Couchbase

Two systems that usually sit together

A very common architecture puts a cache in front of a database. The database holds the data. The cache holds copies of whatever is being read often, so that repeated reads do not reach the database.

It works, and it introduces a problem that every team using it has to solve: keeping the two consistent. When data changes, the cached copy is wrong until something removes or updates it. Getting that wrong produces bugs that are intermittent and hard to reproduce.

Couchbase folds the caching layer into the database. Active data is held in memory and written through to disk by the same system that owns the data.

That is the design idea, and it is what distinguishes it from document databases that treat memory purely as a read cache managed separately.

Documents

Data is stored as JSON documents, each under a key.

A document holds named fields, and values can be nested structures or arrays. Documents in the same collection do not have to share a shape, which suits records whose attributes genuinely vary.

Retrieving a document by key is the fastest path, and where the data is in memory it involves no disk access.

Querying with SQL++

Key based retrieval alone is limiting. Applications need to ask for documents matching conditions, and that requires a query language.

Couchbase uses SQL++, which applies SQL style syntax to documents.

Selecting fields, filtering, joining, grouping and ordering are all present and recognisable to anyone who has written SQL. The extensions handle what JSON adds: reaching into nested objects, and working with arrays inside a document.

The practical value is the shortened learning curve. A team that knows SQL can query document data without adopting an entirely different way of expressing questions.

Indexes matter as they do anywhere. A query with no suitable index has to examine far more documents than necessary.

Services and where they run

Couchbase separates its work into services: storing and retrieving data, running queries, maintaining indexes, and full text search.

These can run on different nodes.

The reason is that these workloads stress different resources. Query processing is demanding on processing capacity. Index maintenance has its own cost. Holding data in memory needs memory.

Running them separately means a cluster can be shaped to the workload, giving more nodes to query processing where queries are heavy without also paying for storage capacity that is not needed.

It is a more involved deployment decision than a single uniform node type, and it is what allows the resources to match the actual load.

Replication between clusters

Cross data centre replication copies data between separate clusters.

This supports running in more than one location, so each region reads from a cluster near it, and it provides a copy in another location for continuity if one becomes unavailable.

Replication between clusters is asynchronous, so the copies are not identical at every instant. Applications spanning regions need to account for that, particularly where the same record may be written in two places.

Who uses it

Couchbase is used by application developers and platform teams working on systems with high read rates and flexible record structures, including user profiles, catalogues, reservations and similar workloads where documents are read constantly and response time matters.

Points to consider

Couchbase is available in a community edition and in commercial editions including a managed cloud service. The official website is the reference for what each provides.

The memory first design implies memory planning. Performance depends on the working set fitting in memory, so understanding how much of the data is actively used is part of sizing a cluster rather than an afterthought.

Separating services adds flexibility and adds decisions. A small deployment may not need it, and a large one benefits from thinking about node roles deliberately rather than by default.

As with other document databases, flexible structure moves responsibility to the application. Without discipline, a collection accumulates documents in several shapes and every reader has to cope with all of them.

Getting started

The official documentation covers the data model, SQL++, indexing, services and replication. Storing a few documents and querying them with SQL++ shows quickly how close the syntax is to SQL and where the extensions for nested data come in.