Colog.Concurrent

NOTE: Many thanks to Alexander Vershilov for the implementation.

For the speed reasons you may want to dump logs asynchronously. This is especially useful when application threads are CPU bound while logs emitting is I/O bound. This approach allows to mitigate bottlenecks from the I/O.

When writing an application user should be aware of the tradeoffs that concurrent log system can provide, in this module we explain potential tradeoffs and describe if certain building blocks are affected or not.

1. Unbounded memory usage - if there is no backpressure mechanism the user threads, they may generate more logs that can be written in the same amount of time. In those cases messages will be accumulated in memory. That will lead to extended GC times and application may be killed by the operating systems mechanisms.
2. Persistence requirement - sometimes application may want to ensure that logs were written before it can continue. This is not a case with concurrent log systems in general, and some logs may be lost when application exits before dumping all logs.
3. Non-precise logging - sometimes it may happen that there can be logs reordering (in case if thread was moved to another capability).

In case if your application is a subject of those problems you may consider not using concurrent logging system in other cases concurrent logger may be a good default for you.

Documentation

Concurrent logger consists of the basic parts (see schema below).

1. Logger in application thread. This logger is evaluated in the application thread and has an access to all the context available in that thread and monad, this logger can work in any m.
2. Communication channel with backpressure support. In addition to the channel we have a converter that puts the user message to the communication channel. This converter works in the user thread. Such a logger usually works in IO but it's possible to make it work in STM as well. At this point library provides only IO version, but it can be lifted to any MonadIO by the user.
3. Logger thread. This is the thread that performs actual write to the sinks. Loggers there do not have access to the users thread state, unless that state was passed in the message.

 +-------------------------+                  +--------------------------------+
 |                         |                  | Logger        |   Sink-1       |
 |   Application Thread    |                  | Thread    +--->                |
 |   -----------------     |  +-----------+   |           |   +----------------+
 |                         |  |           |   +---------+ |   +----------------+
 |           +-------------+  |  channel  |   | Shared  +----->   Sink-2       |
 |           | application||  |          +----> logger  | |   |                |
 |           | logger    +----->          |   +---------+ |   +----------------+
 |           +-------------+  |           |   |           |   +----------------+
 |                         |  +-----------+   |           +--->   Sink3        |
 |                         |                  |               |                |
 |                         |                  |               +----------------+
 |                         |                  |                                |
 +-------------------------+                  +--------------------------------+

So usually user should write the logging system in the way that all LogAction that populate and filter information should live in the application logger. All loggers that do serialization and formatting should live in shared logger.

If more concurrency is needed it's possible to build multilayer systems:

  +-------------+                         +-------+
  | application |---+                 +---| sink-1|
  +-------------+   |   +---------+   |   +-------+
                    +---| logger  |---+
                        +---------+   |   +-------+
                                      +---| sink-2|
                                          +-------+

In this approach application will be concurrently write logs to the logger, then logger will be concurrently writing to all sinks.

Simple API provides a handy easy to use API that can be used directly in application without dealing with internals. Based on users feedback internal implementation of the simple API may change, especially in early versions of the library. But the guarantee that we give is that no matter what implementation is it will be kept with reasonable defaults and will be applicable to a generic application.

withBackgroundLogger

Arguments

:: MonadIO m

=> Capacity

Capacity of messages to handle; bounded channel size

-> LogAction IO msg

Action that will be used in a forked thread

-> (LogAction m msg -> IO a)

Continuation action

-> IO a

An exception safe way to create background logger. This method will fork a thread that will run 'shared worker', see schema above.

Capacity - provides a backpressure mechanism and tells how many messages in flight are allowed. In most cases defCapacity will work well. See forkBackgroundLogger for more details.

LogAction - provides a logger action, this action does not have access to the application state or thread info, so you should only pass methods that serialize and dump data there.

main :: IO ()
main =
  withBackgroundLogger
     defCapacity
     logByteStringStdout
     (log -> usingLoggerT log $ do
        logMsg @ByteString "Starting application..."
        logMsg @ByteString "Finishing application..."
     )

defCapacity :: Capacity

Default capacity size, (4096)

Extended API

Extended API explains how asynchronous logging is working and provides basic building blocks for writing your own combinators. This is the part of the public API and will not change without prior notice.

The main abstraction for the concurrent worker is BackgroundWorker. This is a wrapper of the thread, that has communication channel to talk to, and threadId.

Background worker may provide a backpressure mechanism, but does not provide notification of completeness unless it's included in the message itself.

data BackgroundWorker msg

Wrapper for the background thread that may receive messages to process.

backgroundWorkerWrite :: BackgroundWorker msg -> msg -> STM ()

Method for communication with the thread.

killBackgroundLogger :: BackgroundWorker msg -> IO ()

Stop background logger thread.

The thread is blocked until background thread will finish processing all messages that were written in the channel.

Background logger

forkBackgroundLogger :: Capacity -> LogAction IO msg -> IO (BackgroundWorker msg)

Creates background logger with given Capacity, takes a LogAction that should describe how to write logs.

capacity - parameter tells how many in flight messages are allowed, if that value is reached then user's thread that emits logs will be blocked until any message will be written. Usually if value should be chosen reasonably high and if this value is reached it means that the application environment experience severe problems.

N.B. The LogAction will be run in the background thread so that logger should not add any thread specific context to the message.

N.B. On exit, even in case of exception thread will dump all values that are in the queue. But it will stop doing that in case if another exception will happen.

convertToLogAction :: MonadIO m => BackgroundWorker msg -> LogAction m msg

Convert a given 'BackgroundWorker msg' into a 'LogAction msg' that will send log message to the background thread, without blocking the thread.

If logger dies for any reason then thread that emits logs will receive BlockedIndefinitelyOnSTM exception.

You can extend result worker with all functionality available with co-log. This logger will have an access to the thread state.

Worker thread

While generic background logger is enough for the most of the usecases, sometimes you may want even more.

There are at least two cases where that may happen:

1. You need to modify logger, for example different threads wants to write to different sources. Or you want to change lgo mechanism in runtime.
2. You may want to implement some notification machinery that allows you to guarantee that your logs were written before processing further.

In order to solve those problems worker thread abstraction was introduced. This is a worker that accepts any action and performs that.

mkBackgroundThread :: Capacity -> IO (BackgroundWorker (IO ()))

Create a background worker with a given capacity. If capacity is reached, then the thread that tries to write logs will be blocked.

This method is more generic than forkBackgroundLogger but it's less effective, as you have to pass entire closure to be run and that leads to extra memory usage and indirect calls happening.

When closed it will dump all pending messages, unless another asynchronous exception will arrive, or synchronous exception will happen during the logging.

runInBackgroundThread :: BackgroundWorker (IO ()) -> LogAction IO msg -> LogAction IO msg

Run logger action asynchronously in the worker thread. Logger is executed in the other thread entirely, so if logger takes any thread related context it will be read from the other thread.

Usage example

Consider following example. (Leaving resource control aside).

data M msg = M (MVar ()) msg

notificationLogger :: MonadIO m => LoggerAction m msg -> LoggerAction m (M msg)
notificationLogger logger = LogAction $ (M lock msg) ->
   (unLogger logger msg) finally (putMVar lock ())

example = do
   worker <- mkBackgroundThread defCapacity
   lock <- newEmptyMVar
   -- Log message with default logger.
   unLogger
      (runInBackgroundThread worker
      (notificationLogger $ withLogByteStringFile "/var/log/myapp/log")
      (M lock "my message")
   -- Log message with a different logger.
   unLogger
      (runInBackgroundThread worker
      (withLogByteStringFile "varlogmyapplog")
      ("another message")
   -- Block until first message is logged.
   _ <- takeMVar lock