Time series data has become one of the fastest growing categories of data in modern systems. Sensors, machines, applications, logs, and user interactions all generate continuous streams of time stamped events. Storing, querying, and analyzing these events efficiently requires a database that can handle high ingestion rates, fast analytical queries, and long term retention.
In this guide, we review the leading time series databases available today. Each option brings its own strengths and architectural trade-offs, so the right choice depends on your use case, data model, and performance requirements.
What Makes a Good Time Series Database
Before reviewing the top solutions, here are the core capabilities that matter most.
High ingestion throughput: Time series workloads often involve millions of events per second. The database must absorb sustained or bursty data streams without backpressure.
Fast analytical queries: Dashboards, anomaly detection, and monitoring systems rely on fast range scans, aggregations, and window functions.
Efficient storage for large datasets: Compression, columnar storage, tiered retention, and time based partitioning help keep storage affordable while maintaining performance.
Flexible schemas: Modern time series data often includes metadata, tags, JSON payloads, geospatial coordinates, or vectors that must be stored together.
Easy integration with analytics and monitoring tools: Support for SQL, Grafana, Python, and BI tools makes it easier for teams to work with the data.
The Top Time Series Databases in 2026
InfluxDB
InfluxDB is one of the most well known time series databases and is widely used for metrics, monitoring, and observability workloads. It is built around the TICK stack and offers Flux, a custom query language designed for time series analysis.
Strengths:
- Mature ecosystem for metrics and monitoring
- Good ingestion performance for medium scale workloads
- Retention policies, downsampling, and built in lifecycle management
- Cloud native managed offering
Limitations:
- Historically relied on InfluxQL and Flux, which increased complexity when working across versions
- SQL support is available in InfluxDB 3 and Cloud, but not evenly across all editions and deployments
- Shifts in storage engines and query layers require careful planning when upgrading or migrating
- Less suited for workloads mixing time series with complex JSON, geospatial, or vector data
TimescaleDB
TimescaleDB extends PostgreSQL with time series capabilities. It uses hypertables to partition data by time and space, and integrates well with the PostgreSQL ecosystem.
Strengths:
- Full SQL support
- Easy to adopt for teams already using PostgreSQL
- Good compression features
- Strong ecosystem and documentation
Limitations:
- Ingestion performance depends heavily on write patterns
- Scaling is limited by PostgreSQL constraints
- Horizontal scale out is more complex compared to distributed databases
- Best suited for smaller clusters and workloads that do not grow rapidly
ClickHouse
ClickHouse is a high performance columnar database that excels at analytical workloads. While not built specifically for time series, it is widely used for logs, events, and metrics due to its strong compression and fast scans.
Strengths:
- Exceptional query speed on very large datasets
- Highly efficient columnar storage with strong compression
- Proven performance at massive scale
- Rich ecosystem for analytics and observability workloads
- Suitable for logs, events, and time series style analytical queries
Limitations:
- Updates and deletes are handled through background mutations, so high frequency row level changes are not ideal
- Best suited for append oriented workloads rather than mixed transactional and analytical patterns
- Cluster operations and tuning require specialized knowledge of storage engines, partitioning, and part management
- Operational complexity grows with scale, especially for teams new to ClickHouse
CrateDB
CrateDB is a distributed time series database designed for real time analytics across high volume and diverse data types. It handles time series, JSON, vectors, full-text search, geospatial data, and relational workloads in a single SQL engine.
Strengths:
- Distributed SQL for high concurrency
- Fast ingestion and millisecond query readiness
- Columnar storage for analytical speed
- Unlimited cardinality and long term retention
- Automatic indexing for filters, aggregations, search, and vector queries
- Unified support for metrics, logs, events, geospatial shapes, and embeddings
- Easy integration with Grafana, Python, and BI tools
- Cloud and self-managed deployment options
Limitations:
- Designed for server side environments, not for in process embedded use as a library
- More capable than metric only monitoring systems, which makes it better suited for mixed analytics workloads rather than very simple dashboarding setups
CrateDB is a strong choice for IoT, industrial analytics, smart mobility, cybersecurity analytics, and AI powered applications that combine time series data with complex search or vector similarity.
QuestDB
QuestDB is a performance focused time series database with a SQL interface and a strong emphasis on ingestion speed for append-only workloads.
Strengths:
- Very fast ingestion and querying
- Simple SQL interface
- Good for financial tick data or real time trading workloads
- Lightweight architecture
Limitations:
- Limited support for complex data types such as JSON or geospatial
- Limited horizontal scaling options
- Less suited for mixed analytics or multi model workloads
VictoriaMetrics
VictoriaMetrics is a time series solution optimized for Prometheus compatible monitoring and observability use cases.
Strengths:
- Efficient at storing metrics
- Drop in replacement for Prometheus
- Good compression
- Designed for monitoring pipelines
Limitations:
- Focused almost entirely on metrics
- Not ideal for logs, events, or diversified time series data
- Limited flexibility compared to general purpose engines
Which Time Series Database Should You Choose
Here is a quick decision guide based on common scenarios.
| Vendor | When this is best |
| InfluxDB | If you need a straightforward metrics and monitoring database for small to medium scale environments. |
| TimescaleDB | If wou want PostgreSQL compatibility and are working with moderate ingestion rates. |
| ClickHouse | If your primary goal is fast analytical queries on very large datasets and you can accept operational complexity. |
| CrateDB | If you need real time ingestion, live analytics, and support for complex multi model data such as JSON, geospatial shapes, logs, events, and vectors. This is especially relevant in industrial IoT, mobility, logistics, and AI driven applications. |
| QuestDB | If you need peak ingestion performance for append only, financial, or trading workloads. |
| VictoriaMetrics | If your workload is strictly Prometheus compatible metrics and monitoring. |
Every effort has been made to provide accurate and up to date information. Because database technologies change frequently, some details may differ from the latest product versions. Always refer to official documentation for the most current information.
Conclusion
There is no single best time series database for all situations. The right choice depends on scale, data types, retention needs, and the types of analytics you plan to run. As data sources multiply and AI driven use cases emerge, flexibility, real time performance, and unified analytics increasingly define the new generation of time series platforms.
If you want to experience how CrateDB handles high volume time series workloads:
- Read more how CrateDB is a solid choice as a time series database for real-time analytics
- Try CrateDB Cloud for free
- Or explore the documentation to get started.
CrateDB is recognized as a leader by G2 in the Time Series Database category, you can check reviews and ranking here.
