Software Alternatives & Reviews

Apache Log4j VS Apache Flume

Compare Apache Log4j VS Apache Flume and see what are their differences

Apache Log4j logo Apache Log4j

Log4j is a logging framework (APIs) written in Java.

Apache Flume logo Apache Flume

Apache Flume is a distributed, reliable, and available service for efficiently collecting, aggregating, and moving large amounts of log data
  • Apache Log4j Landing page
    Landing page //
    2023-06-17
  • Apache Flume Landing page
    Landing page //
    2018-09-29

Category Popularity

0-100% (relative to Apache Log4j and Apache Flume)
Log Management
71 71%
29% 29
Big Data
0 0%
100% 100
Monitoring Tools
79 79%
21% 21
Machine Data Analytics
100 100%
0% 0

User comments

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Social recommendations and mentions

Based on our record, Apache Log4j seems to be a lot more popular than Apache Flume. While we know about 26 links to Apache Log4j, we've tracked only 1 mention of Apache Flume. We are tracking product recommendations and mentions on various public social media platforms and blogs. They can help you identify which product is more popular and what people think of it.

Apache Log4j mentions (26)

  • Studying Log4Shell
    The official website. The vulnerability was introduced in 2.0-beta7 which was released in 2013. Source: about 1 year ago
  • Apache POI Setup Logging Error
    What you need is log4j-core, what you downloaded is some kind of connector between log4j and JUL. Tbh I don't know what JUL is, but that's not important. You can get log4j-core on from the official website - https://logging.apache.org/log4j/2.x/ or in maven repo. In case you're not using maven, I highly, highly recommend you using it for managing your dependencies. Source: about 1 year ago
  • 5 Best Logging Solutions for Java
    Log4J(https://logging.apache.org/log4j/2.x/) is a Java-based logging framework. It is a part of Apache Logging Services. It was also the most popular and widely used Java logging solution until the exposure of its Log4Shell vulnerability last year. - Source: dev.to / over 1 year ago
  • Reduce Security Risks by Keeping Dependencies Up-To-Date with GitHub Actions and Dependabot
    Almost nothing is more ubiquitous in applications than logging libraries. No matter which type of application - hastily thrown-together prototypes, decades-old enterprise monoliths, newly built event-driven serverless apps - there is always the need to log. Even in non-production-grade applications where standard observability patterns such as monitoring and alerting might not be applied - logging is usually... - Source: dev.to / about 2 years ago
  • System Logger
    Most applications currently use Log4J2 or SLF4J. Both provide a compatible System.Logger implementation. - Source: dev.to / about 2 years ago
View more

Apache Flume mentions (1)

  • 7 Open-Source Log Management Tools that you may consider in 2023
    Apache Flume is an open-source log management tool designed to efficiently collect, aggregate, and transport large volumes of log data from various sources to a centralized data store, such as HDFS or Hbase. It excels in handling large amounts of log data in real-time and is highly scalable, able to handle the load from multiple servers, network devices, and applications. - Source: dev.to / over 1 year ago

What are some alternatives?

When comparing Apache Log4j and Apache Flume, you can also consider the following products

Datadog - See metrics from all of your apps, tools & services in one place with Datadog's cloud monitoring as a service solution. Try it for free.

Fluentd - Fluentd is a cross platform open source data collection solution originally developed at Treasure Data.

LOGBack - Logging framework

Apache Flink - Flink is a streaming dataflow engine that provides data distribution, communication, and fault tolerance for distributed computations.

tinylog - tinylog is a lightweight logging framework with static logger class for Java and can be configured...

Graylog - Graylog is an open source log management platform for collecting, indexing, and analyzing both structured and unstructured data.