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\title { Garage}
\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}
\begin { document}
\begin { frame}
\centering
\includegraphics [width=.3\linewidth] { ../../sticker/Garage.pdf}
\vspace { 1em}
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{ \large \bf Alex Auvolat, Deuxfleurs Association}
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\vspace { 1em}
\url { https://garagehq.deuxfleurs.fr/}
Matrix channel: \texttt { \# garage:deuxfleurs.fr}
\end { frame}
\begin { frame}
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\frametitle { Who I am}
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\begin { columns} [t]
\begin { column} { .2\textwidth }
\centering
\adjincludegraphics [width=.4\linewidth, valign=t] { assets/alex.jpg}
\end { column}
\begin { column} { .6\textwidth }
\textbf { Alex Auvolat} \\
PhD; co-founder of Deuxfleurs
\end { column}
\begin { column} { .2\textwidth }
~
\end { column}
\end { columns}
\vspace { 2em}
\begin { columns} [t]
\begin { column} { .2\textwidth }
\centering
\adjincludegraphics [width=.5\linewidth, valign=t] { assets/deuxfleurs.pdf}
\end { column}
\begin { column} { .6\textwidth }
\textbf { Deuxfleurs} \\
A non-profit self-hosting collective,\\
member of the CHATONS network
\end { column}
\begin { column} { .2\textwidth }
\centering
\adjincludegraphics [width=.7\linewidth, valign=t] { assets/logo_ chatons.png}
\end { column}
\end { columns}
\end { frame}
\begin { frame}
\frametitle { Our objective at Deuxfleurs}
\begin { center}
\textbf { Promote self-hosting and small-scale hosting\\
as an alternative to large cloud providers}
\end { center}
\vspace { 2em}
\visible <2->{
Why is it hard?
}
\visible <3->{
\vspace { 2em}
\begin { center}
\textbf { \underline { Resilience} } \\
{ \footnotesize (we want good uptime/availability with low supervision)}
\end { center}
}
\end { frame}
\begin { frame}
\frametitle { How to make a \underline { stable} system}
Enterprise-grade systems typically employ:
\vspace { 1em}
\begin { itemize}
\item RAID
\item Redundant power grid + UPS
\item Redundant Internet connections
\item Low-latency links
\item ...
\end { itemize}
\vspace { 1em}
$ \to $ it's costly and only worth it at DC scale
\end { frame}
\begin { frame}
\frametitle { How to make a \underline { resilient} system}
\only <1,4-5>{
Instead, we use:
\vspace { 1em}
\begin { itemize}
\item \textcolor <2->{ gray} { Commodity hardware (e.g. old desktop PCs)}
\vspace { .5em}
\item <4-> \textcolor <5->{ gray} { Commodity Internet (e.g. FTTB, FTTH) and power grid}
\vspace { .5em}
\item <5-> \textcolor <6->{ gray} { \textbf { Geographical redundancy} (multi-site replication)}
\end { itemize}
}
\only <2>{
\begin { center}
\includegraphics [width=.8\linewidth] { assets/atuin.jpg}
\end { center}
}
\only <3>{
\begin { center}
\includegraphics [width=.8\linewidth] { assets/neptune.jpg}
\end { center}
}
\only <6>{
\begin { center}
\includegraphics [width=.5\linewidth] { assets/inframap.jpg}
\end { center}
}
\end { frame}
\begin { frame}
\frametitle { How to make this happen}
\begin { center}
\only <1>{ \includegraphics [width=.8\linewidth] { assets/slide1.png} } %
\only <2>{ \includegraphics [width=.8\linewidth] { assets/slide2.png} } %
\only <3>{ \includegraphics [width=.8\linewidth] { assets/slide3.png} } %
\end { center}
\end { frame}
\begin { frame}
\frametitle { Distributed file systems are slow}
File systems are complex, for example:
\vspace { 1em}
\begin { itemize}
\item Concurrent modification by several processes
\vspace { 1em}
\item Folder hierarchies
\vspace { 1em}
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\item Other requirements of the POSIX spec (e.g.~locks)
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\end { itemize}
\vspace { 1em}
Coordination in a distributed system is costly
\vspace { 1em}
Costs explode with commodity hardware / Internet connections\\
{ \small (we experienced this!)}
\end { frame}
\begin { frame}
\frametitle { A simpler solution: object storage}
Only two operations:
\vspace { 1em}
\begin { itemize}
\item Put an object at a key
\vspace { 1em}
\item Retrieve an object from its key
\end { itemize}
\vspace { 1em}
{ \footnotesize (and a few others)}
\vspace { 1em}
Sufficient for many applications!
\end { frame}
\begin { frame}
\frametitle { A simpler solution: object storage}
\begin { center}
\includegraphics [height=6em] { ../2020-12-02_ wide-team/img/Amazon-S3.jpg}
\hspace { 3em}
\includegraphics [height=5em] { assets/minio.png}
\hspace { 3em}
\includegraphics [height=6em] { ../../logo/garage_ hires_ crop.png}
\end { center}
\vspace { 1em}
S3: a de-facto standard, many compatible applications
\vspace { 1em}
MinIO is self-hostable but not suited for geo-distributed deployments
\vspace { 1em}
\textbf { Garage is a self-hosted drop-in replacement for the Amazon S3 object store}
\end { frame}
\begin { frame}
\frametitle { The data model of object storage}
Object storage is basically a key-value store:
\vspace { 1em}
\begin { center}
\begin { tabular} { |l|p{ 8cm} |}
\hline
\textbf { Key: file path + name} & \textbf { Value: file data + metadata} \\
\hline
\hline
\texttt { index.html} &
\texttt { Content-Type: text/html; charset=utf-8} \newline
\texttt { Content-Length: 24929} \newline
\texttt { <binary blob>} \\
\hline
\texttt { img/logo.svg} &
\texttt { Content-Type: text/svg+xml} \newline
\texttt { Content-Length: 13429} \newline
\texttt { <binary blob>} \\
\hline
\texttt { download/index.html} &
\texttt { Content-Type: text/html; charset=utf-8} \newline
\texttt { Content-Length: 26563} \newline
\texttt { <binary blob>} \\
\hline
\end { tabular}
\end { center}
\end { frame}
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\begin { frame}
\frametitle { Garage's architecture}
\begin { center}
\includegraphics [width=.35\linewidth] { assets/garage.drawio.pdf}
\end { center}
\end { frame}
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\begin { frame}
\frametitle { Two big problems}
\begin { enumerate}
\item \textbf { How to place data on different nodes?} \\
\vspace { 1em}
\underline { Constraints:} heterogeneous hardware\\
\underline { Objective:} $ n $ copies of everything, maximize usable capacity, maximize resilience\\
\vspace { 1em}
$ \to $ the Dynamo model + optimization algorithms
\vspace { 2em}
\item <2-> \textbf { How to guarantee consistency?} \\
\vspace { 1em}
\underline { Constraints:} slow network (geographical distance), node unavailability/crashes\\
\underline { Objective:} maximize availability, read-after-write guarantee\\
\vspace { 1em}
$ \to $ CRDTs, monotonicity, read and write quorums
\end { enumerate}
\end { frame}
\section { Problem 1: placing data}
\begin { frame}
\frametitle { Key-value stores, upgraded: the Dynamo model}
\textbf { Two keys:}
\begin { itemize}
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\item Partition key: used to divide data into partitions { \small (a.k.a.~shards)}
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\item Sort key: used to identify items inside a partition
\end { itemize}
\vspace { 1em}
\begin { center}
\begin { tabular} { |l|l|p{ 3cm} |}
\hline
\textbf { Partition key: bucket} & \textbf { Sort key: filename} & \textbf { Value} \\
\hline
\hline
\texttt { website} & \texttt { index.html} & (file data) \\
\hline
\texttt { website} & \texttt { img/logo.svg} & (file data) \\
\hline
\texttt { website} & \texttt { download/index.html} & (file data) \\
\hline
\hline
\texttt { backup} & \texttt { borg/index.2822} & (file data) \\
\hline
\texttt { backup} & \texttt { borg/data/2/2329} & (file data) \\
\hline
\texttt { backup} & \texttt { borg/data/2/2680} & (file data) \\
\hline
\hline
\texttt { private} & \texttt { qq3a2nbe1qjq0ebbvo6ocsp6co} & (file data) \\
\hline
\end { tabular}
\end { center}
\end { frame}
\begin { frame}
\frametitle { Key-value stores, upgraded: the Dynamo model}
\begin { itemize}
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\item Data with different partition keys is stored independently,\\
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on a different set of nodes\\
\vspace { .5em}
$ \to $ no easy way to list all partition keys\\
$ \to $ no cross-shard transactions\\
\vspace { 2em}
\item Placing data: hash the partition key, select nodes accordingly\\
\vspace { .5em}
$ \to $ distributed hash table (DHT)
\vspace { 2em}
\item For a given value of the partition key, items can be listed using their sort keys
\end { itemize}
\end { frame}
\begin { frame}
\frametitle { How to spread files over different cluster nodes?}
\textbf { Consistent hashing (Dynamo):}
\vspace { 1em}
\begin { center}
\only <1>{ \includegraphics [width=.40\columnwidth] { assets/consistent_ hashing_ 1.pdf} } %
\only <2>{ \includegraphics [width=.40\columnwidth] { assets/consistent_ hashing_ 2.pdf} } %
\only <3>{ \includegraphics [width=.40\columnwidth] { assets/consistent_ hashing_ 3.pdf} } %
\only <4>{ \includegraphics [width=.40\columnwidth] { assets/consistent_ hashing_ 4.pdf} } %
\end { center}
\end { frame}
\begin { frame}
\frametitle { Constraint: location-awareness}
\begin { center}
\includegraphics [width=\linewidth] { assets/location-aware.png}
\end { center}
\vspace { 2em}
Garage replicates data on different zones when possible
\end { frame}
\begin { frame}
\frametitle { Constraint: location-awareness}
\begin { center}
\includegraphics [width=.8\linewidth] { assets/map.png}
\end { center}
\end { frame}
\begin { frame}
\frametitle { Issues with consistent hashing}
\begin { itemize}
\item Consistent hashing doesn't dispatch data based on geographical location of nodes
\vspace { 1em}
\item <2-> Geographically aware adaptation, try 1:\\
data quantities not well balanced between nodes
\vspace { 1em}
\item <3-> Geographically aware adaptation, try 2:\\
too many reshuffles when adding/removing nodes
\end { itemize}
\end { frame}
\begin { frame}
\frametitle { How to spread files over different cluster nodes?}
\textbf { Garage's method: build an index table}
\vspace { 1em}
Realization: we can actually precompute an optimal solution
\vspace { 1em}
\visible <2->{
\begin { center}
\begin { tabular} { |l|l|l|l|}
\hline
\textbf { Partition} & \textbf { Node 1} & \textbf { Node 2} & \textbf { Node 3} \\
\hline
\hline
Partition 0 & Io (jupiter) & Drosera (atuin) & Courgette (neptune) \\
\hline
Partition 1 & Datura (atuin) & Courgette (neptune) & Io (jupiter) \\
\hline
Partition 2 & Io(jupiter) & Celeri (neptune) & Drosera (atuin) \\
\hline
\hspace { 1em} $ \vdots $ & \hspace { 1em} $ \vdots $ & \hspace { 1em} $ \vdots $ & \hspace { 1em} $ \vdots $ \\
\hline
Partition 255 & Concombre (neptune) & Io (jupiter) & Drosera (atuin) \\
\hline
\end { tabular}
\end { center}
}
\vspace { 1em}
\visible <3->{
The index table is built centrally using an optimal algorithm,\\
then propagated to all nodes
}
\end { frame}
\begin { frame}
\frametitle { The relationship between \emph { partition} and \emph { partition key} }
\begin { center}
\begin { tabular} { |l|l|l|l|}
\hline
\textbf { Partition key} & \textbf { Partition} & \textbf { Sort key} & \textbf { Value} \\
\hline
\hline
\texttt { website} & Partition 12 & \texttt { index.html} & (file data) \\
\hline
\texttt { website} & Partition 12 & \texttt { img/logo.svg} & (file data) \\
\hline
\texttt { website} & Partition 12 & \texttt { download/index.html} & (file data) \\
\hline
\hline
\texttt { backup} & Partition 42 & \texttt { borg/index.2822} & (file data) \\
\hline
\texttt { backup} & Partition 42 & \texttt { borg/data/2/2329} & (file data) \\
\hline
\texttt { backup} & Partition 42 & \texttt { borg/data/2/2680} & (file data) \\
\hline
\hline
\texttt { private} & Partition 42 & \texttt { qq3a2nbe1qjq0ebbvo6ocsp6co} & (file data) \\
\hline
\end { tabular}
\end { center}
\vspace { 1em}
\textbf { To read or write an item:} hash partition key
\\ \hspace { 5cm} $ \to $ determine partition number (first 8 bits)
\\ \hspace { 5cm} $ \to $ find associated nodes
\end { frame}
\begin { frame}
\frametitle { Garage's internal data structures}
\centering
\includegraphics [width=.75\columnwidth] { assets/garage_ tables.pdf}
\end { frame}
\begin { frame}
\frametitle { Storing and retrieving files}
\begin { center}
\only <1>{ \includegraphics [width=.45\linewidth] { assets/garage2a.drawio.pdf} } %
\only <2>{ \includegraphics [width=.45\linewidth] { assets/garage2b.drawio.pdf} } %
\end { center}
\end { frame}
\section { Problem 2: ensuring consistency}
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\begin { frame}
\frametitle { Consensus vs weak consistency}
\hspace { 1em}
\begin { minipage} { 7cm}
\textbf { Consensus-based systems:}
\vspace { 1em}
\begin { itemize}
\item \textbf { Leader-based:} a leader is elected to coordinate
all reads and writes
\vspace { 1em}
\item \textbf { Linearizability} of all operations\\
(strongest consistency guarantee)
\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}
\item \textbf { Costly} , the leader is a bottleneck;
leader elections on failure take time
\end { itemize}
\end { minipage}
\hfill
\begin { minipage} { 7cm} \visible <2->{
\textbf { Weakly consistent systems:}
\vspace { 1em}
\begin { itemize}
\item \textbf { Nodes are equivalent} , any node
can originate a read or write operation
\vspace { 1em}
\item \textbf { Read-after-write consistency} with quorums,
eventual consistency without
\vspace { 1em}
\item \textbf { Operations have to commute} , i.e.~we
can only implement CRDTs
\vspace { 1em}
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\item \textbf { Fast} , no single bottleneck;\\
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works the same with offline nodes
\end { itemize}
} \end { minipage}
\hspace { 1em}
\end { frame}
\begin { frame}
\frametitle { Consensus vs weak consistency}
\begin { center}
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\textbf { From a theoretical point of view:} \\
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\end { center}
\vspace { 2em}
\hspace { 1em}
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\begin { minipage} { 6.5cm}
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\underline { Consensus-based systems:}
\vspace { 1em}
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Require \textbf { additional assumptions} such as a fault detector or a strong RNG\\
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(FLP impossibility theorem)
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\end { minipage}
\hfill
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\begin { minipage} { 6.5cm}
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\underline { Weakly consistent systems:}
\vspace { 1em}
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Can be implemented in \textbf { any\\ asynchronous message passing\\ distributed system} with node crashes
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\end { minipage}
\hspace { 1em}
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\vspace { 3em}
\begin { center}
They represent \textbf { different classes of computational capability} \\
\end { center}
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\end { frame}
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\begin { frame}
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\frametitle { Consensus vs weak consistency}
\begin { center}
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\textbf { The same objects cannot be implemented in both models.}
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\end { center}
\vspace { 2em}
\hspace { 1em}
\begin { minipage} { 6.5cm}
\underline { Consensus-based systems:}
\vspace { 1em}
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\textbf { Any sequential specification} \\ ~
\vspace { 1em}
\textbf { Easier to program for} : just write your program as if it were sequential on a single machine
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\end { minipage}
\hfill
\begin { minipage} { 6.5cm}
\underline { Weakly consistent systems:}
\vspace { 1em}
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\textbf { Only CRDTs} \\ (conflict-free replicated data types)
\vspace { 1em}
Part of the complexity is \textbf { reported to the consumer of the API} \\ ~
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\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}
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\begin { itemize}
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\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}
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\item <2-> \textbf { This API can be implemented using only weak primitives} \\
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(e.g. in the asynchronous message passing model with no further assumption)\\
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$ \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:}
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\vspace { 1em}
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\begin { itemize}
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\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}
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\vspace { 1em}
\item Particularly \textbf { sensitive to higher latency} between nodes
\end { itemize}
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\end { itemize}
\end { frame}
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\begin { frame}
\frametitle { Performance gains in practice}
\begin { center}
\includegraphics [width=.8\linewidth] { assets/endpoint-latency-dc.png}
\end { center}
\end { frame}
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\begin { frame}
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\frametitle { What can we implement without consensus?}
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\begin { itemize}
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\item Any \textbf { conflict-free replicated data type} (CRDT)
\vspace { 1em}
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\item <2-> Non-transactional key-value stores such as S3 are equivalent to a simple CRDT:\\
a map of \textbf { last-writer-wins registers} (each key is its own CRDT)
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\vspace { 1em}
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\item <3-> \textbf { Read-after-write consistency} can be implemented
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using quorums on read and write operations
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\vspace { 1em}
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\item <4-> \textbf { Monotonicity of reads} can be implemented with repair-on-read\\
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(makes reads more costly, not implemented in Garage)
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\end { itemize}
\end { frame}
\begin { frame}
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\frametitle { CRDTs and quorums: read-after-write consistency}
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\begin { figure}
\centering
\def \svgwidth { .8\textwidth }
\only <1>{ \import { assets/} { lattice1.pdf_ tex} } %
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\end { figure}
\end { frame}
\begin { frame}
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\frametitle { CRDTs and quorums: read-after-write consistency}
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\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}
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\frametitle { CRDTs and quorums: monotonic-reads consistency}
\begin { figure}
\centering
\def \svgwidth { .8\textwidth }
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\only <2>{ \import { assets/} { latticeB_ 2.pdf_ tex} } %
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\end { figure}
\end { frame}
\begin { frame}
\frametitle { CRDTs and quorums: monotonic-reads consistency}
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\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}
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\textbf { Algorithm $ monotonic \_ read ( ) $ :} { \small (a.k.a. repair-on-read)}
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\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}
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This makes reads slower in some cases, and is \textbf { not implemented in Garage} .
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\end { frame}
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\begin { frame}
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\frametitle { A hard problem: layout changes}
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\begin { itemize}
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\item We rely on quorums $ k > n / 2 $ within each partition:\\
$$ n = 3 ,~~~~~~~k \ge 2 $$
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\item <2-> When rebalancing, the set of nodes responsible for a partition can change:\\
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$$ \{ n _ A, n _ B, n _ C \} \to \{ n _ A, n _ D, n _ E \} $$
\vspace { .01em}
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\item <3-> During the rebalancing, $ D $ and $ E $ don't yet have the data,\\
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~~~~~~~~~~~~~~~~~~~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...}
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\end { itemize}
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\end { frame}
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\section { Going further than the S3 API}
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\begin { frame}
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\frametitle { Using Garage for everything}
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\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}
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\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}
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\vspace { 1em}
\item <2-> Exposes the partitoning mechanism of Garage\\
K2V = partition key / sort key / value (like Dynamo)
\vspace { 1em}
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\item <3-> Weakly consistent, CRDT-friendly\\
$ \to $ no support for transactions (not ACID)
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\vspace { 1em}
\item <4-> Cryptography-friendly: values are binary blobs
\end { itemize}
\end { frame}
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\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}
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\item <2-> Values are opaque binary blobs, \textbf { K2V cannot resolve conflicts} by itself\\
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(e.g. by implementing a CRDT)
\vspace { 1.5em}
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\item <3-> Solution: \textbf { we keep both!} \\
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$ \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}
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\item <2-> When calling $ write ( ) $ , the client sends \textbf { the causality token from its last read}
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\vspace { 1.5em}
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\item <3-> The causality token represents the set of values \textbf { already seen by the client} \\
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$ \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}
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\item <4-> Internally, the causality token is \textbf { a vector clock}
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\end { itemize}
\end { frame}
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\begin { frame}
\frametitle { Application: an e-mail storage server}
\begin { center}
\only <1>{ \includegraphics [width=.9\linewidth] { assets/aerogramme.png} } %
\end { center}
\end { frame}
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\begin { frame}
\frametitle { Aerogramme data model}
\begin { center}
\only <1>{ \includegraphics [width=.4\linewidth] { assets/aerogramme_ datatype.drawio.pdf} } %
\only <2->{ \includegraphics [width=.9\linewidth] { assets/aerogramme_ keys.drawio.pdf} \vspace { 1em} } %
\end { center}
\visible <3->{ Aerogramme encrypts all stored values for privacy\\
(Garage server administrators can't read your mail)}
\end { frame}
\begin { frame}
\frametitle { Different deployment scenarios}
\begin { center}
\only <1>{ \includegraphics [width=.9\linewidth] { assets/aerogramme_ components1.drawio.pdf} } %
\only <2>{ \includegraphics [width=.9\linewidth] { assets/aerogramme_ components2.drawio.pdf} } %
\end { center}
\end { frame}
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\begin { frame}
\frametitle { A new model for building resilient software}
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How to build an application using only Garage as a data store:
\vspace { 1em}
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\begin { enumerate}
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\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}
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\item <2-> Store big blobs (files) using the S3 API
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\vspace { 1em}
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\item <3-> Let Garage manage sharding, replication, failover, etc.
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\end { enumerate}
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\end { frame}
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\section { Conclusion}
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\begin { frame}
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\frametitle { Perspectives}
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\begin { itemize}
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\item Fix the consistency issue when rebalancing
\vspace { 1em}
\item Write about Garage's architecture and properties,\\
and about our proposed architecture for (E2EE) apps over K2V+S3
\vspace { 1em}
\item Continue developing Garage; finish Aerogramme; build new applications...
\vspace { 1em}
\item Anything else?
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\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}
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