Using Logstash and ElasticSearch to Process Eliot Logs


Logstash, Elasticsearch and Kibana change frequently. These instructions might not be quite accurate.

ElasticSearch is a search and analytics engine which can be used to store Eliot logging output. The logs can then be browsed by humans using the Kibana web UI, or on the command-line using the logstash-cli tool. Automated systems can access the logs using the ElasticSearch query API. Logstash is a log processing tool that can be used to load Eliot log files into ElasticSearch. The combination of ElasticSearch, Logstash, and Kibana is sometimes referred to as ELK.

Example Logstash Configuration

Assuming each Eliot message is written out as a JSON message on its own line (which is the case for eliot.to_file() and eliot.logwriter.ThreadedFileWriter), the following Logstash configuration will load these log messages into an in-process ElasticSearch database:


input {
  stdin {
    codec => json_lines {
      charset => "UTF-8"

filter {
  date {
    # Parse Eliot timestamp filed into the special @timestamp field Logstash
    # expects:
    match => [ "timestamp", "UNIX" ]
    target => ["@timestamp"]

output {
  # Stdout output for debugging:
  stdout {
    codec => rubydebug

  elasticsearch {
    # We make the document id unique (for a specific index/mapping type pair) by
    # using the relevant Eliot fields. This means replaying messages will not
    # result in duplicates, as long as the replayed messages end up in the same
    # index.
    document_id => "%{task_uuid}_%{task_level}"

We can then pipe JSON messages from Eliot into ElasticSearch using Logstash:

$ python examples/ | logstash web -- agent --config logstash_standalone.conf

You can then use the Kibana UI to search and browse the logs by visiting http://localhost:9292/.