Choose PostgreSQL for a relational SQL database with ACID transactions, foreign keys and rich queries on one server or a small cluster. Choose Cassandra when your data has to spread across many machines or sites, you want a masterless design, and you're happy to model data around the queries you'll run.
Apache Cassandra and PostgreSQL both turn up on database shortlists, but they come from different families: a distributed, wide-column NoSQL store, and a relational engine with object features that builds on the SQL standard. Developers and architects ask which to pick when a new system's needs aren't settled yet, or when an existing database is starting to strain.
One node defeats the point of Cassandra
Cassandra's own documentation says there's little point running it as a single node. Its strengths come from spreading data over many machines: there's no leader, every member of the cluster can stand in for every other, and they keep each other informed through a gossip protocol. On one server you take on the design's trade-offs without getting its benefits.
Postgres is the opposite. Its SQL features, transactions and constraints all work from a single instance, so a small project can start on one machine and add complexity only when it needs to. If you can't yet say why you'd need several machines, that alone points you one way.
So the first question isn't which is faster. It's how many machines, and how many places, your data has to live in.
The two compared row by row
| Cassandra | Postgres | |
|---|---|---|
| Family | Distributed NoSQL | Object-relational |
| Data model | Partitioned wide-column | SQL, used and extended |
| Consistency | Eventual | ACID |
| Layout | Leaderless cluster | Single instance or small cluster |
| Licence | Open source | Free to use, modify and distribute |
Eventual consistency and ACID in plain English
These two phrases decide more than any feature list. ACID means a transaction either happens completely or not at all, and once it's confirmed, every reader sees the same result. Move money between two accounts in a system like that and you'll never see the money in both places, or in neither.
Eventually consistent means copies of the data on different machines can briefly disagree, and they settle on the same value over time. In return, any node can do the same job as any other, so no single machine is in charge. For a social feed, a sensor log or a shopping basket that's often an acceptable trade. For a ledger, stock levels or anything legal, most teams want the stronger guarantee.
So ask what a short-lived disagreement would mean for your users. If the honest answer is a mildly stale page, a distributed store is on the table. If it's a support ticket or an audit finding, start relational.
When Cassandra fits the job
It suits data that has to live across many machines or sites. Its design combines distributed storage techniques from Amazon's Dynamo with Google's Bigtable data model, and the partitioned wide-column approach means you plan tables around the questions you'll ask, not around neat relationships between records.
That's a different habit from relational design. It works best when you know your access patterns up front, because you write data the way you'll read it. If your queries change every month, expect more remodelling than you'd like. If you're also weighing a document store, our comparison with MongoDB covers that choice.
When PostgreSQL fits the job
PostgreSQL suits applications where records relate to each other: orders linked to customers, accounts linked to users. It has been ACID-compliant since 2001, and it supports foreign keys, triggers, updatable views and multiversion concurrency control, so the database itself guards the integrity of your data.
Two of those terms are worth unpacking. A trigger runs your own logic automatically when rows change, so a rule such as keeping an audit record lives in the database rather than in every app that writes to it. Multiversion concurrency control keeps several versions of a row while it's being changed, which lets reads carry on without waiting for writes to finish.
It's also extensible. You can add new data types, functions, operators and index methods, which lets you stretch it into jobs beyond classic tables without changing database. Its licence lets anyone use, modify and distribute it free of charge for any purpose. Comparing other relational options? See how it measures up against MySQL.
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What running each one involves
Day-to-day care is where these two differ most, and it's easy to underestimate. A single relational instance needs backups, monitoring, the occasional upgrade and someone who understands its query plans. A small team can usually look after that alongside other work.
A distributed cluster adds a second layer of jobs: replacing failed machines, keeping copies of data in step, planning capacity across several servers or sites, and upgrading members one at a time without taking the whole thing down. None of that is exotic, but it's real work every month, and it lands on whoever is on call.
So be honest about who will do it. A design that looks perfect on a whiteboard can still be the wrong pick if nobody on the team wants the pager that comes with it. Put a name next to each of those jobs before you commit, and if a column stays empty, that's your answer for now.
Common ground
Both are open source. Both reward planning: a distributed store punishes a data model that ignores your queries, and a relational schema without the right indexes slows down as tables grow.
Questions to answer before you choose
- How many machines or sites must hold the data, now and in two years?
- Do you need transactions across related records, such as an order and its stock count?
- Can you list your main queries today, or will they keep changing?
- Who on the team has run a distributed database before?
- What happens to the business if two copies of a record briefly disagree?
- Could you start on a single relational instance and move only the busiest data later?
If most answers point to one machine, related records and changing queries, start relational. If they point to many sites and fixed access patterns, build a prototype of the distributed option on several nodes, never one.
Frequently asked questions
Can I use both in one system?
Yes. Some architectures keep transactional records in a relational database and push widely distributed data elsewhere. Keep the boundary between them clear.
Is it hard to move from one to the other later?
Yes, because the data models differ. Moving between relational tables and query-shaped wide-column tables means redesigning, not just copying.
Which is easier to learn?
SQL is familiar ground for most developers, so a relational database is the quicker start. A distributed store asks you to learn its data modelling approach as well.
What should a proof of concept include?
Realistic data volumes, your real queries, and a failure test: switch a node or the server off and see what your application does.
How do I know my app has outgrown one server?
When you've tuned queries and added indexes and the machine is still the bottleneck, or when the data must physically sit in several regions.
What happens when I add a node to a Cassandra cluster?
Because any node can do the same job as any other, a new one joins as an equal rather than as a follower, and gossip spreads word of it to the rest. Follow Cassandra's documentation for the exact steps to bring it in safely.
