OPC UA 和 Clickhouse 集成

由 InfluxData 构建的开源数据连接器 Telegraf 提供支持,实现强大的性能和轻松集成。

info

对于大规模实时查询,这不是推荐的配置。为了实现查询和压缩优化、高速摄取和高可用性,您可能需要考虑 OPC UA 和 InfluxDB

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#1

时间序列数据库
来源:DB Engines

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2,800+

贡献者

目录

强大的性能,无限的扩展能力

收集、组织和处理海量高速数据。当您将任何数据视为时间序列数据时,它会更有价值。InfluxDB 是排名第一的时间序列平台,旨在通过 Telegraf 进行扩展。

查看入门方法

输入和输出集成概览

OPC UA 插件提供了一个用于从 OPC UA 服务器设备检索数据的接口,从而促进有效的数据收集和监控。

Telegraf 的 SQL 插件使用简单的表模式和动态列生成将收集的指标发送到 SQL 数据库。当配置为 ClickHouse 时,它会调整 DSN 格式和类型转换设置,以确保无缝数据集成。

集成详细信息

OPC UA

OPC UA 插件从使用 OPC UA 协议通信的设备检索数据,使您可以收集和监控来自 OPC UA 服务器的数据。

Clickhouse

Telegraf 的 SQL 插件旨在通过基于传入指标动态创建表和列,将指标数据写入 SQL 数据库。当配置为 ClickHouse 时,它使用 clickhouse-go v1.5.4 驱动程序,该驱动程序采用独特的 DSN 格式和一组专门的类型转换规则,将 Telegraf 的数据类型直接映射到 ClickHouse 的原生类型。这种方法确保了在高吞吐量环境中的最佳存储和检索性能,使其非常适合实时分析和大规模数据仓库。动态模式创建和精确的类型映射实现了详细的时间序列数据日志记录,这对于监控现代分布式系统至关重要。

配置

OPC UA


[[inputs.opcua]]
  ## Metric name
  # name = "opcua"
  #
  ## OPC UA Endpoint URL
  # endpoint = "opc.tcp://localhost:4840"
  #
  ## Maximum time allowed to establish a connect to the endpoint.
  # connect_timeout = "10s"
  #
  ## Maximum time allowed for a request over the established connection.
  # request_timeout = "5s"

  # Maximum time that a session shall remain open without activity.
  # session_timeout = "20m"
  #
  ## Security policy, one of "None", "Basic128Rsa15", "Basic256",
  ## "Basic256Sha256", or "auto"
  # security_policy = "auto"
  #
  ## Security mode, one of "None", "Sign", "SignAndEncrypt", or "auto"
  # security_mode = "auto"
  #
  ## Path to cert.pem. Required when security mode or policy isn't "None".
  ## If cert path is not supplied, self-signed cert and key will be generated.
  # certificate = "/etc/telegraf/cert.pem"
  #
  ## Path to private key.pem. Required when security mode or policy isn't "None".
  ## If key path is not supplied, self-signed cert and key will be generated.
  # private_key = "/etc/telegraf/key.pem"
  #
  ## Authentication Method, one of "Certificate", "UserName", or "Anonymous".  To
  ## authenticate using a specific ID, select 'Certificate' or 'UserName'
  # auth_method = "Anonymous"
  #
  ## Username. Required for auth_method = "UserName"
  # username = ""
  #
  ## Password. Required for auth_method = "UserName"
  # password = ""
  #
  ## Option to select the metric timestamp to use. Valid options are:
  ##     "gather" -- uses the time of receiving the data in telegraf
  ##     "server" -- uses the timestamp provided by the server
  ##     "source" -- uses the timestamp provided by the source
  # timestamp = "gather"
  #
  ## Client trace messages
  ## When set to true, and debug mode enabled in the agent settings, the OPCUA
  ## client's messages are included in telegraf logs. These messages are very
  ## noisey, but essential for debugging issues.
  # client_trace = false
  #
  ## Include additional Fields in each metric
  ## Available options are:
  ##   DataType -- OPC-UA Data Type (string)
  # optional_fields = []
  #
  ## Node ID configuration
  ## name              - field name to use in the output
  ## namespace         - OPC UA namespace of the node (integer value 0 thru 3)
  ## identifier_type   - OPC UA ID type (s=string, i=numeric, g=guid, b=opaque)
  ## identifier        - OPC UA ID (tag as shown in opcua browser)
  ## tags              - extra tags to be added to the output metric (optional); deprecated in 1.25.0; use default_tags
  ## default_tags      - extra tags to be added to the output metric (optional)
  ##
  ## Use either the inline notation or the bracketed notation, not both.
  #
  ## Inline notation (default_tags not supported yet)
  # nodes = [
  #   {name="", namespace="", identifier_type="", identifier="", tags=[["tag1", "value1"], ["tag2", "value2"]},
  #   {name="", namespace="", identifier_type="", identifier=""},
  # ]
  #
  ## Bracketed notation
  # [[inputs.opcua.nodes]]
  #   name = "node1"
  #   namespace = ""
  #   identifier_type = ""
  #   identifier = ""
  #   default_tags = { tag1 = "value1", tag2 = "value2" }
  #
  # [[inputs.opcua.nodes]]
  #   name = "node2"
  #   namespace = ""
  #   identifier_type = ""
  #   identifier = ""
  #
  ## Node Group
  ## Sets defaults so they aren't required in every node.
  ## Default values can be set for:
  ## * Metric name
  ## * OPC UA namespace
  ## * Identifier
  ## * Default tags
  ##
  ## Multiple node groups are allowed
  #[[inputs.opcua.group]]
  ## Group Metric name. Overrides the top level name.  If unset, the
  ## top level name is used.
  # name =
  #
  ## Group default namespace. If a node in the group doesn't set its
  ## namespace, this is used.
  # namespace =
  #
  ## Group default identifier type. If a node in the group doesn't set its
  ## namespace, this is used.
  # identifier_type =
  #
  ## Default tags that are applied to every node in this group. Can be
  ## overwritten in a node by setting a different value for the tag name.
  ##   example: default_tags = { tag1 = "value1" }
  # default_tags = {}
  #
  ## Node ID Configuration.  Array of nodes with the same settings as above.
  ## Use either the inline notation or the bracketed notation, not both.
  #
  ## Inline notation (default_tags not supported yet)
  # nodes = [
  #  {name="node1", namespace="", identifier_type="", identifier=""},
  #  {name="node2", namespace="", identifier_type="", identifier=""},
  #]
  #
  ## Bracketed notation
  # [[inputs.opcua.group.nodes]]
  #   name = "node1"
  #   namespace = ""
  #   identifier_type = ""
  #   identifier = ""
  #   default_tags = { tag1 = "override1", tag2 = "value2" }
  #
  # [[inputs.opcua.group.nodes]]
  #   name = "node2"
  #   namespace = ""
  #   identifier_type = ""
  #   identifier = ""

  ## Enable workarounds required by some devices to work correctly
  # [inputs.opcua.workarounds]
    ## Set additional valid status codes, StatusOK (0x0) is always considered valid
  # additional_valid_status_codes = ["0xC0"]

  # [inputs.opcua.request_workarounds]
    ## Use unregistered reads instead of registered reads
  # use_unregistered_reads = false

Clickhouse

[[outputs.sql]]
  ## Database driver
  ## Valid options include mssql, mysql, pgx, sqlite, snowflake, clickhouse
  driver = "clickhouse"

  ## Data source name
  ## For ClickHouse, the DSN follows the clickhouse-go v1.5.4 format.
  ## Example DSN: "tcp://localhost:9000?debug=true"
  data_source_name = "tcp://localhost:9000?debug=true"

  ## Timestamp column name
  timestamp_column = "timestamp"

  ## Table creation template
  ## Available template variables:
  ##  {TABLE}        - table name as a quoted identifier
  ##  {TABLELITERAL} - table name as a quoted string literal
  ##  {COLUMNS}      - column definitions (list of quoted identifiers and types)
  table_template = "CREATE TABLE {TABLE} ({COLUMNS})"

  ## Table existence check template
  ## Available template variables:
  ##  {TABLE} - table name as a quoted identifier
  table_exists_template = "SELECT 1 FROM {TABLE} LIMIT 1"

  ## Initialization SQL (optional)
  init_sql = ""

  ## Maximum amount of time a connection may be idle. "0s" means connections are never closed due to idle time.
  connection_max_idle_time = "0s"

  ## Maximum amount of time a connection may be reused. "0s" means connections are never closed due to age.
  connection_max_lifetime = "0s"

  ## Maximum number of connections in the idle connection pool. 0 means unlimited.
  connection_max_idle = 2

  ## Maximum number of open connections to the database. 0 means unlimited.
  connection_max_open = 0

  ## Metric type to SQL type conversion for ClickHouse.
  ## The conversion maps Telegraf metric types to ClickHouse native data types.
  [outputs.sql.convert]
    conversion_style = "literal"
    integer          = "Int64"
    text             = "String"
    timestamp        = "DateTime"
    defaultvalue     = "String"
    unsigned         = "UInt64"
    bool             = "UInt8"
    real             = "Float64"

输入和输出集成示例

OPC UA

  1. 基本配置:使用 OPC UA 服务器端点和所需的指标设置插件。这允许 Telegraf 开始从配置的节点收集指标。

  2. 节点 ID 设置:使用配置指定特定节点,例如温度传感器,以实时监控其值。例如,配置节点 ns=3;s=Temperature 以直接收集温度数据。

  3. 组配置:通过将多个节点分组到一个配置下,简化对多个节点的监控——这为该组中的所有节点设置默认值,从而减少设置中的冗余。

Clickhouse

  1. 高容量数据的实时分析:使用该插件将来自大规模系统的流式指标馈送到 ClickHouse 中。此设置支持超快的查询性能和近乎实时的分析,非常适合监控高流量应用程序。

  2. 时间序列数据仓库:将插件与 ClickHouse 集成以创建强大的时间序列数据仓库。此用例允许组织存储详细的历史指标,并执行复杂的查询以进行趋势分析和容量规划。

  3. 分布式环境中的可扩展监控:利用该插件在 ClickHouse 中动态创建每个指标类型的表,从而更轻松地管理和查询来自大量分布式系统的数据,而无需事先定义模式。

  4. 物联网部署的优化存储:部署该插件以将来自物联网传感器的数据摄取到 ClickHouse 中。其高效的模式创建和原生类型映射有助于处理海量数据,从而实现实时监控和预测性维护。

反馈

感谢您成为我们社区的一份子!如果您有任何一般性反馈或在这些页面上发现了任何错误,我们欢迎并鼓励您提出意见。请在 InfluxDB 社区 Slack 中提交您的反馈。

强大的性能,无限的扩展能力

收集、组织和处理海量高速数据。当您将任何数据视为时间序列数据时,它会更有价值。InfluxDB 是排名第一的时间序列平台,旨在通过 Telegraf 进行扩展。

查看入门方法

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