elasticsearch | Elasticsearch on Mesos | Job Orchestrator library

answer found here : https://suay.site/?p=526

in short :

retrieve the key and add the key :

I faced the same problem, but I used to make exactly the same image where everything works to this day. I can't figure out what has changed.

But if you need to urgently solve the problem, use my in-person image:

use fields.Nested:

It depends on what is the goal of the parent POM? If it's only for the consolidation dependency versions, you can use a version catalog. A version catalog is a list of dependencies, represented as dependency coordinates, that a user can pick from when declaring dependencies in a build script.

settings.gradle

TL;DR: You can pass conflicts=proceed to the update_by_query API if you want it to continue working even when hitting conflicts.

More details: The update_by_query page explains:

When you submit an update by query request, Elasticsearch gets a snapshot of the data stream or index when it begins processing the request and updates matching documents using internal versioning. When the versions match, the document is updated and the version number is incremented. If a document changes between the time that the snapshot is taken and the update operation is processed, it results in a version conflict and the operation fails. You can opt to count version conflicts instead of halting and returning by setting conflicts to proceed.

So basically, your update and update_by_query are trying to update the same documents, conflicting with each other. Using conflicts=proceed makes that operation say "oh well, I'll just continue to update the other docs".

It's not related to ES version.

Update max_expansions to more than 50.

max_expansions : Maximum number of variations created.

With 3 grams letter & digits as token_chars, ideal max_expansion will be (26 alphabets + 10 digits) * 3

Short answer:

In your Windows %userprofile% directory (typically C:\Users\) create or edit the file .wslconfig with the following:

For adding the documents you could use bulk indexing for example by collecting the documents to index in a list/array or whatever and when a predefined size is reached - like 500 entries - then do a bulk insert of these.

For deleting there is no bulk operation, but you could collect ids to delete in a list or array again with am maximum size and then use ElasticsearchOperations.idsQuery(List) to create a query for these ids and pass this into the delete(query) method.

Edit 29.09.2021:

the idsQuery was just added in the 4.3 branch, it is simplemented like this (https://github.com/spring-projects/spring-data-elasticsearch/blob/main/src/main/java/org/springframework/data/elasticsearch/core/AbstractElasticsearchRestTransportTemplate.java#L193-L200):

There is no simple way to accomplish your goal. The automatic processing implied for the sequence under [:b :c] is also problematic.

You can get partway there using the Tupelo Forest library. See the Lightning Talk video from Clojure/Conj 2017.

I did some additional work in data destructuring that you may find useful building the tupelo.core/destruct macro (see examples here). You could follow a similar outline to build a recursive solution to your specific problem.

A related project is Meander. I have worked on my own version which is like a generalized version of tupelo.core/destruct. Given data like this