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    Indexes: Map-Reduce Indexes

    Map-Reduce indexes allow you to perform complex aggregations of data. The first stage, called the map, runs over documents and extracts portions of data according to the defined mapping function(s). Upon completion of the first phase, reduction is applied to the map results and the final outcome is produced.

    The idea behind map-reduce indexing is that aggregation queries using such indexes are very cheap. The aggregation is performed only once and the results are stored inside the index. Once new data comes into the database or existing documents are modified, the map-reduce index will keep the aggregation results up-to-date. The aggregations are never done during querying to avoid expensive calculations that could result in severe performance degradation. When you make the query, RavenDB immediately returns the matching results directly from the index.

    For a more in-depth look at how map reduce works, you can read this post: RavenDB 4.0 Unsung Heroes: Map/reduce.

    Creating

    When it comes to index creation, the only difference between simple indexes and the map-reduce ones is an additional reduce function defined in index definition. To deploy an index we need to create a definition and deploy it using one of the ways described in the creating and deploying article.

    Example I - Count

    Let's assume that we want to count the number of products for each category. To do it, we can create the following index using LoadDocument inside:

    public static class Products_ByCategory extends AbstractIndexCreationTask {
    public static class Result {
        private String category;
        private String count;

        public String getCategory() {
            return category;
        }

        public void setCategory(String category) {
            this.category = category;
        }

        public String getCount() {
            return count;
        }

        public void setCount(String count) {
            this.count = count;
        }
    }

    public Products_ByCategory() {
        map = "docs.Products.Select(product => new { " +
            "    Product = Product, " +
            "    CategoryName = (this.LoadDocument(product.Category, \"Categories\")).Name " +
            "}).Select(this0 => new { " +
            "    Category = this0.CategoryName, " +
            "    Count = 1 " +
            "})";

        reduce = "results.GroupBy(result => result.Category).Select(g => new { " +
            "    Category = g.Key, " +
            "    Count = Enumerable.Sum(g, x => ((int) x.Count)) " +
            "})";
    }
}
    public static class Products_ByCategory extends AbstractJavaScriptIndexCreationTask {
    public Products_ByCategory() {
        setMaps(Sets.newHashSet("map('products', function(p){\n" +
            "            return {\n" +
            "                Category: load(p.Category, 'Categories').Name,\n" +
            "                Count: 1\n" +
            "            }\n" +
            "        })"));

        setReduce("groupBy(x => x.Category)\n" +
            "    .aggregate(g => {\n" +
            "        return {\n" +
            "            Category: g.key,\n" +
            "            Count: g.values.reduce((count, val) => val.Count + count, 0)\n" +
            "        };\n" +
            "    })");
    }
}

    and issue the query:

    List<Products_ByCategory.Result> results = session
    .query(Products_ByCategory.Result.class, Products_ByCategory.class)
    .whereEquals("Category", "Seafood")
    .toList();
    from 'Products/ByCategory'
where Category == 'Seafood'

    The above query will return one result for Seafood with the appropriate number of products from that category.

    Example II - Average

    In this example, we will count an average product price for each category. The index definition:

    public static class Products_Average_ByCategory extends AbstractIndexCreationTask {
    public static class Result {
        private String category;
        private double priceSum;
        private double priceAverage;
        private int productCount;

        public String getCategory() {
            return category;
        }

        public void setCategory(String category) {
            this.category = category;
        }

        public double getPriceSum() {
            return priceSum;
        }

        public void setPriceSum(double priceSum) {
            this.priceSum = priceSum;
        }

        public double getPriceAverage() {
            return priceAverage;
        }

        public void setPriceAverage(double priceAverage) {
            this.priceAverage = priceAverage;
        }

        public int getProductCount() {
            return productCount;
        }

        public void setProductCount(int productCount) {
            this.productCount = productCount;
        }
    }

    public Products_Average_ByCategory() {
        map = "docs.Products.Select(product => new { " +
            "    Product = Product, " +
            "    CategoryName = (this.LoadDocument(product.Category, \"Categories\")).Name " +
            "}).Select(this0 => new { " +
            "    Category = this0.CategoryName, " +
            "    PriceSum = this0.Product.PricePerUnit, " +
            "    PriceAverage = 0, " +
            "    ProductCount = 1 " +
            "})";

        reduce = "results.GroupBy(result => result.Category).Select(g => new { " +
            "    g = g, " +
            "    ProductCount = Enumerable.Sum(g, x => ((int) x.ProductCount)) " +
            "}).Select(this0 => new { " +
            "    this0 = this0, " +
            "    PriceSum = Enumerable.Sum(this0.g, x0 => ((decimal) x0.PriceSum)) " +
            "}).Select(this1 => new { " +
            "    Category = this1.this0.g.Key, " +
            "    PriceSum = this1.PriceSum, " +
            "    PriceAverage = this1.PriceSum / ((decimal) this1.this0.ProductCount), " +
            "    ProductCount = this1.this0.ProductCount " +
            "})";
    }
}
    public static class Product_Average_ByCategory extends AbstractJavaScriptIndexCreationTask {
    public Product_Average_ByCategory() {
        setMaps(Sets.newHashSet("map('products', function(product){\n" +
            "    return {\n" +
            "        Category: load(product.Category, 'Categories').Name,\n" +
            "        PriceSum: product.PricePerUnit,\n" +
            "        PriceAverage: 0,\n" +
            "        ProductCount: 1\n" +
            "    }\n" +
            "})"));

        setReduce("groupBy(x => x.Category)\n" +
            "        .aggregate(g => {\n" +
            "          var pricesum = g.values.reduce((sum,x) => x.PriceSum + sum,0);\n" +
            "          var productcount = g.values.reduce((sum,x) => x.ProductCount + sum,0);\n" +
            "          return {\n" +
            "            Category: g.key,\n" +
            "            PriceSum: pricesum,\n" +
            "            ProductCount: productcount,\n" +
            "            PriceAverage: pricesum / productcount\n" +
            "          }\n" +
            "        })");
    }
}

    and the query:

    List<Products_Average_ByCategory.Result> results = session
    .query(Products_Average_ByCategory.Result.class, Products_Average_ByCategory.class)
    .whereEquals("Category", "Seafood")
    .toList();
    from 'Products/Average/ByCategory'
where Category == 'Seafood'

    Example III - Calculations

    This example illustrates how we can put some calculations inside an index using on one of the indexes available in the sample database (Product/Sales).

    We want to know how many times each product was ordered and how much we earned for it. In order to extract that information, we need to define the following index:

    public static class Product_Sales extends AbstractIndexCreationTask {
    public static class Result {
        private String product;
        private int count;
        private double total;

        public String getProduct() {
            return product;
        }

        public void setProduct(String product) {
            this.product = product;
        }

        public int getCount() {
            return count;
        }

        public void setCount(int count) {
            this.count = count;
        }

        public double getTotal() {
            return total;
        }

        public void setTotal(double total) {
            this.total = total;
        }
    }

    public Product_Sales() {
        map = "docs.Orders.SelectMany(order => order.Lines, (order, line) => new { " +
            "    Product = line.Product, " +
            "    Count = 1, " +
            "    Total = (((decimal) line.Quantity) * line.PricePerUnit) * (1M - line.Discount) " +
            "})";


        reduce = "results.GroupBy(result => result.Product).Select(g => new { " +
            "    Product = g.Key, " +
            "    Count = Enumerable.Sum(g, x => ((int) x.Count)), " +
            "    Total = Enumerable.Sum(g, x0 => ((decimal) x0.Total)) " +
            "})";
    }
}
    public static class Product_Sales extends AbstractJavaScriptIndexCreationTask {
    public Product_Sales() {
        setMaps(Sets.newHashSet("map('orders', function(order){\n" +
            "            var res = [];\n" +
            "            order.Lines.forEach(l => {\n" +
            "              res.push({\n" +
            "                Product: l.Product,\n" +
            "                Count: 1,\n" +
            "                Total:  (l.Quantity * l.PricePerUnit) * (1- l.Discount)\n" +
            "              })\n" +
            "            });\n" +
            "            return res;\n" +
            "        })"));

        setReduce("groupBy(x => x.Product)\n" +
            "    .aggregate(g => {\n" +
            "        return {\n" +
            "            Product : g.key,\n" +
            "            Count: g.values.reduce((sum, x) => x.Count + sum, 0),\n" +
            "            Total: g.values.reduce((sum, x) => x.Total + sum, 0)\n" +
            "        }\n" +
            "    })");
    }
}

    and send the query:

    List<Product_Sales.Result> results = session
    .query(Product_Sales.Result.class, Product_Sales.class)
    .toList();
    from 'Product/Sales'

    Reduce Results as Artificial Documents

    In addition to storing the aggregation results in the index, the map-reduce indexes can also output reduce results as documents to a specified collection. In order to create such documents, called artificial, you need to define the target collection using the OutputReduceToCollection property in the index definition.

    public static class Product_Sales_ByMonth extends AbstractIndexCreationTask {
    public static class Result {
        private String product;
        private Date month;
        private int count;
        private double total;

        public String getProduct() {
            return product;
        }

        public void setProduct(String product) {
            this.product = product;
        }

        public Date getMonth() {
            return month;
        }

        public void setMonth(Date month) {
            this.month = month;
        }

        public int getCount() {
            return count;
        }

        public void setCount(int count) {
            this.count = count;
        }

        public double getTotal() {
            return total;
        }

        public void setTotal(double total) {
            this.total = total;
        }
    }

    public Product_Sales_ByMonth() {
        map = "docs.Orders.SelectMany(order => order.Lines, (order, line) => new { " +
            "    Product = line.Product, " +
            "    Month = new DateTime(order.OrderedAt.Year, order.OrderedAt.Month, 1), " +
            "    Count = 1, " +
            "    Total = (((decimal) line.Quantity) * line.PricePerUnit) * (1M - line.Discount) " +
            "})";

        reduce = "results.GroupBy(result => new { " +
            "    Product = result.Product, " +
            "    Month = result.Month " +
            "}).Select(g => new { " +
            "    Product = g.Key.Product, " +
            "    Month = g.Key.Month, " +
            "    Count = Enumerable.Sum(g, x => ((int) x.Count)), " +
            "    Total = Enumerable.Sum(g, x0 => ((decimal) x0.Total)) " +
            "})";

        outputReduceToCollection = "MonthlyProductSales";
    }
}
    public static class Product_Sales_ByMonth extends AbstractJavaScriptIndexCreationTask {
    public Product_Sales_ByMonth() {
        setMaps(Sets.newHashSet("map('orders', function(order){\n" +
            "            var res = [];\n" +
            "            order.Lines.forEach(l => {\n" +
            "            res.push({\n" +
            "                Product: l.Product,\n" +
            "                Month: new Date( (new Date(order.OrderedAt)).getFullYear(),(new Date(order.OrderedAt)).getMonth(),1),\n" +
            "                Count: 1,\n" +
            "                Total: (l.Quantity * l.PricePerUnit) * (1- l.Discount)\n" +
            "            })\n" +
            "        });\n" +
            "        return res;\n" +
            "    })"));

        setReduce("groupBy(x => ({Product: x.Product, Month: x.Month}))\n" +
            "    .aggregate(g => {\n" +
            "        return {\n" +
            "            Product: g.key.Product,\n" +
            "            Month: g.key.Month,\n" +
            "            Count: g.values.reduce((sum, x) => x.Count + sum, 0),\n" +
            "            Total: g.values.reduce((sum, x) => x.Total + sum, 0)\n" +
            "        }\n" +
            "    })");

        setOutputReduceToCollection("MonthlyProductSales");
    }
}

    Writing map-reduce outputs into documents allows you to define additional indexes on top of them that give you the option to create recursive map-reduce operations. This way, you can do daily/monthly/yearly summaries very cheaply and easy.

    In addition, you can also apply the usual operations on documents (e.g. data subscriptions or ETL).

    Recursive indexing loop

    It is forbidden to output reduce results to a collection when:

    • It is a collection that the current index is already working on
      (e.g. index on DailyInvoices collections outputs to DailyInvoices)
    • It is a collection that the current index is loading a document from
      (e.g. index has LoadDocument(id, "Invoices") outputs to Invoices)
    • it is a collection that is processed by another map-reduce index, that outputs results to a collection that the current index is working on
      (e.g. one index indexes the Invoices collection and outputs to the DailyInvoices collection, and a second index indexes the DailyInvoices collection and outputs to the Invoices collection)

    The reason these scenarios are forbidden is that they result in infinite indexing loop. Attempting to create such indexes will produce a detailed error.

    Output to an Existing collection

    Creating a map-reduce index which defines an output collection that already exists and contains documents, will result in an error. Please delete all documents from the target collection before creating the index or output the results to a different collection.

    Modification of Artificial Documents

    Artificial documents can be loaded and queried just like regular documents.
    However, it is not recommended to edit artificial documents manually since any index results update would overwrite all manual modifications made in them.

    Artificial Document IDs

    The identifiers of artificial documents are generated as:

    • <OutputCollectionName>/<hash-of-reduce-key>

    For the above sample index, the document ID can be:

    • MonthlyProductSales/13770576973199715021

    The numeric part is the hash of the reduce key values, in this case: hash(Product, Month).

    If the aggregation value for a given reduce key changes then we overwrite the artificial document. It will get removed once there is no result for a given reduce key.

    Artificial Document Flags

    Documents generated by map-reduce indexes get the following @flags metadata:

    "@flags": "Artificial, FromIndex"

    Those flags are used internally by the database to filter out artificial documents during replication.

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