From 0c4462924497540c96f30cd143acb763e1139d96 Mon Sep 17 00:00:00 2001 From: fjy Date: Mon, 10 Mar 2014 14:16:19 -0700 Subject: [PATCH] some more minor paper edits --- publications/whitepaper/druid.pdf | Bin 588183 -> 588374 bytes publications/whitepaper/druid.tex | 9 ++++++--- .../whitepaper/figures/avg_query_latency.pdf | Bin 6245 -> 6233 bytes 3 files changed, 6 insertions(+), 3 deletions(-) diff --git a/publications/whitepaper/druid.pdf b/publications/whitepaper/druid.pdf index da4c703ba6bb6d744dc92f4c602f70870ce2936a..ea0edd1441f2e41cfda91c34a55304ab7a500224 100644 GIT binary patch delta 30729 zcmV(;K-<5U^CQ;uBY=bfgaWh!z0U$RF_Xa-&SZ&N-t$@2@|}-QV;cHM#en3U*ITx_T&v}h}a`)1uj!~bbM&8<3HTQ&NVw)*7z zAEqYGw$zONBu$*nacbgrn(*8;SzpcFA!3p0MZ7&+$YY-?nPYt}@*EJN$9i*s$TYxv zZcRCi;bBFRM^_j0W7KkAv+t&I)6Y!H6X$mdCnlBUg8T7vmyy|C_PwIh6vvJ zpbWvX8qhNRngdv-0n1{2S~tm(-eba_ZJL2aZ3drhnG=Yp2LM~Ga{!BKfa|L3PcZ`> zguK{T42+*MUM`p9Q|&fqx}@ zuw@}R6kD7j!*nqgn<4fkZin7|60)1e_Mwi)f%3GNFU9)Bv}!$V#V>`Xp~aTISk1N- 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We can see that based on the descriptions in Table~\ref{tab:ingest_datasources}, latencies vary significantly and the 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 -data producer was delivering data. The results are shown in Figure~\ref{fig:ingestion_rate}. +metrics. We see some lower latencies on simple data sets because that was the +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} \centering @@ -1039,7 +1043,6 @@ of functionality as Druid, some of Druid’s optimization techniques such as usi inverted indices to perform fast filters are also used in other data stores \cite{macnicol2004sybase}. -\newpage \section{Conclusions} \label{sec:conclusions} In this paper, we presented Druid, a distributed, column-oriented, real-time diff --git a/publications/whitepaper/figures/avg_query_latency.pdf b/publications/whitepaper/figures/avg_query_latency.pdf index f74fbd941acbcb8e87b00eacd1175ab5b3fb0168..5ae784e97c5ecd7ac9cc90981ba1d2cef6d6ad78 100644 GIT binary patch delta 2358 zcmZX}c{~%01IKY!$Sp?X7`9wt!!}k^ z)csfW{?`g!m$=~2;`5v!0wxU9DW3VAr5RTuGPXsdZ^(9}oRIsYFj+2G@g^C*ZQ{~D zE1Pnhxa0*O&1mJdjyfUz>uuYvIG~zEXX99mIM&ob%&!|sM={6u!kEMjc3LOHuP7lzs zu8C9W{FzbYB~usy*mpXf0j#~TEGX8dd@@Q6<%G@TU)rw`>TQ*s)IB6}J2h~Qc_q3a z=tWE3ebphv6tfWC>W~VF?8kf@uH-0=oWSIbrVVQ-w|si{oRd&)wcZWwauDg!{Q9;= z*erWwdczBsJ~xiP?LP?5e)t2q1aQ5Kzb<{(<*qO{Ys>Gbb5Vki*JQmN)3-$QEsE< zn=L7Tm1zSQU83ONPXy;Kh~~GP!{lnJ(M%-31Yb=|-OpgO@w`V6)e>nPq*1h0jJz`I zpI8Noc-^JM1De(_oeZSI zqa)At@*<$A78g&YLSx^#LyOi`2bV)^kFqf>Y0v@i*d32dG1@V|$tFqKE7ckA$l*nJ znB>Oe5qfhS06e#Fu)?QBs}?lcY^nzIX1$F5mZ9KF85LHNTFs0DYfC6BeM`&0-N6TD z$XCS#@gqkG+v1TwAJRFqei)x_T)i)cKD{WAFN8o9Txdpz&xJFalGEbZBrb}DgBgX_ zsz%e$#lG+|(8Jv~a?bDG*)%uMjsqdml0!?4+3)F!m6QKsGkJet|74fAG;h~{=e0>s z&TsK8*K;N0cf}V>vl``UWN`9XsZ*Y4ehK+XBgE9FFSXqwo;JF>cPYo56QQ!WYx-lC z$|P#I0#(RfLrp@8D47*KasI;7%P*b|O1wxD# ztKz!4`<&t89q)4NKMl;Uc*STrMCU%L1ou3ekJZp$fE*>OZ~0~P2cS=_^tp&i?<{g+ z$u6c+CAYTi3|rqd#n;vgSo+a5h@z_F=oqYltqK1~p=kQ&ZWO8+i%|ma-51#ux<5;V zZ99P6ee*xRmgptap^t;>d`s}ohzt=EjWS)Xw_)6&MJ2K%Cf14{(@HZ&&@Ji zH*24mG>*2%Ng^?m7l>MpHmyFzJR@=olu32l09`XHFAYX5L(t4;4ERpMoZmaaejS*p z3h;woUi7`1-uMI)JOfk2*vgoKc@{+J3`YyK9 zZK*=k4+e(k8JOueBu_#h@}cfo*X%Q#_SqP>$`w`4P~xz`fW^8N9E0@7T? 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