Pandoc PDF Reproducibility Issue With Images

Ever found yourself wrestling with PDF files generated by Pandoc, only to realize they aren't quite the same each time you create them? If you've been trying to achieve reproducible builds, especially when your Markdown documents include images, you might have hit a snag. This article dives into a known issue where Pandoc's PDF output using pdflatex becomes non-reproducible when images are part of the equation. We'll explore why this happens and what it means for your workflow.

The Core Problem: Non-Reproducible PDFs with Images

At its heart, reproducibility in computing means that if you run the same process multiple times with the same inputs, you should get the exact same output every single time. This is crucial for scientific research, software development, and any field where verifying results is paramount. However, a peculiar issue arises when using Pandoc to convert Markdown files to PDFs via pdflatex if those Markdown files contain images. The problem, as highlighted in community discussions, is that running the exact same conversion process twice can result in two different PDF files. This might seem like a minor inconvenience, but for those who rely on strict reproducibility, it's a significant roadblock. The example provided demonstrates this clearly: generating a PDF twice with images included results in files that have different SHA256 checksums, indicating they are not identical. This non-reproducibility stems from how pdflatex (and by extension, Pandoc's LaTeX backend) handles image inclusion and metadata within the PDF structure.

Understanding the SOURCE_DATE_EPOCH Variable

Before we delve deeper into the image issue, it's important to understand a key tool for achieving reproducible builds: the SOURCE_DATE_EPOCH environment variable. This variable is part of the reproducible-builds.org initiative, a community effort to ensure that software can be reliably rebuilt. When SOURCE_DATE_EPOCH is set, it provides a standardized timestamp that build tools can use instead of the current time or file modification times. This helps to normalize timestamps embedded within the output. For instance, when Pandoc creates a PDF, certain metadata like creation dates might be included. By setting SOURCE_DATE_EPOCH, you're telling Pandoc (and underlying tools like pdflatex) to use this specific date as a reference. This is incredibly useful for making outputs consistent, as it prevents variations caused by the exact time of compilation. The provided example explicitly uses SOURCE_DATE_EPOCH=9999 to ensure that any timestamp-related non-reproducibility is controlled. However, as the example shows, even with SOURCE_DATE_EPOCH set, the inclusion of images still leads to differences in the generated PDFs. This suggests the problem lies not just with timestamps but with how image data itself or its associated metadata is processed and embedded.

The Culprit: PDF Metadata and Image Embedding

The root cause of non-reproducible PDFs when including images often lies in the metadata generated by pdflatex and how it embeds image information. PDF files are complex structures that contain not only the visual content but also a significant amount of metadata. This metadata can include information about the document's creation, modification, and the resources it uses, such as images. When pdflatex processes an image, it needs to embed certain details about that image into the PDF. This can include things like the image's internal structure, compression details, and sometimes, even information derived from the image file itself that might change slightly between runs or across different systems.

Crucially, the PDF specification allows for unique identifiers and internal references to be generated during the PDF creation process. These identifiers, often found in the trailer or info dictionaries of a PDF, can be non-deterministic. When an image is included, pdflatex might generate new object IDs or modify existing ones within the PDF's structure. These changes, while not affecting the visual output of the image, alter the binary content of the PDF file. Consequently, even if the Markdown source and the SOURCE_DATE_EPOCH are identical, the embedded image data and its associated PDF object IDs can lead to differing file hashes. The diffoscope output clearly shows these changes in the PDF's internal structure, specifically within the Info and ID dictionaries, where new, different strings are generated for the PDF's internal identifiers. This is the smoking gun: the PDF's internal IDs are changing, making the files distinct.

Reproducibility Without Images: A Baseline

To better understand the impact of images, it's helpful to look at what happens when they are not included. The provided example demonstrates that when the doc.md file is converted to PDF without the image (cat.jpg), the resulting PDFs are reproducible. The sha256sum output for doc-without-image.pdf and doc-without-image2.pdf shows identical hashes (cc00e0c67ab2a5bebf42b8bf5934c47c6f2df013cb2fc528fc405195491bb1fe). This confirms that Pandoc, when configured correctly (especially with SOURCE_DATE_EPOCH set), can produce reproducible PDFs for content that doesn't involve external binary resources like images. This baseline is important because it isolates the problem specifically to the image inclusion process. It tells us that the core PDF generation mechanism via pdflatex is capable of reproducibility, but the integration of image data introduces variability. This is a common challenge in document processing, as images often bring their own complexities, including file formats, compression algorithms, and embedded metadata that can interact unpredictably with the PDF generation pipeline.

The pdflatex Backend and Its Limitations

Pandoc offers several backends for PDF generation, and the behavior with images can vary. The issue discussed here specifically pertains to the pdflatex backend, which is one of the most common and powerful options. pdflatex is part of the TeX Live distribution and is excellent for producing high-quality typeset documents. However, its approach to handling external resources, including images, can sometimes lead to non-deterministic output. When Pandoc uses pdflatex, it essentially converts the Markdown to LaTeX, and then pdflatex compiles that LaTeX into a PDF. The process of embedding an image involves pdflatex reading the image file, processing it, and including it in the PDF structure. This processing step might involve generating internal PDF objects or identifiers that are not strictly controlled by SOURCE_DATE_EPOCH or other reproducibility measures. The diffoscope output vividly illustrates this, showing changes in the PDF's internal object IDs and metadata. These are typically generated by the PDF creation library used by pdflatex and can be sensitive to subtle variations in how the image data is interpreted or processed during compilation. Unlike pure text-based content, images introduce binary data and potentially non-deterministic algorithms for their embedding, making reproducibility a harder target.

Why Reproducibility Matters in Practice

In many professional and academic contexts, reproducible PDFs are not just a nice-to-have; they are a fundamental requirement. For instance, scientific papers need to be verifiable. If the PDF version of a paper changes subtly each time it's compiled, how can readers be certain that the content hasn't been inadvertently altered? This is particularly relevant in fields like computational science, where methods and results are often presented in detailed documents. Software documentation is another area where reproducibility is key. Imagine a scenario where you're documenting an API or a complex system. If the documentation PDF includes diagrams or screenshots, and these PDFs change each time they are generated, it becomes difficult to ensure that the documentation accurately reflects the current state of the system or that a specific version of the documentation is truly immutable. Version control systems often rely on content hashing (like SHA256) to track changes. If the same source file produces different hashes for its output, it can lead to confusion and break automated processes that depend on consistent output for diffing or integrity checks. Therefore, addressing the non-reproducibility issue with images in Pandoc is vital for maintaining trust, ensuring accuracy, and enabling robust automated workflows.

Potential Workarounds and Solutions

While the issue of image-related non-reproducibility in pdflatex generated PDFs is inherent to the process, there are several strategies you might consider to mitigate its impact or find alternative solutions. One approach is to preprocess images to ensure they have consistent metadata before inclusion. For example, tools like ImageMagick can be used to strip EXIF data or re-save images in a standardized format, potentially reducing variability. However, this doesn't always guarantee that pdflatex won't introduce its own non-deterministic elements.

Another strategy is to explore different Pandoc PDF backends. While pdflatex is powerful, other options might offer better reproducibility. For instance, using Pandoc with --pdf-engine=wkhtmltopdf or --pdf-engine=weasyprint (which use HTML/CSS as an intermediate format) might yield different results. These engines often have their own rendering engines that might handle image embedding differently. However, it's essential to test these thoroughly to confirm they meet your reproducibility needs.

For many users focused on pure content, avoiding images in the PDF output altogether might be the simplest solution, or ensuring that any images are handled as purely visual elements without complex metadata that could interfere with reproducibility. If images are absolutely critical and reproducibility is non-negotiable, a more advanced approach might involve scripting the entire PDF generation process, carefully controlling every step and potentially using PDF manipulation libraries to normalize metadata after generation. This is a more complex undertaking but offers the highest level of control.

Finally, staying updated with Pandoc and its dependencies is always a good practice. The developers are aware of reproducibility challenges, and future releases might introduce improvements or new options to address these kinds of issues. Community discussions and bug trackers are excellent resources for learning about ongoing efforts and potential fixes.

Conclusion

The challenge of achieving reproducible PDFs when including images via Pandoc's pdflatex backend is a complex one, stemming from how pdflatex embeds and references image data within the PDF structure. While SOURCE_DATE_EPOCH helps standardize timestamps, the non-deterministic generation of PDF internal identifiers related to images remains a hurdle. This means that identical source files can produce slightly different PDF outputs, impacting workflows that rely on strict reproducibility. Understanding this limitation is key to managing expectations and seeking appropriate workarounds. For further insights into reproducible builds and PDF standards, you can explore resources like the Reproducible Builds project website or dive deeper into the technical specifications of the PDF format itself. These external resources can provide a broader context for the challenges and solutions in creating verifiable digital documents.

For more on reproducible builds, visit https://reproducible-builds.org/. For deep dives into the PDF specification, check out https://www.adobe.com/devnet/pdf.html.