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2026-11-01 | 🐢 Solving Slow Obsidian Publishing 🤖

🎯 The Problem

📚 A vault with thousands of notes can turn a daily publishing task into a wait of tens of minutes.
🔍 I investigated Enveloppe’s publishing flow and compared ways to shrink that wait without pretending that an unmeasured bottleneck is a proven root cause.
📏 The key finding is that the current source contains several operations that scale with the number of published notes, but I found no official large-vault benchmark or guarantee that any one of them explains a particular user’s delay.

🧭 Three Plans

🅰️ One plan was to replace Enveloppe immediately with a custom static-site pipeline, trading its built-in Obsidian-aware conversion for maximum control.
🅱️ A second plan was to tune what gets published and measure the existing workflow first, keeping the current publishing system while reducing unnecessary work.
✅ The third plan was to move rendering and deployment into CI, which could isolate work from the Obsidian client but would require reproducing its plugin-dependent behavior.
🏆 The best starting point is plan two: measure one representative publish, reduce the eligible batch, and only then decide whether a pipeline change is justified.

🔬 What Enveloppe Does

📌 Enveloppe is a one-way publisher, not a vault backup or bidirectional sync tool.
🏷️ Its shared-note selection is based on the share: true frontmatter property, and its commands include uploading one note, all shared notes, or only new shared notes, refreshing published notes, and purging notes no longer shared or present.
🔄 The source processes an upload-all batch sequentially: it awaits publishing one shared note before moving on to the next.
🧰 Publishing a note includes checking its state, reading and converting its content, processing linked or embedded files according to settings, and making GitHub API calls to write the result.
🧷 Conversion can rewrite Obsidian links, process embeds and attachments, and render Dataview queries using the installed Dataview plugin’s API.
🧮 Dataview queries are evaluated in sequence, and DataviewJS waits briefly for its rendered output before converting it to Markdown.
🌿 Refresh discovers repository content and, for matching published files, checks GitHub commit history against Obsidian’s last-edited time.
🚦 Enveloppe’s command documentation also describes estimating GitHub API requests and checking the API rate limit before upload, refresh, or purge.

🐌 Likely Sources of Latency

🥇 The strongest code-based suspect is sequential network work: the upload-all loop waits for every note, so the latency of many per-note operations accumulates instead of overlapping.
🥈 Refresh is another plausible suspect because the implementation checks commit history for existing published files one at a time; a large published set can therefore create many serial requests.
🥉 Rich notes can add local conversion work: repeated link rewriting, embed traversal, attachment handling, and Dataview rendering all happen as part of preparing content.
📈 These are plausible scaling costs inferred from the implementation, not measured durations or a confirmed explanation for every slow vault.
⚠️ GitHub request limits or transient network/API delays can add time or interrupt a large batch, but the code alone does not show that rate limiting caused this particular report.
🕵️ The fastest way to distinguish the causes is to record elapsed time for file discovery, conversion, and GitHub operations on one ordinary batch, along with note count, embed and Dataview counts, and request totals.

🛠️ Options to Try

🧹 Tune Enveloppe First

🔹 Publish only notes that genuinely belong on the public site, rather than marking every potentially useful note as shared.
🔹 Prefer the new-note and refresh commands when their timestamp semantics fit the change instead of uploading every shared note each day.
🔹 Remember that refresh compares note edit time with the last GitHub commit: a changed Dataview result does not necessarily edit the source Markdown, so force an upload when the rendered query output itself must be refreshed.
🔹 Review embed and attachment settings, especially if a note pulls in large or deeply linked content that the site does not need.
🔹 Keep daily batches small and publish a focused note directly when a full-vault command is unnecessary.
⚖️ Pros: low migration cost, keeps Enveloppe’s Obsidian-aware link and Dataview behavior, and can sharply reduce work if the current batch includes many irrelevant notes.
⚠️ Cons: it does not remove sequential processing or per-file GitHub checks, and selective publishing can miss content if the sharing rules are not maintained.
📈 Expected speedup: potentially large when most shared notes were being re-uploaded unnecessarily; modest when nearly every shared note needs fresh conversion and upload.
🧩 Complexity: low.

🧪 Measure and Improve the Existing Plugin Workflow

🔹 Compare a small representative batch with a full batch and note whether time is spent before the first upload, during local conversion, or between GitHub requests.
🔹 If Dataview-heavy notes dominate, reduce expensive queries or publish their results as generated static content with an explicit refresh step.
🔹 If embedded content dominates, publish fewer embeds or move large media into an asset workflow that avoids repeatedly traversing the same material.
🔹 If API calls dominate, report the request count and batch size to the plugin maintainers with a reproducible profile; a bounded-concurrency implementation or batched remote-state lookup might help, but should be tested against API limits and ordering assumptions.
⚖️ Pros: evidence targets the actual bottleneck and can produce an upstream fix useful to all users.
⚠️ Cons: profiling may take time, and an optimization to Enveloppe would need to be implemented and released by its maintainers or carried as a local fork.
📈 Expected speedup: unknown until measured; high only if the measured dominant work is reduced or safely overlapped.
🧩 Complexity: low for timing a workflow, medium to high for implementing and maintaining plugin changes.

🏗️ Move Site Building to CI

🔹 Keep Obsidian as the editing environment, but have a Git push or scheduled workflow build and deploy a selected content repository.
🔹 Tools such as Quartz provide a command-line static-site workflow; a custom GitHub Actions pipeline can filter changed notes, run a generator, and deploy the output.
⚖️ Pros: builds run outside the Obsidian client, are repeatable, and can use incremental build tooling when the chosen generator supports it.
⚠️ Cons: moving work to CI does not make the work disappear; fetching, link resolution, Dataview-equivalent rendering, asset copying, deletions, and private-note exclusion still need a reliable design.
📈 Expected speedup: potentially large for interactive publishing if the pipeline builds only changed inputs, but small or negative if each run still rebuilds and uploads the whole site.
🧩 Complexity: medium for a standard generator and high if the pipeline must reproduce arbitrary Obsidian plugin behavior.

🌱 Choose a Different Publishing Plugin

🔹 Digital Garden is another selective Obsidian-to-site workflow, while Obsidian Git is useful for Git-based backup and synchronization rather than for transforming notes into a website.
⚖️ Pros: a different publisher may better match the desired site workflow, and a Git sync plugin can simplify the source-repository handoff.
⚠️ Cons: switching publishers does not prove that the same volume of parsing, rendering, or network work will be faster; feature compatibility and migration effort must be checked.
📈 Expected speedup: unknown without a benchmark on the same vault, same published set, and same output requirements.
🧩 Complexity: medium for evaluating and migrating to a publisher, low to medium for setting up Git sync, with a separate site build still required.

🧾 A Practical Decision

🧪 First, time the current workflow and capture the number of shared notes, changed notes, Dataview blocks, embeds, attachments, and GitHub requests.
🎯 Next, try a selective daily publish and compare the same measurements, while explicitly checking whether Dataview output changed without a source-file edit.
📊 Then compare a CI build or alternate publisher using the same content subset and a checklist of required links, embeds, query output, asset behavior, and deletion handling.
🛡️ Keep private-note filtering and deletion behavior as acceptance criteria, not afterthoughts; a faster pipeline is not an improvement if it publishes the wrong files.
⏱️ No defensible percentage speedup can be quoted before those comparisons, so the estimates here are qualitative expectations rather than benchmark results.

📚 Research Notes

🔎 I reviewed Enveloppe’s command documentation and its upload, file-discovery, conversion, link, embed, and Dataview source modules.
📅 The inspected source revision was dated July 31, 2026, while the command documentation reported a January 2025 update; settings and behavior should be confirmed against the installed plugin version.
📚 Alternative references reviewed were the Quartz project documentation, the Digital Garden project documentation, and the Obsidian Git project README.
🚫 No official Enveloppe performance benchmark or confirmed large-vault limit was found, so source-based scaling concerns are clearly separated from measured facts.

📚 Book Recommendations

• 📘 Designing Data-Intensive Applications by Martin Kleppmann
• 📗 The Pragmatic Programmer by David Thomas and Andrew Hunt
• 📙 Building Evolutionary Architectures by Neal Ford, Rebecca Parsons, and Patrick Kua
• 📕 Release It! Design and Deploy Production-Ready Software by Michael Nygard