////
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    The ASF licenses this file to You under the Apache License, Version 2.0
    (the "License"); you may not use this file except in compliance with
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         http://www.apache.org/licenses/LICENSE-2.0

    Unless required by applicable law or agreed to in writing, software
    distributed under the License is distributed on an "AS IS" BASIS,
    WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
    See the License for the specific language governing permissions and
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////
= Performance
Remko Popma <rpopma@apache.org>; Ralph Goers <rgoers@apache.org>

++++
<link rel="stylesheet" type="text/css" href="../css/tables.css">
++++

////
One of the often-cited arguments against logging is its
computational cost. This is a legitimate concern as even moderately
sized applications can generate thousands of log requests. Much
effort was spent measuring and tweaking logging performance. Log4j
claims to be fast and flexible: speed first, flexibility second.
////

Apart from functional requirements, an important reason for selecting a
logging library is often how well it fulfills non-functional
requirements like reliability and performance.

This page compares the performance of a number of logging frameworks
(java.util.logging "JUL", Logback, Log4j 1.2 and Log4j 2.6), and
documents some performance trade-offs for Log4j 2 functionality.

[#benchmarks]
== Benchmarks

Performance can mean different things to different people. Common terms
in this context are throughput and latency: _Throughput_ is a measure of
capacity and can be expressed in a single number: how many messages can
be logged in a certain period of time. _Response time latency_ is how
long it takes to log a message. This cannot be expressed in a single
number because each measurement has its own response time and we are
often most interested in the outliers: how many there were and how large
they were.

When evaluating a logging framework's performance these may be useful
questions to ask:

[qanda]
What is its *peak throughput*?::
Many systems that react to external
events need to log bursts of messages followed by periods of relative
quiet. This number is the maximum throughput measured over a short
period of time and gives some idea of how well the logging library deals
with bursts. For systems that need to log a lot at a constant high rate
(for example, batch jobs) this is less likely to be a useful measure of
performance.

What is the *maximum sustained throughput*?::
This is the throughput
averaged over a long time. This is a useful measure of the "upper limit"
of the capacity of the logging library. It is not recommended that
reactive applications actually log at this rate since under this load
they will likely experience jitter and large response time spikes.

What is its *response time* behaviour under various loads?::
This is the
most important question for applications that need to react to external
events in a timely manner. Response time is the total amount of time it
takes to log a message and is the sum of the service time and wait time.
The *service time* is the time it takes to do the work to log the
message. As the workload increases, the service time often varies
little: to do X amount of work it always takes X amount of time. The
*wait time* is how long the request had to wait in a queue before being
serviced. _As the workload increases, wait time often grows to many
times the service time._

[[responseTimeVsServiceTime]]

.Why Care About Response Time Latency?
****
[width="100%",cols="50%,50%"]
|===
a|
What is often measured and reported as _latency_ is actually _service
time_, and omits that a service time spike adds wait time for many
subsequent events. This may present results that are more optimistic
than what users experience.

The graph on the right illustrates how much more optimistic service time
is than response time. The graph shows response time and service time
for the same system under a load of 100,000 messages per second. Out of
24 million measurements, only ~50 are more than 250 microseconds, less
than 0.001%. In a service time-only graph this would hardly be visible.
However, the depending on the load it will take a while to catch up
after a spike.

The response time graph shows that in reality many more events are
impacted by these delays than the service time numbers alone would
suggest.

To learn more, watch Gil Tene's eye-opening presentation
http://www.infoq.com/presentations/latency-response-time[How NOT to
measure latency].


|link:images/ResponseTimeVsServiceTimeAsyncLoggers.png[image:images/ResponseTimeVsServiceTimeAsyncLoggers.png[image,width=480,height=288]]
|===
****

[#loglibComparison]
== Logging Library Performance Comparison

[#asyncLogging]
=== Asynchronous Logging - Peak Throughput Comparison

Asynchronous logging is useful to deal with bursts of events. How this
works is that a minimum amount of work is done by the application thread
to capture all required information in a log event, and this log event
is then put on a queue for later processing by a background thread. As
long as the queue is sized large enough, the application threads should
be able to spend very little time on the logging call and return to the
business logic very quickly.

It turns out that the choice of queue is extremely important for peak
throughput. Log4j 2's Async Loggers use a
https://lmax-exchange.github.io/disruptor/[lock-free data structure],
whereas Logback, Log4j 1.2 and Log4j 2's Asynchronous Appenders use an
ArrayBlockingQueue. With a blocking queue, multi-threaded applications
often experience lock contention when trying to enqueue the log event.

The graph below illustrates the difference a lock-free data structure
can make to throughput in multi-threaded scenarios. _Log4j 2 scales
better with the number of threads: an application with more threads can
log more. The other logging libraries suffer from lock contention and
total throughput stays constant or drops when more threads are logging.
This means that with the other logging libraries, each individual thread
will be able to log less._

Bear in mind that this is _peak_ throughput: Log4j 2's Async Loggers
give better throughput up to a point, but once the queue is full, the
appender thread needs to wait until a slot becomes available in the
queue, and throughput will drop to the maximum sustained throughput of
the underlying appenders at best.

image:images/async-throughput-comparison.png[Peak throughput comparison]

For details, see the link:manual/async.html[Async Loggers] manual page.

[#asyncLoggingResponseTime]
=== Asynchronous Logging Response Time

Response time behaviour varies a lot with the workload and the number of
threads that log concurrently. The link:manual/async.html#Latency[Async
Loggers] manual page and the
link:manual/garbagefree.html#Latency[garbage-free logging] manual page
provide some graphs showing response time behaviour under various loads.

This section shows another graph showing response time latency behaviour
under a modest total workload of 64,000 messages per second, with 4
threads logging concurrently. At this load and on this hardware/OS/JVM
configuration, lock contention and context switches play less of a role
and the pauses are mostly caused by minor garbage collections. Garbage
collection pause duration and frequency can vary a lot: when testing the
Log4j 1.2.17 Async Appender a minor GC pause of 7 milliseconds occurred
while the Log4j 2 Async Appender test only saw a GC pause of a little
over 2 milliseconds. This does not necessarily mean that one is better
than the other.

Generally, garbage-free async loggers had the best response time
behaviour in all configurations we tested.

image:images/ResponseTimeAsyncLogging4Threads@16kEach.png[Response time comparison]

The above result was obtained with the ResponseTimeTest class which can
be found in the Log4j 2 unit test source directory, running on JDK
1.8.0_45 on RHEL 6.5 (Linux 2.6.32-573.1.1.el6.x86_64) with 10-core Xeon
CPU E5-2660 v3 @2.60GHz with hyperthreading switched on (20 virtual
cores).

[#asyncLoggingWithParams]
=== Asynchronous Logging Parameterized Messages

Many logging libraries offer an API for logging parameterized messages.
This enables application code to look something like this:

[source,java]
----
logger.debug("Entry number: {} is {}", i, entry[i]);
----

In the above example, the fully formatted message text is not created
unless the DEBUG level is enabled for the logger. Without this API, you
would need three lines of code to accomplish the same:

[source,java]
----
if (logger.isDebugEnabled()) {
    logger.debug("Entry number: " + i + " is " + entry[i].toString());
}
----

If the DEBUG level _is_ enabled, then at some point the message needs to
be formatted. When logging asynchronously, the message parameters may be
changed by the application thread before the background thread had a
chance to log the message. This would show the wrong values in the log
file. To prevent this, Log4j 2, Log4j 1.2 and Logback format the message
text in the application thread _before_ passing off the log event to the
background thread.

This is the safe thing to do, but the formatting has a performance cost.
The graph below compares the throughput of logging messages with
parameters using various logging libraries. These are all asynchronous
logging calls, so these numbers do not include the cost of disk I/O and
represent _peak_ throughput.

JUL (java.util.logging) does not have a built-in asynchronous Handler.
https://docs.oracle.com/javase/8/docs/api/java/util/logging/MemoryHandler.html[`MemoryHandler`]
is the nearest thing available so we included it here. MemoryHandler
does _not_ do the safe thing of taking a snapshot of the current
parameter state (it just keeps a reference to the original parameter
objects), and as a result it is very fast when single-threaded. However,
when more application threads are logging concurrently, the cost of lock
contention outweighs this gain.

In absolute numbers, _Log4j 2's Async Loggers perform well compared to
the other logging frameworks, but notice that the message formatting
cost increases sharply with the number of parameters. In this area,
Log4j 2 still has work to do to improve: we would like to keep this cost
more constant._

image:images/ParamMsgThrpt1-4T.png[image]

The results above are for JUL (java.util.logging) 1.8.0_45, Log4j 2.6,
Log4j 1.2.17 and Logback 1.1.7, and were obtained with the
http://openjdk.java.net/projects/code-tools/jmh/[JMH] Java benchmark
harness. See the AsyncAppenderLog4j1Benchmark,
AsyncAppenderLog4j2Benchmark, AsyncAppenderLogbackBenchmark,
AsyncLoggersBenchmark and the MemoryHandlerJULBenchmark source code in
the log4j-perf module.

[#asyncLoggingWithLocation]
=== Asynchronous Logging with Caller Location Information

Some layouts can show the class, method and line number in the
application where the logging call was made. In Log4j 2, examples of
such layout options are HTML
link:layouts.html#HtmlLocationInfo[locationInfo], or one of the patterns
link:layouts.html#PatternClass[%C or $class],
link:layouts.html#PatternFile[%F or %file],
link:layouts.html#PatternLocation[%l or %location],
link:layouts.html#PatternLine[%L or %line],
link:layouts.html#PatternMethod[%M or %method]. In order to provide
caller location information, the logging library will take a snapshot of
the stack, and walk the stack trace to find the location information.

The graph below shows the performance impact of capturing caller
location information when logging asynchronously from a single thread.
Our tests show that _capturing caller location has a similar impact
across all logging libraries, and slows down asynchronous logging by
about 30-100x_.

image:images/AsyncWithLocationThrpt1T-labeled.png[image]

The results above are for JUL (java.util.logging) 1.8.0_45, Log4j 2.6,
Log4j 1.2.17 and Logback 1.1.7, and were obtained with the
http://openjdk.java.net/projects/code-tools/jmh/[JMH] Java benchmark
harness. See the AsyncAppenderLog4j1LocationBenchmark,
AsyncAppenderLog4j2LocationBenchmark,
AsyncAppenderLogbackLocationBenchmark, AsyncLoggersLocationBenchmark and
the MemoryHandlerJULLocationBenchmark source code in the log4j-perf
module.

[#fileLoggingComparison]
=== Synchronous File Logging - Sustained Throughput Comparison

This section discusses the maximum sustained throughput of logging to a
file. In any system, the maximum sustained throughput is determined by
its slowest component. In the case of logging, this is the appender,
where the message formatting and disk I/O takes place. For this reason
we will look at simple _synchronous_ logging to a file, without queues
or background threads.

The graph below compares Log4j 2.6's RandomAccessFile appender to the
respective File appenders of Log4j 1.2.17, Logback 1.1.7 and Java util
logging (JUL) on Oracle Java 1.8.0_45. ImmediateFlush was set to false
for all loggers that support this. The JUL results are for the
XMLFormatter (which in our measurements was about twice as fast as the
SimpleFormatter).

_Log4j 2's sustained throughput drops a little when more threads are
logging simultaneously, but its fine-grained locking pays off and
throughput stays relatively high. The other logging frameworks'
throughput drops dramatically in multi-threaded applications: Log4j 1.2
has 1/4th of its single-threaded capacity, Logback has 1/10th of its
single-threaded capacity, and JUL steadily drops from 1/4th to 1/10th of
its single-threaded throughput as more threads are added._

image:images/SyncThroughputLoggerComparisonLinux.png[image]

The synchronous logging throughput results above are obtained with the
http://openjdk.java.net/projects/code-tools/jmh/[JMH] Java benchmark
harness. See the FileAppenderBenchmark source code in the log4j-perf
module.

=== Synchronous File Logging - Response Time Comparison

Response time for synchronous file logging varies a lot with the
workload and the number of threads. Below is a sample for a workload of
32,000 events per second, with 2 threads logging 16,000 events per
second each.

image:images/SynchronousFileResponseTime2T32k-labeled.png[image]

The above result was obtained with the ResponseTimeTest class which can
be found in the Log4j 2 unit test source directory, running on JDK
1.8.0_45 on RHEL 6.5 (Linux 2.6.32-573.1.1.el6.x86_64) with 10-core Xeon
CPU E5-2660 v3 @2.60GHz with hyperthreading switched on (20 virtual
cores).

////
TODO
=== Synchronous Socket Sustained Throughput Comparison

=== Synchronous Syslog Sustained Throughput Comparison
////

[#filtering]
=== Filtering by Level

The most basic filtering a logging framework provides is filtering by
log level. When logging is turned off entirely or just for a set of
Levels, the cost of a log request consists of a number of method
invocations plus an integer comparison. Unlike Log4j, Log4j 2 Loggers
don't "walk a hierarchy". Loggers point directly to the Logger
configuration that best matches the Logger's name. This incurs extra
overhead when the Logger is first created but reduces the overhead every
time the Logger is used.

=== Advanced Filtering

Both Logback and Log4j 2 support advanced filtering. Logback calls them
TurboFilters while Log4j 2 has a single Filter object. Advanced
filtering provides the capability to filter LogEvents using more than
just the Level before the events are passed to Appenders. However, this
flexibility does come with some cost. Since multi-threading can also
have an impact on the performance of advanced filtering, the chart below
shows the difference in performance of filtering based on a Marker or a
Marker's parent.

The "Simple Marker" comparison checks to see if a Marker that has no
references to other markers matches the requested Marker. The "Parent
Marker" comparison checks to see if a Marker that does have references
to other markers matches the requested Marker.

It appears that coarse-grained synchronization in SLF4J can impact
performance in multi-threaded scenarios. See
http://jira.qos.ch/browse/SLF4J-240[SLF4J-240].

image:images/MarkerFilterCostComparison.png[image]

Log4j and Logback also support filtering on a value in the Log4j
ThreadContext vs filtering in Logback on a value in the MDC. The graph
below shows that the performance difference between Log4j 2 and Logback
is small for the ThreadContext filter.

image:images/ThreadContextFilterCostComparison.png[image]

The Filter comparison results above are obtained with the
http://openjdk.java.net/projects/code-tools/jmh/[JMH] Java benchmark
harness. See the MarkerFilterBenchmark and MDCFilterBenchmark in the
log4j-perf module for details on these benchmarks.

[#tradeoffs]
== Trade-offs

[#whichAppender]
=== Which Log4j 2 Appender to Use?

Assuming that you selected Log4j 2 as your logging framework, next you
may be interested in learning what the performance trade-offs are for
selecting a specific Log4j 2 configuration. For example, there are three
appenders for logging to a file: the File, RandomAccessFile and
MemoryMappedFile appenders. Which one should you use?

If performance is all you care about, the graphs below show your best
choice is either the MemoryMappedFile appender or the RandomAccessFile
appender. Some things to bear in mind:

* MemoryMappedFile appender does not have a rolling variant yet.
* When the log file size exceeds the MemoryMappedFile's region length,
the file needs to be remapped. This can be a very expensive operation,
taking several seconds if the region is large.
* MemoryMappedFile appender creates a presized file from the beginning
and fills it up gradually. This can confuse tools like `tail`; many such
tools don't work very well with memory mapped files.
* On Windows, using a tool like `tail` on a file created by
RandomAccessFile appender can hold a lock on this file which may prevent
Log4j from opening the file again when the application is restarted. In
a development environment where you expect to restart your application
regularly while using tools like tail to view the log file contents, the
File appender may be a reasonable trade-off between performance and
flexibility. For production environments performance may have higher
priority.

The graph below shows sustained throughput for the console and file
appenders in Log4j 2.6, and for reference also provides the 2.5
performance.

It turns out that the garbage-free text encoding logic in 2.6 gives
these appenders a performance boost compared to Log4j 2.5. It used to be
that the RandomAccessFile appender was significantly faster, especially
in multi-threaded scenarios, but with the 2.6 release the File appender
performance has improved and the performance difference between these
two appender is smaller.

Another takeaway is just how much of a performance drag logging to the
console can be. Considering logging to a file and using a tool like
`tail` to watch the file change in real time.

image:images/Log4j2AppenderThroughputComparison-linux.png[image]

On Windows, the results are similar but the RandomAccessFile and
MemoryMappedFile appenders outperform the plain File appender in
multi-threaded scenarios. The absolute numbers are higher on Windows: we
don't know why but it looks like Windows handles lock contention better
than Linux.

image:images/Log4j2AppenderThroughputComparison-windows.png[image]

The Log4j 2 appender comparison results above are obtained with the
http://openjdk.java.net/projects/code-tools/jmh/[JMH] Java benchmark
harness. See the Log4j2AppenderComparisonBenchmark source code in the
log4j-perf module.

////
The user should be aware of the following performance issues.

=== Logging performance when logging is turned off.

When logging is turned off entirely or just for a set of Levels, the
cost of a log request consists of two method invocations plus an integer
comparison. On a 2.53 GHz Intel Core 2 Duo MacBook Pro calling
isDebugEnabled 10 million times produces an average result in
nanoseconds of:

....
            Log4j: 4
            Logback: 5
            Log4j 2: 3

....

The numbers above will vary slightly from run to run so the only
conclusion that should be drawn is that all 3 frameworks perform
similarly on this task.

However, The method invocation involves the "hidden" cost of parameter
construction.

For example,

....
              logger.debug("Entry number: " + i + " is " + String.valueOf(entry[i]));

....

incurs the cost of constructing the message parameter, i.e. converting
both integer `i` and `entry[i]` to a String, and concatenating
intermediate strings, regardless of whether the message will be logged
or not. This cost of parameter construction can be quite high and it
depends on the size of the parameters involved. A comparison run on the
same hardware as above yields:

....
            Log4j: 188
            Logback: 183
            Log4j 2: 188

....

Again, no conclusion should be drawn regarding relative differences
between the frameworks on this task, but it should be obvious that it is
considerably more expensive than simply testing the level.

The best approach to avoid the cost of parameter construction is to use
Log4j 2's formatting capabilities. For example, instead of the above
write:

....
            logger.debug("Entry number: {} is {}", i, entry[i]);

....

Using this approach, a comparison run again on the same hardware
produces:

....
            Log4j: Not supported
            Logback: 9
            Log4j 2: 4

....

These results show that the difference in performance between the call
to isDebugEnabled and logger.debug is barely discernible.

In some circumstances one of the parameters to logger.debug will be a
costly method call that should be avoided if debugging is disabled. In
those cases write:

....
            if(logger.isDebugEnabled() {
                logger.debug("Entry number: " + i + " is " + entry[i].toString());
            }

....

This will not incur the cost of whatever the toString() method needs to
do if debugging is disabled. On the other hand, if the logger is enabled
for the debug level, it will incur twice the cost of evaluating whether
the logger is enabled or not: once in `isDebugEnabled` and once in
`debug`. This is an insignificant overhead because evaluating a logger
takes about 1% of the time it takes to actually log.

Certain users resort to pre-processing or compile-time techniques to
compile out all log statements. This leads to perfect performance
efficiency with respect to logging. However, since the resulting
application binary does not contain any log statements, logging cannot
be turned on for that binary. This seems to be a disproportionate price
to pay in exchange for a small performance gain.

The performance of deciding whether to log or not to log when logging is
turned on.
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^

Unlike Log4j, Log4j 2 Loggers don't "walk a hierarchy". Loggers point
directly to the Logger configuration that best matches the Logger's
name. This incurs extra overhead when the Logger is first created but
reduces the overhead every time the Logger is used.

=== Actually outputting log messages

This is the cost of formatting the log output and sending it to its
target destination. Here again, a serious effort was made to make
layouts (formatters) perform as quickly as possible. The same is true
for appenders. One of the fundamental tenets of Log4j 2 is to use
immutable objects whenever possible and to lock at the lowest
granularity possible. However, the cost of actually formatting and
delivering log events will never be insignificant. For example, the
results of writing to a simple log file using the same format using
Log4j, Logback and Log4j 2 are:

....
              Log4j: 1651
              Logback: 1419
              Log4j 2.0: 1542

....

As with many of the other results on this page the differences between
the frameworks above should be considered insignificant. The values will
change somewhat on each execution and changing the order the frameworks
are tested or adding calls to System.gc() between the tests can cause a
variation in the reported times. However, these results show that
actually writing out the events can be at least 1000 times more
expensive than when they are disabled, so it is always recommended to
take advantage of Log4j 2's fine-grained filtering capabilities.
////
