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July 17, 2011
Blue and gray watering wand spraying multiple thin streams of water upward over a green lawn
Kodos — Python Regex Debugger
Blue and gray watering wand spraying multiple thin streams of water upward over a green lawn
DenyHosts — SSH Attack Blocker
Blue and gray watering wand spraying multiple thin streams of water upward over a green lawn
Scratchy — Apache Log Analyzer
Blue and gray watering wand spraying multiple thin streams of water upward over a green lawn
Canyonero — Project Management
Open Source Projects by Phil Schwartz

Creating a Python Interactive Plot for Live Network Traffic Statistics

Creating a Python-Based Interactive Plot for Live Network Traffic Statistics turns a stream of changing counters into something a sysadmin can read at a glance. It also fits naturally beside my DenyHosts, Kodos and Scratchy projects: focused open-source utilities that show how practical Linux problems can be solved with clear interfaces and inspectable code.

The aim is a compact viewer, not a packet-inspection console. It should run beside a terminal, use unprivileged interface counters by default, update smoothly, retain useful history and explain plainly when a platform cannot provide the requested data.

Decide what the graph measures

Start by choosing interface counters: bytes sent and received, packets, errors and discards. They are cheap to read, expose trends clearly and avoid storing packet contents. Rates are more useful than cumulative totals, so each sample should be converted into a delta before plotting.

Decide whether the display covers one interface or all active interfaces. A single NIC view makes a busy tunnel easy to isolate, while an aggregate view is better for a small home server. Keep both available, but label units consistently.

Packet capture can be an optional mode rather than the foundation. Raw sockets require elevated privileges and introduce legal, privacy and performance concerns. The default path should use psutil.net_io_counters() or Linux’s /proc/net/dev, with packet details handled by a separate tool if needed.

Separate collection from rendering

Run collection in a worker thread and keep Matplotlib’s event loop on the main thread. A bounded queue prevents a slow redraw from causing unbounded memory growth. The worker publishes timestamped samples and never calls plotting functions directly.

The value of a client-server streaming example is that it makes the lifecycle of a long-lived channel explicit. A .NET team can adapt those ideas when feeding remote telemetry; a local Python viewer can use the same separation with a queue and a short refresh timer.

Use a rolling deque for the visible history and aggregate old samples into coarser buckets. When a counter resets or an interface disappears, emit a gap marker rather than drawing a misleading spike. That event model keeps the chart honest during reconnects.

Build an interface that stays readable

Matplotlib’s FuncAnimation is sufficient for a compact application, provided the redraw path stays small. Draw the axes once, update line data in place and use blitting where the backend supports it. A fixed window, such as 60 or 300 seconds, should be selectable.

Expose a dark or light theme, clear units and a pause control. Keyboard shortcuts are useful on a minimal desktop, while checkboxes or buttons work better in a full graphical interface. Every control must remain responsive during packet bursts.

Avoid animations that move the entire layout on every sample. Fixed axes with a moving time range reduce flicker and make rate changes easier to compare. Annotate pauses, sampling errors and counter resets directly on the plot.

Keep CPU and memory under control

Sampling too quickly can consume more CPU than the traffic being measured. Start with one-second intervals, calculate byte rates from counter differences and smooth only the display. The underlying data should remain available for precise inspection.

Bound the sample count by time and memory. A ten-minute window at one sample per second is modest; retaining a full day at the same rate needs aggregation or disk-backed storage. Downsample old points before they reach the renderer.

If collection must inspect packets, isolate it in a separate process so a malformed capture cannot freeze the interface. Use a modest capture buffer, drop policy and timeout. Benchmark against a busy link rather than only an idle workstation.

Treat capture and data handling securely

Principle of least privilege shapes the whole design. Interface counters usually need no special rights; packet capture does. Do not run the viewer as root merely to simplify setup, and document the exact capability required by any optional capture mode.

Collect metadata, not payload, unless the feature has a clear purpose and consent. Network activity can expose browsing habits, service usage and internal hostnames. A local-only binding, short retention and explicit opt-in logging reduce those risks.

Write errors to a rotating log and keep secrets out of configuration files. Validate interface names against the current host to prevent command injection if shell commands are involved. The site should also make the data-retention behaviour visible to users.

Package the project with a practical release checklist

A small package with a stable command-line interface fits this portfolio well. Users should be able to select an interface, sampling interval, time window and output theme without editing source code. The README should show a terminal session and a screenshot of the running plot.

These choices make downloads predictable and give contributors a clear route to the project. Licence text, release notes and project statistics should sit beside the download link, consistent with a transparent open-source portfolio.

Test against Australian network conditions

Australia provides useful and varied test conditions. On the NBN, retail ISPs can deliver the same line over FTTP, FTTN or HFC, while upload speeds are often lower than downloads. The viewer should expose both directions and cope with brief counter pauses.

Mobile 4G and 5G links add variable latency and handover gaps that fixed-line tests miss. During a busy weekday morning around 9am in Sydney and Melbourne, usage can rise quickly, although regional paths may behave differently. The graph should mark those gaps honestly.

Cloud deployment is equally relevant, with major cloud regions available in Sydney and Melbourne and latency varying by ISP and location. In plain Aussie phrasing, when data is “a bit patchy”, the tool should say so rather than hide it. Align retention and access guidance with current ACSC advice, and the result feels built for the market it serves.