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While TimescaleDB, an extension of PostgreSQL, works perfectly with structured time series data like metrics (e.g., temperature, sensors, voltage, tags, and telemetry), it is not fit for unstructured time series blob-like data such as images, log files, and vibration data (that need to be stored in chunks). Despite being easier to query using easy SQL syntax and Hyper functions, TimescaleDB underperforms when BLOB data more prominent than 10KB is uploaded.
Whether indexing drawbacks or processing a large number of queries per second due to limited scalability when it comes to high ingestion, TimescaleDB degrades in performance for unstructured data where blob data needs to be stored. However, why worry when there is ReductStore, a time series object storage database that writes and reads even large BLOBs sequentially, ensuring the highest ingestion rate?
The current article demonstrates the use cases in which TimescaleDB lacks performance, scalability, and blob storage larger than 10 KB. Moreover, databases like Reductstore are more cost-effective and can process billions of requests per second.
Why Look for Alternatives to TimescaleDB?
TimescaleDB is very fast for time series data. An alternative is needed when dealing with records higher than a few kilobytes (like storing an image). Otherwise, you need to store those records in a blob storage and save the address in TimescaleDB. Also, if you need to selectively replicate historical blob data from one instance to another (streaming data), it’s not that easy with TimescaleDB. TimescaleDB is unsuitable for IoT, Logs, Robotics telemetry, and blob storage and is well-suited only for analytics and SQL-based time series.
Popular TimescaleDB Alternatives
1. InfluxDB
When it comes to high-speed ingestion for real-time time series data with minimal indexing overhead, InfluxDB is a good alternative to TimescaleDB.
2. Prometheus
Due to its higher latency and lack of built-in alerting, Prometheus is a better option than TimescaleDB for monitoring and alerting real-time metrics.
3. Graphite
Graphite is a good alternative to TimescaleDB and even Prometheus when higher visualization is required.
4. ReductStore
Those are small use cases, though. Modern trends include robotics data, images from computer vision applications, industrial data, logs, files, and AI/ML-based data. For such scenarios, ample, high-frequency blob data storage is required, in which ReductStore excels.
It is a better alternative to TimescaleDB for use cases like Logs, IoT, AI/ML-based telemetry, audio, videos, or Industrial IoT data due to High Ingestion Workload with extensive Blob data
Since it has a blob-first architecture, efficient retention, and quick access to time-series blobs without performance bottlenecks, it is a better alternative to TimescaleDB for high-ingestion workloads involving extensive binary data.
Fast Query and Efficiency – Whether indexing overhead or latency is an issue, it outperforms TimescaleDB, as this does not require indexing and only supports appending sequential data reading. Index Bloating is also not an issue, ensuring consistent performance without additional storage overhead.
High Scalability for High Ingestion Workload – ReductStore is more scalable for high ingestion workloads, as it supports sequential writing and does not suffer from indexing slowdowns like TimescaleDB.
More Efficient Support for IoT, logs, AI/ML-based telemetry, and Time-Series Blob Storage – There is no denying that ReductStore is more efficient for IoT, logs, AI/ML-based telemetry, and other forms of time-series blob storage.
TimescaleDB is suitable for SQL-based time-series data analytics, and ReductStore is best for storing, retrieving, and analyzing large BLOB time-series object data with high ingestion rates.
Conclusion
While TimescaleDB is well-suited for SQL-based time series data and analytics, it fails for high ingestion data involving large binary data. It also has lower query speed and efficiency for such use cases and is less suitable for monitoring and alerting real-time metrics and advanced visualization. Time-series databases like InfluxDB, Graphite, and Prometheus are better alternatives in such scenarios.
However, modern data trends increasingly demand databases that support Logs, IoT, AI/ML-based telemetry, and other forms of Blob data, essential for applications like autonomous vehicles, IoT-based data, or large-scale image processing in ML workflows. ReductStore offers support for such high-ingestion time-series blob data.