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docu #18

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7 changes: 4 additions & 3 deletions README.md
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Expand Up @@ -121,15 +121,16 @@ NanoCube is 100x or more times faster than Pandas.
#### Point query aggregating 5% of rows
A barely selective, filtering on 2 dimensions that affects and aggregates 5% of rows.
NanoCube is consistently 10x faster than Pandas. But you can already see, that the
aggregation in Numpy become slightly more dominant.
aggregation in Numpy become more dominant -> compare the lines of the number of returned
records and the NanoCube response time, they are almost parallel.

![Point query aggregating 5% of rows](benchmarks/charts/l.png)

#### Point query aggregating 50% of rows
A non-selective query, filtering on 1 dimension that affects and aggregates 50% of rows.
Here, most of the time is spent in Numpy, aggregating the rows. The more
rows, the closer Pandas and NanoCube get as both rely on Numpy for
aggregation.
rows, the closer Pandas and NanoCube get as both rely finally on Numpy for
aggregation, which is very fast.

![Point query aggregating 50% of rows](benchmarks/charts/xl.png)

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