目录
输入和输出集成概述
Docker 输入插件允许您使用 Docker Engine API 从 Docker 容器收集指标,从而增强容器化应用程序的可视性和监控。
此输出插件为将 Telegraf 收集的指标直接路由到 TimescaleDB 提供了可靠且高效的机制。通过利用 PostgreSQL 强大的生态系统以及 TimescaleDB 的时间序列优化,它支持高性能数据摄取和高级查询功能。
集成详情
Docker
Telegraf 的 Docker 输入插件从 Docker Engine API 收集有价值的指标,从而深入了解正在运行的容器。此插件利用官方 Docker 客户端与 Engine API 接口,允许用户监控各种容器状态、资源分配和性能指标。该插件具有按名称和状态过滤容器的选项,以及可自定义的标签和标签,支持在各种环境中(无论是在本地系统上还是在 Kubernetes 等编排平台中)灵活监控容器化应用程序。此外,它还通过要求访问 Docker 守护程序的权限来解决安全问题,并强调在容器化环境中部署时进行正确的配置。
TimescaleDB
TimescaleDB 是一个开源时间序列数据库,作为 PostgreSQL 的扩展构建,旨在高效处理大规模、面向时间的数据。TimescaleDB 于 2017 年推出,是为了响应对能够管理海量数据、具有高插入速率和复杂查询的强大、可扩展解决方案日益增长的需求。通过利用 PostgreSQL 熟悉的 SQL 接口并使用专门的时间序列功能对其进行增强,TimescaleDB 在希望将时间序列功能集成到现有关系数据库中的开发人员中迅速普及。它的混合方法允许用户受益于 PostgreSQL 的灵活性、可靠性和生态系统,同时为时间序列数据提供优化的性能。
该数据库在需要快速摄取数据点并结合对历史周期进行复杂分析查询的环境中尤其有效。TimescaleDB 具有许多创新功能,例如将数据透明地分区为可管理块的超表和内置的连续聚合。这些功能可以显着提高查询速度和资源效率。
配置
Docker
[[inputs.docker]]
## Docker Endpoint
## To use TCP, set endpoint = "tcp://[ip]:[port]"
## To use environment variables (ie, docker-machine), set endpoint = "ENV"
endpoint = "unix:///var/run/docker.sock"
## Set to true to collect Swarm metrics(desired_replicas, running_replicas)
## Note: configure this in one of the manager nodes in a Swarm cluster.
## configuring in multiple Swarm managers results in duplication of metrics.
gather_services = false
## Only collect metrics for these containers. Values will be appended to
## container_name_include.
## Deprecated (1.4.0), use container_name_include
container_names = []
## Set the source tag for the metrics to the container ID hostname, eg first 12 chars
source_tag = false
## Containers to include and exclude. Collect all if empty. Globs accepted.
container_name_include = []
container_name_exclude = []
## Container states to include and exclude. Globs accepted.
## When empty only containers in the "running" state will be captured.
# container_state_include = []
# container_state_exclude = []
## Objects to include for disk usage query
## Allowed values are "container", "image", "volume"
## When empty disk usage is excluded
storage_objects = []
## Timeout for docker list, info, and stats commands
timeout = "5s"
## Whether to report for each container per-device blkio (8:0, 8:1...),
## network (eth0, eth1, ...) and cpu (cpu0, cpu1, ...) stats or not.
## Usage of this setting is discouraged since it will be deprecated in favor of 'perdevice_include'.
## Default value is 'true' for backwards compatibility, please set it to 'false' so that 'perdevice_include' setting
## is honored.
perdevice = true
## Specifies for which classes a per-device metric should be issued
## Possible values are 'cpu' (cpu0, cpu1, ...), 'blkio' (8:0, 8:1, ...) and 'network' (eth0, eth1, ...)
## Please note that this setting has no effect if 'perdevice' is set to 'true'
# perdevice_include = ["cpu"]
## Whether to report for each container total blkio and network stats or not.
## Usage of this setting is discouraged since it will be deprecated in favor of 'total_include'.
## Default value is 'false' for backwards compatibility, please set it to 'true' so that 'total_include' setting
## is honored.
total = false
## Specifies for which classes a total metric should be issued. Total is an aggregated of the 'perdevice' values.
## Possible values are 'cpu', 'blkio' and 'network'
## Total 'cpu' is reported directly by Docker daemon, and 'network' and 'blkio' totals are aggregated by this plugin.
## Please note that this setting has no effect if 'total' is set to 'false'
# total_include = ["cpu", "blkio", "network"]
## docker labels to include and exclude as tags. Globs accepted.
## Note that an empty array for both will include all labels as tags
docker_label_include = []
docker_label_exclude = []
## Which environment variables should we use as a tag
tag_env = ["JAVA_HOME", "HEAP_SIZE"]
## Optional TLS Config
# tls_ca = "/etc/telegraf/ca.pem"
# tls_cert = "/etc/telegraf/cert.pem"
# tls_key = "/etc/telegraf/key.pem"
## Use TLS but skip chain & host verification
# insecure_skip_verify = false
TimescaleDB
# Publishes metrics to a TimescaleDB database
[[outputs.postgresql]]
## Specify connection address via the standard libpq connection string:
## host=... user=... password=... sslmode=... dbname=...
## Or a URL:
## postgres://[user[:password]]@localhost[/dbname]?sslmode=[disable|verify-ca|verify-full]
## See https://postgresql.ac.cn/docs/current/libpq-connect.html#LIBPQ-CONNSTRING
##
## All connection parameters are optional. Environment vars are also supported.
## e.g. PGPASSWORD, PGHOST, PGUSER, PGDATABASE
## All supported vars can be found here:
## https://postgresql.ac.cn/docs/current/libpq-envars.html
##
## Non-standard parameters:
## pool_max_conns (default: 1) - Maximum size of connection pool for parallel (per-batch per-table) inserts.
## pool_min_conns (default: 0) - Minimum size of connection pool.
## pool_max_conn_lifetime (default: 0s) - Maximum connection age before closing.
## pool_max_conn_idle_time (default: 0s) - Maximum idle time of a connection before closing.
## pool_health_check_period (default: 0s) - Duration between health checks on idle connections.
# connection = ""
## Postgres schema to use.
# schema = "public"
## Store tags as foreign keys in the metrics table. Default is false.
# tags_as_foreign_keys = false
## Suffix to append to table name (measurement name) for the foreign tag table.
# tag_table_suffix = "_tag"
## Deny inserting metrics if the foreign tag can't be inserted.
# foreign_tag_constraint = false
## Store all tags as a JSONB object in a single 'tags' column.
# tags_as_jsonb = false
## Store all fields as a JSONB object in a single 'fields' column.
# fields_as_jsonb = false
## Name of the timestamp column
## NOTE: Some tools (e.g. Grafana) require the default name so be careful!
# timestamp_column_name = "time"
## Type of the timestamp column
## Currently, "timestamp without time zone" and "timestamp with time zone"
## are supported
# timestamp_column_type = "timestamp without time zone"
## Templated statements to execute when creating a new table.
# create_templates = [
# '''CREATE TABLE {{ .table }} ({{ .columns }})''',
# ]
## Templated statements to execute when adding columns to a table.
## Set to an empty list to disable. Points containing tags for which there is
## no column will be skipped. Points containing fields for which there is no
## column will have the field omitted.
# add_column_templates = [
# '''ALTER TABLE {{ .table }} ADD COLUMN IF NOT EXISTS {{ .columns|join ", ADD COLUMN IF NOT EXISTS " }}''',
# ]
## Templated statements to execute when creating a new tag table.
# tag_table_create_templates = [
# '''CREATE TABLE {{ .table }} ({{ .columns }}, PRIMARY KEY (tag_id))''',
# ]
## Templated statements to execute when adding columns to a tag table.
## Set to an empty list to disable. Points containing tags for which there is
## no column will be skipped.
# tag_table_add_column_templates = [
# '''ALTER TABLE {{ .table }} ADD COLUMN IF NOT EXISTS {{ .columns|join ", ADD COLUMN IF NOT EXISTS " }}''',
# ]
## The postgres data type to use for storing unsigned 64-bit integer values
## (Postgres does not have a native unsigned 64-bit integer type).
## The value can be one of:
## numeric - Uses the PostgreSQL "numeric" data type.
## uint8 - Requires pguint extension (https://github.com/petere/pguint)
# uint64_type = "numeric"
## When using pool_max_conns > 1, and a temporary error occurs, the query is
## retried with an incremental backoff. This controls the maximum duration.
# retry_max_backoff = "15s"
## Approximate number of tag IDs to store in in-memory cache (when using
## tags_as_foreign_keys). This is an optimization to skip inserting known
## tag IDs. Each entry consumes approximately 34 bytes of memory.
# tag_cache_size = 100000
## Cut column names at the given length to not exceed PostgreSQL's
## 'identifier length' limit (default: no limit)
## (see https://postgresql.ac.cn/docs/current/limits.html)
## Be careful to not create duplicate column names!
# column_name_length_limit = 0
## Enable & set the log level for the Postgres driver.
# log_level = "warn" # trace, debug, info, warn, error, none
输入和输出集成示例
Docker
-
监控容器化应用程序的性能:使用 Docker 输入插件来跟踪 Docker 容器中运行的应用程序的 CPU、内存、磁盘 I/O 和网络活动。通过收集这些指标,DevOps 团队可以主动管理资源分配、排除性能瓶颈并确保不同环境中的最佳应用程序性能。
-
与 Kubernetes 集成:利用此插件收集由 Kubernetes 编排的 Docker 容器的指标。通过滤除不必要的 Kubernetes 标签并专注于关键指标,团队可以简化其监控解决方案并创建仪表板,以深入了解 Kubernetes 集群中运行的微服务的整体健康状况。
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容量规划和资源优化:使用 Docker 输入插件收集的指标来执行 Docker 部署的容量规划。分析使用模式有助于识别未充分利用的资源和过度配置的容器,从而指导根据实际使用趋势进行向上或向下扩展的决策。
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容器异常的自动警报:根据通过 Docker 插件收集的指标设置警报规则,以通知团队资源使用率异常飙升或服务中断。这种主动监控方法有助于维护服务可靠性并优化容器化应用程序的性能。
TimescaleDB
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实时物联网数据摄取:使用该插件实时收集和存储来自数千个物联网设备的传感器数据。此设置有助于立即分析,帮助组织监控运营效率并快速响应不断变化的条件。
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云应用程序性能监控:利用该插件将来自分布式云应用程序的详细性能指标馈送到 TimescaleDB。这种集成支持实时仪表板和警报,使团队能够快速识别和缓解性能瓶颈。
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历史数据分析和报告:实施一个系统,将长期指标存储在 TimescaleDB 中,以进行全面的历史分析。这种方法使企业能够执行趋势分析、生成详细报告并根据存档的时间序列数据做出数据驱动的决策。
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自适应警报和异常检测:将插件与自动异常检测工作流程集成。通过将指标持续流式传输到 TimescaleDB,机器学习模型可以分析数据模式并在发生异常时触发警报,从而增强系统可靠性和主动维护。
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