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This iteration of the RedMonk programming Language Rankings is brought to you by Amazon Web Services. AWS manages a variety of developer communities where you can join and learn more about building modern applications in your preferred language.
Even by our standards, dropping the Q1 programming language rankings the same month we run the Q3 numbers is quite the delay. While the usual travel and school vacation delays have applied, however, the drawn out process in this case is deliberate on our part. As has been discussed in recent iterations of these rankings, the arrival of AI has had a significant and accelerating impact on Stack Overflow, which comprises one half of the data used to both plot and rank languages twice a year.
My colleague Rachel has been studying this impact in detail and has more on it here, but for our purposes it’s enough to know that Stack Overflow’s value from an observational standpoint is not what it once was, and that has a tangible impact as we’ll see. Still to be determined on our end is whether Stack Overflow should continue to be used, and if not what a reasonable alternative might be. Stay tuned for more details on that front when we get to the Q3 rankings, which will presumably be in Q1 of next year.
In the meantime, however, as a reminder, this work is a continuation of the work originally performed by Drew Conway and John Myles White late in 2010. While the specific means of collection has changed, the basic process remains the same: we extract language rankings from GitHub and Stack Overflow, and combine them for a ranking that attempts to reflect both code (GitHub) and discussion (Stack Overflow) traction. The idea is not to offer a statistically valid representation of current usage, but rather to correlate language discussion and usage in an effort to extract insights into potential future adoption trends.
Our Current Process
The data source used for the GitHub portion of the analysis is the GitHub Archive. We query languages by pull request in a manner similar to the one GitHub used to assemble the State of the Octoverse. Our query is designed to be as comparable as possible to the previous process.
- Language is based on the base repository language. While this continues to have the caveats outlined below, it does have the benefit of cohesion with our previous methodology.
- We exclude forked repos.
- We use the aggregated history to determine ranking (though based on the table structure changes this can no longer be accomplished via a single query.)
- For Stack Overflow, we simply collect the required metrics using their useful data explorer tool.
With that description out of the way, please keep in mind the other usual caveats.
- To be included in this analysis, a language must be observable within both GitHub and Stack Overflow. If a given language is not present in this analysis, that’s why.
- No claims are made here that these rankings are representative of general usage more broadly. They are nothing more or less than an examination of the correlation between two populations we believe to be predictive of future use, hence their value.
- There are many potential communities that could be surveyed for this analysis. GitHub and Stack Overflow are used here first because of their size and second because of their public exposure of the data necessary for the analysis. We encourage, however, interested parties to perform their own analyses using other sources.
- All numerical rankings should be taken with a grain of salt. We rank by numbers here strictly for the sake of interest. In general, the numerical ranking is substantially less relevant than the language’s tier or grouping. In many cases, one spot on the list is not distinguishable from the next. The separation between language tiers on the plot, however, is generally representative of substantial differences in relative popularity.
- In addition, the further down the rankings one goes, the less data available to rank languages by. Beyond the top tiers of languages, depending on the snapshot, the amount of data to assess is minute, and the actual placement of languages becomes less reliable the further down the list one proceeds.
- Languages that have communities based outside of Stack Overflow such as Mathematica will be under-represented on that axis. It is not possible to scale a process that measures one hundred different community sites, both because many do not have public metrics available and because measuring different community sites against one another is not statistically valid.
With that, here is the first quarter plot for 2025.
1 JavaScript
2 Python
3 Java
4 PHP
5 C#
6 TypeScript
7 CSS
7 C++
9 Ruby
10 C
11 Swift
12 Go
12 R
14 Shell
14 Kotlin
14 Scala
17 Objective-C
18 PowerShell
19 Rust
20 Dart
If you’re tracking from our last iteration of the rankings – and Rachel has the entire history of the Top 20 rankings charted here, the only change within our Top 20 languages is Dart dropping from a tie with Rust at 19 into sole possession of 20. In the decade and a half that we have been ranking these languages, this is by far the least movement within the top 20 that we have seen. While this is to some degree attributable to a general stasis that has settled over the rankings in recent years, the extraordinary lack of movement is likely also in part a manifestation of Stack Overflow’s decline in query volume. As that long time developer site sees fewer questions, it becomes less impactful in terms of driving volatility on its half of the rankings axis, and potentially less suggestive of trends moving forward. As mentioned above, we’re not yet at a point where Stack Overflow’s role in our rankings has been deprecated, but the conversations at least are happening behind the scenes.
With that, some results of note:
- TypeScript (6): even acknowledging the general lack of movement within, it’s notable that TypeScript has effectively stalled just outside the Top 10. On the one hand, it can piggyback on the ubiquity of JavaScript while offering important safety provisions, but on the other, it has a reputation of not scaling particularly well. This reputation, in fact, has led Microsoft to reimplement the TypeScript compiler and tools in Go. The question now is whether this reimplementation will lead to greater performance, leading to greater adoption and more usage, or whether the fact that Microsoft felt it needed to be reimplemented in the first place could throw shade on the language. It will be interesting to watch, assuming we have data enough to observe any potential impact.
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Kotlin (14) / Scala (14): both the JVM-based languages held their gains from our last ranking and it’s unclear what their prospects are for moving up more significantly. In 2015, when Go entered our rankings at 17, Scala was at 14 and jumped up briefly to 11 two years later. In 2023, however, Go passed Scala – having already been ranked above Kotlin – and has maintained that role ever since. And with Go finding new fans in companies like Microsoft and Rust making gains among other server side workloads, particularly those with security concerns, Kotlin and Scala’s growth paths are not assured.
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Dart (20) / Rust (19): while Dart technically dropped one spot, that far down the rankings the actual differences are marginal at best. These two languages, which have little to nothing in common and are aimed at very different users and workloads, have tended to move in lockstep and this quarter’s run does not represent much of an exception in that regard.
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Ballerina (64) / Bicep (79) / Grain / Moonbit / Zig (86): among the “languages we’re paying attention to” set, there was little more movement than within our Top 20, and for the most part movement among them was down. Grain and Moonbit remained unranked, while Ballerina dropped from 61 to 64, Bicep dropped from 78 to 79. Zig, however, did manage to jump, if only one spot from 87 to 86 – it probably does not hurt that Mitchell Hashimoto is a major fan. It is worth noting for these emerging languages, however, that they may be disproportionately impacted by Stack Overflow’s decline. In every case where the languages are ranked, they perform better within our GitHub rankings than they do within Stack Overflow’s: 62 vs 66 for Ballerina, 69 vs 73 for Bicep and 70 vs 83 for Zig. In Zig’s case in particular, then, it is possible that faster growth in code as measured by GitHub is being dragged down by the steep decline in query volume on Stack Overflow. Which is yet another reason why we’re carefully evaluating our options moving forward, but in the meantime we’ll keep all of these languages on our “to watch” list.
Credit: My colleague Rachel Stephens wrote the queries that are responsible for the GitHub axis in these rankings. She is also responsible for the query design for the Stack Overflow data.
