createDeviceEngine

Syntax

createDeviceEngine(name, metrics, dummyTable, outputTable, [keyColumn],[keepOrder])

Arguments

name is a string of the engine name. It is the only identifier of a reactive state engine on a data/compute node. It can have letter, number and "_" and must start with a letter.

metrics is metacode specifying the formulas for calculation. It can include one or more expressions, built-in functions, or user-defined functions. User-defined functions must have a single return value and must not contain embedded for loops or any loops that exceed 100 iterations. It can also include a constant or a vector of constants (in which case, the output column must be of the array vector type). For more information about metacode, refer to Metaprogramming.

dummyTable is a table object whose schema must be the same as the subscribed stream table. Whether dummyTable contains data does not matter.

outputTable is the output table for the results. It can be an in-memory table or a DFS table. Create an empty table and specify the column names and types before calling the function.

keyColumn (optional) is a STRING scalar/vector indicating the grouping column(s).

keepOrder (optional) specifies whether to preserve the insertion order of the records in the output table. The default value is false, meaning data is sorted by keyColumn.

Details

Create a device engine which conducts the calculations defined in the metrics with GPU acceleration.

The following functions can be accelerated using GPUs:

• Basic unary operation: not, neg, cast, log, log2, log10 ,log1p ,abs, sign, sqrt, sin, sinh, asin, asinh, cos, cosh, acos, acosh, tan, tanh, atan, atanh, reciprocal, cbrt, exp, exp2, expm1

• Basic binary operation: add, sub, mul, div, ratio, pow, lt, gt, le, ge, eq, ne, and, or, or__, max, min

• Binary operation on integers: mod, bitAnd, bitOr, bitXor, lshift, rshift

• Ternary operation: iif

• Unary moving functions: mavg, msum, mcount, mprod, mvar, mvarp, mstd, mstdp, mskew, mkurtosis, mmin, mmax, mimin, mimax, sma, wma, mfirst, mlast, mrank, mmaxPositiveStreak, mmed, mpercentile, mmad (useMedian is currently not supported)

• TALib-series Unary moving functions: sma, ema, wma, dema, tema, trima, t3, wilder, gema, linearTimeTrend, ma, talib (only the m- /moving functions and mTopN-/moving TopN functions are accepted)

• Binary moving functions: mcorr, mbeta, mcovar, mwsum, mwavg

• Other moving functions: linearTimeTrend, mslr

• Unary cumulative functions: cumsum, cumprod, cummin, cummax, cumvar, cumvarp, cumstd, cumstdp, cumnunique, cumfirstNot, cumlastNot, cumavg, cumcount, cumPositiveStreak

• Binary cumulative functions: cumcorr, cumcovar, cumbeta, cumwsum, cumwavg

• Order-sensitive functions: deltas, ratios, ffill, move, prev, next, percentChange, iterate, prevState, ewmMean, ewmVar, ewmStd, ewmCov, ewmCorr

  Note: For ewmVar, ewmStd, ewmCov, and ewmCorr, the adjust parameter must be set to false and bias must be true.

• Moving TopN functions: msumTopN, mavgTopN, mstdpTopN, mstdTopN, mvarTopN, mvarpTopN, mwsumTopN, mcorrTopN, mcovarTopN, mbetaTopN, mskewTopN, mkurtosisTopN

• Row-based functions: rowMin, rowMax, rowAnd, rowOr, rowXor, rowProd, rowSum, rowSum2, rowSize, rowCount, rowAvg, rowVar, rowVarp, rowStd, rowStdp

• Time-based moving functions: tmsum, tmsum2, tmavg, tmprod, tmcount, tmvar, tmvarp, tmstd, tmstdp, tmcovar, tmcorr, tmwavg, tmwsum, tmbeta, tmfirst, tmlast, tmmin, tmmax, tmskew, tmkurtosis, tmove

• Other functions: TrueRange, topRange, lowRange, stateMavg

Details

// create a device engine
dummyTb = table(1:0, `sym`id`value, [SYMBOL,INT,DOUBLE])
share table(100:0, `sym`id`flag`value`factor, [SYMBOL,INT,SYMBOL,DOUBLE,DOUBLE]) as result
de = createDeviceEngine(name="myDe", metrics=[<id>,<"flag"+"_A">,<value>,<mavg(value,5)>], dummyTable=dummyTb, outputTable=result, keyColumn="sym")


// simulate data
data1 = table(take("A", 100) as sym, 1..100 as id, double(10+1..100) as value)
data2 = table(take("B", 100) as sym, 1..100 as id, double(20+1..100) as value)
data3 = table(take("C", 100) as sym, 1..100 as id, double(30+1..100) as value)
data = data1.unionAll(data2).unionAll(data3).sortBy!(`id)

// write data
de.append!(data)
select top 10 * from result