#FILEBEATS SET DOCUMENT ID CODE#

This should be stored in a file called convert_csv_to_json.js which is referenced in the filebeat.yml configuration that we presented above.

#FILEBEATS SET DOCUMENT ID CODE#

: false JavaScript processor codeīelow we present the JavaScript code that we use for converting lines in a CSV file into JSON objects. max_procs: 1 # This code will not work correctly on multiple threads We use the following filebeat.yml configuration to call the CSV processor as well as our custom JavaScript. This CSV is intentionally written in an inconsistent format, to ensure that the code is working correctly for different formats: first_col,col2,col3,fourth_colĥ678,first 2,456,'third 2' Filebeat configuration This file will be used to show how CSV can be converted into JSON. We will store the following data in a file called test.csv. The custom script processor will apply custom JavaScript code to each event (in our case, to each to CSV line), which converts the CSV values into key-value pairs in a JSON object.

The code presented in this blog makes use of the CSV processor as well as a custom script processor. Processors are executed on data as it passes through Filebeat. We should be aware the existence of the registry, as the registry will prevent sending the same CSV data to Elasticsearch multiple times, which can be undesirable when testing. This is tracked in the Filebeat registry. CodeĪll code presented in this blog is available at: A note on the Filebeat registryīecause Filebeat is designed for sending log lines from files which are actively being written, it keeps track of the most recent log entry that it has sent to Elasticsearch, and ensures that each entry is only sent once. This technique therefore eliminates the need for additional ingest node or Logstash processing that would otherwise be required. On the other hand, the approach presented in this blog will automatically extract field names from the CSV header, and then generate key-value pairs based on each row’s values combined with the field names that are extracted from the header row. When using the CSV processor, additional processing (and hard-coding of the field names) is generally required in an ingest node or in Logstash to add the correct field names back into the extracted data. However, this processor does not create key-value pairs to maintain the relation between the column names and the extracted values. Motivationįilebeat supports a CSV processor which extracts values from a CSV string, and stores the result in an array. The technique and code presented in this article is intended for ingesting an existing CSV file a single time, and it then terminates Filebeat immediately after the file has been ingested. On the other hand, the technique described in this blog is not intended to run on a CSV file that continually has lines added to it. Note that Filebeat is intended for sending log lines into Elasticsearch. This will be accomplished by using a built-in CSV processor as well as a custom JavaScript processor which will be applied to every line in a CSV file. In this blog I will show how Filebeat can be used to convert CSV data into JSON-formatted data that can be sent into an Elasticsearch cluster. In such cases, one option is to use Filebeat for uploading such CSV data into an Elasticsearch cluster. Or one may wish to use Elasticsearch and Kibana for analysing a dataset that is only available in a spreadsheet.

For example, one may need to get Master Data that is created in a spreadsheet into Elasticsearch where it could be used for enriching Elasticsearch documents. For various reasons it may be useful to import such data into Elasticsearch. Many organisations use excel files for creating and storing important data.

Filebeats set document id