Easy lifehacks

Is Fluentd reliable?

Is Fluentd reliable?

Proven Reliability and Performance In fact, according to the survey by Datadog, Fluentd is the 7th top technologies running on Docker container environments. Some Fluentd users collect data from thousands of machines in real-time. Thanks to its small memory footprint (30~40MB), you can save a lot of memory at scale.

Why flume is used in Hadoop?

What is Apache Flume in Hadoop? Apache Flume is a reliable and distributed system for collecting, aggregating and moving massive quantities of log data. Apache Flume is used to collect log data present in log files from web servers and aggregating it into HDFS for analysis.

Is Fluentd better than Logstash?

FluentD and Logstash are both open source data collectors used for Kubernetes logging. Logstash is centralized while FluentD is decentralized. FluentD offers better performance than Logstash.

What is fluentd written in?

C
Ruby
Fluentd/Programming languages

Who uses fluentd?

Who uses Fluentd? 155 companies reportedly use Fluentd in their tech stacks, including Alibaba Travels, deleokorea, and ViaVarejo.

What is Fluentd written in?

Does Flume provide 100% reliability to the data flow?

Answer: Flume generally offers the end-to-end reliability of the flow. Also, it uses a transactional approach to the data flow, by default. In addition, Source and sink encapsulate in a transactional repository provides the channels. Hence, it offers 100% reliability to the data flow.

What is the difference between Flume and Kafka?

Kafka runs as a cluster which handles the incoming high volume data streams in the real time. Flume is a tool to collect log data from distributed web servers. Kafka will treat each topic partition as an ordered set of messages.

Is Fluentd same as Logstash?

FluentD and Logstash are both open source data collectors used for Kubernetes logging. Logstash is centralized while FluentD is decentralized. FluentD offers better performance than Logstash. In fact, FluentD offers many benefits over Logstash.

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Ruth Doyle