Package: MsBackendSql
Authors: Johannes Rainer [aut, cre] (ORCID: https://orcid.org/0000-0002-6977-7147), Chong Tang
[ctb], Laurent Gatto [ctb] (ORCID: https://orcid.org/0000-0002-1520-2268)
Compiled: Mon Nov 3 12:07:03 2025
Introduction
The Spectra
Bioconductor package provides a flexible and expandable infrastructure
for Mass Spectrometry (MS) data. The package supports interchangeable
use of different backends that provide additional file support
or different ways to store and represent MS data. The MsBackendSql
package provides backends to store data from whole MS experiments in SQL
databases. The data in such databases can be easily (and efficiently)
accessed using Spectra objects that use the
MsBackendSql class as an interface to the data in the
database. Such Spectra objects have a minimal memory
footprint and hence allow analysis of very large data sets even on
computers with limited hardware capabilities. For certain operations,
the performance of this data representation is superior to that of other
low-memory (on-disk) data representations such as
Spectra’s MsBackendMzR backend. Finally, the
MsBackendSql supports also remote data access to e.g. a
central database server hosting several large MS data sets.
Installation
The package can be installed with the BiocManager
package. To install BiocManager use
install.packages("BiocManager") and, after that,
BiocManager::install("MsBackendSql") to install this
package.
Creating and using MsBackendSql SQL databases
MsBackendSql SQL databases can be created either by
importing (raw) MS data from MS data files using the
createMsBackendSqlDatabase() or using the
backendInitialize() function by providing in addition to
the database connection also the full MS data to import as a
DataFrame. In the first example we use the
createMsBackendSqlDatabase() function to import the full MS
data from the provided MS data files into an (empty) database. Below we
first create an empty SQLite database (in a temporary file) and use the
createMsBackendSqlDatabase() function to create all
necessary tables in that database and import the MS data from two mzML
files (from the r Biocpkg("msdata") package).
library(RSQLite)
dbfile <- tempfile()
con <- dbConnect(SQLite(), dbfile)
library(Spectra)
library(MsBackendSql)
fls <- dir(system.file("sciex", package = "msdata"), full.names = TRUE)
createMsBackendSqlDatabase(con, fls)
dbDisconnect(con)By default (with parameters blob = TRUE and
peaksStorageMode = "blob2") the peaks data matrix of each
spectrum is stored as a BLOB data type into the database (one
entry per spectrum). This has advantages on the performance to extract
the peaks data from the database, but does not allow to filter
individual peaks by their m/z or intensity values directly in
the database. As an alternative (using blob = FALSE) it is
also possible to store the individual m/z and intensity values
in separate columns of the database table. This long table
format results however in considerably larger databases (with
potentially poorer performance). Note also that the code and backend is
optimized for MySQL/MariaDB databases by taking advantage of table
partitioning and specialized table storage options. Any other SQL
database server is however also supported (also portable, self-contained
SQLite databases). In fact, performance for MsBackendSql
databases with peaks data stored as BLOB data type is similar
for SQLite and MySQL/MariaDB databases.
The MsBackendSql package provides two backends to interact
with such databases: the MsBackendSql class and the
MsBackendOfflineSql class, that inherits all properties and
functions from the former, but does not store the connection to the
database within the object. The MsBackendOfflineSql object
thus supports parallel processing and allows to save/load the object
(e.g. using save and saveRDS). The
MsBackendOfflineSql might therefore be used as the
preferred backend to SQL databases for most applications.
To access the data in the database we create below a
Spectra object providing the database connection
information in the constructor call and specifying to use the
MsBackendOfflineSql as backend (parameter
source). We stored the data to a SQLite database, thus we
provide the database name (SQLite database file name) and the SQLite DBI
driver with parameters dbname and drv. Which
parameters are required to connect to the database depends on the SQL
database and the used driver. For a MySQL/MariaDB database we would use
the MariaDB() driver and would have to provide the database
name, user name, password as well as the host name and port through
which the database is accessible.
sps <- Spectra(dbname = dbfile, source = MsBackendOfflineSql(), drv = SQLite())
sps## MSn data (Spectra) with 1862 spectra in a MsBackendOfflineSql backend:
## msLevel precursorMz polarity
## <integer> <numeric> <integer>
## 1 1 NA 1
## 2 1 NA 1
## 3 1 NA 1
## 4 1 NA 1
## 5 1 NA 1
## ... ... ... ...
## 1858 1 NA 1
## 1859 1 NA 1
## 1860 1 NA 1
## 1861 1 NA 1
## 1862 1 NA 1
## ... 35 more variables/columns.
## Use 'spectraVariables' to list all of them.
## Database: /tmp/RtmpGFApvq/fileef2322cd20eSpectra objects allow also to change the backend to any
other backend (extending MsBackend) using the
setBackend() function. Below we use this function to first
load all data into memory by changing from the
MsBackendOfflineSql to a MsBackendMemory.
sps_mem <- setBackend(sps, MsBackendMemory())
sps_mem## MSn data (Spectra) with 1862 spectra in a MsBackendMemory backend:
## msLevel rtime scanIndex
## <integer> <numeric> <integer>
## 1 1 0.280 1
## 2 1 0.559 2
## 3 1 0.838 3
## 4 1 1.117 4
## 5 1 1.396 5
## ... ... ... ...
## 1858 1 258.636 927
## 1859 1 258.915 928
## 1860 1 259.194 929
## 1861 1 259.473 930
## 1862 1 259.752 931
## ... 35 more variables/columns.
## Processing:
## Switch backend from MsBackendOfflineSql to MsBackendMemory [Mon Nov 3 12:07:10 2025]With this function it is also possible to change from any backend to
a MsBackendOfflineSql (or MsBackendSql) in
which case a new database is created and all data from the originating
backend is stored in this database. To change the backend to an
MsBackendOfflineSql we need to provide the connection
information to the SQL database as additional parameters. These
parameters are the same that need to be passed to a
dbConnect() call to establish the connection to the
database. These parameters include the database driver (parameter
drv), the database name and eventually the user name, host
etc (see ?dbConnect for more information). In the simple
example below we store the data into a SQLite database and thus only
need to provide the database name, which corresponds SQLite database
file. In our example we store the data into a temporary file.
Optionally, setBackend() supports also the parameters
blob and peaksDataStorage described above for
the createMsBackendSqlDatabase() function.
sps2 <- setBackend(sps_mem, MsBackendOfflineSql(), drv = SQLite(),
dbname = tempfile())
sps2## MSn data (Spectra) with 1862 spectra in a MsBackendOfflineSql backend:
## msLevel precursorMz polarity
## <integer> <numeric> <integer>
## 1 1 NA 1
## 2 1 NA 1
## 3 1 NA 1
## 4 1 NA 1
## 5 1 NA 1
## ... ... ... ...
## 1858 1 NA 1
## 1859 1 NA 1
## 1860 1 NA 1
## 1861 1 NA 1
## 1862 1 NA 1
## ... 35 more variables/columns.
## Use 'spectraVariables' to list all of them.
## Database: /tmp/RtmpGFApvq/fileef21ce61e67
## Processing:
## Switch backend from MsBackendOfflineSql to MsBackendMemory [Mon Nov 3 12:07:10 2025]
## Switch backend from MsBackendMemory to MsBackendOfflineSql [Mon Nov 3 12:07:10 2025]Similar to any other Spectra object we can retrieve the
available spectra variables using the
spectraVariables() function.
spectraVariables(sps)## [1] "msLevel" "rtime"
## [3] "acquisitionNum" "scanIndex"
## [5] "dataStorage" "dataOrigin"
## [7] "centroided" "smoothed"
## [9] "polarity" "precScanNum"
## [11] "precursorMz" "precursorIntensity"
## [13] "precursorCharge" "collisionEnergy"
## [15] "isolationWindowLowerMz" "isolationWindowTargetMz"
## [17] "isolationWindowUpperMz" "peaksCount"
## [19] "totIonCurrent" "basePeakMZ"
## [21] "basePeakIntensity" "electronBeamEnergy"
## [23] "ionisationEnergy" "lowMZ"
## [25] "highMZ" "mergedScan"
## [27] "mergedResultScanNum" "mergedResultStartScanNum"
## [29] "mergedResultEndScanNum" "injectionTime"
## [31] "filterString" "spectrumId"
## [33] "ionMobilityDriftTime" "scanWindowLowerLimit"
## [35] "scanWindowUpperLimit" "spectrum_id_"The MS peak data can be accessed using either the mz(),
intensity() or peaksData() functions. Below we
extract the peaks matrix of the 5th spectrum and display the first 6
rows.
## mz intensity
## [1,] 105.0347 0
## [2,] 105.0362 164
## [3,] 105.0376 0
## [4,] 105.0391 0
## [5,] 105.0405 328
## [6,] 105.0420 0All data (peaks data or spectra variables) are
always retrieved on-the-fly from the database resulting
thus in a minimal memory footprint for the Spectra
object.
print(object.size(sps), units = "KB")## 115 KbThe backend supports also adding additional spectra variables or changing their values. Below we add 10 seconds to the retention time of each spectrum.
sps$rtime <- sps$rtime + 10Such operations do however not change the data in the database (which is always considered read-only) but are cached locally within the backend object (in memory). The size in memory of the object is thus higher after changing that spectra variable.
print(object.size(sps), units = "KB")## 129.6 KbSuch $<- operations can also be used to
cache spectra variables (temporarily) in memory which can
eventually improve performance. Below we test the time it takes to
extract the MS level from each spectrum from the database, then cache
the MS levels in memory using $msLevel <- and test the
timing to extract these cached variable.
system.time(msLevel(sps))## user system elapsed
## 0.009 0.000 0.009
sps$msLevel <- msLevel(sps)
system.time(msLevel(sps))## user system elapsed
## 0.005 0.000 0.004We can also use the reset() function to reset
the data to its original state (this will cause any local spectra
variables to be deleted and the backend to be initialized with the
original data in the database).
sps <- reset(sps)Performance considerations
Database systems and data storage modes
The performance of storing and retrieving MS data from an
MsBackendSql respectively SQL database can also depend on
the type of database used as well as storage modes and the database
layout used by MsBackendSql.
Database systems
Performance comparison have been made for small and large data sets using different SQL database systems and MsBackendSql has been optimized based on these results. For MariaDB database systems, for example, the Aria storage engine is used by default as it has considerable advantages over other MariaDB engines.
Performance of MariaDB and SQLite is comparable, even for very large data sets/databases. See this GitHub issue for performance comparison between MariaDB and SQLite.
Performance evaluation of SQLite and duckdb are provided in this GitHub issue. MsBackendSql long format database layout (see next section for details on available database layouts) with duckdb is clearly faster than with SQLite. For the blob2 database layout SQLite has advantages. Also, extracting individual spectra variables or filtering by e.g. retention time is slower for duckdb.
MsBackendSql database layouts/storage modes
MsBackendSql defines different database table layouts and
hence ways to store the MS data. The most intuitive way to store MS data
would be the long format
(peaksStorageMode = "long") which saves the m/z
and intensity values of each mass peak as a single row. While this would
allow to filter e.g. the peaks data by m/z and/or intensity
values already on the SQL level, it significantly increases the size of
the database. This is in particular true for SQLite-based
databases. The default storage mode
(peaksStorageMode = "blob2") stores the complete peaks
matrix (i.e. the two-column numerical matrix of m/z and
intensity values) of spectrum as one entity to the database. This entry
is stored as a binary data type (BLOB) in the database table (one row
per spectrum). This reduces the size of the database and well as the
time to extract (peaks) data. On the downside, such databases will only
be readable and usable with MsBackendSql.
For MsBackendSql in the long peaks storage mode it is suggested to use duckdb as database backend.
Performance comparison with other backends
The need to retrieve any spectra data on-the-fly from the database
has an impact on the performance of data access functions of
Spectra objects using
MsBackendSql/MsBackendOfflineSql backends. To
evaluate this we compare below the performance of the
MsBackendSql to other Spectra backends,
specifically, the MsBackendMzR which is the default backend
to read and represent raw MS data, and the MsBackendMemory
backend that keeps all MS data in memory (and is thus not suggested for
larger MS experiments). Similar to the MsBackendMzR, also
the MsBackendSql keeps only a limited amount of data in
memory. These on-disk backends need thus to retrieve spectra
and MS peaks data on-the-fly from either the original raw data files (in
the case of the MsBackendMzR) or from the SQL database (in
the case of the MsBackendSql). The in-memory backend
MsBackendMemory is supposed to provide the fastest data
access since all data is kept in memory.
Below we thus create Spectra objects from the same data
but using the different backends.
con <- dbConnect(SQLite(), dbfile)
sps <- Spectra(con, source = MsBackendSql())
sps_mzr <- Spectra(fls, source = MsBackendMzR())
sps_im <- setBackend(sps_mzr, backend = MsBackendMemory())At first we compare the memory footprint of the 3 backends.
print(object.size(sps), units = "KB")## 113.3 Kb
print(object.size(sps_mzr), units = "KB")## 401.4 Kb
print(object.size(sps_im), units = "KB")## 54509.1 KbThe MsBackendSql has the lowest memory footprint of all
3 backends because it does not keep any data in memory. The
MsBackendMzR keeps all spectra variables, except the MS
peaks data, in memory and has thus a larger size. The
MsBackendMemory keeps all data (including the MS peaks
data) in memory and has thus the largest size in memory.
Next we compare the performance to extract the MS level for each
spectrum from the 4 different Spectra objects.
library(microbenchmark)
microbenchmark(msLevel(sps),
msLevel(sps_mzr),
msLevel(sps_im))## Unit: microseconds
## expr min lq mean median uq max
## msLevel(sps) 5099.312 5197.5805 5399.92681 5252.5980 5512.8195 9188.728
## msLevel(sps_mzr) 372.165 404.8655 427.70709 415.8015 443.6335 636.949
## msLevel(sps_im) 10.489 12.9340 19.52523 20.0480 22.3470 57.617
## neval
## 100
## 100
## 100Extracting MS levels is thus slowest for the
MsBackendSql, which is not surprising because both other
backends keep this data in memory while the MsBackendSql
needs to retrieve it from the database.
We next compare the performance to access the full peaks data from
each Spectra object.
microbenchmark(peaksData(sps, BPPARAM = SerialParam()),
peaksData(sps_mzr, BPPARAM = SerialParam()),
peaksData(sps_im, BPPARAM = SerialParam()),
times = 10)## Unit: microseconds
## expr min lq mean
## peaksData(sps, BPPARAM = SerialParam()) 35548.619 45401.928 133085.07
## peaksData(sps_mzr, BPPARAM = SerialParam()) 484020.661 485190.435 513019.36
## peaksData(sps_im, BPPARAM = SerialParam()) 332.982 352.157 1760.66
## median uq max neval
## 51993.706 263136.088 268657.08 10
## 491609.997 498077.969 711223.46 10
## 663.234 761.963 12536.09 10As expected, the MsBackendMemory has the fasted access
to the full peaks data. The MsBackendSql outperforms
however the MsBackendMzR providing faster access to the m/z
and intensity values.
Performance can be improved for the MsBackendMzR using
parallel processing. Note that the MsBackendSql does
not support parallel processing and thus parallel
processing is (silently) disabled in functions such as
peaksData().
m2 <- MulticoreParam(2)
microbenchmark(peaksData(sps, BPPARAM = m2),
peaksData(sps_mzr, BPPARAM = m2),
peaksData(sps_im, BPPARAM = m2),
times = 10)## Unit: microseconds
## expr min lq mean median
## peaksData(sps, BPPARAM = m2) 36615.101 48989.678 84057.7731 59040.4850
## peaksData(sps_mzr, BPPARAM = m2) 442654.344 465956.800 719433.6651 783246.1660
## peaksData(sps_im, BPPARAM = m2) 410.807 738.709 771.1649 800.7505
## uq max neval
## 67326.607 337546.954 10
## 824711.354 1100012.445 10
## 916.251 935.306 10We next compare the performance of subsetting operations.
microbenchmark(filterRt(sps, rt = c(50, 100)),
filterRt(sps_mzr, rt = c(50, 100)),
filterRt(sps_im, rt = c(50, 100)))## Unit: microseconds
## expr min lq mean median
## filterRt(sps, rt = c(50, 100)) 1761.359 1805.517 1988.5741 1833.4290
## filterRt(sps_mzr, rt = c(50, 100)) 1239.234 1301.791 1486.5582 1335.3385
## filterRt(sps_im, rt = c(50, 100)) 386.412 417.560 447.5003 437.6375
## uq max neval
## 1876.825 14443.741 100
## 1471.558 9151.458 100
## 459.538 1031.486 100The two on-disk backends MsBackendSql and
MsBackendMzR show a comparable performance for this
operation. This filtering does involves access to a spectra variables
(the retention time in this case) which, for the
MsBackendSql needs first to be retrieved from the backend.
The MsBackendSql backend allows however also to
cache spectra variables (i.e. they are stored within the
MsBackendSql object). Any access to such cached spectra
variables can eventually be faster because no dedicated SQL query is
needed.
To evaluate the performance of a pure subsetting operation
we first define the indices of 10 random spectra and subset the
Spectra objects to these.
idx <- sample(seq_along(sps), 10)
microbenchmark(sps[idx],
sps_mzr[idx],
sps_im[idx])## Unit: microseconds
## expr min lq mean median uq max neval
## sps[idx] 132.107 139.716 150.0258 149.339 156.3370 201.857 100
## sps_mzr[idx] 657.768 684.007 705.2798 692.929 703.3175 1590.591 100
## sps_im[idx] 229.439 237.689 247.0975 246.846 252.8920 349.273 100Here the MsBackendSql outperforms the other backends
because it does not keep any data in memory and hence does not need to
subset these. The two other backends need to subset the data they keep
in memory which is in both cases a data frame with either a reduced set
of spectra variables or the full MS data.
At last we compare also the extraction of the peaks data from the
such subset Spectra objects.
sps_10 <- sps[idx]
sps_mzr_10 <- sps_mzr[idx]
sps_im_10 <- sps_im[idx]
microbenchmark(peaksData(sps_10),
peaksData(sps_mzr_10),
peaksData(sps_im_10),
times = 10)## Unit: microseconds
## expr min lq mean median uq
## peaksData(sps_10) 1731.864 1945.493 2388.2116 2109.574 3006.454
## peaksData(sps_mzr_10) 50087.777 50528.140 51915.3793 51865.618 53176.986
## peaksData(sps_im_10) 385.049 417.249 544.1631 563.337 651.466
## max neval
## 3171.012 10
## 53970.307 10
## 682.013 10The MsBackendSql outperforms the
MsBackendMzR while, not unexpectedly, the
MsBackendMemory provides fasted access.
Considerations for database systems/servers
The backends from the MsBackendSql package use standard SQL calls to retrieve MS data from the database and hence any SQL database system (for which an R package is available) is supported. SQLite-based databases would represent the easiest and most user friendly solution since no database server administration and user management is required. Indeed, performance of SQLite is very high, even for very large data sets. Server-based databases on the other hand have the advantage to enable a centralized storage and control of MS data (inclusive user management etc). Also, such server systems would also allow data set or server-specific configurations to improve performance.
A comparison between a SQLite-based with a MariaDB-based MsBackendSql database for a large data set comprising over 8,000 samples and over 15,000,000 spectra is available here. In brief, performance to extract data was comparable and for individual spectra variables even faster for the SQLite database. Only when more complex SQL queries were involved (combining several primary keys or data fields) the more advanced MariaDB database outperformed SQLite.
Other properties of the MsBackendSql
The MsBackendSql backend does not support parallel
processing since the database connection can not be shared across the
different (parallel) processes. Thus, all methods on
Spectra objects that use a MsBackendSql will
automatically (and silently) disable parallel processing even if a
dedicated parallel processing setup was passed along with the
BPPARAM method.
Some functions on Spectra objects require to load the MS
peak data (i.e., m/z and intensity values) into memory. For very large
data sets (or computers with limited hardware resources) such function
calls can cause out-of-memory errors. One example is the
lengths() function that determines the number of peaks per
spectrum by loading the peak matrix first into memory. Such functions
should ideally be called using the peaksapply() function
with parameter chunkSize (e.g.,
peaksapply(sps, lengths, chunkSize = 5000L)). Instead of
processing the full data set, the data will be first split into chunks
of size chunkSize that are stepwise processed. Hence, only
data from chunkSize spectra is loaded into memory in one
iteration.
Summary
The MsBackendSql provides an MS data representations and
storage mode with a minimal memory footprint (in R) that is still
comparably efficient for standard processing and subsetting operations.
This backend is specifically useful for very large MS data sets, that
could even be hosted on remote (MySQL/MariaDB) servers. A potential use
case for this backend could thus be to set up a central storage place
for MS experiments with data analysts connecting remotely to this server
to perform initial data exploration and filtering. After subsetting to a
smaller data set of interest, users could then retrieve/download this
data by changing the backend to e.g. a MsBackendMemory,
which would result in a download of the full data to the user
computer’s memory.
Session information
## R Under development (unstable) (2025-10-31 r88977)
## Platform: x86_64-pc-linux-gnu
## Running under: Ubuntu 24.04.3 LTS
##
## Matrix products: default
## BLAS: /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3
## LAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.26.so; LAPACK version 3.12.0
##
## locale:
## [1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C
## [3] LC_TIME=en_US.UTF-8 LC_COLLATE=en_US.UTF-8
## [5] LC_MONETARY=en_US.UTF-8 LC_MESSAGES=en_US.UTF-8
## [7] LC_PAPER=en_US.UTF-8 LC_NAME=C
## [9] LC_ADDRESS=C LC_TELEPHONE=C
## [11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C
##
## time zone: UTC
## tzcode source: system (glibc)
##
## attached base packages:
## [1] stats4 stats graphics grDevices utils datasets methods
## [8] base
##
## other attached packages:
## [1] microbenchmark_1.5.0 RSQLite_2.4.3 MsBackendSql_1.11.1
## [4] Spectra_1.21.0 BiocParallel_1.45.0 S4Vectors_0.49.0
## [7] BiocGenerics_0.57.0 generics_0.1.4 BiocStyle_2.39.0
##
## loaded via a namespace (and not attached):
## [1] sass_0.4.10 MsCoreUtils_1.21.0 stringi_1.8.7
## [4] hms_1.1.4 digest_0.6.37 evaluate_1.0.5
## [7] bookdown_0.45 blob_1.2.4 fastmap_1.2.0
## [10] jsonlite_2.0.0 ProtGenerics_1.43.0 progress_1.2.3
## [13] mzR_2.45.0 DBI_1.2.3 BiocManager_1.30.26
## [16] codetools_0.2-20 textshaping_1.0.4 jquerylib_0.1.4
## [19] cli_3.6.5 rlang_1.1.6 crayon_1.5.3
## [22] Biobase_2.71.0 bit64_4.6.0-1 cachem_1.1.0
## [25] yaml_2.3.10 tools_4.6.0 parallel_4.6.0
## [28] memoise_2.0.1 ncdf4_1.24 fastmatch_1.1-6
## [31] vctrs_0.6.5 R6_2.6.1 lifecycle_1.0.4
## [34] fs_1.6.6 htmlwidgets_1.6.4 IRanges_2.45.0
## [37] bit_4.6.0 clue_0.3-66 MASS_7.3-65
## [40] ragg_1.5.0 cluster_2.1.8.1 pkgconfig_2.0.3
## [43] desc_1.4.3 pkgdown_2.1.3.9000 bslib_0.9.0
## [46] Rcpp_1.1.0 data.table_1.17.8 systemfonts_1.3.1
## [49] xfun_0.54 knitr_1.50 htmltools_0.5.8.1
## [52] rmarkdown_2.30 compiler_4.6.0 prettyunits_1.2.0
## [55] MetaboCoreUtils_1.19.0