Yeah, should be this:
GET /_search { "aggs": { "tasks": { "terms": { "field": "task" } } } }See https://www.elastic.co/guide/en/elasticsearch/reference/current/search-aggregations-bucket-terms-aggregation.html

This https://stackoverflow.com/questions/65109764/wildcard-search-issue-with-long-datatype-in-elasticsearch says long types can be converted to string to do the search

well I still see some ES errors in the logs
clearml-apiserver | [2021-07-07 14:02:17,009] [9] [ERROR] [clearml.service_repo] Returned 500 for events.add_batch in 65750ms, msg=General data error: err=('500 document(s) failed to index.', [{'index': {'_index': 'events-training_stats_scalar-d1bd92a3b039400cbafc60a7a5b1e52b', '_type': '_doc', '_id': 'c2068648d2fe5da975665985f44c20b6', 'status':..., extra_info=[events-training_stats_scalar-d1bd92a3b039400cbafc60a7a5b1e52b][0] primary shard is not active Timeout: [1m], request: [BulkShardRequest [[events-training_stats_scalar-d1bd92a3b039400cbafc60a7a5b1e52b][0]] containing [500] requests and a refresh]

however 504 is very extreme, I'm not sure it's related to the timeout on the server side, you might want to increase the ELB timeout

So it looks like it tries to register a batch of 500 documents

but not as much as the ELB reports

more than 120s?

SuccessfulKoala55 I deleted all :monitor:machine and :monitor:gpu series, but only deleted ~20M documents out of 320M documents in the events-training_debug_image-xyz . I would like now to understand which experiments contain most of the document to delete them. I would like to aggregate the number of document per experiment. Is there a way do that using the ES REST api?

SuccessfulKoala55 Thanks to that I was able to identify the most expensive experiments. How can I count the number of documents for a specific series? Ie. I suspect that the loss, that is logged every iteration, is responsible for most of the documents logged, and I want to make sure of that

Maybe the agent could be adapted to have a max_batch_size parameter?

You might need to specify number of buckets if you don't get all of the experiments, but since it's a single shard, I think it'll be ordered by descending bucket size anyway

Just do a sub-aggregation for the metric field (and if you like more details, a sub-sub aggregation for the variant field)

but the issue was the shard not being active, it's not the number of documents

Thanks a lot, I will play with that!

500 is relatively low...

Something like that?
curl "localhost:9200/events-training_stats_scalar-adx3r00cad1bdfvsw2a3b0sa5b1e52b/_search?pretty" -H 'Content-Type: application/json' -d' { "query": { "bool": { "must": [ { "match": { "variant": "loss_model" } }, { "match": { "task": "8f88e4b8cff84f23bde74ed4b7213ec6" } } ] } }, "aggs": { "series": { "terms": { "field": "iter" } } } } '

Why not do:
curl "localhost:9200/events-training_stats_scalar-adx3r00cad1bdfvsw2a3b0sa5b1e52b/_search?pretty" -H 'Content-Type: application/json' -d' { "query": { "bool": { "must": [ { "match": { "variant": "loss_model" } } ] } }, "aggs": { "terms": { "field": "task" } } }For all tasks?

Now, I know the experiments having the most metrics. I want to downsample these metrics by 10, ie only keep iterations that are multiple of 10. How can I query (to delete) only the documents ending with 0?

I guess using a delete by query with a match on the field value suffix or something similar?

Here I have to do it for each task, is there a way to do it for all tasks at once?

Hmm, that's something I don't know 🙂

Why do you do aggs on the "iter" field?

Ha nice, good one! Thanks!

From what I can find there's a prefix query, but not a suffix - this can be done using a regex or a wildcard, but that's relatively expensive

"Can only use wildcard queries on keyword and text fields - not on [iter] which is of type [long]"

But I would need to reindex everything right? Is that a expensive operation?

There is no way to filter on long types? I can’t believe it

Ok, I guess I’ll just delete the whole loss series. Thanks!

Reindex is very expensive 🙂

Same for regexp, damn