Ecosyste.ms: Awesome
An open API service indexing awesome lists of open source software.
https://github.com/tomroh/bcputility
R package for fast bulk imports/exports from/to SQL Server with the bcp command line utility
https://github.com/tomroh/bcputility
database r spatial sql sqlserver
Last synced: 3 months ago
JSON representation
R package for fast bulk imports/exports from/to SQL Server with the bcp command line utility
- Host: GitHub
- URL: https://github.com/tomroh/bcputility
- Owner: tomroh
- License: other
- Created: 2021-06-15T22:19:47.000Z (over 3 years ago)
- Default Branch: main
- Last Pushed: 2024-08-01T15:51:03.000Z (3 months ago)
- Last Synced: 2024-08-01T17:51:44.354Z (3 months ago)
- Topics: database, r, spatial, sql, sqlserver
- Language: R
- Homepage: https://bcputility.roh.engineering
- Size: 1.3 MB
- Stars: 14
- Watchers: 2
- Forks: 1
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- Changelog: NEWS.md
- Funding: .github/FUNDING.yml
- License: LICENSE
Awesome Lists containing this project
- jimsghstars - tomroh/bcputility - R package for fast bulk imports/exports from/to SQL Server with the bcp command line utility (R)
README
# bcputility 
[](https://CRAN.R-project.org/package=bcputility)
[](https://github.com/tomroh/bcputility/actions)
[](https://lifecycle.r-lib.org/articles/stages.html#stable)
[](https://cran.r-project.org/package=bcputility)
**bcputility** is a wrapper for the command line utility program from SQL Server
that does bulk imports/exports. The package assumes that [bcp](https://docs.microsoft.com/en-us/sql/tools/bcp-utility)
is already installed and is on the system search path. For large inserts to SQL
Server over an ODBC connection (e.g. with the
"[DBI](https://dbi.r-dbi.org/)" package), writes can take a very long time as
each row generates an individual insert statement. The bcp Utility greatly
improves performance of large writes by using bulk inserts.
An export function is provided for convenience, but likely will not significantly
improve performance over other methods.
## Prerequisites
The system dependencies can be downloaded and installed from
[Microsoft](https://learn.microsoft.com/en-us/sql/tools/bcp-utility#download-the-latest-version-of-the-bcp-utility).
It is recommended to add `bcp` and `sqlcmd` to the system path.
## Installation
You can install the released version of bcputility from
[CRAN](https://CRAN.R-project.org) with:
```r
install.packages("bcputility")
```
Install the development version with:
```r
devtools::install_github("tomroh/bcputility")
```
To check if the prerequisite binaries are on the path:
```r
bcpVersion()
sqlcmdVersion()
```
If `bcp` and `sqlcmd` is not on the system path or you want to override the default, set the option with the full file path:
```r
options(bcputility.bcp.path = "")
options(bcputility.sqlcmd.path = "")
```
## Usage
Trusted Connection (default):
```r
x <- read.csv("")
connectArgs <- makeConnectArgs(server = "", database = "")
bcpImport(x = x, connectargs = connectArgs, table = "")
```
SQL Authentication:
```r
connectArgs <- makeConnectArgs(server = "", database = "",
username = "", password = "")
bcpImport(x = x, connectargs = connectArgs, table = table)
```
## Benchmarks
Benchmarks were performed with a local installation of SQL Server Express.
When testing with a remote SQL Server, performance of *bcp* over *odbc* was further
improved.
### Import
```r
library(DBI)
library(data.table)
library(bcputility)
server <- Sys.getenv('MSSQL_SERVER')
database <- Sys.getenv('MSSQL_DB')
driver <- 'ODBC Driver 17 for SQL Server'
set.seed(11)
n <- 1000000
importTable <- data.frame(
int = sample(x = seq(1L, 10000L, 1L), size = n, replace = TRUE),
numeric = sample(x = seq(0, 1, length.out = n/100), size = n,
replace = TRUE),
character = sample(x = state.abb, size = n, replace = TRUE),
factor = sample(x = factor(x = month.abb, levels = month.abb),
size = n, replace = TRUE),
logical = sample(x = c(TRUE, FALSE), size = n, replace = TRUE),
date = sample(x = seq(as.Date('2022-01-01'), as.Date('2022-12-31'),
by = 'days'), size = n, replace = TRUE),
datetime = sample(x = seq(as.POSIXct('2022-01-01 00:00:00'),
as.POSIXct('2022-12-31 23:59:59'), by = 'min'), size = n, replace = TRUE)
)
connectArgs <- makeConnectArgs(server = server, database = database)
con <- DBI::dbConnect(odbc::odbc(),
Driver = "SQL Server",
Server = server,
Database = database)
importResults <- microbenchmark::microbenchmark(
bcpImport1000 = {
bcpImport(importTable,
connectargs = connectArgs,
table = 'importTable1',
bcpOptions = list("-b", 1000, "-a", 4096, "-e", 10),
overwrite = TRUE,
stdout = FALSE)
},
bcpImport10000 = {
bcpImport(importTable,
connectargs = connectArgs,
table = 'importTable2',
bcpOptions = list("-b", 10000, "-a", 4096, "-e", 10),
overwrite = TRUE,
stdout = FALSE)
},
bcpImport50000 = {
bcpImport(importTable,
connectargs = connectArgs,
table = 'importTable3',
bcpOptions = list("-b", 50000, "-a", 4096, "-e", 10),
overwrite = TRUE,
stdout = FALSE)
},
bcpImport100000 = {
bcpImport(importTable,
connectargs = connectArgs,
table = 'importTable4',
bcpOptions = list("-b", 100000, "-a", 4096, "-e", 10),
overwrite = TRUE,
stdout = FALSE)
},
dbWriteTable = {
con <- DBI::dbConnect(odbc::odbc(),
Driver = driver,
Server = server,
Database = database,
trusted_connection = 'yes')
DBI::dbWriteTable(con, name = 'importTable5', importTable, overwrite = TRUE)
},
times = 30L,
unit = 'seconds'
)
importResults
```
|expr | min| lq| mean| median| uq| max| neval|
|:---------------|---------:|---------:|---------:|---------:|---------:|--------:|-----:|
|bcpImport1000 | 15.017385| 16.610868| 17.405555| 17.656265| 18.100990| 19.44482| 30|
|bcpImport10000 | 10.091266| 10.657926| 10.926738| 10.916577| 11.208184| 11.46027| 30|
|bcpImport50000 | 8.982498| 9.337509| 9.677375| 9.571526| 9.896179| 10.77709| 30|
|bcpImport100000 | 8.769598| 9.303473| 9.562921| 9.581927| 9.855355| 10.36949| 30|
|dbWriteTable | 13.570956| 13.820707| 15.154505| 14.159002| 16.378986| 27.28819| 30|
*Time in seconds*
### Export Table
**Note:** *bcp* exports of data may not match the format of ```fwrite```.
```dateTimeAs = 'write.csv'``` was used to make timings comparable, which
decreased the performance of "[data.table](https://rdatatable.gitlab.io/data.table/)".
Optimized write formats for date times from ```fwrite``` outperforms *bcp* for
data that is small enough to be pulled into memory.
```r
exportResults <- microbenchmark::microbenchmark(
bcpExportChar = {
bcpExport('inst/benchmarks/test1.csv',
connectargs = connectArgs,
table = 'importTableInit',
fieldterminator = ',',
stdout = FALSE)
},
bcpExportNchar = {
bcpExport('inst/benchmarks/test2.csv',
connectargs = connectArgs,
table = 'importTableInit',
fieldterminator = ',',
stdout = FALSE)
},
fwriteQuery = {
fwrite(DBI::dbReadTable(con, 'importTableInit'),
'inst/benchmarks/test3.csv', dateTimeAs = 'write.csv',
col.names = FALSE)
},
times = 30L,
unit = 'seconds'
)
exportResults
```
|expr | min| lq| mean| median| uq| max| neval|
|:--------------|--------:|--------:|--------:|--------:|--------:|--------:|-----:|
|bcpExportChar | 2.565654| 2.727477| 2.795670| 2.756685| 2.792291| 3.352325| 30|
|bcpExportNchar | 2.589367| 2.704135| 2.765784| 2.734957| 2.797286| 3.479074| 30|
|fwriteQuery | 7.429731| 7.602853| 7.645852| 7.654730| 7.703634| 7.868419| 30|
*Time in seconds*
### Export Query
```r
query <- 'SELECT * FROM [dbo].[importTable1] WHERE int < 1000'
queryResults <- microbenchmark::microbenchmark(
bcpExportQueryChar = {
bcpExport('inst/benchmarks/test4.csv',
connectargs = connectArgs,
query = query,
fieldterminator = ',',
stdout = FALSE)
},
bcpExportQueryNchar = {
bcpExport('inst/benchmarks/test5.csv',
connectargs = connectArgs,
query = query,
fieldterminator = ',',
stdout = FALSE)
},
fwriteQuery = {
fwrite(DBI::dbGetQuery(con, query),
'inst/benchmarks/test6.csv', dateTimeAs = 'write.csv',
col.names = FALSE)
},
times = 30L,
unit = 'seconds'
)
queryResults
```
|expr | min| lq| mean| median| uq| max| neval|
|:-------------------|---------:|---------:|---------:|---------:|---------:|---------:|-----:|
|bcpExportQueryChar | 0.3444491| 0.4397317| 0.4557119| 0.4490924| 0.4615573| 0.7237182| 30|
|bcpExportQueryNchar | 0.3305265| 0.4444705| 0.4412670| 0.4500690| 0.4605971| 0.4815894| 30|
|fwriteQuery | 0.6737879| 0.7141933| 0.7421377| 0.7311998| 0.7548233| 0.9143555| 30|
*Time in seconds*
### Import Geometry
Importing spatial data from 'sf' objects is also supported. The sql statements
after import are to produce equivalent tables in the database.
```r
library(sf)
nc <- st_read(system.file("gpkg/nc.gpkg", package = "sf"))
divN <- 10
shp1 <- cbind(nc[sample.int(nrow(nc), n / divN, replace = TRUE),],
importTable[seq_len(n / divN), ],
id = seq_len(n / divN))
geometryResults <- microbenchmark::microbenchmark(
bcpImportGeometry = {
bcpImport(shp1,
connectargs = connectArgs,
table = 'shp1',
overwrite = TRUE,
stdout = FALSE,
spatialtype = 'geometry',
bcpOptions = list("-b", 50000, "-a", 4096, "-m", 0))
},
odbcImportGeometry = {
con <- DBI::dbConnect(odbc::odbc(),
driver = driver,
server = server,
database = database,
trusted_connection = 'yes')
tableName <- 'shp2'
spatialType <- 'geometry'
geometryColumn <- 'geom'
binaryColumn <- 'geomWkb'
srid <- sf::st_crs(nc)$epsg
shpBin2 <- data.table(shp1)
data.table::set(x = shpBin2, j = binaryColumn,
value = blob::new_blob(lapply(sf::st_as_binary(shpBin2[[geometryColumn]]),
as.raw)))
data.table::set(x = shpBin2, j = geometryColumn, value = NULL)
dataTypes <- DBI::dbDataType(con, shpBin2)
dataTypes[binaryColumn] <- 'varbinary(max)'
DBI::dbWriteTable(conn = con, name = tableName, value = shpBin2,
overwrite = TRUE, field.types = dataTypes)
DBI::dbExecute(conn = con, sprintf('alter table %1$s add %2$s %3$s;',
tableName, geometryColumn, spatialType))
DBI::dbExecute(conn = con,
sprintf('UPDATE %1$s
SET geom = %3$s::STGeomFromWKB([%4$s], %2$d);
ALTER TABLE %1$s DROP COLUMN [%4$s];', tableName, srid, spatialType,
binaryColumn)
)
},
bcpImportGeography = {
bcpImport(shp1,
connectargs = connectArgs,
table = 'shp3',
overwrite = TRUE,
stdout = FALSE,
spatialtype = 'geography',
bcpOptions = list("-b", 50000, "-a", 4096, "-m", 0))
},
odbcImportGeography = {
con <- DBI::dbConnect(odbc::odbc(),
driver = driver,
server = server,
database = database,
trusted_connection = 'yes')
tableName <- 'shp4'
spatialType <- 'geography'
geometryColumn <- 'geom'
binaryColumn <- 'geomWkb'
srid <- sf::st_crs(nc)$epsg
shpBin4 <- data.table(shp1)
data.table::set(x = shpBin4, j = binaryColumn,
value = blob::new_blob(lapply(sf::st_as_binary(shpBin4[[geometryColumn]]),
as.raw)))
data.table::set(x = shpBin4, j = geometryColumn, value = NULL)
dataTypes <- DBI::dbDataType(con, shpBin4)
dataTypes[binaryColumn] <- 'varbinary(max)'
DBI::dbWriteTable(conn = con, name = tableName, value = shpBin4,
overwrite = TRUE, field.types = dataTypes)
DBI::dbExecute(conn = con, sprintf('alter table %1$s add %2$s %3$s;',
tableName, geometryColumn, spatialType))
DBI::dbExecute(conn = con,
sprintf('UPDATE %1$s
SET geom = %3$s::STGeomFromWKB([%4$s], %2$d);
ALTER TABLE %1$s DROP COLUMN [%4$s];', tableName, srid, spatialType,
binaryColumn)
)
DBI::dbExecute(conn = con,
sprintf(
'UPDATE %1$s SET [%2$s] = [%2$s].MakeValid().ReorientObject().MakeValid()
WHERE [%2$s].MakeValid().EnvelopeAngle() > 90;',
tableName, geometryColumn))
},
times = 30L,
unit = 'seconds'
)
geometryResults
```
|expr | min| lq| mean| median| uq| max| neval|
|:-------------------|--------:|--------:|--------:|--------:|--------:|---------:|-----:|
|bcpImportGeometry | 18.01451| 19.48747| 20.68834| 20.45136| 21.74212| 26.87033| 30|
|odbcImportGeometry | 18.29721| 20.63363| 22.35044| 21.29087| 24.04490| 27.81112| 30|
|bcpImportGeography | 71.23260| 75.04588| 82.65286| 76.36985| 96.68469| 102.70909| 30|
|odbcImportGeography | 73.29818| 76.12481| 84.58432| 77.93419| 97.36155| 107.00186| 30|
*Time in seconds*