Advanced usage • pool

This article discusses some more advanced features of the pool package, namely customizing your pool and using pool with database transactions.

library(pool)

Get started by installing the packages you need:

install.packages(c("shiny", "DBI", "pool"))

Customizing your pool

First, let’s get to know our Pool object:

library(pool)

pool <- dbPool(
  drv = RMySQL::MySQL(),
  dbname = "shinydemo",
  host = "shiny-demo.csa7qlmguqrf.us-east-1.rds.amazonaws.com",
  username = "guest",
  password = "guest"
)
pool
#> <Pool> of MySQLConnection objects
#>   Objects checked out: 0
#>   Available in pool: 1
#>   Max size: Inf
#>   Valid: TRUE

As you can see, printing gives you basic information about your pool. This can be useful to learn how many connections you have open (both free/idle and in use).

But for this section, let’s turn our attention to other parameters that you can pass to dbPool(): minSize, maxSize, and idleTimeout.

When created, the pool will initialize minSize connections, and keeps them around until they’re requested. If all the idle connections are taken up when another request for a connection comes up, the pool will create a new connection. It’ll keep doing this as needed until it gets to maxSize connections at which point it will error.

Any connection that is created when we’re over minSize will have a timer attached to it: from the moment it is returned back to the pool, a countdown of idleTimeout seconds will start. If that connection is not requested again during that period, it will be destroyed when the countdown finishes. If it is requested and checked out of the pool, the countdown will the reset when it is returned back to the pool.

The optimal values of these three parameters depend on how you’re using your pool, as they represent a tradeoff between how adaptable your pool is and how efficient it is. Large values for all three parameters would mean that your pool is highly adaptable (it can handle spikes in traffic easily), but potentially not very efficient (it might be creating and holding on to connections that aren’t needed). On the other hand, small values for these parameters would mean that your pool is very strict about the number of connections it has (it will very rarely allow for idle connections) which will lead to an efficient pool if traffic is consistent. However, this type of pool won’t be able to handle spikes in traffic easily: on one hand, it will often have to do the computationally expensive fetching of connections directly from the database, since it doesn’t hold on to idle connections for long; on the other hand, once it hits the maxSize number of connections, it won’t be able to scale up any further.

Considering where your pool falls on this spectrum, you should choose the value for these arguments accordingly. For example, if you want a stable pool that will adapt and scale up easily (and you’re not too worried about efficiency), you could do something like:

library(DBI)
library(pool)

pool <- dbPool(
  drv = RMySQL::MySQL(),
  dbname = "shinydemo",
  host = "shiny-demo.csa7qlmguqrf.us-east-1.rds.amazonaws.com",
  username = "guest",
  password = "guest",
  minSize = 10,
  idleTimeout = 60 * 60
)

poolClose(pool)

Transactions

So far, we’ve recommended you always use the pool object directly when you need to query the database. There’s one challenge where this is not possible: transactions. Because for a transaction, you need to have access to the same connection for longer than a single query. The following will not necessary work because the pool might give you a different connection for each

dbBegin(pool)
dbExecute(pool, A)
dbGetQuery(pool, B)
dbCommit(pool)

DBI provides a helper for this case, but it doesn’t work either:

DBI::dbWithTransaction(pool, {
  dbExecute(pool, A)
  dbGetQuery(pool, B)
})

You can instead use pool::poolWithTransaction():

pool::poolWithTransaction(pool, function(con) {
  dbGetQuery(con, A)
  dbGetQuery(con, B)
})