ElasticSearch之cat anomaly detectors API

合集 - ElasticSearch(59)

1.ElasticSearch之安装2023-11-21 2.ElasticSearch之查看集群的参数2023-11-22 3.ElasticSearch之健康状态2023-11-22 4.ElasticSearch的日志配置2023-11-22 5.ElasticSearch之cat aliases API2023-11-23 6.ElasticSearch之Health API2023-11-23 7.ElasticSearch之Nodes info API2023-11-23 8.ElasticSearch之系统关键配置2023-11-23 9.ElasticSearch之cat allocation API2023-11-23

10.ElasticSearch之cat anomaly detectors API2023-11-23

11.ElasticSearch之cat component templates API2023-11-23 12.ElasticSearch之配置2023-11-23 13.ElasticSearch之cat count API2023-11-23 14.ElasticSearch之cat data frame analytics API2023-11-24 15.ElasticSearch之禁用交换分区2023-11-24 16.ElasticSearch之虚拟内存2023-11-24 17.ElasticSearch之文件描述符的数量2023-11-25 18.ElasticSearch之线程的数量2023-11-25 19.ElasticSearch之cat datafeeds API2023-11-25 20.ElasticSearch之cat fielddata API2023-11-25 21.ElasticSearch之cat health API2023-11-25 22.ElasticSearch之cat indices API2023-11-28 23.ElasticSearch之cat master API2023-11-28 24.ElasticSearch之cat nodeattrs API2023-11-28 25.ElasticSearch之线程池2023-11-28 26.ElasticSearch之cat nodes API2023-11-28 27.ElasticSearch之cat pending tasks API2023-11-29 28.ElasticSearch之cat plugins API2023-11-29 29.ElasticSearch之cat recovery API2023-11-29 30.ElasticSearch之cat repositories API2023-11-29 31.ElasticSearch之cat segments API2023-11-29 32.ElasticSearch之cat shards API2023-11-29 33.ElasticSearch之cat task management API2023-11-29 34.ElasticSearch之cat templates API2023-11-29 35.ElasticSearch之cat thread pool API2023-11-29 36.ElasticSearch之Search settings2023-11-29 37.ElasticSearch之cat trained model API2023-11-29 38.ElasticSearch之cat transforms API2023-11-29 39.ElasticSearch之Merge2023-11-30 40.ElasticSearch之Force merge API2023-11-30 41.ElasticSearch之Task management API2023-11-30 42.ElasticSearch之Slow Log2023-12-01 43.ElasticSearch之Analyze index disk usage API2023-12-02 44.ElasticSearch之Clear cache API2023-12-02 45.ElasticSearch之Create index API2023-12-02 46.ElasticSearch之Clone index API2023-12-02 47.ElasticSearch之Close index API2023-12-02 48.ElasticSearch之Open index API2023-12-02 49.ElasticSearch之Delete index API2023-12-02 50.ElasticSearch之Exists API2023-12-02 51.ElasticSearch之Get index API2023-12-02 52.ElasticSearch之Get index settings API2023-12-02 53.ElasticSearch之Index stats API2023-12-02 54.ElasticSearch之Refresh API2023-12-02 55.ElasticSearch之Shard request cache settings2023-12-09 56.ElasticSearch之Node query cache settings2023-12-11 57.ElasticSearch之Index modules2023-12-17 58.ElasticSearch之集群中的节点2024-10-05 59.ElasticSearch之网络配置2024-10-06

 curl -X GET "https://localhost:9200/_cat/ml/anomaly_detectors?v=true&pretty" --cacert $ES_HOME/config/certs/http_ca.crt -u "elastic:ohCxPH=QBE+s5=*lo7F9"

执行结果输出如下：

 curl -X GET "https://localhost:9200/_cat/ml/anomaly_detectors?v=true&pretty" --cacert $ES_HOME/config/certs/http_ca.crt -u "elastic:ohCxPH=QBE+s5=*lo7F9"
id state data.processed_records model.bytes model.memory_status forecasts.total buckets.count

查看帮助，命令如下：

 curl -X GET "https://localhost:9200/_cat/ml/anomaly_detectors?v=true&help=true&pretty" --cacert $ES_HOME/config/certs/http_ca.crt -u "elastic:ohCxPH=QBE+s5=*lo7F9"

执行结果输出如下：

 id                               |                                    | the job_id
state                            | s                                  | the job state
opened_time                      | ot                                 | the amount of time the job has been opened
assignment_explanation           | ae                                 | why the job is or is not assigned to a node
data.processed_records           | dpr,dataProcessedRecords           | number of processed records
data.processed_fields            | dpf,dataProcessedFields            | number of processed fields
data.input_bytes                 | dib,dataInputBytes                 | total input bytes
data.input_records               | dir,dataInputRecords               | total record count
data.input_fields                | dif,dataInputFields                | total field count
data.invalid_dates               | did,dataInvalidDates               | number of records with invalid dates
data.missing_fields              | dmf,dataMissingFields              | number of records with missing fields
data.out_of_order_timestamps     | doot,dataOutOfOrderTimestamps      | number of records handled out of order
data.empty_buckets               | deb,dataEmptyBuckets               | number of empty buckets
data.sparse_buckets              | dsb,dataSparseBuckets              | number of sparse buckets
data.buckets                     | db,dataBuckets                     | total bucket count
data.earliest_record             | der,dataEarliestRecord             | earliest record time
data.latest_record               | dlr,dataLatestRecord               | latest record time
data.last                        | dl,dataLast                        | last time data was seen
data.last_empty_bucket           | dleb,dataLastEmptyBucket           | last time an empty bucket occurred
data.last_sparse_bucket          | dlsb,dataLastSparseBucket          | last time a sparse bucket occurred
model.bytes                      | mb,modelBytes                      | model size
model.memory_status              | mms,modelMemoryStatus              | current memory status
model.bytes_exceeded             | mbe,modelBytesExceeded             | how much the model has exceeded the limit
model.memory_limit               | mml,modelMemoryLimit               | model memory limit
model.by_fields                  | mbf,modelByFields                  | count of 'by' fields
model.over_fields                | mof,modelOverFields                | count of 'over' fields
model.partition_fields           | mpf,modelPartitionFields           | count of 'partition' fields
model.bucket_allocation_failures | mbaf,modelBucketAllocationFailures | number of bucket allocation failures
model.categorization_status      | mcs,modelCategorizationStatus      | current categorization status
model.categorized_doc_count      | mcdc,modelCategorizedDocCount      | count of categorized documents
model.total_category_count       | mtcc,modelTotalCategoryCount       | count of categories
model.frequent_category_count    | mfcc,modelFrequentCategoryCount    | count of frequent categories
model.rare_category_count        | mrcc,modelRareCategoryCount        | count of rare categories
model.dead_category_count        | mdcc,modelDeadCategoryCount        | count of dead categories
model.failed_category_count      | mfcc,modelFailedCategoryCount      | count of failed categories
model.log_time                   | mlt,modelLogTime                   | when the model stats were gathered
model.timestamp                  | mt,modelTimestamp                  | the time of the last record when the model stats were gathered
forecasts.total                  | ft,forecastsTotal                  | total number of forecasts
forecasts.memory.min             | fmmin,forecastsMemoryMin           | minimum memory used by forecasts
forecasts.memory.max             | fmmax,forecastsMemoryMax           | maximum memory used by forecasts
forecasts.memory.avg             | fmavg,forecastsMemoryAvg           | average memory used by forecasts
forecasts.memory.total           | fmt,forecastsMemoryTotal           | total memory used by all forecasts
forecasts.records.min            | frmin,forecastsRecordsMin          | minimum record count for forecasts
forecasts.records.max            | frmax,forecastsRecordsMax          | maximum record count for forecasts
forecasts.records.avg            | fravg,forecastsRecordsAvg          | average record count for forecasts
forecasts.records.total          | frt,forecastsRecordsTotal          | total record count for all forecasts
forecasts.time.min               | ftmin,forecastsTimeMin             | minimum runtime for forecasts
forecasts.time.max               | ftmax,forecastsTimeMax             | maximum run time for forecasts
forecasts.time.avg               | ftavg,forecastsTimeAvg             | average runtime for all forecasts (milliseconds)
forecasts.time.total             | ftt,forecastsTimeTotal             | total runtime for all forecasts
node.id                          | ni,nodeId                          | id of the assigned node
node.name                        | nn,nodeName                        | name of the assigned node
node.ephemeral_id                | ne,nodeEphemeralId                 | ephemeral id of the assigned node
node.address                     | na,nodeAddress                     | network address of the assigned node
buckets.count                    | bc,bucketsCount                    | bucket count
buckets.time.total               | btt,bucketsTimeTotal               | total bucket processing time
buckets.time.min                 | btmin,bucketsTimeMin               | minimum bucket processing time
buckets.time.max                 | btmax,bucketsTimeMax               | maximum bucket processing time
buckets.time.exp_avg             | btea,bucketsTimeExpAvg             | exponential average bucket processing time (milliseconds)
buckets.time.exp_avg_hour        | bteah,bucketsTimeExpAvgHour        | exponential average bucket processing time by hour (milliseconds)

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posted @ 2023-11-23 21:14 jackieathome 阅读(12) 评论(0) 编辑收藏举报

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	curl -X GET "https://localhost:9200/_cat/ml/anomaly_detectors?v=true&pretty" --cacert $ES_HOME/config/certs/http_ca.crt -u "elastic:ohCxPH=QBE+s5=*lo7F9"
	id state data.processed_records model.bytes model.memory_status forecasts.total buckets.count

	id \| \| the job_id
	state \| s \| the job state
	opened_time \| ot \| the amount of time the job has been opened
	assignment_explanation \| ae \| why the job is or is not assigned to a node
	data.processed_records \| dpr,dataProcessedRecords \| number of processed records
	data.processed_fields \| dpf,dataProcessedFields \| number of processed fields
	data.input_bytes \| dib,dataInputBytes \| total input bytes
	data.input_records \| dir,dataInputRecords \| total record count
	data.input_fields \| dif,dataInputFields \| total field count
	data.invalid_dates \| did,dataInvalidDates \| number of records with invalid dates
	data.missing_fields \| dmf,dataMissingFields \| number of records with missing fields
	data.out_of_order_timestamps \| doot,dataOutOfOrderTimestamps \| number of records handled out of order
	data.empty_buckets \| deb,dataEmptyBuckets \| number of empty buckets
	data.sparse_buckets \| dsb,dataSparseBuckets \| number of sparse buckets
	data.buckets \| db,dataBuckets \| total bucket count
	data.earliest_record \| der,dataEarliestRecord \| earliest record time
	data.latest_record \| dlr,dataLatestRecord \| latest record time
	data.last \| dl,dataLast \| last time data was seen
	data.last_empty_bucket \| dleb,dataLastEmptyBucket \| last time an empty bucket occurred
	data.last_sparse_bucket \| dlsb,dataLastSparseBucket \| last time a sparse bucket occurred
	model.bytes \| mb,modelBytes \| model size
	model.memory_status \| mms,modelMemoryStatus \| current memory status
	model.bytes_exceeded \| mbe,modelBytesExceeded \| how much the model has exceeded the limit
	model.memory_limit \| mml,modelMemoryLimit \| model memory limit
	model.by_fields \| mbf,modelByFields \| count of 'by' fields
	model.over_fields \| mof,modelOverFields \| count of 'over' fields
	model.partition_fields \| mpf,modelPartitionFields \| count of 'partition' fields
	model.bucket_allocation_failures \| mbaf,modelBucketAllocationFailures \| number of bucket allocation failures
	model.categorization_status \| mcs,modelCategorizationStatus \| current categorization status
	model.categorized_doc_count \| mcdc,modelCategorizedDocCount \| count of categorized documents
	model.total_category_count \| mtcc,modelTotalCategoryCount \| count of categories
	model.frequent_category_count \| mfcc,modelFrequentCategoryCount \| count of frequent categories
	model.rare_category_count \| mrcc,modelRareCategoryCount \| count of rare categories
	model.dead_category_count \| mdcc,modelDeadCategoryCount \| count of dead categories
	model.failed_category_count \| mfcc,modelFailedCategoryCount \| count of failed categories
	model.log_time \| mlt,modelLogTime \| when the model stats were gathered
	model.timestamp \| mt,modelTimestamp \| the time of the last record when the model stats were gathered
	forecasts.total \| ft,forecastsTotal \| total number of forecasts
	forecasts.memory.min \| fmmin,forecastsMemoryMin \| minimum memory used by forecasts
	forecasts.memory.max \| fmmax,forecastsMemoryMax \| maximum memory used by forecasts
	forecasts.memory.avg \| fmavg,forecastsMemoryAvg \| average memory used by forecasts
	forecasts.memory.total \| fmt,forecastsMemoryTotal \| total memory used by all forecasts
	forecasts.records.min \| frmin,forecastsRecordsMin \| minimum record count for forecasts
	forecasts.records.max \| frmax,forecastsRecordsMax \| maximum record count for forecasts
	forecasts.records.avg \| fravg,forecastsRecordsAvg \| average record count for forecasts
	forecasts.records.total \| frt,forecastsRecordsTotal \| total record count for all forecasts
	forecasts.time.min \| ftmin,forecastsTimeMin \| minimum runtime for forecasts
	forecasts.time.max \| ftmax,forecastsTimeMax \| maximum run time for forecasts
	forecasts.time.avg \| ftavg,forecastsTimeAvg \| average runtime for all forecasts (milliseconds)
	forecasts.time.total \| ftt,forecastsTimeTotal \| total runtime for all forecasts
	node.id \| ni,nodeId \| id of the assigned node
	node.name \| nn,nodeName \| name of the assigned node
	node.ephemeral_id \| ne,nodeEphemeralId \| ephemeral id of the assigned node
	node.address \| na,nodeAddress \| network address of the assigned node
	buckets.count \| bc,bucketsCount \| bucket count
	buckets.time.total \| btt,bucketsTimeTotal \| total bucket processing time
	buckets.time.min \| btmin,bucketsTimeMin \| minimum bucket processing time
	buckets.time.max \| btmax,bucketsTimeMax \| maximum bucket processing time
	buckets.time.exp_avg \| btea,bucketsTimeExpAvg \| exponential average bucket processing time (milliseconds)
	buckets.time.exp_avg_hour \| bteah,bucketsTimeExpAvgHour \| exponential average bucket processing time by hour (milliseconds)