“VictoriaMetrics Just Works - and Uses Fewer Hardware Resources Compared to Other Tools!”
- Internet
- San Francisco, USA
- 500+ Employees
Grammarly's digital writing assistant supports more than 30 million DAUs and 30,000 teams write more clearly and effectively every day. In building a product that scales across multiple platforms and devices, Grammarly works to empower users whenever and wherever they communicate. Grammarly's values-driven team is growing to support our expanding user base and to continue developing our writing assistant into a truly comprehensive communication partner. With a working model that balances remote work with in-person collaboration at Grammarly's hubs in San Francisco, Kyiv, New York, and Vancouver, the Grammarly team strives to help people around the world connect and be understood. Mission: Improve lives by improving communication.
Main Benefits of Using VictoriaMetrics
10x Cost Savings
Ingestion Types Flexibility
Performance
Easy to Get Started
Responsive VM Developers
Great Support & Docs
Challenge
The maintenance and scaling of our previous on-premises monitoring system was hard and required major engineering time and resources to maintain.
The stability of the previous solution was unreliable..
Our previous system struggled with storing frequently changing metrics (the moderate churn rate was a concern).
The overall costs of the previous solution were too high.
Solution
Ingestion type flexibility (support for Graphite, OpenMetrics, etc.) was definitely a winning feature and important benefit for us.
VictoriaMetrics comes with good documentation and is easy to bootstrap.
The high level of responsiveness of the VictoriaMetrics developers and support team during our research phase and production have made us extremely happy customers.
Delivered 10x cost savings versus our prior monitoring solution.
Why VictoriaMetrics Was Chosen Over Other Solutions
Great On-Premises Solution
Outperformed Competitive Solutions in Benchmarks
Strong POC Results
Direct Access & Great Support by VM Developers
- After trying out SaaS solutions we decided to go with an in-house setup. Out of the various in-house tools we had short-listed, we decided to try VM first during a PoC taking into account publicly available benchmarking with competitive solutions. The PoC results and the VictoriaMetrics developers' help made it an easy decision to move forward with a VictoriaMetrics solution.
Technical Stats
Median memory usage during the last 24h
sum(avg_over_time(process_resident_memory_bytes[24h]))
618 GiB
The average number of cpu cores used during the last 24h
sum(rate(process_cpu_seconds_total[24h]))
~66 CPU cores
The maximum number of active time series during the last 24 hours
sum(max_over_time(vm_cache_entries{type="storage/hour_metric_ids"}[24h]))~120 Mil
Daily time series churn rate
sum(increase(vm_new_timeseries_created_total[24h]))
~74 Mil
The average ingestion rate over the last 24h
sum(rate(vm_rows_inserted_total[24h]))
3.16Mil datapoints/sec
The total number of datapoints
sum(vm_rows{type=~"storage/.+"})57.7 Tri
The total number of entries in inverted index
sum(vm_rows{type="indexdb"}))295 Bil
Data size on disk
sum(vm_data_size_bytes{type=~"storage/.+"})72.8 TiB
Index size on disk:
sum(vm_data_size_bytes{type="indexdb"})7.6 TiB
The average datapoint size on disk
sum(vm_data_size_bytes) / sum(vm_rows)
~1.5 B
The average range query rate over the last 24h
sum(rate(vm_http_requests_total{path=~".*/api/v1/query_range"}[24h]))~1.4 req/s
The average instant query rate over the last 24h
sum(rate(vm_http_requests_total{path=~".*/api/v1/query"}[24h]))~47 req/s
Average range query duration quantiles over the last 24h
max(avg_over_time(vm_request_duration_seconds{path=~".*/api/v1/query_range"}[24h])) by (quantile)1 2.29s 0.500 0.006s 0.900 0.3s 0.970 1.04s 0.990 2.21s
Average instant query duration quantiles over the last 24h
max(avg_over_time(vm_request_duration_seconds{path=~".*/api/v1/query"}[24h])) by (quantile)1 19.8s 0.500 0.007s 0.900 0.2s 0.970 1s 0.990 2.6s