Profile

Introduction

Time-series databases are designed to handle large amounts of data that are time-stamped. They are optimized for storing and querying time-series data, which makes them ideal for use cases such as IoT, financial data, and log data.

Key Characteristics

Structured data
Optimized for time-series data
High write throughput
Efficient storage and retrieval of time-series data
Support for complex queries
Limited support for transactions and joins

CAP Theorem

General CAP theorem handling

Time-series databases typically prioritize availability and partition tolerance over consistency.

Guarantee of consistency

Eventual consistency is typically used in time-series databases.
The database achieves eventual consistency using vector clocks and conflict resolution techniques.

Guarantee of availability

Time-series databases are designed to be highly available.
They achieve availability through techniques such as replication and sharding.

Guarantee of partition tolerance

Time-series databases are designed to be highly partition tolerant.
They achieve partition tolerance through techniques such as replication and sharding.

Usage

Best usage

Time-series databases are best suited for storing and querying time-series data.
They are ideal for use cases like IoT, financial, and log data.

Neutral usage

Time-series databases can also be used for storing and querying non-time-series data.
However, they may not be the best choice for non-time-series data use cases.

Worst usage

Time-series databases are unsuited for use cases requiring complex transactions or join.

System Design Role

Time-series databases are well-suited for systems that require efficient storage and retrieval of time-series data.
They are ideal for systems that require high write throughput and support for complex queries.

Data Model

Time-series databases typically use a non-relational data model.
The data is organized into time series, which are collections of time-stamped data points.
The advantages of the data model include efficient storage and retrieval of time-series data.
The disadvantages of the data model include limited support for transactions and join.

Query Language

Time-series databases typically use a NoSQL query language.
The query language is optimized for querying time-series data.
The advantages of the query language include efficient querying of time-series data.
The disadvantages of the query language include limited support for transactions and join.

Scalability

How to make it performant

Time-series databases can be made performant through techniques such as indexing and compression.

High traffic handling

Time-series databases are well-suited for high-write workloads. They can also handle high-read workloads but may require additional resources.

How to scale it

Time-series databases can be scaled horizontally through techniques such as sharding. Vertical scaling may also be an option but may be limited by hardware constraints.

Usage in distributed systems

Time-series databases can be used in distributed systems. Considerations include data partitioning, replication, and consistency.

Replication

Time-series databases typically use replication to achieve high availability and partition tolerance.
Best practices for replication include using multiple replicas and ensuring consistency across replicas.

In Practice

Best Practices

Best practices for using time-series databases include optimizing queries, using compression, and monitoring performance.

Common Pitfalls

Common pitfalls to avoid when using time-series databases include not optimizing queries, not using compression, and not monitoring performance.

Examples

Prometheus
InfluxDB
TimescaleDB
OpenTSDB.