38 results found

1. Allow configuration of 100mb memory limit per aggregation pipeline stage

   In this old thread from 2016 (https://groups.google.com/forum/#!topic/mongodb-user/LCeFZZRz5EY) it was asked whether there was a way to increase the 100mb in memory limit of each stage of an aggregation pipeline. The responses centered around two points:

   1. If too much memory is used per aggregation pipeline stage then it will reduce performance for the overall MongoDB database, impacting other queries negatively.
   2. You can set allowDiskUse: true and revert to performing these pipeline stages on disk when they exceed 100mb.

   I believe this subject needs to be revisited for the following reasons:

   1. “Too much memory” is very subjective, and the 100mb number is arbitrary. The hardware for a typical MongoDB database setup in 2016 is not the same as in 2020. And what is the MongoDB use case for one company isn’t the use case for another company. Company A might have top of the line hardware they are running MongoDB on and only have a few concurrent users, and need large in memory aggregation queries to run fast so want to set a limit of 200mb or even higher. Company B might have lesser hardware and many concurrent users, and want to set the limit to 50mb or less.
   2. The allowDiskUse: true option is nice to have, but it obviously comes at a huge performance hit once your aggregation pipeline stages exceed the 100mb limit, even on high performance SSD drives.

   This really comes down to who knows best what the limit should be, the company’s developers and database / server ops teams or the MongoDB developers? The MongoDB developers obviously have much more knowledge on the inner workings of Mongo, but that advice can still be taken into account by having a default limit of 100mb. I believe very strongly that today in 2020 this limit should be configurable so that we can customize the behavior for our own specific situation. The documentation can of course provide a warning that changing the setting can impact performance (although everyone already knows that.)

   If you want to get into the details of why my company needs to increase this limit, and why it has been holding us back from switching from SQL Server to MongoDB for the last year (despite spending 6 months developing a working Mongo solution) then we can get into specifics. But really that will distract away from the central issue, which is that different customers have different scenarios, and a “one size fits all” solution isn’t appropriate and needs to be customizable.

   In this old thread from 2016 (https://groups.google.com/forum/#!topic/mongodb-user/LCeFZZRz5EY) it was asked whether there was a way to increase the 100mb in memory limit of each stage of an aggregation pipeline. The responses centered around two points:

   1. If too much memory is used per aggregation pipeline stage then it will reduce performance for the overall MongoDB database, impacting other queries negatively.
   2. You can set allowDiskUse: true and revert to performing these pipeline stages on disk when they exceed 100mb.

   I believe this subject needs to be revisited for the following reasons:

   1. “Too much memory” is very subjective, and the 100mb… more
   26 votes

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2. Add operator that would calculate distance between 2 geolocation points

   It would be great to have operator that would calculate distance between 2 geolocation points, and not to do it manually with big aggregate queries.

   I suggest to add 2 new operators that would calculate distance in two different ways, as discussed in this Community Post: https://www.mongodb.com/community/forums/t/how-to-calculate-distance-between-two-geolocation-points/173045

   19 votes

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3. geoContain

   Dear all,
   according to the attached image, I have some documents (in blue, with id from 1 to 5) having a geographic extent and a search area (in yellow).
   I need to find all documents where search area is completly inside the document's geometry.
   Using different words, I need to find all documents where geometry completly covers the given search area.
   In my sample, the geo query should return the document with id 1.
   This kind of query has a opposite logic than the $geoWithin

   Could you provide a $geoContain functionality in the next future?

   •  

   10 votes

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4. Handle Daylight Saving Time when $densify is used on a date field

   When using "day" as "unit" for a $densify pipeline stage on a date field, the date is always advanced of 24 hours. This is however not always the expected result in timezones in which the year has one 23-hour and one 25-hour long day, because of Daylight Saving Time.

   It would be useful to have the possibility to pass an optional timezone parameter in the $densify stage and, when present, have the stage account for these exceptions when appropriate.

   Here follows an example.

   Assume we have a collection containing the following documents:

   db.densifyDateExample.insertMany([
       {_id: "a", d: ISODate("2022-10-28T22:00:00Z")},
       {_id: "b", d: ISODate("2022-10-29T22:00:00Z")},
       {_id: "c", d: ISODate("2022-10-30T23:00:00Z")},
       {_id: "d", d: ISODate("2022-10-31T23:00:00Z")},
       {_id: "e", d: ISODate("2022-11-01T23:00:00Z")},
       {_id: "f", d: ISODate("2022-11-02T23:00:00Z")}
   ])
   

   Keep in mind that on 2022-10-30, in EU countires, the time switched from CEST to CET. So those are the UTC representations of the beginning of the days from Oct 29th to Nov 3rd.

   Now, if we run the following pipeline:

   db.densifyDateExample.aggregate([{
       $densify: {
           "field": "d",
           "range": {
               "bounds": [ISODate("2022-10-25T22:00:00.000Z"), ISODate("2022-11-06T23:00:00.000Z")],
               "step": 1,
               "unit": "day"
           }
       }
   }])
   

   We get the following result:

   { "d" : ISODate("2022-10-25T22:00:00Z") }
   { "d" : ISODate("2022-10-26T22:00:00Z") }
   { "d" : ISODate("2022-10-27T22:00:00Z") }
   { "_id" : "a", "d" : ISODate("2022-10-28T22:00:00Z") }
   { "_id" : "b", "d" : ISODate("2022-10-29T22:00:00Z") }
   { "d" : ISODate("2022-10-30T22:00:00Z") }
   { "_id" : "c", "d" : ISODate("2022-10-30T23:00:00Z") }
   { "d" : ISODate("2022-10-31T22:00:00Z") }
   { "_id" : "d", "d" : ISODate("2022-10-31T23:00:00Z") }
   { "d" : ISODate("2022-11-01T22:00:00Z") }
   { "_id" : "e", "d" : ISODate("2022-11-01T23:00:00Z") }
   { "d" : ISODate("2022-11-02T22:00:00Z") }
   { "_id" : "f", "d" : ISODate("2022-11-02T23:00:00Z") }
   { "d" : ISODate("2022-11-03T22:00:00Z") }
   { "d" : ISODate("2022-11-04T22:00:00Z") }
   { "d" : ISODate("2022-11-05T22:00:00Z") }
   { "d" : ISODate("2022-11-06T22:00:00Z") }
   

   While - after somehow telling $densify in which timezone we are working - the following would be way more useful:

   { "d" : ISODate("2022-10-25T22:00:00Z") }
   { "d" : ISODate("2022-10-26T22:00:00Z") }
   { "d" : ISODate("2022-10-27T22:00:00Z") }
   { "_id" : "a", "d" : ISODate("2022-10-28T22:00:00Z") }
   { "_id" : "b", "d" : ISODate("2022-10-29T22:00:00Z") }
   { "_id" : "c", "d" : ISODate("2022-10-30T23:00:00Z") }
   { "_id" : "d", "d" : ISODate("2022-10-31T23:00:00Z") }
   { "_id" : "e", "d" : ISODate("2022-11-01T23:00:00Z") }
   { "_id" : "f", "d" : ISODate("2022-11-02T23:00:00Z") }
   { "d" : ISODate("2022-11-03T23:00:00Z") }
   { "d" : ISODate("2022-11-04T23:00:00Z") }
   { "d" : ISODate("2022-11-05T23:00:00Z") }
   { "d" : ISODate("2022-11-06T23:00:00Z") }
   

   When using "day" as "unit" for a $densify pipeline stage on a date field, the date is always advanced of 24 hours. This is however not always the expected result in timezones in which the year has one 23-hour and one 25-hour long day, because of Daylight Saving Time.

   It would be useful to have the possibility to pass an optional timezone parameter in the $densify stage and, when present, have the stage account for these exceptions when appropriate.

   Here follows an example.

   Assume we have a collection containing the following documents:

   db.densifyDateExample.insertMany([
       {_id: "a", d: ISODate("2022-10-28T22:00:00Z")},
       {_id: "b", d:

   … more
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5. Raise the limit of 16 MB JSON between aggregation stages

   When doing analytics, the 16 MB JSON limit between aggregation stages restricts the ability to process large amount of data. allowDiskUse does not help with all the various $operators and stages that we use. See ticket 00774514 for details.

   7 votes

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6. Support compound/multiple grouping keys in $bucket

   We often need to compute statistical/summarizing aggregations grouped by more than one field where all fields are of a $bucket-able type.

   An example, would be to count all orders grouped by their status and some custom time ranges of their creation date.
   This can be achieved by using $group in combination with a $switch expression (sometimes simplified with $trunc), however, that is cumbersome and prevents efficient grouping since e.g. no binary search can be employed to identify the bucket boundaries efficiently.

   The query syntax of $bucket would not need to change much. It would simply need to allow for nested arrays within the boundaries parameter whereby each nested array specifies the requested grouping range.

   We often need to compute statistical/summarizing aggregations grouped by more than one field where all fields are of a $bucket-able type.

   An example, would be to count all orders grouped by their status and some custom time ranges of their creation date.
   This can be achieved by using $group in combination with a $switch expression (sometimes simplified with $trunc), however, that is cumbersome and prevents efficient grouping since e.g. no binary search can be employed to identify the bucket boundaries efficiently.

   The query syntax of $bucket would not need to change much. It would simply need to allow for nested… more

   6 votes

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7. `$getField` to work with a dynamic `field`

   Currently $getField works only when field resolves at query-compile-time to a string. It would be nice if it worked also when field resolves to a string at runtime.

   See this Jira ticket - https://jira.mongodb.org/browse/SERVER-67030

   5 votes

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8. Document scoped RBAC - Permission for collection document fields

   Roles and accesses can be defined on the basis of collections that define roles for users.
   It would be nice if these access permissions could be made within the scope of the fields under the collection and the query results would be returned accordingly.

   Current:
   
privileges: [
{ resource: { db: "users", collection: "user" }, actions: [ "find"] },
}


   Expected:
   
{ resource: { db: "users", collection: "user", field: "email" }, actions: [ "find"] },


   3 votes

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9. support parallel query executions include find(), aggregation()

   To use multi-core environment and enhance the query performance w/ a large amount of documents, need a parallel execution.
   Sharding or microsharding is not an alternative in this case.

   2 votes

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10. Flatten arrays in group stage

    Have group operators to flatten document arrays into a single one with or without repeated elements.
    So ->
    doc1 = {arr: [1,2,3,4], gr: "group"}, doc2 = {arr: [5, 6, 7, 8], gr: "group"}
    {$group: {id: "$gr", arrays: {$***: "$arr"} } }
    =>
    {id: "gr", arrays: [1, 2, 3, 4, 5, 6, 7, 8]}

    2 votes

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11. $merge

    Report number of docs matched, merged, skipped, etc. from a $merge stage. Alternatively, return the merged doc results as a pipeline result to pass to additional stages.

    2 votes

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12. geo

    It would be nice to get the length of an LineString of a geo-json object or the possibility to write an aggregation to calculate it.

    2 votes

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13. hint support for $graphLookup

    Currently you can supply a hint to the aggregation call in order to tell MongoDB to use a specific index for the initial $match. But there is currently no way to specify which index to use for a $graphLookup later in the pipeline.

    I would like an optional hint property on the $graphLookup stage.

    2 votes

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14. Support expressions in $densify range bounds

    The $densify aggregation pipeline stage seems unable to evaluate range bounds expressions, requiring the range bounds to be constant.

    See the following example (the collection testcoll contains a single documents with only the _id field):

    sometestdb> db.testcoll.aggregate([{$addFields: {a: 1}}, {$densify: {field: "a", range: {bounds: [0, 5], step: 1}}}])
    
    [
    
      { a: 0 },
    
      { _id: ObjectId("6284a16d64553eaf74b1e189"), a: 1 },
    
      { a: 2 },
    
      { a: 3 },
    
      { a: 4 }
    
    ]
    
    
    
    sometestdb> db.testcoll.aggregate([{$addFields: {a: 1}}, {$densify: {field: "a", range: {bounds: [{$toInt: "0"}, 5], step: 1}}}])
    
    MongoServerError: A bounding array must be an ascending array of either two dates or two numbers
    

    The first aggregate() call gives the correct result, and I'd expect the second one to evaluate the $toInt and yield the same result. Of course $toInt isn't a particularly useful use case but there are indeed useful computations which an user may want to do on the range bounds; for example, when using dates, one may want to truncate the start and end date to the hour or to the day before they are used for the densify operation.

    The $densify aggregation pipeline stage seems unable to evaluate range bounds expressions, requiring the range bounds to be constant.

    See the following example (the collection testcoll contains a single documents with only the _id field):

    sometestdb> db.testcoll.aggregate([{$addFields: {a: 1}}, {$densify: {field: "a", range: {bounds: [0, 5], step: 1}}}])
    
    [
    
      { a: 0 },
    
      { _id: ObjectId("6284a16d64553eaf74b1e189"), a: 1 },
    
      { a: 2 },
    
      { a: 3 },
    
      { a: 4 }
    
    ]
    
    
    
    sometestdb> db.testcoll.aggregate([{$addFields: {a: 1}}, {$densify: {field: "a", range: {bounds: [{$toInt: "0"}, 5], step: 1}}}])
    
    MongoServerError: A bounding array must be an ascending array of either two dates or

    … more
    2 votes

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15. support $lookup for update aggregation

    We frequently denormalise either full documents or subsets to different documents in order to speed up reading, create indexes or paginate/sort on fields.

    Consider a user collection and a task collection, if a task can be assigned to the user, it makes sense to just put the user document on the task they are assigned. But an update to a user now requires you to update the user both in the user collection and all tasks with that user in the tasks collection.

    This can be achieved but does introduce some complexity, however the introduction of updates using aggregation pipelines could eliminate this problem if they supported the $lookup operator.

    So we could achieve the desired result using two simple operations

    db.collection('users').updateOne({id: '1234'}, {$set: ...})
    db.collection('tasks').updateMany({"user.id": '1234'}, [
    {
    $lookup: {
    from: 'users',
    localField: 'user.id',
    foreignField: 'id',
    as: 'user'
    }
    }
    ]);

    This would make it really simple to synchronise data between documents in different collections

    We frequently denormalise either full documents or subsets to different documents in order to speed up reading, create indexes or paginate/sort on fields.

    Consider a user collection and a task collection, if a task can be assigned to the user, it makes sense to just put the user document on the task they are assigned. But an update to a user now requires you to update the user both in the user collection and all tasks with that user in the tasks collection.

    This can be achieved but does introduce some complexity, however the introduction of updates using aggregation pipelines… more

    2 votes

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16. Make $merge support DELETE operation

    Currently, the $merge only supports insert/update/upsert/merge behaviour. It would be great to support delete behaviour. A common use case would be in-place document deduplication/clean-up in a collection.

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17. Sorting support on Array fields.

    Presently, I'm attempting to arrange(sort) the documents within my collection based on a key nested within an array object. However, sorting isn't functioning as expected in this scenario.

    A proper explanation with example is here what I mean.
    https://www.mongodb.com/community/forums/t/how-to-sort-documents-in-collection-on-basis-of-array-fields/270867?u=samrat_n_a

    1 vote

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18. JSON

    I'm converting a lot of data for export from MongoDB -> Postgresql

    I would like an aggregation function to convert an object to JSON

    {
    "$addField": {
    "_id": true,
    "json": {
    '$convert': {
    'input': '$subdocument',
    'to': 'json', # // Idealy, +1 for msgpack https://msgpack.org/
    }
    }
    }
    }

    thus object.json would be a STRING "{abc: true}"

    FYI: Postgresql supports JSON(B) in it's field structures: https://www.postgresql.org/docs/current/datatype-json.html

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19. define the random seed manually, for $rand and $sample

    It will be great if an additional paramater to define the seed for $rand and $sample could be use.

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20. $group all fields

    $group should have the ability to allow specifying all fields in a document (without explicitly defining them all, which can lead to duplicating dozens of lines just to do "key: $first")

    This will help users that use $unwind and then want to $group the results without having to do a subsequent $lookup and $mergeObjects (or similar) to get the final document structure they're looking for.

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    1 comment  ·  Query and Aggregation Pipeline  ·  Delete…  ·  Admin →
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