CouchDB vs Memcached

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CouchDB

443
544
+ 1
139
Memcached

6.6K
4.9K
+ 1
470
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CouchDB vs Memcached: What are the differences?

Developers describe CouchDB as "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. On the other hand, Memcached is detailed as "High-performance, distributed memory object caching system". Memcached is an in-memory key-value store for small chunks of arbitrary data (strings, objects) from results of database calls, API calls, or page rendering.

CouchDB and Memcached can be primarily classified as "Databases" tools.

"JSON" is the primary reason why developers consider CouchDB over the competitors, whereas "Fast object cache" was stated as the key factor in picking Memcached.

CouchDB and Memcached are both open source tools. It seems that Memcached with 8.99K GitHub stars and 2.6K forks on GitHub has more adoption than CouchDB with 4.24K GitHub stars and 835 GitHub forks.

According to the StackShare community, Memcached has a broader approval, being mentioned in 755 company stacks & 267 developers stacks; compared to CouchDB, which is listed in 61 company stacks and 31 developer stacks.

Decisions about CouchDB and Memcached

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.

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Karan Kaushik
Senior Software Developer at Shyplite · | 5 upvotes · 31.8K views

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

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James Bender
Lead Application Architect at TekPartners · | 4 upvotes · 4K views

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.

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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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Pros of CouchDB
Pros of Memcached
  • 43
    JSON
  • 30
    Open source
  • 18
    Highly available
  • 12
    Partition tolerant
  • 11
    Eventual consistency
  • 7
    Sync
  • 5
    REST API
  • 4
    Attachments mechanism to docs
  • 4
    Multi master replication
  • 3
    Changes feed
  • 1
    REST interface
  • 1
    js- and erlang-views
  • 138
    Fast object cache
  • 128
    High-performance
  • 90
    Stable
  • 65
    Mature
  • 33
    Distributed caching system
  • 11
    Improved response time and throughput
  • 3
    Great for caching HTML
  • 2
    Putta

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Cons of CouchDB
Cons of Memcached
    Be the first to leave a con
    • 2
      Only caches simple types

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    What is 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.

    What is Memcached?

    Memcached is an in-memory key-value store for small chunks of arbitrary data (strings, objects) from results of database calls, API calls, or page rendering.

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    What tools integrate with CouchDB?
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    Blog Posts

    Dec 22 2020 at 9:26PM

    Pinterest

    Amazon EC2MemcachedC lang+4
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    Jun 6 2019 at 5:11PM

    AppSignal

    RedisRubyKafka+9
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    GitHubDockerReact+17
    35
    33651
    GitHubPythonNode.js+47
    53
    70915
    JavaScriptGitHubNode.js+26
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    JavaScriptGitHubPython+42
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    What are some alternatives to CouchDB and Memcached?
    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.
    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.
    Cloudant
    Cloudant’s distributed database as a service (DBaaS) allows developers of fast-growing web and mobile apps to focus on building and improving their products, instead of worrying about scaling and managing databases on their own.
    MariaDB
    Started by core members of the original MySQL team, MariaDB actively works with outside developers to deliver the most featureful, stable, and sanely licensed open SQL server in the industry. MariaDB is designed as a drop-in replacement of MySQL(R) with more features, new storage engines, fewer bugs, and better performance.
    RethinkDB
    RethinkDB is built to store JSON documents, and scale to multiple machines with very little effort. It has a pleasant query language that supports really useful queries like table joins and group by, and is easy to setup and learn.
    See all alternatives