Azure Stream Analytics: Multiple Windows + JOINS

Question

My architecture:

• 1 EventHub with 8 Partitions & 2 TPUs
• 1 Streaming Analytics Job
• 6 Windows based on the same input (from 1mn to 6mn)

Sample Data:

{side: 'BUY', ticker: 'MSFT', qty: 1, price: 123, tradeTimestamp: 10000000000}
{side: 'SELL', ticker: 'MSFT', qty: 1, price: 124, tradeTimestamp:1000000000}

The EventHub PartitionKey is ticker

I would like to emit every second, the following data:

(Total quantity bought / Total quantity sold) in the last minute, last 2mn, last 3mn and more

What I tried:

WITH TradesWindow AS (
    SELECT
        windowEnd = System.Timestamp,
        ticker,
        side,
        totalQty = SUM(qty)
    FROM [Trades-Stream] TIMESTAMP BY tradeTimestamp PARTITION BY PartitionId
    GROUP BY ticker, side, PartitionId, HoppingWindow(second, 60, 1)
),
TradesRatio1MN AS (
    SELECT 
        ticker = b.ticker,
        buySellRatio = b.totalQty / s.totalQty
    FROM TradesWindow b /* SHOULD I PARTITION HERE TOO ? */
    JOIN TradesWindow s /* SHOULD I PARTITION HERE TOO ? */
    ON s.ticker = b.ticker AND s.side = 'SELL'
    AND DATEDIFF(second, b, s) BETWEEN 0 AND 1
    WHERE b.side = 'BUY'
)

 /* .... More windows.... */

/* FINAL OUTPUT: Joining all the windows */
SELECT
   buySellRatio1MN = bs1.buySellRatio,
   buySellRatio2MN = bs2.buySellRatio
   /* more windows */
INTO [output]
FROM buySellRatio1MN bs1 /* SHOULD I PARTITION HERE TOO ? */
JOIN buySellRatio2MN bs2 /* SHOULD I PARTITION HERE TOO ? */
ON bs2.ticker = bs1.ticker
AND DATEDIFF(second, bs1, bs2) BETWEEN 0 AND 1

Issues:

• This requires 6 EventHub Consumer groups (each one can only have 5 readers), why ? I don't have 5x6 SELECT statements on the input, why then ?
• The output doesn't seem consistent (I don't know if my JOINs are correct).
• Sometimes the job doesn't output at all (maybe some partitioning problem ? see the comments in the code about partitioning)

Briefly, is there a better way to achieve this ? I couldn't find anything in the doc and examples about having multiple windows and joining them then joining the results of the previous joins from only 1 input.

Answer 1

For the first question, this depend of the internal implementation of the scale out logic. See details here.

For the output of the join, I don't see the whole query but if you join a query with a 1 minute window with a query with a 2 minute window with a 1s time "buffer" you will only an output every 2 minutes. UNION operator will be better for this.

From your sample and your goal, I think there is a much easier way to write this query using UDA (User Defined Aggregate).

For this I will define a UDA function called "ratio" first:

function main() {
this.init = function () {
    this.sumSell = 0.0;
    this.sumBuy  = 0.0;
}

this.accumulate = function (value, timestamp) {
    if (value.side=="BUY") {this.sumBuy+=value.qty};
    if (value.side=="SELL") {this.sumSell+=value.qty};
   }

this.computeResult = function () {
    if(this.sumSell== 0) {
        result = 0;
    }
    else {
        result =  this.sumBuy/this.sumSell;
    }
    return result;

}
}

Then I can simply use this SQL query for a 60 seconds window:

SELECT
  windowEnd = System.Timestamp,
  ticker,
  uda.ratio(iothub) as ratio
FROM iothub PARTITION BY PartitionId
GROUP BY ticker, PartitionId, SlidingWindow(second, 60)