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### Describe Your Changes fsync() ensures that the data is written to disk. In production this is needed for data durability. However, during the development, when the unit tests are run, this level of durability is not needed. Therefore fsync() can be disabled which will makes test runs two times faster. The disabling is done by setting the `DISABLE_FSYNC_FOR_TESTING` environment variable. The valid values for this variable are the same as the values of the arg of `go doc strconv.ParseBool`: ``` 1, t, T, TRUE, true, True, 0, f, F, FALSE, false, False. ``` Any other value means `false`. The variable is set for all test build targets. Compare running times: Build Target | DISABLE_FSYNC_FOR_TESTING=0 | DISABLE_FSYNC_FOR_TESTING=1 ----------------- | ------------------------------------------------ | ------------------------------------------------- make test | 1m5s | 0m22s make test-race | 3m1s | 1m42s make test-pure | 1m7s | 0m20s make test-full | 1m21s | 0m32s make test-full-386 | 1m42s | 0m36s When running tests for a given package, fsync can be disabled as follows: ```shell DISABLE_FSYNC_FOR_TESTING=1 go test ./lib/storage ``` Disabling fsync() is intended for testing purposes only and the name of the variables reflects that. What could also have been done but haven't: - lib/filestream/filestream.go: `Writer.MustFlush()` also uses f.Sync() but nothing has been done to it, because the Writer.MustFlush() is not used anywhere in the VM codebase. A side question: what is the general policy for the unused code? - lib/filestream/filestream.go: Writer.Write() calls `adviceDontNeed()` which calls unix.Fdatasync(). Disabling it could potentially improve running time, but running tests with this code disabled has shown otherwise. ### Checklist The following checks are **mandatory**: - [ x] My change adheres [VictoriaMetrics contributing guidelines](https://docs.victoriametrics.com/contributing/). --------- Signed-off-by: Artem Fetishev <wwctrsrx@gmail.com>
VictoriaMetrics
VictoriaMetrics is a fast, cost-saving, and scalable solution for monitoring and managing time series data. It delivers high performance and reliability, making it an ideal choice for businesses of all sizes.
Here are some resources and information about VictoriaMetrics:
- Documentation: docs.victoriametrics.com
- Case studies: Grammarly, Roblox, Wix,....
- Available: Binary releases, Docker images, Source code
- Deployment types: Single-node version, Cluster version, and Enterprise version
- Changelog: CHANGELOG, and How to upgrade
- Community: Slack, Twitter, LinkedIn, YouTube
Yes, we open-source both the single-node VictoriaMetrics and the cluster version.
Prominent features
VictoriaMetrics is optimized for timeseries data, even when old time series are constantly replaced by new ones at a high rate, it offers a lot of features:
- Long-term storage for Prometheus or as a drop-in replacement for Prometheus and Graphite in Grafana.
- Powerful stream aggregation: Can be used as a StatsD alternative.
- Ideal for big data: Works well with large amounts of time series data from APM, Kubernetes, IoT sensors, connected cars, industrial telemetry, financial data and various Enterprise workloads.
- Query language: Supports both PromQL and the more performant MetricsQL.
- Easy to setup: No dependencies, single small binary, configuration through command-line flags, but the default is also fine-tuned; backup and restore with instant snapshots.
- Global query view: Multiple Prometheus instances or any other data sources may ingest data into VictoriaMetrics and queried via a single query.
- Various Protocols: Support metric scraping, ingestion and backfilling in various protocol.
- Prometheus exporters, Prometheus remote write API, Prometheus exposition format.
- InfluxDB line protocol over HTTP, TCP and UDP.
- Graphite plaintext protocol with tags.
- OpenTSDB put message.
- HTTP OpenTSDB /api/put requests.
- JSON line format.
- Arbitrary CSV data.
- Native binary format.
- DataDog agent or DogStatsD.
- NewRelic infrastructure agent.
- OpenTelemetry metrics format.
- NFS-based storages: Supports storing data on NFS-based storages such as Amazon EFS, Google Filestore.
- And many other features such as metrics relabeling, cardinality limiter, etc.
Enterprise version
In addition, the Enterprise version includes extra features:
- Anomaly detection: Automation and simplification of your alerting rules, covering complex anomalies found in metrics data.
- Backup automation: Automates regular backup procedures.
- Multiple retentions: Reducing storage costs by specifying different retentions for different datasets.
- Downsampling: Reducing storage costs and increasing performance for queries over historical data.
- Stable releases with long-term support lines (LTS).
- Comprehensive support: First-class consulting, feature requests and technical support provided by the core VictoriaMetrics dev team.
- Many other features, which you can read about on the Enterprise page.
Contact us if you need enterprise support for VictoriaMetrics. Or you can request a free trial license here, downloaded Enterprise binaries are available at Github Releases.
We strictly apply security measures in everything we do. VictoriaMetrics has achieved security certifications for Database Software Development and Software-Based Monitoring Services. See Security page for more details.
Benchmarks
Some good benchmarks VictoriaMetrics achieved:
- Minimal memory footprint: handling millions of unique timeseries with 10x less RAM than InfluxDB, up to 7x less RAM than Prometheus, Thanos or Cortex.
- Highly scalable and performance for data ingestion and querying, 20x outperforms InfluxDB and TimescaleDB.
- High data compression: 70x more data points may be stored into limited storage than TimescaleDB, 7x less storage space is required than Prometheus, Thanos or Cortex.
- Reducing storage costs: 10x more effective than Graphite according to the Grammarly case study.
- A single-node VictoriaMetrics can replace medium-sized clusters built with competing solutions such as Thanos, M3DB, Cortex, InfluxDB or TimescaleDB. See VictoriaMetrics vs Thanos, Measuring vertical scalability, Remote write storage wars - PromCon 2019.
- Optimized for storage: Works well with high-latency IO and low IOPS (HDD and network storage in AWS, Google Cloud, Microsoft Azure, etc.).
Community and contributions
Feel free asking any questions regarding VictoriaMetrics:
If you like VictoriaMetrics and want to contribute, then please read these docs.
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