I have access logs such as below stored in a mongodb instance:
Time Service Latency
[27/08/2013:11:19:22 +0000] "POST Service A HTTP/1.1" 403
[27/08/2013:11:19:24 +0000] "POST Service B HTTP/1.1" 1022
[27/08/2013:11:22:10 +0000] "POST Service A HTTP/1.1" 455
Is there an analytics function like PERCENTILE_DISC
in Oracle to calculate the percentile?
I would like to calculate latency percentiles over a period of time.
Percentiles can be calculated using the formula n = (P/100) x N, where P = percentile, N = number of values in a data set (sorted from smallest to largest), and n = ordinal rank of a given value. Percentiles are frequently used to understand test scores and biometric measurements.
The 95th percentile is a number that is greater than 95% of the numbers in a given set. The reason this statistic is so useful in measuring data throughput is that it gives a very accurate picture of the maximum traffic generated on an interface. This is a standard measure used in interpreting performance data.
Starting Mongo 4.4
, the $group
stage has a new aggregation operator $accumulator
allowing custom accumulations of documents as they get grouped, via javascript user defined functions.
Thus, in order to find the 20th percentile:
// { "a" : 25, "b" : 12 }
// { "a" : 89, "b" : 73 }
// { "a" : 25, "b" : 7 }
// { "a" : 25, "b" : 17 }
// { "a" : 89, "b" : 14 }
// { "a" : 89, "b" : 17 }
// { "a" : 25, "b" : 24 }
// { "a" : 25, "b" : 15 }
// { "a" : 25, "b" : 22 }
// { "a" : 25, "b" : 94 }
db.collection.aggregate([
{ $group: {
_id: "$a",
percentile: {
$accumulator: {
accumulateArgs: ["$b"],
init: function() { return []; },
accumulate: function(bs, b) { return bs.concat(b); },
merge: function(bs1, bs2) { return bs1.concat(bs2); },
finalize: function(bs) {
bs.sort(function(a, b) { return a - b });
return bs[Math.floor(bs.length*.2) + 1];
},
lang: "js"
}
}
}}
])
// { "_id" : 89, "percentile" : 17 }
// { "_id" : 25, "percentile" : 15 }
The accumulator:
b
(accumulateArgs
)init
)b
items in an array (accumulate
and merge
)b
items (finalize
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