I am trying to compute some simple statistic over log Event like download with elasticSearch. I am wondering what is the difference between the Count API and Bucket Aggregate Filter combine with Value Count aggregate ? Is there any benefit of using aggregate over the count api ?
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I am new to DynamoDB and I am looking for suggestions / recommendations. There's a use case where we have a paginated API and we have to search for multiple values of an indexed attribute. Since DynamoDB allows only one value to be searched for an indexed attribute in a single query, a batch call should be done. However, since it requires pagination (batch call would make the pagination complicated), therefore currently, the required IDs are fetched from ElasticSearch for those multiple values (in a paginated way) after which the complete documents are fetched from DynamoDB based on IDs obtained from ElasticSearch. Is this the correct approach or is there any better alternative?
I am using Elastic Cloud v 7.5.2. I am trying to transform the index, where i want the term count to be aggregated. In Kibana UI, Define Pivot does not have provision to take terms aggregation. How to achieve it? Is the version didn't support or we can achieve the same using Transform API?
we have a field eventType which will have values like task-started, task-completed, task-inprogress. Each document will have an jobId and each job can have multiple tasks. I need to transform the index to a new index in such a way where task-started, task-completed and task-inprogress will be separate field and it will have value count aggregated to it.
Our ultimate goal, in Kibana we need to show additional columns which will have percentage and ratio of these task fields.
I'd like to automate a features creation process for large dataset with elastic search.
I'd like to know if it is possible to create a new field in my dataset that will be the result of an aggregation.
I'm currently working on log from a network and wants to implement the moving average (the mean of a field during the past x days) of the filed "bytes_in".
After spending time reading the doc and example, I wasn't able to do so ...
You have two possibilities:
By using the Rollup API you can create a job that will allow you to summarize data on the go and store it in a dedicated index.
A detailed example can be found in this blog article.
By using the Data Frame Transform API, you can pivot your data into a new entity-centric index, aggregate your data in various ways and store the results in a dedicated index.
I have a data log entry stored in elasticsearch, each with its own timestamp. I now have a dashboard that can get the aggregation by day / week using Date Histogram aggregation.
Now I want to get the data in chunk (data logs are written several time per transaction, spanning for up to several minutes) by analyzing the "cluster" of logs according to its timestamp to identify whether it's the same "transaction". Would that be possible for Elastic search to automatically analyze the meaningful bucket and aggregate the data accordingly?
Another approach I'm trying is to group the data by transaction ID - however there's a warning that to do this I need to enable fielddata which will use a significant amount of memory. Any suggestion?
In a Kafka server I have N types of messages, one for each IOT application. I want to store these messages in Elastisearch in different indexes. Do you know which is the most optimizing method for that use case in order to have the lower time response for request regarding every message type ?
Furthermore, it is adivised to create an index per day like this: "messageType-%{+YYYY.MM.dd}"; Is this a way for my use case?
Finally, concerning the previous way, if I have a request with a time range for instance from 2016.06.01 to 2016.07.04, does elasticsearch search directly in the indexes "messageType-%{+2016.06.01}", "messageType-%{+2016.06.02}", ..., "messageType-%{+2016.07.04}" ?
Thanks in advance,
J
If you plan to purge docs after a certain time, creating indexes based on time is a good idea because you can drop indexes after certain time.
You can search against all indexes or more preferably you should specify the indexes you want to search against.
For example, you could do a search against /index1,index2/_search where you determine index1, index2 from the query or you can just hit /_search which will search all indexes (slower)