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publications/whitepaper/druid.tex
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@ -810,16 +810,20 @@ to do so because infrastructure cost is still a consideration.
\label{fig:query_percentiles}
\end{figure}
We also present Druid benchmarks with TPC-H data. Most TPC-H queries do not
directly apply to Druid, so we selected similiar queries to demonstrate Druid's
query performance. As a comparison, we also provide the results of the same
queries using MySQL with MyISAM (InnoDB was slower in our experiments). We
selected MySQL to benchmark against because of its universal popularity. We
choose not to select another open source column store because we were not
confident we could correctly tune it for optimal performance. The results for
the 1 GB TPC-H data set are shown in Figure~\ref{fig:tpch_1gb} and the results
of the 100 GB data set are shown in Figure~\ref{fig:tpch_100gb}. We benchmarked
Druid's scan rate at 50.6 million rows/second/core.
We also present Druid benchmarks with TPC-H data. Our setup used Amazon EC2
m3.2xlarge (CPU: Intel(R) Xeon(R) CPU E5-2680 v2 @ 2.80GHz) instances for
historical nodes. Most TPC-H queries do not directly apply to Druid, so we
selected similiar queries to demonstrate Druid's query performance. As a
comparison, we also provide the results of the same queries using MySQL with
MyISAM (InnoDB was slower in our experiments). Our MySQL setup was an Amazon
RDS instance that also ran on an m3.2xlarge node.We selected MySQL to benchmark
against because of its universal popularity. We choose not to select another
open source column store because we were not confident we could correctly tune
it for optimal performance. The results for the 1 GB TPC-H data set are shown
in Figure~\ref{fig:tpch_1gb} and the results of the 100 GB data set are shown
in Figure~\ref{fig:tpch_100gb}. We benchmarked Druid's scan rate at
53,539,211.1 rows/second/core for count(*) over a given interval and 36,246,530
rows/second/core for an aggregation involving floats.
\begin{figure}
\centering
@ -836,12 +840,14 @@ Druid's scan rate at 50.6 million rows/second/core.
\end{figure}
Finally, we present our results of scaling Druid to meet increasing data
volumes with the TPC-H 100 GB data set. We observe that when we increased the
number of cores from 8 to 48, we do not always display linear scaling.
The increase in speed of a parallel computing system is often limited by the
time needed for the sequential operations of the system, in accordance with
Amdahl's law \cite{amdahl1967validity}. Our query results and query speedup are
shown in Figure~\ref{fig:tpch_scaling}.
volumes with the TPC-H 100 GB data set. Our distributed cluster used Amazon EC2
c3.2xlarge (Intel(R) Xeon(R) CPU E5-2670 v2 @ 2.50GHz) instances for broker
nodes. We observe that when we increased the number of cores from 8 to 48, we
do not always display linear scaling. The increase in speed of a parallel
computing system is often limited by the time needed for the sequential
operations of the system, in accordance with Amdahl's law
\cite{amdahl1967validity}. Our query results and query speedup are shown in
Figure~\ref{fig:tpch_scaling}.
\begin{figure}
\centering