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What “best performance” means for a Java logger
A logger can produce many messages per second while still adding unacceptable delay to the thread that calls it. Throughput measures messages processed over time; call latency measures how long an individual logging call takes. For application requests, the distribution and tail of that latency can matter as much as the average.
Peak throughput is not the same as sustained throughput. An asynchronous logger may accept messages quickly while work accumulates in a queue. Once the queue fills, callers may have to wait, and the lasting rate is limited by the slowest part of the pipeline. Apache Log4j’s performance documentation puts it plainly: “In any system, the maximum sustained throughput is determined by its slowest component.” Apache Log4j performance manual.
- Throughput: how many records the system handles over time, reported separately for peak and sustained operation.
- Call latency: how long the application thread spends logging, including any waiting caused by formatting, I/O or a full queue.
- Tail latency: whether a minority of slow calls creates request delays even when average latency looks acceptable.
What the published comparisons show—and what they do not
Apache’s historical file benchmark compared Log4j 2.6 using RandomAccessFile, Log4j 1.2.17, Logback 1.1.7 and java.util.logging (JUL) 1.8.0_45 on Oracle Java 1.8.0_45. The test disabled ImmediateFlush where supported; JUL used XMLFormatter because it was about twice as fast as SimpleFormatter in that measurement. Apache reported that Log4j 2 held up better as concurrent threads increased, while the other tested implementations lost more throughput. These results are evidence about that setup, not a current ranking of the frameworks. Apache’s historical performance comparisons.
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A free scan shows the junk files, broken settings and background clutter dragging Windows down - then fixes them in one click.Free scan · Windows 10 & 11The same historical page describes asynchronous tests using JMH and old versions of JUL, Log4j 2, Log4j 1 and Logback. It notes that message parameter count and formatting affect cost. Its caller-location tests reported asynchronous logging about 30–100 times slower when location information was captured in the tested cases. That figure is a warning about stack inspection, not a multiplier to expect with every current version or workload.
A separate public project describes a Java 25 benchmark comparing Log4j 2, Logback and JUL. Its available description does not establish enough detail about the full workload, destination, machine, results or independent review to support an overall winner. Java Logging Framework Benchmark repository.
Rank #2
How logging mode changes the result
Synchronous logging
With synchronous logging, the calling thread performs or waits for the logging work. This makes the call’s cost visible on the application path, but can be appropriate when a record must be handled synchronously as part of business logic. It is especially important not to treat ordinary diagnostic messages and audit or business-critical records as interchangeable.
Asynchronous loggers and appenders
Log4j 2 asynchronous loggers use the LMAX Disruptor; asynchronous appenders instead pass output work to a separate thread through a queue. Both approaches can let application code resume sooner, but neither eliminates formatting or output I/O. Queue capacity and the destination’s processing rate still govern sustained behavior, and callers can wait when buffers or queues fill. Apache Log4j asynchronous logger manual.
Asynchronous operation is not automatically faster for every deployment. Extra threads consume resources, and a CPU-constrained or single-vCPU environment may not benefit. If records must be synchronously durable or logging is part of business logic, assess that reliability requirement before choosing an asynchronous mode; Log4j advises synchronous logging for business-critical use cases.
What to control in a fair comparison
Formatting and output can dominate the apparent cost of a framework. Log4j’s manual notes that layouts can materially affect total logging performance. A benchmark that measures one formatter writing to a file does not settle how another layout, console output or a production collector will behave. Apache Log4j performance manual.
Rank #4
- Versions and runtime: record the exact framework versions, JDK and hardware; test the versions you intend to deploy.
- Mode and concurrency: compare synchronous logging, asynchronous loggers or asynchronous appenders as relevant, at both single-thread and realistic multi-thread loads.
- Destination and formatting: use the actual sink where possible, and keep layout, encoding, flush and buffering settings comparable.
- Message shape and features: reproduce message sizes, parameterized versus preformatted messages, structured/context data and caller-location capture.
- Results and reliability: report throughput and call-latency distributions, distinguish peak from sustained rates, and observe what happens as queues fill. Decide whether records may be delayed or dropped before interpreting a speed result.
A practical way to choose
- Define the requirement. Decide whether the constraint is request-path latency, sustained message rate, burst handling, or reliable synchronous handling of particular records.
- Build a production-shaped test. Use current framework versions on the target JDK and hardware, with representative messages, layouts, concurrency, flush policy and output destination.
- Warm up and repeat. Allow the runtime and output buffers to reach steady operation, repeat runs, and report the configuration alongside results. A historical Apache recipe illustrates why: it warmed the JVM with 200,000 messages of 500 characters, repeated warm-up ten times, waited ten seconds for I/O and buffers to catch up, then timed fixed logger calls over five measured repetitions and averaged them. The recipe and its hardware and versions are old; its useful lesson is to disclose methodology, not to copy its numbers as a modern standard. Historical Log4j asynchronous benchmark methodology.
- Check sustained behavior. Run long enough to see whether queues stabilize or fill, and whether the destination can keep pace. Compare tail latency as well as average call cost and total message rate.
- Choose against your acceptance criteria. Select the implementation and mode that meet the application’s throughput, latency, reliability and operational needs—not the one with the highest isolated peak number.
So, is Log4j 2 faster than Logback?
It can be in a particular configuration, and Apache’s historical tests found Log4j 2 performed strongly as concurrency increased. That does not establish that Log4j 2 is faster than Logback in every current application. The answer depends on versions, JDK, logging mode, layout, output sink, concurrency and whether you value peak throughput, sustained throughput or low call latency. Benchmark those conditions directly before treating a framework as the winner.
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