# How Time Series Databases Work
Introduction to Time Series Databases
Time series databases (TSDBs) are specialized database management systems designed to handle time-stamped data efficiently. Unlike traditional relational databases, TSDBs are optimized for storing, retrieving, and analyzing data points that are indexed by time.
Core Characteristics of Time Series Data
Time series data has several unique characteristics that make it different from other types of data:
- Temporal nature: Each data point is associated with a timestamp
- Append-only: New data is typically added without modifying existing records
- High volume: Time series data often comes in large, continuous streams
- Time-based queries: Most queries focus on time ranges rather than individual records
How Time Series Databases Store Data
Keyword: how time series database work
Specialized Storage Structures
TSDBs use specialized storage structures to optimize for time series data:
- Time-ordered storage: Data is stored in chronological order for efficient time-range queries
- Columnar storage: Many TSDBs use column-oriented storage for better compression
- Data partitioning: Data is often partitioned by time intervals (hourly, daily, etc.)
Compression Techniques
Time series databases employ various compression techniques:
- Delta encoding: Storing differences between consecutive values
- Run-length encoding: Compressing sequences of identical values
- Bit packing: Efficient storage of small integers
Query Processing in Time Series Databases
TSDBs optimize query processing for time-based operations:
- Time-range scans: Efficiently retrieve all data within a specific time window
- Downsampling: Aggregate data at different time resolutions
- Continuous queries: Support for streaming data processing
Popular Time Series Database Architectures
Log-Structured Merge Trees (LSM Trees)
Many modern TSDBs use LSM tree architectures that:
- Buffer writes in memory before flushing to disk
- Periodically merge and compact data files
- Provide high write throughput
In-Memory Architectures
Some TSDBs prioritize speed by keeping data primarily in memory:
- Faster query response times
- Periodic snapshots to disk for durability
- Higher hardware requirements
Use Cases for Time Series Databases
TSDBs are particularly well-suited for:
- Monitoring systems: Tracking metrics from servers, applications, and networks
- IoT applications: Collecting sensor data from devices
- Financial data: Storing market data and trading information
- Industrial telemetry: Recording equipment performance metrics