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Time Series Database in One Line of Clojure

If you ever worked in the financial sector, specifically high frequency trading, a time series database is a well known tool that orders up all those quotes, orders, trades for financial pleasure.

The are many of these databases available. The Wall Street being The Wall Street would of course primarily use proprietary ones, since, well, it’s proprietary :), but giving them a credit: they do outperform open source ones by a lot, at least presently (talking about millions per second).

Disrupting Time Series Business

So I decided to write an open source time series database that will outperform them all not necessarily by performance, but definitely by clarity and size. Get ready for this one line.

If you read this far that means you are ready, so let’s begin by creating a database:

(def db (sorted-map-by >))

Oh, by the way we are done. It’s the one and only: The Time Series Database.

Map is King of Data

Let’s use it. First we’ll need some data:

(def data
  {1449088877092 {:GOOG {:bid 762.74 :offer 762.79}}
   1449088876590 {:AAPL {:bid 116.60 :offer 116.70}}
   1449088877601 {:MSFT {:bid 55.22 :offer 55.27}}
   1449088877203 {:TSLA {:bid 232.57 :offer 232.72}}
   1449088875914 {:NFLX {:bid 128.95 :offer 129.05}}
   1449088870005 {:FB {:bid 105.96 :offer 106.6}}})

The format is simple {timestamp data}.

Now a query to have a database as a value with this data:

(defn with [db data] (merge db data))

And finally some time based queries, like before and after:

(defn before [database ts] (into {} (subseq database > ts)))
(defn after [database ts] (into {} (subseq database < ts)))



(before (with db data) 1449088877091)
{1449088876590 {:AAPL {:bid 116.6, :offer 116.7}},
 1449088875914 {:NFLX {:bid 128.95, :offer 129.05}},
 1449088870005 {:FB {:bid 105.96, :offer 106.6}}}
(after (with db data) 1449088877091)
{1449088877601 {:MSFT {:bid 55.22, :offer 55.27}},
 1449088877203 {:TSLA {:bid 232.57, :offer 232.72}},
 1449088877092 {:GOOG {:bid 762.74, :offer 762.79}}}

Beware, you, other time series databases!

P.S. Of course there is a possibility of events that came in at the exact same millisecond, so here is another line that solves it

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