aggregation

How can I write an Elasticsearch terms aggregation that splits the buckets by the entire term rather than individual tokens? For example, I would like to aggregate by state, but the following returns new, york, jersey and california as individual buckets, not New York and New Jersey and California as the buckets as expected: curl -XPOST "http://localhost:9200/my_index/_search" -d' { "aggs" : { "states" : { "terms" : { "field" : "states", "size": 10 } } } }' My use case is like the one described here https://www.elastic.co/guide/en/elasticsearch/guide/current/aggregations-and-analysis.html with

I am learning core data and particularly working on aggregation. Current what I want to do : count the number of records from the table which is in to-many relationship with inverse relationship on some criteria . Currently I am doing this : NSExpression *ex = [NSExpression expressionForFunction:@"count:" arguments:[NSArray arrayWithObject:[NSExpression expressionForKeyPath:@"ddname"]]]; NSPredicate *pred = [NSPredicate predicateWithFormat:@"ddtype == 'Home'"]; NSExpressionDescription *ed = [[NSExpressionDescription alloc] init]; [ed setName:@"countDDEvents"]; [ed setExpression:ex]; [ed

Using ES 1.2.1 My aggregation { "size": 0, "aggs": { "cities": { "terms": { "field": "city","size": 300000 } } } } The issue is that some city names have spaces in them and aggregate separately. For instance Los Angeles { "key": "Los", "doc_count": 2230 }, { "key": "Angeles", "doc_count": 2230 }, I assume it has to do with the analyzer? Which one would I use to not split on spaces? For fields that you want to perform aggregations on I would recommend either the keyword analyzer or do not analyze the field at all. From the keyword analyzer documentation: An analyzer of type keyword that

I'm dealing with this problem. I'm creating math problems, each one has response. For example. If my question is about the "result of 5x + 15 = 2?" , I'll be waiting just one answer (as integer). If my question is about says "give me the area and permiter of this shape" , I'll be waiting two answers (as doubles). In another one, I'll be waiting as response a string And anothers, I can have several answers or responses with various datatypes. My big question is. How would be the relation between the classes question and response. Also I was dealing if this should be an association, aggregation

I have a Pandas data frame like this: test = pd.DataFrame({ 'Date' : ['2016-04-01','2016-04-01','2016-04-02', '2016-04-02','2016-04-03','2016-04-04', '2016-04-05','2016-04-06','2016-04-06'], 'User' : ['Mike','John','Mike','John','Mike','Mike', 'Mike','Mike','John'], 'Value' : [1,2,1,3,4.5,1,2,3,6] }) As you can see below, the data set does not have observations for every day necessarily: Date User Value 0 2016-04-01 Mike 1.0 1 2016-04-01 John 2.0 2 2016-04-02 Mike 1.0 3 2016-04-02 John 3.0 4 2016-04-03 Mike 4.5 5 2016-04-04 Mike 1.0 6 2016-04-05 Mike 2.0 7 2016-04-06 Mike 3.0 8 2016-04-06 John

I'm wondering what everyone is using for logging, log management and log aggregation on their systems. I am working in a company which uses .NET for all it's applications and all systems are Windows based. Currently each application looks after its own logging and notifications of failures (e.g. if app A fails it will send out its own 'call for help' to an admin). While this current practice works its a bit hacky and hard to manage. I've been trying to find some options for making this work better and I've come up with the following: log4net & Chainsaw (ah, if it works). Logging via log4net or