Pinning transitive R dependencies for fun and reproducible builds
Transitive dependencies (n)
- Packages that your dependencies depend on, and packages those packages depend on and so on and so on and so on and so on and so on
- The packages which are most likely to break your builds and are the most difficult to manage
Like many teams that work with large amounts of external software, we run into issues with our transitive dependencies. In general, transitive dependencies are a hard problem to solve. Many package managers (e.g. Cargo, NPM) and helper tools (e.g. pip-compile, Bundler) now generate a “lockfile” to guarantee reproducibility in installing transitive dependencies. The first time you install all of your dependencies, the tool runs its dependency resolution algorithm to determine which packages to install at what versions, taking into account the version requirements all packages have of other packages. The output of the dependency resolution is then stored in a lockfile (Gemfile.lock, cargo.lock, package-lock.json, etc.), which is stored alongside the file specifying dependencies. With subsequent installations, the same exact version of all dependencies are installed.
Our problem
While most of our business logic is written in Python, with its dependencies managed by “pip-compile”, we rely on some external R libraries for processing biological data. In the name of stability, we pinned our R dependencies using devtools to specify a version during installation. R’s default package installation scheme, however, doesn’t deal well with pinned versions of packages. Because there is no equivalent of a lockfile, devtools will install the specified pinned versions of packages, but the most recent versions of all other packages. This behaviour subverts the stability and reproducibility of pinning package versions, since transitive dependencies can update and break our builds. This is a recent example of an R build failing for exactly that reason:
After tons of digging, I tracked down the source of our build failure. It turns out, the latest vctrs update required rlang version 0.4.0 or greater, which caused R to skip installing vctrs since we pin rlang at 0.3.1. vctrs is 3 layers down the dependency graph from a package we explicitly install, but its update still broke our tests and prevented us from deploying. Eventually, I decided that we had to do something to prevent these errors moving forward.
Our solution
In thinking about the problem, we recognized that there were two distinct steps that a dependency-locking tool needs to accomplish: resolving version requirements and calculating an installation order.
Resolving version requirements
As I alluded to earlier, the approach that most package managers and tools take to resolve version requirements is to apply a dependency resolution algorithm. Dependency resolution algorithms are able to parse version requirements between packages and resolve which version of each package to install. Given how complicated the description is, dependency resolution algorithms can be complex. For that reason, we decided on a simpler approach: using package versions from an already-built docker container. Essentially, we are manually resolving dependencies, and then storing the output of that resolution for future use.
To get all of the installed R package versions on a system, you can use this R one-liner:
print(as.data.frame(installed.packages()[, c(1, 3:4)])[1:2])
It looks fairly complicated, but all that it does is to convert specific parts of “installed.packages” (a structure holding information about all installed R packages) to a data frame, then it does more filtering on the data frame and prints it out. What is nice about this is that the output format is easily machine readable, so we can convert it into a TSV file for use by other scripts. This is a simplified version of the script we use to run the R script on a specified docker container and convert the output to TSV:
docker run -it "$docker_image" Rscript -e "$(cat ./list_dependencies.R)" \
| tr -d '\r'
| tail -n+2
| awk '{ print $1 "\t" $3 }'
We run the above R script inside $docker_image, then use “tr” to remove the windows line endings and “tail” to remove the column headers, then use “awk” to print out two of the columns with a tab in between. Although this requires a bit more manual input than a dependency resolution algorithm, it was much simpler to implement and was perfectly sufficient for our problem.
Resolving installation order
The other main problem that we had to address is installation order. Since our solution uses “devtools::install_version” to install packages at specific versions, we had to pay attention to the order in which we installed packages. Even if we install every single transient dependency manually, we can still run into issues if we install a package before its dependencies. While installing a package, R checks to see if its dependencies are installed, and if they aren’t already installed it automatically installs the latest version of that dependency. Which is a problem, because R automatically picking the latest version of dependencies is the exact problem we are trying to avoid in the first place.
To better understand the problem and our solution, it is helpful to think of this problem in terms of a dependency graph:
A dependency graph is a way of representing a package’s transitive dependencies based on which packages depend on each other. In the very simple example above, “callr” depends on “processx” and “R6”, “R6” doesn’t depend on anything, “processx” depends on “R6” and “ps”, and “ps” doesn’t depend on anything.
Now that we understand what a dependency graph is, it is easier to see what we mean by installing packages in the proper order. To install packages in the correct order, we look at the dependency graph and install packages “from the bottom up”. That is, we only want to install a package once all packages pointed to by that package have already been installed. This type of ordering is called a “topological ordering” in graph theory.
So that we can perform a topological sort on the graph to get a topological ordering, we have to ensure that the graph has two general properties. The first property is that the graph is “directed”, meaning that connections between nodes encode a direction, and are usually depicted as arrows. For dependency graphs, the arrows’ directions encode which package on each edge depends on the other. The second is that it is acyclic, meaning that there are no cycles in the dependency graph, since packages would fail to install if cycles existed because each package in the cycle would be waiting on another package in the cycle before it could install. Since both of these conditions hold in dependency graphs, we can perform a topological sort on the graphs to easily determine an installation order.
Once we can transform a dependency graph into an installation order, the rest of the code is easy. The general outline of what our code for installation order resolution does is the following:
- Scrape CRAN’s website for each package to find out what it “depends” on and what it “imports” (since there are two ways of specifying what we think of as a “dependency” in R).
- Use the information from CRAN to build up a dependency tree. This is different from a dependency graph, because each node only has one arrow pointing into it and packages are duplicated if multiple packages depend on them.
- Convert the dependency tree into a dependency graph.
- Apply a topological sort to the graph to get the installation order, using depth first search.
From there, we combine the installation order information with the version information to generate an R script like the following:
# Generated from cranlock
options(warn=2)
options(Ncpus=parallel::detectCores())
options(repos=structure(c(CRAN="https://cran.revolutionanalytics.com")))
devtools::install_version('codetools', version='0.2-15')
devtools::install_version('iterators', version='1.0.10')
devtools::install_version('foreach', version='1.4.4')
devtools::install_version('doParallel', version='1.0.11')
devtools::install_version('data.table', version='1.11.0')
devtools::install_version('getopt', version='1.20.3')
devtools::install_version('optparse', version='1.4.4')
devtools::install_version('lazyeval', version='0.2.1')
Conclusion
As long as you do it properly, pinning dependencies can save you a ton of time by drastically reducing surprise build failures.
Although we couldn’t find any, there could still be a more fully fleshed-out method of pinning R transitive dependencies out there. If there is, we would love to hear about it.
If you are interested in reading the full code, it is hosted on Github here: https://github.com/AlexsLemonade/cranlock
Transitive dependencies (n)
- Packages that your dependencies depend on, and packages those packages depend on and so on and so on and so on and so on and so on
- The packages which are most likely to break your builds and are the most difficult to manage
Like many teams that work with large amounts of external software, we run into issues with our transitive dependencies. In general, transitive dependencies are a hard problem to solve. Many package managers (e.g. Cargo, NPM) and helper tools (e.g. pip-compile, Bundler) now generate a “lockfile” to guarantee reproducibility in installing transitive dependencies. The first time you install all of your dependencies, the tool runs its dependency resolution algorithm to determine which packages to install at what versions, taking into account the version requirements all packages have of other packages. The output of the dependency resolution is then stored in a lockfile (Gemfile.lock, cargo.lock, package-lock.json, etc.), which is stored alongside the file specifying dependencies. With subsequent installations, the same exact version of all dependencies are installed.
Our problem
While most of our business logic is written in Python, with its dependencies managed by “pip-compile”, we rely on some external R libraries for processing biological data. In the name of stability, we pinned our R dependencies using devtools to specify a version during installation. R’s default package installation scheme, however, doesn’t deal well with pinned versions of packages. Because there is no equivalent of a lockfile, devtools will install the specified pinned versions of packages, but the most recent versions of all other packages. This behaviour subverts the stability and reproducibility of pinning package versions, since transitive dependencies can update and break our builds. This is a recent example of an R build failing for exactly that reason:
After tons of digging, I tracked down the source of our build failure. It turns out, the latest vctrs update required rlang version 0.4.0 or greater, which caused R to skip installing vctrs since we pin rlang at 0.3.1. vctrs is 3 layers down the dependency graph from a package we explicitly install, but its update still broke our tests and prevented us from deploying. Eventually, I decided that we had to do something to prevent these errors moving forward.
Our solution
In thinking about the problem, we recognized that there were two distinct steps that a dependency-locking tool needs to accomplish: resolving version requirements and calculating an installation order.
Resolving version requirements
As I alluded to earlier, the approach that most package managers and tools take to resolve version requirements is to apply a dependency resolution algorithm. Dependency resolution algorithms are able to parse version requirements between packages and resolve which version of each package to install. Given how complicated the description is, dependency resolution algorithms can be complex. For that reason, we decided on a simpler approach: using package versions from an already-built docker container. Essentially, we are manually resolving dependencies, and then storing the output of that resolution for future use.
To get all of the installed R package versions on a system, you can use this R one-liner:
print(as.data.frame(installed.packages()[, c(1, 3:4)])[1:2])
It looks fairly complicated, but all that it does is to convert specific parts of “installed.packages” (a structure holding information about all installed R packages) to a data frame, then it does more filtering on the data frame and prints it out. What is nice about this is that the output format is easily machine readable, so we can convert it into a TSV file for use by other scripts. This is a simplified version of the script we use to run the R script on a specified docker container and convert the output to TSV:
docker run -it "$docker_image" Rscript -e "$(cat ./list_dependencies.R)" \
| tr -d '\r'
| tail -n+2
| awk '{ print $1 "\t" $3 }'
We run the above R script inside $docker_image, then use “tr” to remove the windows line endings and “tail” to remove the column headers, then use “awk” to print out two of the columns with a tab in between. Although this requires a bit more manual input than a dependency resolution algorithm, it was much simpler to implement and was perfectly sufficient for our problem.
Resolving installation order
The other main problem that we had to address is installation order. Since our solution uses “devtools::install_version” to install packages at specific versions, we had to pay attention to the order in which we installed packages. Even if we install every single transient dependency manually, we can still run into issues if we install a package before its dependencies. While installing a package, R checks to see if its dependencies are installed, and if they aren’t already installed it automatically installs the latest version of that dependency. Which is a problem, because R automatically picking the latest version of dependencies is the exact problem we are trying to avoid in the first place.
To better understand the problem and our solution, it is helpful to think of this problem in terms of a dependency graph:
A dependency graph is a way of representing a package’s transitive dependencies based on which packages depend on each other. In the very simple example above, “callr” depends on “processx” and “R6”, “R6” doesn’t depend on anything, “processx” depends on “R6” and “ps”, and “ps” doesn’t depend on anything.
Now that we understand what a dependency graph is, it is easier to see what we mean by installing packages in the proper order. To install packages in the correct order, we look at the dependency graph and install packages “from the bottom up”. That is, we only want to install a package once all packages pointed to by that package have already been installed. This type of ordering is called a “topological ordering” in graph theory.
So that we can perform a topological sort on the graph to get a topological ordering, we have to ensure that the graph has two general properties. The first property is that the graph is “directed”, meaning that connections between nodes encode a direction, and are usually depicted as arrows. For dependency graphs, the arrows’ directions encode which package on each edge depends on the other. The second is that it is acyclic, meaning that there are no cycles in the dependency graph, since packages would fail to install if cycles existed because each package in the cycle would be waiting on another package in the cycle before it could install. Since both of these conditions hold in dependency graphs, we can perform a topological sort on the graphs to easily determine an installation order.
Once we can transform a dependency graph into an installation order, the rest of the code is easy. The general outline of what our code for installation order resolution does is the following:
- Scrape CRAN’s website for each package to find out what it “depends” on and what it “imports” (since there are two ways of specifying what we think of as a “dependency” in R).
- Use the information from CRAN to build up a dependency tree. This is different from a dependency graph, because each node only has one arrow pointing into it and packages are duplicated if multiple packages depend on them.
- Convert the dependency tree into a dependency graph.
- Apply a topological sort to the graph to get the installation order, using depth first search.
From there, we combine the installation order information with the version information to generate an R script like the following:
# Generated from cranlock
options(warn=2)
options(Ncpus=parallel::detectCores())
options(repos=structure(c(CRAN="https://cran.revolutionanalytics.com")))
devtools::install_version('codetools', version='0.2-15')
devtools::install_version('iterators', version='1.0.10')
devtools::install_version('foreach', version='1.4.4')
devtools::install_version('doParallel', version='1.0.11')
devtools::install_version('data.table', version='1.11.0')
devtools::install_version('getopt', version='1.20.3')
devtools::install_version('optparse', version='1.4.4')
devtools::install_version('lazyeval', version='0.2.1')
Conclusion
As long as you do it properly, pinning dependencies can save you a ton of time by drastically reducing surprise build failures.
Although we couldn’t find any, there could still be a more fully fleshed-out method of pinning R transitive dependencies out there. If there is, we would love to hear about it.
If you are interested in reading the full code, it is hosted on Github here: https://github.com/AlexsLemonade/cranlock
Related Post
We are excited to announce that our next virtual workshop, Introduction to Single-cell RNA-Seq, will run from June 10-14, 2024! In this workshop, Data Lab staff will introduce researchers studying pediatric cancer to the R programming language, the Tidyverse R packages for data science, single-cell RNA-seq data analysis, and annotating cell types.
Applications are open for the Data Lab's next workshop! We are holding a two-day course on Reproducible Research Practices and the Open Single-cell Pediatric Cancer Atlas (OpenScPCA) project from May 14-15, 2024. Please note that the OpenScPCA module is an optional part of the workshop. The course begins with an introduction to principles and techniques to achieve reproducible results in computational cancer research. On day two, you can choose to continue the workshop and learn how to put your skills to use for OpenScPCA, our new pediatric cancer research project.
Are you attending the American Association for Cancer Research (AACR) annual meeting in San Diego, CA? Visit the Alex’s Lemonade Stand Foundation (ALSF) Grants and Data Lab teams at booth 3755 in the exhibit hall from April 7-10 and during poster sessions on April 8. We will announce a new collaborative project and share exciting news about the Single-cell Pediatric Cancer Atlas Portal and training opportunities!
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Are you attending the American Association for Cancer Research (AACR) annual meeting in Orlando, FL this year? Visit Alex's Lemonade Stand Foundation (ALSF) at booth 369 in the exhibit hall from April 16-19! You'll find information about ALSF's grants program, the Childhood Cancer Data Lab and more. The Data Lab will also be holding office hours during select time slots.
The Data Lab is excited to announce that our next training workshop will be held virtually from March 13-17, 2023! During this workshop, we will cover advanced topics in the analysis of single-cell RNA-seq data for researchers studying pediatric cancer. The workshop will take place each day from 12-5pm Eastern. Each day consists of lectures and designated time for attendees to work on exercise materials and their own projects with our staff available for consultation. You’ll need a laptop with internet access and to install Zoom and Slack. You will log into an RStudio Server hosted by the Data Lab from your web browser. Pediatric cancer researchers are encouraged to apply now!
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The workshop will last from 9AM to 5PM on October 14th, 15th, and 16th at the CCDL offices at 1429 Walnut St Philadelphia, PA, 19102.
MultiPLIER is a machine learning approach that brings big data to bear on rare diseases. It’s also an example of the scientific approach and ethos of the CCDL, and the publication is a great opportunity to share how the CCDL is developing new technologies to accelerate research into cures for childhood cancers!
The Childhood Cancer Data Lab powered by Alex's Lemonade Stand Foundation is hosting a workshop to introduce childhood cancer researchers to reproducible analysis of bulk and single-cell transcriptomic data.
The Childhood Cancer Data Lab (CCDL), an initiative of Alex's Lemonade Stand Foundation develops tools, trainings, and methods to empower childhood cancer researchers. The work at the CCDL is focused and impactful. There are multiple opportunities and challenges for you to apply and grow your skills as a scientist or as an engineer.
The Childhood Cancer Data Lab powered by Alex's Lemonade Stand Foundation is hosting a workshop to introduce childhood cancer researchers to reproducible analysis of bulk and single-cell transcriptomic data.
At this hands-on, 3-day session held in Houston, researchers learned data science skills that could accelerate their own work. Drawing on skills learned at the workshop, childhood cancer researchers can perform basic analyses of their work to make informed decisions on how to proceed with their own research. Don’t just take our word for it, though. Read more about the workshop’s incredibly valuable benefits through its attendees’ perspectives.
The goal of our refine.bio project is to download, process, and make available gene expression datasets that can be analyzed together, or in parts, depending on a researcher’s need. Childhood cancer researchers need to be able to use data generated through multiple profiling technologies including microarrays and RNA-sequencing.
There are countless log blog posts out there about the benefits of good logging, how to log well, and how much to log. Going through them all can be a real log blog slog. Wouldn't it be cool if you could log like this:logger.info("Something happened!", job=job.id, user=user.id) and get an easily searchable output.
Caffeine is a stimulant that can induce alertness in certain individuals when consumed at an appropriate quantity. Caffeine is often obtained by ingesting caffeine-containing solutions. However, no protocol for obtaining caffeine from dehydrated, roasted beans using materials typically available in a Philadelphia office has been described in the published literature.
Alex’s Lemonade Stand Foundation (ALSF) staunchly believes that stronger scientific sharing practices will accelerate the pace of discovery and finding cures for children with cancer. Robust sharing improves reproducibility, minimizes redundant studies and maximizes our return on research investment.
Earlier this year, Alex’s Lemonade Stand Foundation identified single-cell gene expression profiling as an opportunity to build an atlas of cell types within tumors that could be broadly reused by pediatric cancer researchers.
This year was a big one for the CCDL. In our mission to empower pediatric cancer experts poised for big discoveries with the knowledge, data and methods to reach them we launched a software product, developed and delivered training workshops on single-cell and bulk RNA-seq analysis, and hired our data science team among other milestones.
I’m a scientist at Sage Bionetworks, a nonprofit research organization in Seattle, WA. My work focuses on a family of rare pediatric diseases (NF): neurofibromatosis type 1, type 2, and schwannomatosis.
Our particular process is designed to source opportunities from our team members and external stakeholders, convert those opportunities into a set of potential goals, and then select the goals that we expect will most advance our mission.
The ability to restore scroll position is often critical for website usability. It helps users keep the flow of navigation when going back and forth between different pages. Most modern browsers take care of restoring the scroll position automatically, but it doesn’t always work for Single Page Applications where the content is generated on the client’s side, often asynchronously.
Carnegie Mellon University Libraries is partnering with the Childhood Cancer Data Lab (CCDL), founded by Alex’s Lemonade Stand Foundation, to host a Data Analysis workshop using CCDL materials.
The CCDL will have a team of scientists at the American Association for Cancer Research 2020 Annual Meeting in sunny San Diego! Our team members are excited to talk to researchers studying pediatric cancer at Booth 1601.
To help keep pediatric cancer research moving forward, here are 3 ways the CCDL is helping the research community during this time: refine.bio, virtual workshops, and the Open Pediatric Brain Tumor Atlas project.
We know that pandemic-related university closures mean that the demand for opportunities for pediatric cancer researchers to increase their analytical skills has never been higher. As such, we are delighted to announce a pilot virtual workshop running from May 4-8, 2020!
Here at the Childhood Cancer Data Lab, we value transparency and the practice of open science. Much of the work we’ve done and the products that we build hinge on the generosity and openness of other scientists. In this post, as part of National Brain Tumor Awareness month, we want to talk about a project that our science team has been working on over the last few months (and to do so in a way that aligns with our values).
The workshop will take place on June 22 - 26, 2020 from noon - 5pm Eastern. Each day consists of lectures and designated time for attendees to work on exercise materials and their own projects with CCDL staff available for consultation.
When the CCDL (along with everyone else) realized that we would have to conduct our bioinformatics training workshops remotely, we had to make some quick decisions about how we were going to do it. Most of the instructional materials for our in person workshops were already online, so we knew we had a good base to work from. We just needed to figure how to adapt the live instruction.
At Alex’s Lemonade Stand Foundation’s Childhood Cancer Data Lab, we’re excited to be helping out with an upcoming event hosted by the Children’s Tumor Foundation. If you participate, you may meet members of our team who are mentoring and judging.
The workshop will take place on March 22 - 26, 2021 from noon - 5pm Eastern. Each day consists of lectures and designated time for attendees to work on exercise materials and their own projects with CCDL staff available for consultation
The workshop will take place on June 28- July 2, 2021 from noon to 5pm eastern. Each day consists of lectures and designated time for attendees to work on exercise materials and their own projects with CCDL staff available for consultation.
Hack4Rare is a virtual event that calls for healthcare startups, developers, solutions architects, and hackathon enthusiasts to join researchers, clinicians and patients in developing solutions built around a number of rare diseases including neurofibromatosis, PTEN Hamartoma Tumor Syndrome, RASopathies and Desmoid Tumors.
The workshop will take place on September 20 - 24, 2021 from noon - 5pm Eastern. Each day consists of lectures and designated time for attendees to work on exercise materials and their own projects with CCDL staff available for consultation.
Introducing refine.bio examples. Here, users can access a variety of example analyses implemented in R, such as clustering and heat maps, differential expression analysis, and pathway analysis, for use with refine.bio data.
The workshop will take place on November 1-5, 2021 from noon to 5pm eastern. Each day consists of lectures and designated time for attendees to work on exercise materials and their own projects with our staff available for consultation.
I work at the Childhood Cancer Data Lab, where we use very big data to find cures for childhood cancers. To move data around the internet at very high speeds, we are forced to use a proprietary software suite called Aspera. If somebody could make a Free Software alternative, the future of the internet would be way more awesome! Best of all, you can be the one to do it!
When my daughter Alex was diagnosed with cancer and throughout her battle, we saw how our community of people rallied around our family. No one knew quite how to help, but they were willing to do whatever was needed to ease the burden we faced.
'Work smarter not harder’ is useless advice if you don’t know how to ‘work smarter’. But the Childhood Cancer Data Lab's work and processes may be the smartest I’ve ever had the pleasure of learning and adopting.
The Data Lab will hold our first virtual workshop of the year from March 14-18, 2022!In this workshop, we will introduce researchers studying pediatric cancer to the R programming language, the Tidyverse R packages for data science, single-cell RNA-seq data analysis, and pathway analyses.
At the Data Lab, we are big proponents of automating the boring stuff so we can spend more time thinking about the fun stuff. But how exactly do we do that, and what does it mean to automate the boring stuff?
November marked the final Childhood Cancer Data Lab training workshop for 2021. We held four week-long virtual workshops this year, teaching 88 researchers the data science skills they need to examine their own data.
Before working as a Data Scientist at the Childhood Cancer Data Lab, I spent time in my PhD and post-doctoral fellowship in two very different research environments. Each had their own unique way of doing research. I found that some things worked really well and others were not as successful.
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