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mangle
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google / mangle

mangle

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This week on Hacker News

Nov 7–Nov 14 UTC · Direct repository links and separately labeled related project stories. Indexed through 11/14/2026, 12:54:37 PM UTC.

No direct repository links were found in the indexed HN discussions over the past seven days. Older discussions are available below.

Mentions over time

HN mentions by year11 dated mentions

Observed mentions: 2022-11-26 17:31:13 UTC to 2026-02-05 19:58:22 UTC.

0123452022-01-01 00:00:00 UTC to 2023-01-01 00:00:00 UTC (exclusive): 2 mentions; 2 submissions, 0 comments20222023-01-01 00:00:00 UTC to 2024-01-01 00:00:00 UTC (exclusive): 1 mentions; 0 submissions, 1 comments2024-01-01 00:00:00 UTC to 2025-01-01 00:00:00 UTC (exclusive): 5 mentions; 0 submissions, 5 comments20242025-01-01 00:00:00 UTC to 2026-01-01 00:00:00 UTC (exclusive): 2 mentions; 1 submissions, 1 comments2026-01-01 00:00:00 UTC to 2027-01-01 00:00:00 UTC (exclusive): 1 mentions; 0 submissions, 1 comments. Incomplete calendar period.2026*

* An incomplete calendar period is marked with an outlined bar. Its count is not directly comparable with a full period.

Counts come from the repository’s dated source evidence. Index cutoff: 2026-11-02 08:16:59 UTC.

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PeriodStart (UTC)End (exclusive, UTC)MentionsSubmissionsComments
20222022-01-01 00:00:00 UTC2023-01-01 00:00:00 UTC220
20232023-01-01 00:00:00 UTC2024-01-01 00:00:00 UTC101
20242024-01-01 00:00:00 UTC2025-01-01 00:00:00 UTC505
20252025-01-01 00:00:00 UTC2026-01-01 00:00:00 UTC211
2026 (partial)2026-01-01 00:00:00 UTC2027-01-01 00:00:00 UTC101

What Hacker News says

11 sources
burakemir·Feb 5, 2026
The approach described here - model things as graph - can really be applied to model any domains. If you are into this type of modelling, you may find value in Mangle, a datalog-based logic programming language and deductive database library. You do not need to invent dozens of DSLs but can do it all in one. And without all the RDF trouble. https://github.com/google/mangle HN discussion https://news.ycombinator.com/item?id=33756800 Talk at REBASE 2025 "From Facts to Theories" https://youtu.be/UjOEHSZDBH8?si=qAjnkBQfPKMVaOPW
In Company as Code
burakemir·Jun 15, 2025
I made some progress porting mangle datalog to Rust https://github.com/google/mangle/tree/main/rust - it is in the same repo as the golang implementation. It is slow going, partly since it is not a priority, partly because I suffer from second system syndrome. Mangle Rust should deal with any size data through getting and writing facts to disk via memory mapping. The golang implementation is in-memory. This post is nice because it parses datalog and mentions the LSM tree, and much easier to follow than the data frog stuff. There are very many datalog implementations in Rust (ascent, crepe) that use proc-macros. The downside is that they won't handle getting queries at runtime. For the static analysis use case where queries/programs are fixed, the proc macro approach might be better.
In Datalog in Rust
burakemir·Nov 16, 2024
Here is a proof that you can translate non-recursive datalog into relational algebra and vice versa: https://github.com/google/mangle/blob/main/docs/spec_explain... Since Logica is translated to SQL it should benefit from all the query optimistic goodness that went into the SQL engine that runs the resulting queries. I personally see the disadvantages of SQL in that it is not really modular, you cannot have libraries, tests and such. Disclosure: I wrote Mangle (the link goes to the Mangle repo), another datalog, different way of extending, no SQL translation but an engine library.
In Logica – Declarative logic programming language for data
burakemir·Aug 24, 2024
I am sure Clojure is great but it really shows the need for an "overview of datalog" that is not embedded in the context of a programming language with it's own syntactic choices. My own humble attempt is here https://github.com/google/mangle ... I had come across datalog in Clojure and thought it is a complete nonstarter. Most people (including me) are not in a position to start using Clojure if all they want is a query capability. IMHO what most people would want a datalog implementation that is very easy to integrate into their existing setup, with their existing data. Yet for extensibility it is also important to enable users to write datalog queries in it's own special syntax so one can have type checking, modules, declarations in a way that is independent of the host programming language. From a teaching point of view, one does nee
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d an example database. Here is "employee department boss", with a volunteering spin: https://github.com/google/mangle/blob/main/docs/example_volu... There is a long history of approaches to integrate querying into programming languages (eg LINQ). Trade off here between making an easy to use yet powerful query language, keeping it simple and integrating it tightly into the host language or using a DSL along clear interface.
In An Overview of Datalog (2010)
burakemir·Aug 11, 2024
For the subproblem of being able to unify and query various data sources in different formats, I would suggest to take a look at Datalog and specifically Mangle, my implementation of it. I don't want to plug the project here but more describe the approach. Usually your data will comfortably fit in a file. Your data getter emits these files in facts (essentially relations). If you want structures data, it can also be a single column that is of some struct type (similar to protobuf). With all data available the problem becomes one of querying. With a good enough query language and system, you write these can data transformations via Datalog rules which roughly correspond to database views. It is always possible to write queries in code in a general purpose language, but is a bit clumsy and hard to get an overview or reuse. It may also be po
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ssible to do SQL but it SQL is not very compositional and you ask yourself whether the base data representation should be adapted refactored. Essentially you do not want to think about the optimal schema or set of structs but just do transformations you need in the lowest friction way. With Datalog you may benefit from a unified representation (everything is "facts") and the transformations to useful different formats (different kinds of facts) can be factored and reused. It may mean duplication and denormalization but usually that does not matter. Mangle supports aggregation and even calling some custom functions during query evaluation. The repo is at https://github.com/google/mangle and obviously there remains a lot to do, the API is unstable, there are bugs and the type checker is not finished... but a number of people and projects seem to use it. Even if you do not use it, it may give you how to use facts (relations) as a unified data structure for your project.
In Imagining a personal data pipeline
burakemir·Jul 18, 2024
Mangle is a language that includes "textbook datalog" as a subset https://github.com/google/mangle ; like any real-world datalog language, it extends datalog with various facilities to make it practical. It was discussed on HN https://news.ycombinator.com/item?id=33756800 and is implemented in go. There is the beginnings of a Rust implementation meanwhile. If you are looking for datalog in the textbooks, here are some references: https://github.com/google/mangle/blob/main/docs/bibliography... A graph DBs short intro to datalog: just like the edges of a graph could be represented as a simple table (src, target), you could consider a database tuple or a datalog or prolog fact foo(x1, ..., xN) as a "generalized edge." The nice thing about datalog is then that as one is able to express a connections in an elegant way as "foo(...X...), bar(...
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X...)" (a conjunction, X being a "node"), whereas in the SQL world one has to deal with a clumsy JOIN statement to express the same thing.
In Ask HN: What's Prolog like in 2024?
burakemir·May 8, 2023
Logic programming offers a good foundation for anything that people call "rule engines." Within logic programming, there is some variation on the degree of declarativeness. Datalog is arguably the minimal core logic programming, similar to what the lambda calculus achieves for functional programming. Unfortunately, it has been forgotten outside of database and query processing realm. A resurgence has happened in recent years, as PL researchers and also industry have discovered the virtues of datalog (e.g. Flix, DataFun). My own attempt at making this more widely known is here https://github.com/google/mangle, a language from the datalog family and its implementation as a go library. As the example shows: plain "rules" (or: plain datalog) is rarely enough to capture everything that one wants to express: the question then is, how to combine
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a pure declarative "kernel" with more general purpose programming (e.g. mapping a list). PROLOG offered one answer, already in the 1980s, but I fully reject it: the fact that the writing a program in the wrong order with negation and recursion makes it non-terminating is not something we'd want everyone to deal with. Datalog with stratified recursion is somewhat better, as "layers of rules" is a concept that is easy to understand. In mainstream programming languages, the possibility of writing non-terminating programs also exists, but is rarely an issue. That is why I believe a good combination of declarative and general-purpose has to make it really easy to recognize which parts of a program are in the declarative, terminating, safe kernel and which parts require more attention from the programmer.
In Prolog for data science

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