Apache CouchDB vs. Apache HBase

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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Hbase is well suited for large organizations with millions of operations performing on tables, real-time lookup of records in a table, range queries, random reads and writes and online analytics operations. Hbase cannot be replaced for traditional databases as it cannot support all the features, CPU and memory intensive. Observed increased latency when using with MapReduce job joins.

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• 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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• Scalability. HBase can scale to trillions of records.
• Fast. HBase is extremely fast to scan values or retrieve individual records by key.
• HBase can be accessed by standard SQL via Apache Phoenix.
• Integrated. I can easily store and retrieve data from HBase using Apache Spark.
• It is easy to set up DR and backups.
• Ingest. It is easy to ingest data into HBase via shell, Java, Apache NiFi, Storm, Spark, Flink, Python and other means.

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• 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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• There are very few commands in HBase.
• Stored procedures functionality is not available so it should be implemented.
• HBase is CPU and Memory intensive with large sequential input or output access while as Map Reduce jobs are primarily input or output bound with fixed memory. HBase integrated with Map-reduce jobs will result in random latencies.

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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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There's really not anything else out there that I've seen comparable for my use cases. HBase has never proven me wrong. Some companies align their whole business on HBase and are moving all of their infrastructure from other database engines to HBase. It's also open source and has a very collaborative community.

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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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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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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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Cassandra os great for writes. But with large datasets, depending, not as great as HBASE. Cassandra does support parquet now. HBase still performance issues. Cassandra has use cases of being used as time series. HBase, it fails miserably. GeoSpatial data, Hbase does work to an extent. HA between the two are almost the same.

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• 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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• As Hbase is a noSql database, here we don't have transaction support and we cannot do many operations on the data.
• Not having the feature of primary or a composite primary key is an issue as the architecture to be defined cannot be the same legacy type. Also the transaction concept is not applicable here.
• The way data is printed on console is not so user-friendly. So we had to use some abstraction over HBase (eg apache phoenix) which means there is one new component to handle.

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