similar sync feature. Attempted to use mongo. I attempted to use rethink. Rethink for the win. Super clear l. I had it running in minutes on my local machine and I believe it’s supposed to scale easy. Mongo wasn’t as easy and there free online db is so slow what’s the point. Very easy to find mongo code examples and use rethink code in its place. I wish I went this route years ago. All that corporate google Amazon crap get bent. The reason they have so much power in the world is cause you guys are giving it to them.
So, we started using foundationDB for an OLAP system although the inbuilt tools for some core things like aggregation and filtering were negligible, with the high through put of the DB, we were able to handle it on the application. The system has been running pretty well for the past 6 months, although the data load isn’t very high yet, the performance is fairly promising
Our application data all goes in SQL. We will use something like Cosmos or Couch DB if one or both of these conditions are true: * We need to ingest a large amount of bulk data from a third party, and integrating it straight into an RDBMS with referential integrity checks would create a performance hit * We need to ingest a large amount of data that does not have a clearly defined, or consistent schema. In either case, we will have a process that migrates the data from Cosmos/Couch to SQL in a way that doesn't create a noticeable performance hit and ensures that we are not introducing bad data to the system. Because of this, there is a third condition that must be met: the data that is coming in must be something that the users will not need immediately, i.e. stock ticker information, real-time telemetry from other systems for performance/safety monitoring, etc.
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 CouchDB
- JSON43
- Open source30
- Highly available18
- Partition tolerant12
- Eventual consistency11
- Sync7
- REST API5
- Attachments mechanism to docs4
- Multi master replication4
- Changes feed3
- REST interface1
- js- and erlang-views1
Pros of RocksDB
- Very fast5
- Made by Facebook3
- Consistent performance2
- Ability to add logic to the database layer where needed1
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