the log data following:
{"time":"2020-05-21 10:10:56.210","level":"Info","transID":1,"span":{"traceID":521,"spanID":1,"tags":[{"userID":93}]},"msg":"TransStart","extend":{"type":"Login"}}
{"time":"2020-05-21 10:10:57.213","level":"Info","transID":1,"span":{"traceID":522,"spanID":2,"tags":[{"userID":93}]}}
{"time":"2020-05-21 10:10:58.218","level":"Error","transID":1,"span":{"traceID":523,"spanID":3,"tags":[{"userID":93}]},"extend":{"result":"Failed"}}
{"time":"2020-05-21 10:11:56.210","level":"Info","transID":2,"span":{"traceID":524,"spanID":1,"tags":[{"userID":932229}]},"msg":"TransStart","extend":{"type":"Login"}}
{"time":"2020-05-21 10:11:57.213","level":"Info","transID":2,"span":{"traceID":525,"spanID":2,"tags":[{"userID":93}]}}
{"time":"2020-05-21 10:11:58.218","level":"Info","transID":2,"span":{"traceID":526,"spanID":3,"tags":[{"userID":93}]},"extend":{"result":"Succ"}}
{"time":"2020-05-21 10:12:56.210","level":"Info","transID":3,"span":{"traceID":527,"spanID":1,"tags":[{"userID":93}]},"msg":"TransStart","extend":{"type":"Login"}}
{"time":"2020-05-21 10:12:57.213","level":"Info","transID":3,"span":{"traceID":528,"spanID":2,"tags":[{"userID":93}]}}
{"time":"2020-05-21 10:12:58.218","level":"Error","transID":3,"span":{"traceID":529,"spanID":3,"tags":[{"userID":93}]},"extend":{"result":"Failed"}}
{"time":"2020-05-21 10:13:56.210","level":"Info","transID":4,"span":{"traceID":530,"spanID":1,"tags":[{"userID":93}]},"msg":"TransStart","extend":{"type":"Login"}}
{"time":"2020-05-21 10:13:57.213","level":"Info","transID":4,"span":{"traceID":531,"spanID":2,"tags":[{"userID":93}]}}
{"time":"2020-05-21 10:13:58.218","level":"Info","transID":4,"span":{"traceID":532,"spanID":3,"tags":[{"userID":93}]},"extend":{"result":"Succ"}}
now I want to statistic the login's seccess rate from these logs.
there are some logs, but according to "transID" can distinguish event,
so we can know the user log in fourth times, and "Failed" twice, "Succ" twice
The question is coming： how can I calculate the succedd rate through aggregation?
there is my ES aggs code:
"size": 0,
"aggs": {
"trans_id": {
"terms": {
"field": "transID"
},
"aggs":{
"login":{
"filter": {
"term": {
"extend.type.keyword": "Login"
}
}
},
"login_succ":{
"filter": {
"term": {
"extend.result.keyword": "Succ"
}
}
},
"succ_script":{
"bucket_script": {
"buckets_path":{
"succ_count": "login_succ>_count",
"total_count": "login>_count"
},
"format": "#.##",
"script":" params.succ_count/params.total_count",
"gap_policy": "skip"
}
}
}
}
}
the response :
"aggregations" : {
"trans_id" : {
"doc_count_error_upper_bound" : 0,
"sum_other_doc_count" : 0,
"buckets" : [
{
"key" : 5,
"doc_count" : 4,
"login" : {
"doc_count" : 0
},
"login_succ" : {
"doc_count" : 0
},
"succ_script" : {
"value" : null
}
},
{
"key" : 1,
"doc_count" : 3,
"login" : {
"doc_count" : 1
},
"login_succ" : {
"doc_count" : 0
},
"succ_script" : {
"value" : 0.0,
"value_as_string" : "0"
}
},
{
"key" : 2,
"doc_count" : 3,
"login" : {
"doc_count" : 1
},
"login_succ" : {
"doc_count" : 1
},
"succ_script" : {
"value" : 1.0,
"value_as_string" : "1"
}
},
{
"key" : 3,
"doc_count" : 3,
"login" : {
"doc_count" : 1
},
"login_succ" : {
"doc_count" : 0
},
"succ_script" : {
"value" : 0.0,
"value_as_string" : "0"
}
},
{
"key" : 4,
"doc_count" : 3,
"login" : {
"doc_count" : 1
},
"login_succ" : {
"doc_count" : 1
},
"succ_script" : {
"value" : 1.0,
"value_as_string" : "1"
}
}
]
}