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Loading Sample Dataedit
The tutorials in this section rely on the following data sets:
- The complete works of William Shakespeare, suitably parsed into fields. Download this data set by clicking here: shakespeare.json.
- A set of fictitious accounts with randomly generated data. Download this data set by clicking here: accounts.zip
- A set of randomly generated log files. Download this data set by clicking here: logs.jsonl.gz
Two of the data sets are compressed. Use the following commands to extract the files:
unzip accounts.zip gunzip logs.jsonl.gz
The Shakespeare data set is organized in the following schema:
{
"line_id": INT,
"play_name": "String",
"speech_number": INT,
"line_number": "String",
"speaker": "String",
"text_entry": "String",
}
The accounts data set is organized in the following schema:
{
"account_number": INT,
"balance": INT,
"firstname": "String",
"lastname": "String",
"age": INT,
"gender": "M or F",
"address": "String",
"employer": "String",
"email": "String",
"city": "String",
"state": "String"
}
The schema for the logs data set has dozens of different fields, but the notable ones used in this tutorial are:
{
"memory": INT,
"geo.coordinates": "geo_point"
"@timestamp": "date"
}
Before we load the Shakespeare and logs data sets, we need to set up mappings for the fields. Mapping divides the documents in the index into logical groups and specifies a field’s characteristics, such as the field’s searchability or whether or not it’s tokenized, or broken up into separate words.
Use the following command in a terminal (eg bash) to set up a mapping for the Shakespeare data set:
PUT /shakespeare
{
"mappings" : {
"_default_" : {
"properties" : {
"speaker" : {"type": "keyword" },
"play_name" : {"type": "keyword" },
"line_id" : { "type" : "integer" },
"speech_number" : { "type" : "integer" }
}
}
}
}
This mapping specifies the following qualities for the data set:
- Because the speaker and play_name fields are keyword fields, they are not analyzed. The strings are treated as a single unit even if they contain multiple words.
- The line_id and speech_number fields are integers.
The logs data set requires a mapping to label the latitude/longitude pairs in the logs as geographic locations by
applying the geo_point type to those fields.
Use the following commands to establish geo_point mapping for the logs:
PUT /logstash-2015.05.18
{
"mappings": {
"log": {
"properties": {
"geo": {
"properties": {
"coordinates": {
"type": "geo_point"
}
}
}
}
}
}
}
PUT /logstash-2015.05.19
{
"mappings": {
"log": {
"properties": {
"geo": {
"properties": {
"coordinates": {
"type": "geo_point"
}
}
}
}
}
}
}
PUT /logstash-2015.05.20
{
"mappings": {
"log": {
"properties": {
"geo": {
"properties": {
"coordinates": {
"type": "geo_point"
}
}
}
}
}
}
}
The accounts data set doesn’t require any mappings, so at this point we’re ready to use the Elasticsearch
bulk API to load the data sets with the following commands:
curl -H 'Content-Type: application/x-ndjson' -XPOST 'localhost:9200/bank/account/_bulk?pretty' --data-binary @accounts.json curl -H 'Content-Type: application/x-ndjson' -XPOST 'localhost:9200/shakespeare/_bulk?pretty' --data-binary @shakespeare.json curl -H 'Content-Type: application/x-ndjson' -XPOST 'localhost:9200/_bulk?pretty' --data-binary @logs.jsonl
These commands may take some time to execute, depending on the computing resources available.
Verify successful loading with the following command:
GET /_cat/indices?v
You should see output similar to the following:
health status index pri rep docs.count docs.deleted store.size pri.store.size yellow open bank 5 1 1000 0 418.2kb 418.2kb yellow open shakespeare 5 1 111396 0 17.6mb 17.6mb yellow open logstash-2015.05.18 5 1 4631 0 15.6mb 15.6mb yellow open logstash-2015.05.19 5 1 4624 0 15.7mb 15.7mb yellow open logstash-2015.05.20 5 1 4750 0 16.4mb 16.4mb