パッケージ: victoria-metrics (1.112.0+ds1-4)

fast, cost-effective and scalable time series database

VictoriaMetrics is a fast, cost-effective and scalable time-series database. It can be used as long-term remote storage for Prometheus.

Prominent features:

 * Supports Prometheus querying API, so it can be used as Prometheus
   drop-in replacement in Grafana. VictoriaMetrics implements MetricsQL
   query language, which is inspired by PromQL.
 * Supports global query view. Multiple Prometheus instances may write
   data into VictoriaMetrics. Later this data may be used in a single query.
 * High performance and good scalability for both inserts and selects.
   Outperforms InfluxDB and TimescaleDB by up to 20x.
 * Uses 10x less RAM than InfluxDB when working with millions of unique time
   series (aka high cardinality).
 * Optimized for time series with high churn rate. Think about
   prometheus-operator metrics from frequent deployments in Kubernetes.
 * High data compression, so up to 70x more data points may be crammed into
   limited storage comparing to TimescaleDB.
 * Optimized for storage with high-latency IO and low IOPS (HDD and network
   storage in AWS, Google Cloud, Microsoft Azure, etc).
 * A single-node VictoriaMetrics may substitute moderately sized clusters
   built with competing solutions such as Thanos, M3DB, Cortex, InfluxDB or
   TimescaleDB.
 * Easy operation:
   - VictoriaMetrics consists of a single small executable without external
     dependencies.
   - All the configuration is done via explicit command-line flags with
     reasonable defaults.
   - All the data is stored in a single directory pointed by
     -storageDataPath flag.
   - Easy and fast backups from instant snapshots to S3 or GCS with
     vmbackup / vmrestore.
   - Data migration between VictoriaMetrics, Prometheus and InfluxDB with
     vmctl.
 * Storage is protectedfrom corruption on unclean shutdown (i.e. OOM,
   hardware reset or kill -9) thanks to the storage architecture.
 * Supports metrics' scraping, ingestion and backfilling (#backfilling)
   via the following protocols:
   - Metrics from Prometheus exporters such as node_exporter.
   - Prometheus remote write API
   - InfluxDB line protocol
   - Graphite plaintext protocol with tags if -graphiteListenAddr is set.
   - OpenTSDB put message if -opentsdbListenAddr is set.
   - HTTP OpenTSDB /api/put requests if -opentsdbHTTPListenAddr is set.
   - /api/v1/import.
 * Ideally works with big amounts of time series data from Kubernetes, IoT
   sensors, connected cars, industrial telemetry, financial data and
   various Enterprise workloads.
 * Has open source cluster version.

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