kevcodez

db.assets.find({}, {
  projection: {
    "lastPrice": 0
  }
})

kevcodez

db.assets.find({}, {
  projection: {
    "lastPrice": 0,
    "marketCap": 0
  }
})

kevcodez

db.assets.find({
  marketCap: { $gt: 10000 }
}, { 
  projection: { isin: 1 }
})

kevcodez

db.assets.find({}, {
  projection: {
    marketCap: 1,
    lastPrice: 1,
    marketShares: { $divide: ["$marketCap", "$lastPrice"] } 
  }
})

kevcodez

{
    projection: {
        myLargeArray: {
            $slice: ['$myLargeArray', 0, 10]
        } 
    }
}

kevcodez

db.holdings.aggregate([
    {
        $group: {
            _id: '$portfolio',
            assetIdentifiers: {
                $addToSet: '$asset.identifier',
            },
        },
    },
    {

kevcodez

db.assets
  .find({ ipoDate: { $gt: ISODate('2022-01-01') } })
  .readPref('secondaryPreferred')

kevcodez

const client = await MongoClient.connect(mongoConfig().mongoUrl, {
  compressors: 'snappy', // make sure Snappy is installed
});

kevcodez

async function getQuotesForPeriod(
  assetIdentifier: string,
  from: Date,
  to: Date
): Promise<Quote[]> {
  const quotesCollection = await getQuotesCollection();

  /**
   * When fetching multiple years of data, queries are fast, but retrieval not so much.
   * To improve query performance, we split the interval into a few chunks and query/fetch the data in parallel,

kevcodez

const client = await MongoClient.connect('mongodb-url', {
  minPoolSize: 20,
  maxPoolSize: 100,
});