Apache Flume vs Logstash

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Apache Flume

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Apache Flume vs Logstash: What are the differences?

Key Differences between Apache Flume and Logstash

Apache Flume and Logstash are two popular data collection tools that are used for efficiently ingesting and processing data from various sources. Although they serve similar purposes, there are several key differences between the two.

  1. Architecture: Apache Flume follows a distributed and fault-tolerant architecture, which allows it to handle large amounts of data efficiently. It uses a pull-based model, where agents pull data from sources and push it to sinks. On the other hand, Logstash follows a more centralized architecture, with a single central processing unit that receives and processes data.

  2. Ease of Use: Apache Flume is known for its simplicity and ease of use. It provides a user-friendly interface and configuration options, making it easy for users to set up and configure data flows. Logstash, on the other hand, offers a more flexible and complex configuration system, making it suitable for advanced users with more complex data processing requirements.

  3. Data Transformation: Apache Flume is primarily focused on the reliable ingestion of data, and it does not provide extensive data transformation capabilities out of the box. It can perform basic transformations, but for more complex transformations, additional tools may be required. Logstash, on the other hand, offers a wide range of built-in filters and transformation capabilities, allowing users to manipulate and enrich the data during the ingestion process.

  4. Plugin Ecosystem: Apache Flume has a limited number of plugins available, which may restrict its flexibility in certain use cases. On the other hand, Logstash has a vibrant and extensive plugin ecosystem, with a wide range of community-contributed plugins available for various purposes. This allows users to easily extend the functionality of Logstash and integrate it with other systems and tools.

  5. Scalability: Apache Flume is designed to handle massive amounts of data efficiently and is highly scalable. It supports horizontal scalability, allowing users to add more agents or sources to handle increasing data volumes. Logstash, on the other hand, may face scalability challenges when dealing with large data volumes, as it relies on a centralized processing unit. However, Logstash can be deployed in a distributed manner using multiple instances to overcome scalability limitations.

  6. Community and Support: Apache Flume has a strong and active community support, with regular updates and bug fixes being released by the Apache Software Foundation. Logstash also has a solid community support, but it is primarily governed and maintained by Elastic, the company behind the Elasticsearch. Users of Logstash can benefit from the extensive documentation, forums, and support provided by Elastic.

In summary, Apache Flume and Logstash have distinct differences in terms of their architecture, ease of use, data transformation capabilities, plugin ecosystems, scalability, and community support. The choice between the two depends on specific use cases and requirements, with Apache Flume being a good choice for simple and reliable data ingestion, and Logstash offering more advanced features and flexibility for complex data processing needs.

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Pros of Apache Flume
Pros of Logstash
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    • 69
      Free
    • 18
      Easy but powerful filtering
    • 12
      Scalable
    • 2
      Kibana provides machine learning based analytics to log
    • 1
      Great to meet GDPR goals
    • 1
      Well Documented

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    Cons of Apache Flume
    Cons of Logstash
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      • 4
        Memory-intensive
      • 1
        Documentation difficult to use

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      What is Apache Flume?

      It is a distributed, reliable, and available service for efficiently collecting, aggregating, and moving large amounts of log data. It has a simple and flexible architecture based on streaming data flows. It is robust and fault tolerant with tunable reliability mechanisms and many failover and recovery mechanisms. It uses a simple extensible data model that allows for online analytic application.

      What is Logstash?

      Logstash is a tool for managing events and logs. You can use it to collect logs, parse them, and store them for later use (like, for searching). If you store them in Elasticsearch, you can view and analyze them with Kibana.

      Need advice about which tool to choose?Ask the StackShare community!

      What companies use Apache Flume?
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      See which teams inside your own company are using Apache Flume or Logstash.
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      What tools integrate with Apache Flume?
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        Blog Posts

        May 21 2019 at 12:20AM

        Elastic

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        What are some alternatives to Apache Flume and Logstash?
        Apache Spark
        Spark is a fast and general processing engine compatible with Hadoop data. It can run in Hadoop clusters through YARN or Spark's standalone mode, and it can process data in HDFS, HBase, Cassandra, Hive, and any Hadoop InputFormat. It is designed to perform both batch processing (similar to MapReduce) and new workloads like streaming, interactive queries, and machine learning.
        Apache Storm
        Apache Storm is a free and open source distributed realtime computation system. Storm makes it easy to reliably process unbounded streams of data, doing for realtime processing what Hadoop did for batch processing. Storm has many use cases: realtime analytics, online machine learning, continuous computation, distributed RPC, ETL, and more. Storm is fast: a benchmark clocked it at over a million tuples processed per second per node. It is scalable, fault-tolerant, guarantees your data will be processed, and is easy to set up and operate.
        Kafka
        Kafka is a distributed, partitioned, replicated commit log service. It provides the functionality of a messaging system, but with a unique design.
        Apache Flink
        Apache Flink is an open source system for fast and versatile data analytics in clusters. Flink supports batch and streaming analytics, in one system. Analytical programs can be written in concise and elegant APIs in Java and Scala.
        Apache NiFi
        An easy to use, powerful, and reliable system to process and distribute data. It supports powerful and scalable directed graphs of data routing, transformation, and system mediation logic.
        See all alternatives