Apache CouchDB vs. Apache Spark

Overview
ProductRatingMost Used ByProduct SummaryStarting Price
CouchDB
Score 6.2 out of 10
N/A
Apache CouchDB is an HTTP + JSON document database with Map Reduce views and bi-directional replication. The Couch Replication Protocol is implemented in a variety of projects and products that span computing environments from globally distributed server-clusters, over mobile phones to web browsers.N/A
Apache Spark
Score 8.7 out of 10
N/A
N/AN/A
Pricing
Apache CouchDBApache Spark
Editions & Modules
No answers on this topic
No answers on this topic
Offerings
Pricing Offerings
CouchDBApache Spark
Free Trial
NoNo
Free/Freemium Version
NoNo
Premium Consulting/Integration Services
NoNo
Entry-level Setup FeeNo setup feeNo setup fee
Additional Details
More Pricing Information
Community Pulse
Apache CouchDBApache Spark
Considered Both Products
CouchDB
Chose Apache CouchDB
It stacks up well against Mongo DB. Mongo DB definitely has more marketing and developer and customer mindshare because it is so widely known.
Apache Spark

No answer on this topic

Top Pros
Top Cons
Features
Apache CouchDBApache Spark
NoSQL Databases
Comparison of NoSQL Databases features of Product A and Product B
Apache CouchDB
7.9
2 Ratings
11% below category average
Apache Spark
-
Ratings
Performance8.02 Ratings00 Ratings
Availability8.52 Ratings00 Ratings
Concurrency8.52 Ratings00 Ratings
Security6.02 Ratings00 Ratings
Scalability8.02 Ratings00 Ratings
Data model flexibility7.02 Ratings00 Ratings
Deployment model flexibility9.02 Ratings00 Ratings
Best Alternatives
Apache CouchDBApache Spark
Small Businesses
IBM Cloudant
IBM Cloudant
Score 8.1 out of 10

No answers on this topic

Medium-sized Companies
IBM Cloudant
IBM Cloudant
Score 8.1 out of 10
Cloudera Manager
Cloudera Manager
Score 9.9 out of 10
Enterprises
IBM Cloudant
IBM Cloudant
Score 8.1 out of 10
IBM Analytics Engine
IBM Analytics Engine
Score 8.7 out of 10
All AlternativesView all alternativesView all alternatives
User Ratings
Apache CouchDBApache Spark
Likelihood to Recommend
9.0
(10 ratings)
10.0
(23 ratings)
Likelihood to Renew
9.0
(9 ratings)
10.0
(1 ratings)
Usability
8.0
(1 ratings)
10.0
(3 ratings)
Support Rating
-
(0 ratings)
8.7
(4 ratings)
Implementation Rating
9.0
(1 ratings)
-
(0 ratings)
User Testimonials
Apache CouchDBApache Spark
Likelihood to Recommend
Apache
Great for REST API development, if you want a small, fast server that will send and receive JSON structures, CouchDB is hard to beat. Not great for enterprise-level relational database querying (no kidding). While by definition, document-oriented databases are not relational, porting or migrating from relational, and using CouchDB as a backend is probably not a wise move as it's reliable, but It may not always be highly available.
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Apache
Well suited: To most of the local run of datasets and non-prod systems - scalability is not a problem at all. Including data from multiple types of data sources is an added advantage. MLlib is a decently nice built-in library that can be used for most of the ML tasks. Less appropriate: We had to work on a RecSys where the music dataset that we used was around 300+Gb in size. We faced memory-based issues. Few times we also got memory errors. Also the MLlib library does not have support for advanced analytics and deep-learning frameworks support. Understanding the internals of the working of Apache Spark for beginners is highly not possible.
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Pros
Apache
  • It can replicate and sync with web browsers via PouchDB. This lets you keep a synced copy of your database on the client-side, which offers much faster data access than continuous HTTP requests would allow, and enables offline usage.
  • Simple Map/Reduce support. The M/R system lets you process terabytes of documents in parallel, save the results, and only need to reprocess documents that have changed on subsequent updates. While not as powerful as Hadoop, it is an easy to use query system that's hard to screw up.
  • Sharding and Clustering support. As of CouchDB 2.0, it supports clustering and sharding of documents between instances without needing a load balancer to determine where requests should go.
  • Master to Master replication lets you clone, continuously backup, and listen for changes through the replication protocol, even over unreliable WAN links.
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Apache
  • Rich APIs for data transformation making for very each to transform and prepare data in a distributed environment without worrying about memory issues
  • Faster in execution times compare to Hadoop and PIG Latin
  • Easy SQL interface to the same data set for people who are comfortable to explore data in a declarative manner
  • Interoperability between SQL and Scala / Python style of munging data
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Cons
Apache
  • NoSQL DB can become a challenge for seasoned RDBMS users.
  • The map-reduce paradigm can be very demanding for first-time users.
  • JSON format documents with Key-Value pairs are somewhat verbose and consume more storage.
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Apache
  • Memory management. Very weak on that.
  • PySpark not as robust as scala with spark.
  • spark master HA is needed. Not as HA as it should be.
  • Locality should not be a necessity, but does help improvement. But would prefer no locality
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Likelihood to Renew
Apache
Because our current solution S3 is working great and CouchDB was a nightmare. The worst is that at first, it seemed fine until we filled it with tons of data and then started to create views and actually delete.
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Apache
Capacity of computing data in cluster and fast speed.
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Usability
Apache
Couchdb is very simple to use and the features are also reduced but well implemented. In order to use it the way its designed, the ui is adequate and easy. Of course, there are some other task that can't be performed through the admin ui but the minimalistic design allows you to use external libraries to develop custom scripts
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Apache
The only thing I dislike about spark's usability is the learning curve, there are many actions and transformations, however, its wide-range of uses for ETL processing, facility to integrate and it's multi-language support make this library a powerhouse for your data science solutions. It has especially aided us with its lightning-fast processing times.
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Support Rating
Apache
No answers on this topic
Apache
1. It integrates very well with scala or python. 2. It's very easy to understand SQL interoperability. 3. Apache is way faster than the other competitive technologies. 4. The support from the Apache community is very huge for Spark. 5. Execution times are faster as compared to others. 6. There are a large number of forums available for Apache Spark. 7. The code availability for Apache Spark is simpler and easy to gain access to. 8. Many organizations use Apache Spark, so many solutions are available for existing applications.
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Implementation Rating
Apache
it support is minimal also hw requirements. Also for development, we can have databases replicated everywhere and the replication is automagical. once you set up the security and the rules for replication, you are ready to go. The absence of a model let you build your app the way you want it
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Apache
No answers on this topic
Alternatives Considered
Apache
It has been 5+ years since we chose CouchDB. We looked an MongoDB, Cassandra, and probably some others. At the end of the day, the performance, power potential, and simplicity of CouchDB made it a simple choice for our needs. No one should use just because we did. As I said early, make sure you understand your problems, and find the right solution. Some random reading that might be useful: http://www.julianbrowne.com/article/viewer/brewers-cap-theorem https://www.couchbase.com/nosql-resources/why-nosql\ https://www.infoq.com/articles/cap-twelve-years-later-how-the-rules-have-changed
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Apache
Spark in comparison to similar technologies ends up being a one stop shop. You can achieve so much with this one framework instead of having to stitch and weave multiple technologies from the Hadoop stack, all while getting incredibility performance, minimal boilerplate, and getting the ability to write your application in the language of your choosing.
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Return on Investment
Apache
  • It has saved us hours and hours of coding.
  • It is has taught us a new way to look at things.
  • It has taught us patience as the first few weeks with CouchDB were not pleasant. It was not easy to pick up like MongoDB.
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Apache
  • Business leaders are able to take data driven decisions
  • Business users are able access to data in near real time now . Before using spark, they had to wait for at least 24 hours for data to be available
  • Business is able come up with new product ideas
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