Data Systems
Time-Series Databases
Time-series data is one of the fastest growing data types. Master the specialized databases, data models, and optimization techniques for handling massive volumes of timestamped data.
- Compression β Specialized algorithms reduce storage by 10-100x
- Retention β Automatic data lifecycle management
- Aggregation β Pre-computed rollups for fast queries
Time-series databases turn mountains of data into actionable insights.
What Is Time-Series Data?
Time-Series Characteristics
| Characteristic | Implication |
|---|---|
| Append-only | Data is written once, never modified |
| Time-ordered | Natural ordering by timestamp |
| High write volume | Millions of points per second |
| Time-bounded queries | Most queries filter by time range |
| Old data less valuable | Aggregation and downsampling needed |
| Real-time ingestion | Low-latency write path critical |
The Time-Series Data Model
Time-Series Compression
Time-series databases use specialized compression algorithms:
| Algorithm | Technique | Typical Ratio |
|---|---|---|
| Delta encoding | Store differences between values | 2-5x |
| Gorilla (Facebook) | XOR with previous value | 10-20x |
| Dictionary encoding | Map repeated values to IDs | 5-10x |
| Run-length encoding | Compress consecutive identical values | Variable |
InfluxDB Architecture
| Component | Purpose |
|---|---|
| TSM Engine | Time-structured merge tree for storage |
| Write Path | WAL + in-memory cache for fast writes |
| Query Engine | Flux query language for processing |
| Continuous Queries | Automatic downsampling and aggregation |
| Retention Policies | Automatic data expiration |
TimescaleDB Architecture
Hypertables
Retention and Downsampling
Query Optimization Patterns
| Pattern | Technique |
|---|---|
| Time-range pruning | Skip chunks outside query range |
| Pre-computed aggregates | Use materialized views for common queries |
| Continuous aggregates | Automatically update summaries on write |
| Partition elimination | Only scan relevant time partitions |
| Columnar storage | Efficient compression for analytics |
Practice Exercises
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Data Modeling: Design the schema for an IoT sensor data system that collects temperature, humidity, and pressure from 10,000 sensors every second. Include retention and downsampling strategies.
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Compression Analysis: Given 10M data points per day with 8-byte values, estimate the storage needed with and without Gorilla compression (assume 12x ratio).
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Query Design: Write a TimescaleDB query to find the top 10 sensors with the highest average temperature over the last 24 hours, bucketed by 5-minute intervals.
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Architecture Decision: Compare InfluxDB and TimescaleDB for a monitoring platform. What are the trade-offs in terms of query language, storage efficiency, and operational complexity?
What to Learn Next
-> Cassandra Deep Dive Cassandra architecture, data modeling, and operational patterns.
-> Kafka Deep Dive Event streaming, partitioning, and exactly-once semantics.
-> Stream Processing Real-time data processing with Flink, Spark Streaming, and Kafka Streams.
-> Data Partitioning Sharding strategies, consistent hashing, and partition keys.
-> Choosing the Right Database Systematic framework for database selection.
-> DynamoDB Deep Dive DynamoDB internals, partitioning, and global tables.