Filter/aggregate one elasticsearch index of time series data by timestamps found in another index - elasticsearch

Question

The Data
So I have reams of different types of time series data. Currently i've chosen to put each type of data into their own index because with the exception of 4 fields, all of the data is very different. Also the data is sampled at different rates and are not guaranteed to have common timestamps across the same sub-second window so fusing them all into one large document is also not a trivial task.
The Goal
One of our common use cases that i'm trying to see if I can solve entirely in Elasticsearch is to return an aggregation result of one index based on the time windows returned from a query of another index. Pictorially:
This is what I want to accomplish.
Some Considerations
For small enough signal transitions on the "condition" data, I can just use a date histogram and some combination of a top hits sub aggregation, but this quickly breaks down when I have 10,000's or 100,000's of occurrences of "the condition". Further this is just one "case", I have 100's of sets of similar situations that i'd like to get the overall min/max from.
The comparisons are basically amongst what I would consider to be sibling level documents or indices, so there doesn't seem to be any obvious parent->child relationship that would be flexible enough over the long run, at least with how the data is currently structured.
It feels like there should be an elegant solution instead of brute force building the date ranges outside of Elasticsearch with the results of one query and feeding 100's of time ranges into another query.
Looking through the documentation it feels like some combination of Elasticsearch scripting and some of the pipelined aggregations are going to be what i want, but no definitive solutions are jumping out at me. I could really use some pointers in the right direction from the community.
Thanks.

Answer 1

I found a "solution" that worked for me for this problem. No answers or even comments from anyone yet, but i'll post my solution in case someone else comes along looking for something like this. I'm sure there is a lot of opportunity for improvement and optimization and if I discover such a solution (likely through a scripted aggregation) i'll come back and update my solution.
It may not be the optimal solution but it works for me. The key was to leverage the top_hits, serial_diff and bucket_selector aggregators.
The "solution"
def time_edges(index, must_terms=[], should_terms=[], filter_terms=[], data_sample_accuracy_window=200):
"""
Find the affected flights and date ranges where a specific set of terms occurs in a particular ES index.
index: the Elasticsearch index to search
terms: a list of dictionaries of form { "term": { "<termname>": <value>}}
"""
query = {
"size": 0,
"timeout": "5s",
"query": {
"constant_score": {
"filter": {
"bool": {
"must": must_terms,
"should": should_terms,
"filter": filter_terms
}
}
}
},
"aggs": {
"by_flight_id": {
"terms": {"field": "flight_id", "size": 1000},
"aggs": {
"last": {
"top_hits": {
"sort": [{"#timestamp": {"order": "desc"}}],
"size": 1,
"script_fields": {
"timestamp": {
"script": "doc['#timestamp'].value"
}
}
}
},
"first": {
"top_hits": {
"sort": [{"#timestamp": {"order": "asc"}}],
"size": 1,
"script_fields": {
"timestamp": {
"script": "doc['#timestamp'].value"
}
}
}
},
"time_edges": {
"histogram": {
"min_doc_count": 1,
"interval": 1,
"script": {
"inline": "doc['#timestamp'].value",
"lang": "painless",
}
},
"aggs": {
"timestamps": {
"max": {"field": "#timestamp"}
},
"timestamp_diff": {
"serial_diff": {
"buckets_path": "timestamps",
"lag": 1
}
},
"time_delta_filter": {
"bucket_selector": {
"buckets_path": {
"timestampDiff": "timestamp_diff"
},
"script": "if (params != null && params.timestampDiff != null) { params.timestampDiff > " + str(data_sample_accuracy_window) + "} else { false }"
}
}
}
}
}
}
}
}
return es.search(index=index, body=query)
Breaking things down
Get filter the results by 'Index 2'
"query": {
"constant_score": {
"filter": {
"bool": {
"must": must_terms,
"should": should_terms,
"filter": filter_terms
}
}
}
},
must_terms is the required value to be able to get all the results for "the condition" stored in "Index 2".
For example, to limit results to only the last 10 days and when condition is the value 10 or 12 we add the following must_terms
must_terms = [
{
"range": {
"#timestamp": {
"gte": "now-10d",
"lte": "now"
}
}
},
{
"terms": {"condition": [10, 12]}
}
]
This returns a reduced set of documents that we can then pass on into our aggregations to figure out where our "samples" are.
Aggregations
For my use case we have the notion of "flights" for our aircraft, so I wanted to group the returned results by their id and then "break up" all the occurences into buckets.
"aggs": {
"by_flight_id": {
"terms": {"field": "flight_id", "size": 1000},
...
}
}
}
You can get the rising edge of the first occurence and the falling edge of the last occurence using the top_hits aggregation
"last": {
"top_hits": {
"sort": [{"#timestamp": {"order": "desc"}}],
"size": 1,
"script_fields": {
"timestamp": {
"script": "doc['#timestamp'].value"
}
}
}
},
"first": {
"top_hits": {
"sort": [{"#timestamp": {"order": "asc"}}],
"size": 1,
"script_fields": {
"timestamp": {
"script": "doc['#timestamp'].value"
}
}
}
},
You can get the samples in between using a histogram on a timestamp. This breaks up your returned results into buckets for every unique timestamp. This is a costly aggregation, but worth it. Using the inline script allows us to use the timestamp value for the bucket name.
"time_edges": {
"histogram": {
"min_doc_count": 1,
"interval": 1,
"script": {
"inline": "doc['#timestamp'].value",
"lang": "painless",
}
},
...
}
By default the histogram aggregation returns a set of buckets with the document count for each bucket, but we need a value. This is what is required for serial_diff aggregation to work, so we have to do a token max aggregation on the results to get a value returned.
"aggs": {
"timestamps": {
"max": {"field": "#timestamp"}
},
"timestamp_diff": {
"serial_diff": {
"buckets_path": "timestamps",
"lag": 1
}
},
...
}
We use the results of the serial_diff to determine whether or not two bucket are approximately adjacent. We then discard samples that are adjacent to eachother and create a combined time range for our condition by using the bucket_selector aggregation. This will throw out buckets that are smaller than our data_sample_accuracy_window. This value is dependent on your dataset.
"aggs": {
...
"time_delta_filter": {
"bucket_selector": {
"buckets_path": {
"timestampDiff": "timestamp_diff"
},
"script": "if (params != null && params.timestampDiff != null) { params.timestampDiff > " + str(data_sample_accuracy_window) + "} else { false }"
}
}
}
The serial_diff results are also critical for us to determine how long our condition was set. The timestamps of our buckets end up representing the "rising" edge of our condition signal so the falling edge is unknown without some post-processing. We use the timestampDiff value to figure out where the falling edge is.