Using Pipelines or not .. a shakespearean dilemma!

I have two different applications running on two different servers, shipping logs and exposing server API information to Logstash.

The output from each application has a different data rate. Let's say:

  • App1 sends data @ 1x rate
  • App2 sends data @ 15x rate

Each app sends 4 different types of logs (json/log4j, different fields, etc.).
Also the shipping method for the 4 datasets is different, 2 come from filebeat and 1 from the jdbc_input plugin.
Mixing the data from the two apps is not a current priority.

Currently, for App1, I have 9 logstash configuration files:
4 datasets x 2 files (input, filter) + 1 common output file = 9 config files


  1. Filebeat --> Grok --> Elasticsearch
  2. Filebeat --> Grok --> Elasticsearch
  3. JDBC Input (Postgres) --> Mutate/Replace --> Elasticsearch
  4. HTTP Plugin (public API) --> N/A --> Elasticsearch

I wonder what's the best practice for onboarding App2.

Some of the questions I have, are:

  • How can I develop my logstash configuration for App2 without impacting the operation of App1, i.e. not having to restart logstash while I develop my grok filters?
  • Should I be using multiple pipelines? If so, should I join all config files (input, filter, output) into one or do pipeline-to-pipeline (in beta)?
  • If I'm running a single node (which I am), what's best from a performance perspective? (introductory note seems to argue that pipelines are the way to go in this case)
  • Should I use 1 pipeline per data set or per application/server (data source)?
  • How should I think about tradeoffs between complexity of pipelines, performance and ability to keep onboarding new datasets from new apps?

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