Zeitreihen

• Timestamping: Every data point associated with a specific time
• Sequential Nature: Data arrives in a continuous, ordered flow
• High Volumes: Generated at high frequencies, leading to massive datasets
• Trends, Patterns, and Anomalies: Focus on identifying temporal dynamics

• Immutability: Typically append-only, rarely updated or deleted

• Beispiele (link)

• Charakteristica (link)

• Datenmodelle (link)

• Charakteristica (link)

• Verarbeitung (link)

• Reguläre/Irreguläre Zeitreihendaten (link)

Beispiele

IOT

• Predictive Maintenance: Analyzing historical sensor data to predict equipment failures
• Smart Homes: Monitoring and controlling appliances
• Industrial Automation: Tracking machine performance in real-time
• Environmental Monitoring: Analyzing air quality, weather patterns, water levels

DevOps and System Monitoring

• Tracking Infrastructure Metrics: CPU, memory, network performance
• Monitoring Application Performance: Latency, throughput, error rates
• Real-Time Alerting for System Anomalies: Notifications based on thresholds or deviations

Financial Markets

• Tracking Stock Prices and Market Trends: Analyzing economic indicators over time
• High-Frequency Trading (HFT): Near-instantaneous response times
• Algorithmic Trading: Automated strategies based on real-time and historical data
• Risk Management: Monitoring financial metrics over time
• Market Analysis: Identifying patterns and trends for investment decisions

Other Use Cases

• Healthcare: Continuous patient vitals monitoring
• Energy Sector: Utility usage management, grid optimization
• Environmental Monitoring: Tracking climate change, weather patterns
• Product Analytics: Tracking user interactions with applications
• Website Traffic Analysis: Understanding user journeys
• Logistics and Asset Tracking: Real-time monitoring of shipments
• Anomaly Detection: Identifying unusual patterns across domains

Kardinalität

• Große Kardinalität (link)

• Speicherverbrauch (link)

Zeitbasierte Analyse

• Tumbling Window (link)

• Hopping Window (link)

• Session window (link)

• Sliding Window (link)

• Snapshot window (link)

Konzepte Zeitreihendatenbanksysteme

• Time as Primary Index: Architecturally designed with timestamp as core organizing principle.Faster writes and efficient time-based queries
• Optimized for Sequential Data: Engineered for continuous, append-only data streams
• Built-in Data Retention Policies: Automated expiration or downsampling of older data
• Time-Aware Data Structures: Time partitioning (chunking) for efficient querying. Specialized compression algorithms

• Specialized Query Languages: Optimized for time-based analysis (bucketing, moving averages)

• Columnar Storage: Stores data by columns, improving analytical query performance and- compression
• Time-Based Partitioning: Divides data into time-based segments (chunks) for efficient- management and querying
• Specialized Indexing: Optimized for time ranges, enabling fast retrieval within temporal- boundaries
• Data Compression: Tailored algorithms to exploit temporal coherence and value patterns.2
• In-Memory Processing: For recent and frequently accessed data, enabling low-latency operations

Zeitreihendatenbanksysteme

• Ranking (link)

InfluxDB

• InfluxDB 3.0 Under the Hood (link)

• Flight, DataFusion, Arrow, and Parquet: Using the FDAP Architecture to build InfluxDB 3.0 (link)

• Apache Arrow introduction (link)

• Apache Parquet File Format (link)

Prometheus

• Docs (link)

• PromQL: A Developer’s Guide to Prometheus Query Language (link)

TimescaleDB

• Timescale architecture for real-time analytics (link)