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ReQL command: group

Command syntax

sequence.group([field | function...,]) → grouped_stream
r.group(sequence, [field | function...,]) → grouped_stream

Description

Takes a stream and partitions it into multiple groups based on the fields or functions provided.

Two options are available via optArg: index can be the name of an index to group on (in place of a field. The multi flag, a boolean (default false), allows single documents to be assigned to multiple groups, similar to the behavior of multi-indexes. When multi is true and the grouping value is an array, documents will be placed in each group that corresponds to the elements of the array. If the array is empty the row will be ignored.

The data returned by group will be a List<GroupedResult>:

public class GroupedResult<G,V> {
    public final G group;
    public final List<V> values;

    public GroupedResult(G group, List<V> values){
        this.group = group;
        this.values = values;
    }
}

Suppose that the table games has the following data:

[
    {"id": 2, "player": "Bob", "points": 15, "type": "ranked"},
    {"id": 5, "player": "Alice", "points": 7, "type": "free"},
    {"id": 11, "player": "Bob", "points": 10, "type": "free"},
    {"id": 12, "player": "Alice", "points": 2, "type": "free"}
]

Example: Group games by player.

r.table("games").group("player").run(conn);

To show the returned data, we’ll use JSON representation again, with group and values as the fields corresponding to the elements in each GroupedResult.

[
    {
        "group": "Alice",
        "values": [
            {"id": 5, "player": "Alice", "points": 7, "type": "free"},
            {"id": 12, "player": "Alice", "points": 2, "type": "free"}
        ]
    },
    {
        "group": "Bob",
        "values": [
            {"id": 2, "player": "Bob", "points": 15, "type": "ranked"},
            {"id": 11, "player": "Bob", "points": 10, "type": "free"}
        ]
    }
]

Commands chained after group will be called on each of these grouped sub-streams, producing grouped data.

Example: What is each player’s best game?

r.table("games").group("player").max("points").run(conn);

[
    {
        "group": "Alice",
        "values": {"id": 5, "player": "Alice", "points": 7, "type": "free"}
    },
    {
        "group": "Bob",
        "values": {"id": 2, "player": "Bob", "points": 15, "type": "ranked"}
    }
]

Commands chained onto grouped data will operate on each grouped datum, producing more grouped data.

Example: What is the maximum number of points scored by each player?

r.table("games").group("player").max("points").g("points").run(conn);

[
    {
        "group": "Alice",
        "values": 7
    },
    {
        "group": "Bob",
        "values": 15
    }
]

You can also group by more than one field.

Example: What is the maximum number of points scored by each player for each game type?

r.table("games").group("player", "type").max("points").g("points").run(conn);

[
    {
        "group": ["Alice", "free"],
        "values": 7
    }
    {
        "group": ["Bob", "free"],
        "values": 10,
    },
    {
        "group": ["Bob", "ranked"],
        "values": 15,
    }
]

You can also group by a function.

Example: What is the maximum number of points scored by each player for each game type?

r.table("games").group(
    game -> game.pluck("player", "type")
).max("points").g("points").run(conn);

[
    {
        "group": {"player": "Alice", "type": "free"},
        "values": 7
    },
    {
        "group": {"player": "Bob", "type": "free"},
        "values": 10
    },
    {
        "group": {"player": "Bob", "type": "ranked"},
        "values": 15
    }
]

Using a function, you can also group by date on a ReQL date field.

Example: How many matches have been played this year by month?

r.table("matches").group(
    match -> r.array(match.g("date").year(), match.g("date").month())
).count().run(conn);

[
    {
        "group": [2014, 2],
        "values": 2
    },
    {
        "group": [2014, 3],
        "values": 2
    },
    {
        "group": [2014, 4],
        "values": 1
    },
    {
        "group": [2014, 5],
        "values": 3
    }
]

You can also group on an index (primary key or secondary).

Example: What is the maximum number of points scored by game type?

r.table("games").group().optArg("index", "type")
 .max("points").g("points").run(conn);

[
    {
        "group": "free",
        "values": 10
    },
    {
        "group": "ranked",
        "values": 15
    }
]

Organizing by value with multi

Suppose that the table games2 has the following data:

[
    { "id": 1, "matches": {"a": [1, 2, 3], "b": [4, 5, 6]} },
    { "id": 2, "matches": {"b": [100], "c": [7, 8, 9]} },
    { "id": 3, "matches": {"a": [10, 20], "c": [70, 80]} }
]

Using the multi option we can group data by match A, B or C.

r.table("games2").group(
    row -> row.g("matches").keys()
).optArg("multi", true).run(conn);

[
    {
        "group": "a",
        "values": [ <id 1>, <id 3> ]
    },
    {
        "group": "b",
        "values": [ <id 1>, <id 2> ]
    },
    {
        "group": "c",
        "values": [ <id 2>, <id 3> ]
    }
]

(The full result set is abbreviated in the figure; <id 1>, <id 2> and <id 3> would be the entire documents matching those keys.)

Example: Use map and sum to get the total points scored for each match.

r.table("games2").group(
    row -> row.g("matches").keys()
).optArg("multi", true).ungroup().map(
    doc -> r.hashMap("match", doc.g("group")).with(
        "total", doc.g("reduction").sum(
            set -> set.g("matches").bracket(doc.g("group")).sum()
        )
    )
).run(conn);

[
    { "match": "a", "total": 36 },
    { "match": "b", "total": 115 },
    { "match": "c", "total": 174 }
]

The inner sum adds the scores by match within each document; the outer sum adds those results together for a total across all the documents.

Ungrouping

If you want to operate on all the groups rather than operating on each group (e.g. if you want to order the groups by their reduction), you can use ungroup to turn a grouped stream or grouped data into an array of objects representing the groups.

The format of the array returned by ungroup is the same as the default native format of grouped data in the JavaScript driver and Data Explorer.

Example: Ungrouping grouped data.

r.table('games').group('player').max('points')['points'].ungroup().run(conn);

[
    {
        "group": "Alice",
        "reduction": 7
    },
    {
        "group": "Bob",
        "reduction": 15
    }
]

Ungrouping is useful e.g. for ordering grouped data, or for inserting grouped data into a table.

Example: What is the maximum number of points scored by each player, with the highest scorers first?

r.table("games").group("player").max("points").g("points").ungroup()
 .orderBy(r.desc("reduction")).run(conn);

[
    {
        "group": "Bob",
        "reduction": 15
    },
    {
        "group": "Alice",
        "reduction": 7
    }
]

Implementation Details

When grouped data are returned to the client, they are transformed into a client-specific native type. (Something similar is done with times.) In Java, grouped data are transformed into an List. If you instead want to receive the raw pseudotype from the server, you can specify group_format: "raw" as an optional argument to run:

Example: Get back the raw GROUPED_DATA pseudotype.

r.table("games").group("player").avg("points").run(conn).optArg("group_format", "raw");

{
    "$reql_type$": "GROUPED_DATA",
    "data": [
        ["Alice", 4.5],
        ["Bob", 12.5]
    ]
}

You might also want to use the ungroup command (see above), which will turn the grouped data into an array of objects on the server.

Performance Details

If you run a query that returns a grouped stream, it will be automatically converted to grouped data before being sent back to you (there is currently no efficient way to stream groups from RethinkDB). This grouped data is subject to the array size limit, by default 100,000 elements (see run for details on how to use the array_limit argument to change this).

In general, operations on grouped streams will be efficiently distributed, and operations on grouped data won’t be. You can figure out what you’re working with by putting typeOf on the end of your query. Below are efficient and inefficient examples.

Example: Efficient operation.

// r.table("games").group("player").typeOf().run(conn);
// Returns "GROUPED_STREAM"
r.table("games").group("player").min("points").run(conn); // EFFICIENT

Example: Inefficient operation.

// r.table("games").group("player").orderBy("score").typeOf().run(conn);
// Returns "GROUPED_DATA"
r.table("games").group("player").orderBy("score").nth(0).run(conn); // INEFFICIENT

What does it mean to be inefficient here? When operating on grouped data rather than a grouped stream, all of the data has to be available on the node processing the query. This means that the operation will only use one server’s resources, and will require memory proportional to the size of the grouped data it’s operating on. (In the case of the orderBy in the inefficient example, that means memory proportional to the size of the table.) The array limit is also enforced for grouped data, so the orderBy example would fail for tables with more than 100,000 rows without changing the arrayLimit option to run.

More Examples

Example: What is the maximum number of points scored by each player in free games?

r.table("games").filter(
    game -> game.g("type").eq("free")
).group("player").max("points").g("points").run(conn);

[
    {
        "group": "Alice",
        "values": 7
    },
    {
        "group": "Bob",
        "values": 10
    }
]

Example: What is each player’s highest even and odd score?

r.table("games").group(
    "name", game -> game.g("points").mod(2)
).max("points").g("points").run(conn);

[
    {
        "group": ["Alice", 1],
        "values": 7
    },
    {
        "group": ["Bob", 0],
        "values": 10
    },
    {
        "group": ["Bob", 1],
        "values": 15
    }
]

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