It's Time to Stop Building KV Databases(buttondown.com)
buttondown.com
It's Time to Stop Building KV Databases
https://buttondown.com/jaffray/archive/its-time-to-stop-building-kv-databases/
63 comments
Sometimes a KV datastore is the right abstraction, though. Caching is an excellent example, but also for distributed session storage, configuration management, nonce enforcement, etc.
KV stores can be used to build distributed relational databases.
KV stores can be used to build relational databases in general.
Whether it's a good idea depends on your goal, and what alternative buildings blocks you have available.
(Eg if you are building your distributed relational database to run on top of lots of computers with spinning hard disks, you might want to expose some more characteristics of the hard disk directly to your database, so you can manage them; instead of trying to hide them behind an abstraction.)
Whether it's a good idea depends on your goal, and what alternative buildings blocks you have available.
(Eg if you are building your distributed relational database to run on top of lots of computers with spinning hard disks, you might want to expose some more characteristics of the hard disk directly to your database, so you can manage them; instead of trying to hide them behind an abstraction.)
K:V - maps / dictionaries can be the correct tool for some jobs.
I think I'd prefer to stop calling _large_ resources that are only K:V a 'database' though.
A 'database' shouldn't require SQL, but a distributed filesystem, however similar, isn't quite a database.
I think I'd prefer to stop calling _large_ resources that are only K:V a 'database' though.
A 'database' shouldn't require SQL, but a distributed filesystem, however similar, isn't quite a database.
Rebuttal: a filesystem is a database.
I was immediately offended by your rebuttal and gave it some thought. It is an interesting definitional boundary.
Perhaps the distinction is more pragmatic than fundamentally technical. We typically use the term "database" to describe systems designed primarily for structured data management with query capabilities, while filesystems optimize for hierarchical storage of opaque binary objects.
Therefore a KV is not a database either.
Perhaps the distinction is more pragmatic than fundamentally technical. We typically use the term "database" to describe systems designed primarily for structured data management with query capabilities, while filesystems optimize for hierarchical storage of opaque binary objects.
Therefore a KV is not a database either.
Filesystems are more like a graph database than a pure key value store.
I think 'database' is a term with multiple (related) meanings depending on context.
Another example is the term 'colour'. Depending on context, it sometimes makes sense to call black and white and grey 'colours', and sometimes it's better to treat them as something else.
I think 'database' is a term with multiple (related) meanings depending on context.
Another example is the term 'colour'. Depending on context, it sometimes makes sense to call black and white and grey 'colours', and sometimes it's better to treat them as something else.
Filesystems are structured and have queries.
On "IBM i", all filesystem units are also objects. They have relationships, as well as hierarchy.
Huh? Offended?!
Filesystems present a durable way to store hierarchical binary/textual data. They normally have a very well-defined api used to provide a primitive query language. Sounds a lot like a database, no?
Even internally they are very similar: journalling, paging, tree indexes are normally present in typical popular implementations.
In some classic OS-s there is no separation at all between the concepts of a database and a filesystem.
In a way, a generic durable database can be though of as a special kind of a filesystem. And vice versa.
Filesystems present a durable way to store hierarchical binary/textual data. They normally have a very well-defined api used to provide a primitive query language. Sounds a lot like a database, no?
Even internally they are very similar: journalling, paging, tree indexes are normally present in typical popular implementations.
In some classic OS-s there is no separation at all between the concepts of a database and a filesystem.
In a way, a generic durable database can be though of as a special kind of a filesystem. And vice versa.
Storage Engine (using a store type) -> Data Store (ordered by data model) -> Database (providing semantics & management)
k->v is a data store (using disk|inmem|networked storage engines).
A database is a complete system for management of data. They come (or used to come) in various data model flavors: hierarchical, graph, relational, etc.
k->v is a data store (using disk|inmem|networked storage engines).
A database is a complete system for management of data. They come (or used to come) in various data model flavors: hierarchical, graph, relational, etc.
Related philosophical question - is a spreadsheet a database?
It's certainly a base for data and can be used to implement all core concepts of relational calculus but it isn't designed for such and doesn't do so with performance in mind. Conversely, filesystems are often implemented using B-trees as many RDBMSes are but aren't designed for many of the operations one might typically ascribe to a database.
Nomenclature is tricky... how does that saying about the two hardest problems in CS go again?
It's certainly a base for data and can be used to implement all core concepts of relational calculus but it isn't designed for such and doesn't do so with performance in mind. Conversely, filesystems are often implemented using B-trees as many RDBMSes are but aren't designed for many of the operations one might typically ascribe to a database.
Nomenclature is tricky... how does that saying about the two hardest problems in CS go again?
I would hazard to say that anything that can be queried in a way more efficient than a full scan is a database, or can be used as a database. A spreadsheet, a filesystem, a hash table (aka "KV store"), even a sorted list, like a library catalog.
> Conversely, filesystems are often implemented using B-trees as many RDBMSes are but aren't designed for many of the operations one might typically ascribe to a database.
Filesystems are not a relational database, sure, but the word "database" in the context of computer systems, computer science, IT and technology in general doesn't mean "relational database".
Filesystems are definitely a hierarchical database, which is different to a relational database.
Filesystems are not a relational database, sure, but the word "database" in the context of computer systems, computer science, IT and technology in general doesn't mean "relational database".
Filesystems are definitely a hierarchical database, which is different to a relational database.
Spreadsheets: no index, not a database.
I sort of disagree. KV databases are so fundamental, that they are considered one of the most foundational tech to any advanced database management system.
Think of KV databases as a persistent associative mapping/hash map that needs to store data in a safe and secure way, then we can build advanced stuff on top of it. Take TiDB for example, it is a distributed database based on MySQL (its own query language can be considered as a subset of MySQL), but actually most of the heavylifting is handled by TiKV, which is a distributed KV datastore with Raft distributed consensus.
And then SurrealDB also leveraged TiKV to build their own graph-document hybrid database product...as one of the data transport. P.S.: used to be a contributor for SurrealDB.
Think of KV databases as a persistent associative mapping/hash map that needs to store data in a safe and secure way, then we can build advanced stuff on top of it. Take TiDB for example, it is a distributed database based on MySQL (its own query language can be considered as a subset of MySQL), but actually most of the heavylifting is handled by TiKV, which is a distributed KV datastore with Raft distributed consensus.
And then SurrealDB also leveraged TiKV to build their own graph-document hybrid database product...as one of the data transport. P.S.: used to be a contributor for SurrealDB.
KV databases are also the least efficient architecture possible if your data models or workloads are non-trivial. They are relatively simple to design and build, which is a positive attribute, but they are not that capable in any kind of theoretical sense. Other architectures preserve far more spatial and temporal locality when representing data models.
If your workload has even a whiff of analytics to it, operational or slow-time, KV databases are almost the pathological architecture in theory. Their intrinsically poor locality exacts a steep performance price.
These database architectures are all equivalent in the same sense that almost everything is a Turing Machine. Some manifestations and implementations are much more efficient than others in the real world. While I am not as emotionally invested in it as the article’s author seems to be, he is generally correct that KV databases have poor properties for most applications.
If your workload has even a whiff of analytics to it, operational or slow-time, KV databases are almost the pathological architecture in theory. Their intrinsically poor locality exacts a steep performance price.
These database architectures are all equivalent in the same sense that almost everything is a Turing Machine. Some manifestations and implementations are much more efficient than others in the real world. While I am not as emotionally invested in it as the article’s author seems to be, he is generally correct that KV databases have poor properties for most applications.
Deepseek just used FoundationDB to build a parallel filesystem. Parallel filesystem are a big deal -- their number, including proprietary ones, is probably in single digits.
Parallel Filesystems aren't a new or novel concept, and there have been lots of implementations.
The first one I encountered was DrFTPD circa 2004. But these days, any object storage system qualifies because they all support varying replication schemes and reading from any valid in-sync replica.
The first one I encountered was DrFTPD circa 2004. But these days, any object storage system qualifies because they all support varying replication schemes and reading from any valid in-sync replica.
Now we are getting into the definition of what a parallel filesystem is.
In my book, a parallel filesystem is not just pooling together a bunch of nodes, but something that can actually support the synchronized accesses needed by a parallel workload. So not just decoupling between data and metadata, but scaling out of the metadata layer as well.
That and a hierarchical namespace (I could be sold on compromising some POSIX compliance for performance reasons, but it has to fundamentally be a hierarchical namespace with similar semantics). So object stores would not qualify.
In my book, a parallel filesystem is not just pooling together a bunch of nodes, but something that can actually support the synchronized accesses needed by a parallel workload. So not just decoupling between data and metadata, but scaling out of the metadata layer as well.
That and a hierarchical namespace (I could be sold on compromising some POSIX compliance for performance reasons, but it has to fundamentally be a hierarchical namespace with similar semantics). So object stores would not qualify.
Object stores are not filesystems. They have a paths, but they are not hierarchical.
unless it's a hierarchical object store, like the one i am using.
thinking more about this, the difference between a hierarchical and non-hierarchical storage i the existence of additional indexes representing the hierarchy.
in most filesystems files are addressed by an inode. and directories are just lists of inodes. if you remove those then you end up with a KV store: inode->file.
consequently i see no difficulty to convert a KV store into a hierarchy by adding the necessary tables/directories representing the hierarchy.
in most filesystems files are addressed by an inode. and directories are just lists of inodes. if you remove those then you end up with a KV store: inode->file.
consequently i see no difficulty to convert a KV store into a hierarchy by adding the necessary tables/directories representing the hierarchy.
In a hierarchically system renaming a directory is an O(1) operation, in a non-hierarchical system with paths it is an O(n) operation.
the KV store i am talking about doesn't use paths, it uses IDs. the paths are in a hierarchical directory that is added on top of the KV store, or alongside of it. the result is a filesystem.
Is this a good take? I'm just a below average dev and trying to figure it out.
Never believe that any tool is good or bad. That's always going to be a generalization, and therefore wrong. Learn as many tools as possible, and know which use cases they're good at and which use cases they're bad at. If someone implies the tool is bad for all use cases, know that we all live in our own bubbles and are ignorant about the plethora of other use cases that exist in the world.
> Never believe that any tool is good or bad. That's always going to be a generalization, and therefore wrong.
Some generalisations are close enough to true to be worth it.
Eg I'm fairly confident to generalise and say that for most beginners in 2025 picking Python is a better choice than PHP or Cobol.
Of course, you can come up with some contrived scenarios where the beginner would be better served with Cobol.
Some generalisations are close enough to true to be worth it.
Eg I'm fairly confident to generalise and say that for most beginners in 2025 picking Python is a better choice than PHP or Cobol.
Of course, you can come up with some contrived scenarios where the beginner would be better served with Cobol.
> Eg I'm fairly confident to generalise and say that for most beginners in 2025 picking Python is a better choice than PHP or Cobol.
Limiting choices to “pick one” seems contrived. Beginners should learn to program and think like programmers, which means learning multiple languages and tools. Programming languages have far more in common with each other than not and the sooner a beginner thinks of themselves as a problem solver rather than a Python programmer the better.
> Of course, you can come up with some contrived scenarios where the beginner would be better served with Cobol.
I don’t think looking at job opportunities and pay qualify as “contrived scenarios.” PHP has a huge footprint in web applications and every beginner steered away from PHP to the saturated Python world forgoes a lot of opportunity. And again nothing prevents learning both. COBOL as usual gets trotted out as the dinosaur, but right now in the current tech job apocalypse knowing COBOL would get a lot more job offers than knowing Python.
Limiting choices to “pick one” seems contrived. Beginners should learn to program and think like programmers, which means learning multiple languages and tools. Programming languages have far more in common with each other than not and the sooner a beginner thinks of themselves as a problem solver rather than a Python programmer the better.
> Of course, you can come up with some contrived scenarios where the beginner would be better served with Cobol.
I don’t think looking at job opportunities and pay qualify as “contrived scenarios.” PHP has a huge footprint in web applications and every beginner steered away from PHP to the saturated Python world forgoes a lot of opportunity. And again nothing prevents learning both. COBOL as usual gets trotted out as the dinosaur, but right now in the current tech job apocalypse knowing COBOL would get a lot more job offers than knowing Python.
Thanks for illustrating my point.
It's an opinion.
IMO key value stores tend to live in the space between a third normal form ultra relational UML diagram database like the college textbooks assure you exist and a high chaos cowboy document storage system like mongodb.
They enable you to make a lot of things up as you go and iterate on your design. I like them because they remove a lot of ceremony around letting me get on with persisting things without having to ALTER TABLE or CREATE TABLE and all that entails. At the same time, they're constrained and often organized in a way that storing big ol' json blobs aren't. I like them for doing multiplayer gamedev things.
IMO key value stores tend to live in the space between a third normal form ultra relational UML diagram database like the college textbooks assure you exist and a high chaos cowboy document storage system like mongodb.
They enable you to make a lot of things up as you go and iterate on your design. I like them because they remove a lot of ceremony around letting me get on with persisting things without having to ALTER TABLE or CREATE TABLE and all that entails. At the same time, they're constrained and often organized in a way that storing big ol' json blobs aren't. I like them for doing multiplayer gamedev things.
The relational normal forms are actually pretty practical.
But you are right that in practice you sometimes want to deviate from them. And then it is still useful to be aware of what the normal form of your database _would_ be, and how you are deviating.
Similarly to how sometimes you might want to manually unroll a loop in your code, and it's still useful to keep in mind conceptually how the original loop would have looked like.
But you are right that in practice you sometimes want to deviate from them. And then it is still useful to be aware of what the normal form of your database _would_ be, and how you are deviating.
Similarly to how sometimes you might want to manually unroll a loop in your code, and it's still useful to keep in mind conceptually how the original loop would have looked like.
> ALTER TABLE or CREATE TABLE
Fundamentally this isn't a theoretical limitation of the relational model, instead it is a historical artefact that "doesn't have to be that way".
Systems like Kubernetes or Azure Resource Manager show how this ought to have been implemented: Declarative resource definitions via an API with idempotency as a core paradigm.
I.e.: Instead of the web developer having to figure out the delta change between two schema versions, they should just "declare" that they want some specific schema. This could happen with every code deployment, with most such changes doing nothing. If the schema does change, then it's up to the database engine to figure out how to make it happen, much like how a Kubernetes cluster reconciles the cluster state with a deployment definition.
KV stores become popular because they do this implicitly, with no explicit schema at all.
Relational databases (or even KV stores!) could instead use an explicit schema with automatic reconciliation instead of manual delta changes implemented by developers.
TL;DR: The tooling is bad. KV is an over-reaction to this, but instead we just needed better tools.
Fundamentally this isn't a theoretical limitation of the relational model, instead it is a historical artefact that "doesn't have to be that way".
Systems like Kubernetes or Azure Resource Manager show how this ought to have been implemented: Declarative resource definitions via an API with idempotency as a core paradigm.
I.e.: Instead of the web developer having to figure out the delta change between two schema versions, they should just "declare" that they want some specific schema. This could happen with every code deployment, with most such changes doing nothing. If the schema does change, then it's up to the database engine to figure out how to make it happen, much like how a Kubernetes cluster reconciles the cluster state with a deployment definition.
KV stores become popular because they do this implicitly, with no explicit schema at all.
Relational databases (or even KV stores!) could instead use an explicit schema with automatic reconciliation instead of manual delta changes implemented by developers.
TL;DR: The tooling is bad. KV is an over-reaction to this, but instead we just needed better tools.
Django (python web framework) migrations have worked similar to this for well over a decade: You modify the models, then generate the migrations from the models and commit those.
The changes aren't generated on the fly at runtime because it can prompt you for things it suspects or can't figure out, for example if you rename a column the naive way would be an add+delete, which erases data. If the types are the same, it checks whether you wanted that or a rename, and generates the appropriate migration.
The changes aren't generated on the fly at runtime because it can prompt you for things it suspects or can't figure out, for example if you rename a column the naive way would be an add+delete, which erases data. If the types are the same, it checks whether you wanted that or a rename, and generates the appropriate migration.
> TL;DR: The tooling is bad.
I make my own tools. KV makes it really easy to stop having to care about the database layer really early in a project and live entirely in code. It's fine.
I make my own tools. KV makes it really easy to stop having to care about the database layer really early in a project and live entirely in code. It's fine.
Is he right that this imaginary future product might be a better solution than current KV datastores? Maybe - he makes a good case, it certainly sounds worth pursuing, and he can point to a case where it sort of works well already (FoundationDB).
Doen that imply that you should give up on KV datastores today, when this product category he's asking for barely exists? No, obviously not.
Doen that imply that you should give up on KV datastores today, when this product category he's asking for barely exists? No, obviously not.
Like most articles that make strong assertive statements like this, it's an oversimplification. Every tool has its place. The author clearly wants to use SQL, and seems to have a problem that would benefit from it, so they should use a SQL DB and not try to use a KV DB.
He’s basically just asking for SQL without the query planner because it’s obnoxious and how often do you have unconstrained arbitrary queries hitting the database that you don’t have the chance to vet anyways? The database is hidden behind applications 100% of the time; why is this a predominant design use case
KVs give you this behavior, they just drop everything else with it
KVs give you this behavior, they just drop everything else with it
You usually want a query planner, else you'll end up writing a query planner yourself to produce efficient queries.
What is sometimes needed is query plan stability, lack of surprises, and influencing the planner. This is very attainable in the existing SQL databases and is a core feature of the older ones, like Oracle.
What is sometimes needed is query plan stability, lack of surprises, and influencing the planner. This is very attainable in the existing SQL databases and is a core feature of the older ones, like Oracle.
I’m not really talking about eliminating the query compilation step — that’s always useful. The dynamic query compilation however — the planner executing on every submitted query — is generally less so but fundamental to RDBMS goals. Query plan stability is the point — if you’re stabilizing the query planner, then you’re explicitly opting out of its dynamic capabilities. You don’t need a planner, you need a query compiler (in that it’s one and done, and you can even allow the thing the luxury of having actual time to optimize)
For proper data warehouses with multiple applications talking to it, planning is probably more useful than not. For the common modern situation with small/medium sized applications with a 1:1 relationship to their database, I’ve found it fairly rare that my data distribution changes significantly over time — and where it does, a lot more work needs to happen around it anyways.
Hints are the effective solution but they’re opt-in, weird non-standard extensions to the language and intrinsically tied to the planning engine; the big issue though is that they mainly exist to coerce/override the heuristics, which mainly fall over because planners are built to re-execute on every query with no real time to optimize/explore properly (you’ll give a C++ codebase hours to explore, but the RDBMS is granted mere milliseconds — a great injustice)
For proper data warehouses with multiple applications talking to it, planning is probably more useful than not. For the common modern situation with small/medium sized applications with a 1:1 relationship to their database, I’ve found it fairly rare that my data distribution changes significantly over time — and where it does, a lot more work needs to happen around it anyways.
Hints are the effective solution but they’re opt-in, weird non-standard extensions to the language and intrinsically tied to the planning engine; the big issue though is that they mainly exist to coerce/override the heuristics, which mainly fall over because planners are built to re-execute on every query with no real time to optimize/explore properly (you’ll give a C++ codebase hours to explore, but the RDBMS is granted mere milliseconds — a great injustice)
Very opinionated clickbait if you ask me.
[deleted]
Yes, it’s good
Yeah! Then I can stop grousing about yet another grammatical peeve: It's "key/value" pairs, not "key-value" pairs (which would mean pairs of key values).
I do like this approach in theory, but agreed I don't think that the devex has been solved and I don't know how many people would value a different approach as SQL feels like king in many places... FWIW it's kind of the approach that IndexedDB in the web takes, however that the API is quite bad IMO.
Or maybe there is a higher level DSL that you could apply to create query plans (something like MongoDB aggregation pipelines maybe?), but it quickly becomes basically the same as SQL.
Or maybe there is a higher level DSL that you could apply to create query plans (something like MongoDB aggregation pipelines maybe?), but it quickly becomes basically the same as SQL.
The author doesn't know it, but he's asking for Permazen - exactly what you're asking for:
https://github.com/permazen/permazen/blob/master/README.md
It's a bit like the record layer in FoundationDB but more advanced. You specify query plans manually, so you can't accidentally forget an index for example.
https://github.com/permazen/permazen/blob/master/README.md
It's a bit like the record layer in FoundationDB but more advanced. You specify query plans manually, so you can't accidentally forget an index for example.
>> SQL feels like king in many places
I think it is because most people can make something work with SQL.
I think it is because most people can make something work with SQL.
Finally! Someone else reaching the conclusion that the query planner is really annoying and for most queries I would just like to skip it.
I don't want the dynamic nature of the planner. I don't want to send SQL over the wire, I want to send the already completed plan that I either generated or wrote by hand. So many annoying performance bugs are because the planner did the slow thing. Just let me write/adjust it.
I don't want the dynamic nature of the planner. I don't want to send SQL over the wire, I want to send the already completed plan that I either generated or wrote by hand. So many annoying performance bugs are because the planner did the slow thing. Just let me write/adjust it.
Amazon reached the same conclusion and widely prefer dynamodb for this reason
This depends entirely on your use-case though right? It's not generic advice.
If your use-case is a data warehouse, then you absolutely want more than a K/V database and likely dynamic query plans because the point is dynamic usage. If your use-case is the serving frontend for a >1m request per second API, then sure, you probably don't want the complexity of a relational database and query planner.
Most things are somewhere in the middle and need to give serious consideration to this.
If your use-case is a data warehouse, then you absolutely want more than a K/V database and likely dynamic query plans because the point is dynamic usage. If your use-case is the serving frontend for a >1m request per second API, then sure, you probably don't want the complexity of a relational database and query planner.
Most things are somewhere in the middle and need to give serious consideration to this.
The complexity of a relational database is usually very much needed, with all the joins, subqueries, etc.
The poster complains about query plans being unnecessarily dynamic; for certain queries, it should be pinned, and only changed in a controlled way. Compare it to something like pip or npm; not being able to pin versions of certain packages could be a source of endless frustrations.
Pinning a query plan to a query could very well be a feature of a relational DB, an it is. Postgres (pg_hint_plan extension), Oracle (a bunch), MS SQL (somehow), they all have ways to pin the query plan. Not sending SQL is calling a stored procedure, also a long-standing feature of relational databases.
Knowing your tech stack goes a long way in battling frustration.
The poster complains about query plans being unnecessarily dynamic; for certain queries, it should be pinned, and only changed in a controlled way. Compare it to something like pip or npm; not being able to pin versions of certain packages could be a source of endless frustrations.
Pinning a query plan to a query could very well be a feature of a relational DB, an it is. Postgres (pg_hint_plan extension), Oracle (a bunch), MS SQL (somehow), they all have ways to pin the query plan. Not sending SQL is calling a stored procedure, also a long-standing feature of relational databases.
Knowing your tech stack goes a long way in battling frustration.
> Not sending SQL is calling a stored procedure
That's not what I mean, I don't want to bother with the SQL layer at all. I want to generate the query plan from the client side and send it off to be executed.
And to my knowledge the hinting extensions don't actually allow you to skip the ceremony and supply your own plan, just (in a very hacky way) adjust it as it runs.
That's not what I mean, I don't want to bother with the SQL layer at all. I want to generate the query plan from the client side and send it off to be executed.
And to my knowledge the hinting extensions don't actually allow you to skip the ceremony and supply your own plan, just (in a very hacky way) adjust it as it runs.
All I want is K-V store with indexes.
Let database enforce serialization format (JSON, BSON, MessagePack, protobuf.. anything really) + create and maintain indices, using this fancy crash-proof logic it has. That'll cover 95% of all my database needs.
(OP also asks for row-based layout, types, and non-trivial language. I think those parts are entirely optional)
Let database enforce serialization format (JSON, BSON, MessagePack, protobuf.. anything really) + create and maintain indices, using this fancy crash-proof logic it has. That'll cover 95% of all my database needs.
(OP also asks for row-based layout, types, and non-trivial language. I think those parts are entirely optional)
A K-V store by construction has an index (over K). But you seem to also want referential integrity and transactions, not small features, limiting the implementation quite a lot.
You can attain what you desire by using an RDBMS, and having all tables with one key column, and a TEXT column with your serialized non-key fields; it's going to be a fun approximation of 6NF. Realistically, you can have all joinable columns as normal columns, indexed as you desire, and the rest of the columns as a serialized blob.
When you want high parallelism for guaranteed independent segments of data, use sharding.
You can attain what you desire by using an RDBMS, and having all tables with one key column, and a TEXT column with your serialized non-key fields; it's going to be a fun approximation of 6NF. Realistically, you can have all joinable columns as normal columns, indexed as you desire, and the rest of the columns as a serialized blob.
When you want high parallelism for guaranteed independent segments of data, use sharding.
FoundationDB supports referential integrity and indexes in a layer on top of the key value store.
https://apple.github.io/foundationdb/layer-concept.html
https://apple.github.io/foundationdb/layer-concept.html
+1.
Indexes, triggers (very good abstraction covering everything from computed fields to dependent fields), transactions.
Indexes, triggers (very good abstraction covering everything from computed fields to dependent fields), transactions.
Use foundationdb then
Is weird, but what the OP was asking for, actually exist before in the case of FoxPro and similar tools.
In Fox, you write more or less `physical query plans` as syntax:
BTW, I think this idea can be move even further and my take is at https://tablam.org
In Fox, you write more or less `physical query plans` as syntax:
USE customer && Opens Customer table
CLEAR
SCAN FOR UPPER(country) = 'SWEDEN'
? contact, company, city
ENDSCAN
BTW, I think this idea can be move even further and my take is at https://tablam.org
Realistically, what should happen is reusable queries in the database with pre-cached plans as well as planner scripting and eliminating the index chooser for standard transactional queries. For real-time ad-hoc queries, the planners can be used, but for the ones happening 1000s of times a second... best to stick with a cached plan.
All you need is MemcacheD
Honestly though, why not just take the existing SQL standard and trim it down then?