Multi-term queries are, in their most generic definition, queries with several terms. These terms could be completely unrelated, or they could be about the same topic, or they could even be part of a single specific concept.
All these scenarios call for different configurations. This articles discusses some of the options with Elasticsearch. Assuming elasticsearch is up and running on your local machine, you can download the script which creates the data set used in the following examples. These are the created documents:. The first example of query is for the simple case of terms which may or may not be related.
In this scenario, we decide to use a classic match query. This kind of query does not impose any restriction between multiple terms, but of course will promote documents which contain more query terms.
The documents in position 2 and 3 share the same score, because they have the same number of matches two terms and the same document lenght. Precision on multi-term query can be controlled by specifying some arbitrary threashold for the number of terms which should be matched. For example, we can re-write the query as:. The output will be basically the same, with the exeption of having only the top four documents.
You can experiment with different thresholds for minimum match, keeping in mind that there is a balance to find between removing unrelevant documents and not losing the relevant ones. What if we need an exact match of the query? More precisely, what if we need to match all the query terms in their relative position?
The previous query can be rewritten as:. We immediately see that the query returns an empty result set: there is no document about quick brown dogs. Now we can see how the query retrieves only one document, precisely document 2, the only one to match the exact phrase. Sometimes a phrase match can be too restrictive. This concept is less restrictive than a pure phrase match, but still stronger than a general purpose query. In order to achieve proximity search, we simply need to define the search window, so how far we allow the terms to be.
We immediately see that the second document is also relevant to the query, but it was missed by the original phrase match. We can also try with a bigger slop, e.Although Elasticsearch offers an efficient scoring algorithm, it may often be inadequate in e-commerce contexts. Most users tend to care only about the topmost number of results. If you can present the topmost results according to user preference, then your conversion rate is likely to increase significantly.
This knowledge can help you achieve a user-customizable list of results. For this post, we will be using hosted Elasticsearch on Qbox. You can sign up or launch your cluster hereor click "Get Started" in the header navigation.
If you need help setting up, refer to " Provisioning a Qbox Elasticsearch Cluster. However, the more common meaning of relevance is the algorithm that calculates the similarity of the contents of a full-text field in comparison to a full-text query string. The table above lists the component factors that determine the score of a document in Elasticsearch.
Term Frequency tf is a measure of the number of occurrences of a term in a document in context. If the occurrence count is high, the score will be high, and the chances for inclusion of that document as relevant will be high. Inverse Document Frequency idf is a measurement of how frequently the search terms occur across a set of documents.
Typically, if the search term commonly occurs across many documents, the score will be low. Frequent occurrence of rare words will typically boost the score value. Coord is a measurement of matching on multiple search terms, and a higher value of this measurement will increase the overall score.Relevant search results (with elasticsearch) - Jettro Coenradie - Codemotion Amsterdam 2017
Consider a search for the two terms, "woolen" and "jacket. A document that has both of these words will get a higher rank than documents containing either of the search terms.
For example, if the search term finds a match in a title field instead of the content field, it may achieve relevance. Although it is not directly related to document relevance, a querynorm is a measure for comparing queries when you are using a combination of query types.
You can also affect the score with either of the boost factors index time boost and query time boost. Boosting a specific field can cause it to have more significance in the score calculation.
All documents that pass the Boolean model then go on to scoring with the Vector Space Model. Of course, we want to see how we can use all of these elements to calculate the score for a document.
We do not recommend using it in production, but it can be very helpful as you develop and refine your queries. Elasticsearch actually offers many methods for calculating the score for each match. Consider this statement:. In the 0. In addition to the built-in functions, you can create more functions with a script. Use the filters to restrict the results to only the ones that match your criteria.I would like to ask for help with following issue.
My goal is to list events of followers and then sort the events by count of followers who are following the event. First part of task is pretty simple, I can match events of related users by simple terms query, something like. I want to order the result set by number of matched followers. Since I went through lot of issues here, stack, elastic githubI thought the way will be using function score.
Most of the people recommends doing more terms queries and boost each and single one, but this is unusable for me, since the number of my followers is relative it will be different for every user. Is there any terms query related parameter which would boost documents by count of matched terms? I was also thinking about aggregations but they don't fit such a task, so from my point of view I will have to do some magic via script sorting, but first I would like to try to ask for help here.
This topic was automatically closed 28 days after the last reply. New replies are no longer allowed. Score boosting for terms query Elasticsearch.Tag: elasticsearch. I am trying to run a simple elasticsearch terms query as follows using the sense chrome extension :.
This is exactly where your problem is : the field is analyzed, however, a terms query look directly for terms, which are not analyzed see here. Write your query like this, i.
We can use patterns occuring in the index names to be identified and can specify whether it can be created automatically if it is not already existing. An example would be action. When in your own Dockerfile you use the CMD instruction, you override the one from the parents Dockerfiles. Since your CMD only starts gunicorn, Elasticsearch is "near real-time" by nature, i.
While it may seem enough in a majority of cases, it might not, such as in your case.
Elasticsearch - Query DSL
If you need your documents to be available immediately, you need to refresh your indices explicitly by Index elasticSearchIndex.
Type "business". Id result. DocAsUpsert ; You can achieve this by using scripting. For more reference in You can use Terms Aggregation for this.
Meaning, if you search for A8DF5A, this is exactly what is searching for upper-case letters included. But, your macAddr and insturmentName fields and others are just strings. Meaning, they use the standard analyzer which lowercases the terms. So, you are searching Did you try to use extension method Suffix? This is how you can modify your query Suffix "english". Type TextQueryType.
Hope it helps You can achieve that with a simple terms aggregation parametrized with an include property which you can use to specify either a regexp e. I think you don't have all the requirements set, yet. The limit filter doesn't limit the number of documents that are returned, just the number of documents that the query executes on each shard.
If you want to limit the number of documents returned, you need to use the size parameter or in your case the Python slicing API like I am not sure you can do this as the Discovery section already uses the timestamp aggregation. Can you explain what are you trying to do? There are ways to add customer aggregations in the visualizations. If you open up the advanced section on the aggregation in the visualization youTag: elasticsearch. Elasticsearch allows you to boost a field that you are searching on, but is it possible to "boost" the importance of a specific word in the query itself?
For example, I want to search for "Minnesota health care", and I want "Minnesota" to be more important than "health care" because I don't want health care information from other states. Not sure if that is possible. Ideally I would be able to do this all programmatically, with a list of words that are considered "most important" for the application, and those could be found in incoming queries and "boosted".
Since the exception complains about a NumberFormatException, you should try sending the date as a long instead of a Date object since this is how dates are stored internally. See I'm calling date. You can achieve that with a simple terms aggregation parametrized with an include property which you can use to specify either a regexp e.
You can use low level client to pass raw json. There are a couple issues in your code: Issue 1: When you create your document in the second snippet, you're not using the correct mapping type and your body doesn't include the correct field name as declared in your mapping: client.
After searching some more, I got the impression that this same scrollId is by design. After the timeout has expired which is reset after each call Elasticsearch scan and scroll - add to new index.
So you can only get one opened scroll per index. I am not sure you can do this as the Discovery section already uses the timestamp aggregation. Can you explain what are you trying to do? There are ways to add customer aggregations in the visualizations. If you open up the advanced section on the aggregation in the visualization you Since you're using the elasticsearch-river-couchdb plugin, you can configure the river with a groovy script that will remove all the fields but the ones you specify.
An example is given in the official documentation of the plugin and simply amounts to add the following the script to the couchdb object When in your own Dockerfile you use the CMD instruction, you override the one from the parents Dockerfiles.
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Since your CMD only starts gunicorn, Write your query like this, i. Index elasticSearchIndex. Type "business". Id result. DocAsUpsert ; Here the frequency is calculated across all the You can achieve this by using scripting. For more reference in You will need to setup a multifield as the dash is causing the terms to be split.
I don't think you can do this with terms.We had known that Full text queries will analyse query string before executing. These queries are usually used for structured data numbers, dates, enums…rather than full text fields.
The default boost value is 1.
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Non-necessary Non-necessary.Its been used quite a bit at the Open Knowledge Foundation over the last few years. Plus, as its easy to setup locally its an attractive option for digging into data on your local machine. This post therefore provides a simple introduction and guide to querying ElasticSearch that provides a short overview of how it all works together with a good set of examples of some of the most standard queries.
The following examples utilize the cURL command line utility. If you prefer, you you can just open the relevant urls in your browser:. Basic queries can be done using only query string parameters in the URL. Basic queries like this have the advantage that they only involve accessing a URL and thus, for example, can be performed just using any web browser. However, this method is limited and does not give you access to most of the more powerful query features. Basic queries use the q query string parameter which supports the Lucene query parser syntax and hence filters on specific fields e.
There are a variety of other options e. More powerful and complex queries, including those that involve faceting and statistical operations, should use the full ElasticSearch query language and API.
In the query language queries are written as a JSON structure and is then sent to the query endpoint details of the query langague below. There are two options for how a query is sent to the search endpoint:. Queries are JSON objects with the following structure each of the main sections has more detail below :. Query objects are built up of sub-components.
Querying ElasticSearch - A Tutorial and Guide
These sub-components are either basic or compound. Compound sub-components may contains other sub-components while basic may not. Filters, are really special kind of queries that are: mostly basic though boolean compounding is alllowed ; limited to one field or operation and which, as such, are especially performant.
Examples, of filters are full list on RHS at the bottom of the query-dsl page :. Rather than attempting to set out all the constraints and options of the query-dsl we now offer a variety of examples. This will perform a full-text style query across all fields. The query string supports the Lucene query parser syntax and hence filters on specific fields e.
For full details see the query-string documentation.