![]() ![]() It can store data on NFS-based storages such as Amazon EFS and Google Filestore.It ideally works with big amounts of time series data from APM, Kubernetes, IoT sensors, connected cars, industrial telemetry, financial data and various Enterprise workloads.It can deal with high cardinality issues and high churn rate issues via series limiter.It supports powerful stream aggregation, which can be used as a statsd alternative.InfluxDB line protocol over HTTP, TCP and UDP.Metrics scraping from Prometheus exporters.It supports metrics' scraping, ingestion and backfilling via the following protocols:.OOM, hardware reset or kill -9) thanks to the storage architecture. It protects the storage from data corruption on unclean shutdown (i.e.See vertical scalability benchmarks, comparing Thanos to VictoriaMetrics cluster and Remote Write Storage Wars talk from PromCon 2019. A single-node VictoriaMetrics may substitute moderately sized clusters built with competing solutions such as Thanos, M3DB, Cortex, InfluxDB or TimescaleDB.See disk IO graphs from these benchmarks. ![]() It is optimized for storage with high-latency IO and low IOPS (HDD and network storage in AWS, Google Cloud, Microsoft Azure, etc).It provides high data compression, so up to 70x more data points may be stored into limited storage comparing to TimescaleDB according to these benchmarks and up to 7x less storage space is required compared to Prometheus, Thanos or Cortex according to this benchmark.It is optimized for time series with high churn rate. ![]()
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