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demo/containers/dbsdata/data/
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client/qldbtools/db-collection-py-1/
mrva-overview.aux
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\documentclass[11pt]{article}
% Load the geometry package to set margins
\usepackage[margin=1cm]{geometry}
% Load CM Bright for math
\usepackage{amsmath} % Standard math package
\usepackage{amssymb} % Additional math symbols
\usepackage{cmbright} % Sans-serif math font that complements Fira Sans
% Font configuration
% \usepackage{bera}
% or
% Load Fira Sans for text
\usepackage{fontspec}
\setmainfont{Fira Sans} % System-installed Fira Sans
\renewcommand{\familydefault}{\sfdefault} % Set sans-serif as default
% pseudo-code with math
\usepackage{listings}
\usepackage{float}
\usepackage{xcolor}
\usepackage{colortbl}
% Set TT font
% \usepackage{inconsolata}
% or
\setmonofont{IBMPlexMono-Light}
% Define custom settings for listings
\lstset{
language=Python,
basicstyle=\ttfamily\small, % Monospaced font
commentstyle=\itshape\color{gray}, % Italic and gray for comments
keywordstyle=\color{blue}, % Keywords in blue
stringstyle=\color{red}, % Strings in red
mathescape=true, % Enable math in comments
breaklines=true, % Break long lines
numbers=left, % Add line numbers
numberstyle=\tiny\color{gray}, % Style for line numbers
frame=single, % Add a frame around the code
}
\usepackage{newfloat} % Allows creating custom float types
% Define 'listing' as a floating environment
\DeclareFloatingEnvironment[
fileext=lol,
listname=List of Listings,
name=Listing
]{listing}
% To prevent floats from moving past a section boundary but still allow some floating:
\usepackage{placeins}
% used with \FloatBarrier
\usepackage[utf8]{inputenc}
\usepackage[T1]{fontenc}
\usepackage{graphicx}
\usepackage{longtable}
\usepackage{wrapfig}
\usepackage{rotating}
\usepackage[normalem]{ulem}
\usepackage{amsmath}
\usepackage{amssymb}
\usepackage{capt-of}
\usepackage{hyperref}
\usepackage{algorithm}
\usepackage{algpseudocode}
% Title, Author, and Date (or Report Number)
\title{MRVA for CodeQL}
\author{Michael Hohn}
\date{Technical Report 20250224}
\hypersetup{
pdfauthor={Michael Hohn},
pdftitle={MRVA for CodeQL},
pdfkeywords={},
pdfsubject={},
pdfcreator={Emacs 29.1},
pdflang={English}}
\begin{document}
\maketitle
\tableofcontents
\section{MRVA System Architecture Summary}
The MRVA system is organized as a collection of services. On the server side, the
system is containerized using Docker and comprises several key components:
\begin{itemize}
\item {\textbf{Server}}: Acts as the central coordinator.
\item \textbf{Agents}: One or more agents that execute tasks.
\item \textbf{RabbitMQ}: Handles messaging between components.
\item \textbf{MinIO}: Provides storage for both queries and results.
\item \textbf{HEPC}: An HTTP endpoint that hosts and serves CodeQL databases.
\end{itemize}
On the client side, users can interact with the system in two ways:
\begin{itemize}
\item {\textbf{VSCode-CodeQL}}: A graphical interface integrated with Visual Studio Code.
\item \textbf{gh-mrva CLI}: A command-line interface that connects to the server in a similar way.
\end{itemize}
This architecture enables a robust and flexible workflow for code analysis, combining a containerized back-end with both graphical and CLI front-end tools.
The full system details can be seen in the source code. This document provides an
overview.
\section{Distributed Query Execution in MRVA}
\subsection{Execution Overview}
The \textit{MRVA system} is a distributed platform for executing \textit{CodeQL
queries} across multiple repositories using a set of worker agents. The system is
{containerized} and built around a set of core services:
\begin{itemize}
\item \textbf{Server}: Coordinates job distribution and result aggregation.
\item \textbf{Agents}: Execute queries independently and return results.
\item \textbf{RabbitMQ}: Handles messaging between system components.
\item \textbf{MinIO}: Stores query inputs and execution results.
\item \textbf{HEPC}: Serves CodeQL databases over HTTP.
\end{itemize}
Clients interact with MRVA via \texttt{VSCode-CodeQL} (a graphical interface) or
\texttt{gh-mrva CLI} (a command-line tool), both of which submit queries to the
server.
The execution process follows a structured workflow:
\begin{enumerate}
\item A client submits a set of queries $\mathcal{Q}$ targeting a repository
set $\mathcal{R}$.
\item The server enqueues jobs and distributes them to available agents.
\item Each agent retrieves a job, executes queries against its assigned repository, and accumulates results.
\item The agent sends results back to the server, which then forwards them to the client.
\end{enumerate}
This full round-trip can be expressed as:
\begin{equation}
\text{Client} \xrightarrow{\mathcal{Q}} \text{Server}
\xrightarrow{\text{enqueue}}
\text{Queue} \xrightarrow{\text{dispatch}} \text{Agent}
\xrightarrow{\mathcal{Q}(\mathcal{R}_i)}
\text{Server} \xrightarrow{\mathcal{Q}(\mathcal{R}_i} \text{Client}
\end{equation}
where the Client submits queries to the Server, which enqueues jobs in the
Queue. Agents execute the queries, returning results $\mathcal{Q}(\mathcal{R}_i)$
to the Server and ultimately back to the Client.
A more rigorous description of this is in section \ref{sec:full-round-trip}.
\subsection{System Structure Overview}
This design allows for scalable and efficient query execution across multiple
repositories, whether on a single machine or a distributed cluster. The key idea
is that both setups follow the same structural approach:
\begin{itemize}
\item \textbf{Single machine setup:}
\begin{itemize}
\item Uses \textit{at least 5 Docker containers} to manage different
components of the system.
\item The number of \textit{agent containers} (responsible for executing
queries) is constrained by the available \textit{RAM and CPU cores}.
\end{itemize}
\item \textbf{Cluster setup:}
\begin{itemize}
\item Uses \textit{at least 5 virtual machines (VMs) and / or Docker containers}.
\item The number of \textit{agent VMs} is limited by \textit{network bandwidth
and available resources} (e.g., distributed storage and inter-node communication
overhead).
\end{itemize}
\end{itemize}
Thus:
\begin{itemize}
\item The {functional architecture is identical} between the single-machine and cluster setups.
\item The {primary difference} is in \textit{scale}:
\begin{itemize}
\item A single machine is limited by \textit{local CPU and RAM}.
\item A cluster is constrained by \textit{network and inter-node coordination overhead} but allows for higher overall compute capacity.
\end{itemize}
\end{itemize}
\subsection{Messages and their Types}
\label{sec:msg-types}
The following table enumerates the types (messages) passed from Client to Server.
\begin{longtable}{|p{5cm}|p{5cm}|p{5cm}|}
\hline
\rowcolor{gray!20} \textbf{Type Name} & \textbf{Field} & \textbf{Type} \\
\hline
\endfirsthead
\hline
\rowcolor{gray!20} \textbf{Type Name} & \textbf{Field} & \textbf{Type} \\
\hline
\endhead
\hline
\endfoot
\hline
\endlastfoot
ServerState & NextID & () $\rightarrow$ int \\
& GetResult & JobSpec $\rightarrow$ IO (Either Error AnalyzeResult) \\
& GetJobSpecByRepoId & (int, int) $\rightarrow$ IO (Either Error JobSpec) \\
& SetResult & (JobSpec, AnalyzeResult) $\rightarrow$ IO () \\
& GetJobList & int $\rightarrow$ IO (Either Error \textbf{[AnalyzeJob]}) \\
& GetJobInfo & JobSpec $\rightarrow$ IO (Either Error JobInfo) \\
& SetJobInfo & (JobSpec, JobInfo) $\rightarrow$ IO () \\
& GetStatus & JobSpec $\rightarrow$ IO (Either Error Status) \\
& SetStatus & (JobSpec, Status) $\rightarrow$ IO () \\
& AddJob & AnalyzeJob $\rightarrow$ IO () \\
\hline
JobSpec & sessionID & int \\
& nameWithOwner & string \\
\hline
AnalyzeResult & spec & JobSpec \\
& status & Status \\
& resultCount & int \\
& resultLocation & ArtifactLocation \\
& sourceLocationPrefix & string \\
& databaseSHA & string \\
\hline
ArtifactLocation & Key & string \\
& Bucket & string \\
\hline
AnalyzeJob & Spec & JobSpec \\
& QueryPackLocation & ArtifactLocation \\
& QueryLanguage & QueryLanguage \\
\hline
QueryLanguage & & string \\
\hline
JobInfo & QueryLanguage & string \\
& CreatedAt & string \\
& UpdatedAt & string \\
& SkippedRepositories & SkippedRepositories \\
\hline
SkippedRepositories & AccessMismatchRepos & AccessMismatchRepos \\
& NotFoundRepos & NotFoundRepos \\
& NoCodeqlDBRepos & NoCodeqlDBRepos \\
& OverLimitRepos & OverLimitRepos \\
\hline
AccessMismatchRepos & RepositoryCount & int \\
& Repositories & \textbf{[Repository]} \\
\hline
NotFoundRepos & RepositoryCount & int \\
& RepositoryFullNames & \textbf{[string]} \\
\hline
Repository & ID & int \\
& Name & string \\
& FullName & string \\
& Private & bool \\
& StargazersCount & int \\
& UpdatedAt & string \\
\end{longtable}
\section{Symbols and Notation}
\label{sec:orgb695d5a}
We define the following symbols for entities in the system:
\begin{center}
\begin{tabular}{lll}
Concept & Symbol & Description \\[0pt]
\hline
\href{vscode://file//Users/hohn/work-gh/mrva/gh-mrva/README.org:39:1}{Client} & \(C\) & The source of the query submission \\[0pt]
Server & \(S\) & Manages job queue and communicates results back to the client \\[0pt]
Job Queue & \(Q\) & Queue for managing submitted jobs \\[0pt]
Agent & \(\alpha\) & Independently polls, executes jobs, and accumulates results \\[0pt]
Agent Set & \(A\) & The set of all available agents \\[0pt]
Query Suite & \(\mathcal{Q}\) & Collection of queries submitted by the client \\[0pt]
Repository List & \(\mathcal{R}\) & Collection of repositories \\[0pt]
\(i\)-th Repository & \(\mathcal{R}_i\) & Specific repository indexed by \(i\) \\[0pt]
\(j\)-th Query & \(\mathcal{Q}_j\) & Specific query from the suite indexed by \(j\) \\[0pt]
Query Result & \(r_{i,j,k_{i,j}}\) & \(k_{i,j}\)-th result from query \(j\) executed on repository \(i\) \\[0pt]
Query Result Set & \(\mathcal{R}_i^{\mathcal{Q}_j}\) & Set of all results for query \(j\) on repository \(i\) \\[0pt]
Accumulated Results & \(\mathcal{R}_i^{\mathcal{Q}}\) & All results from executing all queries on \(\mathcal{R}_i\) \\[0pt]
\end{tabular}
\end{center}
\section{Full Round-Trip Representation}
\label{sec:full-round-trip}
The full round-trip execution, from query submission to result delivery, can be summarized as:
\[
C \xrightarrow{\mathcal{Q}} S \xrightarrow{\text{enqueue}} Q
\xrightarrow{\text{poll}}
\alpha \xrightarrow{\mathcal{Q}(\mathcal{R}_i)} S \xrightarrow{\mathcal{R}_i^{\mathcal{Q}}} C
\]
\begin{itemize}
\item \(C \to S\): Client submits a query suite \(\mathcal{Q}\) to the server.
\item \(S \to Q\): Server enqueues the query suite \((\mathcal{Q}, \mathcal{R}_i)\) for each repository.
\item \(Q \to \alpha\): Agent \(\alpha\) polls the queue and retrieves a job.
\item \(\alpha \to S\): Agent executes the queries and returns the accumulated results \(\mathcal{R}_i^{\mathcal{Q}}\) to the server.
\item \(S \to C\): Server sends the complete result set \(\mathcal{R}_i^{\mathcal{Q}}\) for each repository back to the client.
\end{itemize}
\section{Result Representation}
For the complete collection of results across all repositories and queries:
\[
\mathcal{R}^{\mathcal{Q}} = \bigcup_{i=1}^{N} \bigcup_{j=1}^{M}
\left\{ r_{i,j,1}, r_{i,j,2}, \dots, r_{i,j,k_{i,j}} \right\}
\]
where:
\begin{itemize}
\item \(N\) is the total number of repositories.
\item \(M\) is the total number of queries in \(\mathcal{Q}\).
\item \(k_{i,j}\) is the number of results from executing query
\(\mathcal{Q}_j\)
on repository \(\mathcal{R}_i\).
\end{itemize}
An individual result from the \(i\)-th repository, \(j\)-th query, and \(k\)-th result is:
\[
r_{i,j,k}
\]
\[
C \xrightarrow{\mathcal{Q}} S \xrightarrow{\text{enqueue}} Q \xrightarrow{\text{dispatch}} \alpha \xrightarrow{\mathcal{Q}(\mathcal{R}_i)} S \xrightarrow{r_{i,j}} C
\]
Each result can be further indexed to track multiple repositories and result sets.
\section{Execution Loop in Pseudo-Code}
\begin{listing}[h] % h = here, t = top, b = bottom, p = page of floats
\caption{Distributed Query Execution Algorithm}
\begin{lstlisting}[language=Python]
# Distributed Query Execution with Agent Polling and Accumulated Results
# Initialization
$\mathcal{R}$ = set() # Repository list
$Q$ = [] # Job queue
$A$ = set() # Set of agents
$\mathcal{R}_i^{\mathcal{Q}}$ = {} # Result storage for each repository
# Initialize result sets for each repository
for $R_i$ in $\mathcal{R}$:
$\mathcal{R}_i^{\mathcal{Q}} = \{\}$ # Initialize empty result set
# Enqueue the entire query suite for all repositories
for $R_i$ in $\mathcal{R}$:
$Q$.append(($\mathcal{Q}$, $R_i$)) # Enqueue $(\mathcal{Q}, \mathcal{R}_i)$ pair
# Processing loop while there are jobs in the queue
while $Q \neq \emptyset$:
# Agents autonomously poll the queue
for $\alpha$ in $A$:
if $\alpha$.is_available():
$(\mathcal{Q}, \mathcal{R}_i)$ = $Q$.pop(0) # Agent polls a job
# Agent execution begins
$\mathcal{R}_i^{\mathcal{Q}} = \{\}$ # Initialize results for repository $R_i$
for $\mathcal{Q}_j$ in $\mathcal{Q}$:
# Execute query $\mathcal{Q}_j$ on repository $\mathcal{R}_i$
$r_{i,j,1}, \dots, r_{i,j,k_{i,j}}$ = $\alpha$.execute($\mathcal{Q}_j$, $R_i$)
# Store results for query $j$
$\mathcal{R}_i^{\mathcal{Q}_j} = \{r_{i,j,1}, \dots, r_{i,j,k_{i,j}}\}$
# Accumulate results
$\mathcal{R}_i^{\mathcal{Q}} = \mathcal{R}_i^{\mathcal{Q}} \cup \mathcal{R}_i^{\mathcal{Q}_j}$
# Send all accumulated results back to the server
$\alpha$.send_results($S$, ($\mathcal{Q}$, $R_i$, $\mathcal{R}_i^{\mathcal{Q}}$))
# Server sends results for $(\mathcal{Q}, \mathcal{R}_i)$ back to the client
$S$.send_results_to_client($C$, ($\mathcal{Q}$, $R_i$, $\mathcal{R}_i^{\mathcal{Q}}$))
\end{lstlisting}
\end{listing}
\FloatBarrier
\section{Execution Loop in Pseudo-Code, algorithmic}
\begin{algorithm}
\caption{Distribute a set of queries $\mathcal{Q}$ across repositories
$\mathcal{R}$ using agents $A$}
\begin{algorithmic}[1] % Line numbering enabled
\Procedure{DistributedQueryExecution}{$\mathcal{Q}, \mathcal{R}, A$}
\ForAll{$\mathcal{R}_i \in \mathcal{R}$}
\Comment{Initialize result sets for each repository and query}
\State $\mathcal{R}_i^{\mathcal{Q}} \gets \left\{ \, \right\}$
\EndFor
\State $Q \gets \left\{ \, \right\}$ \Comment{Initialize empty job queue}
\ForAll{$\mathcal{R}_i \in \mathcal{R}$}
\Comment{Enqueue the entire query suite across all repositories}
\State $S \xrightarrow{\text{enqueue}(\mathcal{Q}, \mathcal{R}_i)} Q$
\EndFor
\While{$Q \neq \emptyset$}
\Comment{Agents poll the queue for available jobs}
\ForAll{$\alpha \in A$ \textbf{where} $\alpha$ \text{is available}}
\State $\alpha \xleftarrow{\text{poll}(Q)}$ \Comment{Agent autonomously retrieves a job}
% --- Begin Agent Execution Block ---
\State \textbf{(Agent Execution Begins)}
\State $\mathcal{R}_i^{\mathcal{Q}} \gets \left\{ \, \right\}$ \Comment{Initialize result set for this repository}
\ForAll{$\mathcal{Q}_j \in \mathcal{Q}$}
\State $\mathcal{R}_i^{\mathcal{Q}_j} \gets \left\{ r_{i,j,1}, r_{i,j,2}, \dots, r_{i,j,k_{i,j}} \right\}$
\Comment{Collect results for query $j$ on repository $i$}
\State $\mathcal{R}_i^{\mathcal{Q}} \gets \mathcal{R}_i^{\mathcal{Q}}
\cup \mathcal{R}_i^{\mathcal{Q}_j}$
\Comment{Accumulate results}
\EndFor
\State $\alpha \xrightarrow{(\mathcal{Q}, \mathcal{R}_i, \mathcal{R}_i^{\mathcal{Q}})} S$
\Comment{Agent sends all accumulated results back to server}
\State \textbf{(Agent Execution Ends)}
% --- End Agent Execution Block ---
\State $S \xrightarrow{(\mathcal{Q}, \mathcal{R}_i, \mathcal{R}_i^{\mathcal{Q}})} C$
\Comment{Server sends results for repository $i$ back to the client}
\EndFor
\EndWhile
\EndProcedure
\end{algorithmic}
\end{algorithm}
\FloatBarrier
\end{document}
%%% Local Variables:
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%%% TeX-command-extra-options: "-synctex=1 -shell-escape -interaction=nonstopmode"
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@@ -11,22 +11,6 @@
{
"name": "mrvaserver",
"path": "../mrvaserver"
},
{
"name": "mrva-docker",
"path": "../mrva-docker"
},
{
"name": "mrvahepc",
"path": "../mrvahepc"
},
{
"name": "gh-mrva",
"path": "../gh-mrva"
},
{
"name": "vscode-codeql",
"path": "../vscode-codeql"
}
],
"settings": {