some more minor paper edits

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fjy 2014-03-10 14:16:19 -07:00
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@ -897,8 +897,12 @@ of the data sources we selected is shown in Table~\ref{tab:ingest_datasources}.
We can see that based on the descriptions in We can see that based on the descriptions in
Table~\ref{tab:ingest_datasources}, latencies vary significantly and the Table~\ref{tab:ingest_datasources}, latencies vary significantly and the
ingestion latency is not always a factor of the number of dimensions and ingestion latency is not always a factor of the number of dimensions and
metrics. We see some lower latencies on simple data sets because that was the rate that the metrics. We see some lower latencies on simple data sets because that was the
data producer was delivering data. The results are shown in Figure~\ref{fig:ingestion_rate}. rate that the data producer was delivering data. The results are shown in
Figure~\ref{fig:ingestion_rate}. We define throughput as the number of events a
real-time node can ingest and also make queryable. If too many events are sent
to the real-time node, those events are blocked until the real-time node has
capacity to accept them.
\begin{figure} \begin{figure}
\centering \centering
@ -1039,7 +1043,6 @@ of functionality as Druid, some of Druids optimization techniques such as usi
inverted indices to perform fast filters are also used in other data inverted indices to perform fast filters are also used in other data
stores \cite{macnicol2004sybase}. stores \cite{macnicol2004sybase}.
\newpage
\section{Conclusions} \section{Conclusions}
\label{sec:conclusions} \label{sec:conclusions}
In this paper, we presented Druid, a distributed, column-oriented, real-time In this paper, we presented Druid, a distributed, column-oriented, real-time