What is opentsdb?

OpenTSDB (Open Time Series Database) is a free, open‑source system that stores, indexes, and serves large amounts of time‑stamped data-think measurements that are recorded over time, like CPU usage, temperature, or website traffic. It sits on top of HBase (a NoSQL database) and uses the Hadoop ecosystem to handle massive data volumes efficiently.

Let's break it down

  • Time series data: A series of data points, each attached to a specific timestamp (e.g., “CPU 70% at 12:00 PM”).
  • Metric: The name of what you’re measuring (e.g., “cpu.utilization”).
  • Tag: Key‑value pairs that add context (e.g., host=web01, region=us‑east).
  • HBase backend: OpenTSDB writes the data into HBase tables, which provide scalable storage across many servers.
  • API/CLI: You can insert data and query it via HTTP REST calls, a command‑line tool, or client libraries in languages like Java, Python, or Go.
  • Visualization: Results are often displayed with Grafana, Kibana, or other dashboards.

Why does it matter?

Because modern applications generate billions of data points every day, you need a system that can keep them all, retrieve them quickly, and let you analyze trends over days, months, or years. OpenTSDB gives you:

  • Scalability: Handles petabytes of data without a single point of failure.
  • Speed: Fast reads for real‑time monitoring and historical analysis.
  • Flexibility: Tags let you slice and dice data in many ways without redesigning the schema.
  • Cost‑effectiveness: Built on open‑source components, so you avoid expensive proprietary licenses.

Where is it used?

  • Infrastructure monitoring: Data centers track CPU, memory, network latency, power usage, etc.
  • IoT platforms: Sensors report temperature, humidity, or motion at regular intervals.
  • Financial services: Stock tick data, transaction volumes, and risk metrics are stored as time series.
  • Telecommunications: Call quality, bandwidth, and device statistics are logged continuously.
  • Gaming: Player counts, latency, and in‑game events are recorded for analytics.

Good things about it

  • Highly scalable: Leverages HBase’s distributed architecture.
  • Open source: Free to use, modify, and integrate with other tools.
  • Rich query language: Supports aggregations, down‑sampling, and complex filters.
  • Strong community: Active contributors, documentation, and plugins.
  • Tag‑centric model: Makes ad‑hoc analysis easy without schema changes.

Not-so-good things

  • Operational complexity: Requires a working HBase/Hadoop cluster, which can be hard to set up and maintain.
  • Latency for writes: HBase’s write path can add a few milliseconds, which may be too slow for ultra‑low‑latency use cases.
  • Limited built‑in visualization: You need external tools (Grafana, etc.) to view data nicely.
  • Steep learning curve: Understanding HBase, region servers, and the OpenTSDB data model takes time.
  • Resource intensive: Large clusters consume significant CPU, memory, and storage, leading to higher infrastructure costs if not tuned properly.