mirror of
https://git.deuxfleurs.fr/Deuxfleurs/garage.git
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899 lines
26 KiB
TeX
899 lines
26 KiB
TeX
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\begin{frame}
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\vfill
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\centering
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\begin{beamercolorbox}[sep=8pt,center,shadow=true,rounded=true]{title}
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}
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\title{Garage}
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\subtitle{a lightweight and robust geo-distributed data storage system}
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\author{Alex Auvolat, Deuxfleurs Association}
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\date{Inria, 2023-01-18}
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\begin{document}
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\begin{frame}
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\centering
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\includegraphics[width=.3\linewidth]{../../sticker/Garage.pdf}
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\vspace{1em}
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{\large\bf Alex Auvolat, Deuxfleurs Association}
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\vspace{1em}
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\url{https://garagehq.deuxfleurs.fr/}
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Matrix channel: \texttt{\#garage:deuxfleurs.fr}
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\end{frame}
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\begin{frame}
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\frametitle{Who I am}
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\begin{columns}[t]
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\begin{column}{.2\textwidth}
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\centering
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\adjincludegraphics[width=.4\linewidth, valign=t]{assets/alex.jpg}
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\end{column}
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\begin{column}{.6\textwidth}
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\textbf{Alex Auvolat}\\
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PhD; co-founder of Deuxfleurs
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\end{column}
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\begin{column}{.2\textwidth}
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~
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\end{column}
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\end{columns}
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\vspace{2em}
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\begin{columns}[t]
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\begin{column}{.2\textwidth}
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\centering
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\adjincludegraphics[width=.5\linewidth, valign=t]{assets/deuxfleurs.pdf}
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\end{column}
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\begin{column}{.6\textwidth}
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\textbf{Deuxfleurs}\\
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A non-profit self-hosting collective,\\
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member of the CHATONS network
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\end{column}
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\begin{column}{.2\textwidth}
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\centering
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\adjincludegraphics[width=.7\linewidth, valign=t]{assets/logo_chatons.png}
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\end{column}
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\end{columns}
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\end{frame}
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\begin{frame}
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\frametitle{Our objective at Deuxfleurs}
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\begin{center}
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\textbf{Promote self-hosting and small-scale hosting\\
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as an alternative to large cloud providers}
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\end{center}
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\vspace{2em}
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\visible<2->{
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Why is it hard?
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}
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\visible<3->{
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\vspace{2em}
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\begin{center}
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\textbf{\underline{Resilience}}\\
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{\footnotesize (we want good uptime/availability with low supervision)}
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\end{center}
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}
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\end{frame}
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\begin{frame}
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\frametitle{How to make a \underline{stable} system}
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Enterprise-grade systems typically employ:
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\vspace{1em}
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\begin{itemize}
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\item RAID
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\item Redundant power grid + UPS
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\item Redundant Internet connections
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\item Low-latency links
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\item ...
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\end{itemize}
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\vspace{1em}
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$\to$ it's costly and only worth it at DC scale
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\end{frame}
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\begin{frame}
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\frametitle{How to make a \underline{resilient} system}
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\only<1,4-5>{
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Instead, we use:
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\vspace{1em}
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\begin{itemize}
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\item \textcolor<2->{gray}{Commodity hardware (e.g. old desktop PCs)}
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\vspace{.5em}
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\item<4-> \textcolor<5->{gray}{Commodity Internet (e.g. FTTB, FTTH) and power grid}
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\vspace{.5em}
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\item<5-> \textcolor<6->{gray}{\textbf{Geographical redundancy} (multi-site replication)}
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\end{itemize}
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}
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\only<2>{
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\begin{center}
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\includegraphics[width=.8\linewidth]{assets/atuin.jpg}
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\end{center}
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}
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\only<3>{
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\begin{center}
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\includegraphics[width=.8\linewidth]{assets/neptune.jpg}
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\end{center}
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}
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\only<6>{
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\begin{center}
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\includegraphics[width=.5\linewidth]{assets/inframap.jpg}
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\end{center}
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}
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\end{frame}
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\begin{frame}
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\frametitle{How to make this happen}
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\begin{center}
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\only<1>{\includegraphics[width=.8\linewidth]{assets/slide1.png}}%
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\only<2>{\includegraphics[width=.8\linewidth]{assets/slide2.png}}%
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\only<3>{\includegraphics[width=.8\linewidth]{assets/slide3.png}}%
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\end{center}
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\end{frame}
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\begin{frame}
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\frametitle{Distributed file systems are slow}
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File systems are complex, for example:
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\vspace{1em}
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\begin{itemize}
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\item Concurrent modification by several processes
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\vspace{1em}
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\item Folder hierarchies
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\vspace{1em}
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\item Other requirements of the POSIX spec
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\end{itemize}
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\vspace{1em}
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Coordination in a distributed system is costly
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\vspace{1em}
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Costs explode with commodity hardware / Internet connections\\
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{\small (we experienced this!)}
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\end{frame}
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\begin{frame}
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\frametitle{A simpler solution: object storage}
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Only two operations:
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\vspace{1em}
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\begin{itemize}
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\item Put an object at a key
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\vspace{1em}
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\item Retrieve an object from its key
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\end{itemize}
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\vspace{1em}
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{\footnotesize (and a few others)}
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\vspace{1em}
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Sufficient for many applications!
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\end{frame}
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\begin{frame}
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\frametitle{A simpler solution: object storage}
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\begin{center}
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\includegraphics[height=6em]{../2020-12-02_wide-team/img/Amazon-S3.jpg}
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\hspace{3em}
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\includegraphics[height=5em]{assets/minio.png}
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\hspace{3em}
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\includegraphics[height=6em]{../../logo/garage_hires_crop.png}
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\end{center}
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\vspace{1em}
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S3: a de-facto standard, many compatible applications
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\vspace{1em}
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MinIO is self-hostable but not suited for geo-distributed deployments
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\vspace{1em}
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\textbf{Garage is a self-hosted drop-in replacement for the Amazon S3 object store}
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\end{frame}
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\begin{frame}
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\frametitle{The data model of object storage}
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Object storage is basically a key-value store:
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\vspace{1em}
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\begin{center}
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\begin{tabular}{|l|p{8cm}|}
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\hline
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\textbf{Key: file path + name} & \textbf{Value: file data + metadata} \\
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\hline
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\hline
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\texttt{index.html} &
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\texttt{Content-Type: text/html; charset=utf-8} \newline
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\texttt{Content-Length: 24929} \newline
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\texttt{<binary blob>} \\
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\hline
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\texttt{img/logo.svg} &
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\texttt{Content-Type: text/svg+xml} \newline
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\texttt{Content-Length: 13429} \newline
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\texttt{<binary blob>} \\
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\hline
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\texttt{download/index.html} &
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\texttt{Content-Type: text/html; charset=utf-8} \newline
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\texttt{Content-Length: 26563} \newline
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\texttt{<binary blob>} \\
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\hline
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\end{tabular}
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\end{center}
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\end{frame}
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\begin{frame}
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\frametitle{Two big problems}
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\begin{enumerate}
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\item \textbf{How to place data on different nodes?}\\
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\vspace{1em}
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\underline{Constraints:} heterogeneous hardware\\
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\underline{Objective:} $n$ copies of everything, maximize usable capacity, maximize resilience\\
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\vspace{1em}
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$\to$ the Dynamo model + optimization algorithms
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\vspace{2em}
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\item<2-> \textbf{How to guarantee consistency?}\\
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\vspace{1em}
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\underline{Constraints:} slow network (geographical distance), node unavailability/crashes\\
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\underline{Objective:} maximize availability, read-after-write guarantee\\
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\vspace{1em}
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$\to$ CRDTs, monotonicity, read and write quorums
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\end{enumerate}
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\end{frame}
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\section{Problem 1: placing data}
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\begin{frame}
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\frametitle{Key-value stores, upgraded: the Dynamo model}
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\textbf{Two keys:}
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\begin{itemize}
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\item Partition key: used to divide data into partitions (shards)
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\item Sort key: used to identify items inside a partition
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\end{itemize}
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\vspace{1em}
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\begin{center}
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\begin{tabular}{|l|l|p{3cm}|}
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\hline
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\textbf{Partition key: bucket} & \textbf{Sort key: filename} & \textbf{Value} \\
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\hline
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\hline
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\texttt{website} & \texttt{index.html} & (file data) \\
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\hline
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\texttt{website} & \texttt{img/logo.svg} & (file data) \\
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\hline
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\texttt{website} & \texttt{download/index.html} & (file data) \\
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\hline
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\hline
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\texttt{backup} & \texttt{borg/index.2822} & (file data) \\
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\hline
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\texttt{backup} & \texttt{borg/data/2/2329} & (file data) \\
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\hline
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\texttt{backup} & \texttt{borg/data/2/2680} & (file data) \\
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\hline
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\hline
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\texttt{private} & \texttt{qq3a2nbe1qjq0ebbvo6ocsp6co} & (file data) \\
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\hline
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\end{tabular}
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\end{center}
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\end{frame}
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\begin{frame}
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\frametitle{Key-value stores, upgraded: the Dynamo model}
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\begin{itemize}
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\item Data with different partition keys is stored independantly,\\
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on a different set of nodes\\
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\vspace{.5em}
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$\to$ no easy way to list all partition keys\\
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$\to$ no cross-shard transactions\\
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\vspace{2em}
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\item Placing data: hash the partition key, select nodes accordingly\\
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\vspace{.5em}
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$\to$ distributed hash table (DHT)
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\vspace{2em}
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\item For a given value of the partition key, items can be listed using their sort keys
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\end{itemize}
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\end{frame}
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\begin{frame}
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\frametitle{How to spread files over different cluster nodes?}
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\textbf{Consistent hashing (Dynamo):}
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\vspace{1em}
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\begin{center}
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\only<1>{\includegraphics[width=.40\columnwidth]{assets/consistent_hashing_1.pdf}}%
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\only<2>{\includegraphics[width=.40\columnwidth]{assets/consistent_hashing_2.pdf}}%
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\only<3>{\includegraphics[width=.40\columnwidth]{assets/consistent_hashing_3.pdf}}%
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\only<4>{\includegraphics[width=.40\columnwidth]{assets/consistent_hashing_4.pdf}}%
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\end{center}
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\end{frame}
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\begin{frame}
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\frametitle{Constraint: location-awareness}
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\begin{center}
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\includegraphics[width=\linewidth]{assets/location-aware.png}
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\end{center}
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\vspace{2em}
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Garage replicates data on different zones when possible
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\end{frame}
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\begin{frame}
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\frametitle{Constraint: location-awareness}
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\begin{center}
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\includegraphics[width=.8\linewidth]{assets/map.png}
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\end{center}
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\end{frame}
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\begin{frame}
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\frametitle{Issues with consistent hashing}
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\begin{itemize}
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\item Consistent hashing doesn't dispatch data based on geographical location of nodes
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\vspace{1em}
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\item<2-> Geographically aware adaptation, try 1:\\
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data quantities not well balanced between nodes
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\vspace{1em}
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\item<3-> Geographically aware adaptation, try 2:\\
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too many reshuffles when adding/removing nodes
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\end{itemize}
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\end{frame}
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\begin{frame}
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\frametitle{How to spread files over different cluster nodes?}
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\textbf{Garage's method: build an index table}
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\vspace{1em}
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Realization: we can actually precompute an optimal solution
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\vspace{1em}
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\visible<2->{
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\begin{center}
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\begin{tabular}{|l|l|l|l|}
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\hline
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\textbf{Partition} & \textbf{Node 1} & \textbf{Node 2} & \textbf{Node 3} \\
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\hline
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\hline
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Partition 0 & Io (jupiter) & Drosera (atuin) & Courgette (neptune) \\
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\hline
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Partition 1 & Datura (atuin) & Courgette (neptune) & Io (jupiter) \\
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\hline
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Partition 2 & Io(jupiter) & Celeri (neptune) & Drosera (atuin) \\
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\hline
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\hspace{1em}$\vdots$ & \hspace{1em}$\vdots$ & \hspace{1em}$\vdots$ & \hspace{1em}$\vdots$ \\
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\hline
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Partition 255 & Concombre (neptune) & Io (jupiter) & Drosera (atuin) \\
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\hline
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\end{tabular}
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\end{center}
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}
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\vspace{1em}
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\visible<3->{
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The index table is built centrally using an optimal algorithm,\\
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then propagated to all nodes
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}
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\end{frame}
|
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\begin{frame}
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\frametitle{The relationship between \emph{partition} and \emph{partition key}}
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\begin{center}
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\begin{tabular}{|l|l|l|l|}
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\hline
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\textbf{Partition key} & \textbf{Partition} & \textbf{Sort key} & \textbf{Value} \\
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\hline
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\hline
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\texttt{website} & Partition 12 & \texttt{index.html} & (file data) \\
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\hline
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\texttt{website} & Partition 12 & \texttt{img/logo.svg} & (file data) \\
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\hline
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\texttt{website} & Partition 12 &\texttt{download/index.html} & (file data) \\
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\hline
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\hline
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\texttt{backup} & Partition 42 & \texttt{borg/index.2822} & (file data) \\
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\hline
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\texttt{backup} & Partition 42 & \texttt{borg/data/2/2329} & (file data) \\
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\hline
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\texttt{backup} & Partition 42 & \texttt{borg/data/2/2680} & (file data) \\
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\hline
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\hline
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\texttt{private} & Partition 42 & \texttt{qq3a2nbe1qjq0ebbvo6ocsp6co} & (file data) \\
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\hline
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\end{tabular}
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\end{center}
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\vspace{1em}
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\textbf{To read or write an item:} hash partition key
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\\ \hspace{5cm} $\to$ determine partition number (first 8 bits)
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\\ \hspace{5cm} $\to$ find associated nodes
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\end{frame}
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\begin{frame}
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\frametitle{Garage's internal data structures}
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\centering
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\includegraphics[width=.75\columnwidth]{assets/garage_tables.pdf}
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\end{frame}
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|
\begin{frame}
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|
\frametitle{Storing and retrieving files}
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\begin{center}
|
|
\only<1>{\includegraphics[width=.45\linewidth]{assets/garage2a.drawio.pdf}}%
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|
\only<2>{\includegraphics[width=.45\linewidth]{assets/garage2b.drawio.pdf}}%
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\end{center}
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\end{frame}
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\section{Problem 2: ensuring consistency}
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|
|
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\begin{frame}
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|
\frametitle{Consensus vs weak consistency}
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|
|
|
\hspace{1em}
|
|
\begin{minipage}{7cm}
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|
\textbf{Consensus-based systems:}
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\vspace{1em}
|
|
\begin{itemize}
|
|
\item \textbf{Leader-based:} a leader is elected to coordinate
|
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all reads and writes
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\vspace{1em}
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|
\item \textbf{Linearizability} of all operations\\
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(strongest consistency guarantee)
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\vspace{1em}
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\item Any sequential specification can be implemented as a \textbf{replicated state machine}
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\vspace{1em}
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\item \textbf{Costly}, the leader is a bottleneck;
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leader elections on failure take time
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\end{itemize}
|
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\end{minipage}
|
|
\hfill
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\begin{minipage}{7cm} \visible<2->{
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|
\textbf{Weakly consistent systems:}
|
|
\vspace{1em}
|
|
\begin{itemize}
|
|
\item \textbf{Nodes are equivalent}, any node
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can originate a read or write operation
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\vspace{1em}
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\item \textbf{Read-after-write consistency} with quorums,
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|
eventual consistency without
|
|
\vspace{1em}
|
|
\item \textbf{Operations have to commute}, i.e.~we
|
|
can only implement CRDTs
|
|
\vspace{1em}
|
|
\item \textbf{Fast}, no single bottleneck;\\
|
|
works the same with offline nodes
|
|
\end{itemize}
|
|
} \end{minipage}
|
|
\hspace{1em}
|
|
\end{frame}
|
|
|
|
\begin{frame}
|
|
\frametitle{Consensus vs weak consistency}
|
|
\begin{center}
|
|
\textbf{From a theoretical point of view:}\\
|
|
|
|
\end{center}
|
|
\vspace{2em}
|
|
|
|
\hspace{1em}
|
|
\begin{minipage}{6.5cm}
|
|
\underline{Consensus-based systems:}
|
|
|
|
\vspace{1em}
|
|
|
|
Require \textbf{additionnal assumptions} such as a fault detector or a strong RNG\\
|
|
(FLP impossibility theorem)
|
|
\end{minipage}
|
|
\hfill
|
|
\begin{minipage}{6.5cm}
|
|
\underline{Weakly consistent systems:}
|
|
|
|
\vspace{1em}
|
|
|
|
Can be implemented in \textbf{any\\asynchronous message passing\\distributed system} with node crashes
|
|
\end{minipage}
|
|
\hspace{1em}
|
|
|
|
\vspace{3em}
|
|
\begin{center}
|
|
They represent \textbf{different classes of computational capability}\\
|
|
\end{center}
|
|
\end{frame}
|
|
|
|
\begin{frame}
|
|
\frametitle{Consensus vs weak consistency}
|
|
\begin{center}
|
|
\textbf{The same objects cannot be implemented in both models.}
|
|
\end{center}
|
|
\vspace{2em}
|
|
|
|
\hspace{1em}
|
|
\begin{minipage}{6.5cm}
|
|
\underline{Consensus-based systems:}
|
|
|
|
\vspace{1em}
|
|
|
|
\textbf{Any sequential specification}\\~
|
|
|
|
\vspace{1em}
|
|
\textbf{Easier to program for}: just write your program as if it were sequential on a single machine
|
|
|
|
\end{minipage}
|
|
\hfill
|
|
\begin{minipage}{6.5cm}
|
|
\underline{Weakly consistent systems:}
|
|
|
|
\vspace{1em}
|
|
|
|
\textbf{Only CRDTs}\\(conflict-free replicated data types)
|
|
|
|
\vspace{1em}
|
|
Part of the complexity is \textbf{reported to the consumer of the API}\\~
|
|
\end{minipage}
|
|
\hspace{1em}
|
|
\end{frame}
|
|
|
|
\begin{frame}
|
|
\frametitle{Understanding the power of consensus}
|
|
\textbf{Consensus:} an API with a single operation, $propose(x)$
|
|
\begin{enumerate}
|
|
\item nodes all call $propose(x)$ with their proposed value;
|
|
\item nodes all receive the same value as a return value, which is one of the proposed values
|
|
\end{enumerate}
|
|
\vspace{1em}
|
|
|
|
\visible<2->{
|
|
\textbf{Equivalent to} a distributed algorithm that gives a total order on all requests
|
|
}
|
|
\vspace{1em}
|
|
|
|
\visible<3->{
|
|
\textbf{Implemented by} this simple replicated state machine:
|
|
\vspace{.5em}
|
|
\begin{figure}
|
|
\centering
|
|
\def\svgwidth{.5\textwidth}
|
|
\large
|
|
\import{assets/}{consensus.pdf_tex}
|
|
\end{figure}
|
|
\vspace{1em}
|
|
}
|
|
\end{frame}
|
|
|
|
\begin{frame}
|
|
\frametitle{Can my object be implemented without consensus?}
|
|
\underline{Given the specification of an API:}
|
|
\vspace{2em}
|
|
\begin{itemize}
|
|
\item \textbf{Using this API, we can implement the consensus object} (the $propose$ function)\\
|
|
$\to$ the API is equivalent to consensus/total ordering of messages\\
|
|
$\to$ the API cannot be implemented in a weakly consistent system
|
|
\vspace{2em}
|
|
\item \textbf{This API can be implemented using only weak primitives}\\
|
|
(e.g. in the asynchronous message passing model with no further assumption)\\
|
|
$\to$ the API is strictly weaker than consensus\\
|
|
$\to$ we can implement it in Garage!
|
|
\end{itemize}
|
|
\end{frame}
|
|
|
|
\begin{frame}
|
|
\frametitle{Why avoid consensus?}
|
|
Consensus can be implemented reasonably well in practice, so why avoid it?
|
|
\vspace{2em}
|
|
\begin{itemize}
|
|
\item \textbf{Software complexity:} RAFT and PAXOS are complex beasts;\\
|
|
harder to prove, harder to reason about
|
|
\vspace{1.5em}
|
|
\item \textbf{Performance issues:}
|
|
\vspace{1em}
|
|
\begin{itemize}
|
|
\item Theoretical requirements (RNG, failure detector) translate into \textbf{practical costs}
|
|
\vspace{1em}
|
|
\item The leader is a \textbf{bottleneck} for all requests;\\
|
|
even in leaderless approaches, \textbf{all nodes must process all operations in order}
|
|
\vspace{1em}
|
|
\item Particularly \textbf{sensitive to higher latency} between nodes
|
|
\end{itemize}
|
|
\end{itemize}
|
|
\end{frame}
|
|
|
|
\begin{frame}
|
|
\frametitle{Performance gains in practice}
|
|
\begin{center}
|
|
\includegraphics[width=.8\linewidth]{assets/endpoint-latency-dc.png}
|
|
\end{center}
|
|
\end{frame}
|
|
|
|
\begin{frame}
|
|
\frametitle{What can we implement without consensus?}
|
|
\begin{itemize}
|
|
\item Any \textbf{conflict-free replicated data type} (CRDT)
|
|
\vspace{1em}
|
|
\item Non-transactional key-value stores such as S3 are equivalent to a simple CRDT:\\
|
|
a \textbf{last-writer-wins registry}
|
|
\vspace{1em}
|
|
\item \textbf{Read-after-write consistency} can be implemented
|
|
using quorums on read and write operations
|
|
\vspace{1em}
|
|
\item \textbf{Monotonicity of reads} can be implemented with repair-on-read\\
|
|
(makes reads more costly, not implemented in Garage)
|
|
\end{itemize}
|
|
\end{frame}
|
|
|
|
\begin{frame}
|
|
\frametitle{CRDTs and quorums: read-after-write consistency}
|
|
\begin{figure}
|
|
\centering
|
|
\def\svgwidth{.8\textwidth}
|
|
\only<1>{\import{assets/}{lattice1.pdf_tex}}%
|
|
\only<2>{\import{assets/}{lattice2.pdf_tex}}%
|
|
\only<3>{\import{assets/}{lattice3.pdf_tex}}%
|
|
\only<4>{\import{assets/}{lattice4.pdf_tex}}%
|
|
\only<5>{\import{assets/}{lattice5.pdf_tex}}%
|
|
\only<6>{\import{assets/}{lattice6.pdf_tex}}%
|
|
\only<7>{\import{assets/}{lattice7.pdf_tex}}%
|
|
\only<8>{\import{assets/}{lattice8.pdf_tex}}%
|
|
\end{figure}
|
|
\end{frame}
|
|
|
|
\begin{frame}
|
|
\frametitle{CRDTs and quorums: read-after-write consistency}
|
|
\textbf{Property:} If node $A$ did an operation $write(x)$ and received an OK response,\\
|
|
\hspace{2cm} and node $B$ starts an operation $read()$ after $A$ received OK,\\
|
|
\hspace{2cm} then $B$ will read a value $x' \sqsupseteq x$.
|
|
|
|
\vspace{1em}
|
|
|
|
\hspace{1em}
|
|
\begin{minipage}{6.8cm}
|
|
\textbf{Algorithm $write(x)$:}
|
|
\begin{enumerate}
|
|
\item Broadcast $write(x)$ to all nodes
|
|
\item Wait for $k > n/2$ nodes to reply OK
|
|
\item Return OK
|
|
\end{enumerate}
|
|
\end{minipage}
|
|
\hfill
|
|
\begin{minipage}{6.8cm}
|
|
\vspace{1em}
|
|
\textbf{Algorithm $read()$:}
|
|
\begin{enumerate}
|
|
\item Broadcast $read()$ to all nodes
|
|
\item Wait for $k > n/2$ nodes to reply\\
|
|
with values $x_1, \dots, x_k$
|
|
\item Return $x_1 \sqcup \dots \sqcup x_k$
|
|
\end{enumerate}
|
|
\end{minipage}
|
|
\hspace{1em}
|
|
|
|
\vspace{2em}
|
|
\textbf{Why does it work?} There is at least one node at the intersection between the two sets of nodes that replied to each request, that ``saw'' $x$ before the $read()$ started ($x_i \sqsupseteq x$).
|
|
\end{frame}
|
|
|
|
\begin{frame}
|
|
\frametitle{CRDTs and quorums: monotonic-reads consistency}
|
|
\begin{figure}
|
|
\centering
|
|
\def\svgwidth{.8\textwidth}
|
|
\only<1>{\import{assets/}{latticeB_1.pdf_tex}}%
|
|
\only<2>{\import{assets/}{latticeB_2.pdf_tex}}%
|
|
\only<3>{\import{assets/}{latticeB_3.pdf_tex}}%
|
|
\only<4>{\import{assets/}{latticeB_4.pdf_tex}}%
|
|
\only<5>{\import{assets/}{latticeB_5.pdf_tex}}%
|
|
\only<6>{\import{assets/}{latticeB_6.pdf_tex}}%
|
|
\only<7>{\import{assets/}{latticeB_7.pdf_tex}}%
|
|
\only<8>{\import{assets/}{latticeB_8.pdf_tex}}%
|
|
\only<9>{\import{assets/}{latticeB_9.pdf_tex}}%
|
|
\only<10>{\import{assets/}{latticeB_10.pdf_tex}}%
|
|
\end{figure}
|
|
\end{frame}
|
|
|
|
\begin{frame}
|
|
\frametitle{CRDTs and quorums: monotonic-reads consistency}
|
|
\textbf{Property:} If node $A$ did an operation $read()$ and received $x$ as a response,\\
|
|
\hspace{2cm} and node $B$ starts an operation $read()$ after $A$ received $x$,\\
|
|
\hspace{2cm} then $B$ will read a value $x' \sqsupseteq x$.
|
|
|
|
\vspace{1em}
|
|
|
|
\textbf{Algorithm $read()$:}
|
|
\begin{enumerate}
|
|
\item Broadcast $read()$ to all nodes
|
|
\item Wait for $k > n/2$ nodes to reply with values $x_1, \dots, x_k$
|
|
\item If $x_i \ne x_j$ for some nodes $i$ and $j$,\\
|
|
\hspace{1cm}then call $write(x_1 \sqcup \dots \sqcup x_k)$ and wait for OK from $k' > n/2$ nodes
|
|
\item Return $x_1 \sqcup \dots \sqcup x_k$
|
|
\end{enumerate}
|
|
|
|
\vspace{1em}
|
|
|
|
This makes reads slower in some cases, and is \textbf{not implemented in Garage}.
|
|
\end{frame}
|
|
|
|
\begin{frame}
|
|
\frametitle{A hard problem: layout changes}
|
|
\begin{itemize}
|
|
\item We rely on quorums $k > n/2$ within each partition:\\
|
|
$$n=3,~~~~~~~k\ge 2$$
|
|
\item When rebalancing, the set of nodes responsible for a partition can change:\\
|
|
$$\{n_A, n_B, n_C\} \to \{n_A, n_D, n_E\}$$
|
|
\vspace{.01em}
|
|
\item During the rebalancing, $D$ and $E$ don't yet have the data,\\
|
|
~~~~~~~~~~~~~~~~~~~and $B$ and $C$ want to get rid of the data to free up space\\
|
|
\vspace{.2em}
|
|
$\to$ quorums only within the new set of nodes don't work\\
|
|
$\to$ how to coordinate? \textbf{currently, we don't...}
|
|
|
|
\end{itemize}
|
|
\end{frame}
|
|
|
|
\section{Going further than the S3 API}
|
|
|
|
\begin{frame}
|
|
\frametitle{Further plans for Garage}
|
|
\begin{center}
|
|
\only<1>{\includegraphics[width=.8\linewidth]{assets/slideB1.png}}%
|
|
\only<2>{\includegraphics[width=.8\linewidth]{assets/slideB2.png}}%
|
|
\only<3>{\includegraphics[width=.8\linewidth]{assets/slideB3.png}}%
|
|
\end{center}
|
|
\end{frame}
|
|
|
|
\begin{frame}
|
|
\frametitle{K2V Design}
|
|
\begin{itemize}
|
|
\item A new, custom, minimal API\\
|
|
\vspace{.5em}
|
|
\begin{itemize}
|
|
\item Single-item operations
|
|
\item Operations on ranges and batches of items
|
|
\item Polling operations to help implement a PubSub pattern
|
|
\end{itemize}
|
|
\vspace{1em}
|
|
\item<2-> Exposes the partitoning mechanism of Garage\\
|
|
K2V = partition key / sort key / value (like Dynamo)
|
|
\vspace{1em}
|
|
\item<3-> Weakly consistent, CRDT-friendly\\
|
|
$\to$ no support for transactions (not ACID)
|
|
\vspace{1em}
|
|
\item<4-> Cryptography-friendly: values are binary blobs
|
|
\end{itemize}
|
|
\end{frame}
|
|
|
|
\begin{frame}
|
|
\frametitle{Handling concurrent values}
|
|
\textbf{How to handle concurrency?} Example:
|
|
\vspace{1em}
|
|
\begin{enumerate}
|
|
\item Client $A$ reads the initial value of a key, $x_0$
|
|
\vspace{1em}
|
|
\item<2-> Client $B$ also reads the initial value $x_0$ of that key
|
|
\vspace{1em}
|
|
\item<3-> Client $A$ modifies $x_0$, and writes a new value $x_1$
|
|
\vspace{1em}
|
|
\item<4-> Client $B$ also modifies $x_0$, and writes a new value $x'_1$,\\
|
|
without having a chance to first read $x_1$\\
|
|
\vspace{1em}
|
|
$\to$ what should the final state be?
|
|
\end{enumerate}
|
|
\end{frame}
|
|
|
|
\begin{frame}
|
|
\frametitle{Handling concurrent values}
|
|
\begin{itemize}
|
|
\item If we keep only $x_1$ or $x'_1$, we risk \textbf{loosing application data}
|
|
\vspace{1.5em}
|
|
\item Values are opaque binary blobs, \textbf{K2V cannot resolve conflicts} by itself\\
|
|
(e.g. by implementing a CRDT)
|
|
\vspace{1.5em}
|
|
\item Solution: \textbf{we keep both!}\\
|
|
$\to$ the value of the key is now $\{x_1, x'_1\}$\\
|
|
$\to$ the client application can decide how to resolve conflicts on the next read
|
|
\end{itemize}
|
|
\end{frame}
|
|
|
|
\begin{frame}
|
|
\frametitle{Keeping track of causality}
|
|
How does K2V know that $x_1$ and $x'_1$ are concurrent?
|
|
\vspace{1em}
|
|
\begin{itemize}
|
|
\item $read()$ returns \textbf{a set of values} and an associated \textbf{causality token}\\
|
|
\vspace{1.5em}
|
|
\item When calling $write()$, the client sends \textbf{the causality token from its last read}
|
|
\vspace{1.5em}
|
|
\item The causality token represents the set of values \textbf{already seen by the client}\\
|
|
$\to$ those values are the \textbf{causal past} of the write operation\\
|
|
$\to$ K2V can keep concurrent values and overwrite all ones in the causal past
|
|
\vspace{1.5em}
|
|
\item Internally, the causality token is \textbf{a vector clock}
|
|
\end{itemize}
|
|
\end{frame}
|
|
|
|
\begin{frame}
|
|
\frametitle{Application: an e-mail storage server}
|
|
\begin{center}
|
|
\only<1>{\includegraphics[width=.9\linewidth]{assets/aerogramme.png}}%
|
|
\end{center}
|
|
\end{frame}
|
|
|
|
\begin{frame}
|
|
\frametitle{A new model for building resilient software}
|
|
\begin{enumerate}
|
|
\item Design a data model suited to K2V\\
|
|
{\footnotesize (see Cassandra docs on porting SQL data models to Cassandra)}
|
|
\vspace{1em}
|
|
\begin{itemize}
|
|
\item Use CRDTs or other eventually consistent data types (see e.g. Bayou)
|
|
\vspace{1em}
|
|
\item Store opaque binary blobs to provide End-to-End Encryption\\
|
|
\end{itemize}
|
|
\vspace{1em}
|
|
\item Store big blobs (files) using the S3 API
|
|
\vspace{1em}
|
|
\item Let Garage manage sharding, replication, failover, etc.
|
|
\end{enumerate}
|
|
\end{frame}
|
|
|
|
\begin{frame}
|
|
\frametitle{Research perspectives}
|
|
\begin{itemize}
|
|
\item TODO
|
|
\end{itemize}
|
|
\end{frame}
|
|
|
|
\begin{frame}
|
|
\frametitle{Where to find us}
|
|
\begin{center}
|
|
\includegraphics[width=.25\linewidth]{../../logo/garage_hires.png}\\
|
|
\vspace{-1em}
|
|
\url{https://garagehq.deuxfleurs.fr/}\\
|
|
\url{mailto:garagehq@deuxfleurs.fr}\\
|
|
\texttt{\#garage:deuxfleurs.fr} on Matrix
|
|
|
|
\vspace{1.5em}
|
|
\includegraphics[width=.06\linewidth]{assets/rust_logo.png}
|
|
\includegraphics[width=.13\linewidth]{assets/AGPLv3_Logo.png}
|
|
\end{center}
|
|
\end{frame}
|
|
|
|
\end{document}
|
|
|
|
%% vim: set ts=4 sw=4 tw=0 noet spelllang=en :
|