eBPF: Revolutionizing Observability for DevOps and SRE Teams#

Whether you’re a system administrator, developer, or any other DevOps or Site Reliability Engineering (SRE) professional, staying ahead in cloud-native computing is crucial. One way to maintain your competitive edge is to embrace the transformative benefits of eBPF (Extended Berkeley Packet Filter).

Beyond advances in security and networking, eBPF-based tools are particularly revolutionizing the observability landscape, providing unprecedented insights into system behavior and application performance with minimal overhead.

Understanding the Kernel Foundation#

1
graph TB
2
    subgraph "Traditional OS Architecture"
3
        subgraph "Applications Layer"
4
            A1[Web Apps] --> A2[Databases]
5
            A3[Microservices] --> A4[APIs]
6
        end
7

8
        subgraph "Operating System"
9
            OS1[User Space] --> OS2[System Calls]
10
            OS2 --> OS3[Kernel Space]
11
        end
12

13
        subgraph "Hardware Layer"
14
            H1[CPU] --> H2[Memory]
15
            H3[Network] --> H4[Storage]
16
        end
17

18
        A1 --> OS1
19
        A2 --> OS1
20
        A3 --> OS1
21
        A4 --> OS1
22

23
        OS3 --> H1
24
        OS3 --> H2
25
        OS3 --> H3
26
        OS3 --> H4
27
    end
28

29
    style OS3 fill:#e1f5fe
30
    style OS2 fill:#f3e5f5

The Critical Role of the Kernel#

Traditionally, an operating system is where observability, security, and networking functionalities take place. Every machine—whether it’s a computer, cell phone, or virtual computing device—has a single kernel. This kernel is not just significant; it’s the most critical part of any operating system because without it, no device would be usable.

All containers on any machine share this common kernel, which has made evolving the operating system kernel extremely challenging for several reasons:

System Reliability: Kernel modifications can destabilize the entire system
Security Concerns: Direct kernel access poses significant security risks
Compatibility Issues: Changes must work across diverse hardware and software configurations
Development Complexity: Kernel programming requires specialized expertise

This has resulted in slower innovation compared to service functionalities beyond the OS—until eBPF changed everything.

Breaking New Ground with eBPF#

Rooted in the Linux kernel, eBPF allows running isolated programs within the operating system kernel, extending OS capabilities without loading new modules or modifying its source code.

1
graph LR
2
    subgraph "eBPF Revolution"
3
        subgraph "Traditional Approach"
4
            T1[Kernel Modules] --> T2[Security Risks]
5
            T3[Source Modifications] --> T4[System Instability]
6
            T5[Complex Development] --> T6[Slow Innovation]
7
        end
8

9
        subgraph "eBPF Approach"
10
            E1[Sandboxed Programs] --> E2[Safety Guaranteed]
11
            E3[Runtime Loading] --> E4[System Stability]
12
            E5[High-Level Languages] --> E6[Rapid Innovation]
13
        end
14
    end
15

16
    style E2 fill:#c8e6c9
17
    style E4 fill:#c8e6c9
18
    style E6 fill:#c8e6c9
19
    style T2 fill:#ffcdd2
20
    style T4 fill:#ffcdd2
21
    style T6 fill:#ffcdd2

Key eBPF Advantages#

eBPF allows application developers to add additional capabilities to the operating system by running sandboxed eBPF programs without compromising safety and execution efficiency. This shift has given rise to a revolution of eBPF-based advancements in operating systems, unlocking application innovation in:

Full-Stack Observability: Complete visibility across all system layers
Performance Troubleshooting: Real-time performance analysis and bottleneck identification
Application Tracing: Detailed execution path analysis
Advanced Networking: Programmable network data path processing
Preventive Security: Proactive threat detection and mitigation

The breakthrough lies in accessing the OS kernel via eBPF, which provides incredible insights into every aspect of application code running on the machine—at lightning speed.

eBPF’s Role in Modern Observability#

Integration with OpenTelemetry#

1
graph TB
2
    subgraph "Modern Observability Stack"
3
        subgraph "Data Collection Layer"
4
            DC1[eBPF Programs] --> DC2[Kernel-Level Metrics]
5
            DC3[OpenTelemetry SDKs] --> DC4[Application Metrics]
6
        end
7

8
        subgraph "Data Processing"
9
            DP1[eBPF Maps] --> DP2[Aggregation]
10
            DP3[OTEL Collectors] --> DP4[Enrichment]
11
        end
12

13
        subgraph "Data Transmission"
14
            DT1[Standardized Formats] --> DT2[OTLP Protocol]
15
            DT3[Vendor Neutral] --> DT4[Interoperability]
16
        end
17

18
        subgraph "Analysis & Visualization"
19
            AV1[Prometheus] --> AV2[Grafana]
20
            AV3[Jaeger] --> AV4[Custom Dashboards]
21
        end
22

23
        DC1 --> DP1
24
        DC3 --> DP3
25
        DP2 --> DT1
26
        DP4 --> DT3
27
        DT2 --> AV1
28
        DT4 --> AV3
29
    end
30

31
    style DC1 fill:#e1f5fe
32
    style DP1 fill:#f3e5f5
33
    style DT1 fill:#e8f5e8
34
    style AV1 fill:#fff3e0

As an open-source project for monitoring and collecting performance data in software applications, OpenTelemetry standardizes observability practices across different languages and environments.

Together, eBPF and OpenTelemetry are rewriting the rules, offering more efficient, flexible, and less intrusive ways to gather critical system data:

OpenTelemetry standardizes data transmission and formatting
eBPF revolutionizes data collection at the kernel level

The eBPF Advantage: A Lightweight Virtual Machine#

Imagine a lightweight virtual machine inside your Linux kernel, running programs that enhance and monitor system performance without disrupting normal operations. That’s eBPF in a nutshell—designed to be safe, efficient, and incredibly powerful.

Programs built on eBPF effortlessly connect with different system events:

Library Function Calls: Hook into application library functions
System Calls: Monitor kernel-userspace interactions
Network Traffic: Analyze packet flows and protocols
Dynamic Tracing: Conduct user-level tracing without instrumentation

Exceptional Data Processing Capabilities#

Automatic Instrumentation Without Manual Intervention#

One of eBPF’s standout features is enabling comprehensive metrics tracking without the need for manual instrumentation:

1
sequenceDiagram
2
    participant App as Application
3
    participant eBPF as eBPF Program
4
    participant Kernel as Kernel
5
    participant Monitor as Monitoring System
6

7
    Note over App,Monitor: Traditional Instrumentation
8
    App->>App: Add logging code
9
    App->>App: Add metrics collection
10
    App->>Monitor: Send metrics (high overhead)
11

12
    Note over App,Monitor: eBPF-Based Observability
13
    App->>Kernel: Normal operations
14
    eBPF->>Kernel: Hook system events
15
    eBPF->>eBPF: Process data in kernel
16
    eBPF->>Monitor: Send processed metrics (low overhead)
17

18
    rect rgb(255, 205, 210)
19
        Note over App: No code changes required
20
    end
21
    rect rgb(200, 230, 200)
22
        Note over eBPF: Automatic data collection
23
    end

Kernel-Level Data Processing Benefits#

The ability to process data at the kernel level drastically reduces the overhead of transferring data between kernel and user space:

Minimal Context Switching: Reduced CPU overhead from kernel-userspace transitions
Real-Time Processing: Data filtering and aggregation at the source
Memory Efficiency: Reduced memory footprint for observability data
Network Optimization: Intelligent packet processing before userspace delivery

Advanced Observability Techniques#

Distinguishing Observability from Troubleshooting#

Modern observability platforms make a clear distinction between two related but different practices:

1
graph TB
2
    subgraph "Observability vs Troubleshooting"
3
        subgraph "Observability"
4
            O1[Continuous Monitoring] --> O2[System State Understanding]
5
            O3[Proactive Insights] --> O4[Trend Analysis]
6
            O5[Performance Baselines] --> O6[Predictive Analytics]
7
        end
8

9
        subgraph "Troubleshooting"
10
            T1[Reactive Response] --> T2[Issue Identification]
11
            T3[Root Cause Analysis] --> T4[Rapid Remediation]
12
            T5[Incident Resolution] --> T6[System Recovery]
13
        end
14

15
        subgraph "eBPF Foundation"
16
            E1[Real-time Data Collection] --> E2[Historical Analysis]
17
            E3[Multi-dimensional Metrics] --> E4[Contextual Information]
18
        end
19

20
        E1 --> O1
21
        E1 --> T1
22
        E2 --> O3
23
        E2 --> T3
24
        E3 --> O5
25
        E3 --> T5
26
        E4 --> O2
27
        E4 --> T2
28
    end
29

30
    style O1 fill:#c8e6c9
31
    style T1 fill:#fff3e0
32
    style E1 fill:#e1f5fe

Observability is the practice of continuously understanding the state of your landscape, both the application and the underlying platform.

Troubleshooting is aimed at remediating an issue as fast as possible.

eBPF-based observability provides strong support for both by retrieving the correct data set through kernel-level instrumentation.

Network Analysis and Service Interactions#

Comprehensive Network Observability#

eBPF excels in network analysis by examining data flow between processes, even across clusters and clouds:

1
#include <vmlinux.h>
2
#include <bpf/bpf_helpers.h>
3
#include <bpf/bpf_tracing.h>
4
#include <bpf/bpf_core_read.h>
5

6
struct network_flow {
7
    __u32 src_ip;
8
    __u32 dst_ip;
9
    __u16 src_port;
10
    __u16 dst_port;
11
    __u8 protocol;
12
    __u64 bytes_sent;
13
    __u64 bytes_received;
14
    __u64 timestamp;
15
    __u32 latency_us;
16
    __u16 status_code;
17
};
18

19
struct {
20
    __uint(type, BPF_MAP_TYPE_HASH);
21
    __uint(max_entries, 65536);
22
    __type(key, struct flow_key);
23
    __type(value, struct network_flow);
24
} active_flows SEC(".maps");
25

26
struct {
27
    __uint(type, BPF_MAP_TYPE_RINGBUF);
28
    __uint(max_entries, 1024 * 1024);
29
} flow_events SEC(".maps");
30

31
// Track TCP connections
32
SEC("tp/sock/inet_sock_set_state")
33
int trace_tcp_state_change(struct trace_event_raw_inet_sock_set_state *ctx) {
34
    if (ctx->protocol != IPPROTO_TCP)
35
        return 0;
36

37
    struct network_flow *flow;
38
    flow = bpf_ringbuf_reserve(&flow_events, sizeof(*flow), 0);
39
    if (!flow)
40
        return 0;
41

42
    flow->src_ip = ctx->saddr;
43
    flow->dst_ip = ctx->daddr;
44
    flow->src_port = ctx->sport;
45
    flow->dst_port = ctx->dport;
46
    flow->protocol = ctx->protocol;
47
    flow->timestamp = bpf_ktime_get_ns();
48

49
    bpf_ringbuf_submit(flow, 0);
50
    return 0;
51
}
52

53
// HTTP traffic analysis
54
SEC("uprobe/http_request_handler")
55
int trace_http_request(struct pt_regs *ctx) {
56
    __u64 pid_tgid = bpf_get_current_pid_tgid();
57

58
    // Extract HTTP method, path, and headers
59
    char *method = (char *)PT_REGS_PARM1(ctx);
60
    char *path = (char *)PT_REGS_PARM2(ctx);
61

62
    struct network_flow *flow;
63
    flow = bpf_ringbuf_reserve(&flow_events, sizeof(*flow), 0);
64
    if (!flow)
65
        return 0;
66

67
    // Populate HTTP-specific metrics
68
    bpf_probe_read_str(&flow->src_ip, 4, method); // Store method in src_ip for demo
69
    flow->timestamp = bpf_ktime_get_ns();
70

71
    bpf_ringbuf_submit(flow, 0);
72
    return 0;
73
}
74

75
// MongoDB operation tracking
76
SEC("uprobe/mongo_operation_start")
77
int trace_mongo_operation(struct pt_regs *ctx) {
78
    __u32 operation_type = (int)PT_REGS_PARM1(ctx);
79
    char *collection = (char *)PT_REGS_PARM2(ctx);
80

81
    struct network_flow *flow;
82
    flow = bpf_ringbuf_reserve(&flow_events, sizeof(*flow), 0);
83
    if (!flow)
84
        return 0;
85

86
    flow->protocol = 27017; // MongoDB default port
87
    flow->timestamp = bpf_ktime_get_ns();
88

89
    bpf_ringbuf_submit(flow, 0);
90
    return 0;
91
}
92

93
char _license[] SEC("license") = "GPL";

Real-Time Protocol Analysis#

eBPF provides insights into service interactions with real-time metrics on:

Throughput: Data transfer rates between services
Latency: Request-response timing analysis
Error Rates: Failed requests and connection issues
Protocol Support: HTTP, HTTPS, MongoDB, Kafka, and custom protocols
Encrypted Connections: Analysis even when connections are encrypted

Multi-Cluster and Multi-Cloud Observability#

For complex environments spanning multiple clusters and clouds, advanced techniques maintain observability:

1
struct trace_context {
2
    __u64 trace_id;
3
    __u64 span_id;
4
    __u64 parent_span_id;
5
    char cluster_id[32];
6
    char service_name[64];
7
    __u64 timestamp;
8
};
9

10
struct {
11
    __uint(type, BPF_MAP_TYPE_HASH);
12
    __uint(max_entries, 100000);
13
    __type(key, __u64);
14
    __type(value, struct trace_context);
15
} distributed_traces SEC(".maps");
16

17
// Inject trace headers for correlation
18
SEC("tc")
19
int inject_trace_headers(struct __sk_buff *skb) {
20
    // Extract existing trace context
21
    struct trace_context *ctx;
22
    __u64 trace_id = extract_trace_id(skb);
23

24
    ctx = bpf_map_lookup_elem(&distributed_traces, &trace_id);
25
    if (!ctx) {
26
        // Create new trace context
27
        struct trace_context new_ctx = {
28
            .trace_id = bpf_get_prandom_u32(),
29
            .span_id = bpf_get_prandom_u32(),
30
            .timestamp = bpf_ktime_get_ns(),
31
        };
32

33
        bpf_probe_read_str(new_ctx.cluster_id, sizeof(new_ctx.cluster_id), "cluster-1");
34
        bpf_map_update_elem(&distributed_traces, &new_ctx.trace_id, &new_ctx, BPF_ANY);
35
    }
36

37
    // Modify packet headers to include trace information
38
    return TC_ACT_OK;
39
}
40

41
// Cross-cluster correlation
42
SEC("kprobe/tcp_sendmsg")
43
int correlate_cross_cluster(struct pt_regs *ctx) {
44
    struct sock *sk = (struct sock *)PT_REGS_PARM1(ctx);
45

46
    // Extract destination information
47
    __u32 dst_ip = BPF_CORE_READ(sk, __sk_common.skc_daddr);
48

49
    // Check if this is cross-cluster communication
50
    if (is_cross_cluster_ip(dst_ip)) {
51
        // Enhance trace context with cluster boundary information
52
        update_cross_cluster_metrics(sk);
53
    }
54

55
    return 0;
56
}
57

58
static int is_cross_cluster_ip(__u32 ip) {
59
    // Implement cluster IP range detection
60
    return 1; // Simplified
61
}
62

63
static void update_cross_cluster_metrics(struct sock *sk) {
64
    // Update metrics for cross-cluster communication
65
}
66

67
char _license[] SEC("license") = "GPL";

Key Metrics Extraction and Analysis#

Actionable Insights from Network Traffic#

eBPF-based solutions don’t just track network traffic; they decode and distill essential information:

1
graph TB
2
    subgraph "eBPF Data Processing Pipeline"
3
        subgraph "Raw Data Collection"
4
            RDC1[Network Packets] --> RDC2[System Calls]
5
            RDC3[Function Calls] --> RDC4[Kernel Events]
6
        end
7

8
        subgraph "Protocol Analysis"
9
            PA1[HTTP Parser] --> PA2[Request/Response]
10
            PA3[Database Parser] --> PA4[Query/Result]
11
            PA5[Message Queue Parser] --> PA6[Topic/Message]
12
        end
13

14
        subgraph "Metric Extraction"
15
            ME1[Response Times] --> ME2[Error Rates]
16
            ME3[Throughput] --> ME4[Resource Usage]
17
            ME5[Status Codes] --> ME6[Custom KPIs]
18
        end
19

20
        subgraph "Data Enrichment"
21
            DE1[Service Discovery] --> DE2[Topology Mapping]
22
            DE3[Business Context] --> DE4[SLA Correlation]
23
        end
24

25
        RDC1 --> PA1
26
        RDC2 --> PA3
27
        RDC3 --> PA5
28
        RDC4 --> PA1
29

30
        PA2 --> ME1
31
        PA4 --> ME3
32
        PA6 --> ME5
33

34
        ME2 --> DE1
35
        ME4 --> DE3
36
        ME6 --> DE2
37
    end
38

39
    style RDC1 fill:#e1f5fe
40
    style PA1 fill:#f3e5f5
41
    style ME1 fill:#e8f5e8
42
    style DE1 fill:#fff3e0

Comprehensive Metrics Portfolio#

Modern eBPF observability platforms extract:

Request Path Analysis
- Complete request flow tracking
- Service dependency mapping
- Bottleneck identification
- Performance optimization opportunities
Status Code Distribution
- HTTP response code analysis
- Error pattern identification
- Success rate monitoring
- SLA compliance tracking
Topic and Queue Metrics
- Message queue throughput
- Topic-specific performance
- Producer/consumer analysis
- Queue depth monitoring
Resource Utilization
- CPU usage per service
- Memory consumption patterns
- Network bandwidth utilization
- Storage I/O characteristics

Implementation Examples#

HTTP Service Monitoring#

1
struct http_metrics {
2
    char method[8];
3
    char path[128];
4
    __u16 status_code;
5
    __u64 request_time;
6
    __u64 response_time;
7
    __u32 request_size;
8
    __u32 response_size;
9
    char user_agent[64];
10
    char client_ip[16];
11
};
12

13
struct {
14
    __uint(type, BPF_MAP_TYPE_RINGBUF);
15
    __uint(max_entries, 2 * 1024 * 1024);
16
} http_events SEC(".maps");
17

18
SEC("uprobe/handle_http_request")
19
int trace_http_request_start(struct pt_regs *ctx) {
20
    struct http_request *req = (struct http_request *)PT_REGS_PARM1(ctx);
21

22
    struct http_metrics *metrics;
23
    metrics = bpf_ringbuf_reserve(&http_events, sizeof(*metrics), 0);
24
    if (!metrics)
25
        return 0;
26

27
    // Extract HTTP request details
28
    bpf_probe_read_str(metrics->method, sizeof(metrics->method),
29
                       BPF_CORE_READ(req, method));
30
    bpf_probe_read_str(metrics->path, sizeof(metrics->path),
31
                       BPF_CORE_READ(req, path));
32
    bpf_probe_read_str(metrics->user_agent, sizeof(metrics->user_agent),
33
                       BPF_CORE_READ(req, user_agent));
34

35
    metrics->request_time = bpf_ktime_get_ns();
36
    metrics->request_size = BPF_CORE_READ(req, content_length);
37

38
    bpf_ringbuf_submit(metrics, 0);
39
    return 0;
40
}
41

42
SEC("uretprobe/handle_http_request")
43
int trace_http_request_end(struct pt_regs *ctx) {
44
    struct http_response *resp = (struct http_response *)PT_REGS_RC(ctx);
45

46
    struct http_metrics *metrics;
47
    metrics = bpf_ringbuf_reserve(&http_events, sizeof(*metrics), 0);
48
    if (!metrics)
49
        return 0;
50

51
    metrics->status_code = BPF_CORE_READ(resp, status_code);
52
    metrics->response_size = BPF_CORE_READ(resp, content_length);
53
    metrics->response_time = bpf_ktime_get_ns();
54

55
    bpf_ringbuf_submit(metrics, 0);
56
    return 0;
57
}
58

59
char _license[] SEC("license") = "GPL";

Database Performance Monitoring#

1
struct db_operation {
2
    char operation[16];     // SELECT, INSERT, UPDATE, DELETE
3
    char table_name[64];    // Target table
4
    __u64 execution_time;   // Query execution time
5
    __u32 rows_affected;    // Number of rows processed
6
    __u32 connection_id;    // Database connection identifier
7
    __u8 error_code;        // Error status (0 = success)
8
    char query_hash[32];    // Hash of the query for grouping
9
};
10

11
struct {
12
    __uint(type, BPF_MAP_TYPE_RINGBUF);
13
    __uint(max_entries, 1024 * 1024);
14
} db_events SEC(".maps");
15

16
// Track database query execution
17
SEC("uprobe/mysql_execute_query")
18
int trace_mysql_query_start(struct pt_regs *ctx) {
19
    void *connection = (void *)PT_REGS_PARM1(ctx);
20
    char *query = (char *)PT_REGS_PARM2(ctx);
21

22
    struct db_operation *op;
23
    op = bpf_ringbuf_reserve(&db_events, sizeof(*op), 0);
24
    if (!op)
25
        return 0;
26

27
    // Extract query type
28
    if (bpf_strncmp(query, "SELECT", 6) == 0) {
29
        bpf_probe_read_str(op->operation, sizeof(op->operation), "SELECT");
30
    } else if (bpf_strncmp(query, "INSERT", 6) == 0) {
31
        bpf_probe_read_str(op->operation, sizeof(op->operation), "INSERT");
32
    } else if (bpf_strncmp(query, "UPDATE", 6) == 0) {
33
        bpf_probe_read_str(op->operation, sizeof(op->operation), "UPDATE");
34
    } else if (bpf_strncmp(query, "DELETE", 6) == 0) {
35
        bpf_probe_read_str(op->operation, sizeof(op->operation), "DELETE");
36
    }
37

38
    op->execution_time = bpf_ktime_get_ns();
39
    op->connection_id = (__u32)(long)connection;
40

41
    // Generate query hash for grouping similar queries
42
    op->query_hash[0] = bpf_get_prandom_u32() % 256;
43

44
    bpf_ringbuf_submit(op, 0);
45
    return 0;
46
}
47

48
// MongoDB operation tracking
49
SEC("uprobe/mongodb_collection_operation")
50
int trace_mongodb_operation(struct pt_regs *ctx) {
51
    char *collection = (char *)PT_REGS_PARM1(ctx);
52
    int operation_type = (int)PT_REGS_PARM2(ctx);
53

54
    struct db_operation *op;
55
    op = bpf_ringbuf_reserve(&db_events, sizeof(*op), 0);
56
    if (!op)
57
        return 0;
58

59
    bpf_probe_read_str(op->table_name, sizeof(op->table_name), collection);
60

61
    switch (operation_type) {
62
        case 1:
63
            bpf_probe_read_str(op->operation, sizeof(op->operation), "FIND");
64
            break;
65
        case 2:
66
            bpf_probe_read_str(op->operation, sizeof(op->operation), "INSERT");
67
            break;
68
        case 3:
69
            bpf_probe_read_str(op->operation, sizeof(op->operation), "UPDATE");
70
            break;
71
        case 4:
72
            bpf_probe_read_str(op->operation, sizeof(op->operation), "DELETE");
73
            break;
74
    }
75

76
    op->execution_time = bpf_ktime_get_ns();
77

78
    bpf_ringbuf_submit(op, 0);
79
    return 0;
80
}
81

82
char _license[] SEC("license") = "GPL";

User-Space Processing and Analytics#

Real-Time Data Processing#

1
#include <stdio.h>
2
#include <stdlib.h>
3
#include <string.h>
4
#include <unistd.h>
5
#include <time.h>
6
#include <bpf/libbpf.h>
7
#include <bpf/bpf.h>
8

9
struct service_metrics {
10
    char service_name[64];
11
    uint64_t request_count;
12
    uint64_t error_count;
13
    uint64_t total_latency;
14
    uint64_t min_latency;
15
    uint64_t max_latency;
16
    time_t last_updated;
17
};
18

19
struct metrics_aggregator {
20
    struct service_metrics services[1000];
21
    int service_count;
22
    time_t collection_start;
23
};
24

25
static struct metrics_aggregator aggregator = {0};
26

27
// Process HTTP events from eBPF
28
static int handle_http_event(void *ctx, void *data, size_t data_sz) {
29
    struct http_metrics *event = data;
30

31
    // Find or create service metrics
32
    struct service_metrics *service = find_or_create_service(event->path);
33
    if (!service) {
34
        return 0;
35
    }
36

37
    // Update metrics
38
    service->request_count++;
39

40
    if (event->status_code >= 400) {
41
        service->error_count++;
42
    }
43

44
    uint64_t latency = event->response_time - event->request_time;
45
    service->total_latency += latency;
46

47
    if (latency < service->min_latency || service->min_latency == 0) {
48
        service->min_latency = latency;
49
    }
50

51
    if (latency > service->max_latency) {
52
        service->max_latency = latency;
53
    }
54

55
    service->last_updated = time(NULL);
56

57
    // Generate real-time insights
58
    if (should_generate_alert(service, event)) {
59
        generate_performance_alert(service, event);
60
    }
61

62
    return 0;
63
}
64

65
static struct service_metrics *find_or_create_service(const char *path) {
66
    // Extract service name from path
67
    char service_name[64];
68
    extract_service_name(path, service_name, sizeof(service_name));
69

70
    // Find existing service
71
    for (int i = 0; i < aggregator.service_count; i++) {
72
        if (strcmp(aggregator.services[i].service_name, service_name) == 0) {
73
            return &aggregator.services[i];
74
        }
75
    }
76

77
    // Create new service entry
78
    if (aggregator.service_count < 1000) {
79
        struct service_metrics *service = &aggregator.services[aggregator.service_count++];
80
        strncpy(service->service_name, service_name, sizeof(service->service_name));
81
        service->last_updated = time(NULL);
82
        return service;
83
    }
84

85
    return NULL;
86
}
87

88
static void extract_service_name(const char *path, char *service_name, size_t size) {
89
    // Simple service name extraction from API path
90
    if (strncmp(path, "/api/v1/users", 13) == 0) {
91
        strncpy(service_name, "user-service", size);
92
    } else if (strncmp(path, "/api/v1/orders", 14) == 0) {
93
        strncpy(service_name, "order-service", size);
94
    } else if (strncmp(path, "/api/v1/payments", 16) == 0) {
95
        strncpy(service_name, "payment-service", size);
96
    } else {
97
        strncpy(service_name, "unknown-service", size);
98
    }
99
}
100

101
static int should_generate_alert(struct service_metrics *service, struct http_metrics *event) {
102
    // Error rate threshold
103
    if (service->request_count > 10) {
104
        double error_rate = (double)service->error_count / service->request_count;
105
        if (error_rate > 0.05) { // 5% error rate threshold
106
            return 1;
107
        }
108
    }
109

110
    // Latency threshold
111
    uint64_t current_latency = event->response_time - event->request_time;
112
    if (current_latency > 5000000000ULL) { // 5 seconds
113
        return 1;
114
    }
115

116
    return 0;
117
}
118

119
static void generate_performance_alert(struct service_metrics *service, struct http_metrics *event) {
120
    time_t now = time(NULL);
121
    char *timestamp = ctime(&now);
122
    timestamp[strlen(timestamp) - 1] = '\0'; // Remove newline
123

124
    double error_rate = service->request_count > 0 ?
125
                       (double)service->error_count / service->request_count * 100.0 : 0.0;
126

127
    double avg_latency = service->request_count > 0 ?
128
                        (double)service->total_latency / service->request_count / 1000000.0 : 0.0;
129

130
    printf("ALERT [%s] Service: %s\n", timestamp, service->service_name);
131
    printf("  Error Rate: %.2f%% (%lu/%lu requests)\n",
132
           error_rate, service->error_count, service->request_count);
133
    printf("  Average Latency: %.2f ms\n", avg_latency);
134
    printf("  Min/Max Latency: %.2f/%.2f ms\n",
135
           service->min_latency / 1000000.0, service->max_latency / 1000000.0);
136
    printf("  Recent Request: %s %s -> %d\n",
137
           event->method, event->path, event->status_code);
138
    printf("\n");
139
}
140

141
// Export metrics in Prometheus format
142
static void export_prometheus_metrics() {
143
    printf("# HELP http_requests_total Total number of HTTP requests\n");
144
    printf("# TYPE http_requests_total counter\n");
145

146
    printf("# HELP http_request_duration_seconds HTTP request latency\n");
147
    printf("# TYPE http_request_duration_seconds histogram\n");
148

149
    for (int i = 0; i < aggregator.service_count; i++) {
150
        struct service_metrics *service = &aggregator.services[i];
151

152
        printf("http_requests_total{service=\"%s\",status=\"success\"} %lu\n",
153
               service->service_name, service->request_count - service->error_count);
154
        printf("http_requests_total{service=\"%s\",status=\"error\"} %lu\n",
155
               service->service_name, service->error_count);
156

157
        if (service->request_count > 0) {
158
            double avg_latency = (double)service->total_latency / service->request_count / 1e9;
159
            printf("http_request_duration_seconds{service=\"%s\",quantile=\"0.5\"} %.6f\n",
160
                   service->service_name, avg_latency);
161
        }
162
    }
163
}
164

165
int main() {
166
    struct bpf_object *obj;
167
    struct ring_buffer *rb;
168
    int err;
169

170
    printf("Starting eBPF-based observability processor...\n");
171

172
    // Load eBPF programs
173
    obj = bpf_object__open_file("http_service_monitor.bpf.o", NULL);
174
    if (libbpf_get_error(obj)) {
175
        fprintf(stderr, "Failed to open eBPF object\n");
176
        return 1;
177
    }
178

179
    err = bpf_object__load(obj);
180
    if (err) {
181
        fprintf(stderr, "Failed to load eBPF object\n");
182
        return 1;
183
    }
184

185
    // Attach programs
186
    struct bpf_link *links[10];
187
    int link_count = 0;
188

189
    struct bpf_program *prog;
190
    bpf_object__for_each_program(prog, obj) {
191
        links[link_count] = bpf_program__attach(prog);
192
        if (libbpf_get_error(links[link_count])) {
193
            printf("Warning: Failed to attach program %s\n", bpf_program__name(prog));
194
            continue;
195
        }
196
        printf("Attached eBPF program: %s\n", bpf_program__name(prog));
197
        link_count++;
198
    }
199

200
    // Set up ring buffer
201
    int map_fd = bpf_object__find_map_fd_by_name(obj, "http_events");
202
    if (map_fd < 0) {
203
        fprintf(stderr, "Failed to find http_events map\n");
204
        return 1;
205
    }
206

207
    rb = ring_buffer__new(map_fd, handle_http_event, NULL, NULL);
208
    if (!rb) {
209
        fprintf(stderr, "Failed to create ring buffer\n");
210
        return 1;
211
    }
212

213
    aggregator.collection_start = time(NULL);
214

215
    printf("eBPF observability system started. Monitoring HTTP traffic...\n");
216
    printf("Press Ctrl-C to export metrics and exit.\n\n");
217

218
    // Process events
219
    while (1) {
220
        err = ring_buffer__poll(rb, 1000);
221
        if (err < 0) {
222
            printf("Error polling ring buffer: %d\n", err);
223
            break;
224
        }
225

226
        // Periodic metrics export
227
        static time_t last_export = 0;
228
        time_t now = time(NULL);
229
        if (now - last_export >= 60) { // Export every minute
230
            printf("\n=== Metrics Export ===\n");
231
            export_prometheus_metrics();
232
            printf("====================\n\n");
233
            last_export = now;
234
        }
235
    }
236

237
    // Cleanup
238
    ring_buffer__free(rb);
239
    for (int i = 0; i < link_count; i++) {
240
        bpf_link__destroy(links[i]);
241
    }
242
    bpf_object__close(obj);
243

244
    return 0;
245
}

Future-Proof Observability with eBPF#

Paradigm Shift in System Monitoring#

eBPF represents more than just a technology; it’s a paradigm shift in observability that provides:

1
graph TB
2
    subgraph "eBPF Observability Benefits"
3
        subgraph "Technical Advantages"
4
            TA1[Kernel-Level Insights] --> TA2[Real-Time Processing]
5
            TA3[Zero Instrumentation] --> TA4[Universal Coverage]
6
            TA5[Minimal Overhead] --> TA6[Production Ready]
7
        end
8

9
        subgraph "Operational Benefits"
10
            OB1[Comprehensive Visibility] --> OB2[Faster MTTR]
11
            OB3[Proactive Monitoring] --> OB4[Predictive Analytics]
12
            OB5[Unified Platform] --> OB6[Reduced Complexity]
13
        end
14

15
        subgraph "Business Impact"
16
            BI1[Improved Reliability] --> BI2[Better User Experience]
17
            BI3[Operational Efficiency] --> BI4[Cost Optimization]
18
            BI5[Risk Mitigation] --> BI6[Competitive Advantage]
19
        end
20

21
        TA2 --> OB1
22
        TA4 --> OB3
23
        TA6 --> OB5
24

25
        OB2 --> BI1
26
        OB4 --> BI3
27
        OB6 --> BI5
28
    </end>
29

30
    style TA1 fill:#e1f5fe
31
    style OB1 fill:#f3e5f5
32
    style BI1 fill:#e8f5e8

Detailed Real-Time System Views#

eBPF provides detailed, real-time views of systems, ensuring operators are always in control:

Complete Service Topology: Automatic discovery of service dependencies
Performance Bottleneck Identification: Real-time identification of performance issues
Security Threat Detection: Continuous monitoring for security anomalies
Resource Optimization: Data-driven insights for resource allocation
Capacity Planning: Predictive analytics for infrastructure scaling

Strategic Advantages for DevOps and SRE Teams#

Understanding and utilizing eBPF provides significant advantages:

Single Cluster Management: Comprehensive visibility into monolithic or single-cluster environments
Multi-Cloud Environments: Unified observability across complex, distributed infrastructures
Scalable Architecture: eBPF programs scale with your infrastructure without proportional overhead
Technology Agnostic: Works across different programming languages, frameworks, and protocols

Integration with Modern DevOps Workflows#

CI/CD Pipeline Integration#

1
name: eBPF Observability Deployment
2
on:
3
  push:
4
    branches: [main]
5
  pull_request:
6
    branches: [main]
7

8
jobs:
9
  build-ebpf:
10
    runs-on: ubuntu-latest
11
    steps:
12
      - uses: actions/checkout@v3
13

14
      - name: Install eBPF dependencies
15
        run: |
16
          sudo apt-get update
17
          sudo apt-get install -y clang libbpf-dev bpftool
18

19
      - name: Compile eBPF programs
20
        run: |
21
          make -C src/ebpf all
22

23
      - name: Test eBPF programs
24
        run: |
25
          sudo make -C src/ebpf test
26

27
      - name: Build user-space components
28
        run: |
29
          make -C src/userspace all
30

31
      - name: Package observability suite
32
        run: |
33
          tar -czf ebpf-observability.tar.gz src/ebpf/*.o src/userspace/processor
34

35
      - name: Upload artifacts
36
        uses: actions/upload-artifact@v3
37
        with:
38
          name: ebpf-observability
39
          path: ebpf-observability.tar.gz
40

41
  deploy-staging:
42
    needs: build-ebpf
43
    runs-on: ubuntu-latest
44
    if: github.ref == 'refs/heads/main'
45
    steps:
46
      - name: Deploy to staging
47
        run: |
48
          # Deploy eBPF observability to staging environment
49
          kubectl apply -f k8s/staging/
50

51
      - name: Run integration tests
52
        run: |
53
          # Test observability functionality
54
          ./tests/integration-tests.sh staging
55

56
  deploy-production:
57
    needs: [build-ebpf, deploy-staging]
58
    runs-on: ubuntu-latest
59
    if: github.ref == 'refs/heads/main'
60
    environment: production
61
    steps:
62
      - name: Deploy to production
63
        run: |
64
          # Deploy eBPF observability to production
65
          kubectl apply -f k8s/production/

Kubernetes Integration#

1
apiVersion: apps/v1
2
kind: DaemonSet
3
metadata:
4
  name: ebpf-observability
5
  namespace: monitoring
6
spec:
7
  selector:
8
    matchLabels:
9
      app: ebpf-observability
10
  template:
11
    metadata:
12
      labels:
13
        app: ebpf-observability
14
    spec:
15
      hostNetwork: true
16
      hostPID: true
17
      containers:
18
        - name: ebpf-agent
19
          image: ebpf-observability:latest
20
          securityContext:
21
            privileged: true
22
            capabilities:
23
              add: ["SYS_ADMIN", "NET_ADMIN", "BPF"]
24
          volumeMounts:
25
            - name: proc
26
              mountPath: /host/proc
27
              readOnly: true
28
            - name: sys
29
              mountPath: /host/sys
30
              readOnly: true
31
            - name: debugfs
32
              mountPath: /sys/kernel/debug
33
          env:
34
            - name: NODE_NAME
35
              valueFrom:
36
                fieldRef:
37
                  fieldPath: spec.nodeName
38
            - name: CLUSTER_NAME
39
              value: "production-cluster"
40
          ports:
41
            - containerPort: 8080
42
              name: metrics
43
            - containerPort: 8081
44
              name: health
45
      volumes:
46
        - name: proc
47
          hostPath:
48
            path: /proc
49
        - name: sys
50
          hostPath:
51
            path: /sys
52
        - name: debugfs
53
          hostPath:
54
            path: /sys/kernel/debug
55
      tolerations:
56
        - operator: Exists
57
          effect: NoSchedule
58
---
59
apiVersion: v1
60
kind: Service
61
metadata:
62
  name: ebpf-observability-metrics
63
  namespace: monitoring
64
spec:
65
  selector:
66
    app: ebpf-observability
67
  ports:
68
    - name: metrics
69
      port: 8080
70
      targetPort: 8080
71
    - name: health
72
      port: 8081
73
      targetPort: 8081

Conclusion#

eBPF is revolutionizing observability for DevOps and SRE teams by providing unprecedented insights into system behavior with minimal overhead and zero manual instrumentation. This technology represents a fundamental shift from traditional monitoring approaches to kernel-level, real-time observability.

Key Benefits Summary#

Automatic Instrumentation: No code changes required for comprehensive monitoring
Universal Coverage: Works across all applications, protocols, and languages
Real-Time Insights: Kernel-level processing provides immediate visibility
Minimal Overhead: Efficient data collection without performance impact
Scalable Architecture: Grows with your infrastructure complexity

Strategic Impact#

For organizations managing cloud-native environments, eBPF provides:

Competitive Advantage: Advanced observability capabilities ahead of traditional monitoring
Operational Excellence: Faster incident response and proactive issue prevention
Cost Optimization: Efficient resource utilization through better visibility
Future Readiness: Technology foundation for next-generation observability needs

Looking Forward#

As cloud-native computing continues to evolve, eBPF-based observability will become increasingly critical for:

Microservices Architecture: Complete service mesh visibility
Multi-Cloud Deployments: Unified observability across cloud providers
Edge Computing: Distributed system monitoring at scale
AI/ML Workloads: Performance optimization for compute-intensive applications

The future of observability is here, and it’s powered by eBPF. Organizations that embrace this technology today will be better positioned to handle the complexity and scale of tomorrow’s distributed systems.

Resources and Further Reading#

Official Documentation#

eBPF Foundation - Official eBPF documentation and resources
OpenTelemetry - Open source observability framework
eBPF Kernel Documentation

Learning Resources#

eBPF: Unlocking the Kernel Documentary - 30-minute documentary about eBPF
Learning eBPF by Liz Rice
BPF Performance Tools by Brendan Gregg

Open Source Projects#

Cilium - eBPF-powered networking and security
Falco - Runtime security monitoring with eBPF
Pixie - Kubernetes observability platform
Parca - Continuous profiling with eBPF

Enterprise Solutions#

SUSE Observability - Enterprise observability platform
SUSE Rancher Prime - Kubernetes management platform

Inspired by the original article by Mark Bakker on SUSE Blog