目录
输入和输出集成概览
Modbus 插件允许您使用各种通信方法从 Modbus 设备收集数据,从而增强您监控和控制工业流程的能力。
此输出插件提供了一种可靠且高效的机制,用于将 Telegraf 收集的指标直接路由到 TimescaleDB。通过利用 PostgreSQL 的强大生态系统以及 TimescaleDB 的时序优化,它支持高性能数据摄取和高级查询功能。
集成详情
Modbus
Modbus 插件通过 Modbus TCP 或 Modbus RTU/ASCII 收集离散输入、线圈、输入寄存器和保持寄存器。
TimescaleDB
TimescaleDB 是一个开源时序数据库,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,是为了响应对稳健、可扩展的解决方案日益增长的需求,该解决方案可以管理海量数据,并具有高插入率和复杂查询。 通过利用 PostgreSQL 熟悉的 SQL 接口并使用专门的时序功能对其进行增强,TimescaleDB 在希望将时序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,同时为时序数据提供优化的性能。
该数据库在需要快速摄取数据点以及对历史时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,例如将数据透明地划分为可管理块的超表和内置的持续聚合。 这些功能可以显着提高查询速度和资源效率。
配置
Modbus
[[inputs.modbus]]
name = "Device"
slave_id = 1
timeout = "1s"
configuration_type = "register"
discrete_inputs = [
{ name = "start", address = [0]},
{ name = "stop", address = [1]},
{ name = "reset", address = [2]},
{ name = "emergency_stop", address = [3]},
]
coils = [
{ name = "motor1_run", address = [0]},
{ name = "motor1_jog", address = [1]},
{ name = "motor1_stop", address = [2]},
]
holding_registers = [
{ name = "power_factor", byte_order = "AB", data_type = "FIXED", scale=0.01, address = [8]},
{ name = "voltage", byte_order = "AB", data_type = "FIXED", scale=0.1, address = [0]},
{ name = "energy", byte_order = "ABCD", data_type = "FIXED", scale=0.001, address = [5,6]},
{ name = "current", byte_order = "ABCD", data_type = "FIXED", scale=0.001, address = [1,2]},
{ name = "frequency", byte_order = "AB", data_type = "UFIXED", scale=0.1, address = [7]},
{ name = "power", byte_order = "ABCD", data_type = "UFIXED", scale=0.1, address = [3,4]},
{ name = "firmware", byte_order = "AB", data_type = "STRING", address = [5, 6, 7, 8, 9, 10, 11, 12]},
]
input_registers = [
{ name = "tank_level", byte_order = "AB", data_type = "INT16", scale=1.0, address = [0]},
{ name = "tank_ph", byte_order = "AB", data_type = "INT16", scale=1.0, address = [1]},
{ name = "pump1_speed", byte_order = "ABCD", data_type = "INT32", scale=1.0, address = [3,4]},
]
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
Modbus
- 基本用法:要从单个设备读取数据,请使用设备名称和 IP 地址配置它,并指定从站 ID 和感兴趣的寄存器。
- 多个请求:您可以通过指定多个 [[inputs.modbus.request]] 部分,在单个配置中定义多个请求以从不同的 Modbus 从站设备获取数据。
- 数据处理:利用缩放功能将原始 Modbus 读数转换为有用的指标,并根据需要调整单位转换。
TimescaleDB
-
实时物联网数据摄取:使用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。 此设置有助于即时分析,帮助组织监控运营效率并对不断变化的条件做出快速响应。
-
云应用程序性能监控:利用插件将分布式云应用程序的详细性能指标馈送到 TimescaleDB。 此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。
-
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。 这种方法允许企业执行趋势分析、生成详细报告并根据存档的时序数据做出数据驱动的决策。
-
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,从而提高系统可靠性和主动维护。
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