Here we show how to load CSV data into ElasticSearch using Logstash.

The file we use is network traffic. There are no heading fields, so we will add them.

Download and Unzip the Data

Download this file from Kaggle. Then unzip it. The resulting file is conn250K.csv. It has 256,670 records.

Next, change permissions on the file, since the permissions are set to no permissions.

chmod 777 conn250K.csv

Now, create this logstash file csv.config, changing the path and server name to match your environment.

input {
  file {
    path => "/home/ubuntu/Documents/esearch/conn250K.csv"
    start_position => "beginning"

filter {
      csv {
        columns => [ "record_id", "duration", "src_bytes", "dest_bytes" ]

output {
  elasticsearch { 
  hosts => ["parisx:9200"] 
  index => "network"


Then start logstash giving that config file name.

sudo bin/logstash -f config/csv.conf

While the load is running, you can list some documents:

curl XGET http://parisx:9200/network/_search?pretty

results in:

"_index" : "network",
        "_type" : "_doc",
        "_id" : "dmx9emwB7Q7sfK_2g0Zo",
        "_score" : 1.0,
        "_source" : {
          "record_id" : "72552",
          "duration" : "0",
          "src_bytes" : "297",
          "host" : "paris",
          "message" : "72552,0,297,9317",
          "@version" : "1",
          "@timestamp" : "2019-08-10T07:45:41.642Z",
          "dest_bytes" : "9317",
          "path" : "/home/ubuntu/Documents/esearch/conn250K.csv"

You can run this query to follow when the data load is complete, which is when the document count is 256,670.

curl XGET http://parisx:9200/_cat/indices?v

Create Index Pattern in Kibana

Open Kibana.

Create the Index Pattern. Don’t use @timestamp as a key field as that only refers to the time we loaded the data into Logstash. Unfortunately, the data provided by Kaggle does not include any date, which is strange for network data. But we can use the record_id in later time series analysis.

Now go to the Discover tab and list some documents:

In the next blog post we will show how to use Elasticsearch Machine Learning to do Anomaly Detection on this network traffic.

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Last updated: 08/22/2019

These postings are my own and do not necessarily represent BMC's position, strategies, or opinion.

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About the author

Walker Rowe

Walker Rowe

Walker Rowe is a freelance tech writer and programmer. He specializes in big data, analytics, and programming languages. Find him on LinkedIn or Upwork.