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Aggregate Results with Full Data Objects  

AGGREGATE_BY organizes query results into groups based on one or more property values, returning both count summaries and the full data objects for each group. 
::AGGREGATE_BY(property)
::AGGREGATE_BY(property1, property2)
Returns grouped data with full objects: 
[
    {
        'count': 3,
        'data': [
            {"property1": value1, "property2": "Alice", ...},
            {"property1": value1, "property2": "Charlie", ...},
            {"property1": value1, "property2": "Frank", ...}
        ]
    },
    {
        'count': 2,
        'data': [
            {"property1": value2, "property2": "Bob", ...},
            {"property1": value2, "property2": "Diana", ...}
        ]
    },
    ...
]
AGGREGATE_BY returns the full data objects grouped by the specified properties, providing both count and the actual grouped elements for detailed analysis. This is useful when you need to work with the actual data in each group, not just counts.
When using the SDKs or curling the endpoint, the query name must match what is defined in the queries.hx file exactly.

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Example 1: Aggregate orders by status

QUERY AggregateOrdersByStatus () =>
    orders <- N<Order>
    RETURN orders::AGGREGATE_BY(status)

QUERY CreateOrder (customer_name: String, status: String, total: F64) =>
    order <- AddN<Order>({
        customer_name: customer_name,
        status: status,
        total: total
    })
    RETURN order
Here’s how to run the query using the SDKs or curl 
from helix.client import Client

client = Client(local=True, port=6969)

orders = [
    {"customer_name": "Alice", "status": "pending", "total": 99.99},
    {"customer_name": "Bob", "status": "shipped", "total": 149.99},
    {"customer_name": "Charlie", "status": "pending", "total": 75.50},
    {"customer_name": "Diana", "status": "delivered", "total": 200.00},
    {"customer_name": "Eve", "status": "shipped", "total": 89.99},
    {"customer_name": "Frank", "status": "pending", "total": 125.00},
]

for order in orders:
    client.query("CreateOrder", order)

result = client.query("AggregateOrdersByStatus", {})
print("Orders aggregated by status:", result)

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Example 2: Aggregate products by category with COUNT

QUERY CountProductsByCategory () =>
    products <- N<Product>
    RETURN products::COUNT::AGGREGATE_BY(category)

QUERY CreateProduct (name: String, category: String, price: F64, stock: I32) =>
    product <- AddN<Product>({
        name: name,
        category: category,
        price: price,
        stock: stock
    })
    RETURN product
Here’s how to run the query using the SDKs or curl 
from helix.client import Client

client = Client(local=True, port=6969)

products = [
    {"name": "Laptop", "category": "Electronics", "price": 999.99, "stock": 15},
    {"name": "Mouse", "category": "Electronics", "price": 29.99, "stock": 50},
    {"name": "Desk", "category": "Furniture", "price": 299.99, "stock": 10},
    {"name": "Chair", "category": "Furniture", "price": 199.99, "stock": 20},
    {"name": "Monitor", "category": "Electronics", "price": 399.99, "stock": 25},
    {"name": "Bookshelf", "category": "Furniture", "price": 149.99, "stock": 12},
]

for product in products:
    client.query("CreateProduct", product)

result = client.query("CountProductsByCategory", {})
print("Product count aggregated by category:", result)

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When to Use AGGREGATE_BY vs GROUP_BY

Choose AGGREGATE_BY when:
  • You need the actual data objects in each group
  • You want to perform further processing on grouped items
  • You need to display or return the full records
  • You’re building reports that show detailed breakdowns
Choose GROUP_BY when:
  • You only need counts and summaries
  • Memory efficiency is a priority
  • You’re doing analytics or dashboards showing distributions
  • You don’t need the actual data, just statistics
AGGREGATE_BY returns more data than GROUP_BY, so it uses more memory and bandwidth. Use GROUP_BY when you only need counts.

GROUP_BY

Group results with count summaries only

Grouping Guide

When to use GROUP_BY vs AGGREGATE_BY

COUNT Operation

Count operation and other result operations

Property Access

Property filtering and access patterns