Alternative to Logstash was the Elasticsearch JDBC tool. Which at the time of writing was using port 9300 for transfering data. There were talks of not exposing this port externally in future releases of elaticsearch and hence we went with logstash.


  • The way we have setup logstash and elasticsearch cluster at present is by using Ansible.
  • We have one vm with logstash installed which can connect to the elasticsearch cluster.
  • ReadonlyRest plugin is used for managing access for our cluster.
  • Used the JDBC plugin in order to query for the data with elasticsearch output plugin.
  • Use a cron job for scheduling the logstash to run on a schedule. Our schedule is once every hour.

As of logstash version 5.0, there is an option to enable http compression for requests , so make sure to take advantage of this. As we saw a reduction of up to 10 fold in the data size.


There were two options for getting the updates from oracle db whilst using the JDBC input plugin. Option 1: Modify the job which insert or updates each table that we are ingesting with a lastupdated field. The script that would run at our schedule of every one hour would then query the elasticsearch index for the max_date on the index and pass it to the sql thats run by logstash jdbc plugin. Option 2: Use the sql_last_value plugin parameter which will persist the sql_last_value parameter in the form of a metadata file stored in the configured last_run_metadata_path. Upon query execution, this file will be updated with the current value of sql_last_value. In our case, this meant that we will need to use an insert or update timestamp in our table.

Primary key in the oracle db table is used as the document id in elasticsearch. This means that each updated document will correctly override the document in elasticsearch.

output {
  elasticsearch {
    hosts => ${HOST_STRING}
    index => "${ES_INDEX}"
    document_id => "%{${ES_DOC_ID}}"
    document_type => "${INDEX_TYPE}"
    flush_size => 1000
    http_compression => true

Transform data

Make use of filters in order to do basic data transformations.

Transform table column value to object

mutate {
    rename => { "address.line1" => "[address][line1]" }
    rename => { "address.line2" => "[address][line2]" }

Covert comma delimeted field to array of string

ruby {
    init => "require 'csv'"
    code => "['urls'].each { |type|
        if event.include?(type) then
            if event.get(type) == nil || event.get(type) == 'null' then
                # bin data if not valid CSV
                    event.set(type, CSV.parse(event.get(type))[0])


The setup described in this article doesn’t work well if we need to also remove deleted entries. Consider using a column in our view to indicate if a field was removed or not. But that only works for “soft-deletes” in database.

Move towards using a bus queuing system for ingestion. One project by linkedin that caught my attention that supports oracle db as source for ingestion was databus. But, haven’t managed to get it setup locally (poor documentation at the time of writing).

Full re-index is currently a manual process, even though we a script to perform full re-index.

Further Reading

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