Apache Solr Aggregation Query

Apache Solr is an Open-source model search platform that supports full-text search, distributed index and distributed search. It provides some aggregation query functions to process and analyze data. Solr supports the following types of aggregation queries: 1. Grouping: Group the results according to the specified fields and apply various statistical functions to each group. For example, sales data can be grouped by region and the total sales revenue for each region can be calculated. 2. Statistics: Calculate statistical information for a specified field, such as total, average, minimum, maximum, and quantity. For example, it is possible to calculate the average and highest prices for a product category. 3. Facet: Group according to different values of the specified fields and count the quantity of each value. This can be used to generate filters for different attributes. For example, different product categories can be counted and the number of products under each category can be calculated. 4. Query Filters: Use aggregation functions as query criteria to filter results. Multiple criteria can be specified in the query filter to group and filter the results. 5. Range Facet: Group the values of fields according to a certain range and count the quantity within each range. For example, sales data can be grouped by price range and the sales quantity within each price range can be counted. The following is an example table structure and sample data: Assuming there is a Solr index of product sales records, which includes the following fields: -ID: Product ID -Name: Product Name -Category: Product category -Price: Product price -Region: Sales Region Sample data: [ { "id": "1", "name": "iPhone X", Category: "Phone", "price": 999.99, Region: United States }, { "id": "2", "name": "Samsung Galaxy S20", Category: "Phone", "price": 799.99, Region: United States }, { "id": "3", "name": "MacBook Pro", Category: "Computer", "price": 1499.99, Region: United States }, { "id": "4", "name": "iPad Pro", "Category": "tablet", "price": 799.99, Region: UK }, { "id": "5", "name": "Sony PlayStation 5", Category: "Game console", "price": 499.99, Region: UK } ] Now let's take a look at the implementation of some example aggregation queries: 1. Group query: Query: '/select? Q=*: *&group=true&group. field=region` Result: "grouped": { "region": { "matches": 5, "groups": [ { "GroupValue": "United States", "doclist": { "numFound": 3, "start": 0, "docs": [ { "id": "1", "name": "iPhone X", Category: "Phone", "price": 999.99, Region: United States }, { "id": "2", "name": "Samsung Galaxy S20", Category: "Phone", "price": 799.99, Region: United States }, { "id": "3", "name": "MacBook Pro", Category: "Computer", "price": 1499.99, Region: United States } ] } }, { "GroupValue": "UK", "doclist": { "numFound": 2, "start": 0, "docs": [ { "id": "4", "name": "iPad Pro", "Category": "tablet", "price": 799.99, Region: UK }, { "id": "5", "name": "Sony PlayStation 5", Category: "Game console", "price": 499.99, Region: UK } ] } } ] } } 2. Statistical Query: Query: '/select? Q=*: *&stats=true&stats. field=price&stats. facet=category` Result: "stats": { "stats_fields": { "price": { "min": 499.99, "max": 1499.99, "count": 5, "missing": 0, "sum": 4599.95, "mean": 919.99, "stddev": 365.14 } } }, "facet_counts": { "facet_fields": { "category": [ Phone, 2, Computer, 1, Plate, 1, Game console, 1 ] } } 3. Faceted query: Query: '/select? Q=*: *&facet=true&facet. field=category` Result: "facet_counts": { "facet_fields": { "category": [ Phone, 2, Computer, 1, Plate, 1, Game console, 1 ] } } 4. Query Filter: Query: '/select? Q=*: *&fq=category: Mobile phone` Result: "response": { "numFound": 2, "start": 0, "docs": [ { "id": "1", "name": "iPhone X", Category: "Phone", "price": 999.99, Region: United States }, { "id": "2", "name": "Samsung Galaxy S20", Category: "Phone", "price": 799.99, Region: United States } ] } 5. Scope statistics: Query: '/select? Q=*: *&face=true&face. range=price&f. price. face. range. start=0&f. price. face. range. end=1999.99&f. price. face. range. cap=500` Result: "facet_counts": { "facet_ranges": { "price": { "counts": [ "0.0", 0, "500.0", 0, "1000.0", 2, "1500.0", 1 ], "gap": 500, "start": 0, "end": 1999.99, "before": 0, "after": 0, "between": 3 } } } These are just some basic examples, and Solr also supports more complex aggregation query operations, which can be further studied and practiced according to specific needs.