It is common to have elastic indices with dates, in particular from something like logstash.
So for example, you have indices like foo-2016.05.01, foo-2016.05.02, etc...
When doing a time range query for data. What is the cost of querying indexes that I already know won't have data for that time range?
So for example if time range query only asks for data from 2016.05.02 but I also include the foo-2016.05.01 index in my query.
Is that basically a quick one-op per index where the index knows it has no data in that date range, or will doing this be costly to performance? I'm hoping not only to know the yes/no answer, but to understand why it behaves the way it does.
Short version: it's probably going to be expensive. The cost will be n where n is the number of distinct field values for the date data. If all entries in the index had an identical date field value, it'd be a cheap query of 1 check (and would be pointless since it'd be a binary "all or nothing" response at that point). Of course, the reality is usually that every single doc has a unique date field value (which is incrementing such as in a log), depending on how granular the date is (assuming here that the time is included to seconds or milliseconds). Elasticsearch will check each aggregated, unique date field value of the included indices to try and find documents that match on the field by satisfying the predicates of the range query. This is the nature of the inverted index (indexing documents by their fields).
An easy way to improve performance is to change the Range Query to a Range Filter which caches results and improves performance for requests beyond the first one. Of course, this is only valuable if you're repeating the same range filter over time (the cache is read more than it is written), and if the range is not part of scoring the documents (that is to say those in range are not more valuable that those not in range when returning a set of both - also known as "boosting").
Another way to improve performance is by convention. If you query by day, store each day in its own rolling index and then do pre-search logic to select the indexes to query. This eliminates the need for the filter or query entirely.
Elasticsearch doesn't care about the index name (that includes the date) and it doesn't automagically exclude that index from your range query. It will query all the shards (a copy - be it replica or primary) of all the indices specified in the query. Period.
Kibana, on the other hand, knows based on the time range selected to query specific indices only.
If you know your range will not make sense on some indices, then exclude those from the query before creating the query.
A common approach for logging usecase, in case the current day is most frequently queried is to create an alias. Give it a significant name - like today - that will always point to today's index. Also, common with time based indices is the retention period. For these two tasks - managing the aliases and deleting the "expired" indices - you can use Curator.
In case the most times you care about the current day, use that alias and thus you get rid of the days before today.
In case not, then filter the indices to be queried based on the range before deciding on which indices to run the query.
Related
with our current implementation of search engine we do something like:
search by date range from to (by #timestamp)
get all indices by some prefix (e.g. technical-logs*)
filter out only those indices which applies the range from to (e.g. if from=20230101 and to=20230118 then we select all indices in those ranges with prefix technical-logs-yyyyMMdd)
It seems like that data streams could be beneficial for us. The problem I see is that all indices being created by data streams are hidden by default so I won't be able to see them (by default) therefore I won't be able to query only those indices which I'm interested in (from-to).
Is there some easy mechanism how we can select only indices which we want or does the ES has some functionality for that? I know that there is #timestamp field but I don't know if that is somehow being used also to filtering out only indices which contains given date.
That's the whole point of data streams, i.e. you don't need to know which indices to query, you just query the data stream (i.e. like an alias) or a subset thereof technical-logs* and ES will make sure to only query the underlying indexes that satisfy your constraints (from/to time interval, etc)
Time-series data streams use time bound indices. Each of those backing indices is then sorted by #timestamp so that when you search for a specific time interval, ES will only query the relevant backing indexes.
I know how Elasticsearch index words and strings, but I wonder if there's a different behaviour for timestamps?
We have internal elasticsearch instance that index events ( millions of events per day).
I want to pull once in X seconds all the events that we received in the last X seconds.
Does Elasticsearch index the timestamp in efficient way such that we don't need to traverse all the documents to return the relevant results? How it index this data?
Anything numeric, like date fields, integer fields, geo fields, etc, are not stored in the inverted index, but in BKD trees (since ES 5), which are especially suited for range queries and finding collection of unordered docIDs that meet the time range conditions.
I'm wondering what would be an efficient way to detect the last modified timestamp of an index in Elastic Search. I have read posts of adding a timestamp fields in pipeline but this solution has limitations (e.g. only newly created index supports timestamp update?)
If only a handful of indices are required to track their last modify time, what would be the most efficient way? Would periodic query and compare result between queries give us an approx. last modify time? any other ways to track ES events?
there is a creation_date setting, but not a comparable update_date one. the reasoning behind this is that updating this for every indexing event would be very expensive, even more so in a distributed environment
you could use something like meta, but it has the same limitation as adding a timestamp to individual documents
Good day, everyone. I have a lit bit strange case of using elastic search for me.
There are two different indexes, each index contain one data type.
First type contains next important for this case data:
keyword (text,keyword),
URL (text,keyword)
position (number).
Second type contains next data fields:
keyword (text,keyword)
numberValue (number).
I need to do next things:
1.Group data from the first ind by URL
2.For each object in group calculate new metric (metric A) by next simple formula: position*numberValue*Param
3.For each groups calculate sum of elements metric A we have calculated on stage 1
4.Order by desc result groups by sums we have calculated on stage 3
5.Take some interval of result groups.
Param - param, i need to set for calculation, this is not in elastic.
That is not difficult algorithm, but data in different indices, and i don`t know how to do it fast, and i prefer to do it on elastic search level.
I don`t know how to make effective data search or pipeline of data processing which can help me to implement this case.
I use ES version 6.2.3 if it is important.
Give me some advice, please, how can i implement this algorithm.
By reading 2. you seem to assume keyword is some sort of primary key. Elasticsearch is not an RDB and can only reason over one document at a time, so unless numberValue and position are (indexed) fields of the same document you can't combine them.
The rest of the items seem to be possible to achieve with the help of Aggregation
Elasticsearch takes the length of a document into account when ranking (they call this field normalization). The default behavior is to rank shorter matching documents higher than longer matching documents.
Is there anyway to turn off or modify field normalization at query time? I am aware of the index time omit_norms option, but I would prefer to not reindex everything to try this out.
Also, instead of simply turning off field normalization, I wanted to try out a few things. I would like to take field length into account, but not as heavily as elasticsearch currently does. With the default behavior, a document will rank 2 times higher than a document which is two times longer. I wanted to try a non-linear relationship between ranking and length.