How to Create a Histogram with Elasticsearch Aggregations using NodeJS

Introduction

Elasticsearch aggregations allow you to compute averages, sums, maximums, and minimums. It also let’s you create histograms from your data. Let’s start with what a histogram is in case you’ve forgotten. A histogram is a bar graph that tells you how many there are of each given type. For example, if you had a database of songs, a histogram might tell you how many country, rock, and blues songs there are. The types would be on the x-axis and the number of occurrences would be on the y-axis. If this is the type of calculation you need we’ll show you how to do it step-by-step in Javascript. But if you’d just rather see the example code, click here to jump to Just the Code.

Prerequisites

Before we show you how to create the histogram data, it’s important to make sure a few prerequisites are in place. There are only a few of system requirements for this task: NodeJS needs to be installed Elasticsearch needs to be installed and running In our example, we have Elasticsearch installed locally using the default port of 9200. If your Elasticsearch installation is running on a different server, you’ll need to modify your javascript accordingly. The elasticsearch npm module installed. * A simple npm install elasticsearch should work in most cases.

Use the histogram Aggregation

We love to show by example. In this example we use an index representing a small grocery store called store. The store index contains the type products which lists all the stores products. To keep it simple our dataset only has a small number of products with just a few fields including the id, price, quantity, and department. Here is the json we used to create our dataset:

idnamepricequantitydepartment
1Multi-Grain Cereal4.994Packaged Foods
21lb Ground Beef3.9929Meat and Seafood
3Dozen Apples2.4912Produce
4Chocolate Bar1.292Packaged FoodsCheckout
51 Gallon Milk3.2916Dairy
60.5lb Jumbo Shrimp5.2912Meat and Seafood
7Wheat Bread1.295Bakery
8Pepperoni Pizza2.995Frozen
912 Pack Cola5.296Packaged Foods
10Lime Juice0.9920Produce
1112 Pack Cherry Cola5.595Packaged Foods
121 Gallon Soy Milk3.3910Dairy
131 Gallon Vanilla Soy Milk3.499Dairy
141 Gallon Orange Juice3.294Juice

And here is the json we used to make the mapping:

{
    "mappings": {`
        "products": {
            "properties" : {`
                "name": { "type": "text"},
                "price": { "type": "double"},
                "quantity": { "type": "integer"},
                "department": { "type": "keyword"}
            }
        }
    }
}

Now let’s say we wanted a histogram of all our products based on price. How many products are in the price range from $0.00 to $1.00? What about $1.01 to $2.00? This is what a histogram would tell us. Now let’s solve that problem to demonstrate:

File index.js: `js var elasticsearch = require(“elasticsearch”);

var client = new elasticsearch.Client({ hosts: [“http://localhost:9200”] });

/ Histogram Aggregation / client.search({ size : 0, index: ‘store’, type: ‘products’, body: {


  aggs: {
    histogram_by_dollar: {
      histogram:
        {
          field: "price",
          interval: 1.00
        }
    }
  }

} }).then(function(resp) { console.log(“Successful query!”); console.log(JSON.stringify(resp, null, 4)); }, function(err) { console.trace(err.message); }); `

There’s a few steps to unpack so let’s dive in step-by-step: First we required the elasticsearch library because it gives us the library of functions that make it easy to access Elasticsearch. Next we created a variable var client which creates and stores our connection to Elasticsearch. From this point on we’ll use this client to do all our interactions with Elasticsearch. We then use the search function on client to create a query with an aggregator. We specify the index and type to perform the search on. The important part comes in the body where we define the aggregator by using the aggs keyword. We gave our aggregator a name histogram_by_dollar that can be anything but choose something that makes sense. We specify the type of aggregator as histogram. Lastly we specify what field the aggregator should evaluate, price. The range of values for each bucket 1.0. This means it will tell us how many products are in the price range from $0-$1, $1.01-$2, and so on.

Note: We used JSON.stringify to pretty print our results. We also gave a size : 0 because we don’t want any specific products back but only the aggregation data.

We run our NodeJS app and get this response:

$ node index.js

Successful query!
{
    "took": 8,
    "timed_out": false,
    "_shards": {
        "total": 5,
        "successful": 5,
        "skipped": 0,
        "failed": 0
    },
    "hits": {
        "total": 13,
        "max_score": 0,
        "hits": []
    },
    "aggregations": {
        "histogram_by_dollar": {
            "buckets": [
                {
                    "key": 1,
                    "doc_count": 2
                },
                {
                    "key": 2,
                    "doc_count": 2
                },
                {
                    "key": 3,
                    "doc_count": 5
                },
                {
                    "key": 4,
                    "doc_count": 0
                },
                {
                    "key": 5,
                    "doc_count": 4
                }
            ]
        }
    }
}

What we got back was exactly as expected. You can see our aggregator created buckets and the doc_count tells us how many products were in that bucket. The key value tells us which bucket we are looking at.

Conclusion

In this tutorial we demonstrated how to use Elasticsearch aggregations to calculate histogram data based on a specific field and interval. This is a common use case. It’s very good for getting a bird’s eye view of your data. There are many other things you can do with aggregation and you can consult the Elasticsearch documentation to learn more about it. The documentation is also useful if you need help with syntax. We hope you found this tutorial on histogram aggregations helpful and can apply it to your specific application. If you have questions or this didn’t work for you please reach out to us so we can help. Thank you.

Just the Code

If you’re already comfortable with NodeJS and aggregations here’s all the code we used to demonstrate how to create a histogram in Elasticsearch using NodeJS.

var elasticsearch = require("elasticsearch");

var client = new elasticsearch.Client({
  hosts: ["http://localhost:9200"]
});


/* Histogram Aggregation */
client.search({
  size : 0,
  index: 'store',
  type: 'products',
  body: {
      aggs: {
        histogram_by_dollar: {
          histogram:
            {
              field: "price",
              interval: 1.00
            }
        }
      }
  }
}).then(function(resp) {
  console.log("Successful query!");
  console.log(JSON.stringify(resp, null, 4));
}, function(err) {
  console.trace(err.message);
});

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