From 40bff24fc2e2da57f382e4f49a5ffb7c826bbcfb Mon Sep 17 00:00:00 2001 From: Douglas Rumbaugh Date: Tue, 13 May 2025 17:29:40 -0400 Subject: Updates --- chapters/sigmod23/exp-baseline.tex | 12 ++++++------ 1 file changed, 6 insertions(+), 6 deletions(-) (limited to 'chapters/sigmod23/exp-baseline.tex') diff --git a/chapters/sigmod23/exp-baseline.tex b/chapters/sigmod23/exp-baseline.tex index da62766..5585c36 100644 --- a/chapters/sigmod23/exp-baseline.tex +++ b/chapters/sigmod23/exp-baseline.tex @@ -5,7 +5,7 @@ Olken's method on an aggregate B+Tree. We also examine the query performance of a single instance of the SSI in question to establish how much query performance is lost in the dynamization. Unless otherwise specified, IRS and WIRS queries are run with a selectivity of $0.1\%$. Additionally, -the \texttt{OSM} dataset was downsampled to 500 million records, except +the \texttt{OSM} dataset was down-sampled to 500 million records, except for scalability tests. The synthetic uniform and zipfian datasets were generated with 1 billion records. As with the previous section, all benchmarks began by warming up the structure with $10\%$ of the total @@ -50,13 +50,13 @@ resulting in better performance. \end{figure*} In Figures~\ref{fig:wirs-insert} and \ref{fig:wirs-sample} we examine -the performed of \texttt{DE-WIRS} compared to \text{AGG B+TreE} and an +the performed of \texttt{DE-WIRS} compared to \text{AGG B+tree} and an alias-augmented B+Tree. We see the same basic set of patterns in this case as we did with WSS. \texttt{AGG B+Tree} defeats our dynamized index on the \texttt{twitter} dataset, but loses on the others, in terms of insertion performance. We can see that the alias-augmented B+Tree is much more expensive to build than an alias structure, and -so its insertion performance advantage is erroded somewhat compared to +so its insertion performance advantage is eroded somewhat compared to the dynamic structure. For queries we see that the \texttt{AGG B+Tree} performs similarly for WIRS sampling as it did for WSS sampling, but the alias-augmented B+Tree structure is quite a bit slower at WIRS than the @@ -82,7 +82,7 @@ being introduced by the dynamization. We next considered IRS queries. Figures~\ref{fig:irs-insert1} and \ref{fig:irs-sample1} show the results of our testing of single-threaded \texttt{DE-IRS} running in-memory against the in-memory ISAM Tree and -\texttt{AGG B+treE}. The ISAM tree structure can be efficiently bulk-loaded, +\texttt{AGG B+tree}. The ISAM tree structure can be efficiently bulk-loaded, which results in a much faster construction time than the alias structure or alias-augmented B+tree. This gives it a significant update performance advantage, and we see in Figure~\ref{fig:irs-insert1} that \texttt{DE-IRS} @@ -96,7 +96,7 @@ the performance differences. We also consider the scalability of inserts, queries, and deletes, of \texttt{DE-IRS} compared to \texttt{AGG B+tree} across a wide range of data sizes. Figure~\ref{fig:irs-insert-s} shows that \texttt{DE-IRS}'s -insertion performance scales similarly with datasize as the baseline, and +insertion performance scales similarly with data size as the baseline, and Figure~\ref{fig:irs-sample-s} tells a similar story for query performance. Figure~\ref{fig:irs-delete-s} compares the delete performance of the two structures, where \texttt{DE-IRS} is configured to use tagging. As @@ -110,7 +110,7 @@ the B+tree is superior to \texttt{DE-IRS} because of the cost of the preliminary processing that our dynamized structure must do to begin to answer queries. However, as the sample set size increases, this cost increasingly begins to pay off, with \texttt{DE-IRS} quickly defeating -the dynamic structure in averge per-sample latency. One other interesting +the dynamic structure in average per-sample latency. One other interesting note is the performance of the static ISAM tree, which begins on-par with the B+Tree, but also sees an improvement as the sample set size increases. This is because of cache effects. During the initial tree traversal, both -- cgit v1.2.3