# Progress Tracker Service Low-Level Design: Completion Events, Milestone Detection, and Streak Calculation Source: https://www.techinterview.org/post/3233470582/lld-progress-tracker/ Updated: 2026-07-12 · techinterview.org ## What Is a Progress Tracker Service? A progress tracker service ingests completion events from multiple upstream services, detects when users cross configurable milestones, calculates streaks based on activity patterns, and triggers achievement unlocks through a downstream pipeline. It provides a unified view of user progress across a platform, whether that is courses completed, workouts logged, or episodes watched, without requiring each upstream service to implement milestone logic independently. ## Requirements ### Functional Requirements - Ingest completion events (e.g., lesson complete, workout done, episode watched) from upstream services via event stream. - Maintain per-user, per-activity-type cumulative counters and timestamps. - Detect milestone crossings (e.g., 10th, 50th, 100th completion) and emit milestone events. - Calculate daily and weekly streaks: consecutive days or weeks with at least one qualifying completion. - Trigger achievement unlock pipeline when milestone or streak conditions are met. - Expose a progress summary API for profile pages and dashboards. ### Non-Functional Requirements - Ingest up to 100,000 completion events per second at peak. - Streak calculations must be timezone-aware (user local time defines day boundaries). - Progress summary reads under 25 ms P99. - At-least-once event delivery; duplicate events must be idempotent. ## Data Model ### CompletionEvent (immutable log) - `event_id` UUID: idempotency key; unique index prevents duplicate processing. - `user_id`, `activity_type` (e.g., LESSON, WORKOUT, EPISODE). - `content_id`: the specific item completed. - `completed_at` timestamp: used for streak date logic. - `source_service`: which upstream service emitted the event. ### ProgressCounter - `user_id`, `activity_type`: composite primary key. - `total_count` BIGINT: cumulative completions. - `last_milestone_crossed` INTEGER: highest milestone threshold already triggered. - `updated_at` timestamp. ### StreakRecord - `user_id`, `activity_type`, `streak_type` (DAILY, WEEKLY): composite PK. - `current_streak` INTEGER: current consecutive period count. - `longest_streak` INTEGER: all-time record. - `last_activity_date` DATE: in user local timezone. - `streak_broken_at` NULLABLE DATE: when the most recent streak ended. ## Core Algorithms ### Idempotent Event Processing On receipt of a completion event, the processor first attempts to insert the event into the CompletionEvent log using an `INSERT ... ON CONFLICT (event_id) DO NOTHING`. If the insert affects zero rows, the event is a duplicate and processing stops. This guarantees exactly-once counter increments regardless of how many times the event is delivered from the upstream Kafka topic. ### Milestone Detection Milestone thresholds are stored in a sorted configuration list per activity type (e.g., [1, 5, 10, 25, 50, 100, 250, 500]). After incrementing the counter, the processor queries the next uncrossed threshold: the smallest value greater than `last_milestone_crossed`. If `total_count >= next_threshold`, a `MILESTONE_CROSSED` event is emitted to Kafka and `last_milestone_crossed` is updated. Multiple thresholds can be crossed in a single update (e.g., bulk import) — the processor iterates the threshold list until no more are crossed. ### Streak Calculation The streak algorithm converts `completed_at` to the user local date using their stored timezone preference. It then compares `last_activity_date` on the StreakRecord with today local date. If the difference is 0 (same day), no change. If the difference is 1 (consecutive day for DAILY streak), increment `current_streak` and update `last_activity_date`. If the difference is greater than 1, the streak is broken: set `streak_broken_at` to the previous `last_activity_date`, reset `current_streak` to 1, and update `last_activity_date`. Update `longest_streak` if `current_streak` exceeds it. ## API Design - `GET /v1/progress/{user_id}`: returns all ProgressCounters and StreakRecords for the user; used by profile and dashboard pages. - `GET /v1/progress/{user_id}/{activity_type}`: returns counter and streak for a specific activity type. - `POST /v1/events`: internal endpoint for upstream services to submit completion events; accepts batches of up to 100 events. - `GET /v1/milestones/{user_id}`: returns all milestones crossed with timestamps; used by achievement display pages. ## Scalability and Achievement Pipeline ### Ingestion Layer Completion events arrive via a Kafka topic partitioned by user_id, ensuring all events for a single user are processed in order by the same consumer. Each consumer maintains a local write buffer and flushes ProgressCounter and StreakRecord updates to PostgreSQL in micro-batches every 500 ms. This batching reduces database write amplification while keeping progress data fresh within under one second. ### Achievement Unlock Pipeline MILESTONE_CROSSED and STREAK_ACHIEVED events are published to a dedicated Kafka topic consumed by the achievement service. The achievement service evaluates rule trees (e.g., complete 10 lessons AND maintain a 7-day streak) against the current progress state. When all conditions are met, it inserts an AchievementUnlock record and notifies the user via the notification service. Decoupling achievement evaluation from the progress tracker keeps each service focused on a single responsibility and allows achievement rules to be updated without touching the core progress logic. See also: [Netflix Interview Guide 2026: Streaming Architecture, Recommendation Systems, and Engineering Excellence](https://www.techinterview.org/post/3233460274/netflix-interview-guide-2026-streaming-architecture-recommendation-systems-and-engineering-excellence/) See also: [Scale AI Interview Guide 2026: Data Infrastructure, RLHF Pipelines, and ML Engineering](https://www.techinterview.org/post/3233460275/scale-ai-interview-guide-2026-data-infrastructure-rlhf-pipelines-and-ml-engineering/) See also: [Atlassian Interview Guide](https://www.techinterview.org/post/3233459919/atlassian-interview/)