Named Time Series Values

In this example, we define a StockPrice class and use it when appending StockPrice entries.

class StockPrice {

    // Define the names for the entry values
    static TIME_SERIES_VALUES = ["open", "close", "high", "low", "volume"];

    constructor(
        open = 0,
        close = 0,
        high = 0,
        low = 0,
        volume = 0
    ) {
        Object.assign(this, {
            open,
            close,
            high,
            low,
            volume
        });
    }
}

const session = documentStore.openSession();
await session.store(new User("John"), "users/john");

// Get an instance of 'timeSeriesFor', pass:
// * the document ID
// * the time series name
// * the class that will hold the entry's values
const tsf = session.timeSeriesFor("users/john", "StockPrices", StockPrice);

const optionalTag = "companies/kitchenAppliances";
const baseTime = new Date();
baseTime.setUTCHours(0);
const oneDay = 24 * 60 * 60 * 1000;

// Provide the multiple values via the StockPrice class
const price1 = new StockPrice();
price1.open = 52;
price1.close = 54;
price1.high = 63.5;
price1.low = 51.4;
price1.volume = 9824;

// Call 'append' with the custom StockPrice class
let nextDay = new Date(baseTime.getTime() + oneDay);
tsf.append(nextDay, price1, optionalTag);

const price2 = new StockPrice();
price2.open = 54;
price2.close = 55;
price2.high = 61.5;
price2.low = 49.4;
price2.volume = 8400;

nextDay = new Date(baseTime.getTime() + oneDay * 2);
tsf.append(nextDay, price2, optionalTag);

const price3 = new StockPrice();
price3.open = 55;
price3.close = 57;
price3.high = 65.5;
price3.low = 50;
price3.volume = 9020;

nextDay = new Date(baseTime.getTime() + oneDay * 3);
tsf.append(nextDay, price3, optionalTag);

await session.saveChanges();

In this example, we get StockPrice values by name and check whether a stock's closing-time prices are ascending over time.

let goingUp = false;

const allEntries = await session
    .timeSeriesFor("users/john", "StockPrices")
    .get();

// Call 'asTypedEntry' to be able to access the entry's values by their names
// Pass the class type (StockPrice)
const typedEntry1 = allEntries[0].asTypedEntry(StockPrice);

// Access the entry value by its StockPrice class property name (close)
const closePriceDay1 = typedEntry1.value.close;

const typedEntry2 = allEntries[1].asTypedEntry(StockPrice);
const closePriceDay2 = typedEntry2.value.close;

const typedEntry3 = allEntries[2].asTypedEntry(StockPrice);
const closePriceDay3 = typedEntry3.value.close;

// Check if the stock's closing price is rising
if ((closePriceDay2 > closePriceDay1) && (closePriceDay3 > closePriceDay2)) {
    goingUp = true;
}

In this query, we use the custom StockPrice type so we can address trade Volume by name.

const oneDay = 24 * 60 * 60 * 1000;
const startTime = new Date();
const endTime = new Date(startTime.getTime() + 3 * oneDay);

// Note: the 'where' clause must come after the 'between' clause
const tsQueryText = `
    from StockPrices
    between $start and $end
    where Tag == "AppleTech"`;

const query = session.query({ collection: "companies" })
    .whereEquals("address.city", "New York")
    .selectTimeSeries(b => b.raw(tsQueryText), TimeSeriesRawResult)
    .addParameter("start", startTime)
    .addParameter("end", endTime);

// Execute the query:
const results = await query.all();

// Access entries results:
const tsEntries = results[0].results;

// Call 'asTypedEntry' to be able to access the entry's values by their names
// Pass the class type (StockPrice)
const volumeDay1 = tsEntries[0].asTypedEntry(StockPrice).value.volume;
const volumeDay2 = tsEntries[1].asTypedEntry(StockPrice).value.volume;
const volumeDay3 = tsEntries[2].asTypedEntry(StockPrice).value.volume;

from "companies"
where address.city = $p0
select timeseries(
    from StockPrices
    between $start and $end
    where Tag == "AppleTech")
{
   "p0":"New York",
   "start":"2024-06-04T06:02:39.826Z",
   "end":"2024-06-07T06:02:39.826Z"
}