systemd-sparrow

Extending systemd with Sparrow6's power for dynamic, intelligent service management.

Vision

This project explores integrating Sparrow6 with systemd to address systemd's biggest pain points:

• Static configuration for dynamic requirements
• Duplicate code between similar services
• Complex dependency management
• Missing abstractions for common patterns

Why Systemd Needs Sparrow6: Real-World Pain Points

While systemd revolutionized Linux service management, its static configuration model creates significant challenges for modern, dynamic infrastructure. Developers routinely work around these limitations with wrapper scripts, configuration management tools, or by accepting less robust services. Here's what we're addressing:

Current systemd pain points from a developer perspective:

• Static configuration hell - You can't adjust memory limits based on available RAM, can't change CPU quotas based on system load. While systemd-run offers some runtime flexibility, unit files themselves remain static. Developers end up writing wrapper scripts for dynamic resource allocation.

• Template limitations - Systemd's template system (@) only supports basic substitutions (%i, %n, etc.). No conditional logic, no loops, no complex transformations. Need different configs for dev/staging/prod? You're maintaining multiple unit files or complex EnvironmentFile hierarchies.

• Limited conditional execution - While systemd offers ConditionPathExists and similar directives, there's no true branching logic. Want complex conditions like "start only if config valid AND database reachable"? You need ExecStartPre script chains.

• Basic health monitoring - Systemd handles process state and basic sd_notify() integration, but complex health checks (HTTP endpoints, database connectivity, multi-step validations) require external tooling. Alternative init systems like OpenRC can integrate custom health check scripts more naturally.

• Environment-specific configuration - Drop-in directories and EnvironmentFile provide some flexibility, but there's no elegant way to have one unit file that adapts to dev/staging/prod environments without external configuration management.

• Restart strategy limitations - While systemd offers RestartSec and StartLimitBurst, there's no exponential backoff, no different actions for different failure types (e.g., "restart on SIGTERM, alert on SIGSEGV"). Some container orchestrators handle this better.

• Dynamic scaling challenges - Managing services that need to scale based on load requires complex template instantiation with external orchestration. No native way to say "run 2-10 instances based on CPU load."

• External configuration integration - Unit files can't fetch configuration from Consul, etcd, or cloud metadata services. This forces wrapper scripts or configuration management tools into the deployment pipeline.

• Static dependency declaration - Dependencies must be explicitly declared. Can't express "depend on any available database service" or "start after any network storage is mounted." Runit and s6 handle dependency resolution more dynamically.

These limitations force developers into workarounds: wrapper scripts, configuration management tools, or simply accepting less robust services. Our Sparrow6 integration provides a real programming language where systemd expects static configuration.

Two Implementation Approaches

1. Translation Mode (Proposed by @melezhik)

Alexey's original concept: Use Raku/Sparrow6 to generate native systemd unit files.

#!raku
# File: /etc/systemd/system/multi-user.target.wants/beans.service

task-run "beans", "systemd", %(
    after-service => ["nginx", "mysql"],
    after-target => ["syslog", "network"],
    env => %(:RACK_ENV<production>),
    description => "Cool Beans",
    start => "/usr/local/bin/beans start -f",
    stop => "/usr/local/bin/beans stop",
    reload => "kill -s HUP /var/run/beans.pid",
);

Flow:

1. Systemd detects #!raku shebang
2. Spawns: raku -MSparrow6::DSL -e /path/to/service
3. Generates /tmp/beans.unit.out with native systemd format
4. Systemd uses the generated unit file

Advantages:

• ✅ Easy to implement (just spawn raku)
• ✅ Works with existing systemd
• ✅ Immediate configuration management wins
• ✅ Full Raku/Sparrow6 power at generation time

2. Runtime Integration Mode (Extended vision)

Going beyond static generation to true runtime dynamism with asynchronous processing.

#!raku
use Sparrow6::DSL;

task-run "my-webapp", "systemd-dynamic", %(
    description => "Dynamic Web Application",
    exec-start => "/usr/bin/webapp",
    
    # Dynamic resource allocation
    hooks => %(
        resource-check => sub {
            my %metrics = task-run "system-metrics", "resource-monitor";
            return %(
                memory-max => %metrics<free-memory> < 4.GB ?? "512M" !! "2G",
                cpu-quota => %metrics<cpu-load> > 80 ?? "50%" !! "200%",
            );
        },
        
        # Health monitoring with Sparrow6
        health-check => sub {
            task-run "webapp-health", "http-check", %(
                url => "http://localhost:8080/health",
                expect => %(status => 200)
            );
        }
    ),
    
    # Sparrow6 orchestration
    orchestration => %(
        startup-sequence => q:to/SEQUENCE/,
            begin:
                task-run "network-ready", "network-check"
                task-run "storage-mount", "mount-check", %( path => "/data" )
                task-run "config-sync", "consul-fetch", %( key => "webapp/config" )
            end:
        SEQUENCE
    )
);

Comparison: Translation vs Runtime Integration

Translation Approach (Phase 1)

• ✅ Easy C++ integration (just spawn raku -e)
• ✅ Works with existing systemd
• ✅ Immediate win for configuration management
• ❌ Static compilation - no runtime dynamism
• ❌ Can't react to changing system conditions
• ❌ Requires regeneration for changes

Runtime Integration (Full vision)

• ✅ True dynamic behavior during service lifecycle
• ✅ Can adapt to system load, hardware changes, etc.
• ✅ Live system introspection and adaptation
• ✅ Non-blocking asynchronous monitoring
• ❌ Requires deeper systemd modifications
• ❌ Much more complex C++ integration

Example of the Difference

Translation approach generates static configs:

[Service]
ExecStart=/usr/bin/myapp
MemoryMax=2048K  # Static value calculated at unit creation

Runtime integration could evaluate dynamically:

# Evaluated periodically in background threads
MemoryMax={ system-memory() < 4.GB ?? "512M" !! "2G" }
CPUQuota={ current-load() > 80 ?? "50%" !! "200%" }

Most Promising Approach (Sparrow6-Powered)

From a pure Sparrow6 capability standpoint, here's what would create the most excitement:

#!raku
use Sparrow6::DSL;

# Define a service with Sparrow6's full orchestration power
task-run "my-webapp", "systemd-dynamic", %(
    # Basic service definition
    description => "Dynamic Web Application",
    exec-start => "/usr/bin/webapp",
    
    # Sparrow6 hooks for lifecycle management
    hooks => %(
        # Pre-start hook using Sparrow6 tasks
        pre-start => sub {
            # Check dependencies with custom logic
            task-run "check-db", "database-health", %( 
                timeout => 30,
                retry => 5 
            );
            
            # Conditional environment setup
            if task-run("detect-env", "environment-probe")<env> eq 'production' {
                task-run "setup-prod", "production-init";
            }
        },
        
        # Dynamic resource allocation hook
        resource-check => sub {
            my %metrics = task-run "system-metrics", "resource-monitor";
            
            return %(
                memory-max => %metrics<free-memory> < 4.GB ?? "512M" !! "2G",
                cpu-quota => %metrics<cpu-load> > 80 ?? "50%" !! "200%",
                io-weight => %metrics<io-pressure> ?? 100 !! 1000
            );
        },
        
        # Health monitoring with Sparrow6 tasks
        health-check => sub {
            my %health = task-run "webapp-health", "http-check", %(
                url => "http://localhost:8080/health",
                expect => %(status => 200, body => /'ok'/)
            );
            
            if !%health<success> {
                task-run "alert", "notification", %( 
                    message => "Service degraded", 
                    severity => "warning" 
                );
            }
            
            return %health<success>;
        }
    ),
    
    # Sparrow6 orchestration for complex dependencies
    orchestration => %(
        # Use Sparrow6's within/sequence expressions
        startup-sequence => q:to/SEQUENCE/,
            begin:
                task-run "network-ready", "network-check"
                task-run "storage-mount", "mount-check", %( path => "/data" )
                task-run "config-sync", "consul-fetch", %( key => "webapp/config" )
            end:
        SEQUENCE
        
        # Dynamic dependency resolution
        requires => sub {
            my @deps = <network.target>;
            
            # Add dependencies based on system state
            if task-run("check-feature", "feature-flags")<use-redis> {
                @deps.push: "redis.service";
            }
            
            if os() ~~ /centos/ {
                @deps.push: "firewalld.service";
            }
            
            return @deps;
        }
    ),
    
    # Asynchronous monitoring configuration
    monitoring => %(
        interval => 60,  # Check every 60 seconds
        priority => "background"  # Don't block systemd operations
    )
);

This approach leverages Sparrow6's strengths:

• Task orchestration for complex startup sequences
• Plugin ecosystem for reusable components
• DSL power for expressing dependencies
• Cross-platform abstractions (os() detection)
• Built-in testing through Sparrow6's test framework

Key Benefits

1. Dynamic Configuration

# Instead of static values:
MemoryMax=2048K

# Dynamic evaluation:
MemoryMax => { system-memory() < 4.GB ?? "512M" !! "2G" }

2. Reusable Service Definitions

# Simple, reusable service definitions
task-run "nginx service", "systemd-nginx";
task-run "mariadb service", "systemd-mariadb";

3. Complex Orchestration

Using Sparrow6's task orchestration capabilities for sophisticated startup sequences, health checks, and dependency management.

4. Cross-Platform Abstractions

# Sparrow6's os() detection for platform-specific logic
if os() ~~ /centos/ {
    @deps.push: "firewalld.service";
}

5. Non-Blocking Asynchronous Operations

All Sparrow6 evaluations run in background threads managed by Cbeam's message_manager, ensuring systemd's core operations continue at full speed. No external tools like cron are needed - threading is handled entirely within the systemd process. See the Asynchronous Resource Monitoring section for implementation details.

Implementation Roadmap

Phase 1: Translation Mode

• Create systemd Sparrow6 plugin
• Implement basic unit file generation
• Add systemd hook for #!raku detection
• Create plugins for common services (nginx, postgresql, etc.)

Phase 2: Enhanced Translation

• Template support for service families
• Conditional logic compilation
• SparrowHub integration for shared service definitions

Phase 3: Hybrid Mode

• Runtime property evaluation hooks
• Dynamic health check integration
• Resource limit adaptation
• Integrate Cbeam for asynchronous monitoring

Phase 4: Full Runtime Integration

• Complete Sparrow6 runtime bridge
• Event-driven task orchestration
• Live system adaptation
• Full asynchronous architecture with Cbeam

Technical Design

C++ Integration (Phase 1)

// Simple approach: detect and translate Sparrow6 units
bool is_sparrow6_unit(const std::filesystem::path& path) {
    std::ifstream file(path);
    std::string first_line;
    std::getline(file, first_line);
    return first_line == "#!raku" || first_line == "#!sparrow6";
}

if (is_sparrow6_unit(unit_path)) {
    system("raku -MSparrow6::DSL -e " + unit_path);
    // Use generated unit file...
}

C++ Integration Design (Full Runtime)

1. Sparrow6 Runtime Bridge

// Sparrow6-specific runtime integration
class Sparrow6Extension {
private:
    std::unique_ptr<RakuRuntime> raku;
    std::filesystem::path sparrow_cache;
    
public:
    // Initialize Sparrow6 environment
    void initialize() {
        // Set up Sparrow6 environment variables
        setenv("SP6_REPO", "https://sparrowhub.io", 1);
        setenv("SP6_TASK_ROOT", "/etc/systemd/sparrow-tasks", 1);
        
        // Initialize Raku with Sparrow6 modules
        raku = std::make_unique<RakuRuntime>();
        raku->use_module("Sparrow6::DSL");
    }
    
    // Execute Sparrow6 task and return results
    TaskResult run_task(const std::string& task_name, 
                       const std::string& plugin_name,
                       const ServiceContext& context) {
        
        // Build parameters from service context
        auto params = build_sparrow_params(context);
        
        // Execute through Sparrow6 API
        std::string code = fmt::format(
            R"(
            my %result = task-run "{}", "{}", {};
            %result
            )",
            task_name, plugin_name, params
        );
        
        return raku->evaluate<TaskResult>(code);
    }
    
    // Hook for dynamic property evaluation
    std::string evaluate_property(const std::string& property_code,
                                 const ServiceContext& context) {
        // Make context available to Raku code
        inject_context(context);
        
        return raku->evaluate_string(property_code);
    }
};

2. Systemd Service Extension

// Extend systemd's Service class to support Sparrow6 hooks
class Sparrow6Service : public Service {
private:
    Sparrow6Extension sparrow;
    std::map<std::string, std::string> sparrow_hooks;
    
public:
    // Override lifecycle methods to call Sparrow6 hooks
    Result<void> start() override {
        // Run pre-start Sparrow6 tasks
        if (sparrow_hooks.contains("pre-start")) {
            auto result = sparrow.run_inline_task(
                sparrow_hooks["pre-start"], 
                get_context()
            );
            
            if (!result.success) {
                return Error("Pre-start hook failed: " + result.error);
            }
        }
        
        // Continue with normal start
        return Service::start();
    }
    
    // Dynamic resource limit evaluation
    uint64_t get_memory_limit() override {
        if (sparrow_hooks.contains("resource-check")) {
            auto resources = sparrow.run_inline_task(
                sparrow_hooks["resource-check"],
                get_context()
            );
            
            return parse_memory(resources["memory-max"]);
        }
        
        return Service::get_memory_limit();
    }
    
    // Health check through Sparrow6
    bool is_healthy() override {
        if (sparrow_hooks.contains("health-check")) {
            auto health = sparrow.run_inline_task(
                sparrow_hooks["health-check"],
                get_context()
            );
            
            return health["success"].to_bool();
        }
        
        return Service::is_healthy();
    }
};

3. Unit File Parser Extension

// Extend unit file parsing to support Sparrow6 expressions
class Sparrow6UnitParser : public UnitParser {
public:
    std::unique_ptr<Unit> parse(const std::filesystem::path& path) override {
        // Check for Sparrow6 shebang
        if (is_sparrow6_unit(path)) {
            return parse_sparrow6_unit(path);
        }
        
        // Fall back to standard parsing
        return UnitParser::parse(path);
    }
    
private:
    bool is_sparrow6_unit(const std::filesystem::path& path) {
        std::ifstream file(path);
        std::string first_line;
        std::getline(file, first_line);
        return first_line == "#!raku" || first_line == "#!sparrow6";
    }
    
    std::unique_ptr<Unit> parse_sparrow6_unit(const std::filesystem::path& path) {
        // Execute Sparrow6 code to generate unit definition
        Sparrow6Extension sparrow;
        
        // For translation mode: generate static unit
        if (translation_mode) {
            auto unit_data = sparrow.generate_unit(path);
            return parse_generated_unit(unit_data);
        }
        
        // For runtime mode: create dynamic unit
        return std::make_unique<Sparrow6Service>(path, sparrow);
    }
};

4. Kernel-Based Resource Monitoring

To achieve truly efficient resource monitoring without polling, we leverage Linux kernel capabilities for event-driven notifications:

PSI (Pressure Stall Information) Integration

Modern kernels (4.20+) provide pressure metrics that we can monitor efficiently:

// Message types for kernel events
struct PSIEvent {
    std::string service_id;
    PSIMetrics metrics;
    int psi_fd;
    int epoll_fd;
};

struct EpollWatchRequest {
    std::string service_id;
    std::string pressure_file;
    PSIConfig config;
};

class PSIMonitor {
private:
    cbeam::concurrency::message_manager<std::variant<PSIEvent, EpollWatchRequest>> monitor;
    
    static constexpr size_t EPOLL_WATCHER_ID = 1;
    static constexpr size_t PSI_PROCESSOR_ID = 2;
    static constexpr size_t SPARROW_TRIGGER_ID = 3;
    
public:
    PSIMonitor() {
        // Handler that sets up epoll watching for PSI files
        monitor.add_handler(EPOLL_WATCHER_ID, [this](auto msg) {
            std::visit([this](auto&& event) {
                using T = std::decay_t<decltype(event)>;
                if constexpr (std::is_same_v<T, EpollWatchRequest>) {
                    setup_psi_watch(event);
                }
            }, msg);
        }, nullptr, nullptr, "psi_epoll_watcher");
        
        // Handler that processes PSI events
        monitor.add_handler(PSI_PROCESSOR_ID, [this](auto msg) {
            std::visit([this](auto&& event) {
                using T = std::decay_t<decltype(event)>;
                if constexpr (std::is_same_v<T, PSIEvent>) {
                    process_psi_event(event);
                }
            }, msg);
        }, nullptr, nullptr, "psi_processor");
    }
    
    void monitor_service(const std::string& service_id, const PSIConfig& config) {
        // Send request to set up monitoring
        monitor.send_message(EPOLL_WATCHER_ID, 
            EpollWatchRequest{service_id, "/proc/pressure/memory", config});
    }
    
private:
    void setup_psi_watch(const EpollWatchRequest& req) {
        // Set up PSI monitoring
        int psi_fd = open(req.pressure_file.c_str(), O_RDWR | O_NONBLOCK);
        
        // Configure threshold
        std::string threshold = fmt::format("{} {} {}", 
            req.config.level,           // "some" or "full"
            req.config.threshold_us,    // stall time threshold
            req.config.window_us        // time window
        );
        
        write(psi_fd, threshold.c_str(), threshold.length());
        
        // Set up epoll
        int epoll_fd = epoll_create1(EPOLL_CLOEXEC);
        struct epoll_event ev;
        ev.events = EPOLLPRI;
        epoll_ctl(epoll_fd, EPOLL_CTL_ADD, psi_fd, &ev);
        
        // Poll in a loop and send events as messages
        struct epoll_event events[1];
        while (true) {
            int nfds = epoll_wait(epoll_fd, events, 1, -1);
            
            if (nfds > 0) {
                PSIMetrics metrics = read_psi_metrics(psi_fd);
                monitor.send_message(PSI_PROCESSOR_ID, 
                    PSIEvent{req.service_id, metrics, psi_fd, epoll_fd});
            }
        }
    }
    
    void process_psi_event(const PSIEvent& event) {
        // Forward to Sparrow6 handler
        monitor.send_message(SPARROW_TRIGGER_ID, event);
    }
    
    PSIMetrics read_psi_metrics(int fd) {
        char buffer[256];
        pread(fd, buffer, sizeof(buffer), 0);
        // Parse PSI format: "some avg10=0.00 avg60=0.00 avg300=0.00 total=0"
        return parse_psi_output(buffer);
    }
};

cgroups v2 Memory Events

For fine-grained memory monitoring per service:

// Message types for memory monitoring
struct MemoryWatchRequest {
    std::string service_id;
    MemoryConfig config;
};

struct MemoryEvent {
    std::string service_id;
    uint64_t current_bytes;
    uint64_t limit_bytes;
    bool is_oom_imminent;
};

class CgroupMemoryMonitor {
private:
    cbeam::concurrency::message_manager<std::variant<MemoryWatchRequest, MemoryEvent>> monitor;
    
    static constexpr size_t MEMORY_WATCHER_ID = 1;
    static constexpr size_t MEMORY_EVENT_ID = 2;
    
public:
    CgroupMemoryMonitor() {
        // Handler that sets up memory monitoring for a service
        monitor.add_handler(MEMORY_WATCHER_ID, [this](auto msg) {
            std::visit([this](auto&& req) {
                using T = std::decay_t<decltype(req)>;
                if constexpr (std::is_same_v<T, MemoryWatchRequest>) {
                    setup_memory_watch(req);
                }
            }, msg);
        }, nullptr, nullptr, "memory_watcher");
    }
    
    void monitor_service_memory(const std::string& service_id, const MemoryConfig& config) {
        monitor.send_message(MEMORY_WATCHER_ID, 
            MemoryWatchRequest{service_id, config});
    }
    
private:
    void setup_memory_watch(const MemoryWatchRequest& req) {
        // Get service's cgroup path
        std::string cgroup_path = fmt::format("/sys/fs/cgroup/system.slice/{}.service", 
                                             req.service_id);
        
        // Monitor memory.events for OOM and high pressure
        int mem_events_fd = open((cgroup_path + "/memory.events").c_str(), O_RDONLY);
        int event_fd = eventfd(0, EFD_CLOEXEC);
        
        // Set up memory.high threshold monitoring
        if (req.config.high_threshold) {
            std::string threshold_path = cgroup_path + "/memory.pressure";
            int threshold_fd = open(threshold_path.c_str(), O_WRONLY);
            
            // Configure pressure monitoring
            std::string config_str = fmt::format("some {} {}", 
                req.config.high_threshold, req.config.window_us);
            write(threshold_fd, config_str.c_str(), config_str.length());
            close(threshold_fd);
        }
        
        // Set up epoll for event notification
        int epoll_fd = epoll_create1(EPOLL_CLOEXEC);
        struct epoll_event ev;
        ev.events = EPOLLIN;
        ev.data.fd = event_fd;
        epoll_ctl(epoll_fd, EPOLL_CTL_ADD, event_fd, &ev);
        
        // Monitor in epoll loop
        struct epoll_event events[1];
        while (true) {
            int nfds = epoll_wait(epoll_fd, events, 1, -1);
            
            if (nfds > 0) {
                uint64_t counter;
                read(event_fd, &counter, sizeof(counter));
                
                // Read current memory usage
                auto stats = read_memory_stats(cgroup_path);
                
                // Send memory event message
                monitor.send_message(MEMORY_EVENT_ID, 
                    MemoryEvent{
                        req.service_id, 
                        stats.current, 
                        stats.limit,
                        stats.current > stats.limit * 0.9
                    });
            }
        }
    }
    
    MemoryStats read_memory_stats(const std::string& cgroup_path) {
        // Read memory.current and memory.max
        MemoryStats stats;
        std::ifstream current(cgroup_path + "/memory.current");
        std::ifstream limit(cgroup_path + "/memory.max");
        current >> stats.current;
        limit >> stats.limit;
        return stats;
    }
};

Integrated Monitoring Architecture

Combining kernel events with Sparrow6 hooks using Cbeam's message-driven architecture:

// Unified message type for all kernel events
using KernelEvent = std::variant<
    PSIEvent,
    MemoryEvent,
    ResourceAdjustmentRequest,
    Sparrow6TaskRequest
>;

class KernelEventMonitor {
private:
    cbeam::concurrency::message_manager<KernelEvent> event_manager;
    PSIMonitor psi_monitor;
    CgroupMemoryMonitor cgroup_monitor;
    Sparrow6Extension sparrow;
    
    static constexpr size_t KERNEL_EVENT_ID = 1;
    static constexpr size_t SPARROW_EXECUTOR_ID = 2;
    static constexpr size_t RESOURCE_ADJUSTER_ID = 3;
    
public:
    KernelEventMonitor() {
        // Handler for all kernel events
        event_manager.add_handler(KERNEL_EVENT_ID, [this](KernelEvent event) {
            std::visit([this](auto&& e) {
                handle_kernel_event(e);
            }, event);
        }, nullptr, nullptr, "kernel_event_handler");
        
        // Handler for Sparrow6 task execution
        event_manager.add_handler(SPARROW_EXECUTOR_ID, [this](KernelEvent event) {
            std::visit([this](auto&& e) {
                using T = std::decay_t<decltype(e)>;
                if constexpr (std::is_same_v<T, Sparrow6TaskRequest>) {
                    execute_sparrow6_task(e);
                }
            }, event);
        }, nullptr, nullptr, "sparrow6_executor");
        
        // Handler for resource adjustments
        event_manager.add_handler(RESOURCE_ADJUSTER_ID, [this](KernelEvent event) {
            std::visit([this](auto&& e) {
                using T = std::decay_t<decltype(e)>;
                if constexpr (std::is_same_v<T, ResourceAdjustmentRequest>) {
                    apply_resource_adjustment(e);
                }
            }, event);
        }, nullptr, nullptr, "resource_adjuster");
    }
    
    void setup_monitoring(const std::string& service_id, const MonitoringConfig& config) {
        // Configure PSI monitoring
        if (config.enable_psi) {
            psi_monitor.monitor_service(service_id, config.psi_config);
        }
        
        // Configure cgroup-specific memory monitoring
        if (config.enable_cgroup_memory) {
            cgroup_monitor.monitor_service_memory(service_id, config.memory_config);
        }
    }
    
private:
    template<typename T>
    void handle_kernel_event(const T& event) {
        if constexpr (std::is_same_v<T, PSIEvent>) {
            // PSI pressure event - create Sparrow6 task request
            event_manager.send_message(SPARROW_EXECUTOR_ID,
                Sparrow6TaskRequest{
                    "pressure-response",
                    "resource-adjust",
                    %(
                        service => event.service_id,
                        pressure => event.metrics,
                        type => "memory"
                    )
                });
        } else if constexpr (std::is_same_v<T, MemoryEvent>) {
            // Memory event - create Sparrow6 task request
            event_manager.send_message(SPARROW_EXECUTOR_ID,
                Sparrow6TaskRequest{
                    "memory-pressure",
                    "oom-prevention",
                    %(
                        service => event.service_id,
                        current_usage => event.current_bytes,
                        limit => event.limit_bytes,
                        oom_imminent => event.is_oom_imminent
                    )
                });
        }
    }
    
    void execute_sparrow6_task(const Sparrow6TaskRequest& req) {
        // Execute Sparrow6 task and get adjustment
        auto result = sparrow.run_task(req.task_name, req.plugin_name, req.params);
        
        // Send adjustment request if needed
        if (result.requires_adjustment) {
            event_manager.send_message(RESOURCE_ADJUSTER_ID,
                ResourceAdjustmentRequest{
                    req.params["service"],
                    result.adjustments
                });
        }
    }
    
    void apply_resource_adjustment(const ResourceAdjustmentRequest& req) {
        // Apply systemd resource adjustments
        auto& service = get_service(req.service_id);
        
        if (req.adjustments.contains("memory-max")) {
            service.set_memory_limit(parse_memory(req.adjustments["memory-max"]));
        }
        
        if (req.adjustments.contains("cpu-quota")) {
            service.set_cpu_quota(parse_percentage(req.adjustments["cpu-quota"]));
        }
        
        // Log the adjustment
        log_resource_adjustment(req);
    }
};

This architecture provides true message-driven kernel event processing:

• No Manual Thread Creation: All threading handled by message_manager
• Clean Separation: Different handlers for different responsibilities
• Scalable: Add more handlers to distribute load across cores
• Type-Safe: std::variant ensures compile-time safety
• Testable: Each handler can be tested independently

This kernel-based approach provides:

• Zero CPU overhead: No polling, kernel wakes us only when thresholds are exceeded
• Microsecond latency: Direct kernel notifications vs. periodic checks
• System-aware decisions: PSI provides holistic system pressure information
• Per-service precision: cgroups v2 gives exact per-service metrics
• Proactive management: Act before OOM killer or service degradation

5. Asynchronous Resource Monitoring with Cbeam

To enable true dynamic resource management without blocking systemd's core operations, we use Cbeam's message_manager for asynchronous processing:

// Message types for resource monitoring
struct ResourceCheckRequest {
    std::string service_id;
    std::string plugin;
    std::map<std::string, std::string> params;
};

struct ResourceUpdateResult {
    std::string service_id;
    bool requires_update;
    std::map<std::string, std::string> properties;
};

struct TimerTick {
    std::chrono::steady_clock::time_point timestamp;
};

class Sparrow6AsyncMonitor {
private:
    using MonitorMessage = std::variant<ResourceCheckRequest, ResourceUpdateResult, TimerTick>;
    cbeam::concurrency::message_manager<MonitorMessage> monitor;
    Sparrow6Extension sparrow;
    
    static constexpr size_t RESOURCE_CHECK_ID = 1;
    static constexpr size_t RESULT_PROCESSOR_ID = 2;
    static constexpr size_t TIMER_ID = 3;
    
public:
    void initialize() {
        // Handler for resource check requests - runs Sparrow6 tasks
        monitor.add_handler(RESOURCE_CHECK_ID, [this](MonitorMessage msg) {
            std::visit([this](auto&& req) {
                using T = std::decay_t<decltype(req)>;
                if constexpr (std::is_same_v<T, ResourceCheckRequest>) {
                    // Execute Sparrow6 task asynchronously
                    auto result = sparrow.run_task("resource-monitor", 
                                                  req.plugin, req.params);
                    
                    // Send result to processor
                    monitor.send_message(RESULT_PROCESSOR_ID,
                        ResourceUpdateResult{
                            req.service_id,
                            result.requires_update,
                            result.properties
                        });
                }
            }, msg);
        }, nullptr, nullptr, "sparrow6_resource_check");
        
        // Handler for processing results and applying updates
        monitor.add_handler(RESULT_PROCESSOR_ID, [this](MonitorMessage msg) {
            std::visit([this](auto&& result) {
                using T = std::decay_t<decltype(result)>;
                if constexpr (std::is_same_v<T, ResourceUpdateResult>) {
                    if (result.requires_update && 
                        meets_change_threshold(result.service_id, result)) {
                        update_service_properties(result.service_id, result.properties);
                    }
                }
            }, msg);
        }, nullptr, nullptr, "result_processor");
        
        // Timer handler sends periodic check requests
        monitor.add_handler(TIMER_ID, [this](MonitorMessage msg) {
            std::visit([this](auto&& tick) {
                using T = std::decay_t<decltype(tick)>;
                if constexpr (std::is_same_v<T, TimerTick>) {
                    for (auto& [service_id, config] : monitored_services) {
                        if (should_check(service_id, tick.timestamp)) {
                            monitor.send_message(RESOURCE_CHECK_ID, 
                                ResourceCheckRequest{service_id, config.plugin, 
                                                   config.params});
                        }
                    }
                }
            }, msg);
        }, nullptr, nullptr, "sparrow6_timer");
        
        // Start timer loop
        start_timer_loop();
    }
    
    void register_service(const std::string& service_id, 
                         const MonitoringConfig& config) {
        monitored_services[service_id] = config;
    }
    
private:
    std::map<std::string, MonitoringConfig> monitored_services;
    
    void start_timer_loop() {
        // Send timer ticks every second
        monitor.add_handler(TIMER_ID + 1, [this](MonitorMessage) {
            while (true) {
                std::this_thread::sleep_for(std::chrono::seconds(1));
                monitor.send_message(TIMER_ID, 
                    TimerTick{std::chrono::steady_clock::now()});
            }
        }, nullptr, nullptr, "timer_generator");
        
        // Kickstart the timer
        monitor.send_message(TIMER_ID + 1, TimerTick{});
    }
    
    bool meets_change_threshold(const std::string& service_id,
                               const ResourceUpdateResult& result) {
        // Implement hysteresis to prevent oscillation
        auto& config = monitored_services[service_id];
        // Compare with configured thresholds
        return true; // simplified
    }
};

This message-driven architecture provides:

• Non-blocking Operation: All Sparrow6 evaluations happen in handler threads managed by message_manager
• No Manual Threading: message_manager handles all thread lifecycle
• Configurable Parallelism: Add more handlers for the same ID to scale across cores
• Message-Based Communication: Clean decoupling between components
• Zero systemd Latency: Main systemd operations continue unimpeded

Why This Would Be Revolutionary

This approach would solve systemd's biggest pain points:

• Dynamic adaptation instead of static configs - Resources adjust to runtime conditions
• Reusable components through Sparrow6 plugins
• Testing built-in via Sparrow6's test framework
• Cross-distro compatibility through Sparrow6's abstractions
• Gradual adoption - start with translation, evolve to runtime
• True asynchronous monitoring - System conditions evaluated continuously without blocking
• Kernel-native efficiency - Zero-overhead monitoring through PSI and cgroups v2

The Sparrow6 ecosystem would provide immediate value through:

• Pre-built service definitions on SparrowHub
• Community-contributed health checks and monitors
• Battle-tested dependency management patterns
• Integration with existing Sparrow6 configuration management

The integration of kernel monitoring enables:

• Real-time responsiveness: Microsecond-latency reactions to system pressure
• Proactive resource management: Prevent OOM and service degradation before they occur
• System-wide awareness: PSI provides holistic view of resource contention
• Zero polling overhead: Kernel notifies only when action is needed

The integration of Cbeam enables:

• Advanced monitoring capabilities: Pattern matching in logs, network condition monitoring, external API integration
• Predictive scaling: Use historical data to anticipate resource needs
• Complex decision logic: Implement sophisticated algorithms without performance penalties
• Reliable change management: Hysteresis and thresholds prevent service instability

Examples

Basic Service

#!raku
task-run "webserver", "systemd", %(
    description => "My Web Server",
    exec-start => "/usr/bin/myapp",
    restart => "always",
    after-service => ["network.target"],
);

Service with Conditional Logic

#!raku
my $env = %*ENV<DEPLOYMENT_ENV> // 'development';

task-run "api-server", "systemd", %(
    description => "API Server ({$env})",
    exec-start => "/usr/bin/api-server",
    
    env => %(
        NODE_ENV => $env,
        WORKERS => $env eq 'production' ?? 8 !! 2,
        LOG_LEVEL => $env eq 'production' ?? 'error' !! 'debug',
    ),
    
    memory-max => $env eq 'production' ?? '4G' !! '1G',
    
    after-service => do {
        my @deps = ["network.target"];
        @deps.push("postgresql.service") if $env eq 'production';
        @deps.push("redis.service") if $env ~~ /staging|production/;
        @deps;
    },
);

Service with Health Checks

#!raku
task-run "microservice", "systemd-with-health", %(
    exec-start => "/usr/bin/microservice",
    
    health-check => %(
        type => "http",
        url => "http://localhost:3000/health",
        interval => 30,
        timeout => 5,
        
        on-failure => sub {
            task-run "alert", "slack-notification", %(
                channel => "#ops",
                message => "Microservice health check failed"
            );
        }
    ),
);

Service with Dynamic Memory Management

#!raku
task-run "webapp", "systemd-dynamic", %(
    description => "Web Application with Dynamic Resources",
    exec-start => "/usr/bin/webapp",
    
    # Initial static configuration
    memory-max => "2G",
    cpu-quota => "100%",
    
    # Asynchronous monitoring configuration
    monitoring => %(
        interval => 60,  # Check every 60 seconds
        
        resource-check => sub {
            my $available = get-available-memory();
            my $load = get-system-load();
            my $connections = get-active-connections();
            
            # Complex logic without blocking systemd
            my $optimal-memory = do {
                when $available < 2.GB { "512M" }
                when $available < 4.GB { "1G" }
                when $connections > 1000 { "4G" }
                default { "2G" }
            };
            
            return %(
                memory-max => $optimal-memory,
                cpu-quota => $load > 80 ?? "50%" !! "200%",
                io-weight => $connections > 500 ?? 100 !! 1000
            );
        },
        
        # Hysteresis to prevent oscillation
        change-threshold => %(
            memory-max => "100M",  # Only update if change > 100M
            cpu-quota => "20%"     # Only update if change > 20%
        )
    )
);

Service with Kernel-Based Pressure Monitoring

#!raku
task-run "database-service", "systemd-pressure-aware", %(
    exec-start => "/usr/bin/postgres",
    
    # Enable kernel-based monitoring
    kernel-monitoring => %(
        # PSI monitoring for system-wide pressure
        psi => %(
            enabled => True,
            memory => %(
                level => "some",      # Trigger on some tasks stalled
                threshold => 150000,  # 150ms stall time
                window => 1000000     # in 1 second window
            ),
            cpu => %(
                level => "full",      # Trigger on all tasks stalled
                threshold => 100000,  # 100ms stall time
                window => 1000000     # in 1 second window
            )
        ),
        
        # cgroup v2 memory events
        cgroup-memory => %(
            enabled => True,
            high-threshold => "80%",  # Alert at 80% of limit
            oom-score => -500        # Protect from OOM killer
        )
    ),
    
    # Sparrow6 hooks triggered by kernel events
    pressure-response => sub ($event) {
        given $event<type> {
            when 'memory-pressure' {
                # Reduce cache size to free memory
                task-run "postgres-tune", "cache-adjust", %(
                    shared_buffers => "128MB",
                    effective_cache_size => "512MB"
                );
            }
            when 'cpu-pressure' {
                # Reduce concurrent connections
                task-run "postgres-tune", "connection-limit", %(
                    max_connections => 50
                );
            }
            when 'memory-high' {
                # Emergency memory reduction
                task-run "postgres-tune", "emergency-gc";
            }
        }
    }
);

Service with Pattern-Based Monitoring

#!raku
task-run "security-service", "systemd-pattern-monitor", %(
    exec-start => "/usr/bin/security-app",
    
    monitoring => %(
        interval => 30,
        
        # Monitor logs for security patterns
        pattern-check => sub {
            my $log-content = slurp("/var/log/security-app/access.log");
            
            # Use Raku's powerful regex engine
            my $suspicious = $log-content ~~ m:g/
                '401' .* 'admin' |
                'DROP' .* 'TABLE' |
                '../' ** 3..*
            /;
            
            if $suspicious.elems > 10 {
                task-run "alert", "security-notification", %(
                    severity => "high",
                    matches => $suspicious.elems
                );
                
                # Activate defensive mode
                return %(
                    environment => %(
                        SECURITY_MODE => "restrictive",
                        RATE_LIMIT => "aggressive"
                    )
                );
            }
            
            return %();  # No changes needed
        }
    )
);

Service with Adaptive Resource Management

#!raku
task-run "ml-inference", "systemd-adaptive", %(
    exec-start => "/usr/bin/ml-server",
    
    # Combine kernel monitoring with Sparrow6 intelligence
    adaptive-resources => %(
        # Let kernel tell us about pressure
        kernel-signals => True,
        
        # Sparrow6 decides how to respond
        adaptation-strategy => sub ($signals) {
            my %response;
            
            # Memory pressure detected by PSI
            if $signals<psi><memory><avg10> > 20 {
                %response<memory-strategy> = do {
                    # Check what's using memory
                    my $model-size = task-run "ml-metrics", "model-memory";
                    my $cache-size = task-run "ml-metrics", "cache-memory";
                    
                    # Intelligent decision making
                    if $cache-size > $model-size * 0.5 {
                        # Cache is too large, reduce it
                        task-run "ml-config", "reduce-cache", %(
                            max-cache => $model-size * 0.3
                        );
                        "cache-reduced"
                    } else {
                        # Offload to disk if possible
                        task-run "ml-config", "enable-offloading";
                        "offloading-enabled"
                    }
                };
            }
            
            # CPU pressure detected
            if $signals<psi><cpu><avg10> > 30 {
                %response<cpu-strategy> = do {
                    # Reduce batch size for lower latency
                    my $current-batch = task-run "ml-metrics", "batch-size";
                    task-run "ml-config", "set-batch-size", %(
                        size => max(1, $current-batch / 2)
                    );
                    "batch-size-reduced"
                };
            }
            
            return %response;
        }
    )
);

Contributing

This is a collaborative project between @melezhik and @acrion. We're looking for contributors interested in:

• Creating Sparrow6 plugins for common services
• Implementing the C++ integration layer (especially kernel event handling)
• Testing on different Linux distributions
• Documenting patterns and best practices
• Developing kernel monitoring integrations