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Did you sign up for the new White House app? Don’t use it until you read this, because it puts your privacy and data security at risk. Patrick Quirk takes an impressive technical piece and distills it for those of us who are not developers or coders. His article is based on original research by...
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Sr. Content Developer at Microsoft, working remotely in PA, TechBash conference organizer, former Microsoft MVP, Husband, Dad and Geek.
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Learn multi-model support in GitHub Copilot SDK: configure GPT-5 and Claude in C#, compare capabilities, build model-agnostic .NET apps.
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This has results for Postgres versions 12.22 through 18.3 with the Insert Benchmark on a small server. My previous post for the same hardware with results up to Postgres 18.1 is here. This post also has results for:
- all 17.x releases from 17.0 through 17.9
- 18.2 with and without full page writes enabled
- both 1 and 4 users
Postgres continues to be boring in a good way. It is hard to find performance regressions. Performance wasn't always stable, but I am reluctant to expect it to show no changes because there are sources of variance beyond the DBMS, especially HW (a too-hot SSD or CPU will run slower). Sometimes perf changes because there are obvious perf bugs, sometimes it changes for other reasons.
tl;dr for a CPU-bound workload
- performance is stable from Postgres 12 through 18
- performance is stable from Postgres 17.0 through 17.9
- disabling full-page writes improves throughput on write-heavy benchmark steps
tl;dr for an IO-bound workload
- performance is mostly stable from Postgres 12 through 18
- performance is stable from Postgres 17.0 through 17.9
- disabling full-page writes improves throughput on write-heavy benchmark steps
- in a few cases there are large improvements to point-query throughput on the qp1000 benchmark step. I will try to explain that soon.
Builds, configuration and hardware
I compiled Postgres from source using -O2 -fno-omit-frame-pointer for versions 12.22, 13.23, 14.22, 15.17, 16.13, 17.0 to 17.9, 18.2 and 18.3.
The server is an Beelink SER7 with a Ryzen 7 7840HS CPU with 8 cores and AMD SMT disabled, 32G of RAM. Storage is one SSD for the OS and an NVMe SSD for the database using ext-4 with discard enabled. The OS is Ubuntu 24.04.
For versions prior to 18, the config file is named conf.diff.cx10a_c8r32 and they are as similar as possible and here for versions 12, 13, 14, 15, 16 and 17.
For Postgres 18 in most cases I used a config named conf.diff.cx10b_c8r32 (aka cx10b) which is as similar as possible to the configs for versions 17 and earlier. But for tests with full-page writes disabled I used additional configs to compare with results from the cx10b config.
- cx10b_fpw0
- this adds full_page_writes=off while cx10b has =on
- cx10b_wallz4
- this adds wal_compression=lz4 while cx10b has =off
- cx10b_fpw0_wallz4
- this adds full_page_writes=off and wal_compression=lz4
The Benchmark
The benchmark is explained here and is run with 1 client 4 clients. In each case each client uses a separate table. I repeated it with two workloads:
- CPU-bound
- for 1 user the values for X, Y, Z are 30M, 40M, 10M
- for 4 users the values for X, Y, Z are 10M, 16M, 4M
- IO-bound
- for 1 user the values for X, Y, Z are 800M, 4M, 1M
- for 4 users the values for X, Y, Z are 200M, 4M, 1M
The point query (qp100, qp500, qp1000) and range query (qr100, qr500, qr1000) steps are run for 1800 seconds each.
The benchmark steps are:
- l.i0
- insert X rows per table in PK order. The table has a PK index but no secondary indexes. There is one connection per client.
- l.x
- create 3 secondary indexes per table. There is one connection per client.
- l.i1
- use 2 connections/client. One inserts Y rows per table and the other does deletes at the same rate as the inserts. Each transaction modifies 50 rows (big transactions). This step is run for a fixed number of inserts, so the run time varies depending on the insert rate.
- l.i2
- like l.i1 but each transaction modifies 5 rows (small transactions) and Z rows are inserted and deleted per table.
- Wait for S seconds after the step finishes to reduce variance during the read-write benchmark steps that follow. The value of S is a function of the table size.
- qr100
- use 3 connections/client. One does range queries and performance is reported for this. The second does does 100 inserts/s and the third does 100 deletes/s. The second and third are less busy than the first. The range queries use covering secondary indexes. If the target insert rate is not sustained then that is considered to be an SLA failure. If the target insert rate is sustained then the step does the same number of inserts for all systems tested. This step is frequently not IO-bound for the IO-bound workload.
- qp100
- like qr100 except uses point queries on the PK index
- qr500
- like qr100 but the insert and delete rates are increased from 100/s to 500/s
- qp500
- like qp100 but the insert and delete rates are increased from 100/s to 500/s
- qr1000
- like qr100 but the insert and delete rates are increased from 100/s to 1000/s
- qp1000
- like qp100 but the insert and delete rates are increased from 100/s to 1000/s
Results: overview
The performance reports are here for:
The summary sections from the performances report have 3 tables. The first shows absolute throughput by DBMS tested X benchmark step. The second has throughput relative to the version from the first row of the table. The third shows the background insert rate for benchmark steps with background inserts. The second table makes it easy to see how performance changes over time. The third table makes it easy to see which DBMS+configs failed to meet the SLA.
Below I use relative QPS to explain how performance changes. It is: (QPS for $me / QPS for $base) where $me is the result for some version. The base version is Postgres 12.22 for the latest point releases comparison, 17.0 for the 17.x releases comparison and 18.2 with the cx10b config for the full-page writes comparison.
When relative QPS is > 1.0 then performance improved over time. When it is < 1.0 then there are regressions. The Q in relative QPS measures:
When relative QPS is > 1.0 then performance improved over time. When it is < 1.0 then there are regressions. The Q in relative QPS measures:
- insert/s for l.i0, l.i1, l.i2
- indexed rows/s for l.x
- range queries/s for qr100, qr500, qr1000
- point queries/s for qp100, qp500, qp1000
This statement doesn't apply to this blog post, but I keep it here for copy/paste into future posts. Below I use colors to highlight the relative QPS values with red for <= 0.95, green for >= 1.05 and grey for values between 0.95 and 1.05.
Results: CPU-bound
The performance summaries are here for:
- 1 user: latest point releases, all 17.x releases and full-page writes
- 4 users: latest point releases, all 17.x releases and full-page writes
For latest point releases at 1 user
- there is either no change or a small improvement for l.i0 (load in PK order), l.x (create indexes) and the read-write tests (qr*, qp*).
- for l.i1 and l.i2 (random write-only) throughput drops by 5% to 10% from 12.22 to 13.23 and has been stable since then (throughput in 18.2 is similar to 13.23. The CPU per operation overhead (cpupq here) increases after 12.22 for the l.i2 step but there wasn't an obvious increase for the l.i1 step - but the way I measure this is far from perfect. The results I share here are worse than what I measured in December 2025.
For latest point releases at 4 users
- there might be a small (3%) regression for l.i0 (load in PK order) in 18.2 vs 12.22. Perhaps this is noise. From vmstat and iostat metrics there aren't obvious changes.
- throughput in 18.2 is better than 12.22 for all other benchmark steps
For all 17.x releases at 1 user
- throughput is stable from 17.0 to 17.9 for all benchmark steps except l.i1 and l.i2 (random writes) where there might be a 5% regression late in 17.x. This might be from new CPU overhead - see cpupq here.
For all 17.x releases at 4 users
- throughput is stable with small improvements from 17.0 to 17.9
For full-page writes at 1 user
- throughput improves by ~5% for l.i1 and l.i2 (random writes) when full-page writes are disabled and KB written to storage per commit drops by ~20% -- see wkbpi here.
- enabling wal_compression=lz4 decreases write throughput for all write-heavy steps when full-page writes are enabled. The impact is smaller when full page writes are disabled.
For full-page writes at 4 users
- throughput improves by <= 5% for all write-heavy steps when full-page writes are disabled
- the impact from wal_compression=lz4 isn't obvious
Results: IO-bound
The performance summaries are here for:
- 1 user: latest point releases, all 17.x releases and full-page writes
- 4 users: latest point releases, all 17.x releases and full-page writes
For latest point releases at 1 user
- there are small (<= 10%) improvements for l.i0 (load in PK order) and l.x (create index). I don't see anything obvious in vmstat and iostat metrics to explain this.
- there are small (<= 10%) regressions for l.i1 and l.i2 (random writes) that might be from a sequence of small regressions from 13.x through 18.x. I don't see anything obvious in vmstat and iostat metrics to explain this.
- throughput is unchanged for the range-query read+write tests (qr*)
- throughput improves by ~1.4X for the point-query read+write tests (qp*). This improvement arrived in 13.x. This can be explained by large drops in CPU overhead (cpupq) and context switch rates (cspq) -- see here.
- the results here are similar to what I measured in December 2025
For latest point releases at 4 users
- there are small (~10%) regressions for l.i0 (load in PK order) that arrived in 17.x. The context switch rate (cspq) increases in 17.x.
- there are small (<= 20%) improvements for l.x (create index) that arrived in 13.x
- there are large regressions for l.i1 and l.i2 (random writes) that arrive in 15.x through 18.x. There are large increases in CPU overhead (cpupq) -- see here.
- throughput is unchanged for the range-query read+write tests (qr*)
- throughput improves for the point-query read+write tests (qp*) at higher write rates (qp500, qp1000).
For all 17.x releases at 1 user
- throughput is stable with a few exceptions
- for qp1000 (point-query, read+write) it improves by ~5% in 17.1 and is then stable to 17.9
- in 17.9 there are large (~1.4x) improvements for all of the point-query, read+write tests
- the changes in throughput for qp1000 might be explained by a small drop in CPU overhead per query (cpupq) that arrived in 17.1 and a large drop that arrived in 17.9 -- see here.
For all 17.x releases at 4 users
- throughput for most steps (l.i0, l.x, qr*, qp100, qp500) is stable
- throughput for l.i1 and l.i2 (random writes) has more variance
- throughput for qp1000 drops by up to 10% from 17.3 through 17.8 and in those cases the CPU overhead increased -- see cpupq here.
For full-page writes at 1 user
- throughput improves by 6% for l.i1 (random writes) when full-page writes are disabled
- throughput improved for qp* tests when either full-page writes were disabled or lz4 was used for log_compression. That is harder to explain, perhaps it is noise.
For full-page writes at 4 users
- throughput improves by 20% for l.i1 (random writes) when full-page writes are disabled
- throughput improved for qp* tests when either full-page writes were disabled or lz4 was used for log_compression. That is harder to explain, perhaps it is noise.
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Exciting new browser library from Cheng Lou, previously a React core developer and the original creator of the react-motion animation library.
Pretext solves the problem of calculating the height of a paragraph of line-wrapped text without touching the DOM. The usual way of doing this is to render the text and measure its dimensions, but this is extremely expensive. Pretext uses an array of clever tricks to make this much, much faster, which enables all sorts of new text rendering effects in browser applications.
Here's one demo that shows the kind of things this makes possible:
The key to how this works is the way it separates calculations into a call to a prepare() function followed by multiple calls to layout().
The prepare() function splits the input text into segments (effectively words, but it can take things like soft hyphens and non-latin character sequences and emoji into account as well) and measures those using an off-screen canvas, then caches the results. This is comparatively expensive but only runs once.
The layout() function can then emulate the word-wrapping logic in browsers to figure out how many wrapped lines the text will occupy at a specified width and measure the overall height.
I had Claude build me this interactive artifact to help me visually understand what's going on, based on a simplified version of Pretext itself.
The way this is tested is particularly impressive. The earlier tests rendered a full copy of the Great Gatsby in multiple browsers to confirm that the estimated measurements were correct against a large volume of text. This was later joined by the corpora/ folder using the same technique against lengthy public domain documents in Thai, Chinese, Korean, Japanese, Arabic, and more.
Via @_chenglou
Tags: browsers, css, javascript, testing, react, typescript
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What your .NET exceptions are telling attackers (and how to stop it)
6 minutes by Adrian Bailador
Unhandled exceptions in .NET APIs expose stack traces, database schemas, and internal paths to anyone watching. Adrian explains how to implement secure error handling in ASP.NET Core to protect your application without sacrificing observability.
Your .NET code is moving at 2026 speeds. Your CI is still in 2015.
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The evolution of validation techniques in ASP.NET
8 minutes by Bipin Joshi
Bipin describes how validation in ASP.NET started as a UI feature tied to page controls in Web Forms, then moved to model decorators in MVC, and finally became a pipeline service in ASP.NET Core. Today it runs automatically, rejecting bad requests before they reach business logic. Blazor brings back stateful UI but keeps validation model driven. Across all versions, validation shifted from a convenience tool to a core part of how systems enforce boundaries.
Speed up .NET CI with test sharding
3 minutes by Gérald Barré
Test sharding splits your test suite into smaller subsets that run in parallel across multiple CI jobs, so you wait for the slowest shard instead of one long run. Meziantou.ShardedTest is a .NET tool that handles this by listing, sorting, and distributing tests across shards automatically. Gérald argues that it works well when your test stage is slow, but adds little value if tests are small or mostly waiting on IO. He suggests you can combine it with built-in framework parallelization for even faster feedback.
MAUI Avalonia now supports Linux and WebAssembly
6 minutes by Tim Miller
Avalonia now has a backend for .NET MAUI, letting developers deploy MAUI apps to Linux and WebAssembly. Setup takes just four steps and requires no extra bootstrapping code. The project also improved Avalonia itself, including new navigation controls now available to all Avalonia 12 users. Both native and drawn UI options are supported, giving developers more flexibility over how their apps look across platforms.
.NET synchronisation APIs: Out-of-process synchronisation
6 minutes by Ricardo Peres
This is the second in a series of posts on .NET synchronisation. Ricardo talks about using the synchronisation APIs in out-of-process context, meaning, to synchronise different processes, not threads. This can be achieved out of the box with three synchronisation objects: Mutex, Semaphore and EventWaitHandle. Ricardo also describes Shared files which offer another option but can leave stale locks after a crash. On Windows, you can also control access using permissions tied to specific users or groups.
And the most popular article from the last issue was:
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Apple's most legendary computer has two legacies: there's the computer itself, and there's the commercial. That commercial. Only a couple of days before Steve Jobs debuted the computer that would both help cement his legacy and contribute to his unceremonious exile from Apple, the company dropped a Super Bowl ad that is still one of the most iconic commercials of all time. It raised both the hype and the stakes for the Macintosh in a big way.
The Macintosh wasn't a great computer, at least at first. It didn't have enough memory; there wasn't enough software that supported it; it wasn't customizable in the ways PC users needed at the time. I …
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