I intend to analyse multiple data sets on the same time series (daily EOD). I will need to use computed columns. Use column A + B to create column C (store net result of calculation in column C). Is this functionality available using the MongoDB / Arctic database?
I would also intend to search the data... for example: What happens when the advance decline thrust pushes over 70 when the cumulative TICK was below -100,000 in the past 'n days'
Two data sets: Cumulative TICK and the Advance Decline Thrust (Uses advancers / decliners data). So they would be stored in the database, then I would want to have the capability to search for the above condition. This is achievable with the mongoDB / Arctic database structure?
Just looking for some general information before I move to a DB format. Currently everything I had created is on excel / VBA now its alrady out grown!
Any information greatly appreciated.
Note: I will use the same database for weekly, monthly, yearly and 1 minute, 3 minute, 5 minute 60 minute TICK/TIME based bars - not feeding live but updated EOD
yes, this can be done with arctic. Arctic can store pandas dataframes, and an operation like you have mentioned is trivial in pandas. Arctic is just a store, so you'd want to read the data out of arctic (data is stored in symbols in arctic) and then perform your transform, and then write the data back. Any of the storage engines (VersionStore, TickStore, or ChunkStore) should work for this.
Related
We have an API that queries an Influx database and a report functionality was implemented so the user can query data using a start and end date.
The problem is that when a longer period is chosen(usually more than 8 weeks), we get a timeout from influx, query takes around 13 seconds to run. When the query returns a dataset successfully, we store that in cache.
The most time-consuming part of the query is probably comparison and averages we do, something like this:
SELECT mean("value") AS "mean", min("value") AS "min", max("value") AS "max"
FROM $MEASUREMENT
WHERE time >= $startDate AND time < $endDate
AND ("field" = 'myFieldValue' )
GROUP BY "tagname"
What would be the best approach to fix this? I can of course limit the amount of weeks the user can choose, but I guess that's not the ideal fix.
How would you approach this? Increase timeout? Batch query? Any database optimization to be able to run this faster?
In such cases where you allow user to select in days, I would suggest to have another table that stores the result (min, max and avg) of each day as a document. This table can be populated using some job after end of the day.
You can also think changing the document per day to per week or per month, based on how you plot the values. You can also add more fields like in your case, tagname and other fields.
Reason why this is superior to using a cache: When you use a cache, you can store the result of the query, so you have to compute for every different combination in realtime. However, in this case, the cumulative results are already available with much smaller dataset to compute.
Based on your query, I assume you are using InfluxDB v1.X. You could try Continuous Queries which are InfluxQL queries that run automatically and periodically on realtime data and store query results in a specified measurement.
In your case, for each report, you could generate a CQ and let your users to query it.
e.g.:
Step 1: create a CQ
CREATE CONTINUOUS QUERY "cq_basic_rp" ON "db"
BEGIN
SELECT mean("value") AS "mean", min("value") AS "min", max("value") AS "max"
INTO "mean_min_max"
FROM $MEASUREMENT
WHERE "field" = 'myFieldValue' // note that the time filter is not here
GROUP BY time(1h), "tagname" // here you can define the job interval
END
Step 2: Query against that CQ
SELECT * FROM "mean_min_max"
WHERE time >= $startDate AND time < $endDate // here you can pass the user's time filter
Since you already ask InfluxDB to run these aggregates continuously based on the specified interval, you should be able to trade space for time.
In my Anylogic model I succesfully create plots of datasets that count the number of trucks arriving from terminals each hour in my simulation. Now, I want to add the actual/"observed" number of trucks arriving at a terminal, to compare my simulation to these numbers. I added these numbers in a database table (see picture below). Is there a simple way of adding this data to the plot?
I tried it by creating a variable that reads the database table for every hour and adding that to a dataset (like can be seen in the pictures below), but this did not work unfortunately (the plot was empty).
Maybe simply delete the variable and fill the dataset at the start of the model by looping through the dbase table data. Use the dbase query wizard to create a for-loop. Something like this should work:
int numEntries = (int) selectFrom(observed_arrivals).count();
DataSet myDataSet = new DataSet(numEntries);
List<Tuple> rows = selectFrom(observed_arrivals).list();
for (Tuple
row : rows) {
myDataSet.add(row.get( observed_arrivals.hour ), row.get( observed_arrivals.terminal_a ));
}
myChart.addDataSet(myDataSet);
You don't explain why it "didn't work" (what errors/problems did you get?), nor where you defined these elements.
(1) Since you want both observed (empirical) and simulated arrivals per terminal, datasets for each should be in the Terminal agent. And then the replicated plot (in Main) can have two data entries referring to data sets terminals(index).observedArrivals and terminals(index).simulatedArrivals or whatever you name them.
(2) Using getHourOfDay to add to the observed dataset is wrong because that just returns 0-23 (i.e., the hour in the current day for the current model date). Your database table looks like it has hours since model start, so you just want time(HOUR) to get the model time in elapsed hours (irrespective of what the model time unit is). Or possibly time(HOUR) - 1 if you only want to update the empirical arrivals for the hour at the end of that hour (i.e., at the same time that you updated the simulated arrivals).
(3) Using a Variable to get the database value each hour doesn't work because a variable's initial value is only evaluated once at model initialisation. You want an hourly cyclic Event in Terminal instead which adds the relevant row's value. (You need to use the Insert Database Query wizard to generate the relevant Java code for the query you need in the event's action.)
(4) Because you have a database table with specifically-named columns for each terminal (columns terminal_a and presumably terminal_b etc.) that makes it slightly more awkward. (This isn't proper relational table design where, instead of 4 columns for the 4 terminals, you'd instead have two columns for terminal_id and observed_value with a row for each time period and terminal combination.)
So your database query expression (in your Terminal agents) will need to use the SQL format (not the QueryDSL format) so that you can 'stitch in' the correct column name into the SQL.
I have a dataset including 3 columns :
ID transac (The unique ID of the transaction - Dimension)
Source (The source of the transaction - Dimension)
Amount € (The amount of the transaction - Stat)
screenshot of my dataset
To Count the number of transactions (for one or more sources), i use COUNT_DISTINCT function
I want to make the sum of the transactions amounts (for one or more sources). But i don't want to additionate the amounts of the transactions with the same ID !
Is there a way to do this calcul with a DataStudio function ?
Thanks for your answers. :-)
EDIT : I saw that we could do this type of calculation via SQL here and I would like to do this in DataStudio (so that I don't have to pre-calculate the amounts per source.)
IMO, your dataset contains wrong data. Each value should be relative only to that line, but this is not the case: if the total is =20, each line should describe the participation of that line to the total. With 4 sources, each line should be =5 or something else that sums 20.
To solve it in DataStudio, you need something like CALCULATE function in PowerBI, but currently DataStudio doesn't support this feature.
But there are some options to consider to repair your data:
If you're sure there are always 4 sources, just create a new calculated field with the expression Amount/4 and SUM it. It is not an elegant solution, but it works.
If your data source is Google Sheets, you can easily repair the data using formulas, like in this example:
Link to spreadsheet
For this spreadsheet, I used this formula in adjusted_amount column: =C2/COUNTIF(A:A,A2). With this column in DataStudio, just use the usual SUM aggregation function to summarize it correctly.
I have a Google Data Studio dashboard that loads really slowly since it's using Google Sheets as a Data Source. I migrated the same data to BigQuery then used it as my new Data Source however, I came across an issue:
When creating a calculated field, the new calculated field is not tagged as Auto in the Default Aggregation I still have to select Sum as a Default Aggregation. This causes problems in my report. Also, it's not Blue, where normal fields are shown as green, and calculated fields are shown as Blue.
When I was using Google Sheets, I could do direct computations in the calculated fields.
Example:
Handle Time = Talk Time / Number of calls
I just create a calculated field called Handle Time, then put the formula Talk Time / Number of calls
Now, I need to create 3 separate Calculated Fields:
Calculated Field 1: SUM(Talk Time)
Calculated Field 2: SUM(Number of calls)
Calculated Field 3: Calculated Field 1 / Calculated Field 2
This is even though I already tagged them as Sum in the Default Aggregation. Can anyone help me understand what I'm doing wrong?
Solution:
A single calculated field will do the trick; the aggregation of each respective field needs to be stated explicitly in the calculated field:
SUM(Talk Time) / SUM(Number of calls)
Why the Change?
To elaborate, the change was part of the Data Modeling update on 31st October 2020; one of the benefits of explicitly stating the aggregation is that it offers greater flexibility with the ability to aggregate fields as required when creating a calculated field, for example, something like:
MAX(Talk Time) - MIN(Talk Time) / COUNT(Handle Time) * AVG(Handle Time) / COUNT_DISTINCT(Text_Field1) * COUNT(Text_Field2)
Speed
Regarding speed, where the Data Set is large and static (daily updates are fine and real time data is not required), then a Data Extract would be a good option.
Dimensions are shown as green, metrics are shown as blue. Data imported from other sources, particularly from Google sheets tend to show metrics as green but when you add them to a chart or table they get recognised as metrics and change to blue.
Background Information: We have an incident time tracker that tracks how long each user spends with a representative before the issue can be closed. We want to determine the average volume of incidents that are being handled for each hour. To say this in another way: We want to get an hourly baseline for each day of the week that will show us the average total call length within the specific time period. Eg: We want to average the total length of every call on Monday from 9AM-10AM for all the weeks in the database, and the same for other hourly intervals.
The simplest way to think of this is that I want AVG(SUM) for the specific time periods, but Tableau does not allow me to do this.
Tableau Output:
This is the desired, target visualization that I am looking for from Tableau.
SQL Query:
I have written a SQL query that returns the answer:
We are looking at two columns: start_time (time stamp) and interval_seconds(float)
In the inner query I use the hour_start function which truncates the date/time value to the hour start, so I can group by the hour and day of the week in the outer query.
SQL Results:
Question:
Is there a way to solve this problem ENTIRELY in Tableau that would get me the result that I am looking for without having to write any SQL code?
Files Stored on Drive
CSV File:
https://drive.google.com/open?id=0B4nMLxIVTDc7NEtqWlpHdVozRXc
Tableau Worksheet:
https://drive.google.com/open?id=0B4nMLxIVTDc7M3A4Q0JxbGdlTE0
You can use Level of Detail expressions to compute the SUM(interval_seconds) at the hour level and then use AVG to calculate the number you are looking for.
I created a couple of calculations:
hour which is defined as: DATETRUNC('hour',[start_time])
this should be equivalent to your hour_start(start_time).
and interval_hours which is defined as {FIXED [hour] : SUM([interval_seconds])/3600 }
This calculates the aggregate for each start_time truncated to the hour.
After this, you simply calculate AVG(interval_hours) and use it in your view.
I put a workbook in dropbox: https://www.dropbox.com/s/3hfvz8w529g9f46/Interval%20Time%20Baseline.twbx?dl=0
Although the chart looks similar to yours, the numbers I came up with are somewhat different from the "SQL Results" you show. Was the data you provided slightly different?