To be explicit, with pipelining the order of operations of our very first example will be the following:

• Client: INCR X
• Client: INCR X
• Client: INCR X
• Client: INCR X
• Server: 1
• Server: 2
• Server: 3
• Server: 4

IMPORTANT NOTE: While the client sends commands using pipelining, the server will be forced to queue the replies, using memory. So if you need to send a lot of commands with pipelining, it is better to send them as batches each containing a reasonable number, for instance 10k commands, read the replies, and then send another 10k commands again, and so forth. The speed will be nearly the same, but the additional memory used will be at most the amount needed to queue the replies for these 10k commands.

It's not just a matter of RTT

Pipelining is not just a way to reduce the latency cost associated with the round trip time, it actually greatly improves the number of operations you can perform per second in a given Redis server. This is because without using pipelining, serving each command is very cheap from the point of view of accessing the data structures and producing the reply, but it is very costly from the point of view of doing the socket I/O. This involves calling the read() and write() syscall, that means going from user land to kernel land. The context switch is a huge speed penalty.

When pipelining is used, many commands are usually read with a single read() system call, and multiple replies are delivered with a single write() system call. Consequently, the number of total queries performed per second initially increases almost linearly with longer pipelines, and eventually reaches 10 times the baseline obtained without pipelining, as shown in this figure.

A real world code example

In the following benchmark we'll use the Redis Ruby client, supporting pipelining, to test the speed improvement due to pipelining:

require 'rubygems'
require 'redis'

def bench(descr)
  start = Time.now
  yield
  puts "#{descr} #{Time.now - start} seconds"
end

def without_pipelining
  r = Redis.new
  10_000.times do
    r.ping
  end
end

def with_pipelining
  r = Redis.new
  r.pipelined do |rp|
    10_000.times do
      rp.ping
    end
  end
end

bench('without pipelining') do
  without_pipelining
end
bench('with pipelining') do
  with_pipelining
end

Running the above simple script yields the following figures on my Mac OS X system, running over the loopback interface, where pipelining will provide the smallest improvement as the RTT is already pretty low:

without pipelining 1.185238 seconds
with pipelining 0.250783 seconds

FOR-ONE-SECOND:
    Redis.SET("foo","bar")
END