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7 results found

  1. Implement $bucket and $group on indexed values with sub-linear runtime

    We noticed that sum $bucket and $group aggregations such as $min, $max, $count are unexpectedly slow even when fully covered by an index, (partially) because the DB scans through the entire index rather than employing optimization approaches such as binary search.

    An example pipeline that should return instantaneous but scans through the entire index (confirmed on v4.4 and v5):
    [
    {
    $match: {
    status: "DELIVERED",
    },
    },
    {
    $group: {
    id: {
    status: "$status",
    },
    min: {
    $min: "$modify
    time",
    },
    },
    },
    ]
    with an index { status: 1, modify_time: 1}

    Another example is $bucket (same index):
    [
    {…

    6 votes

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  2. Avoid truncating the query on the Atlas profiler or system.profile collection

    Slow running queries that are captured in system.profile collection or on profiler page of Atlas are truncated if the query is too long. As an Application DBA, it would be difficult to analyse the query without figuring out the actual query. The current limitation of command document is 50Kb. Request you to consider this limitation to avoid truncation of queries.

    1 vote

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  3. throttle sessions which use too much resources

    We have different types of applications :
    1. Writer - to load data into mongodb from different data sources
    2. Reader - to read data and display to end user.

    Normally, there is strict SLA for reader , but no SLA (or less restricted) for writers. We want to make sure that writer will not impact reader in case when for some reason a lot of data arrived from external sources. So, we would like to slow down writers for the sake of readers.

    Writers can saturate CPUs and IO, that's why we want an option to leave some room…

    1 vote

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  4. Improve sorting performance

    Sorting always ends up doing a collection scan when the selected index for the find/match does meet the sort requirement. The sort effectively makes the performance worse by 15-25 times for the "matched" dataset which runs into 10s of thousands (not millions) of documents

    1 vote

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  5. Build MongoDB with PGO

    I would like to see support for PGO (and even LLVM Bolt) in the upstream. Would be awesome if MongoDB will distribute PGO-optimized binaries, so the users will be able to see an additional performance boost "for free". At least describe to the users somewhere in the documentation, how they could achieve a boost for their own scenarios with PGO.

    1 vote

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  6. Boost the performance of bioinformatic annotation queries

    The documents to be selected look something like this:

    {
    "_id": {
    "$oid": "6272c580d4400d8cb10d5406"
    },
    "#CHROM": 1,
    "POS": 286747,
    "ID": "rs369556846",
    "REF": "A",
    "ALT": "G",
    "QUAL": ".",
    "FILTER": ".",
    "INFO": [{
    "RS": 369556846,
    "RSPOS": 286747,
    "dbSNPBuildID": 138,
    "SSR": 0,
    "SAO": 0,
    "VP": "0x050100000005150026000100",
    "WGT": 1,
    "VC": "SNV",
    "CAF": [{
    "$numberDecimal": "0.9381"
    }, {
    "$numberDecimal": "0.0619"
    }],
    "COMMON": 1,
    "TOPMED": [{
    "$numberDecimal": "0.88411856523955147"
    }, {
    "$numberDecimal": "0.11588143476044852"
    }]
    },
    ["SLO", "ASP", "VLD", "G5", "KGPhase3"]
    ]
    }

    For a basic annotation (https://en.wikipedia.org/wiki/SNP_annotation) scenario, we need such query:

    {'ID': {'$in': ['rs369556846', 'rs2185539', 'rs2519062', 'rs149363311', 'rs55745762', <...>]}}
    , where <...> means hundreds/thousands…

    1 vote

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  7. There is a specific collection that I need more performance than others. Is there a way to assign more ram/memory to a specific collection?

    There is a specific collection that I need more performance than others. Is there a way to assign more ram/memory to a specific collection?

    1 vote

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