Project 13: The Intelligent Logger (Custom Rosbag Filters)

A bag-filtering tool that reads MCAP/SQLite bags and outputs filtered data.

Quick Reference

Attribute Value
Difficulty Level 2: Intermediate
Time Estimate 1-2 weeks
Main Programming Language Python
Alternative Programming Languages C++
Coolness Level Level 2: Practical but Forgettable
Business Potential 2. The Micro-SaaS / Pro Tool
Prerequisites Python, rosbag2 CLI, basic serialization
Key Topics Rosbag2 Architecture, Serialization Formats, Data Filtering & Indexing

1. Learning Objectives

By completing this project, you will:

  1. Explain how Rosbag2 Architecture affects ROS 2 behavior in this project.
  2. Implement the core pipeline for Project 13 and validate it with a deterministic demo.
  3. Measure and document performance or correctness under at least one stress condition.
  4. Produce artifacts (configs, logs, scripts) that make the system reproducible.

2. All Theory Needed (Per-Concept Breakdown)

Rosbag2 Architecture

Fundamentals

Rosbag2 Architecture is the plugin-based storage and serialization design of rosbag2. In ROS 2, this concept defines how nodes coordinate, exchange data, and enforce guarantees. At a minimum you should be able to name the primary entities involved, identify where configuration lives, and explain how storage plugin and metadata influence behavior. When you debug a system, you will almost always inspect sqlite or mcap first because those details surface mismatches early. The practical goal is to build a mental map that connects the API knobs you change to the wire-level or runtime effects you observe. If you can explain this concept without naming a single ROS 2 command, you know it as a systems principle rather than a tooling trick, which is exactly what you need for production robotics.

Deep Dive into the concept

A deeper look at Rosbag2 Architecture starts by tracing data from the API surface to the middleware. Every time you configure storage plugin or metadata, ROS 2 expresses that intent in the rmw layer, which then maps the intent into DDS-RTPS structures. The mapping is not always one-to-one: a single policy or field can affect multiple runtime behaviors, including buffering, matching, and timing. This is why a simple change in sqlite can cause a subscriber to stop receiving data, or why two vendors can discover each other but never exchange payloads. The useful diagnostic strategy is to observe the graph (who matched), then the transport (what packets appear), and finally the runtime state (queues, deadlines, timers).

Failure modes cluster around mismatched assumptions. If mcap is configured incorrectly, you may see data on one machine but not another, or discover that messages arrive but are rejected silently. If reader/writer is too restrictive, you will observe a graph that looks healthy but never transitions into active data flow. In embedded settings, this can appear as missed deadlines or watchdog resets rather than explicit errors. A robust design therefore includes explicit validation: log the effective policy, emit version identifiers, and test a known-good baseline before you change parameters. This project forces that discipline because you will create repeatable experiments and capture deterministic outputs, so you can explain not only what happened but why it happened.

How this fits on projects

This concept directly shapes how you implement and validate Project 13. You will configure it, observe it, and stress it under controlled conditions.

Definitions & key terms

  • storage plugin: storage plugin in the context of Rosbag2 Architecture and ROS 2 systems.
  • metadata: metadata in the context of Rosbag2 Architecture and ROS 2 systems.
  • sqlite: sqlite in the context of Rosbag2 Architecture and ROS 2 systems.
  • mcap: mcap in the context of Rosbag2 Architecture and ROS 2 systems.
  • reader/writer: reader/writer in the context of Rosbag2 Architecture and ROS 2 systems.

Mental model diagram (ASCII)

[User Code] -> [Rosbag2 Architecture] -> [rmw/DDS] -> [Wire/Runtime Effects]
       |             |               |                 |
   Config/API     Policies        Entities         Observability

How it works (step-by-step, with invariants and failure modes)

  1. A node configures the concept through API calls or config files.
  2. The rmw layer translates the settings into DDS/RTPS fields (storage plugin, metadata).
  3. Peers evaluate compatibility, matching, or timing using sqlite and mcap.
  4. The runtime queues or state machines enforce the policy and emit data.
  5. Observability tools (logs, CLI, packet capture) confirm reader/writer behavior.

Minimal concrete example

ros2 bag info input.mcap

Common misconceptions

  • Assuming defaults are identical across vendors.
  • Believing that discovery implies data flow without validating compatibility.

Check-your-understanding questions

  1. Explain how Rosbag2 Architecture changes runtime behavior in ROS 2.
  2. Predict what happens if storage plugin conflicts with metadata.
  3. Why might two nodes discover each other but still exchange no data?

Check-your-understanding answers

  1. It alters matching, buffering, or timing constraints expressed via DDS/RTPS.
  2. The endpoints fail to match or drop messages due to incompatible policy/encoding.
  3. QoS or policy mismatch prevents writer-reader matching or delivery.

Real-world applications

  • dataset collection
  • debugging and playback

Where you’ll apply it

  • You will apply it in Section 5.4 (Concepts You Must Understand First), Section 5.10 (Implementation Phases), and Section 6.2 (Critical Test Cases).
  • Also used in: P14-the-cloud-bridge-ros2-to-mqttzenoh.md and other projects in this series.

References

  • rosbag2 documentation
  • MCAP spec

Key insights

  • Rosbag2 Architecture is the lever that connects configuration to observable system behavior.

Summary

This concept is the bridge between theory and runtime evidence. Mastery means you can predict outcomes, not just observe them.

Homework/Exercises to practice the concept

  1. Capture or log a minimal trace where this concept is visible.
  2. Change one policy/setting and predict the system impact before running it.
  3. Explain the failure mode you expect if the configuration is wrong.

Solutions to the homework/exercises

  1. The trace should show the concept-specific fields or events you expect.
  2. Your prediction should name which endpoints match and how latency/loss changes.
  3. A wrong configuration should lead to mismatch, dropped data, or timeouts.

Serialization Formats

Fundamentals

Serialization Formats is how ROS 2 messages are serialized for bags and wire transport. In ROS 2, this concept defines how nodes coordinate, exchange data, and enforce guarantees. At a minimum you should be able to name the primary entities involved, identify where configuration lives, and explain how CDR and endianness influence behavior. When you debug a system, you will almost always inspect type support or serialization first because those details surface mismatches early. The practical goal is to build a mental map that connects the API knobs you change to the wire-level or runtime effects you observe. If you can explain this concept without naming a single ROS 2 command, you know it as a systems principle rather than a tooling trick, which is exactly what you need for production robotics.

Deep Dive into the concept

A deeper look at Serialization Formats starts by tracing data from the API surface to the middleware. Every time you configure CDR or endianness, ROS 2 expresses that intent in the rmw layer, which then maps the intent into DDS-RTPS structures. The mapping is not always one-to-one: a single policy or field can affect multiple runtime behaviors, including buffering, matching, and timing. This is why a simple change in type support can cause a subscriber to stop receiving data, or why two vendors can discover each other but never exchange payloads. The useful diagnostic strategy is to observe the graph (who matched), then the transport (what packets appear), and finally the runtime state (queues, deadlines, timers).

Failure modes cluster around mismatched assumptions. If serialization is configured incorrectly, you may see data on one machine but not another, or discover that messages arrive but are rejected silently. If deserialization is too restrictive, you will observe a graph that looks healthy but never transitions into active data flow. In embedded settings, this can appear as missed deadlines or watchdog resets rather than explicit errors. A robust design therefore includes explicit validation: log the effective policy, emit version identifiers, and test a known-good baseline before you change parameters. This project forces that discipline because you will create repeatable experiments and capture deterministic outputs, so you can explain not only what happened but why it happened.

How this fits on projects

This concept directly shapes how you implement and validate Project 13. You will configure it, observe it, and stress it under controlled conditions.

Definitions & key terms

  • CDR: CDR in the context of Serialization Formats and ROS 2 systems.
  • endianness: endianness in the context of Serialization Formats and ROS 2 systems.
  • type support: type support in the context of Serialization Formats and ROS 2 systems.
  • serialization: serialization in the context of Serialization Formats and ROS 2 systems.
  • deserialization: deserialization in the context of Serialization Formats and ROS 2 systems.

Mental model diagram (ASCII)

[User Code] -> [Serialization Formats] -> [rmw/DDS] -> [Wire/Runtime Effects]
       |             |               |                 |
   Config/API     Policies        Entities         Observability

How it works (step-by-step, with invariants and failure modes)

  1. A node configures the concept through API calls or config files.
  2. The rmw layer translates the settings into DDS/RTPS fields (CDR, endianness).
  3. Peers evaluate compatibility, matching, or timing using type support and serialization.
  4. The runtime queues or state machines enforce the policy and emit data.
  5. Observability tools (logs, CLI, packet capture) confirm deserialization behavior.

Minimal concrete example

cdr_serialize(msg, buffer); cdr_deserialize(buffer, msg)

Common misconceptions

  • Assuming defaults are identical across vendors.
  • Believing that discovery implies data flow without validating compatibility.

Check-your-understanding questions

  1. Explain how Serialization Formats changes runtime behavior in ROS 2.
  2. Predict what happens if CDR conflicts with endianness.
  3. Why might two nodes discover each other but still exchange no data?

Check-your-understanding answers

  1. It alters matching, buffering, or timing constraints expressed via DDS/RTPS.
  2. The endpoints fail to match or drop messages due to incompatible policy/encoding.
  3. QoS or policy mismatch prevents writer-reader matching or delivery.

Real-world applications

  • custom bag tools
  • protocol bridges

Where you’ll apply it

  • You will apply it in Section 5.4 (Concepts You Must Understand First), Section 5.10 (Implementation Phases), and Section 6.2 (Critical Test Cases).
  • Also used in: P14-the-cloud-bridge-ros2-to-mqttzenoh.md and other projects in this series.

References

  • ROS 2 type support docs
  • CDR spec

Key insights

  • Serialization Formats is the lever that connects configuration to observable system behavior.

Summary

This concept is the bridge between theory and runtime evidence. Mastery means you can predict outcomes, not just observe them.

Homework/Exercises to practice the concept

  1. Capture or log a minimal trace where this concept is visible.
  2. Change one policy/setting and predict the system impact before running it.
  3. Explain the failure mode you expect if the configuration is wrong.

Solutions to the homework/exercises

  1. The trace should show the concept-specific fields or events you expect.
  2. Your prediction should name which endpoints match and how latency/loss changes.
  3. A wrong configuration should lead to mismatch, dropped data, or timeouts.

Data Filtering & Indexing

Fundamentals

Data Filtering & Indexing is selective extraction of messages while preserving bag integrity. In ROS 2, this concept defines how nodes coordinate, exchange data, and enforce guarantees. At a minimum you should be able to name the primary entities involved, identify where configuration lives, and explain how index rebuild and topic filter influence behavior. When you debug a system, you will almost always inspect time window or metadata update first because those details surface mismatches early. The practical goal is to build a mental map that connects the API knobs you change to the wire-level or runtime effects you observe. If you can explain this concept without naming a single ROS 2 command, you know it as a systems principle rather than a tooling trick, which is exactly what you need for production robotics.

Deep Dive into the concept

A deeper look at Data Filtering & Indexing starts by tracing data from the API surface to the middleware. Every time you configure index rebuild or topic filter, ROS 2 expresses that intent in the rmw layer, which then maps the intent into DDS-RTPS structures. The mapping is not always one-to-one: a single policy or field can affect multiple runtime behaviors, including buffering, matching, and timing. This is why a simple change in time window can cause a subscriber to stop receiving data, or why two vendors can discover each other but never exchange payloads. The useful diagnostic strategy is to observe the graph (who matched), then the transport (what packets appear), and finally the runtime state (queues, deadlines, timers).

Failure modes cluster around mismatched assumptions. If metadata update is configured incorrectly, you may see data on one machine but not another, or discover that messages arrive but are rejected silently. If ordering is too restrictive, you will observe a graph that looks healthy but never transitions into active data flow. In embedded settings, this can appear as missed deadlines or watchdog resets rather than explicit errors. A robust design therefore includes explicit validation: log the effective policy, emit version identifiers, and test a known-good baseline before you change parameters. This project forces that discipline because you will create repeatable experiments and capture deterministic outputs, so you can explain not only what happened but why it happened.

How this fits on projects

This concept directly shapes how you implement and validate Project 13. You will configure it, observe it, and stress it under controlled conditions.

Definitions & key terms

  • index rebuild: index rebuild in the context of Data Filtering & Indexing and ROS 2 systems.
  • topic filter: topic filter in the context of Data Filtering & Indexing and ROS 2 systems.
  • time window: time window in the context of Data Filtering & Indexing and ROS 2 systems.
  • metadata update: metadata update in the context of Data Filtering & Indexing and ROS 2 systems.
  • ordering: ordering in the context of Data Filtering & Indexing and ROS 2 systems.

Mental model diagram (ASCII)

[User Code] -> [Data Filtering & Indexing] -> [rmw/DDS] -> [Wire/Runtime Effects]
       |             |               |                 |
   Config/API     Policies        Entities         Observability

How it works (step-by-step, with invariants and failure modes)

  1. A node configures the concept through API calls or config files.
  2. The rmw layer translates the settings into DDS/RTPS fields (index rebuild, topic filter).
  3. Peers evaluate compatibility, matching, or timing using time window and metadata update.
  4. The runtime queues or state machines enforce the policy and emit data.
  5. Observability tools (logs, CLI, packet capture) confirm ordering behavior.

Minimal concrete example

keep msgs where speed > 1.0; update bag metadata

Common misconceptions

  • Assuming defaults are identical across vendors.
  • Believing that discovery implies data flow without validating compatibility.

Check-your-understanding questions

  1. Explain how Data Filtering & Indexing changes runtime behavior in ROS 2.
  2. Predict what happens if index rebuild conflicts with topic filter.
  3. Why might two nodes discover each other but still exchange no data?

Check-your-understanding answers

  1. It alters matching, buffering, or timing constraints expressed via DDS/RTPS.
  2. The endpoints fail to match or drop messages due to incompatible policy/encoding.
  3. QoS or policy mismatch prevents writer-reader matching or delivery.

Real-world applications

  • dataset curation
  • incident analysis

Where you’ll apply it

  • You will apply it in Section 5.4 (Concepts You Must Understand First), Section 5.10 (Implementation Phases), and Section 6.2 (Critical Test Cases).
  • Also used in: P14-the-cloud-bridge-ros2-to-mqttzenoh.md and other projects in this series.

References

  • rosbag2 API docs
  • data engineering references

Key insights

  • Data Filtering & Indexing is the lever that connects configuration to observable system behavior.

Summary

This concept is the bridge between theory and runtime evidence. Mastery means you can predict outcomes, not just observe them.

Homework/Exercises to practice the concept

  1. Capture or log a minimal trace where this concept is visible.
  2. Change one policy/setting and predict the system impact before running it.
  3. Explain the failure mode you expect if the configuration is wrong.

Solutions to the homework/exercises

  1. The trace should show the concept-specific fields or events you expect.
  2. Your prediction should name which endpoints match and how latency/loss changes.
  3. A wrong configuration should lead to mismatch, dropped data, or timeouts.

3. Project Specification

3.1 What You Will Build

A bag-filtering tool that reads MCAP/SQLite bags and outputs filtered data.

Included features:

  • Deterministic startup with explicit configuration.
  • Observability (logs/CLI output) that exposes discovery/data flow.
  • A reproducible demo and a failure case.

Excluded on purpose:

  • Full robot control stacks or SLAM pipelines.
  • Custom GUIs beyond CLI output.

3.2 Functional Requirements

  1. **Decoding serialized data: **Decoding serialized data -> Convert raw bytes to ROS messages.
  2. **Filtering logic: **Filtering logic -> Use speed thresholds or topic filters.
  3. **Re-indexing: **Re-indexing -> Ensure output bag is valid.
  4. Deterministic startup: The project must start with a reproducible, logged configuration.
  5. Observability: Provide CLI or log output that confirms each major component is working.

3.3 Non-Functional Requirements

  • Performance: Must meet the throughput/latency targets documented in the benchmark.\n- Reliability: Must handle common network or runtime failures gracefully.\n- Usability: CLI flags and logs must make configuration and diagnosis obvious.

3.4 Example Usage / Output

$ python3 bag_filter.py --in input.mcap --out fast.mcap --min-speed 1.0
[INFO] 120k -> 8k messages

3.5 Data Formats / Schemas / Protocols

bag metadata.yaml (topics, message counts, duration)

3.6 Edge Cases

  • Missing metadata
  • Unsupported storage plugin
  • Corrupted message

3.7 Real World Outcome

By the end of this project you will have a reproducible system that produces the same observable signals every time you run it. You will be able to point to console output, captured packets, or bag files and explain exactly why the result is correct. You will also be able to force a failure and demonstrate a clean error path.

3.7.1 How to Run (Copy/Paste)

# Build
colcon build --packages-select project_13
# Run
source install/setup.bash
# Start the main node/tool
./run_project_13.sh

3.7.2 Golden Path Demo (Deterministic)

$ python3 bag_filter.py --in input.mcap --out fast.mcap --min-speed 1.0
[INFO] 120k -> 8k messages

3.7.3 Failure Demo (Deterministic)

$ python3 bag_filter.py --in missing.mcap --out out.mcap
[ERROR] input bag not found

4. Solution Architecture

4.1 High-Level Design

[Input/Config] -> [Core Engine] -> [ROS 2/DDS] -> [Observability Output]

4.2 Key Components

Component Responsibility Key Decisions
Bag Reader Stream messages from MCAP/SQLite Minimal memory usage
Filter Engine Apply rules by topic/time/content Deterministic selection
Bag Writer Write output and metadata Rebuild indexes

4.3 Data Structures (No Full Code)

filter.json
{"topics": ["/odom"], "min_speed": 1.0}

4.4 Algorithm Overview

Key Algorithm: Core Pipeline

  1. Open bag
  2. Iterate messages
  3. Filter & write
  4. Update metadata

Complexity Analysis:

  • Time: O(n) over messages/events processed
  • Space: O(1) to O(n) depending on buffering

5. Implementation Guide

5.1 Development Environment Setup

# Install ROS 2 and dependencies
sudo apt-get update
sudo apt-get install -y ros-$ROS_DISTRO-ros-base python3-colcon-common-extensions

5.2 Project Structure

project-root/
|-- src/
|   |-- main.cpp
|   |-- config.yaml
|   `-- utils.cpp
|-- scripts/
|   `-- run_project.sh
|-- tests/
|   `-- test_core.py
`-- README.md

5.3 The Core Question You’re Answering

“How can I extract only the useful parts of large robot datasets?”

5.4 Concepts You Must Understand First

Stop and research these before coding:

  1. Rosbag2 Architecture
    • What breaks if this is misconfigured?
    • How will you observe it?
  2. Serialization Formats
    • What breaks if this is misconfigured?
    • How will you observe it?
  3. Data Filtering & Indexing
    • What breaks if this is misconfigured?
    • How will you observe it?

5.5 Questions to Guide Your Design

  1. Will you filter by topic, time, or content?
  2. How will you validate output bag integrity?

5.6 Thinking Exercise

Design a filtering rule for extracting only “interesting” events.

5.7 The Interview Questions They’ll Ask

  1. “What storage formats does rosbag2 support?”
  2. “Why is MCAP useful?”

5.8 Hints in Layers

Hint 1: Use rosbag2_py SequentialReader Hint 2: Preserve metadata Hint 3: Use a streaming reader Process messages one at a time to avoid RAM spikes. Hint 4: Validate with ros2 bag info Confirm storage id and topic counts after filtering.

5.9 Books That Will Help

Topic Book Chapter
Topic Book Chapter
Python Data “Fluent Python” Ch. 9

5.10 Implementation Phases

Phase 1: Foundation (1-2 days)

Goals:

  • Reproduce the baseline example from the original project outline.
  • Validate toolchain, dependencies, and environment variables.

Tasks:

  1. Create the repository and baseline project structure.
  2. Run a minimal example to confirm discovery/data flow.

Checkpoint: You can reproduce the minimal example and collect logs.

Phase 2: Core Functionality (1-2 weeks)

Goals:

  • Implement the full feature set from the requirements.
  • Instrument key metrics and logs.

Tasks:

  1. Implement each component and integrate them.
  2. Add CLI/config flags for core parameters.

Checkpoint: Golden path demo succeeds with deterministic output.

Phase 3: Polish & Edge Cases (3-5 days)

Goals:

  • Handle failure scenarios and document them.
  • Create a short report/README describing results.

Tasks:

  1. Add error handling, timeouts, and validation.
  2. Capture failure demo output and metrics.

Checkpoint: Failure demo yields the expected errors and exit codes.

5.11 Key Implementation Decisions

Decision Options Recommendation Rationale
Transport UDP, shared memory, serial UDP for baseline Simplest to observe and debug
QoS Default, tuned Default then tune Establish baseline before optimization

6. Testing Strategy

6.1 Test Categories

Category Purpose Examples
Unit Tests Validate parsers and helpers Packet decoder, config parser
Integration Tests End-to-end ROS 2 flow Publisher -> Subscriber -> Metrics
Edge Case Tests Failures & mismatches Wrong domain ID, missing config

6.2 Critical Test Cases

  1. Test 1: Baseline message flow works end-to-end.
  2. Test 2: Configuration mismatch produces a clear, actionable error.
  3. Test 3: Performance/latency stays within documented bounds.

6.3 Test Data

Use a fixed dataset or fixed random seed to make metrics reproducible.

7. Common Pitfalls & Debugging

7.1 Frequent Mistakes

Pitfall Symptom Solution
QoS mismatch Discovery works but no data Align policies explicitly
Misconfigured env vars No nodes discovered Print and validate env on startup
Network filtering Intermittent data Check firewall and multicast settings

7.2 Debugging Strategies

  • Start from the graph: confirm discovery before tuning QoS.
  • Capture packets: validate that RTPS traffic appears on expected ports.

7.3 Performance Traps

If throughput is low, check for unnecessary serialization, small history depth, or lack of shared memory.

8. Extensions & Challenges

8.1 Beginner Extensions

  • Add verbose logging and a dry-run mode.
  • Add a simple configuration file parser.

8.2 Intermediate Extensions

  • Add metrics export to CSV or JSON.
  • Add automated regression tests.

8.3 Advanced Extensions

  • Implement cross-vendor compatibility validation.
  • Add chaos testing with randomized loss/latency patterns.

9. Real-World Connections

9.1 Industry Applications

  • Fleet robotics where reliability must be guaranteed under lossy Wi-Fi.
  • Industrial systems that require deterministic startup and clear failure modes.
  • ROS 2 core repositories (rcl, rmw, rosidl)
  • DDS vendors: Fast DDS, Cyclone DDS

9.3 Interview Relevance

  • Explain QoS compatibility and discovery failures.
  • Describe how to debug why nodes discover but do not communicate.

10. Resources

10.1 Essential Reading

  • “Mastering ROS 2 for Robotics Programming” (focus on the sections related to Rosbag2 Architecture)
  • ROS 2 official docs for the specific APIs used in this project

10.2 Video Resources

  • ROS 2 community talks on middleware and DDS
  • Vendor tutorials on discovery and QoS

10.3 Tools & Documentation

  • ROS 2 CLI and rclcpp/rclpy docs
  • Wireshark or tcpdump for network visibility
  • Project 12: Builds prerequisite concepts
  • Project 14: Extends the middleware layer

11. Self-Assessment Checklist

11.1 Understanding

  • I can explain Rosbag2 Architecture without notes
  • I can explain how QoS and discovery interact
  • I understand why the system fails when policies mismatch

11.2 Implementation

  • All functional requirements are met
  • Golden path demo succeeds
  • Failure demo produces expected errors

11.3 Growth

  • I can explain this project in a technical interview
  • I documented lessons learned and configs
  • I can reproduce the results on another machine

12. Submission / Completion Criteria

Minimum Viable Completion:

  • Golden path demo output matches documentation
  • At least one failure scenario is documented
  • Metrics or logs demonstrate correct behavior

Full Completion:

  • All minimum criteria plus:
  • Compatibility verified across at least two QoS settings
  • Results written to a short report

Excellence (Going Above & Beyond):

  • Automated regression tests for discovery/QoS behavior
  • Clear compatibility matrix or benchmark chart