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CouchDB vs HBase: What are the differences?
CouchDB: HTTP + JSON document database with Map Reduce views and peer-based replication. 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; HBase: The Hadoop database, a distributed, scalable, big data store. 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.
CouchDB and HBase can be primarily classified as "Databases" tools.
"JSON" is the primary reason why developers consider CouchDB over the competitors, whereas "Performance" was stated as the key factor in picking HBase.
CouchDB and HBase are both open source tools. CouchDB with 4.24K GitHub stars and 835 forks on GitHub appears to be more popular than HBase with 2.91K GitHub stars and 2.01K GitHub forks.
Acadar, Third Iron, and SocialDecode are some of the popular companies that use CouchDB, whereas HBase is used by Pinterest, HubSpot, and Yammer. CouchDB has a broader approval, being mentioned in 61 company stacks & 31 developers stacks; compared to HBase, which is listed in 54 company stacks and 18 developer stacks.
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 HBase
- Performance9
- OLTP5
- Fast Point Queries1