This is the Rich Hickey talk from 2016: https://www.youtube.com/watch?v=oyLBGkS5ICk. If you listen to it, it was made in an effort to adjust the community's trajectory. I too would like to hear if adjustments did happen.
Basically the way I interpreted it[1] is that "major version" is a meaningless concept. If you're making incompatible changes, rename the package. If that's a disincentive to making incompatible changes -- great!
> Rich Hickey pointed out last year that the convention of bumping the major version of a library to indicate incompatibility conveys no more actionable information than just changing the name of the library.
Yes, I guess Hickey is applying this idea of immutability not only to data structures but also to APIs and even databases[0] with the proprietary database service Datomic. Interestingly Datomic uses Datalog as query language, so it's straight forward to apply at least some of those ideas with the Racket Datalog package[1].
As of now I have a basic web forum working with all data storage done in Datalog (except for file uploads!). I'll post some code once the design is a bit more settled. It's still in the breaking-things-all-the-time phase.
What I find a bit tricky about Datalog is that relations are stored together with other facts, which in my mind feels a bit like storing code in a database, but maybe I just haven't wrapped my head around it yet.
Does anyone here have favourite articles or talks about logic programming?
> What I find a bit tricky about Datalog is that relations are stored together with other facts, which in my mind feels a bit like storing code in a database, but maybe I just haven't wrapped my head around it yet.
I can't speak for Datalog, but I've used Datomic.
If it's the same, then a 'fact' is comprised of an entity (the id), + an attribute, + a value.
The relationships are made by storing an entity id into the value slot of another fact. Thus the model is both flat (being a list of facts) and hierarchical (they can point to each other). It pretty much becomes a graph database. Is that what you mean?
> I can't speak for Datalog, but I've used Datomic.
Cool!
> The relationships are made by storing an entity id into the value slot of another fact. Thus the model is both flat (being a list of facts) and hierarchical (they can point to each other). It pretty much becomes a graph database. Is that what you mean?
Yes, exactly! What I worry about (because I'm fairly new to logic programming) is whether it could potentially be difficult to update a program where storage of business logic and storage of data aren't separated?
If we assume we have a Hacker News web app where we have a fact: One day Alice submits a story with the title "How to peel onions"; and we are thinking "Why on earth did she post that here?!?" So we add this relation to our code: A story is `irrelevant` if it has the word "onion" in the title. Then, the next day we get another fact: Now Bob has submitted a story called "How onion routing works". This new story by Bob then makes us reconsider our definition of `irrelevant`.
In a typical imperative program we'd just edit the code and redefine the `irrelevant` predicate, and it would take effect next time we run the program (or instantly if we enter it at a repl). But here in our logic program we store this `irrelevant` relation in our graph database, so even though we have removed it from our code, it is still sitting there in the database along with all the facts, outside the reach and responsibility of git, or whichever VCS we're using.
Yes, so my question is: How do you practically deal with changes to business logic in logic programming where data storage and relation storage is one and the same? Perhaps Datomic just avoids this issue somehow? I may also be missing or misunderstanding something.
Can't say I know what the options are since I don't know Datalog or the DB you're using, but is this reasonable?:
-----------------------------------------------------------
Entity | Attribute | Value
-----------------------------------------------------------
person-id-001 | name | Alice
person-id-001 | stories | [story-id-001, story-id-002...]
story-id-001 | headline | "How to peel onions"
irrelevant-word-001 | stories | [story-id-001, ...]
irrelevant-word-001 | word | onion
-----------------------------------------------------------
So if you decide that onion is no longer irrelevant then delete the entity 'irrelevant-word-001'. Which seems, at least to me, better than making code pushes.
So all of this assumes a few things:
- Your DB supports a cardinality of 'many' items in the value slot.
- Your query language can perform joins.
Of course none of this helps when someone changes a headline, but only full-text search DB's will help you do that.
So you would also need to remove the value 'irrelevant-word-001' from the above.
At least this is how I would do it in Datomic anyway.
What's interesting (at least to me) is that Datomic has a function called 'retractEntity' [1] which auto-magically removes all references of an entity in any value slot when you retract the entity. Man I love Datomic :)
> So if you decide that onion is no longer irrelevant then delete the entity 'irrelevant-word-001'. Which seems, at
least to me, better than making code pushes.
I can make sense of that when there's just one instance of this app running. Yet imagine the scenario where the web app has been published, and suddenly other people are running this web app. If the business logic is then changed, somehow I'd have to tell those people: "Oh btw, when you're running `git pull` next time, then you just also gotta run this query to retract some of the old relations from the database."
Definitely not a problem for me yet, but I can just smell it coming. I could add those retractions to the code, but they would have to stay there indefinitely, because it's not possible to tell if those retractions have taken place on everyone's databases yet.
Maybe I'm over-thinking this.
> What's interesting (at least to me) is that Datomic has a function called 'retractEntity'
Looks like the one called `~` in Racket's Datalog[0].
Is this irrelevant-word example a real feature you're building into the app or a contrived example to understand Racket DataLog DB use?
If it's a real feature, and I'm assuming it is. Then I'm also assuming that when a story is submitted you're parsing the title and adding the relationship to the current set of irrelevant-words that are stored.
So the question's are:
1. How are you going to remove the past relationships between stories and an irrelevant word that's getting removed? (looks like we've answered this).
2. How are you going to make sure past stories gain the relationship to newly added irrelevant words?
3. How are you going to handle title changes.
After you get handle on these then what you really need to do provide an interface, from within the apps admin tools, to trigger the noted functionality. This way the business logic is in the app and it's modifying the data.
> Is this irrelevant-word example a real feature you're building into the app or a contrived example to understand Racket DataLog DB use?
It's a simplified and slightly contrived illustration of an issue in this pre-alpha code I haven't published yet, perhaps just because I haven't thought of a name for the project yet. But yes, let's assume it's a real feature for now.
> 3. How are you going to handle title changes.
I like Hickey's idea of accretion of data - with a timestamp! - and not forgetting previous facts. I think he's talking about it in The Database as a Value[0]. So, a story could have a few different titles in the database, and the newest one is the one you get to see.
The thing is, it's easy to add a timestamp to facts as a way of not considering old facts without forgetting them, but not to relations. For example in Racket Datalog[1]:
(! (voted "i4cu" 'up 134))
can easily be get a timestamp:
(! (voted "i4cu" 'up 134 1542151773))
but that would not really make sense in a relation like
(! (root A B) (ancestor A B) (parent null A))
So, I don't feel there's a need for ever retracting facts, because timestamps solve that. (Even when deleting something, you could just add the fact that is has been deleted.) But with relations (a.k.a. business logic) I think I will need to retract things, which is tricky because this logic is not only present in the code but also in the database. This was the problem I was asking about.
> 2. How are you going to make sure past stories gain the relationship to newly added irrelevant words?
Datalog queries reflect the current set of facts and relations, so the possible irrelevance of a story would not be stored anywhere, so it wouldn't need to be updated.
> 1. How are you going to remove the past relationships between stories and an irrelevant word that's getting removed?
Some as above. For example, in a place oriented program a story object could have a boolean attribute `irrelevant?`, whereas I'm sending a query every time this value is needed, so no stale `irrelevance` attributes are stored anywhere.
> After you get handle on these then what you really need to do provide an interface, from within the apps admin tools, to trigger the noted functionality. This way the business logic is in the app and it's modifying the data.
Yes, that is kind of there already, as in having functionality for changing titles. The `irrelevant` functionality is not there now.
Ok, I think I just need to work on getting this code publishable, because it might be easier to discuss tangible examples.
Yeah, Datomic doesn't expect the relationships to be stored in the DB. It stores a bunch of indexes for you and it has a great query language, but that's it.
So where will you're data be? In a local data structure? I read your racket Datalog link, but it doesn't show any details for the database side (i.e. durability etc.) even though it's labelled a database.
Also, I'm curious what made you choose a graph db. It seems like you're inheriting a lot of complexity and I'm wondering what the benefit is over a more traditional sql or nosql db.
> So where will you're data be? In a local data structure?
Yes, I think. The database just stored in memory but it can be serialized and saved to the disk using `write-theory`[0] and loaded `read-theory`. That is what I'm doing for now, and it's a very naive and inefficient to do a full database dump rather than just appending new data, and I presume it's particularly in this area where Datomic is way more optimised and well thought out.
> Also, I'm curious what made you choose a graph db. It seems like you're inheriting a lot of complexity and I'm wondering what the benefit is over a more traditional sql or nosql db.
Well, I did the initial work on the web app: creating user accounts, adding posts and replies, and then I got to data storage. Initially I did a News-style flat-file database, just saving data as lists in files, that are then loaded into memory when the program starts. It mostly worked but also felt a bit complicated, and I thought that perhaps I should just use a proper database?
What I like about news.arc is that you can just launch it without any configuration, so MySQL and PostGreSQL were out of the question, and I started reading a bit about SQLite. But I've also had this fascination with logic programming, from what people are posting here[1][2], and from reading a bit of The Reasoned Schemer, and I watched some of those Rich Hickey talks again, where he talks about Datalog, which happens to be available for Racket.
There are just some things that are incredibly simple in declarative/logic programming. For example, if you have facts about stories being `parent` of their replies, then it's simple to just define the `ancestor` relation, and when you have the `ancestor` relation, you automagically get `descendants` without having to write any code, because it's just the inverse of `ancestor`:
(! (:- (ancestor A B)
(parent A B)))
(! (:- (ancestor A B)
(parent A C)
(ancestor C B)))
But, I've also bumped into some questions - more practical than theorical - and that is why it's interested hearing about your experience with Datomic, and why I'm asking here.
So, SQLite is still on the table. I'm not too familiar with NoSQL, but my impression is that they are all about speed and scalability of data storage. I haven't used MongoDB but isn't it essentially just like storing JSON in a file, except faster? It would be interesting if any of those could be used in conjunction with Datalog though, if don't add too complexity for the sake of increased speed.
Datomic uses DataLog as part of its query language, but that's pretty much where the comparison should end. Things like "treating the database as a value", and features such as data accretion that Rich talks about have nothing to do with DataLog. They're features of Datomic. So for example when you mention never retracting data, well your data size is going to continuously grow unless you write your own data management layer on top. Datomic, on the other hand, does this for you. When you want to query the database over time, then you're going to need to store time intervals for all of your data and incorporate that into each query. Where as in Datomic (which has a time log) you can pass in the DB itself as a value (with an associated time interval) and Datomic will make sure your queries are working against the dataset that accounts for the time interval.
I'm pointing this out because it seems to me that you're doing (or are going to be doing) a lot of work that may not be worth it for what you're trying to accomplish.
Nosql
> I'm not too familiar with NoSQL, but my impression is that they are all about speed and scalability of data storage.
Yes and No. Often speed can be a feature Nosql dbs advertise, but really, for me anyway, it's about flexibility and ease of use. Traditional RDBMS, for example, require creating schemas. Many Nosql databases don't require a schema at all which makes it easier to use and more flexible to change. Nosql's are often a key-value store so it can be really easy to take a hash-map or table of data from your code and just dump it into an nosql datastore and be able to query it.
My personal favourite is Redis and it might be worth considering for your app.
You can:
- store a value under a key [1]
- store table data [2]
- store values in a set [3] (which allows intersection/difference queries)
- store values in a sorted-set [4] (which allows you query by some numerical value like timestamp)
- use it to manage relationships [5]
The reasons I mention Redis is that the HN app is very well suited to it. HN only keeps 'x' amount of data in memory. And in Redis the data lives in memory. Also Redis allows you to set expiry times on data for auto eviction [6]. And Redis also supports ordered lists [7] which can make it useful for lisp based languages.
However it's not embedded. And if that's a requirement I'd almost suggest you move away from Racket and adopt a language that has more options for embeddable databases. I guess if you're willing to roll your own (and it looks like you may be) then that's awesome too.
But in case you decide otherwise... The library I use is Redis Carmine [8], but there are Racket clients [9].
> Datomic uses DataLog as part of its query language, but that's pretty much where the comparison should end. Things like "treating the database as a value", and features such as data accretion that Rich talks about have nothing to do with DataLog.
I'm not sure I totally agree with this. I think that apart from talking about the design of Datomic, he also has a more general point against what he calls PLOP (PLace Oriented Programming), which Datalog does address.
For example in plain Racket a value is lost if something else is put in its place:
Hickey is also mentioning how git doesn't do PLOP in that it doesn't throw out your commit history (without you asking it to do so).
> The reasons I mention Redis is that the HN app is very well suited to it. HN only keeps 'x' amount of data in memory. And in Redis the data lives in memory. Also Redis allows you to set expiry times on data for auto eviction [6].
Interesting. Just checked news.arc, and yes `initload*` is set to 15000. Interesting idea from Redis with expiry times. I'll check it out. I hadn't considered the scenario of storing text enough to max out on memory, because it would probably be premature optimisation, but good to keep in mind. I'd like to give Redis/Rackdis a try; thanks for the suggestion. I've been hosting an Etherpad Lite instance, and Redis was painless to setup.
> I'm pointing this out because it seems to me that you're doing (or are going to be doing) a lot of work that may not be worth it for what you're trying to accomplish.
Yes, my priorities here are definitely to make the code as brief and simple as possible, and to not have to do to much work. With plain Datalog it's very little work to timestamp a fact, and it's also kind of necessary, e.g. to figure out which fact is most recent, when previous facts are not removed. I'm just trying to get the gist of Hickey's ideas here.
> PLOP (PLace Oriented Programming), which Datalog does address.
Yeah, I was thinking more along the lines that Datomic has built-in functionality to address the caching, cache eviction, and indexing that goes along with all that data accumulation. But you're correct, DataLog does accumulate facts.
> Interesting. Just checked news.arc, and yes `initload*` is set to 15000.
I did the same thing, about 6 or 7 years ago, that you're doing now. I ported HN to Clojure (which is actually how I learned Clojure). If memory serves me correctly when I was doing the work I realized I needed a real DB if I wanted to support load balancing. i.e. I needed to centralize the data for the authentication and fnid session info. I think Arc calls them fnids... You probably know better than I do now, but Arc has all this code to expire these session fnids and so, for me, Redis was just a good fit for that task.
Anyways, I'll be sure to take a look at the final result of your work.
> I did the same thing, about 6 or 7 years ago, that you're doing now. I ported HN to Clojure (which is actually how I learned Clojure).
Cool!
> I needed to centralize the data for the authentication and fnid session info. I think Arc calls them fnids... You probably know better than I do now, but Arc has all this code to expire these session fnids and so, for me, Redis was just a good fit for that task.
The Racket web server is quite "batteries included" and comes with these different managers[0] for dealing with expiration of sessions/continuations, such as the LRU manager:
> The memory limit is set to `memory-threshold` bytes. Continuations start with 24 life points. Life points are deducted at the rate of one every 10 minutes, or one every 5 seconds when the memory limit is exceeded. Hence the maximum life time for a continuation is 4 hours, and the minimum is 2 minutes.
> If the load on the server spikes—as indicated by memory usage—the server will quickly expire continuations, until the memory is back under control. If the load stays low, it will still efficiently expire old continuations.
When I was referring to load balancing and centralizing the data I was referring to many web servers sharing a centralized/external source for auth/session data.
I'm unfamiliar with racket's web server 'servlets'. The docs are little unclear (at least to me). Can these servlets live on a separate server so that the data can be shared between web servers? I'm guessing that was/is not a requirement for you, but I'm just interested in knowing if that's how it can work.
Uh oh, you're getting me interested in Racket now. I can't have that... I have too many projects :)
edit: I guess at the end of the day these servlets are web-servers right, so you can, even if you have to do it over http and build an api.
> Can these servlets live on a separate server so that the data can be shared between web servers?
Probably. I assume that serializable continuations[0] from stateless servlets can just be stored wherever, like in Redis or something, instead of in the memory of one server.
> I ported HN to Clojure
If that is something you have published, it'd be fun to see, whether it's finished or not.
> Uh oh, you're getting me interested in Racket now.
My impression is that Clojure is faster, less verbose partly due to clever syntax and provides more immutable data structures than Racket. But when it comes to documentation and error messages, I find Racket more coherent and comprehensible.
Say, if I wanted to connect to a SQL databse, with Racket I'd use the DB module[1], end of discussion. But with Clojure there's Korma, ClojureQL, Persist, HoneySQL, Yesql, a JDBC wrapper from Clojure contrib, SQLingvo, oj, Suricatta, aggregate, Hyperion, HugSQL, and probably a few more[2][3]. That multitude of libraries with similar purpose may be useful in some cases, sure, but also potentially a bit overwhelming for beginners, so I guess that's why I found it easier to get started with Racket.
> If that is something you have published, it'd be fun to see, whether it's finished or not.
I actually tried to look it out the other day during this conv, but it's buried somewhere unavailable right now. If I find/get to it I'll post.
> That multitude of libraries with similar purpose may be useful in some cases, sure, but also potentially a bit overwhelming for beginners, so I guess that's why I found it easier to get started with Racket.
Agreed. Navigating the volume libraries and the options available is a real pain in the beginning, but once you get past that, then it's not bad at all. At the same time, take a look at the quality of Clojure's Redis Carmine Library vs. Racket's Redis Libraries. Miles apart.
