Using elasticsearch, I'd like to get a histogram facet for a price field in my model. Without knowing beforehand the min and max prices, what I'd like is to have the histogram cover the entire range of prices, with a set number of intervals, say 10. I can see from the documentation at
http://www.elasticsearch.org/guide/reference/api/search/facets/histogram-facet.html
that I can specify the price range for each interval, but this would give me some unspecified number of intervals. I'd like to have some specific number of intervals that evenly cover the entire range of values for the price field. Is there any way to do this?
I know that one solution could be to query my database for the min and max values, and then figure out the appropriate interval size, but that goes against one of the main points of using elasticsearch, which is to not have to hit the db for search related queries.
You can query elasticsearch for min and max values using Statistical Facet
You can track progress on the implementation of this feature, referred to as auto_histogram, at https://github.com/elastic/elasticsearch/issues/31828
Related
I have been struggling for a few days with this problem. Anyone kind enough to show some interested will be highly appreciated.
I have the table shown below.
Suppose columns represent months. I would like to know up to which months' orders have been used up.
I have tried criteria with sums of demand up to that point but I cannot seem to use criteria with the sum of total demand and an array of sums of "total units ordered".
F.e. =COUNTIF(SUM($S$2:($S$2:S$2))<SUM($S$1:S$1) is not possible.
I have tried using an index-match combo but i would have to deduct the previous max sum of "total units ordered" that meets the condition up to the previous cell.
Is that possible without using vba?
Thanks in advance for your interest and time spent.
You can use a standard method of getting running totals using Sumif, combined with Match:
=ArrayFormula(match(sumif(column(S1:Z1),"<="&column(S1:Z1),S1:Z1),sumif(column(S2:Z2),"<"&column(S2:Z2),S2:Z2))-1)
I put rows 3 and 4 in just as a check of my calculations and to show the results of the two Sumifs evaluations - they aren't necessary.
You may wish to specify what should happen if the demands add up to exactly 3000, for example. The above formula would actually go to the next month, so may need some refinement if that is not what you want.
i write sensor data every second to an influxdb database. Displaying weekly, monthly or yearly summaries in grafana is quite slow since it needs to query many thousand values.
To speed things up, i was thinking about using a cron job to run a queries like
select mean(sensor1) into data_avg_1h from data where time > start and time <= end group by time(1h)
select mean(sensor1) into data_avg_1d from data where time > start and time <= end group by time(1d)
select mean(sensor1) into data_avg_1w from data where time > start and time <= end group by time(1w)
This would mean i need more storage, but queries run much faster.
Is this a bodge job or acceptable and is there a more clever way to do something like that?
Yes. It is perfectly ok and it is also recommended to downsample the data like you have mentioned in the question.
However, instead of using a cronjob it will be better to use Continuous query feature of InfluxDB to achieve the same result.
Downsampling & Contious Query Documentation.
Please be aware that when storing the average value for short period, if you want to calculate the average for a longer period from this downsampled data you will have to calculate the weighted average. Otherwise, you will calculating the average of average which, may not be equal to the average value calculated from the Original data.
This is because, each downsampled average value might be having different number of datapoints.
So while calculating the mean on regular interval store the number of data points received in that interval. This way you will be able to calculate the weighted average.
I'm setting up a Google Sheet that will calculate the most effective purchase size of specific agricultural inputs (fertilizer, chemical, etc). I set up the price data in its own tab with a separate row for each input name + size.
To keep it easy for the user I'd like to require only the input name, # of gallons per acre, and acres and then have a formula spit out the total cost and most effective purchase (bulk if > X gallons, X # of 250 gallon containers + X 55 drums, etc). How can I use the input name plus a wildcard to find the appropriate purchase size?
https://docs.google.com/spreadsheets/d/1bMOPuk2qhmVuJT7vE_ni3KFxfcgKvwTwkM4p50xQF_0/edit?usp=sharing
I tried:
=ArrayFormula(iferror(INDEX('Data (Current)'!H2:H,SMALL(IF($A2&"*"='Data (Current)'!A2:A,ROW('Data (Current)'!A2:A)-1),1))))
...but it returns blank so I'm guessing the reference $A2&"*" to the input name isn't working properly. When I replace it with a string found in the 'Data (Current)' tab then it works fine.
=ArrayFormula(iferror(INDEX('Data (Current)'!H2:H,SMALL(IF($A2&"*"='Data (Current)'!A2:A,ROW('Data (Current)'!A2:A)-1),1))))
I expected the output to be the smallest value (in this case I think it's 5). Then when I change the last number to 2 or 3 it will find the next smallest value, in this case, 55 or 250. Then I can use simple formulas to interact with that and finish the spreadsheet.
Unfortunately, the actual output is nothing, or "".
Sorry if this isn't what you're looking for, as I had some trouble understanding your question.
Presuming what you want is essentially this:
I want to buy Y quantity of item.
I can buy item at cheaper prices if I buy in higher quantities, although sometimes they have a minimum order quantity.
What is the most optimal combination of the options I have to minimize the price I pay?
I'm unsure if there's a simple solution for this within Google Sheets alone. This might be treading more into Apps Script territory.
However, that's not to say that it's not impossible. I've "brute-forced" the above solution above with an iterative-like approach, for the "Chelated Calcium" product: https://docs.google.com/spreadsheets/d/1YSBiSx0IMr4T0R11Dqb-tqOhH4AOTTAWeH2yQfT4X5w
First, list the data in a standardized manner. This includes giving each same product something easy to look it up by. For example, on the Data (Current) tab, I've added 3 columns:
Product Common Name - This is used so that all items of different quantities can be found easily, without needing wildcards.
Gallons - Much easier to parse the data if it it's explicitly laid out.
Minimum Order Gallons - This is your threshold for Bulk. I've set it at an arbitrary 20,000 gallons for Chelated Calcium.
The data here is ordered least-effective first. How you do this will be up to you. In this case, I sorted by the Retail Cost Per Ounce parameter from your sheet, highest first. This eliminates any guesswork about which of the options are most effective, since you can just traverse your options in order. Note: The way I've laid out the formulas will only work IFF the same products are directly next to each other. It won't work if there are other products between them.
On the Field Level Tool tab, standardize your inputs to the Gallons unit. I do this in Total Gallons Needed column (I multiply anything with a "GAL" with 1, and "QUART" with 0.25).
For each item, determine the row numbers where the product begins and ends. This is marked by columns L (Least Efficient Index) and M (Most Efficient Index). I got these results by using the MATCH function.
Set up the iterations, from 0 to N-1. On this sheet, I've set up N=5 iterations, which means that it can traverse 5 different options of the same product only. Since Chelated Calcium only has 4 different options (5 Gal, 30 Gal, 250 Gal, Bulk), 5 is more than enough for this product. If you have products with more options, you may want to have more iterations.
The iterations are on the right side of the Field Level Tool tab.
In your case, you might want to put it on a different tab since the place I put it makes the file look very messy.
In each iteration, I perform the following steps:
To Fulfill - How many gallons still need to be purchased by this iteration?
ThisIndex - What is the row number of this iteration? This is determined by Most Efficient Index - Iteration Number. Remember that since we sorted in order of ascending efficiency, this means that the iteration starts with the most efficient option it can find first. There is a check to make sure that it only outputs a value if it is between the range [Least Efficient Index, Most Efficient Index]. Otherwise, it will be blank to avoid miscalculations by intruding into another product in the Data (Current) tab.
Retail Price, Minimum Gals, Gallons per Order - Simple data extraction for easy usage in the iteration, using INDEX (and indirectly, MATCH by virtue of ThisIndex).
Order - This formula does a couple of things, outlined below:
It checks whether there still remains a valid choice of product at this iteration. It does this by checking whether ThisIndex still exists. If the product doesn't exist, then it will be nulled. This is accomplished by using the IF function.
It will determine if there is a minimum threshold that must be met to purchase this choice. You can see in the 0th iteration, for example, that there is a minimum quantity of 20,000 gallons. If To Fulfill quantity is greater than or equal to the threshold OR there is no threshold, then a purchase is quantified by this column. The mathematics are simply to divide the To Fulfill amount by the Gallons per Order amount to determine the number of orders of this particular product choice. If there is a threshold but the To Fulfill amount doesn't meet it, then this iteration is skipped with a 0 order value.
If the item is already on its least efficient choice (ThisIndex == Least Efficient Index), it will do a CEILING function to ensure that the order is fulfilled. If not, it will do a FLOOR function instead. This is because you cannot order 3.5 units of an item, so they have to be rounded either up or down.
Expenditure - This is simply Order multiplied by the Retail Price, or how much money you spend in this iteration.
Remaining - How much of the product is left unfulfilled at the end of this iteration, to be used as To Fulfill for the next iteration.
Note: If you see formulas that are of the form =IF(ThisIndex, [calculations_here],), that is simply a check to nullify that calculation if ThisIndex is invalid.
Copy the iterations as many times as you want to the right. Something nice to do is to force the iterations to do a CEILING on the very last one to ensure that you never under-buy.
Generate a user-readable string for the purchase suggestion. You can see this on the Suggested Purchase column.
Calculate the Gallons Bought with a simple SUMPRODUCT over all the iterations.
Calculate the total expenditure with a simple SUM over all the iterations.
I hope this is what you were looking for. Regardless, it's at least a fun exercise on how much you can abuse Sheets. ;)
In InfluxDB v1.3, I have a measurement with one field and a tag that can take two values.
I would like to compute (x where mytag=y) - (x where mytag=z), using the last value of each series when needed (something like an http://code.kx.com/wiki/Reference/aj). I would like to do this in one query, if possible.
If the above is not possible, is there a different schema (e.g. using separate measurements) where what I would like to do is feasible? If so, can you please elaborate on the structure and the query?
SELECT difference(mean(x))
FROM <measurement>
WHERE time > now() - 1h and (mytag='y' OR mytag='x')
GROUP BY time(60s), mytag
Functions like difference require an aggregate query (group by time()) as well as an aggregation function for the values within the grouped window (mean above).
Difference then shows the differences between sequential aggregated values for the time period specified, additionally grouped by the two tag values specified.
These can be adjusted depending on your data.
For my case, I need to capture 15 performance metrics for devices and save it to InfluxDB. Each device has a unique device id.
Metrics are written into InfluxDB in the following way. Here I only show one as an example
new Serie.Builder("perfmetric1")
.columns("time", "value", "id", "type")
.values(getTime(), getPerf1(), getId(), getType())
.build()
Writing data is fast and easy. But I saw bad performance when I run query. I'm trying to get all 15 metric values for the last one hour.
select value from perfmetric1, perfmetric2, ..., permetric15
where id='testdeviceid' and time > now() - 1h
For an hour, each metric has 120 data points, in total it's 1800 data points. The query takes about 5 seconds on a c4.4xlarge EC2 instance when it's idle.
I believe InfluxDB can do better. Is this a problem of my schema design, or is it something else? Would splitting the query into 15 parallel calls go faster?
As #valentin answer says, you need to build an index for the id column for InfluxDB to perform these queries efficiently.
In 0.8 stable you can do this "indexing" using continuous fanout queries. For example, the following continuous query will expand your perfmetric1 series into multiple series of the form perfmetric1.id:
select * from perfmetric1 into perfmetric1.[id];
Later you would do:
select value from perfmetric1.testdeviceid, perfmetric2.testdeviceid, ..., permetric15.testdeviceid where time > now() - 1h
This query will take much less time to complete since InfluxDB won't have to perform a full scan of the timeseries to get the points for each testdeviceid.
Build an index on id column. Seems that he engine uses full scan on table to retrieve data. By splitting your query in 15 threads, the engine will use 15 full scans and the performance will be much worse.