Background
The BiocBuildReporter package provides access to years of Bioconductor build
system data, representing a comprehensive record of package builds across:
- Thousands of packages in the Bioconductor ecosystem
- Multiple R versions spanning several years of development
- Multiple platforms including Linux, macOS, and Windows
- Different build stages (install, build, check)
The Bioconductor build system runs regularly, testing all packages to ensure
they meet quality standards and work correctly across different platforms. This
dataset captures the results of these builds, including:
- Build status (OK, WARNING, ERROR, TIMEOUT)
- Package version information
- Git commit information
- Maintainer details
- Propagation status to the community
This vignette demonstrates how to use BiocBuildReporter functions to explore and
analyze this rich dataset to understand:
- Package build history and stability
- Platform-specific issues
- Package growth over time
- Common failure patterns
The Bioconductor Build Report Logs are being processed by
BiocBuildDB. The BiocBuildDB creates
parquet files read by this package for analysis.
Installation and Loading
BiocBuildReporter is a Bioconductor package and can be installed through
BiocManager::install().
if (!"BiocManager" %in% rownames(installed.packages()))
install.packages("BiocManager")
BiocManager::install("BiocBuildReporter", dependencies=TRUE)
After the package is installed, it can be loaded into R workspace by
library(BiocBuildReporter)
We will also load libraries need to run this vignette:
library(BiocBuildReporter)
library(dplyr)
library(ggplot2)
library(tidyr)
Accessing Bioconductor Build Report Data
Getting All Available Tables
The simplest way to start is to download all available data tables. The
get_all_bbs_tables() function retrieves all three parquet files containing
Bioconductor build data:
# Download all available tables
# This will cache the tables for quick subsequent access
get_all_bbs_tables()
The function downloads three tables:
- build_summary: Results of each build stage for every package
- info: Package metadata including version, maintainer, and git information
- propagation_status: Information about package propagation to the community
Getting Individual Tables
You can also retrieve individual tables using get_bbs_table():
# Get the build summary table
build_summary <- get_bbs_table("build_summary")
# Get the info table
info <- get_bbs_table("info")
# Get the propagation status table
propagation_status <- get_bbs_table("propagation_status")
Once downloaded, subsequent calls to these functions will use cached data,
making analysis much faster.
Remote Read vs Local Download
The package allows the option to either download locally and read from a locally
saved BiocFileCache, or to read files remotely. The default is to download
locally and use a cached version of the files for analysis. To read remotely,
calls to get_bbs_table or get_all_bbs_tables should be altered to use the
argument useLocal=FALSE.
info <- get_bbs_table("info", useLocal=FALSE)
Bioconductor devel reports are daily, meaning cached data can get out of date
quickly. If using locally cached data, you can update/re-download the files
using the argument updateLocal=TRUE. The default is not to update.
info <- get_bbs_table("info", useLocal=TRUE, updateLocal=TRUE)
Package-Specific Queries
This section shows usage of BiocBuildReporter provided helper functions for
package specific analysis.
Package Build Results
The get_package_build_results() function retrieves latest build status
information for a package for all available builders on a given Bioconductor
branch:
# Get build results for BiocFileCache on branch RELEASE_3_22
get_package_build_results("BiocFileCache", branch="RELEASE_3_22")
#> # A tibble: 22 × 9
#> package node stage version status endedat git_branch
#> <chr> <chr> <chr> <chr> <chr> <dttm> <chr>
#> 1 BiocFileCache kjohnson1 buildb… 3.0.0 OK 2025-12-21 11:31:00 RELEASE_3…
#> 2 BiocFileCache kjohnson1 builds… 3.0.0 OK 2025-12-19 01:06:04 RELEASE_3…
#> 3 BiocFileCache kjohnson1 checks… 3.0.0 OK 2025-12-19 21:04:05 RELEASE_3…
#> 4 BiocFileCache kjohnson1 install 3.0.0 OK 2025-12-18 20:29:23 RELEASE_3…
#> 5 BiocFileCache kjohnson3 buildb… 3.0.0 OK 2025-11-11 04:12:43 RELEASE_3…
#> 6 BiocFileCache kjohnson3 builds… 3.0.0 OK 2025-11-10 20:48:12 RELEASE_3…
#> 7 BiocFileCache kjohnson3 checks… 3.0.0 OK 2025-11-10 23:57:58 RELEASE_3…
#> 8 BiocFileCache kjohnson3 install 3.0.0 OK 2025-11-10 20:05:16 RELEASE_3…
#> 9 BiocFileCache lconway buildb… 3.0.0 OK 2025-11-04 06:29:51 RELEASE_3…
#> 10 BiocFileCache lconway builds… 3.0.0 OK 2025-11-03 21:17:15 RELEASE_3…
#> # ℹ 12 more rows
#> # ℹ 2 more variables: git_last_commit <chr>, git_last_commit_date <dttm>
This shows:
- Node (builder machine name)
- Build stage (install, build, check)
- Package version
- Build status (OK, WARNING, ERROR, TIMEOUT)
- Date of completion
- Git branch
- Git commit hashes
- Last commit dates
This is useful for showing the last known status of a package on a given
release for all active builders.
Package Error Counts
The package_error_count() function provides statistics on how often a package
has failed during builds:
# Get error counts for BiocFileCache
bfc_errors <- package_error_count("BiocFileCache")
bfc_errors
#> # A tibble: 408 × 6
#> node version stage count_total count_error git_branch
#> <chr> <pckg_vrs> <chr> <int> <int> <chr>
#> 1 machv2 2.0.0 buildbin 1 0 RELEASE_3_13
#> 2 machv2 2.0.0 buildsrc 1 0 RELEASE_3_13
#> 3 machv2 2.0.0 checksrc 1 0 RELEASE_3_13
#> 4 machv2 2.0.0 install 1 0 RELEASE_3_13
#> 5 nebbiolo1 2.0.0 buildsrc 1 0 RELEASE_3_13
#> 6 nebbiolo1 2.0.0 checksrc 1 0 RELEASE_3_13
#> 7 nebbiolo1 2.0.0 install 1 0 RELEASE_3_13
#> 8 tokay2 2.0.0 buildbin 1 0 RELEASE_3_13
#> 9 tokay2 2.0.0 buildsrc 1 0 RELEASE_3_13
#> 10 tokay2 2.0.0 checksrc 1 0 RELEASE_3_13
#> # ℹ 398 more rows
# Filter to a specific branch
bfc_errors_release <- package_error_count("BiocFileCache", branch = "RELEASE_3_22")
bfc_errors_release
#> # A tibble: 22 × 6
#> node version stage count_total count_error git_branch
#> <chr> <pckg_vrs> <chr> <int> <int> <chr>
#> 1 kjohnson1 3.0.0 buildbin 6 0 RELEASE_3_22
#> 2 kjohnson1 3.0.0 buildsrc 8 2 RELEASE_3_22
#> 3 kjohnson1 3.0.0 checksrc 6 2 RELEASE_3_22
#> 4 kjohnson1 3.0.0 install 8 0 RELEASE_3_22
#> 5 kjohnson3 3.0.0 buildbin 8 0 RELEASE_3_22
#> 6 kjohnson3 3.0.0 buildsrc 10 2 RELEASE_3_22
#> 7 kjohnson3 3.0.0 checksrc 8 0 RELEASE_3_22
#> 8 kjohnson3 3.0.0 install 10 0 RELEASE_3_22
#> 9 lconway 3.0.0 buildbin 4 0 RELEASE_3_22
#> 10 lconway 3.0.0 buildsrc 5 1 RELEASE_3_22
#> # ℹ 12 more rows
# Filter to a specific builder
bfc_errors_builder <- package_error_count("BiocFileCache",
builder = "nebbiolo2",
branch = "RELEASE_3_22")
bfc_errors_builder
#> # A tibble: 3 × 6
#> node version stage count_total count_error git_branch
#> <chr> <pckg_vrs> <chr> <int> <int> <chr>
#> 1 nebbiolo2 3.0.0 buildsrc 60 9 RELEASE_3_22
#> 2 nebbiolo2 3.0.0 checksrc 51 7 RELEASE_3_22
#> 3 nebbiolo2 3.0.0 install 60 0 RELEASE_3_22
This returns:
- Node (builder machine name)
- Package version
- Build stage (install, build, check)
- Total number of runs
- Total number of errors
- Git branch
For the devel branch, you can filter to the most recent version:
# Get devel errors
dev_errors <- package_error_count("BiocFileCache", branch = "devel")
# Filter to current devel version
dev_errors |> filter(version == max(version))
#> # A tibble: 11 × 6
#> node version stage count_total count_error git_branch
#> <chr> <pckg_vrs> <chr> <int> <int> <chr>
#> 1 kjohnson3 3.1.0 buildbin 56 0 devel
#> 2 kjohnson3 3.1.0 buildsrc 69 13 devel
#> 3 kjohnson3 3.1.0 checksrc 56 2 devel
#> 4 kjohnson3 3.1.0 install 69 0 devel
#> 5 lconway 3.1.0 buildbin 9 0 devel
#> 6 lconway 3.1.0 buildsrc 11 2 devel
#> 7 lconway 3.1.0 checksrc 9 1 devel
#> 8 lconway 3.1.0 install 11 0 devel
#> 9 nebbiolo1 3.1.0 buildsrc 108 14 devel
#> 10 nebbiolo1 3.1.0 checksrc 94 8 devel
#> 11 nebbiolo1 3.1.0 install 108 0 devel
This is useful for comparing how often a package failed during a given stage vs
how often it attempted that stage. It gives an overview of frquency of failures.
Package Failures Over Time
The package_failure_over_time() function gives and overview of how long a
package has been failing on a given builder:
# Get failure events for BiocFileCache on nebbiolo1 and
# group events in a 24 hour period
package_failures_over_time("BiocFileCache", "nebbiolo1", 24)
#> # A tibble: 21 × 7
#> version episode first_failure last_failure n_failures stages
#> <pckg_vrs> <int> <dttm> <dttm> <int> <chr>
#> 1 3.1.0 12 2025-12-19 02:17:53 2025-12-19 02:17:53 1 checks…
#> 2 3.1.0 11 2025-12-15 02:57:57 2025-12-16 21:29:20 3 builds…
#> 3 3.1.0 10 2025-12-13 02:32:33 2025-12-13 02:32:33 1 checks…
#> 4 3.1.0 9 2025-12-08 21:30:14 2025-12-11 21:27:29 4 builds…
#> 5 3.1.0 8 2025-12-05 21:26:25 2025-12-05 21:26:25 1 builds…
#> 6 3.1.0 7 2025-12-01 21:30:34 2025-12-03 21:39:35 3 builds…
#> 7 3.1.0 6 2025-11-25 21:30:29 2025-11-26 21:28:08 2 builds…
#> 8 3.1.0 5 2025-11-22 02:27:43 2025-11-22 02:27:43 1 checks…
#> 9 3.1.0 4 2025-11-18 02:32:56 2025-11-18 02:32:56 1 checks…
#> 10 3.1.0 3 2025-11-15 02:26:25 2025-11-15 02:26:25 1 checks…
#> # ℹ 11 more rows
#> # ℹ 1 more variable: statuses <chr>
This shows:
- Package version
- Sequential count of number of failure episodes
- Time of first event failure
- Time of last event failure
- Number of failures during that episode
- Stages of failures
- Status of stages
This is useful to track the length of failure events to determine if it is
intermittent or consistent. The grouping of events is given as an argument, in
this example we used 24 hours. This is to account for branches having different
build cadenances and allowing sequential builds to be potentially grouped
together.
Exploratory Data Analysis
This section provides examples of utilizing the report tables for broader
analysis and queries.
Package Growth Over Time
Let’s explore how the number of Bioconductor packages has grown over time:
# Get info table
info <- get_bbs_table("info")
# Count unique packages by branch
package_counts <- info |>
group_by(git_branch) |>
summarise(
n_packages = n_distinct(Package),
.groups = "drop"
) |>
arrange(desc(n_packages))
# Display the counts
package_counts
#> # A tibble: 12 × 2
#> git_branch n_packages
#> <chr> <int>
#> 1 devel 3060
#> 2 RELEASE_3_22 2885
#> 3 RELEASE_3_21 2859
#> 4 RELEASE_3_19 2816
#> 5 RELEASE_3_20 2807
#> 6 RELEASE_3_18 2778
#> 7 RELEASE_3_17 2723
#> 8 RELEASE_3_16 2670
#> 9 RELEASE_3_15 2622
#> 10 RELEASE_3_14 2528
#> 11 RELEASE_3_13 2475
#> 12 master 33
# Visualize package counts by branch
ggplot(package_counts, aes(x = reorder(git_branch, n_packages), y = n_packages)) +
geom_col(fill = "steelblue") +
coord_flip() +
labs(
title = "Number of Packages by Bioconductor Branch",
x = "Branch",
y = "Number of Packages"
) +
theme_minimal()
Build Status Distribution
Understanding the distribution of build statuses helps identify overall system health:
# Get build summary table
build_summary <- get_bbs_table("build_summary")
# Count build statuses
status_counts <- build_summary |>
count(status) |>
arrange(desc(n))
status_counts
#> # A tibble: 4 × 2
#> status n
#> <chr> <int>
#> 1 OK 13091938
#> 2 WARNINGS 641941
#> 3 ERROR 417673
#> 4 TIMEOUT 17687
# Visualize status distribution
ggplot(status_counts, aes(x = reorder(status, n), y = n)) +
geom_col(aes(fill = status)) +
scale_fill_manual(values = c(
"OK" = "green3",
"WARNING" = "orange",
"ERROR" = "red",
"TIMEOUT" = "darkred"
)) +
coord_flip() +
labs(
title = "Distribution of Build Statuses",
x = "Status",
y = "Count"
) +
theme_minimal() +
theme(legend.position = "none")
Build Stage Analysis
Understanding which build stage most often fails:
# Analyze failures by stage
stage_failures <- build_summary |>
filter(status %in% c("ERROR", "TIMEOUT")) |>
count(stage, status) |>
arrange(desc(n))
stage_failures
#> # A tibble: 8 × 3
#> stage status n
#> <chr> <chr> <int>
#> 1 buildsrc ERROR 219184
#> 2 checksrc ERROR 116956
#> 3 install ERROR 79831
#> 4 checksrc TIMEOUT 9484
#> 5 buildsrc TIMEOUT 8114
#> 6 buildbin ERROR 1702
#> 7 buildbin TIMEOUT 57
#> 8 install TIMEOUT 32
# Visualize
ggplot(stage_failures, aes(x = stage, y = n, fill = status)) +
geom_col() +
scale_fill_manual(values = c("ERROR" = "red", "TIMEOUT" = "darkred")) +
labs(
title = "Build Failures by Stage",
x = "Build Stage",
y = "Number of Failures",
fill = "Status"
) +
theme_minimal()
Most Problematic Packages
Identify packages with the highest error rates:
# Find packages with most errors
package_errors <- build_summary |>
filter(status %in% c("ERROR", "TIMEOUT")) |>
count(package, status) |>
group_by(package) |>
summarise(
total_errors = sum(n),
.groups = "drop"
) |>
arrange(desc(total_errors))
# Top 10 packages with most errors
head(package_errors, 10)
#> # A tibble: 10 × 2
#> package total_errors
#> <chr> <int>
#> 1 lapmix 2214
#> 2 netZooR 1751
#> 3 hypeR 1689
#> 4 ChemmineOB 1594
#> 5 XNAString 1526
#> 6 Repitools 1514
#> 7 ccfindR 1480
#> 8 gpuMagic 1468
#> 9 slalom 1399
#> 10 Harshlight 1395
Maintainer Analysis
Analyze package maintenance patterns:
# Get unique packages per maintainer
maintainer_packages <- info |>
group_by(Maintainer) |>
summarise(
n_packages = n_distinct(Package),
packages = paste(unique(Package), collapse = ", "),
.groups = "drop"
) |>
arrange(desc(n_packages))
# Top maintainers by number of packages
head(maintainer_packages, 10)
#> # A tibble: 10 × 3
#> Maintainer n_packages packages
#> <chr> <int> <chr>
#> 1 Bioconductor Package Maintainer 72 GSE62944, hgu2beta7, TENxBrainDat…
#> 2 Aaron Lun 62 celldex, chipseqDBData, DropletTe…
#> 3 Hervé Pagès 25 pasillaBamSubset, RNAseqData.HNRN…
#> 4 VJ Carey 25 harbChIP, leeBamViews, MAQCsubset…
#> 5 Laurent Gatto 24 depmap, RforProteomics, CTdata, h…
#> 6 Marcel Ramos 24 curatedTCGAData, SingleCellMultiM…
#> 7 Michael Love 20 airway, fission, macrophage, null…
#> 8 Jianhong Ou 17 ATACseqQC, ATACseqTFEA, ChIPpeakA…
#> 9 Guangchuang Yu 16 ChIPseeker, clusterProfiler, DOSE…
#> 10 Mike Smith 16 BeadArrayUseCases, HD2013SGI, min…
# Distribution of packages per maintainer
ggplot(maintainer_packages, aes(x = n_packages)) +
geom_histogram(binwidth = 1, fill = "steelblue", color = "white") +
labs(
title = "Distribution of Packages per Maintainer",
x = "Number of Packages",
y = "Number of Maintainers"
) +
theme_minimal()
Temporal Analysis
Analyze build patterns over time:
# Analyze build patterns over time
build_summary <- build_summary |>
mutate(
date = as.Date(startedat),
month = format(startedat, "%Y-%m")
)
# Build activity by month
monthly_builds <- build_summary |>
count(month) |>
mutate(month_date = as.Date(paste0(month, "-01")))
ggplot(monthly_builds, aes(x = month_date, y = n)) +
geom_line(color = "steelblue", linewidth = 1) +
geom_point(color = "steelblue") +
labs(
title = "Build Activity Over Time",
x = "Month",
y = "Number of Builds"
) +
theme_minimal() +
theme(axis.text.x = element_text(angle = 45, hjust = 1))
# Error rate over time
monthly_errors <- build_summary |>
group_by(month) |>
summarise(
total = n(),
errors = sum(status %in% c("ERROR", "TIMEOUT")),
error_rate = errors / total * 100,
.groups = "drop"
) |>
mutate(month_date = as.Date(paste0(month, "-01")))
ggplot(monthly_errors, aes(x = month_date, y = error_rate)) +
geom_line(color = "red", linewidth = 1) +
geom_point(color = "red") +
labs(
title = "Build Error Rate Over Time",
x = "Month",
y = "Error Rate (%)"
) +
theme_minimal() +
theme(axis.text.x = element_text(angle = 45, hjust = 1))
Bioconductor Report Overview
This sections demonstrates usage of BiocBuildReporter helper functions for
broader analysis.
Get Bioconductor Build Report
The get_build_report() function will retrieve the Bioconductor Build Report
for any given day. Optionally you may specify a specific Bioconductor git branch
or build machine name to filter further.
# Retrieves the build report for all packages on December 29, 2025
# Filtering also for RELEASE_3_22 branch and linux "nebbiolo1" build machine
get_build_report("2025-12-29", branch="RELEASE_3_22", builder="nebbiolo2")
#> # A tibble: 3,602 × 12
#> package node stage version status startedat endedat
#> <chr> <chr> <fct> <pckg_> <chr> <dttm> <dttm>
#> 1 ABSSeq nebbiol… inst… 1.64.0 OK 2025-12-29 20:05:54 2025-12-29 20:06:15
#> 2 ABSSeq nebbiol… buil… 1.64.0 OK 2025-12-29 21:11:23 2025-12-29 21:12:29
#> 3 ABarray nebbiol… inst… 1.78.0 OK 2025-12-29 20:16:38 2025-12-29 20:16:47
#> 4 ABarray nebbiol… buil… 1.78.0 OK 2025-12-29 21:11:23 2025-12-29 21:11:36
#> 5 ACE nebbiol… inst… 1.28.0 OK 2025-12-29 20:18:52 2025-12-29 20:19:11
#> 6 ACE nebbiol… buil… 1.28.0 OK 2025-12-29 21:11:23 2025-12-29 21:12:19
#> 7 ACME nebbiol… inst… 2.66.0 OK 2025-12-29 20:14:48 2025-12-29 20:14:54
#> 8 ACME nebbiol… buil… 2.66.0 OK 2025-12-29 21:11:23 2025-12-29 21:11:44
#> 9 ADAM nebbiol… inst… 1.26.0 OK 2025-12-29 20:41:39 2025-12-29 20:42:11
#> 10 ADAM nebbiol… buil… 1.26.0 OK 2025-12-29 21:11:23 2025-12-29 21:12:07
#> # ℹ 3,592 more rows
#> # ℹ 5 more variables: command <chr>, report_md5 <chr>, git_branch <chr>,
#> # git_last_commit <chr>, git_last_commit_date <dttm>
This shows:
- Package name
- Node (builder machine name)
- Build stage (install, build, check)
- Package version
- Status of stage (OK, WARNING, ERROR, TIMEOUT)
- Time started
- Time completed
- Command utilized to initiate run
- Report md5 sum
- Git branch
- Git commit hashes
- Last commit dates
This function is meant to programatically reproduce a daily report for any given
day for any given builder and branch.
Get List of Failing Packages
The get_failing_packages() function will return a list of all the currently
failing packages for a given branch and build machine name:
# returns all failing packages for RELEASE_3_22 branch
# for build machine nebbolo2
get_failing_packages("RELEASE_3_22", "nebbiolo2")
#> # A tibble: 657 × 6
#> git_branch package version node stages statuses
#> <chr> <chr> <pckg_vrs> <chr> <chr> <chr>
#> 1 RELEASE_3_22 AHMassBank 1.10.0 nebbiolo2 checksrc ERROR
#> 2 RELEASE_3_22 AMARETTO 1.26.0 nebbiolo2 buildsrc ERROR
#> 3 RELEASE_3_22 ANCOMBC 2.12.0 nebbiolo2 buildsrc… ERROR
#> 4 RELEASE_3_22 ANF 1.32.0 nebbiolo2 buildsrc ERROR
#> 5 RELEASE_3_22 APAlyzer 1.24.0 nebbiolo2 checksrc ERROR
#> 6 RELEASE_3_22 ASURAT 1.14.0 nebbiolo2 buildsrc… ERROR
#> 7 RELEASE_3_22 AUCell 1.32.0 nebbiolo2 buildsrc ERROR
#> 8 RELEASE_3_22 AWAggregator 1.0.0 nebbiolo2 buildsrc ERROR
#> 9 RELEASE_3_22 AWAggregatorData 1.0.0 nebbiolo2 buildsrc ERROR, …
#> 10 RELEASE_3_22 AlphaMissense.v2023.hg19 3.18.2 nebbiolo2 buildsrc ERROR
#> # ℹ 647 more rows
This shows:
- Git branch
- Package name
- Package version
- Node (builder machine name)
- Build stages (install, build, check)
- Build statuses (OK, WARNING, ERROR, TIMEOUT)
This gives a quick list of all failing packages. If querying the currently
active Bioconductor branches (see get_latest_branches()), maintainers of these
packages should be contacted to fix their packages to avoid deprecation and
removal from Bioconductor.
Conclusion
The BiocBuildReporter package provides powerful tools for analyzing Bioconductor build system data. This vignette demonstrated:
- Data Access: Using
get_bbs_table() and get_all_bbs_tables() to retrieve build data
- Package-Specific Queries: Using
get_package_release_info(), get_package_build_results(), package_error_count() and package_failures_over_time() to analyze individual packages
- Exploratory Analysis: Examining package growth, build statuses, platform differences, and temporal patterns
- Bioconductor Report Overview: Using
get_build_report() and get_failing_packages()
This dataset can help package developers, maintainers, and the Bioconductor community to:
- Monitor package build health
- Identify platform-specific issues
- Track package evolution over time
- Understand common failure patterns
- Improve package quality and reliability
For more information about specific functions, see their documentation with ?function_name.
sessionInfo()
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