In the last few years we’ve seen the emergency of some impressive cloud technology ranging from databases like very reminiscent of current RDMSes except with better scalability (Aurora), to those with novel new designs that take advantage of custom hardware for the guarantees that they need to scale (Spanner). While unbounded data growth may still have a logistical problem for many organizations, the tools that we have today to manage it have never been better.
It can be a little hard to keep track of the new entrants and track how exactly they differ from one another, so here I’ve tried to summarize various offerings and how they compare to one another.
It’s impossible to rate any one as a clear winner because like any consideration in technology, there are trade offs for everything, and organizations will largely have to select technology based on what will be valuable to them. Many of the characteristics in my comparison matrix below were selected based on their importance in building robust software, but I admit there’s some bias there. There’s also some bias in which technologies even show up. There are dozens of options and I’ve excluded the vast majority; the list is scoped down to the some of the most general purpose, most practical, and most interesting.
I put some opinions on favorites in “Closing thoughts” below.
|Database||Concurrent ACID||HA||Horizontally Scalable||Automatic Scalability||Low Latency||Notes|
|Amazon Aurora||✓||✓||✓ Disk only||✓ Single node; see notes||✓|
|Citus||✓||✓||✓||✓||Open source; ACI* is node local|
|MongoDB||✓||✓||✓||Open source; not recommended given modern alternatives (see notes)|
|Postgres||✓||✓||N/A||✓||Open source; HA through Amazon RDS, Heroku Postgres, or Azure Database|
Here’s the meaning of each column:
Concurrent ACID: Whether the database supports ACID (atomicity, consistency, isolation, and durability) guarantees across multiple operations. ACID is a powerful tool for system correctness, and until recently has been a long sought but illusive chimera for distributed databases. I use the term “concurrent ACID” because technically Cosmos guarantees ACID, but only within the confines of a single operation.
HA: Whether the database is highly available (HA). I’ve marked every one on the list as HA, but some are “more HA” than others with CockroachDB, Cosmos, and Spanner leading the way in this respect. The others tend to rely on a single node failovers.
Horizontally Scalable: Whether the database can be scaled horizontally out to additional nodes. Everything on the list except Postgres is, but I’ve included the column to call out the fact that unlike the others, Aurora’s scalability is disk only. That’s not to say that it’s unsuitable for use, but it has some caveats (see “Amazon Aurora” below for details).
Automatic Scalability: Distinguishes databases where data partitioning and balancing is handled manually by the user versus automatically by the database. As an example of a “manual” database, in Citus or MongoDB you explicitly tell the database that you want a table to be distributed and tell it what key should be used for sharding (e.g.
user_id). For comparison, Spanner automatically figures out how to distribute any data stored to it to the nodes it has available, and rebalances as necessary. Both options are workable, but manual distribution has more operational overhead and without care, can lead to unbalanced sharding where larger nodes run disproportionately hot.
Low latency: The extra inter-node coordination overhead used by CockroachDB Cosmos, and Spanner to ensure consistency comes at the cost of being unsuitable where very low latency operations are needed (~1 ms). I cover this in a little more detail below in “Time-based consistency”.
The CAP theorem dictates that given consistency, 100% availability, and partition tolerance, any given database can satisfy a maximum of two of the three.
To explain why I didn’t include CAP in the table above, I’ll quote Eric Brewer (Google VP Infrastructure) writing about Spanner:
Despite being a global distributed system, Spanner claims to be consistent and highly available, which implies there are no partitions and thus many are skeptical. Does this mean that Spanner is a CA system as defined by CAP? The short answer is “no” technically, but “yes” in effect and its users can and do assume CA.
The purist answer is “no” because partitions can happen and in fact have happened at Google, and during (some) partitions, Spanner chooses C and forfeits A. It is technically a CP system. We explore the impact of partitions below.
Given that Spanner always provides consistency, the real question for a claim of CA is whether or not Spanner’s serious users assume its availability. If its actual availability is so high that users can ignore outages, then Spanner can justify an “effectively CA” claim. This does not imply 100% availability (and Spanner does not and will not provide it), but rather something like 5 or more “9s” (1 failure in 10^5 or less). In turn, the real litmus test is whether or not users (that want their own service to be highly available) write the code to handle outage exceptions: if they haven’t written that code, then they are assuming high availability. Based on a large number of internal users of Spanner, we know that they assume Spanner is highly available.
In other words, modern techniques can achieve CP while still keeping availability that’s incredibly good. Like five or more 9s of good. This result is so optimal that modern databases seem to be converging on it. Every database on the list above is CP with varying levels of A (with some caveats 1).
Sophisticated distributed systems like Spanner and CockroachDB tend to need a little more time to coordinate and verify the accuracy of the results that will be returned from any given node, and this makes them less suitable for low latency operations.
Quizlet suggests that the minimum latency for a Spanner operation is ~5 ms. The Spanner paper describes the details of the coordination for various operations in sections 4.1. and 4.2. CockroachDB states very explicitly in their FAQ that it’s not as good of a choice where low latency reads and writes are critical.
The design of Microsoft’s Cosmos isn’t as transparent, but its documentation seems to suggest similar performance characteristics with the median time for reads and writes at 5 ms.
Aurora is a managed relational database that has an SQL interface that’s compatible with MySQL and Postgres. One of its biggest selling points is performance, and it claims to provide 5x the throughput of MySQL and 2x of Postgres running on the same hardware.
Aurora is quite distinctive from any other option on this list because it’s not horizontally scalable at the node level, and its clusters more resemble those of a traditional RDMS with a primary and read replicas. Instead, Amazon has devised a storage-level scaling scheme that allows its tables to grow to sizes significantly larger than you’d see with a traditional RDMS; up to 64 TB per table.
This storage-based scaling has the disadvantage that compute and memory resources (for writes or consistent reads) are limited to a single vertically scaled node , but it also has significant advantages as well: data is always colocated so query latency is very low. It also means that you can’t make a mistake choosing a partition scheme and end up with a few hot shards that need to be rebalanced (which is very easy to do and very hard to fix). It may be a more appropriate choice than solutions like CockroachDB or Spanner for users looking for extensive scalability, but who don’t need it to be infinite.
Citus is a distributed database built on top of Postgres that allows individual tables to be sharded and distributed across any number of nodes. It provides clever concepts like reference tables to help ensure data locality to improve query performance. ACID guarantees are scoped to particular nodes, which is often adequate given that partitioning is designed so that data is colocated.
Most notably, Citus is open source and runs using the Postgres extension API. This reduces the risk of lock in, which is a considerable downside of most of the other options on this list. Compared to Aurora, it also means that you’re more likely to see features from new Postgres releases make it into your database.
A downside compared to CockroachDB and Spanner is that it data is sharded manually, which as noted above, can lead to balancing problems. Another consideration is that it’s built by a fairly new company with a yet unproven business model. Generally when selecting a database, it’s good for peace of mind to know that you’re using something that’s almost certainly going to be around and well-maintained in ten years time. You can be pretty confident of that when the product is made by a behemoth like Amazon, Google, or Microsoft, but less so for smaller companies.
CockroachDB is a product built out of Cockroach Labs, a company founded by ex-Googlers who are known to have been influential in building Google File System and Google Reader. It’s based on the design laid out by the original Spanner paper, and like spanner, uses a time-based mechanic to achieve consistency, but without the benefit of Google’s GPS and atomic clocks.
It provides serializable distributed transactions, foreign keys, and secondary indexes. It’s open source and written in Go which gives it the nice property of being easily installable and runnable in a development environment. Their documentation is refreshingly well-written, easily readable, and honest. Take for example their list of known limitations.
Like Spanner, the additional overhead of guaranteeing distributed consistency means that it’s a poor choice where low latency operations are needed (they admit as much themselves). Like Citus above, the fact that it’s built by a small company with an unproven business model is a downside.
Cosmos is Microsoft’s brand-new cloud database. Its sales pitch tends to come on a little strong. For example, here’s an excerpt where they sell schemaless design, which put most generously is a well known trade off, and less so, an anti-feature:
Both relational and NoSQL databases force you to deal with schema & index management, versioning and migration […] But don’t worry – Cosmos DB makes this problem go away!
That said, it’s got a pretty good set of features:
- Fast and easy geographical distribution.
- A configurable consistency model that allows anything from strong serializability all the way down to eventual consistency which trades the possibility of out-of-order reads for speed.
- SLAs on operation timing that guarantees reads under 10 ms and indexed writes under 15 ms at the 99th percentile.
Like with CockroachDB and Spanner, the distribution of Cosmos makes it less suitable for work requiring very low latency operations. Their documentation suggests a median read and write latency of ~5 ms.
Cosmos is able to provide ACID through the use of stored
primary so that only one script is being handled at a time,
but it’s also doing some bookkeeping the ensure that any
writes can be reverted, thereby ensuring true atomicity
EVAL in Redis). Still, this approach is not
as sophisticated as the MVCC engines used by other
databases on this list because it can’t provide concurrent
MongoDB is a NoSQL data store that stores data as schemaless JSON documents. It doesn’t support ACID transactions, and if that wasn’t enough, since its release in 2009 has had a number of valid criticisms around core database competencies like durability, security, and correctness.
I’ve included it for purposes of comparison and because it still seems to have a lot of mindshare, but it’s not at the same level of sophistication as other systems on this list. Most others have a strict superset of its functionality (albeit with trade offs in a few cases), but also support other critically important features like ACID guarantees. New projects shouldn’t start on MongoDB, and old projects should be thinking about migrating off of it.
Postgres is the trusty workhorse of traditional RDMSes. HA isn’t built in, but is available through offerings from Amazon RDS, Heroku Postgres, or Azure Database (and hopefully Google Cloud SQL soon).
Even though it’s not a perfect fit for the rest of this list, I’ve included it anyway because it’s often still the best option for many use cases. Most organizations don’t have data that’s as big as they think it is, and by consciously restricting bloat, they can get away with a vertically scaled Postgres node. This will lead to a more operable stack, and more options in case it’s ever necessary to migrate between clouds and providers. You can also easily run Postgres locally or in testing, which is very important for friction-free productivity.
Opinion time: the best choice for most people will be to start with Postgres. It’s a battle-tested database with a spectacular number of features and few limitations. It’s open source and widely available so it can easily be run in development, CI, or migrated across every major cloud provider. Vertical scaling will go a long way for organizations who curate their data and offload lower fidelity information to more scalable stores.
After you’re at the scale of AirBnB or Uber, something like Aurora should look interesting. It seems to have many of the advantages of Postgres, and yet still manages to maintain data locality and scalable storage (at the costs of loss of dev/production parity and vendor lock in). Organizations at this tier who run hot and need compute and memory resources that are scalable beyond a single node might benefit from something like Citus instead.
After you’re at the scale of Google, something closer to Spanner is probably the right answer. Although less suitable for low latency operations, its scalability appears to be practically limitless.
The only databases on the list that I’ve seen running in production are MongoDB and Postgres, so take these recommendations with a grain of salt. There’s almost certainly hidden caveats to any of them that will only be uncovered with a lot of hands on experience.
1 The CAP properties of Cosmos and MongoDB are configurable as they can both be made to be eventually consistent.
2 Aurora nodes are currently scalable to 32 vCPUs and 244 GB of memory. Although that is “only” one node, it’s nothing to scoff at and should provide enough runway for the vast majority of use cases.
Did I make a mistake? Please consider sending a pull request.