Agregasi Metrik
Lakukan perhitungan seperti jumlah, rata-rata, minimum, maksimum, dan hitungan pada data Anda menggunakan berbagai jenis agregasi metrik.
Agregasi Metrik adalah pelajaran Elasticsearch & Full Text Search Systems gratis di CoddyKit. Ini adalah pelajaran 1 dari 4. Kamu bisa membaca pelajaran lengkapnya di bawah secara gratis — lalu praktikkan langsung di browser dengan editor kode bawaan dan tutor AI 24/7. Ini adalah bagian dari jalur belajar Elasticsearch & Full Text Search Systems, dan progresmu tersinkronisasi di web dan aplikasi CoddyKit. Kursus Elasticsearch & Full Text Search Systems mencakup 4 pelajaran total.
Bagian dari pelajaran ini belum diterjemahkan dan ditampilkan dalam bahasa Inggris.
Discover Data Insights
What if you could ask "how much total sales did we have last month?" or "what's the average price of our products?" Aggregations in Elasticsearch let you do exactly that!
They provide powerful analytical capabilities beyond simple search. Think of them as SQL's GROUP BY and aggregate functions, but for your search data.
Unpacking Metric Aggregations
Metric aggregations are the simplest type of aggregation. They calculate a single metric (like a sum, average, min, or max) over a set of documents.
They give you numerical summaries of your data, helping you quickly understand key statistics and trends without retrieving all individual documents.
Indexing Sample Sales Data
To demonstrate metric aggregations, let's index some sample sales data. We'll use documents with a price field.
Run this curl command to add our first product:
curl -X PUT "localhost:9200/sales_data/_doc/1?pretty" -H 'Content-Type: application/json' -d'
{
"product": "Laptop",
"price": 1200,
"quantity": 1
}'Adding More Sample Data
Great! To have more data for our aggregations, let's add two more products. Imagine you've run similar curl commands for these:
sales_data/_doc/2:{"product": "Mouse", "price": 25, "quantity": 2}sales_data/_doc/3:{"product": "Keyboard", "price": 75, "quantity": 1}
Now we have a small dataset to work with!
Calculating the Average (`avg`)
The avg aggregation calculates the arithmetic mean of a numeric field. It's perfect for finding the average price of products, average age, or average score.
Let's find the average price of our indexed products:
curl -X GET "localhost:9200/sales_data/_search?pretty" -H 'Content-Type: application/json' -d'
{
"size": 0,
"aggs": {
"average_price": {
"avg": {
"field": "price"
}
}
}
}'Summing Up Values (`sum`)
The sum aggregation calculates the total sum of a numeric field's values. This is ideal for finding total sales, total quantity, or total revenue.
Let's calculate the total price of all indexed products:
curl -X GET "localhost:9200/sales_data/_search?pretty" -H 'Content-Type: application/json' -d'
{
"size": 0,
"aggs": {
"total_price": {
"sum": {
"field": "price"
}
}
}
}'Finding Min and Max (`min`, `max`)
The min and max aggregations return the smallest and largest values of a numeric field, respectively. They help identify outliers or boundary values in your data.
Let's find the cheapest and most expensive product prices:
curl -X GET "localhost:9200/sales_data/_search?pretty" -H 'Content-Type: application/json' -d'
{
"size": 0,
"aggs": {
"min_price": { "min": { "field": "price" } },
"max_price": { "max": { "field": "price" } }
}
}'Counting Values (`value_count`)
The value_count aggregation counts the number of documents that have a value for a specific field. It's different from a document count because it only considers documents where the field exists and is not null.
Let's count how many products have a 'price' field:
curl -X GET "localhost:9200/sales_data/_search?pretty" -H 'Content-Type: application/json' -d'
{
"size": 0,
"aggs": {
"price_count": {
"value_count": {
"field": "price"
}
}
}
}'All Stats in One Go (`stats`)
The stats aggregation is a convenience aggregation that computes min, max, sum, avg, and count (of values) all at once for a numeric field.
It saves you from writing five separate aggregations and is great for a quick overview!
curl -X GET "localhost:9200/sales_data/_search?pretty" -H 'Content-Type: application/json' -d'
{
"size": 0,
"aggs": {
"price_stats": {
"stats": {
"field": "price"
}
}
}
}'Metric Aggregation Check
Consider a scenario where you have a list of product prices: 100, 50, 200, 50, null.
What would be the result of a value_count aggregation on the 'price' field?
Metric Aggregations Recap
In this lesson, you've learned about Elasticsearch metric aggregations, powerful tools for summarizing your data:
avg: Calculates the arithmetic mean.sum: Computes the total sum.min&max: Finds the smallest and largest values.value_count: Counts non-null field values.stats: A convenient way to get all basic stats at once.
These aggregations are fundamental for understanding the numerical characteristics of your data and are often combined for more complex analysis.
Belajar Elasticsearch & Full Text Search Systems dengan tutor AI — gratis
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- Kursus
- 12
- Pelajaran
- 48
Pertanyaan yang Sering Diajukan
Apakah pelajaran “Agregasi Metrik” gratis?
Ya — teks lengkap “Agregasi Metrik” gratis dibaca di sini di web. Untuk praktiknya secara interaktif (editor kode bawaan dan tutor AI 24/7) dan buka sisa kursus Elasticsearch & Full Text Search Systems, upgrade ke CoddyKit PRO. Kursus Elasticsearch & Full Text Search Systems mencakup 4 pelajaran total.
Apa yang akan aku pelajari di “Agregasi Metrik”?
Lakukan perhitungan seperti jumlah, rata-rata, minimum, maksimum, dan hitungan pada data Anda menggunakan berbagai jenis agregasi metrik. Kamu berlatih Elasticsearch & Full Text Search Systems dengan kode praktik yang langsung kamu jalankan di browser, dan tutor AI 24/7 menjawab pertanyaanmu saat kamu mengerjakan pelajaran ini.
Apakah aku perlu pengalaman untuk memulai Elasticsearch & Full Text Search Systems?
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Berapa lama pelajaran “Agregasi Metrik” memakan waktu?
Sebagian besar pelajaran CoddyKit memakan waktu sekitar 5–10 menit. Setiap pelajaran ringkas dan interaktif, jadi kamu membuat kemajuan stabil dan melanjutkan dari tempat kamu tinggalkan di web dan aplikasi.
Bisakah aku menulis dan menjalankan kode dalam pelajaran Elasticsearch & Full Text Search Systems ini?
Ya. Setiap pelajaran Elasticsearch & Full Text Search Systems menyertakan editor kode bawaan, jadi kamu menulis dan menjalankan kode nyata langsung di browser dan mendapatkan umpan balik AI instan — tidak diperlukan penyiapan lokal.
Semua pelajaran dalam kursus ini
- Agregasi Metrik
- Agregasi Bucket
- Agregasi Pipeline
- Nested dan Subagregasi