北京网站建设百度排名,平面设计公司有什么职位,互联网公司网站建设ppt模板下载,平面设计培训平台如果你对降采样还不是很熟的话#xff0c;请阅读之前的文章 “Elasticsearch#xff1a;对时间序列数据流进行降采样#xff08;downsampling)”。这是一个简化的示例#xff0c;可让你快速了解降采样如何作为 ILM 策略的一部分来减少一组采样指标的存储大小。 该示例使用典…
如果你对降采样还不是很熟的话请阅读之前的文章 “Elasticsearch对时间序列数据流进行降采样downsampling)”。这是一个简化的示例可让你快速了解降采样如何作为 ILM 策略的一部分来减少一组采样指标的存储大小。 该示例使用典型的 Kubernetes 集群监控数据。 要使用 ILM 测试降采样请执行以下步骤
检查先决条件。创建索引生命周期策略。创建索引模板。摄取时间序列数据。查看结果。
以下展示是使用最新的 Elastic Stack 8.11 来进行的。 先决条件
请参阅时间序列数据流先决条件。 集群权限manage_ilm 和 manage_index_templates。索引权限你创建或转换的任何 TSDS 的 create_doc 和 create_index。 要滚动 TSDS你必须具有 manage 权限。在运行此示例之前你可能需要尝试手动运行降采样示例。 创建索引生命周期策略
为你的时间序列数据创建 ILM 策略。 虽然不是必需的但建议使用 ILM 策略来自动管理时间序列数据流索引。
要启用降采样请添加 Downsample 操作并将 fixed_interval 设置为要聚合原始时间序列数据的降采样间隔。
本例中为热阶段配置了 ILM 策略。 降采样发生在索引滚动并且索引时间序列结束时间index.time_series.end_time已过之后因为在此之前源索引仍有望接收主要写入。 在索引结束时间过去之前索引生命周期管理不会继续执行任何期望索引不再接收写入的操作。 在继续等待结束时间之前的索引生命周期管理操作包括 - 删除 (Delete) - 降采样 (Downsample) - 强制合并 (Force merge) - 只读 (Read only) - 可搜索快照 (Searchable snapshot) - 收缩 (Shrink)
PUT _ilm/policy/datastream_policy
{policy: {phases: {hot: {actions: {rollover : {max_age: 5m},downsample: {fixed_interval: 1h}}}}}
}
创建索引模板
这将为基本数据流创建索引模板。 设置时间序列数据流中详细描述了索引模板的可用参数。
为了简单起见在时间序列映射中所有 time_series_metric 参数都设置为 gauge 类型但也可以使 counter 指标类型。 time_series_metric 值确定降采样期间使用的统计表示的类型。
索引模板包含一组静态时间序列维度主机 (host)、命名空间 (namespace)、节点 (node) 和 Pod。 时间序列维度不会因降采样过程而改变。
PUT _index_template/datastream_template
{index_patterns: [datastream*],data_stream: {},template: {settings: {index: {mode: time_series,number_of_replicas: 0,number_of_shards: 2},index.lifecycle.name: datastream_policy},mappings: {properties: {timestamp: {type: date},kubernetes: {properties: {container: {properties: {cpu: {properties: {usage: {properties: {core: {properties: {ns: {type: long}}},limit: {properties: {pct: {type: float}}},nanocores: {type: long,time_series_metric: gauge},node: {properties: {pct: {type: float}}}}}}},memory: {properties: {available: {properties: {bytes: {type: long,time_series_metric: gauge}}},majorpagefaults: {type: long},pagefaults: {type: long,time_series_metric: gauge},rss: {properties: {bytes: {type: long,time_series_metric: gauge}}},usage: {properties: {bytes: {type: long,time_series_metric: gauge},limit: {properties: {pct: {type: float}}},node: {properties: {pct: {type: float}}}}},workingset: {properties: {bytes: {type: long,time_series_metric: gauge}}}}},name: {type: keyword},start_time: {type: date}}},host: {type: keyword,time_series_dimension: true},namespace: {type: keyword,time_series_dimension: true},node: {type: keyword,time_series_dimension: true},pod: {type: keyword,time_series_dimension: true}}}}}}
} 摄取时间序列数据
使用 bulk API 请求自动创建 TSDS 并为一组 10 个文档编制索引。 重要提示在运行此批量请求之前你需要将时间戳更新为当前时间后三到五个小时内。 也就是说搜索 2022-06-21T15 并替换为你当前的日期并将小时调整为你当前的时间加三个小时。 PUT /datastream/_bulk?refresh
{create: {}}
{timestamp:2022-06-21T15:49:00Z,kubernetes:{host:gke-apps-0,node:gke-apps-0-0,pod:gke-apps-0-0-0,container:{cpu:{usage:{nanocores:91153,core:{ns:12828317850},node:{pct:2.77905e-05},limit:{pct:2.77905e-05}}},memory:{available:{bytes:463314616},usage:{bytes:307007078,node:{pct:0.01770037710617187},limit:{pct:9.923134671484496e-05}},workingset:{bytes:585236},rss:{bytes:102728},pagefaults:120901,majorpagefaults:0},start_time:2021-03-30T07:59:06Z,name:container-name-44},namespace:namespace26}}
{create: {}}
{timestamp:2022-06-21T15:45:50Z,kubernetes:{host:gke-apps-0,node:gke-apps-0-0,pod:gke-apps-0-0-0,container:{cpu:{usage:{nanocores:124501,core:{ns:12828317850},node:{pct:2.77905e-05},limit:{pct:2.77905e-05}}},memory:{available:{bytes:982546514},usage:{bytes:360035574,node:{pct:0.01770037710617187},limit:{pct:9.923134671484496e-05}},workingset:{bytes:1339884},rss:{bytes:381174},pagefaults:178473,majorpagefaults:0},start_time:2021-03-30T07:59:06Z,name:container-name-44},namespace:namespace26}}
{create: {}}
{timestamp:2022-06-21T15:44:50Z,kubernetes:{host:gke-apps-0,node:gke-apps-0-0,pod:gke-apps-0-0-0,container:{cpu:{usage:{nanocores:38907,core:{ns:12828317850},node:{pct:2.77905e-05},limit:{pct:2.77905e-05}}},memory:{available:{bytes:862723768},usage:{bytes:379572388,node:{pct:0.01770037710617187},limit:{pct:9.923134671484496e-05}},workingset:{bytes:431227},rss:{bytes:386580},pagefaults:233166,majorpagefaults:0},start_time:2021-03-30T07:59:06Z,name:container-name-44},namespace:namespace26}}
{create: {}}
{timestamp:2022-06-21T15:44:40Z,kubernetes:{host:gke-apps-0,node:gke-apps-0-0,pod:gke-apps-0-0-0,container:{cpu:{usage:{nanocores:86706,core:{ns:12828317850},node:{pct:2.77905e-05},limit:{pct:2.77905e-05}}},memory:{available:{bytes:567160996},usage:{bytes:103266017,node:{pct:0.01770037710617187},limit:{pct:9.923134671484496e-05}},workingset:{bytes:1724908},rss:{bytes:105431},pagefaults:233166,majorpagefaults:0},start_time:2021-03-30T07:59:06Z,name:container-name-44},namespace:namespace26}}
{create: {}}
{timestamp:2022-06-21T15:44:00Z,kubernetes:{host:gke-apps-0,node:gke-apps-0-0,pod:gke-apps-0-0-0,container:{cpu:{usage:{nanocores:150069,core:{ns:12828317850},node:{pct:2.77905e-05},limit:{pct:2.77905e-05}}},memory:{available:{bytes:639054643},usage:{bytes:265142477,node:{pct:0.01770037710617187},limit:{pct:9.923134671484496e-05}},workingset:{bytes:1786511},rss:{bytes:189235},pagefaults:138172,majorpagefaults:0},start_time:2021-03-30T07:59:06Z,name:container-name-44},namespace:namespace26}}
{create: {}}
{timestamp:2022-06-21T15:42:40Z,kubernetes:{host:gke-apps-0,node:gke-apps-0-0,pod:gke-apps-0-0-0,container:{cpu:{usage:{nanocores:82260,core:{ns:12828317850},node:{pct:2.77905e-05},limit:{pct:2.77905e-05}}},memory:{available:{bytes:854735585},usage:{bytes:309798052,node:{pct:0.01770037710617187},limit:{pct:9.923134671484496e-05}},workingset:{bytes:924058},rss:{bytes:110838},pagefaults:259073,majorpagefaults:0},start_time:2021-03-30T07:59:06Z,name:container-name-44},namespace:namespace26}}
{create: {}}
{timestamp:2022-06-21T15:42:10Z,kubernetes:{host:gke-apps-0,node:gke-apps-0-0,pod:gke-apps-0-0-0,container:{cpu:{usage:{nanocores:153404,core:{ns:12828317850},node:{pct:2.77905e-05},limit:{pct:2.77905e-05}}},memory:{available:{bytes:279586406},usage:{bytes:214904955,node:{pct:0.01770037710617187},limit:{pct:9.923134671484496e-05}},workingset:{bytes:1047265},rss:{bytes:91914},pagefaults:302252,majorpagefaults:0},start_time:2021-03-30T07:59:06Z,name:container-name-44},namespace:namespace26}}
{create: {}}
{timestamp:2022-06-21T15:40:20Z,kubernetes:{host:gke-apps-0,node:gke-apps-0-0,pod:gke-apps-0-0-0,container:{cpu:{usage:{nanocores:125613,core:{ns:12828317850},node:{pct:2.77905e-05},limit:{pct:2.77905e-05}}},memory:{available:{bytes:822782853},usage:{bytes:100475044,node:{pct:0.01770037710617187},limit:{pct:9.923134671484496e-05}},workingset:{bytes:2109932},rss:{bytes:278446},pagefaults:74843,majorpagefaults:0},start_time:2021-03-30T07:59:06Z,name:container-name-44},namespace:namespace26}}
{create: {}}
{timestamp:2022-06-21T15:40:10Z,kubernetes:{host:gke-apps-0,node:gke-apps-0-0,pod:gke-apps-0-0-0,container:{cpu:{usage:{nanocores:100046,core:{ns:12828317850},node:{pct:2.77905e-05},limit:{pct:2.77905e-05}}},memory:{available:{bytes:567160996},usage:{bytes:362826547,node:{pct:0.01770037710617187},limit:{pct:9.923134671484496e-05}},workingset:{bytes:1986724},rss:{bytes:402801},pagefaults:296495,majorpagefaults:0},start_time:2021-03-30T07:59:06Z,name:container-name-44},namespace:namespace26}}
{create: {}}
{timestamp:2022-06-21T15:38:30Z,kubernetes:{host:gke-apps-0,node:gke-apps-0-0,pod:gke-apps-0-0-0,container:{cpu:{usage:{nanocores:40018,core:{ns:12828317850},node:{pct:2.77905e-05},limit:{pct:2.77905e-05}}},memory:{available:{bytes:1062428344},usage:{bytes:265142477,node:{pct:0.01770037710617187},limit:{pct:9.923134671484496e-05}},workingset:{bytes:2294743},rss:{bytes:340623},pagefaults:224530,majorpagefaults:0},start_time:2021-03-30T07:59:06Z,name:container-name-44},namespace:namespace26}}
针对我的情况
PUT /datastream/_bulk?refresh
{create: {}}
{timestamp:2023-11-30T06:49:00Z,kubernetes:{host:gke-apps-0,node:gke-apps-0-0,pod:gke-apps-0-0-0,container:{cpu:{usage:{nanocores:91153,core:{ns:12828317850},node:{pct:2.77905e-05},limit:{pct:2.77905e-05}}},memory:{available:{bytes:463314616},usage:{bytes:307007078,node:{pct:0.01770037710617187},limit:{pct:9.923134671484496e-05}},workingset:{bytes:585236},rss:{bytes:102728},pagefaults:120901,majorpagefaults:0},start_time:2021-03-30T07:59:06Z,name:container-name-44},namespace:namespace26}}
{create: {}}
{timestamp:2023-11-30T06:45:50Z,kubernetes:{host:gke-apps-0,node:gke-apps-0-0,pod:gke-apps-0-0-0,container:{cpu:{usage:{nanocores:124501,core:{ns:12828317850},node:{pct:2.77905e-05},limit:{pct:2.77905e-05}}},memory:{available:{bytes:982546514},usage:{bytes:360035574,node:{pct:0.01770037710617187},limit:{pct:9.923134671484496e-05}},workingset:{bytes:1339884},rss:{bytes:381174},pagefaults:178473,majorpagefaults:0},start_time:2021-03-30T07:59:06Z,name:container-name-44},namespace:namespace26}}
{create: {}}
{timestamp:2023-11-30T06:44:50Z,kubernetes:{host:gke-apps-0,node:gke-apps-0-0,pod:gke-apps-0-0-0,container:{cpu:{usage:{nanocores:38907,core:{ns:12828317850},node:{pct:2.77905e-05},limit:{pct:2.77905e-05}}},memory:{available:{bytes:862723768},usage:{bytes:379572388,node:{pct:0.01770037710617187},limit:{pct:9.923134671484496e-05}},workingset:{bytes:431227},rss:{bytes:386580},pagefaults:233166,majorpagefaults:0},start_time:2021-03-30T07:59:06Z,name:container-name-44},namespace:namespace26}}
{create: {}}
{timestamp:2023-11-30T06:44:40Z,kubernetes:{host:gke-apps-0,node:gke-apps-0-0,pod:gke-apps-0-0-0,container:{cpu:{usage:{nanocores:86706,core:{ns:12828317850},node:{pct:2.77905e-05},limit:{pct:2.77905e-05}}},memory:{available:{bytes:567160996},usage:{bytes:103266017,node:{pct:0.01770037710617187},limit:{pct:9.923134671484496e-05}},workingset:{bytes:1724908},rss:{bytes:105431},pagefaults:233166,majorpagefaults:0},start_time:2021-03-30T07:59:06Z,name:container-name-44},namespace:namespace26}}
{create: {}}
{timestamp:2023-11-30T06:44:00Z,kubernetes:{host:gke-apps-0,node:gke-apps-0-0,pod:gke-apps-0-0-0,container:{cpu:{usage:{nanocores:150069,core:{ns:12828317850},node:{pct:2.77905e-05},limit:{pct:2.77905e-05}}},memory:{available:{bytes:639054643},usage:{bytes:265142477,node:{pct:0.01770037710617187},limit:{pct:9.923134671484496e-05}},workingset:{bytes:1786511},rss:{bytes:189235},pagefaults:138172,majorpagefaults:0},start_time:2021-03-30T07:59:06Z,name:container-name-44},namespace:namespace26}}
{create: {}}
{timestamp:2023-11-30T06:42:40Z,kubernetes:{host:gke-apps-0,node:gke-apps-0-0,pod:gke-apps-0-0-0,container:{cpu:{usage:{nanocores:82260,core:{ns:12828317850},node:{pct:2.77905e-05},limit:{pct:2.77905e-05}}},memory:{available:{bytes:854735585},usage:{bytes:309798052,node:{pct:0.01770037710617187},limit:{pct:9.923134671484496e-05}},workingset:{bytes:924058},rss:{bytes:110838},pagefaults:259073,majorpagefaults:0},start_time:2021-03-30T07:59:06Z,name:container-name-44},namespace:namespace26}}
{create: {}}
{timestamp:2023-11-30T06:42:10Z,kubernetes:{host:gke-apps-0,node:gke-apps-0-0,pod:gke-apps-0-0-0,container:{cpu:{usage:{nanocores:153404,core:{ns:12828317850},node:{pct:2.77905e-05},limit:{pct:2.77905e-05}}},memory:{available:{bytes:279586406},usage:{bytes:214904955,node:{pct:0.01770037710617187},limit:{pct:9.923134671484496e-05}},workingset:{bytes:1047265},rss:{bytes:91914},pagefaults:302252,majorpagefaults:0},start_time:2021-03-30T07:59:06Z,name:container-name-44},namespace:namespace26}}
{create: {}}
{timestamp:2023-11-30T06:40:20Z,kubernetes:{host:gke-apps-0,node:gke-apps-0-0,pod:gke-apps-0-0-0,container:{cpu:{usage:{nanocores:125613,core:{ns:12828317850},node:{pct:2.77905e-05},limit:{pct:2.77905e-05}}},memory:{available:{bytes:822782853},usage:{bytes:100475044,node:{pct:0.01770037710617187},limit:{pct:9.923134671484496e-05}},workingset:{bytes:2109932},rss:{bytes:278446},pagefaults:74843,majorpagefaults:0},start_time:2021-03-30T07:59:06Z,name:container-name-44},namespace:namespace26}}
{create: {}}
{timestamp:2023-11-30T06:40:10Z,kubernetes:{host:gke-apps-0,node:gke-apps-0-0,pod:gke-apps-0-0-0,container:{cpu:{usage:{nanocores:100046,core:{ns:12828317850},node:{pct:2.77905e-05},limit:{pct:2.77905e-05}}},memory:{available:{bytes:567160996},usage:{bytes:362826547,node:{pct:0.01770037710617187},limit:{pct:9.923134671484496e-05}},workingset:{bytes:1986724},rss:{bytes:402801},pagefaults:296495,majorpagefaults:0},start_time:2021-03-30T07:59:06Z,name:container-name-44},namespace:namespace26}}
{create: {}}
{timestamp:2023-11-30T06:38:30Z,kubernetes:{host:gke-apps-0,node:gke-apps-0-0,pod:gke-apps-0-0-0,container:{cpu:{usage:{nanocores:40018,core:{ns:12828317850},node:{pct:2.77905e-05},limit:{pct:2.77905e-05}}},memory:{available:{bytes:1062428344},usage:{bytes:265142477,node:{pct:0.01770037710617187},limit:{pct:9.923134671484496e-05}},workingset:{bytes:2294743},rss:{bytes:340623},pagefaults:224530,majorpagefaults:0},start_time:2021-03-30T07:59:06Z,name:container-name-44},namespace:namespace26}}
查看结果
现在你已创建文档并将其添加到数据流中请检查以确认新索引的当前状态。
GET _data_stream
如果尚未应用 ILM 策略你的结果将如下所示。 请注意原始索引名称.ds-datastream-timestamp-000001。
{data_streams: [{name: datastream,timestamp_field: {name: timestamp},indices: [{index_name: .ds-datastream-2023.11.30-000001,index_uuid: muM9y4_ORDK1wLY-t_UtnA,prefer_ilm: true,ilm_policy: datastream_policy,managed_by: Index Lifecycle Management}],generation: 1,status: GREEN,template: datastream_template,ilm_policy: datastream_policy,next_generation_managed_by: Index Lifecycle Management,prefer_ilm: true,hidden: false,system: false,allow_custom_routing: false,replicated: false,time_series: {temporal_ranges: [{start: 2023-11-30T03:21:48.000Z,end: 2023-11-30T07:27:32.000Z}]}},{name: kibana_sample_data_logs,timestamp_field: {name: timestamp},indices: [{index_name: .ds-kibana_sample_data_logs-2023.11.21-000001,index_uuid: JkbH5-8uTvyeZL6icuHN1A,prefer_ilm: true,managed_by: Unmanaged}],generation: 1,status: YELLOW,template: kibana_sample_data_logs,next_generation_managed_by: Unmanaged,prefer_ilm: true,hidden: false,system: false,allow_custom_routing: false,replicated: false},{name: my-data-stream,timestamp_field: {name: timestamp},indices: [{index_name: .ds-my-data-stream-2023.11.30-000001-downsample,index_uuid: KXtWAQCBRlW0sWYCVUu7Fw,prefer_ilm: true,managed_by: Unmanaged},{index_name: .ds-my-data-stream-2023.11.30-000002,index_uuid: yBDLixmpRDahmS4G5_uxQw,prefer_ilm: true,managed_by: Unmanaged}],generation: 4,status: GREEN,template: my-data-stream-template,next_generation_managed_by: Unmanaged,prefer_ilm: true,hidden: false,system: false,allow_custom_routing: false,replicated: false,time_series: {temporal_ranges: [{start: 2023-11-30T02:24:20.000Z,end: 2023-11-30T08:32:32.000Z}]}}]
}
接下来运行搜索查询
GET datastream/_search 经过一段时间后它会变成如下的结果
{data_streams: [{name: datastream,timestamp_field: {name: timestamp},indices: [{index_name: downsample-1h-.ds-datastream-2023.11.30-000001,index_uuid: KQMxcaJATf24XAesWs6Xbw,prefer_ilm: true,ilm_policy: datastream_policy,managed_by: Index Lifecycle Management},{index_name: .ds-datastream-2023.11.30-000002,index_uuid: yKLevDQkQuS-8yqJQb8gWw,prefer_ilm: true,ilm_policy: datastream_policy,managed_by: Index Lifecycle Management}],generation: 3,status: GREEN,template: datastream_template,ilm_policy: datastream_policy,next_generation_managed_by: Index Lifecycle Management,prefer_ilm: true,hidden: false,system: false,allow_custom_routing: false,replicated: false,time_series: {temporal_ranges: [{start: 2023-11-30T03:21:48.000Z,end: 2023-11-30T10:45:44.000Z}]}},{name: kibana_sample_data_logs,timestamp_field: {name: timestamp},indices: [{index_name: .ds-kibana_sample_data_logs-2023.11.21-000001,index_uuid: JkbH5-8uTvyeZL6icuHN1A,prefer_ilm: true,managed_by: Unmanaged}],generation: 1,status: YELLOW,template: kibana_sample_data_logs,next_generation_managed_by: Unmanaged,prefer_ilm: true,hidden: false,system: false,allow_custom_routing: false,replicated: false},{name: my-data-stream,timestamp_field: {name: timestamp},indices: [{index_name: .ds-my-data-stream-2023.11.30-000001-downsample,index_uuid: KXtWAQCBRlW0sWYCVUu7Fw,prefer_ilm: true,managed_by: Unmanaged},{index_name: .ds-my-data-stream-2023.11.30-000002,index_uuid: yBDLixmpRDahmS4G5_uxQw,prefer_ilm: true,managed_by: Unmanaged}],generation: 4,status: GREEN,template: my-data-stream-template,next_generation_managed_by: Unmanaged,prefer_ilm: true,hidden: false,system: false,allow_custom_routing: false,replicated: false,time_series: {temporal_ranges: [{start: 2023-11-30T02:24:20.000Z,end: 2023-11-30T10:45:44.000Z}]}}]
} 默认情况下索引生命周期管理每十分钟检查一次是否符合策略标准。 等待大约十分钟也许可以冲泡一杯咖啡或茶☕然后重新运行 GET _data_stream 请求。
GET _data_stream
ILM 策略生效后原始 .ds-datastream-2023.11.30-000001 索引将替换为新的降采样索引在本例中为 downsample-1h-.ds-datastream-2023.11.30-000001。
{data_streams: [{name: datastream,timestamp_field: {name: timestamp},indices: [{index_name: downsample-1h-.ds-datastream-2023.11.30-000001,index_uuid: KQMxcaJATf24XAesWs6Xbw,prefer_ilm: true,ilm_policy: datastream_policy,managed_by: Index Lifecycle Management},{index_name: .ds-datastream-2023.11.30-000002,index_uuid: yKLevDQkQuS-8yqJQb8gWw,prefer_ilm: true,ilm_policy: datastream_policy,managed_by: Index Lifecycle Management}],generation: 3,status: GREEN,template: datastream_template,ilm_policy: datastream_policy,next_generation_managed_by: Index Lifecycle Management,prefer_ilm: true,hidden: false,system: false,allow_custom_routing: false,replicated: false,time_series: {temporal_ranges: [{start: 2023-11-30T03:21:48.000Z,end: 2023-11-30T10:45:44.000Z}]}},
在数据流上运行搜索查询请注意在查询降采样索引时需要注意一些细微差别。
GET datastream/_search
新的降采样索引仅包含一个文档其中包括基于原始采样指标的 min、max、sum 和 value_count 统计信息。
{took: 1,timed_out: false,_shards: {total: 4,successful: 4,skipped: 0,failed: 0},hits: {total: {value: 1,relation: eq},max_score: 1,hits: [{_index: downsample-1h-.ds-datastream-2023.11.30-000001,_id: 0eL0wC_4-45SnTNFAAABjB7QCwA,_score: 1,_source: {timestamp: 2023-11-30T06:00:00.000Z,_doc_count: 10,kubernetes: {container: {cpu: {usage: {core: {ns: 12828317850},limit: {pct: 0.0000277905},nanocores: {min: 38907,max: 153404,sum: 992677,value_count: 10},node: {pct: 0.0000277905}}},memory: {available: {bytes: {min: 279586406,max: 1062428344,sum: 7101494721,value_count: 10}},majorpagefaults: 0,pagefaults: {min: 74843,max: 302252,sum: 2061071,value_count: 10},rss: {bytes: {min: 91914,max: 402801,sum: 2389770,value_count: 10}},usage: {bytes: {min: 100475044,max: 379572388,sum: 2668170609,value_count: 10},limit: {pct: 0.00009923134},node: {pct: 0.017700378}},workingset: {bytes: {min: 431227,max: 2294743,sum: 14230488,value_count: 10}}},name: container-name-44,start_time: 2021-03-30T07:59:06.000Z},host: gke-apps-0,namespace: namespace26,node: gke-apps-0-0,pod: gke-apps-0-0-0}}}]}
}
使用数据流统计 API 获取数据流的统计信息包括存储大小。
GET /_data_stream/datastream/_stats?humantrue 此示例演示了降采样如何作为 ILM 策略的一部分来工作以减少指标数据的存储大小因为它变得不那么最新且查询频率较低。
你还可以尝试我们的手动运行降采样示例了解降采样如何在 ILM 策略之外工作。
