Topic 4: NoSQL – Time-Series Databases

InfluxDB, TimescaleDB, and Time-Series Workloads

Topic Overview

Time-series databases optimize for workloads where data points are indexed by timestamp and queries typically aggregate or filter by time ranges. These systems handle write-heavy ingestion patterns (millions of points per second) with efficient compression algorithms that exploit temporal locality. Unlike general-purpose databases, time-series databases implement time-based partitioning, automatic downsampling, and retention policies that delete old data. InfluxDB uses a NoSQL line protocol optimized for high-throughput writes, while TimescaleDB extends PostgreSQL with hypertables that automatically partition by time. The choice between SQL and NoSQL approaches affects query expressiveness, tooling ecosystem, and operational complexity. Students must evaluate when specialized time-series databases provide necessary performance versus when general-purpose databases with time-series extensions are sufficient.

Student Presentation Assignments

Student 1:Time-Series Data Fundamentals

Required Coverage:

Must formally define time-series data, specifying characteristics (timestamp ordering, high cardinality, append-only writes) and distinguishing from other temporal data
Must compare write-heavy vs read-heavy workloads, analyzing typical access patterns and how they affect database design
Must explain retention policies and downsampling, specifying how they manage data volume and analyzing trade-offs in data loss vs storage costs
Must justify time-series database selection for at least two applications (e.g., IoT, monitoring), explaining why general-purpose databases are inadequate
Must analyze why general-purpose databases struggle with time-series data, specifying indexing, partitioning, and compression challenges
Must explain time-series data modeling (tags, fields, measurements), analyzing design rationale and comparing with relational normalization approaches, demonstrating why normalization breaks down for time-series workloads

Student 2:InfluxDB (NoSQL Approach)

Required Coverage:

Must explain line protocol and data ingestion, specifying format syntax and analyzing performance characteristics
Must explain storage engine and compression, analyzing why time-series data compresses well (temporal locality, value similarity) and how this affects storage design, not merely stating compression ratios
Must explain Flux query language as a query model, analyzing how it differs from SQL in expressing time-series operations and identifying limitations, not providing a syntax tutorial
Must explain retention policies and automatic data management, specifying how they work and analyzing trade-offs
Must analyze scaling and clustering challenges, identifying specific limitations and comparing solutions (InfluxDB Enterprise vs open source)
Must explain InfluxDB architecture components and data flow, specifying how writes and queries are processed

Student 3:TimescaleDB (SQL-Based Approach)

Required Coverage:

Must explain hypertables and chunking, specifying automatic partitioning strategy and how it differs from manual partitioning
Must evaluate SQL compatibility advantages, analyzing how existing tools and ecosystem integration differ from NoSQL approaches
Must compare indexing strategies for time-series (B-tree vs specialized indexes), analyzing performance trade-offs
Must explain continuous aggregates and pre-computed rollups, specifying how they improve query performance and storage trade-offs
Must compare TimescaleDB with InfluxDB, analyzing SQL vs NoSQL trade-offs in query expressiveness, performance, and operational complexity
Must evaluate PostgreSQL extension model, specifying benefits (ecosystem compatibility) and limitations (PostgreSQL constraints)

Student 4:Choosing the Right Time-Series Database

Required Coverage:

Must evaluate SQL vs NoSQL trade-offs, specifying when to choose each approach based on query patterns and tooling requirements
Must analyze performance benchmarks, comparing ingestion rates and query performance with quantitative data where available
Must evaluate cost considerations, quantifying storage, compute, and operational overhead for different deployment scenarios
Must explain integration with dashboards (e.g., Grafana) as a system design constraint, analyzing how database choice affects visualization integration and operational workflows, not listing dashboard features
Must evaluate at least one production case study, analyzing real-world deployment challenges and solutions
Must identify scenarios where time-series databases are not necessary, justifying when general-purpose databases suffice

Presentation Requirements

All presentations must be 17–20 minutes in duration and include the following components:

Problem Context: What problem this technology solves and why traditional databases struggle
Core Concepts: Clear explanation with correct technical terminology
System Details: How it works in practice with concrete examples
Trade-offs: Strengths, limitations, and when it is appropriate vs not appropriate
Real-World Perspective: At least one realistic application scenario and production considerations

Note: Presentations that only summarize definitions, list features, or copy diagrams without interpretation will receive low marks. Each presentation must demonstrate analytical reasoning through comparisons, trade-off analysis, and justification of design decisions. Reading slides verbatim or presenting material that could be satisfied by reading documentation will be penalized.

Report Requirement: In addition to the presentation, each student must submit an individual PDF report. See Seminar Report Requirements for format, content, and submission details.

Evaluation Criteria

Criterion	Weight	Description
Technical Correctness	30%	Accuracy of technical content, correct use of terminology, absence of errors
Depth of Understanding	25%	Goes beyond surface-level definitions, demonstrates system-level comprehension
Clarity and Structure	20%	Logical flow, clear explanations, appropriate use of examples and visuals
Use of Examples and Trade-offs	15%	Concrete examples, discussion of limitations, comparison with alternatives
Slide Quality and Time Management	10%	Professional formatting, appropriate pacing, stays within time limit

Recommended References

Books:

Kleppmann, Martin. Designing Data-Intensive Applications. O'Reilly Media, 2017. (Note: Time-series databases are not directly covered; Chapter 3 on Storage and Retrieval provides relevant background)

Documentation:

Academic / Technical:

Time-series database survey papers and benchmarks
TimescaleDB research papers on hypertables and compression