I started with the ingestion layer and worked forward, which in hindsight was the wrong order.
Start by clarifying requirements and scale (e.g., number of services, metrics volume, retention, query latency). Then outline a high-level architecture covering collection, ingestion, storage, querying, and alerting, and dive into trade-offs for each component, especially for ML-specific needs like model performance monitoring.
Pro tip: Emphasize how the system supports ML-specific metrics (e.g., model drift, prediction latency) and how you'd handle high-cardinality dimensions without exploding costs—this shows you understand both systems and ML production challenges.
Ask about the number of services, metrics per second, retention period, query patterns, and latency requirements. Also clarify if ML model metrics (e.g., accuracy, drift) need special handling.
Sketch the end-to-end flow: metric sources (services, ML models) -> collection agents -> ingestion pipeline -> storage -> query/visualization -> alerting. Mention key components like Kafka, time-series databases, and query engines.
For each component, discuss design choices and trade-offs: pull vs push collection, stream processing for aggregation, storage options (e.g., TSDB vs data lake), and query optimization (e.g., downsampling, indexing).
Explain how the system scales horizontally (e.g., sharding, partitioning) and ensures reliability (replication, fault tolerance, backpressure). Address how to handle spikes and avoid data loss.
Highlight how ML metrics are integrated: monitoring model performance, data drift, and prediction latency. Discuss alerting on anomalies and integration with ML pipelines for retraining triggers.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Start by defining push and pull models and their typical implementations in ML infrastructure. Then systematically compare them across the four dimensions: reliability, backpressure, service discovery, and failure isolation, using concrete examples. Finally, discuss when to choose each model based on system requirements and constraints.
Pro tip: Emphasize that the choice often depends on the specific use case and existing infrastructure; a hybrid approach can leverage the strengths of both. Also, relate the discussion to ML-specific metrics like model performance and data drift, showing you understand the domain.
Briefly explain that in push, metrics are sent from the source to the collector, while in pull, the collector fetches metrics from the source. Mention common examples like Prometheus (pull) and StatsD (push).
For each dimension (reliability, backpressure, service discovery, failure isolation), describe how push and pull handle it, highlighting tradeoffs. For example, pull provides natural backpressure, while push may require buffering.
Relate the comparison to ML metrics: training metrics (e.g., loss, accuracy) are often pushed from distributed workers, while serving metrics (e.g., latency, QPS) are often pulled from model servers. Mention challenges like high cardinality and dynamic scaling.
Give guidelines on when to choose push vs pull: push for short-lived jobs, serverless, or when the collector cannot reach the source; pull for long-running services, when you need centralized control, or when service discovery is available.
Summarize that there is no one-size-fits-all; often a hybrid model (e.g., push for events, pull for state) works best. Mention that the choice impacts reliability, scalability, and operational complexity.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Blanked for a second on the tradeoffs between doing aggregation early vs late.
Start by clarifying the use case and requirements (latency, accuracy, cost, data volume) before recommending strategies. Then propose a hybrid approach that applies aggregation at multiple layers—client, agent, stream, and storage—each optimized for its role, and justify with trade-offs.
Pro tip: Emphasize that aggregation is not just about reducing data volume but also about enabling real-time insights and reducing downstream compute; mention LinkedIn's specific systems like Kafka, Samza, and Venice to show domain awareness.
Ask about the specific use case: Is it for real-time monitoring, offline analytics, or ML feature generation? Determine latency, accuracy, and cost constraints.
Identify where aggregation can occur: client (pre-aggregation), agent (local rollups), stream processing (windowed aggregations), and storage (materialized views, rollup tables).
For each layer, suggest specific techniques: client-side sampling, agent-side batching, stream processing with windows, and storage-side pre-aggregation.
Discuss trade-offs: client-side reduces network but may lose fidelity; stream processing adds latency but enables real-time; storage-side is efficient for repeated queries but adds storage cost.
Conclude with a balanced recommendation, e.g., lightweight client aggregation, stream processing for real-time metrics, and storage rollups for historical analysis.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Start by clarifying the requirements and constraints of the time-series database, such as query patterns, write throughput, and data volume. Then, systematically address each challenge (high-cardinality labels, downsampling, late/out-of-order data, retention, backfill) by discussing trade-offs and proposing solutions that balance performance, cost, and accuracy. Finally, tie your answer back to LinkedIn's scale and ML use cases, emphasizing practical experience and lessons learned.
Pro tip: Demonstrate awareness of the operational impact: for example, mention how high-cardinality labels can explode index size and query latency, and propose concrete mitigation like label pruning or using a TSDB with native support for high cardinality. Also, highlight that late data handling often requires a trade-off between accuracy and latency, and suggest using watermarks or allowed lateness in stream processing.
Ask about the expected data volume, query patterns (e.g., real-time vs. analytical), write rate, and consistency requirements. This will guide the choice of techniques and trade-offs.
Discuss strategies like limiting label cardinality, using separate indexes or inverted indexes, and choosing a TSDB that handles high cardinality efficiently (e.g., Prometheus with relabeling, or specialized stores like M3DB).
Explain how to implement downsampling (e.g., rollup tables, continuous queries) to reduce storage and improve query performance for older data. Describe retention policies (e.g., time-based deletion, tiered storage) to manage data lifecycle.
Propose mechanisms like watermarks, allowed lateness, and idempotent writes to handle late data. Discuss trade-offs between immediate ingestion and batch correction, and how to ensure correctness.
Outline a backfill strategy: use batch processing to reprocess historical data, ensure idempotency, and coordinate with downsampling and retention policies to avoid inconsistencies.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Honestly the part I was least prepared for.
Start by clarifying the system's scale, read/write patterns, and ML workload characteristics, then propose a capacity plan that balances sharding, replication, and multi-tenant isolation. Walk through trade-offs like shard key selection, replication factor, and isolation models, and tie them to LinkedIn's specific needs such as low-latency feature serving and model training.
Pro tip: Emphasize that capacity planning is iterative: start with a simple model, monitor key metrics, and adjust sharding and replication as data grows. Also, highlight the importance of tenant isolation for compliance and noisy neighbor prevention, especially in a multi-tenant ML platform.
Ask about data volume, query per second (QPS), latency SLAs, and tenant count to understand the capacity needs. Identify ML-specific requirements like feature freshness and training data size.
Choose a shard key that distributes load evenly and supports common queries, such as tenant ID or user ID. Discuss trade-offs between hash-based and range-based sharding, and how to handle hotspots.
Determine replication factor based on consistency needs and failure tolerance. Explain how replication supports read scaling and disaster recovery, and mention trade-offs like increased write latency and storage cost.
Propose isolation models (e.g., shared database with tenant ID, schema-per-tenant, database-per-tenant) and justify based on security, compliance, and performance. Discuss how isolation interacts with sharding and replication.
Cover how capacity planning impacts feature serving, model training, and online/offline consistency. Discuss trade-offs between cost, performance, and complexity, and suggest monitoring and auto-scaling.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Frame the problem as ensuring the reliability and trustworthiness of the monitoring system itself, covering both its components and the ML models it monitors. Discuss testing strategies (unit, integration, end-to-end, chaos) and monitoring techniques (meta-monitoring, health checks, anomaly detection on monitoring data). Emphasize proactive detection of failures and drift in the monitoring pipeline.
Pro tip: Highlight the importance of monitoring the monitoring system's data quality and freshness, as stale or incorrect monitoring data can lead to false confidence or missed incidents. Also, mention that you would apply the same rigor to monitoring the monitoring system as you would to production ML models, including canary deployments and A/B tests for changes.
Map out all parts of the monitoring system (data collection, processing, storage, alerting, dashboards) and enumerate potential failure modes such as data loss, latency, false alerts, and silent failures.
Implement unit tests for individual components, integration tests for data flow, end-to-end tests simulating real incidents, and chaos engineering to test resilience under failures.
Set up health checks, heartbeats, and synthetic probes to continuously verify that the monitoring system is operational and producing expected outputs. Monitor key metrics like data freshness, completeness, and alert delivery latency.
Apply statistical and ML-based anomaly detection on the monitoring system's own metrics to catch subtle issues like gradual degradation or concept drift in the monitored models.
Define escalation paths for when the monitoring system fails, and regularly review incidents to improve tests and monitoring coverage. Use canary deployments for changes to the monitoring system.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.