Couchbase vs PostgreSQL

• Couchbase performance is exceptional both for in-memory and persisted transactions.
• Handling of node failures and cluster rebalancing (high availability).
• Enterprise support from Couchbase themselves
• Good documentation
• Streaming of bucket (database) level mutations via their Database Change Protocol (DCP).
• Replication of datasets between native clients and Couchbase buckets
• Handling of simultaneous writes to the same record with performance penalties

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• The stability it offers, its speed of response and its resource management is excellent even in complex database environments and with low-resource machines.
• The large amount of resources it has in addition to the many own and third-party tools that are compatible that make productivity greatly increase.
• The adaptability in various environments, whether distributed or not, [is a] complete set of configuration options which allows to greatly customize the work configuration according to the needs that are required.
• The excellent handling of referential and transactional integrity, its internal security scheme, the ease with which we can create backups are some of the strengths that can be mentioned.

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• The N1QL engine performs poorly compared to SQL engines due to the number of interactions needed, so if your use case involves the need for a lot of SQL-like query activity as opposed to the direct fetch of data in the form of a key/value map you may want to consider a RDBMS that has support for json data types so that you can more easily mix the use of relational and non-relational approaches to data access.
• You have to be careful when using multiple capabilities (e.g. transactions with Sync Gateway) as you will typically run into problems where one technology may not operate correctly in combination with another.
• There are quality problems with some newly released features, so be careful with being an early adopter unless you really need the capability. We somewhat desperately adopted the use of transactions, but went through multiple bughunt cycles with Couchbase working the kinks out.

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• The query syntax for JSON fields is unwieldy when you start getting into complex queries with many joins.
• I wish there was a distinction (a flag) you could set for automated scripts vs working in the psql CLI, which would provide an 'Are you sure you want to do X?' type prompt if your query is likely to affect more than a certain number of rows. Especially on updates/deletes. Setting the flag in the headless(scripted) flow would disable the prompt.
• Better documentation around JSON and Array aggregation, with more examples of how the data is transformed.

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• Great performance.
• Leading Couchbase Lite capabilities for mobile use.
• Developers' learning curve with replica reads and multi cluster can be long. Needs guidance and nurturing.
• Cluster maintenance during OS patching, etc. has multiple ways to approach. Operational teams may need some guidance.

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• The user-role system has saved us tons of time and thus money. As I mentioned in the "Use Case" section, Postgres is not only used by engineering but also finance to measure how much to charge customers and customer support to debug customer issues. Sure, it's not easy for non-technical employees to psql in and view raw tables, but it has saved engineering hundreds of man-hours that would have had to be spent on building equivalent tools to serve finance or customer support.
• It provides incredibly trustworthy storage for wherever customer data dumped in. In our 6 years of Postgres existence, we have not lost a byte of customer data due to Postgres messing up a transaction or during the multiple times the hard-drives failed (thanks to ACID compliance!).
• This is less significant, but Postgres is also quite easy to manage (unless you are going above and beyond to squeeze out every last bit of performance). There's not much to configure, and the out of the box settings are quite sane. That has saved us engineers lots of time that would have gone into Postgres administration.

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