I am trying to write a Siddhi query to detect if an event didn't happen prior to another within a time limit. The query I have to detect if 'X' didn't ever happen prior to 'Y' in the entire life of the siddhi application is:
from stream[value == 'Y']
and not stream[value == 'X']
I assumed adding a time constraint would work:
from stream[value == 'Y']
and not stream[value == 'X'] for 5 min
However, the 'for' statement never has any effect that I can see. This query is still triggered whether 'X' was 4 minutes ago or 6 minutes ago. I understand that a similar effect can be achieved by checking if 'Y' comes after 'X' within a time limit, but for my purposes I need to know the other way around.
Is this possible with Siddhi? If so can someone please provide a sample query that could achieve this?
Based on your comment I am composing below answer.
Sequence construct in Siddhi will guarantee that no one can enter the state flow at a random point. As an example let's take below sample sequence query.
from every e1=InputStream[state='X'], e2=InputStream[state'Y']
select e1.state as initialState, e2.state as finalState
insert into NextStream;
So in here we are mandating X followed by a consecutive Y. If Y occurs before X sequence construct will handle that and discard Y. So whatever goes into NextStream is guaranteed to satisfy x -> Y state transition.
More relaxed construct of sequence is pattern. It is same as sequence while relaxing the consecutive arrival requirement. Hope this helps!!
Related
I have a query (relationship between CONTRACT <-> ORDERS) that I decided to break up into 2 parts (contract and orders) so I can reuse in another stored procedure.
When I run the code before the break up, it took around 10 secs to run; however, when I use a function for getting the contract, then pump the data into a temp table first, then join to the other parts it takes 2m:30s - why the difference in time?
The function takes less than a second to run and returns only one row i.e. details of one contract (contract_id is the parameter supplied to the function).
The part that is most effecting the performance the (ORDERS) largest table in the query has 4.1 million rows and joins to a few other tables however; if I just run the sub query for orders in isolation with a particular filter i.e. the contract id it takes less than a second to run and just happens to return zero records based for the contract I am testing on (due to filtering on the type of order it is looking for).
Base on the above information you would think 1 sec at most for the function + 1 sec at most to get the orders + summarize = 2 seconds at most, not 2 and half minutes!
Where am I going wrong, how do I begin to isolate the issue in time difference?
I know someone is going to tell me to paste the code but surely it is an issue of the database vs indexes perhaps vs how the compiler performs when dealing with raw code versus broken up code into parts. Is there an area of the code I can look at before having to post my whole code as I have tried variations of OUTER APPLY vs LEFT JOIN from the contract temp table to the orders subquery and both give me about the same result. Any ideas?
I don't think the issue was with the code but the network I was running it on. Although bizarre in the fact I had 2 versions of the proc running side by side and yesterday or rather before the weekend one was running in 10 secs and it is still running in 10 secs 3 days later and my new version (using the function) was taking anywhere between 2 to 3 minutes. This morning it is running at 2 or 3 seconds!! So I don't know if it is the fact I changed from declaring my table structure and using a table variable instead first to where previously I was using SELECT ... INTO #Contract made the difference or the network or precompiling has an affect. Whatever it is it no longer an issue. Should I delete this post?
I’m using watermark to join two streams as you can see below:
val order_wm = order_details.withWatermark("tstamp_trans", "20 seconds")
val invoice_wm = invoice_details.withWatermark("tstamp_trans", "20 seconds")
val join_df = order_wm
.join(invoice_wm, order_wm.col("s_order_id") === invoice_wm.col("order_id"))
My understanding with the above code, it will keep each of the stream for 20 secs. After it comes but, when I’m giving one stream now and the another after 20secs then also both are getting joined. It seems like even after watermark got finished Spark is holding the data in memory. I even tried after 45 seconds and that was getting joined too.
This is creating confusion in my mind regarding watermark.
After it comes but, when I’m giving one stream now and the another after 20secs then also both are getting joined.
That's possible since the time measured is not the time of events as they arrive, but the time that is inside the watermarked field, i.e. tstamp_trans. You have to make sure that the last time in tstamp_trans is 20 seconds after the rows that will participate in the join.
Quoting the doc from: http://spark.apache.org/docs/latest/structured-streaming-programming-guide.html#inner-joins-with-optional-watermarking
In other words, you will have to do the following additional steps in the join.
Define watermark delays on both inputs such that the engine knows how delayed the input can be (similar to streaming aggregations)
Define a constraint on event-time across the two inputs such that the engine can figure out when old rows of one input is not going to be required (i.e. will not satisfy the time constraint) for matches with the other input. This constraint can be defined in one of the two ways.
Time range join conditions (e.g. ...JOIN ON leftTime BETWEEN rightTime AND rightTime + INTERVAL 1 HOUR),
Join on event-time windows (e.g. ...JOIN ON leftTimeWindow = rightTimeWindow).
I will try to explain the problem on an abstract level first:
I have X amount of data as input, which is always going to have a field DATE. Before, the dates that came as input (after some process) where put in a table as output. Now, I am asked to put both the input dates and any date between the minimun date received and one year from that moment. If there was originally no input for some day between this two dates, all fields must come with 0, or equivalent.
Example. I have two inputs. One with '18/03/2017' and other with '18/03/2018'. I now need to create output data for all the missing dates between '18/03/2017' and '18/04/2017'. So, output '19/03/2017' with every field to 0, and the same for the 20th and 21st and so on.
I know to do this programmatically, but on powercenter I do not. I've been told to do the following (which I have done, but I would like to know of a better method):
Get the minimun date, day0. Then, with an aggregator, create 365 fields, each has that "day0"+1, day0+2, and so on, to create an artificial year.
After that we do several transformations like sorting the dates, union between them, to get the data ready for a joiner. The idea of the joiner is to do an Full Outer Join between the original data, and the data that is going to have all fields to 0 and that we got from the previous aggregator.
Then a router picks with one of its groups the data that had actual dates (and fields without nulls) and other group where all fields are null, and then said fields are given a 0 to finally be written to a table.
I am wondering how can this be achieved by, for starters, removing the need to add 365 days to a date. If I were to do this same process for 10 years intead of one, the task gets ridicolous really quick.
I was wondering about an XOR type of operation, or some other function that would cut the number of steps that need to be done for what I (maybe wrongly) feel is a simple task. Currently I now need 5 steps just to know which dates are missing between two dates, a minimun and one year from that point.
I have tried to be as clear as posible but if I failed at any point please let me know!
Im not sure what the aggregator is supposed to do?
The same with the 'full outer' join? A normal join on a constant port is fine :) c
Can you calculate the needed number of 'dublicates' before the 'joiner'? In that case a lookup configured to return 'all rows' and a less-than-or-equal predicate can help make the mapping much more readable.
In any case You will need a helper table (or file) with a sequence of numbers between 1 and the number of potential dublicates (or more)
I use our time-dimension in the warehouse, which have one row per day from 1753-01-01 and 200000 next days, and a primary integer column with values from 1 and up ...
You've identified you know how to do this programmatically and to be fair this problem is more suited to that sort of solution... but that doesn't exclude powercenter by any means, just feed the 2 dates into a java transformation, apply some code to produce all dates between them and for a record to be output for each. Java transformation is ideal for record generation
You've identified you know how to do this programmatically and to be fair this problem is more suited to that sort of solution... but that doesn't exclude powercenter by any means, just feed the 2 dates into a java transformation, apply some code to produce all dates between them and for a record to be output for each. Java transformation is ideal for record generation
Ok... so you could override your source qualifier to achieve this in the selection query itself (am giving Oracle based example as its what I'm used to and I'm assuming your data in is from a table). I looked up the connect syntax here
SQL to generate a list of numbers from 1 to 100
SELECT (MIN(tablea.DATEFIELD) + levquery.n - 1) AS Port1 FROM tablea, (SELECT LEVEL n FROM DUAL CONNECT BY LEVEL <= 365) as levquery
(Check if the query works for you - haven't access to pc to test it at the minute)
Having some problems in a job, and I suspect it is due to a lack of understanding of tJavaFlex. I am generating 10 rows in this test job, and am generating loop inside a tJavaFlex:
So there are 10 rows coming in, and a loop in the Start and End section. I was expecting that for each row coming in, it would generate 10 identical rows coming out. And that I would see iterations 0,1,2,3....9 for each row.
What I got was this. This looks to me like the entire job is running 10 times, and so I have 100 random values coming through the flow from the tRowGenerator.
If I move the for loop into the Main Code section, I get close to the behaviour I was expecting. I am expecting each row when it comes in to be repeated 10 times, and for 1 row coming in to produce 10 output rows. What I get is this.
But even then my tLogRow is only generating one row for each 10 iterations it seems (look at the tLogRow output after iteration 9 above why not 10 items?). I had thought I would be getting 10 rows for each single row coming in and I would see this in the tLogRow.
What I need to do is take a value from a field coming in, do some reg exp parsing and split into an array, and then for each item in the array create lines in the output flow. i.e. 1 row coming in can be turned into x number of rows coming out using a string.split() method.
Can someone explain the behaviour above, and also advise on the best approach to get one value coming in, do some java manipulation and then generate multiple rows coming out?
Any advice appreciated.
Yes you don't use it correctly.
The initial part is for initiate variable. (executed one time before the first tow)
In the principal you put your loop (executed one time at each row)
In the final you store in global variable for example.(executed one time after the last row)
The principal code will be executed at each row in a tjavaflex. So don't put a for loop inside you can do like the example in the screen.
You tjavaflex comportement is normal. you have ten row so each row the for loop wil be executed 10 time (i<10)
You can use it like :
You dont need to create your own loop.
By putting the for loop in the Start code, your main code will be triggered by the loop and by incoming rows, and it will be executed n*r times.
The behaviour of subjob that contains a tJavaFlex, reveils that component before tJavaFlex is included into its starting code, and the after component is included in the ending code, but that may depend to many conditions like data propagation and trigger type.
start code :
System.out.print("tJavaFlex is starting...");
int i = 0;
Main code :
i++;
System.out.print("tJavaFlex inside Main Code...iteration:"+i);
row8.ITEM_NAME = row7.ITEM_NAME;
row8.ITEM_COUNT = row7.ITEM_COUNT;
End code :
System.out.print("tJavaFlex is ending...");
System.out.print(row7.ITEM_NAME);
Instead of main flow in row5, try using iterate flow to connect tJavaFlex
Tough problem I am working on here.
I have a table of CustomerIDs and CallDates. I want to measure whether there is a 'repeat call' within a certain period of time (up to 30 days).
I plan on creating a parameter called RepeatTime which is a range from 0 - 30 days, so the user can slide a scale to see the number/percentage of total repeats.
In Excel, I have this working. I sort CustomerID in order and then sort CallDate from earliest to latest. I then have formulas like:
=IF(AND(CurrentCustomerID = FutureCustomerID, FutureCallDate - CurrentCallDate <= RepeatTime), 1,0)
CurrentCustomerID = the current row, and the FutureCustomerID = the following row (so it is saying if the customer ID is the same).
FutureCallDate = the following row and the CurrentCallDate = the current row. It is subtracting the future call time from the first call time to measure the time in between.
The goal is to be able to see, dynamically, how many customers called in for a specific reason within maybe 4 hours or 1 day or 5 days, etc. All of the way up until 30 days (this is our actual metric but it is good to see the calls which are repeats within a shorter time frame so we can investigate).
I had a similar problem, see here for detailed version Array calculation in Tableau, maxif routine
In your case, that is basically the same thing as mine, so you could apply that solution, but I find it easier to understand the one I'm about to give, I would do:
1) Create a calculated field called RepeatTime:
DATEDIFF('day',MAX(CallDates),LOOKUP(MAX(CallDates),-1))
This will calculated how many days have passed since the last call to the current. You can add a IFNULL not to get Null values for the first entry.
2) Drag CustomersID, CallDates and RepeatTime to the worksheet (can be on the marks tab, don't need to be on rows or column).
3) Configure the table calculation of RepeatTIme, Compute using Advanced..., partitioning CustomersID, Adressing CallDates
Also Sort by Field CallDates, Maximum, Ascending.
This will guarantee the table calculation works properly
4) Now you have a base that you can use for what you need. You can either export it to csv or mdb and connect to it.
The best approach, actually, is to have this RepeatTime field calculated outside Tableau, on your database, so it's already there when you connect to it. But this is a way to use Tableau to do the calculation for you.
Unfortunately there's no direct way to do this directly with your database.