Reconstructing Payloads With Lua

17 Aug 2015 by Parker Selbert

Pipelining commands to minimize I/O, optimizing storage on in the database and minimizing persistence overhead can get you pretty far toward caching bliss. However, there are still some more speed improvements we can squeeze out. It’s time to look at applying Lua scripting to caching operations.

Redis has provided Lua for scripting since version 2.6.0. Typically it is used for transactional semantics and more advanced queuing behavior. Today we are going to look at a data driven use-case for executing Lua scripts in Redis. The possibility to manipulate large volumes of data on the server opens up avenues of performance that are normally prevented by the I/O heavy nature of Redis:

Most Redis work-flows tend to be I/O bound and not CPU bound. Even when you see the CPU at 100% it is likely to be all about protocol handling. This is almost impossible to avoid as Redis commands are much faster than dealing with I/O. With scripting we can put at much better use our bandwidth and CPU power.

Antirez

Wouldn’t it be awesome to construct fully prepared API responses directly from the server? Doing so means fewer commands to execute, fewer I/O operations, less bytes transfered, fewer objects allocated and less work done on the client. Let’s see how it can be done.

Only the Very Basics

There are other primers on Lua integration, so we’ll stay focused on why offloading work to Lua is desirable, and how it can be used to speed up data heavy tasks, rather than the semantics of the language or its integration into Redis. However, as an extremely specific introduction to Lua scripting for our use case, here is a high level overview of scripting with Lua in Redis.

Executing scripts on the server is as simple as using the EVAL command, which can be used from the command line like so:

redis-cli EVAL 'return redis.call("KEYS", "*")' 0

Throughout the rest of this post the examples will all be in Ruby; partially because we’re directly comparing Ruby client performance against Lua on the server, but also because it’s much easier to send dynamic keys.

Build Something That Really Cooks

Continuing from the post on hashed caching, we’ll investigate the process of reconstructing a blog post and all of its associated records. For this example it is assumed that all of the serialized records are stored within fields where the names are posts/1, authors/1, etc. The goal is to efficiently reconstruct the data from multiple hashes into a single payload where all of the posts, authors, and comments are grouped under common keys, like so:

{ "authors": [], "comments": [], "posts": [] }

The sample data we’re working with and the output format are greatly simplified from actual application data. The purpose of this exercise is to explore the performance possibilities of using Lua, so the reconstruction process is what truly matters. Building up json-api compliant payloads would be a great exercise, maybe some other time.

The starting point is a ruby script that performs the following high level steps:

  1. Within a MULTI block, fetch the full contents of each hash for each post via HGETALL.
  2. Iterate over the hashes, normalizing the field names and translating the data into a Hash of Arrays.
# Boilerplate, configuration and data seeding has been left out to emphasize
# the relevant bits of code.

hashes = REDIS.multi do
  ('posts/0'..'posts/30').map { |key| REDIS.hgetall(key) }
end

array_backed_hash = Hash.new { |hash, key| hash[key] = [] }

payload = hashes.each_with_object(array_backed_hash) do |hash, memo|
  hash.each do |key, val|
    root, _ = key.split('/')
    memo[root] << val
  end
end

puts payload.length          #=> 3
puts payload.keys.sort       #=> ['authors', 'comments', 'posts']
puts payload['posts'].length #=> 30

Now, with a working reference in place, we can start translating to Lua and offloading the work. The first step is simply testing that we can pass keys along to the server and execute the commands we expect:

keys = ('posts/1'..'posts/30').to_a

REDIS.eval('return KEYS[1], KEYS[2]', keys) #=> ['posts/1', 'posts/2']

That did it, keys were passed in and are available in the KEYS table. Next we’ll iterate over all of the keys, fetching the contents of each hash. There are a few syntactical jumps here, for example usage of local, for, and ipairs, but nothing tricky is going on. It is all variable declaration and looping over the KEYS table:

script = <<-LUA
  local payload = {}

  for _, key in ipairs(KEYS) do
    payload[key] = key
  end

  return cjson.encode(payload)
LUA

REDIS.eval(script, keys) #=> {"posts\/1":"posts\/1",...

Note the call to cjson.encode for the return value. Without encoding the return value as a string the table will be returned as nil, rather unintuitively. The cjson module is indispensable for client/script interop.

Commands can be executed within scripts through redis.call. Using call the script can use the HGETALL command for each key to build up the payload.

local payload = {}

for _, key in ipairs(KEYS) do
  local hash = redis.call('HGETALL', key)
end

return cjson.encode(payload)

The final step is to loop over each field/value pair in the hash in order to construct our desired payload. This is largely a mechanical translation of the Ruby enumeration we saw earlier. Only the Lua script is being shown here—it’s much more readable with some syntax highlighting.

local payload = {}

for _, key in ipairs(KEYS) do
  local hash = redis.call('HGETALL', key)

  for index = 1, #hash, 2 do
    local field = hash[index]
    local data  = hash[index + 1]
    local root  = string.gsub(field, '(%a)([/\]%d*)', '%1')

    if type(payload[root]) == "table" then
      table.insert(payload[root], data)
    else
      payload[root] = {data}
    end
  end
end

return cjson.encode(payload)

There you have it, the fully translated construction script moved to Lua! The entire purpose of this exercise is to squeeze out more performance from our Redis cache. Naturally, it’s time to do some benchmarking!

Here the script is being loaded once from an external file through SCRIPT LOAD and then referenced with EVALSHA to avoid the overhead of repeatedly sending the same script to the server.

SHA = REDIS.script(:load, IO.read('payload.lua'))

def construct_ruby
  # see above
end

def construct_lua
  REDIS.evalsha(SHA, ('posts/1'..'posts/30').to_a)
end

Benchmark.ips do |x|
  x.report('ruby') { construct_ruby }
  x.report('lua')  { construct_lua }
end

The results are impressive, checking iterations per second for pipelined Ruby and scripted Lua:

When to Reach for Lua

Lua is your performance go-to whenever you want to minimize round trips, guarantee atomicity, or process large swaths of data without slurping it into memory. It is a perfect fit for reliable queues, atomic scheduling, and custom analytics. It can also be indispensable for processing large data sets without slurping all of the data back to the client.

A few parting words of caution. Scripts are evaluated atomically, which in the world of Redis means that no other script or Redis command will be executed in parallel. It’s guaranteed by the single threaded “stop the world” approach. Consequently, EVAL has one major limitation—scripts must be small and fast to prevent blocking other clients.