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    Map-Reduce Visualizer

    Map-Reduce Visualizer Usage Flow

    • 1. Select a Map-Reduce index and the documents to view
    Figure 1. Select an Index

    Figure-1: Select Map-Reduce Index and Document IDs

    • 2. View the graphical representation of the documents and their related 'group-by' reduced key field from the index
    Figure 2. View relation

    Figure-2: Graphical Representation of the Documents and the Reduced Key

    • In the above example, the selected documents orders/1-A, orders/27-A, orders/829-A are related to the key: companies/85-A.
      i.e. All 3 documents have the value 'companies/85-A' in their 'Company' field in the document body.

    • Document ordes/100-A has the value companies/21-A in its 'Company' field.

    • 3. View the detailed view for the key reduced results
    Figure 3. Key details

    Figure-3: Detailed View of the Key Reduced Results

    1. The total number of orders documents made that contain the key 'companies/85-A' - which is 6
      and the total orders amount made by company 'companies/85-A' - which is 2,272.75

    2. A detailed info of these aggregated values showing per document selected.
      Note: per each document, the OrdesCount is '1', as each company makes one order.

    • 4. View details of all entries
    Figure 4. All Entry details

    Figure-4: Detailed View of All Entries

    • A detailed view of all the entries in the index that have 'companies/85-A' for their key (the 'Company' field).
      Only the selected documents are listed explicitly in the Source Document column.

    Map-Reduce Visualizer for Big Data

    • 5. Visualizer results for big data
    Figure 5. Big Data Tree

    Figure-5: Results for Big Data

    • Once past a certain size, RavenDB starts processing the Map-Reduce entries in a treelike fashion.

    • 6. Visualizer details for big data
    Figure 6. Big Data Details

    Figure-6: Details for Big Data

    • When there is a lot of data for a particular key, RavenDB will segment the data,
      so that a minimal number of aggregation operations is required.

    • For example, consider an update to document orders/77-A in the above image.
      First, the Map function is run on the updated document, giving a Map entry to write to page #1047.
      Then, the Reduce function is run on this page, giving the final tally for page #1047.
      We'll then recurse upward, toward page #1391, where the Reduce is run again.

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