Fastest way to upload data via R to PostgresSQL 12 - r

Question

I am using the following code to connect to a PostgreSQL 12 database:
con <- DBI::dbConnect(odbc::odbc(), driver, server, database, uid, pwd, port)
This connects me to a PostgreSQL 12 database on Google Cloud SQL. The following code is then used to upload data:
DBI::dbCreateTable(con, tablename, df)
DBI::dbAppendTable(con, tablename, df)
where df is a data frame I have created in R. The data frame consists of ~ 550,000 records totaling 713 MB of data.
When uploaded by the above method, it took approximately 9 hours at a rate of 40 write operations/second. Is there a faster way to upload this data into my PostgreSQL database, preferably through R?

Answer 1

I've always found bulk-copy to be the best, external to R. The insert can be significantly faster, and your overhead is (1) writing to file, and (2) the shorter run-time.
Setup for this test:
win10 (2004)
docker
postgres:11 container, using port 35432 on localhost, simple authentication
a psql binary in the host OS (where R is running); should be easy with linux, with windows I grabbed the "zip" (not installer) file from https://www.postgresql.org/download/windows/ and extracted what I needed
I'm using data.table::fwrite to save the file because it's fast; in this case write.table and write.csv are still much faster than using DBI::dbWriteTable, but with your size of data you might prefer something quick
DBI::dbCreateTable(con2, "mt", mtcars)
DBI::dbGetQuery(con2, "select count(*) as n from mt")
# n
# 1 0
z1000 <- data.table::rbindlist(replicate(1000, mtcars, simplify=F))
nrow(z1000)
# [1] 32000
system.time({
DBI::dbWriteTable(con2, "mt", z1000, create = FALSE, append = TRUE)
})
# user system elapsed
# 1.56 1.09 30.90
system.time({
data.table::fwrite(z1000, "mt.csv")
URI <- sprintf("postgresql://%s:%s#%s:%s", "postgres", "mysecretpassword", "127.0.0.1", "35432")
system(
sprintf("psql.exe -U postgres -c \"\\copy %s (%s) from %s (FORMAT CSV, HEADER)\" %s",
"mt", paste(colnames(z1000), collapse = ","),
sQuote("mt.csv"), URI)
)
})
# COPY 32000
# user system elapsed
# 0.05 0.00 0.19
DBI::dbGetQuery(con2, "select count(*) as n from mt")
# n
# 1 64000
While this is a lot smaller than your data (32K rows, 11 columns, 1.3MB of data), a speedup from 30 seconds to less than 1 second cannot be ignored.
Side note: there is also a sizable difference between dbAppendTable (slow) and dbWriteTable. Comparing psql and those two functions:
z100 <- rbindlist(replicate(100, mtcars, simplify=F))
system.time({
data.table::fwrite(z100, "mt.csv")
URI <- sprintf("postgresql://%s:%s#%s:%s", "postgres", "mysecretpassword", "127.0.0.1", "35432")
system(
sprintf("/Users/r2/bin/psql -U postgres -c \"\\copy %s (%s) from %s (FORMAT CSV, HEADER)\" %s",
"mt", paste(colnames(z100), collapse = ","),
sQuote("mt.csv"), URI)
)
})
# COPY 3200
# user system elapsed
# 0.0 0.0 0.1
system.time({
DBI::dbWriteTable(con2, "mt", z100, create = FALSE, append = TRUE)
})
# user system elapsed
# 0.17 0.04 2.95
system.time({
DBI::dbAppendTable(con2, "mt", z100, create = FALSE, append = TRUE)
})
# user system elapsed
# 0.74 0.33 23.59
(I don't want to time dbAppendTable with z1000 above ...)
(For kicks, I ran it with replicate(10000, ...) and ran the psql and dbWriteTable tests again, and they took 2 seconds and 372 seconds, respectively. Your choice :-) ... now I have over 650,000 rows of mtcars ... hrmph ... drop table mt ...

Answer 2

I suspect that dbAppendTable results in an INSERT statement per row, which can take a long time for high numbers of rows.
However, you can generate a single INSERT statement for the entire data frame using the sqlAppendTable function and run it by using dbSendQuery explicitly:
res <- DBI::dbSendQuery(con, DBI::sqlAppendTable(con, tablename, df, row.names=FALSE))
DBI::dbClearResult(res)
For me, this was much faster: a 30 second ingest reduced to a 0.5 second ingest.