PostgreSQL Performance Troubleshooting Whitepaper – Using eBPF to Troubleshoot Process Contention in PostgreSQL
To use eBPF for troubleshooting process contention in PostgreSQL, we focus on understanding how processes are interacting with each other, particularly in terms of resource access such as CPU, memory, and I/O. Process contention often manifests as high lock wait times, slow query execution due to CPU scheduling delays, or I/O wait. Here’s how you can approach this using eBPF:
1. Monitoring Lock Contention
PostgreSQL uses various types of locks for managing access to data. Lock contention happens when multiple processes are waiting to acquire a lock held by another process. While PostgreSQL’s own logging and monitoring can track lock waits, eBPF can help identify underlying system-level contention that impacts these locks.
Using eBPF to Trace Lock Contention: You can write an eBPF script to monitor the fcntl system calls, which PostgreSQL uses for advisory locks. The script can record the time spent in these calls and correlate them to specific PIDs (Process IDs). Here’s a simple example using BCC:
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from bcc import BPF bpf_source = """ #include <uapi/linux/ptrace.h> struct lock_data { u32 pid; u64 duration; }; BPF_HASH(start_times, u32); BPF_PERF_OUTPUT(lock_events); int trace_entry(struct pt_regs *ctx) { u32 pid = bpf_get_current_pid_tgid(); u64 ts = bpf_ktime_get_ns(); start_times.update(&pid, &ts); return 0; } int trace_return(struct pt_regs *ctx) { u32 pid = bpf_get_current_pid_tgid(); u64 *tsp, delta; tsp = start_times.lookup(&pid); if (tsp != 0) { delta = bpf_ktime_get_ns() - *tsp; struct lock_data data = {.pid = pid, .duration = delta}; lock_events.perf_submit(ctx, &data, sizeof(data)); start_times.delete(&pid); } return 0; } """ b = BPF(text=bpf_source) b.attach_kprobe(event="sys_fcntl", fn_name="trace_entry") b.attach_kretprobe(event="sys_fcntl", fn_name="trace_return") def print_event(cpu, data, size): event = b["lock_events"].event(data) print(f"PID: {event.pid} Lock duration: {event.duration / 1000000} ms") b["lock_events"].open_perf_buffer(print_event) print("Tracing lock contention... Press Ctrl-C to finish.") while True: try: b.perf_buffer_poll() except KeyboardInterrupt: exit() |
2. Identifying CPU Contention
If PostgreSQL processes are contending for CPU, it can be observed via context switches and scheduler delays. Monitoring these events can reveal if PostgreSQL processes are frequently preempted or spend long times waiting to be scheduled.
Using eBPF to Monitor CPU Contention: You can use a bpftrace script to monitor context switches and scheduler events related to PostgreSQL processes. Here’s a basic example to get you started:
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sudo bpftrace -e ' tracepoint:sched:sched_process_fork { printf("Process forked: %d -> %d\\n", args->parent_pid, args->child_pid); } tracepoint:sched:sched_switch { printf("Context switch: %d -> %d\\n", args->prev_pid, args->next_pid); } ' |
This script prints out every context switch in the system, but you can modify it to focus on PostgreSQL processes by checking if prev_pid or next_pid matches PostgreSQL PIDs.
3. I/O Wait and Performance
I/O wait is another common source of contention. Monitoring disk read/write and blocking I/O operations can provide insights into whether disk access is a bottleneck.
Using eBPF for Disk I/O Monitoring: Again, using BCC or bpftrace, you can trace block device I/O operations. This can help identify slow disk operations that may be affecting PostgreSQL performance.
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sudo bpftrace -e ' tracepoint:block:block_rq_issue { printf("Block I/O request: %d, %s\\n", args->dev, args->rwbs); } ' |
This script needs to be refined to focus on the devices relevant to PostgreSQL.
Combining These Approaches
Combine these monitoring scripts to get a comprehensive view of where contention might be occurring in your PostgreSQL deployment. eBPF can provide deep insights, but it requires careful tuning and understanding of both PostgreSQL internals and system performance characteristics to interpret the data effectively. Each script provides a foundation that should be tailored to your specific environment and needs.
Linux Performance Troubleshooting with eBPF – MinervaDB Webinar
