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" --cacer服务器托管网t $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.to服务器托管网tal | 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)
相关资料
- cat anomaly detectors API
- Post data to jobs API
- API conventions
- HTTP accept header
服务器托管，北京服务器托管，服务器租用 http://www.fwqtg.net
现在很多公司都在使用企业微信了，不但方便公司内部交流和客户交流，还能组建各种小组群，业务群。企业微信群提供一个机器人的功能，方便我们把公司业务信息，或来自外部的信息自动发布到群里。 这里研究一下如何向微信群里发送图片消息，如下图所求，先创建好群机器人，复制或记…
