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Prometheus Remote Write



监听 Prometheus Remote Write 数据,上报到观测云。

配置

前置条件

开启 Prometheus Remote Write 功能,在 prometheus.yml 添加如下配置:

remote_write:
 - url: "http://<datakit-ip>:9529/prom_remote_write"

# If want add some tag, ( __source will not in tag, only show in Datakit expose metrics)
# remote_write:
# - url: "http://<datakit-ip>:9529/prom_remote_write?host=1.2.3.4&foo=bar&__source=<your_source>" 

采集器配置

进入 DataKit 安装目录下的 conf.d/prom 目录,复制 prom_remote_write.conf.sample 并命名为 prom_remote_write.conf。示例如下:

[[inputs.prom_remote_write]]
  ## Path to listen to.
  path = "/prom_remote_write"

  ## accepted methods
  methods = ["PUT", "POST"]

  ## Part of the request to consume.  Available options are "body" and "query".
  # data_source = "body"

  ## output source
  # specify this to output collected metrics to local file
  # if not specified, metrics is sent to datakit io
  # if specified, you can use 'datakit --prom-conf /path/to/this/conf' to debug collected data
  # output = "/abs/path/file"

  ## Metric name filter
  # Regex is supported.
  # Only metric matches one of the regex can pass through. No filter if left empty.
  # metric_name_filter = ["gc", "go"]

  ## Measurement name filter
  # Regex is supported.
  # Only measurement matches one of the regex can pass through. No filter if left empty.
  # This filtering is done before any prefixing rule or renaming rule is applied.
  # measurement_name_filter = ["kubernetes", "container"]

  ## metric name prefix
  # prefix will be added to metric name
  # measurement_prefix = "prefix_"

  ## metric name
  # metric name will be divided by "_" by default.
  # metric is named by the first divided field, the remaining field is used as the current metric name
  # metric name will not be divided if measurement_name is configured
  # measurement_prefix will be added to the start of measurement_name
  # measurement_name = "prom_remote_write"

  ## max body size in bytes, default set to 500MB
  # max_body_size = 0

  ## Optional username and password to accept for HTTP basic authentication.
  ## You probably want to make sure you have TLS configured above for this.
  # basic_username = ""
  # basic_password = ""

  ## If both blacklist and whitelist, all list will cancel.
  ## tags to ignore (blacklist)
  # tags_ignore = ["xxxx"]

  ## tags to ignore with regex (blacklist)
  # tags_ignore_regex = ["xxxx"]

  ## tags whitelist
  # tags_only = ["xxxx"]

  ## tags whitelist with regex
  # tags_only_regex = ["xxxx"]

  ## Indicate whether tags_rename overwrites existing key if tag with the new key name already exists.
  overwrite = false

  ## tags to rename
  [inputs.prom_remote_write.tags_rename]
  # old_tag_name = "new_tag_name"
  # more_old_tag_name = "other_new_tag_name"

  ## Optional setting to map http headers into tags
  ## If the http header is not present on the request, no corresponding tag will be added
  ## If multiple instances of the http header are present, only the first value will be used
  [inputs.prom_remote_write.http_header_tags]
  # HTTP_HEADER = "TAG_NAME"

  ## custom tags
  [inputs.prom_remote_write.tags]
  # some_tag = "some_value"
  # more_tag = "some_other_value"

配置好后,重启 DataKit 即可。

目前可以通过 ConfigMap 方式注入采集器配置来开启采集器。

tags 的处理

可以通过配置 tags 为采集到的指标加上标签,如下:

  ## custom tags
  [inputs.prom_remote_write.tags]
  some_tag = "some_value"
  more_tag = "some_other_value"

注意:黑名单和白名单同时配置,黑白名单会全部失效。

可以通过配置 tags_ignore 忽略指标上的某些标签(黑名单),如下:

  ## tags to ignore
  tags_ignore = ["xxxx"]

可以通过配置 tags_ignore_regex 正则匹配并忽略指标上的标签(黑名单),如下:

  ## tags to ignore with regex
  tags_ignore_regex = ["xxxx"]

可以通过配置 tags_only 配置指标上的标签白名单,如下:

  ## tags white list
  # tags_only = ["xxxx"]

可以通过配置 tags_only_regex 正则匹配指标上的标签白名单,如下:

  ## tags white list with regex
  # tags_only_regex = ["xxxx"]

可以通过配置 tags_rename 重命名指标已有的某些标签名,如下:

  ## tags to rename
  [inputs.prom_remote_write.tags_rename]
  old_tag_name = "new_tag_name"
  more_old_tag_name = "other_new_tag_name"

另外,当重命名后的 tag key 与已有 tag key 相同时,可以通过 overwrite 配置是否覆盖掉已有的 tag key。

注意:对于 DataKit 全局 tag key,此处不支持将它们重命名。

指标

指标集以 Prometheus 发送过来的指标集为准。

命令行调试指标集

DataKit 提供一个简单的调试 prom.conf 的工具,如果不断调整 prom.conf 的配置,可以实现只采集符合一定名称规则的 Prometheus 指标的目的。

Datakit 支持命令行直接调试本采集器的配置文件。在配置 conf.d/promprom_remote_write.confoutput 项,将其配置为一个本地文件路径,之后 prom_remote_write.conf 会将采集到的数据写到文件中,数据就不会上传到中心。

重启 Datakit,让配置文件生效:

datakit service -R

这时 prom_remote_write 采集器将把采集的数据写到 output 指明的本地文件中。

这时执行如下命令,即可调试 prom_remote_write.conf

datakit debug --prom-conf prom_remote_write.conf

参数说明:

  • prom-conf: 指定配置文件,默认在当前目录下寻找 prom_remote_write.conf 文件,如果未找到,会去 <datakit-install-dir>/conf.d/prom 目录下查找相应文件。

输出示例:

================= Line Protocol Points ==================

 prometheus,instance=localhost:9090,job=prometheus,monitor=codelab-monitor target_scrapes_sample_out_of_order_total=0 1634548272855000000
 prometheus,instance=localhost:9090,job=prometheus,monitor=codelab-monitor,scrape_job=node target_sync_failed_total=0 1634548272855000000
 prometheus,instance=localhost:9090,job=prometheus,monitor=codelab-monitor,scrape_job=prometheus target_sync_failed_total=0 1634548272855000000
 prometheus,instance=localhost:9090,job=prometheus,monitor=codelab-monitor,scrape_job=node target_sync_length_seconds_sum=0.000070352 1634548272855000000
 prometheus,instance=localhost:9090,job=prometheus,monitor=codelab-monitor,scrape_job=node target_sync_length_seconds_count=1 1634548272855000000
 prometheus,instance=localhost:9090,job=prometheus,monitor=codelab-monitor,scrape_job=prometheus target_sync_length_seconds_sum=0.000089457 1634548272855000000
 prometheus,instance=localhost:9090,job=prometheus,monitor=codelab-monitor,scrape_job=prometheus target_sync_length_seconds_count=1 1634548272855000000
 prometheus,instance=localhost:9090,job=prometheus,monitor=codelab-monitor template_text_expansion_failures_total=0 1634548272855000000
 prometheus,instance=localhost:9090,job=prometheus,monitor=codelab-monitor template_text_expansions_total=0 1634548272855000000
 prometheus,instance=localhost:9090,job=prometheus,monitor=codelab-monitor treecache_watcher_goroutines=0 1634548272855000000
 prometheus,instance=localhost:9090,job=prometheus,monitor=codelab-monitor treecache_zookeeper_failures_total=0 1634548272855000000
 prometheus,instance=localhost:9090,job=prometheus,monitor=codelab-monitor tsdb_blocks_loaded=1 1634548272855000000
 prometheus,instance=localhost:9090,job=prometheus,monitor=codelab-monitor tsdb_checkpoint_creations_failed_total=0 1634548272855000000
 prometheus,instance=localhost:9090,job=prometheus,monitor=codelab-monitor tsdb_checkpoint_creations_total=1 1634548272855000000
 prometheus,instance=localhost:9090,job=prometheus,monitor=codelab-monitor tsdb_checkpoint_deletions_failed_total=0 1634548272855000000
 prometheus,instance=localhost:9090,job=prometheus,monitor=codelab-monitor tsdb_checkpoint_deletions_total=1 1634548272855000000
 prometheus,instance=localhost:9090,job=prometheus,monitor=codelab-monitor tsdb_clean_start=1 1634548272855000000
 prometheus,instance=localhost:9090,job=prometheus,le=100,monitor=codelab-monitor tsdb_compaction_chunk_range_seconds_bucket=0 1634548272855000000
 prometheus,instance=localhost:9090,job=prometheus,le=400,monitor=codelab-monitor tsdb_compaction_chunk_range_seconds_bucket=0 1634548272855000000
 prometheus,instance=localhost:9090,job=prometheus,le=1600,monitor=codelab-monitor tsdb_compaction_chunk_range_seconds_bucket=0 1634548272855000000
 prometheus,instance=localhost:9090,job=prometheus,le=6400,monitor=codelab-monitor tsdb_compaction_chunk_range_seconds_bucket=0 1634548272855000000
 prometheus,instance=localhost:9090,job=prometheus,le=25600,monitor=codelab-monitor tsdb_compaction_chunk_range_seconds_bucket=0 1634548272855000000
 prometheus,instance=localhost:9090,job=prometheus,le=102400,monitor=codelab-monitor tsdb_compaction_chunk_range_seconds_bucket=0 1634548272855000000
 prometheus,instance=localhost:9090,job=prometheus,le=409600,monitor=codelab-monitor tsdb_compaction_chunk_range_seconds_bucket=0 1634548272855000000
 prometheus,instance=localhost:9090,job=prometheus,le=1.6384e+06,monitor=codelab-monitor tsdb_compaction_chunk_range_seconds_bucket=0 1634548272855000000
 prometheus,instance=localhost:9090,job=prometheus,le=6.5536e+06,monitor=codelab-monitor tsdb_compaction_chunk_range_seconds_bucket=0 1634548272855000000
 prometheus,instance=localhost:9090,job=prometheus,le=2.62144e+07,monitor=codelab-monitor tsdb_compaction_chunk_range_seconds_bucket=0 1634548272855000000
 prometheus,instance=localhost:9090,job=prometheus,le=+Inf,monitor=codelab-monitor tsdb_compaction_chunk_range_seconds_bucket=0 1634548272855000000
 prometheus,instance=localhost:9090,job=prometheus,monitor=codelab-monitor tsdb_compaction_chunk_range_seconds_sum=0 1634548272855000000
 prometheus,instance=localhost:9090,job=prometheus,monitor=codelab-monitor tsdb_compaction_chunk_range_seconds_count=0 1634548272855000000
 prometheus,instance=localhost:9090,job=prometheus,le=4,monitor=codelab-monitor tsdb_compaction_chunk_samples_bucket=0 1634548272855000000
 prometheus,instance=localhost:9090,job=prometheus,le=6,monitor=codelab-monitor tsdb_compaction_chunk_samples_bucket=0 1634548272855000000
 prometheus,instance=localhost:9090,job=prometheus,le=9,monitor=codelab-monitor tsdb_compaction_chunk_samples_bucket=0 1634548272855000000
...
================= Summary ==================

Total time series: 155
Total line protocol points: 487
Total measurements: 6 (prometheus, promhttp, up, scrape, go, node)

输出说明:

  • Line Protocol Points: 产生的行协议点
  • Summary: 汇总结果
    • Total time series: 时间线数量
    • Total line protocol points: 行协议点数
    • Total measurements: 指标集个数及其名称。

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