How to sum multidimensional arrays across documents with MongoDB - arrays

Question

Say I have a lot of the following documents:
{
_id: “abc”,
values: {
0: { 0: 999999, 1: 999999, …, 59: 1000000 },
1: { 0: 2000000, 1: 2000000, …, 59: 1000000 },
…,
58: { 0: 1600000, 1: 1200000, …, 59: 1100000 },
59: { 0: 1300000, 1: 1400000, …, 59: 1500000 }
}
}
{
_id: “def”,
values: {
0: { 0: 999999, 1: 999999, …, 59: 1000000 },
1: { 0: 2000000, 1: 2000000, …, 59: 1000000 },
…,
58: { 0: 1600000, 1: 1200000, …, 59: 1100000 },
59: { 0: 1300000, 1: 1400000, …, 59: 1500000 }
}
}
which is basically a multidimensional array of 60x60 items.
can aggregation (or any other mongodb construct) be used to easily sum the two (or more) matrixes? i.e. values[x][y] of both abc and def are summed together, and the same is done for all other elements?
Ideally the output would be a similar multidimensional array.
This answer seems to suggest this is possible with 1 dimensional array but I am not sure for multidimensional.
EDIT:
This is an example with real data in a format which is slightly different:
db.col.find({}, { _id: 0, hit: 1 })
{ "hit" : [ [ 570, 0, 630, 630, 636, 735, 672, 615, 648, 648, 618, 0 ],
[ 492, 0, 471, 471, 570, 564, 0, 590, 513, 432, 471, 477 ],
[ 387, 0, 0, 0, 0, 0, 0, 456, 0, 480, 351, 415 ],
[ 432, 528, 0, 0, 495, 509, 0, 579, 0, 552, 0, 594 ],
[ 558, 603, 594, 624, 672, 0, 0, 705, 783, 0, 756, 816 ],
[ 0, 858, 951, 1027, 0, 0, 1058, 1131, 0, 0, 1260, 1260 ],
[ 1269, 0, 1287, 0, 1326, 0, 1386, 1386, 1470, 0, 0, 0 ],
[ 1623, 0, 1695, 1764, 1671, 1671, 0, 1824, 1872, 0, 0, 0 ],
[ 1950, 1894, 2034, 2034, 0, 0, 1941, 0, 2070, 1911, 2049, 2055 ],
[ 2052, 2052, 0, 0, 0, 2085, 2007, 2073, 0, 0, 0, 1941 ],
[ 1878, 1896, 0, 1875, 0, 0, 1677, 0, 1722, 0, 1545, 0 ],
[ 0, 0, 1317, 1469, 1501, 1634, 1494, 0, 0, 1290, 0, 0 ],
[ 0, 1485, 1375, 1491, 1530, 1407, 0, 0, 0, 1611, 0, 0 ],
[ 1652, 1800, 1686, 1643, 1923, 0, 0, 0, 1737, 1604, 1797, 0 ],
[ 1842, 1806, 0, 1830, 1896, 1947, 0, 1710, 1734, 1725, 0, 0 ],
[ 0, 0, 1932, 0, 1908, 1878, 1941, 1931, 2007, 2013, 1995, 1995 ],
[ 0, 2025, 2004, 1927, 0, 0, 1939, 1835, 1962, 1863, 0, 1815 ],
[ 0, 0, 1839, 1755, 1821, 1821, 1751, 1656, 0, 0, 1467, 0 ],
[ 0, 1632, 1546, 1449, 0, 1551, 1449, 0, 0, 1554, 0, 1491 ],
[ 1463, 1411, 0, 1491, 0, 0, 1551, 1467, 0, 0, 0, 1464 ],
[ 0, 0, 1311, 0, 0, 1471, 0, 0, 1581, 0, 1368, 1368 ],
[ 1296, 0, 0, 0, 1176, 1381, 0, 1170, 1194, 1194, 1193, 1137 ],
[ 0, 1244, 1221, 1039, 0, 1041, 930, 921, 1033, 813, 0, 0 ],
[ 0, 0, 0, 1010, 0, 0, 918, 783, 0, 609, 693, 645 ] ] }
And this is the appropriate query (thanks to Veeram in the comments for fixing my code):
db.col.aggregate([
{ $project: { _id: 0, hit: 1 } },
{ $unwind: { path: "$hit", includeArrayIndex: "x" } },
{ $unwind: { path: "$hit", includeArrayIndex: "y" } },
{ $group: { _id: { x: "$x", y: "$y" }, hit: { $sum: "$hit" } } },
{ $sort: { "_id.x": 1, "_id.y": 1 } },
{ $group: { _id: "$_id.x", hit: { $push: "$hit" } } },
{ $sort: { "_id": 1 } },
{ $group: { _id: null, hit: { $push: "$hit" } } }
])

Answer 1

You need two operators to deal with dynamic properties: $objectToArray and $arrayToObject. To sum the values from all documents you can try to represent each x,y pair as single document (using $unwind) and then use several $group stages to get single document as a result. To get the initial order of your rows and columns you can apply $sort twice:
db.col.aggregate([
{
$project: {
values: {
$map: {
input: { $objectToArray: "$values" },
as: "obj",
in: { k: "$$obj.k", v: { $objectToArray: "$$obj.v" } }
}
}
}
},
{
$unwind: "$values"
},
{
$unwind: "$values.v"
},
{
$project: {
x: "$values.k",
y: "$values.v.k",
value: "$values.v.v"
}
},
{
$group: {
_id: { x: "$x", y: "$y" },
value: { $sum: "$value" }
}
},
{
$sort: {
"_id.y": 1
}
},
{
$group: {
_id: "$_id.x",
v: { $push: { k: "$_id.y", v: "$value" } }
}
},
{
$sort: {
"_id": 1
}
},
{
$group: {
_id: null,
values: { $push: { k: "$_id", v: "$v" } }
}
},
{
$project: {
values: {
$arrayToObject: {
$map: {
input: "$values",
as: "obj",
in: {
k: "$$obj.k",
v: { $arrayToObject: "$$obj.v" }
}
}
}
}
}
}
])
For your sample data it outputs:
{
"_id" : null,
"values" : {
"0" : {
"0" : 1999998,
"1" : 1999998,
"59" : 2000000
},
"1" : {
"0" : 4000000,
"1" : 4000000,
"59" : 2000000
},
"58" : {
"0" : 3200000,
"1" : 2400000,
"59" : 2200000
},
"59" : {
"0" : 2600000,
"1" : 2800000,
"59" : 3000000
}
}
}