Elasticsearch in Action

6.6. custom scoring with function_score.

Finally, we come to one of the coolest queries that Elasticsearch has to offer: function_score. The function_score query allows you to take control over the relevancy of your results in a fine-grained manner by specifying any number of arbitrary functions to be applied to the score of the documents matching an initial query.

Each function in this case is a small snippet of JSON that influences the score in some way. Sound confusing? Well, we’ll clear it up by the end of this section. We’ll start with the basic structure of the function_score query; the next listing an example that doesn’t perform any fancy scoring.

Listing 6.11. Function_score query basic structure

Simple enough—it looks just like a regular match query inside a function_score wrapper. There’s a new key, functions, that’s currently empty, but don’t worry about that yet; you’ll put things into that array in just a second. This listing is intended to show that the results of this query are going to be the documents that the function_score functions operate on. For example, if you have 30 total documents in the index and the match query for “elasticsearch” in the description field matches 25 of them, the functions inside the array will be applied to those 25 documents.

The function_score query has a number of different functions, and in addition to the original query, each function can take another filter element. You’ll see examples of this as we go into the details about each function in the next sections.

6.6.1. weight

The weight function is the simplest of the bunch; it multiplies the score by a constant number. Note that instead of a regular boost field, which increases the score by a value that gets normalized, weight really does multiply the score by the value.

In the previous example, you’re already matching all of the documents that have “elasticsearch” in the description, so you’ll boost documents that contain “hadoop” in the description as well in the next listing.

Listing 6.12. Using weight function to boost documents containing “hadoop”

The only change to the example was adding the following snippet to the functions array:

"weight": 1.5,

"filter": {"term": {"description": "hadoop"}}

This means that documents that match the term query for “hadoop” in the description will have their score multiplied by 1.5.

You can have as many of these as you’d like. For example, to also increase the score of get-together groups that mention “logstash,” you could specify two different weight functions, as in the following listing.

Listing 6.13. Specifying two weight functions

6.6.2. Combining scores

Let’s talk about how these scores get combined. There are two different factors we need to discuss when talking about scores:

How the scores from each of the individual functions should be combined, called the score_mode

How the score of the functions should be combined with the original query score (searching for

“elasticsearch” in the description in our example), known as boost_mode

The first factor, known as the score_mode parameter, deals with how each of the different functions’ scores are combined. In the previous cURL request you have two functions: one with a weight of 2, the other with a weight of 3. You can set the score_mode parameter to multiply, sum, avg, first, max, or min. If not specified, the scores from each function will be multiplied together.

If first is specified, only the first function with a matching filter will have its score taken into account. For example, if you set score_mode to first and had a document with both “hadoop” and “logstash” in the description, only a boost factor of 2 would be applied, because that’s the first function that matches the document.

The second score-combining setting, known as boost_mode, controls how the score of the original query is combined with the scores of the functions themselves. If not specified, the new score will be the original query score and the combined function’s score multiplied together. You can change this to sum, avg, max, min, or replace. Setting this to replace means that the original query’s score is replaced by the score of the functions.

Armed with these settings, you can tackle the next function score function, which is used for modifying the

score based on a field’s value. The functions we’ll cover are field_value_factor,

script_score, and random_score, as well as the three decay functions: linear, gauss, and exp. We’ll start with the field_value_factor function.

6.6.3. field_value_factor

Modifying the score based on other queries is quite useful, but a lot of people want to use the data inside their documents to influence the score of a document. In this example, you might want to use the number of reviews an event has received to increase the score for that event; this is possible to do by using the field_value_factor function inside a function_score query.

The field_value_factor function takes the name of a field containing a numeric field, optionally multiplies it by a constant number, and then finally applies a math function such as taking the logarithm of the value. Look at the example in the next listing.

Listing 6.14. Using field_value_factor inside a function_score query

The score that comes out of the field_value_factor function here will be

ln(2.5 * doc['reviews'].value)

For a document with a value of 7 in the reviews field, the score would be

ln(2.5 * 7) -> ln(17.5) -> 2.86

Besides ln there are other modifiers: none (default), log, log1p, log2p, ln1p, ln2p, square, sqrt, and reciprocal. One more thing to remember when using field_ value_factor: it

loads all the values of whichever field you’ve specified into memory, so the scores can be calculated quickly; this is part of the field data, which we’ll discuss in section 6.10. But before we talk about that, we’ll cover another function, which can give you finer-grained control over influencing the score by specifying a custom script.

6.6.4. Script

Script scoring gives you complete control over how to change the score. You can perform any sort of scoring inside a script.

As a brief refresher, scripts are written in the Groovy language, and you can access the original score of the document by using _score inside a script. You can access the values of a document using doc['fieldname']. An example of scoring using a slightly more complex script is shown in the next listing.

Listing 6.15. Scoring using a complex script

In this example, you’re using the size of the attendee list to influence the score by multiplying it by a weight and taking the logarithm of it.

Scripting is extremely powerful because you can do anything you’d like inside it, but keep in mind that scripts will be much slower than regular scoring because they must be executed dynamically for each document that matches your query. When using the parameterized script as in listing 6.15, caching the script helps performance.

6.6.5. random

The random_score function gives you the ability to assign random scores to your documents. The advantage of being able to sort documents randomly is the ability to introduce a bit of variation into the first page of results. When searching for get-togethers, sometimes it is nice to not always see the same result at the top.

You can also optionally specify a seed , which is a number passed with the query that will be used to generate the randomness with the function; this lets you sort documents in a random manner, but by using the same random seed, the results will be sorted the same way if the same request is performed again. That’s the only option it supports, so that makes this a simple function.

The next listing shows an example of using it to sort get-togethers randomly.

Listing 6.16. Using random_score function to sort documents randomly

Don’t worry if this doesn’t seem useful yet. Once we’ve covered all of the different functions, we’ll come up with an example that ties them all together at the end of this section. Before we do that, though, there’s one more set of functions we need to discuss: decay functions.

6.6.6. Decay functions

The last set of functions for function_score is the decay functions. They allow you to apply a gradual decay in the score of a document based on some field. There are a number of ways this can be useful. For example, you may want to make get-togethers that occurred more recently have a higher score, with the score gradually tapering off as the get-togethers get older. Another example is with geolocation data; using the decay functions, you can increase the score of results that are closer to a geo point (a user’s location, for example) and decrease the score the farther the group is from the point.

There are three types of decay functions: linear, gauss, and exp. Each decay function follows the same sort of syntax:

"TYPE": {

"origin": "...",

"offset": "...",

"scale": "...",

"decay": "..."

The TYPE can be one of the three types. Each of the types corresponds to a differently shaped curve, shown in figures 6.4, 6.5, and 6.6.

Figure 6.4. Linear curve—scores decrease from the origin at the same rate.

Figure 6.5. Gauss curve—scores decrease more slowly until the scale point is reached and then they decrease faster.

Figure 6.6. Exponential curve—scores drastically drop from the origin.

6.6.7. Configuration options

The configuration options define what the curve will look like; there are four configuration options for each of the three decay curves:

The origin is the center point of the curve, so it’s the point where you’d like the score to be the highest. In the geo-distance example, the origin is most like a person’s current location. In other situations the origin can also be a date or a numeric field.

The offset is the distance away from the originating point, before the score starts to be reduced. In our example, if the offset is set to 1km, it means the score will not be reduced for points within one kilometer from the origin point. It defaults to 0, meaning that scores immediately start to decay as the numeric value moves away from the origin.

The scale and decay options go hand in hand; by setting them, you can say that at the scale value for a field, the score should be reduced to the decay. Sound confusing? It’s much simpler to think of it with actual values. If you set the scale to 5km and the decay to 0.25, it’s the same as saying “at 5 kilometers from my origin point, the score should be 0.25 times the score at the origin.”

The next listing shows an example of Gaussian decay with the get-together data. Listing 6.17. Using Gaussian decay on the geo point location

Let’s look at what’s going on in this listing:

You use a match_all query, which will return all results.

Then you score each result using a Gaussian decay on the score.

The origin point is set in Boulder, Colorado, so the results that come back have the get-togethers in Boulder scored the highest, then results in Denver (a city near Boulder), and so on, as the different get-togethers get farther and farther away from the point of origin.

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InfoQ Homepage Articles Understanding Similarity Scoring in Elasticsearch

Understanding Similarity Scoring in Elasticsearch

Dec 23, 2020 18 min read

Brilian Firdaus

reviewed by

Srini Penchikala

Key Takeaways

  • Relevancy scoring is the backbone of a search engine, understanding how it works is important for creating a good search engine.
  • Elasticsearch uses two kinds of similarity scoring function: TF-IDF before version 5.0 and Okapi BM25 after.
  • TF-IDF measures how much a word is common locally and rare globally to determine how much relevant a query is.
  • Okapi BM25 is based on TF-IDF, it handles the shortcomings of TF-IDF to make the function result more relevant to the user's query.
  • We can use _explain API provided by Elasticsearch to debug the similarity scoring function calculation.  

Sorting a query result is always a hard task to approach. Should you sort it by name, created date, last updated date, or some other factor? If you sort the query results in a product search by name, it’s likely that the first product to appear would not be what the customer was looking to buy.

When creating a Search Engine like product search in the example above, sorting the resulting documents is not always straightforward.

Sorting usually happens by calculating a relevancy or similarity score between the documents in the corpus and the user query. Relevancy score is the backbone of a Search Engine.

Related Sponsored Content

Understanding how to calculate relevancy score is the first step you must take to create a good Search Engine.

With Elasticsearch , we can calculate the relevancy score out of the box. Elasticsearch comes with a built-in relevancy score calculation module called similarity module.

The similarity module uses TF-IDF as its default similarity function until Elasticsearch version 5.0.0.

The latter version uses BM25 , a changed version of TF-IDF , as its default similarity function.

In this article, we will explore TF-IDF and BM25 functions, and how similarity scoring works in Elasticsearch.

Figure 1 below shows the formula of TF-IDF function.

change score elasticsearch

TF-IDF stands for Term Frequency-Inverse Document Frequency. It is a common function used in text analysis and Natural Language Processing to calculate the similarity between words. TF-IDF works by multiplying Term Frequency and Inverse Document Frequency . The former, Term Frequency , is how many times a given word appears in the document. The latter, Inverse Document Frequency , is a calculation that scores how rare your word is in the corpus. The rarer the word is, the higher its score.

When we’re looking for document relevancy with a certain word, we want the word to be:

  • Common Locally : The word appears many times in the document
  • Rare Globally : The word doesn’t appear many times altogether in the corpus.

Documents with a word that is common locally but rare globally are documents that are relevant for the given word. With TF-IDF , we can take into account both the Common Locally and Rare Globally factors of documents when calculating which are the most relevant.

Term Frequency

Term Frequency is the calculation function that takes Common Locally words into account. The value of it is straightforward. You can get it by counting how many times the  word appears in the document in your corpus.

So, we can use Term Frequency to calculate how relevant a document is to  a word, but that alone is not enough. We’d still be left with the following issues:

  • Document Length : Longer documents will be considered more relevant if we only use Term Frequency in our formula. Let’s say that we have a document with 1000 words and another document with 10 words, the chance that our queried word appears more frequently in the document with 1000 words is understandably much higher.
  • Common Words : If we only use Term Frequency and we query common words like "a", "the", "of", *et cetera*, the most relevant document will be a document that has the most common words. Let’s say, for example, we have "The blue keyboard" and "The painting of the farmers and the plants". If we queried "the keyboard", the second document would appear to be more relevant than the first one, even though from a human perspective we know that not to be true.

Because of those problems, using only Term Frequency to calculate similarity score is not recommended. Fortunately, by introducing Inverse Document Frequency , we can avoid both of the aforementioned problems.

Inverse Document Frequency

Inverse Document Frequency is the calculation function that takes Rare Globally words into account. The rarer the word is in the whole corpus, the more important it is. Let’s see the formula of Inverse Document Frequency :

change score elasticsearch

Figure 2. Inverse Document Frequency formula

So we can see that N is the number of documents in our corpus and df is the number of documents that contain the word.

Let’s use an extreme example to illustrate the idea. Suppose that there are 100 documents in our corpus, and each and every document has the word "a" in it. What will happen if we query the word "a"?

change score elasticsearch

Figure 3. Inverse Document Frequency calculation of a word contained in every document

Precisely like we wanted! When the word is not rare globally at all (it exists in every document), the score is reduced to 0. Now let’s look at another example. Like before, we have 100 documents in our corpus. But now, only 1 document has the word "a" in it.

change score elasticsearch

Figure 4. Inverse Document Frequency calculation of a word only contained in one document

As you can see, the rarer the word is in the corpus, the higher the calculation result is.

The Inverse Document Frequency is very important if we query more than one word. Let’s say we queried "a Keyboard" which has two terms, "a" and "Keyboard". We can think "a" is not rare globally while the "keyboard" is. If we only use Term Frequency , a document with more "a" will be shown as the most relevant. We know that it’s wrong, as a document that has 1 "keyboard" should be more relevant compared to a document with 10 "a" and no "keyboard".

TF-IDF Calculation Example

Now that we understand what Term Frequency and Inverse Document Frequency are and why they’re important, let’s look at some examples.

Let’s say that we have five documents containing a product name in the corpus:

  • "Blue Mouse"
  • "Painting of a Blue Mountain with a Blue Sky"
  • "Blue Smartphone"
  • "Red Keyboard"
  • "Black Smartphone"

With those documents in the corpus, which one would be the most relevant if we queried "Blue" to the corpus using TF-IDF ?

We need to calculate the distance between the word "Blue" to each of the documents. Let’s start with the first one, "Blue Mouse".

Remember the formula we learnt earlier:

change score elasticsearch

Figure 5. TF-IDF calculation example

According to this calculation we see that the distance between "Blue" and "Blue Mouse" is 0.51. What about the other documents?

Here are the calculation results of all the documents we listed:

  • "Blue Mouse" = 0.51
  • "Painting of a Blue Mountain with a Blue sky" = 1.02
  • "Blue smartphone" = 0.51
  • "Red Keyboard" = 0
  • "Black Keyboard" = 0

As expected, the document where the word "Blue" appeared the most was  calculated as the most relevant. But what if we add the word "Mouse" on the query?

With "Blue Mouse" as the query, we need to first split it into the terms "Blue" and "Mouse" and calculate the distance of both of them to each of the documents.

The result is:

  • "Blue Mouse" = 0.51 + 1.61 = 2.12
  • "Painting of a Blue Mountain with a Blue sky" = 1.02 + 0 = 1.02
  • "Blue smartphone" = 0.51 + 0 = 0.51

As we can see, the "Blue Mouse" document has become the most relevant, as we expected.

Shortcomings of TF-IDF

TF-IDF works like magic, it can calculate the most relevant documents just the way we want it to! So, why do Elasticsearch and other search engines use BM25 instead of it?

Elasticsearch actually used TF-IDF for calculating similarity score until version 5.0, but then it moved into using BM25 because of these shortcomings:

  • It doesn’t take document length into account : Let’s say that we have a 1,000 word document containing 1 appearance of the word "soccer" and with 10 appearances of the word "soccer". Which document do you think is more relevant to the word "soccer"? It should be the one with 10 words, because there is a greater chance that the document’s topic is about "soccer" compared to the 1,000 words one.
  • The Term Frequency is not saturated : We know from the previous section that IDF will penalize common words. But what if there are some documents that naturally have so many common words? The Term Frequency ’s value will be big. Because the Term Frequency of the TF-IDF function is not saturated, it will boost the irrelevant documents which contain many common words.

Because of those shortcomings, people consider BM25 as the state-of-the-art similarity function.

Okapi BM25 is a similarity score function that is more suitable for modern use cases. Same as TF-IDF , we can get the result of the Okapi BM25 function by multiplying TF and IDF . It’s just that, in Okapi BM25 , the formulas of TF and IDF themselves are different.

Let’s see the formula:

change score elasticsearch

Figure 6. Okapi BM25 formula

Okapi BM25 ’s formula might seem a bit intimidating compared to the TF-IDF . We won’t get into detail about the formula and calculation. But if you’re interested in the formula and calculation, Elasticsearch has a very good article about it.

Why Okapi BM25 is better than TF-IDF for similarity scoring

We know that TF-IDF has shortcomings which make it less well-suited for modern search scoring functions. So, how does the Okapi BM25 overcome those issues?

The first disadvantage of TF-IDF is the fact that it doesn’t take into account the document’s length. In this formula’s denominator we can see that there is fieldLen/avgFieldLen . This means that if the field of the document is longer than the average document length, the function will penalize the document’s score. We can control how much the function penalizes longer documents by changing the b parameter. If we use a large value for b , then the function will penalize the longer document’s score more.

The second disadvantage is "The Term Frequency is not saturated". We know from the previous section that the Term Frequency in the TF-IDF will boost the documents with many appearances of common words. In the Okapi BM25 function, the parameter k1 will determine how saturated the Term Frequency is. The lower the value of k1 is, the more saturated the Term Frequency is. We can see the Term Frequency ’s visualization in the following picture:

change score elasticsearch

Figure 7. Term Frequency Saturation in BM25 - Elasticsearch blog

Okapi BM25 Calculation Example

Now that we know how Okapi BM25 works, let’s try it out. For these examples, we’ll use Elasticsearch’s default k1 and b .

Let’s use the same documents we used in the TF-IDF example, and query "Blue".

  • "Blue Mouse" = 0.29
  • "Painting of a Blue Mountain with a Blue sky" = 0.23
  • "Blue Smartphone" = 0.29

As you can see, the result is different compared to the TF-IDF function. Instead of "Painting of a Blue Mountain with a Blue sky" being the one with the highest score, it’s now lower than "Blue Mouse" and "Blue Smartphone". This happens because we now take the length of the article into consideration, preferring the shorter articles more. We also saturated the Term Frequency . It doesn’t really show in this example, but the score given from the recurrence of a word isn’t as big as the TF-IDF function.

So, let’s try this with  b = 0. What do we get?

  • "Blue Mouse" = 0.24
  • "Painting of a Blue Mountain With a Blue Sky" = 0.34
  • "Blue Smartphone" = 0.24

Since we reduced the b parameter to 0, the function isn’t taking the article length into account anymore. Because of that, the document "Painting of a Blue Mountain With a Blue Sky" becomes the one with the highest score.

Now, let’s try reducing k1 to 0 and see what happens:

  • "Painting of a Blue Mountain With a Blue Sky" = 0.24

All three documents containing the word "Blue" score the same because when the k1 is 0, the Term Frequency won’t contribute to the scoring. If we try to increase the k1 to a higher number, the function will boost the score of the documents with recurring appearances of the queried word.

Similarity scoring in Elasticsearch

Now that we have the basics covered, we can get to similarity scoring in Elasticsearch! In this section we will learn how to configure the similarity scoring and how to calculate it.

From the previous section, we know that Elasticsearch uses Okapi BM25 as a default scoring function. So, let’s insert some documents and see how it works.

Explain API in Elasticsearch

Note: There will be many JSON code blocks from this section onwards. You can use this tool to get a better visualization of JSON format.

Fortunately, if we want to know how Elasticsearch calculates the score, we can use the API it provides, _explain API.

Let’s first create an index and insert some of the documents we used for the TF-IDF examples earlier.

Figure 8. Creating and populating similarity-score index

Let’s start by querying "Blue":

Figure 9. Querying "Blue" to similarity-score index.

Figure 10. "Blue" query result.

The order of the documents is the same as when we calculated them in the Okapi BM25 section, but the score is different. Elasticsearch has a boost parameter, which we will explain in the next section to increase or decrease the score of the query.

Let’s use the _explain API to see the calculation:

Figure 11. Using explain API

We will get a very long response, let’s just look at the first document:

Figure 12. Explain API result

With _explain API, we can know every value used for the score calculation, and also the formula it uses. Note that in these examples I’m using Elasticsearch 7.9. If you don’t use the same version as me, the formula or the boost value might differ from the examples.

You might wonder about the boost parameter you see in the result of _explain API. What does it do? Using boost parameter, you can increase or decrease the score of the term you want. There are 2 types of boost : index-time boost (deprecated) and query-time boost .

The first boost , index-time boost , has been deprecated since version 5.0.0. You can see the reason why Elasticsearch deprecated the index-time boost at the end of this article .

The second type, query-time boost , will change your boost parameter when calculating the score. We can try it by using the _explain API.

To change the boost value, you just add it to the query:

Figure 13. Using query boost

The result will be:

Figure 14. Result of query boost

The default value of boost is 2.2 . Since we used 2 in the query, it will be multiplied by 2 which results in 4.4 .

Shard settings effect on relevance score calculation

Until now, we’ve only used an index with 1 shard, which makes our scoring results consistent and predictable. Different shard settings will affect score calculation in Elasticsearch.

The reason is that when Elasticsearch calculates the score of a document, it only has access to the other documents in the shard. Because of this, the value of IDF and avgLen from the formula differ from shard to shard.

Let’s try creating a new index with 5 shards:

Figure 15. Creating and populating index with 5 number of shards.

If we add the "Blue" query to the index, the result is:

Figure 16. "Blue" query result to an index with 5 number of shards.

The result is different compared to the one with only 1 shard. The document "Painting of a Blue Mountain With a Blue Sky" now has the highest score.

This happens because Elasticsearch stores each of the documents in a different shard, so the value of IDF and avgLen is different for each of the documents.

We can use ?search_type=dfs_query_then_fetch to tell Elasticsearch to retrieve the value needed from each shard before calculating the score.

Figure 17. Querying with dfs_query_then_fetch

The result will be the same as when using only 1 shard:

Figure 18. Query results with dfs_query_then_fetch

The use of ?search_type=dfs_query_then_fetch isn’t really recommended because it will significantly slow down the search. If you have many documents in your index then it will already ensure that the term frequencies are well distributed.

How to change the similarity scoring function in the Elasticsearch

We’ve learned from the previous section the Okapi BM25 is the default scoring function and it has the parameters b and k1 .

What if we want to change the scoring function? Fortunately, Elasticsearch provides a similarity module which we can use to change the scoring function and configure the parameters.

If we want to change the value of b and k1 we can configure it when we’re creating the index:

Figure 19. Creating an index with customized similarity parameters

We can also choose the scoring function to use. For example, if we want to choose LMDirichlet :

Figure 20. Creating an index with another similarity function

Elasticsearch supports the following functions:

  • DFR similarity
  • DFI similarity
  • IB similarity
  • LM Dirichlet similarity
  • LM Jelinek Mercer similarity
  • Scripted similarity

If you want to know about the parameters of each similarity function, you can visit Elasticsearch’s Documentation .

In this article, we’ve learnt about TF-IDF , Okapi BM25 , and scoring in Elasticsearch.

We first learnt about TF-IDF and why we need Term Frequency and Inverse Document Frequency . We also learnt that TF-IDF has shortcomings and how the Okapi BM25 function overcomes those shortcomings.

We also learnt about the scoring in Elasticsearch, how to use _explain API, boosting scores, why shard settings affect the scoring result, how to change BM25 parameters and how the similarity function is used in Elasticsearch.

Similarity scoring can have a significant impact on search performance in Elasticsearch. If you're experiencing issues with search performance, here are 10 easy tips for improvement. If your operation is suffering from search latency, read this guide for explanations and instructions for simple resolution.

  • Similarity Module
  • Practical BM25 - Part 3: Considerations for Picking b and k1 in Elasticsearch
  • What is TF-IDF?
  • Understanding TF-IDF and BM25
  • Practical BM25 - Part 2: The BM25 Algorithm and its Variables

About the Authors

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Community comments

Excellent piece.

by Bas Groot ,

Great intro

by Zheng He ,

Your message is awaiting moderation. Thank you for participating in the discussion.

Very insightful. Always wondered how they do it.

Thanks for writing this great intro. correct me if I'm wrong. considering this statement "Fortunately, by introducing Inverse Document Frequency, we can avoid both of the aforementioned problems.", I believe introducing DF only solves Common Words problem.

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Elasticsearch Guides > Search APIs

Elasticsearch Elasticsearch Function Score: Boosting Relevance with Custom Scoring

By Opster Team

Updated: Jun 22, 2023

Introduction

Function Score is a powerful query in Elasticsearch that allows you to modify the relevance score of documents returned by a query. This is particularly useful when you want to boost the importance of certain documents based on specific criteria, such as recency, popularity, or any other custom factors. In this article, we will dive into the details of the Function Score query and explore how to use it effectively to improve search results.

Understanding Function Score Query

The Function Score query combines the results of a base query with one or more functions that compute a new score for each document. These functions can be based on various factors, such as field values, distance, or even random values. The final score of a document is calculated by combining the original score from the base query with the scores produced by the functions, using a specified score mode.

Here’s a basic structure of a Function Score query:

Function Types

There are several types of functions that can be used in a Function Score query:

1. Field Value Factor: This function modifies the score based on the value of a specific field in the document. You can apply a factor, a modifier (such as log, square, or sqrt), and a missing value to handle documents without the specified field.

2. Decay Functions: Decay functions (linear, exp, and gauss) allow you to reduce the score of documents based on the distance from a specific value in a numeric, geo_point or date field. You can control the rate of decay using the scale, offset, and decay parameters.

3. Script Score: This function allows you to write a custom script (using Painless or another scripting language) to calculate the score based on the document’s fields and other parameters.

4. Random Score: This function assigns a random score to each document, which can be useful for testing or promoting diversity in search results.

5. Weight: This function assigns a static weight to the documents that match the filter, which can be useful for boosting specific documents or groups of documents.

Using Function Score Query

Let’s look at some examples of using Function Score queries to boost search results based on custom criteria.

Example 1: Boosting documents based on recency

Suppose you have an index of blog posts, and you want to boost the relevance of more recent posts. You can use a decay function to achieve this:

In this example, we use a gauss decay function to reduce the score of documents based on the publish_date field. The origin is set to “now”, and the scale, offset, and decay parameters control the rate of decay.

Example 2: Boosting documents based on popularity

If you want to boost the relevance of more popular blog posts, you can use the field_value_factor function:

In this example, we use the field_value_factor function to boost the score of documents based on the views field. The factor, modifier, and missing parameters control the impact of the field value on the score.

Elasticsearch Function Score is a versatile tool for customizing the relevance of search results based on various factors. By understanding the different function types and how to combine them, you can create powerful search experiences that cater to your specific use cases and requirements.

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Scoring and boosting in Elasticsearch

January 1, 2016 • Elasticsearch • Bartosz Konieczny

A subtle difference between filter and full-text search consists on scoring. It's score who distinguishes result corresponding to filter from how well result matches the query.

To work well with Elastcisearch, understanding of scoring is important. Without that, we can explain with difficulty why some documents are returned in higher position than others. It's why the first part of this article begins with explaination of scoring algorithm. After that, we'll try to explore boosting feature which consists on changing score results computed by Elasticsearch.

Scoring in Elasticsearch

Scoring in Elastcisearch consists on associating relevancy values to documents found in search. It's very useful in multiple words full-text search where document can match as well one searched word (for example: "house") as all of them ("my house"). Scores are represented by field called _score . But according to which criteria they are computed ?

To understand scoring calculation ingredients, we'll base on formula used to compute the score. This formula is based on concepts taken from term frequency/inverse document frequency (TF/IDF) and the vector space model concepts. Let's begin by components of the first one:

  • term frequency - according to the occurencies of given term in document, score increases.
  • inverse document frequency - its role is to give an importancy to searched term. For example, some very common words as prepositions (at, since, in, on...) can appear in big number of documents. They will decrease document relevancy while more exotical words, such as "Magnoliidae" will increase it. So even if one document matchs "on" and another one "Magnoliidae", and "on" term is present in almost all index documents (unlike Magnoliidae, present only in several ones), the score of "Magnoliidae" document will be much
  • field-length criteria - the length of field containing searched term(s) can also its influence on scoring. More the field is shorter, better score is computed. To understand that, let's take an analogy with journal articles. If we see words of our center of interest in article titles, we have more chances to read it than only watching on words appearing on a text of half-page length.

Vector space model is used to check how well document is matching multiterm query. Elasticsearch constructs a vector over each index document matching search query. The vector contains weights of all terms defined in the search and present in given document. For example, if we search "and Magnoliidae", document containing only "and" term will have vector looking like [1, 0], where 1 is the weight of "and" and 0 is the weight of "Magnoliidae" term, missing in the document. On the other side, document matching both "and Magnoliidae" terms, wil have a vector like [1, 5]. After, Elaticsearch measures the angle between the query and document vector . If the angle between query and document vector are big, the relevancy is low.

Debuging Elasticsearch score

Each search query allows us to debug scoring and understand score differences between documents. To achieve that we can pass explain parameter in query string, as below http://localhost:9200/waitingforcode/teams/_search?q=name:rc%20roubaix&pretty=true&explain. Elasticsearch will return hits part containing _explanation field:

As you can see, we retrieve there the concepts defined in the first part of this article: tf, idf and fieldNorm. Each of them has associated value, used after to make final score computation. Because our search contains two terms: "rc" and "roubaix", we can find explain parts of both of them.

Formula used to compute final score value is called practical scoring function . It's look like:

Some new functions appeared:

  • queryNorm - query normalization factor, used to simplify the comparison between results of different queries.
  • coord - coordination factor, used to privilege documents containing more of searched terms. In our example we can see that the coordination factor is 0.5. It's because matching document contains only one ("roubaix") from two searched terms.
  • boost - boosting value, allows to give more importancy to some fields containing terms. For example, we can associate boost value "2" to document which title contains searched terms and 0.5 for the rest of fields with them.

Boosting in Elastcisearch

The last parameter quoted in previous part was boost . It helps to modify à posteriori the scores computed by Elasticsearch. It can be implemented at index time or at query time. According to Elasticsearch index boost documentation , boosting at query time should be prefered over boosting at index time for several reasons:

  • Field-length norm precision is lost because index boost value is combined with it and everything is stored in a single byte. In consequency, Elasticsearch isn't able anymore to distinguish the fields with different number of words.
  • Changing index boost needs to reindex all documents while boosting on query time can be adapted with different values for every query.
  • Field weight can be faked in the case when boosted field has multiple values. In this situation, boost is multiplied by itself for every value. By doing it, the weight for given field increases and can not reflect the real match.

To resume, if boost is needed, it's better to use it at query time. But how to do ? If we want to boost a single field, we need to define new attribute in query DSL, boost . Query without this field takes a neutral boost equal to 1. We can also boost one or mutliple indexes. To do that, we need to define indices_boost attribute at query DSL root level.

In our example we'll take the example of newspaper, quoted at the begin of this article. It'll contain 2 fields, title and content. Title field will be boosted while content no. Let's first create new type in index (http://localhost:9200/waitingforcode/_mapping/newspaper):

And save some data (http://localhost:9200/waitingforcode/_bulk):

Now, let's compare results given by query without boost with the query containing negative boosting for title field:

The result returned two hits, one corresponding to document identified by AU74SjgfXaJ2i7zl3W3a , the second for document with id AU74SjgfXaJ2i7zl3W3b . The score was 4.95146 for the first one ("MyTeam won Champions League" title) and 0.2565833 for the second ("New Champions League winner"). Now, we'll try to decreate drastically the importance of title field by boosting it with negative value:

The influence on returned scores are visible immediately. "MyTeam won Champions League" document passed from 4.95146 to -3.7346187, while "New Champions League winner" gained almost 6 points: 6.460153e-7.

This article shows some basic concepts hidden behind Elasticsearch scoring feature. At the begin we can see which ideas are used to define how well given document matches to search criteria. After that, we learn that thanks to explain parameter we can see how many points are attributed for each term defined in matched document. At the end, we can see query boosting, ie. changing score values at query time with the definition of boost field in query DSL.

If you liked it, you should read:

  • Elasticsearch migration from 1.6 to 2.2
  • Reverse nested aggregation in Elasticsearch
  • Parent-children relationship in Elasticsearch

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Custom Score Query and Sort questions

My application needs to have returned hits ordered either by a text field or a date field. I've looked at the Custom Score Query doc ( http://www.elasticsearch.com/docs/elasticsearch/rest_api/query_dsl/custom_score_query/ ) and the Sort doc ( http://www.elasticsearch.com/docs/elasticsearch/rest_api/search/sort/ ), tried them out, and searched the forum. I'm afraid I'm still wondering:

The custom_score queries with script to seem to sort based on script criteria as well (correct me if I am wrong). So, aside from performance differences, what are the functional differences between custom score query with a script and sorting?

Is it the case that custom_scopre query with script actually changes the score values, whereas a sort will not change score values but just return in a different order? I suspected this from the docs, but I'm having trouble testing the idea because all my scores come back 0.0 fore each hit in my tests.
The Sort doc reads, "Note, it is recommended, for single custom based script based sorting, to use custom_score query instead as sorting based on score is faster." So, when would one want (or need) to use sort over custom_score queries with a script to get ordered results? (Perhaps the answers the above answer this.)
One of my searches needs to have results ordered alphabetic by a name field (if there), else an email field. Is it correct to believe this would have to be handled by a custom_score with a script (as I need if-else logic) and a simple sort won't work?
The scripting module doc ( http://www.elasticsearch.com/docs/elasticsearch/modules/scripting/ ) lists fields of type short, string, double, date, long, etc. If I need results ordered by date, what is the best way to store that field from a performance perspective?
Does sharding impact ordered search performance?
Are there any other important performance considerations for ordering results through Elastic Search I should be aware of (aside from the standard Lucene considerations)?
As always, thanks so much your your time and for an awesome technology!

On Wed, Oct 6, 2010 at 7:34 PM, John Chang [email protected] wrote:

My application needs to have returned hits ordered either by a text field or a date field. I've looked at the Custom Score Query doc ( http://www.elasticsearch.com/docs/elasticsearch/rest_api/query_dsl/custom_score_query/ ) and the Sort doc ( http://www.elasticsearch.com/docs/elasticsearch/rest_api/search/sort/ ), tried them out, and searched the forum. I'm afraid I'm still wondering: The custom_score queries with script to seem to sort based on script criteria as well (correct me if I am wrong). So, aside from performance differences, what are the functional differences between custom score query with a script and sorting?

The custom score query allows to provide a custom calculation of the score of each document. With sorting, it will be sorted based on the value of the field, without any custom calculation.

Yea, it changes the score value. If the query would have has a score of 0.25 for a certain document, and your script is (for simplicity sake) "_score * 2", then the score of that document will be 0.5.

You can also provide a script that will produce the sort values (compared with just saying "sort by this field"). If you do so though, and its the only sorting you do, then its usually better to have the same script used, just with a custom score query. Note that this only applied to numeric sorting with float precision.

The sort element can have 2 fields to sort by, first the name, and then the date. If that does not work (i.e. if its not similar names, they just don't exists), then a script can be used with the mentioned "if / else". That script should be a custom sort script and not a custom_score query, since it produces a string, and not a number (which then you could have tried and used custom score).

Note that mvel (the scripting language) gets a bit annoying when trying to implement complex logic (though its very very fast for forumlas). I am working on allowing to provide scripts in other langs.

The simplest would be to add a sort by field on the date field. If you need to access it in a script, then the best way would be to access it as it is stored in the index, which is milliseconds since the epoch in long (this is what you would get when you do: doc['my_date_field'].value.

Basically, each query is a "map / reduce" operation. The query gets executed on the relevant shards, and then gets reduced back to a single response (simplified). So, the more machines you have, and shards gets allocated to them, the faster the search will be. Note that replicas also play a role here (for example, increase the index.number_of_replicas from 1 to 2) since they are searchable as well.

Not sure what you include in the standard Lucene configuration, but elasticsearch has a mechanism which is similar in nature to Lucene FieldCache, so, when you sort on a field (or access it using doc[...] in a script), its terms will be loaded to memory.

No problem, here to help!

-- View this message in context: http://elasticsearch-users.115913.n3.nabble.com/Custom-Score-Query-and-Sort-questions-tp1644004p1644004.html Sent from the Elasticsearch Users mailing list archive at Nabble.com .

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Elasticsearch Scoring Changes In Action

  • Elastic Stack

For the ones started their journey with Elasticsearch before version 5.x sometimes upgrading to the newer versions like 6.x or 7.x bring many challenges. From data type changes to the index structure changes and deprecations, from Transport to REST client and so one. One of those changes that would influence your system, is the scoring algorithm evolution. In this blog, we demonstrate the impact on relevance and scoring if the Elasticsearch changes its algorithms under the hood between major versions. We will show it practically, using a small dataset. If the scoring feature of Elasticsearch is used in your solution and you want to perform an Elasticsearch migration, investigating the impact of these changes would be a part of whole migration process. Otherwise, after a big migration effort may not get satisfied with the result appearing on the first page. In the matter of Similarity, we could find some articles in mimacom blog. Already Rocco Schulz has talked how to store embedded words (or any other vectorized information) into Elasticsearch and then use the script-score (cosine function) to calculate the score of documents. Also, Vinh Nguyên explained the default similarity algorithm since Elasticsearch 5 Okapi BM25 in detail.

What we have in our LAB

In this section, the material utilized in our experiments will be explained. The Elasticsearch versions and APIs, developed clients for reading information from Elasticsearch as well as our dataset employed in the experiments.

Elasticsearch versions

We have selected three versions of Elasticsearch each representing their major versions. I have experienced those differences we are going to talk about, in a recent real project with the following versions of Elasticsearch and that is the reason, I decided to use them in this blog as well.

  • version 2.2.0
  • version 6.4.0
  • version 7.3.0

Our tooling in this experiment

  • Elasticsearch APIs like explain and termvectors
  • A client application: We have developed a client to read statistics from Elasticsearch and put them into another index for further analysis in Kibana. The client reads the _termvectors API per document, does a search (match query) for each of the terms individually and saves the result into the new index. Each record in this new index looks like below.
  • docFreq shows the number of the documents containing the term
  • totalTermFreq is whole frequency of this term
  • averageScore depicts the average score of search result for this term

The dataset is a simple copy of 100 lines of Elasticsearch documentation. It is ingested into the mentioned versions of Elasticsearch with the aid of Logstash. The field containing the data is called “text”. A sample document looks like below.

Dataset is accessible in this GitHub repository

To be able to show differences and design experiments with term combinations (for coordination factor removal) first we will explore some insights about our dataset. To that end, we will use the mentioned client to read from all three versions and establish a new index for each. Then using Kibana to visualize the statistics.

The dataset contains 486 distinct words and the following tables show the top 10 ascending and descending averageScore in different versions.

version 2.2

In this version, the term "appear" ranked on top, with appearance in just one document with a frequency of 4. The terms "times" and "values" following it. On the bottom of the table, the term "similarity" is located even below to stop-words in the English language.

top 10 desc-asc terms

version 6.4

Again "appear" is located on top but none of the terms "times" and "values" even took place in the top ten. In this version, "similarity" and "the" exchanged their spot at the bottom of the table.

top 10 desc-asc terms

version 7.3

It seems in our dataset the term "appear" truly is the most determinant term when it is present in the query. The tables for versions 6.4 and 7.3 are very close to each other but different from version 2.2.

top 10 desc-asc terms

These tables showing, to what extent each individual term in our dataset is important when it is part of the query or in other words, how determinant to be in the process of score calculation. For example, the word “appear” in all three versions got the highest averageScore which means the documents in a match query containing this term would benefit from its higher capability in contributing the final score. On the other hand, we have terms like “the” and “similarity” which are on the bottom of the table and definitely will not play a key role in the score calculation. In the following section, we will use our dataset to show some important factors of the scoring algorithm through different versions.

TF/IDF through different versions

The term rankings vary through different versions of Elasticsearch according to the tables. The very general answer to this variation is the change in the scoring algorithm. To interpret more precisely we will look at the “explain” API of each version with a match query on the term “appear”. The following explain outputs have been pruned to bold out the main concepts.

Version 2.2

By removing the verbose documentation of each component, we can see the contributing elements of the scoring algorithm here. As we could see in this version alongside tf and idf there is one more element which is called "fieldNorm". This factor is calculated in the indexing time. The longer the field the smaller the value. In other words, it makes sure that the shorter fields contribute more to the final score. More details about the equation of each component could be found here .

There are some missing contributors of the algorithm in this example because there are not enough terms in the query to show their impact in the score calculation process. One of them that will be explained in the coming sections is "Coordination Factor".

Version 6.4

In this version, tf and idf have kept their impact in the scoring algorithm, although their computation has been changed (the default similarity module in this version is BM25).

Version 7.3

Apart from supplementing a boosting factor, the computation of tf is modified in this version compared to version 6.4. The term dl in this equation stands for document length and avgdl is the average of all document lengths. It brought back the impact of the "fieldNorm" which was an independent factor in version 2.2, removed in version 6.4 and is part of tf in this version. No change in the computation of idf from version 6.4 to 7.3.

As seen, all versions have two main concepts in common, tf and idf . The former is the frequency of a term in the document and the later stands for Inverse Document Frequency (generally a reverse relation by the number of documents in which a certain term is contained). Some other factors are involved in the scoring algorithm that varies in versions, like coordination factor. It is missing in this example (for version 2.2) because more than one clause (or term) is required to have it involved. We will take a look at it with another example. This factor has been removed since Elasticsearch 6.

The default for similarity in Elasticsearch 2.2 is known as TF/IDF ( detail is here ) which is changed from Elasticsearch 5 to BM25 ( have a look at Vinh's blog ). But according to the main structure of the scoring algorithm, no matter which type of similarity is used, the bigger tf would deliver a bigger score in all versions, the same story is for idf. The difference between similarity modules come from their way of tf and idf calculation. In the older approaches like TF/IDF there is a more direct relation to the number of term frequencies (both in a document and overall documents) but BM25 trying to provide a more mature solution. For example, in TF/IDF the tf is the square of term frequency, which means the more of the same term in a document, the bigger is the tf for it. This approach could overrate the importance of dominant terms which does not let others contribute to the final score. On the other hand, BM25’s diagrams show a raise in the beginning and get stable after a while.

The following diagrams depict the relation of scores and document frequency in different versions for our dataset. As expected generally the same trend is shown by all versions, though the newer versions produce more accurate and cleaner diagrams.

Score and Document Frequency

In the rest of this blog, we will focus on the coordination factor which is removed since Elasticsearch 6. We will see how it could be the source of differences with practical examples.

Coordination Factor

The coordination factor is a score factor based on how many of the query terms are found in the specified document. Quoted from Lucene documentation here . As mentioned, this factor is removed from 6.x onward ( more information ). This factor does not have any impact on the calculation of tf or idf but had its own role in the scoring equation. That means changing the similarity from BM25 to TF/IDF in version 6 will not bring back this factor into the game again because it has been removed from the main equation. Considering statistics from our dataset, we will do some experiments with the combination of different terms to see how the result set could vary even in such a small dataset.

Statistics are mostly shard based if we want to have the precise comparison, we either use a single shard (not just single node) configuration or the same number of shards in all versions. Having just one shard makes the examination more accurate.

In this section, we will run some queries in different versions of Elasticsearch to show the differences by coordination factor. To that end, we have to design a match query with a couple of terms. Let’s start with a single term where the coordination factor would not be in charge because the final query will have just one clause.

query : “algorithm”

The first number is the document ID and the one inside parenthesis depicts the corresponding score. As we could see, there is no difference in rankings in this example among versions.

query : “appear default field similarity”

In this example, we have chosen a combination of terms to expose the differences with presence and absence of coordination factor.

To avoid showing the verbose explain output here for different versions, I will try to explain what happens to the documents with IDs 81 and 59 in this example and why doc 81 appears on top in newer versions but not in 2.2. From the explain output, just the term “appear” is present into doc 81. That means the coordination factor is 0.25 (one out of four) which will be multiplied to the final score in version 2.2 but removed from other versions. Although according to the analysis of our dataset the term “appear” has the biggest tf/idf in all versions, it’s score is multiplied in a small coordination factor because of the absence of other terms in doc 81. In doc 59 all other terms are present but “appear”. According to the statistics from the dataset, the tf/idf for the other terms than "appear" are too small compared to it, but their summation multiplied in 0.75 (three out of four) which provides a bigger score than doc 81 in version 2.2.

Our dataset was a very small one which makes it difficult to show the differences sharply. In the real projects with hundreds of millions of records and each with several lines of text inside, could provide enormous differences in rankings.

Through this blog, we demonstrated practically (in the presence of a small dataset), some differences in scoring algorithms of three different Elasticsearch versions. Sometimes we do a big effort of migration without considering all aspects upfront. Just think we migrate our real data from one version to another and then notice strange behavior in our system because of mismatching numbers. Or completely different result set in the first page for the end-user who had done the same search yesterday. There are some points we should take care of. Never hard code a number derived by the scoring of a specific version because it would not be valid for other versions. If we use parameters in the queries like boosting, they should be re-tuned for newer versions. Sometimes we might also be satisfied with the ranking provided with older versions and want to compensate the impact of breaking changes like removed coordination factor. In those cases, we could use function_score or script_score to simulate the coordination factor.

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Round 4 action from the Mexico Open at Vidanta gets underway Sunday from Vidanta Vallarta, the final event before the TOUR heads to the East Coast. This is the third edition of the Mexico Open being contested at Vidanta Vallarta.

Rookie Jake Knapp took a commanding lead with his Saturday 63, matched only by Ben Silverman. Knapp now sits at 19-under, four strokes ahead of another rookie, Finland's Sami Valimaki. Silverman, Henrik Norlander and Chan Kim all share third place at 12-under.

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Radio on SiriusXM and free at PGATOUR.com/liveaudio :

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New ios 17.3 update warning issued to all iphone users.

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Details have emerged about one of the issues fixed in iOS 17.3, tracked as CVE-2024-23204 and ... [+] reported by security outfit Bitdefender.

Apple’s iOS 17.3 launched a month ago and many security-conscious iPhone users have already upgraded to the latest software. But many more cautious iPhone users prefer to wait to update their device, in case any bugs are introduced.

In the case of iOS 17.3, waiting really isn’t a good idea, because some of the security flaws patched in the upgrade are being exploited in real-life attacks.

Now, with iOS 17.4 set to arrive in a matter of days, details have emerged about one of the issues fixed in iOS 17.3, tracked as CVE-2024-23204 and reported by Jubaer Alnazi, a researcher at security outfit Bitdefender.

“Apple's Shortcuts application, designed to enhance user automation, can inadvertently become a potential vector for privacy breaches,” Alnazi wrote in a blog describing the nature of the vulnerability, its potential impact, and recommended mitigation measures.

What Is CVE-2024-23204 And How Bad Is It?

Fixed in iOS 17.3, CVE-2024-23204 is an issue in Apple’s Shortcuts that could allow an attacker to access sensitive data with certain actions without prompting the user.

The issue was addressed with additional permissions checks, according to Apple’s support page detailing the iOS 17.3 fixes. Reported to the iPhone maker by Alnazi ( @h33tjubaer ), the flaw has been given a CVSS score of 7.5. It came alongside another CVE, CVE-2024-23203.

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Shortcuts is a visual scripting application developed by Apple and provided on its iOS, iPadOS, macOS, and watchOS operating systems. It allows users to share with others—but it’s this flexibility that makes the vulnerability risky.

This is because users can unknowingly import shortcuts that might exploit CVE-2024-23204. “With Shortcuts being a widely used feature for efficient task management, the vulnerability raises concerns about the inadvertent dissemination of malicious shortcuts through diverse sharing platforms,” Alnazi explained.

And for CVE-2024-23204 it was possible to craft a Shortcuts file that would be able to bypass Transparency, Consent and Control (TCC), a security framework in Apple's macOS and iOS that governs access to sensitive user data and system resources by applications. “TCC ensures that apps explicitly request permission from the user before accessing certain data or functionalities, enhancing user privacy and security,” Alnazi wrote.

In his blog and via a video, he demonstrated how an iPhone user could install a malicious shortcut.

So should you be worried? If you use Shortcuts, obviously yes, but otherwise, it’s more important to cover yourself for the already-exploited iPhone flaws fixed in iOS 17.3.

Even if you do use Shortcuts, Sean Wright, head of application security at Featurespace says the issue is relatively difficult to exploit. ““To successfully attack a user, you need them to explicitly install the malicious Shortcut. While not impossible, it’s just another barrier that an attacker would have to overcome. It’s great to see this fixed, and it’s certainly an interesting vulnerability, but I think the likelihood of an attack being successful would be rather limited.”

So what should you do to avoid this issue? The answer is pretty simple—if you haven’t already, update now to iOS 17.3, which’ll mean installing the latest software, iOS 17.3.1 . Bitdefender mirrors this advice, saying iPhone users should update their macOS, ipadOS and watchOS devices to the latest versions now.

In addition, exercise caution when executing shortcuts from untrusted sources and regularly check for security updates and patches from Apple.

Apple iPhone Security—What’s Next?

The next iPhone update will be iOS 17.4, which Apple will release in about a week. The iOS 17.4 update is one of the biggest iPhone upgrades yet—at least if you live in the EU.

That’s because it includes changes to the App Store and iOS ecosystem to allow sideloading in line with the EU Digital Markets Act. This puts Apple on the same footing as Google because the iPhone maker will allow users to download apps from other app stores. At the current time, these will be approved by Apple—adding security—however the iOS 17.4 move does open up EU users to cybersecurity threats.

One of the key benefits of owning an iPhone is the security of closed ecosystem governed by Apple. Unlike rival Google, the iPhone maker owns the hardware, software and operating system. The changes coming in iOS 17.4 will completely transform this.

Apple is doing its best to secure iOS users following the update, with steps such as Notarization of apps, but the iPhone maker acknowledges that less control over the ecosystem does reduce security.

It’s important to note that this change is only coming for EU users, so countries such as the U.K. and U.S. are not affected. In the future, this could change with regulation and user demand, but for now things will remain the same.

There are some cool new features coming in the next update for all iPhone users, such as robust, future-proof security for iMessage and enhancements to the Stolen Device Protection capability.

Meanwhile, iOS 17.4 will come with major security fixes, so keep your eyes peeled for my story covering the release. Increasingly often, Apple is patching bugs being used in real-life attacks. Some security holes are used to perform so-called “zero-click” attacks requiring no interaction from the user to implant spyware on iPhones. While these attacks are highly targeted, the only way to be completely safe is to keep your device up to date, installing the latest software as soon as it arrives.

Updated on 02/25 at 10:05 EST. This article was first published on 02/23 at 09:56 EST. Updated to include information about iOS 17.4, Apple’s next important iPhone upgrade.

Kate O'Flaherty

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Sam Purcell wants to change Mississippi State basketball's fortunes. Does he have time?

change score elasticsearch

TUSCALOOSA, Ala. — At some point, things need to change. For Mississippi State women’s basketball coach Sam Purcell , that point is now, with the Bulldogs having lost four straight games, capped by Sunday’s 87-75 defeat at Alabama .

“We’ve done everything at this point,” Purcell said postgame. “We’ve walked backwards. We’ve walked to the left. I’ve changed my eating patterns. I took my daughter to lunch today. I’ve washed my undergarments. I know that’s too much information, but we’re doing everything.”

The Bulldogs (20-9, 7-7 SEC) are playing far from their best basketball as Purcell’s favorite month — the one where college basketball reigns supreme — approaches.

He can point to a variety of issues, ranging from turnovers to players dealing with illness, but he believes there’s an aspect of misfortune playing into the Bulldogs’ woes.

“Sometimes in sports, you need a little momentum,” Purcell said. “You need some things to break your way. Next thing you know, it’s like our men’s team. Like we talk about them, what a winning streak they’re on. There’s a juice that’s undeniable.”

The men’s team won its fifth straight game with Saturday's victory at LSU , and it’s something Purcell can point to because it’s a spot his team was in earlier this month.

Rather than talk about a four-game skid, he prefers to say the Bulldogs have won five of nine — a reminder of the five-game winning streak his team had, which included an upset of LSU.

“That’s how you’ve got to look at it,” Purcell said. “You’ve got to be optimistic.”

MSU BASEBALL: Catcher Johnny Long asserts message of players-only meeting

However, as the losing streak has grown, time is running out for Mississippi State to find a groove. Last season, it won five of seven to close the regular season and find momentum going into tournament play.

Though the Bulldogs were one-and-done in the SEC tournament, they cracked the field of 68 as one of the last four in. They then became the first team ever on the women’s side to go from a play-in game to a second-round appearance.

This season has been a different path. The Bulldogs got hot in late January, but they’ve faded in late February. With only two games remaining — at Auburn on Thursday and against Missouri on March 3 — there’s an urgency to regain momentum.

Otherwise, if the skid reaches six when Mississippi State walks off the court at Humphrey Coliseum next week, an at-large March Madness bid may depend on an SEC tournament win or two.

“There’s nobody happy in that locker room,” Purcell said. “There’s nobody happy. My young women are working as hard as they can. That’s all we can ask for. You have to have a one-game mentality.”

Stefan Krajisnik is the Mississippi State beat writer for the Clarion Ledger. Contact him at  [email protected]  or follow him on the X platform, formerly known as Twitter,  @skrajisnik3 .

Sort search results edit

Allows you to add one or more sorts on specific fields. Each sort can be reversed as well. The sort is defined on a per field level, with special field name for _score to sort by score, and _doc to sort by index order.

Assuming the following index mapping:

_doc has no real use-case besides being the most efficient sort order. So if you don’t care about the order in which documents are returned, then you should sort by _doc . This especially helps when scrolling .

Sort values edit

The search response includes sort values for each document. Use the format parameter to specify a date format for the sort values of date and date_nanos fields. The following search returns sort values for the post_date field in the strict_date_optional_time_nanos format.

Sort order edit

The order option can have the following values:

The order defaults to desc when sorting on the _score , and defaults to asc when sorting on anything else.

Sort mode option edit

Elasticsearch supports sorting by array or multi-valued fields. The mode option controls what array value is picked for sorting the document it belongs to. The mode option can have the following values:

The default sort mode in the ascending sort order is min  — the lowest value is picked. The default sort mode in the descending order is max  — the highest value is picked.

Sort mode example usage edit

In the example below the field price has multiple prices per document. In this case the result hits will be sorted by price ascending based on the average price per document.

Sorting numeric fields edit

For numeric fields it is also possible to cast the values from one type to another using the numeric_type option. This option accepts the following values: [ "double", "long", "date", "date_nanos" ] and can be useful for searches across multiple data streams or indices where the sort field is mapped differently.

Consider for instance these two indices:

Since field is mapped as a double in the first index and as a long in the second index, it is not possible to use this field to sort requests that query both indices by default. However you can force the type to one or the other with the numeric_type option in order to force a specific type for all indices:

In the example above, values for the index_long index are casted to a double in order to be compatible with the values produced by the index_double index. It is also possible to transform a floating point field into a long but note that in this case floating points are replaced by the largest value that is less than or equal (greater than or equal if the value is negative) to the argument and is equal to a mathematical integer.

This option can also be used to convert a date field that uses millisecond resolution to a date_nanos field with nanosecond resolution. Consider for instance these two indices:

Values in these indices are stored with different resolutions so sorting on these fields will always sort the date before the date_nanos (ascending order). With the numeric_type type option it is possible to set a single resolution for the sort, setting to date will convert the date_nanos to the millisecond resolution while date_nanos will convert the values in the date field to the nanoseconds resolution:

To avoid overflow, the conversion to date_nanos cannot be applied on dates before 1970 and after 2262 as nanoseconds are represented as longs.

Sorting within nested objects. edit

Elasticsearch also supports sorting by fields that are inside one or more nested objects. The sorting by nested field support has a nested sort option with the following properties:

Elasticsearch will throw an error if a nested field is defined in a sort without a nested context.

Nested sorting examples edit

In the below example offer is a field of type nested . The nested path needs to be specified; otherwise, Elasticsearch doesn’t know on what nested level sort values need to be captured.

In the below example parent and child fields are of type nested . The nested.path needs to be specified at each level; otherwise, Elasticsearch doesn’t know on what nested level sort values need to be captured.

Nested sorting is also supported when sorting by scripts and sorting by geo distance.

Missing values edit

The missing parameter specifies how docs which are missing the sort field should be treated: The missing value can be set to _last , _first , or a custom value (that will be used for missing docs as the sort value). The default is _last .

For example:

If a nested inner object doesn’t match with the nested.filter then a missing value is used.

Ignoring unmapped fields edit

By default, the search request will fail if there is no mapping associated with a field. The unmapped_type option allows you to ignore fields that have no mapping and not sort by them. The value of this parameter is used to determine what sort values to emit. Here is an example of how it can be used:

If any of the indices that are queried doesn’t have a mapping for price then Elasticsearch will handle it as if there was a mapping of type long , with all documents in this index having no value for this field.

Geo distance sorting edit

Allow to sort by _geo_distance . Here is an example, assuming pin.location is a field of type geo_point :

geo distance sorting does not support configurable missing values: the distance will always be considered equal to Infinity when a document does not have values for the field that is used for distance computation.

The following formats are supported in providing the coordinates:

Lat lon as properties edit

Lat lon as wkt string edit.

Format in Well-Known Text .

Geohash edit

Lat lon as array edit.

Format in [lon, lat] , note, the order of lon/lat here in order to conform with GeoJSON .

Multiple reference points edit

Multiple geo points can be passed as an array containing any geo_point format, for example

and so forth.

The final distance for a document will then be min / max / avg (defined via mode ) distance of all points contained in the document to all points given in the sort request.

Script based sorting edit

Allow to sort based on custom scripts, here is an example:

Track scores edit

When sorting on a field, scores are not computed. By setting track_scores to true, scores will still be computed and tracked.

Memory considerations edit

When sorting, the relevant sorted field values are loaded into memory. This means that per shard, there should be enough memory to contain them. For string based types, the field sorted on should not be analyzed / tokenized. For numeric types, if possible, it is recommended to explicitly set the type to narrower types (like short , integer and float ).

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  1. Function score query

    The function_score allows you to modify the score of documents that are retrieved by a query. This can be useful if, for example, a score function is computationally expensive and it is sufficient to compute the score on a filtered set of documents.

  2. change _score in elasticsearch to make equal to doc's score field

    1 Answer Sorted by: 2 One solution is to use a function_score query, where you replace the default _score using a field_value_factor score function. It goes like this:

  3. 6.6. Custom scoring with function_score

    Powered by GitBook. 6.6. Custom scoring with function_score. Finally, we come to one of the coolest queries that Elasticsearch has to offer: function_score. The function_score query allows you to take control over the relevancy of your results in a fine-grained manner by specifying any number of arbitrary functions to be applied to the score of ...

  4. Elasticsearch Score: Factors Affecting _score & How to Optimize it

    1. Customize Scoring with Function Score Query: Function Score Query allows you to modify the _score by applying various functions such as field value factor, decay functions, or custom script functions. This can help you tailor the scoring to your specific use case. Example: GET /_search { "query": { "function_score": { "query": { "match": {

  5. Function Score Query, using Field Value Factor

    Boost mode accepts the following parameters: - multiply: Multiply the _score with the function result. - sum: Add the function result to the _score. - max: The higher of the _score and the ...

  6. Customizing scores in Elasticsearch for product recommendations

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  7. Understanding Similarity Scoring in Elasticsearch

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  8. Elasticsearch Constant Score Query: How to Use it, With Examples

    We can change a scoring query, which typically executes in a query context, into a non-scoring filter context by using the constant_score query. The constant_score query retrieves all matching documents with a relevance score equal to the boost parameter value after wrapping a filter query.

  9. Elasticsearch Function Score: How to Use, With Examples

    Function Score is a powerful query in Elasticsearch that allows you to modify the relevance score of documents returned by a query. This is particularly useful when you want to boost the importance of certain documents based on specific criteria, such as recency, popularity, or any other custom factors.

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    The score for the document now 0.042559613. But according to the TF/IDF calculation, we need to see the same score as our first search response. Because nothing changed when we compared it with the first state of the document. Even, though I did not change the first name field, I just changed last_name fields and continued searching on first_name.

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    After that, we'll try to explore boosting feature which consists on changing score results computed by Elasticsearch. Scoring in Elasticsearch. Scoring in Elastcisearch consists on associating relevancy values to documents found in search. It's very useful in multiple words full-text search where document can match as well one searched word ...

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    If the query would have has a score of 0.25. for a certain document, and your script is (for simplicity sake) "_score *. 2", then the score of that document will be 0.5. The Sort doc reads, "Note, it is recommended, for single custom based. script based sorting, to use custom_score query instead as sorting based on.

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    January 15, 2020 by Hossein Yeganeh Elastic Stack For the ones started their journey with Elasticsearch before version 5.x sometimes upgrading to the newer versions like 6.x or 7.x bring many challenges. From data type changes to the index structure changes and deprecations, from Transport to REST client and so one.

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  19. How to improve Elasticsearch search relevance with boolean queries

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  20. Sam Purcell wants to change Mississippi State basketball's fortunes

    TUSCALOOSA, Ala. — At some point, things need to change. For Mississippi State women's basketball coach Sam Purcell, that point is now, with the Bulldogs having lost four straight games ...

  21. Is there a way to turn off scoring when searching in Elasticsearch, to

    800 2 11 26 Add a comment 2 Answers Sorted by: 4 You can use _doc as a sort field. This will make ES return the fields sorted in the order of insertion, and hence it will not do scoring. Here is a thread from the forums that explains more: https://discuss.elastic.co/t/most-efficient-way-to-query-without-a-score/57457/4 Share Improve this answer

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  23. Relevance Tuning Guide, Weights and Boosts edit

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  25. Sort search results

    Sort mode option edit Elasticsearch supports sorting by array or multi-valued fields. The mode option controls what array value is picked for sorting the document it belongs to. The mode option can have the following values: The default sort mode in the ascending sort order is min — the lowest value is picked.