Kibana query contains

Kibana query contains DEFAULT

Kibana is an extremely versatile analysis tool that allows you to perform a wide variety of search queries to find the data you're interested in and build beautiful visualizations and dashboards on top of these queries.

In a previous article, we covered some basic querying types supported in Kibana, such as free-text searches, field-level searches and using operators. In some scenarios, however, and with specific data sets, basic queries will not be enough. They might result in a disappointing "No results found" message or they might result in a huge dataset that is just as frustrating.

This is where additional query types come in handy.

While often defined as advanced, they are not difficult to master and often involve using a specific character and understanding the syntax. In this article, we'll be describing some of these searches — wildcards, fuzzy searches, proximity searches, ranges, regex and boosting.

Wildcards

In some cases, you might not be sure how a term is spelled or you might be looking for documents containing variants of a specific term. In these cases, wildcards can come in handy because they allow you to catch a wider range of results.

There are two wildcard expressions you can use in Kibana: asterisk (*) and question mark (?). * matches any character sequence (including the empty one) and ? matches single characters.

For example, I am shipping AWS ELB access logs which contain a field called . A production instance is spelled incorrectly as "producation" and searching for it directly would not return any results. Instead, I will use a wildcard query, as follows:

I could also use the ? to replace individual characters:

Since these queries are performed across a large number of terms, they can be extremely slow. Never start your query with * or ? and try and be as specific as possible.

Fuzzy Searches

Fuzzy queries search for terms that are within a defined edit distance that you specify in the query. The default edit distance is 2, but an edit distance of 1 should be enough for catching most spelling mistakes. Similar to why you would use wildcards, fuzzy queries will help you out when you're not sure what a specific term looks like.

Fuzzy queries in Kibana are used with a tilde (~) after which you specify the edit distance. In the same example above, we can use a fuzzy search to catch the spelling mistake made in our production ELB instance.

Again, without using fuzziness, the query below would come up short:

But using an edit distance of 2, we can bridge the gap and get some results:

Proximity Searches

Whereas fuzzy queries allow us to specify an edit distance for characters in a word, proximity queries allow us to define an edit distance for words appearing in a different order in a specific phrase.

Proximity queries in Kibana are also executed with a tilde (~) following the words you are looking for in quotation marks. As with fuzzy queries, you define the edit distance after the ~.

For example, say you're looking for a database error but are not sure what the exact message looks like. Using a free-text query will most likely come up empty or display a wide range of irrelevant results, and so a proximity search can come in handy in filtering down results:

Boosting

Boosting in queries allows you to make specific search terms rank higher in importance compared to other terms.

To boost your queries in Kibana, use the ^ character. The default boost value is 1, where 0 and 1 reduce the importance or weight, you want to apply to search results. You can play around with this value for better results.

Regular Expressions

If you're comfortable with regular expressions, they can be quite an effective tool to use in queries. They can be used, for example, for partial and case-insensitive matching or searching for terms containing special characters.

To embed regular expressions in a Kibana query, you need to wrap them in forward-slashes ("/").

In this example, I'm looking for IPs in the message field:

Below, I'm searching apache access logs for requests containing a specific search URL:

I recommend reading up on the syntax and the allowed characters in the documentation. Elasticsearch uses its own regex flavor that might be a bit different from what you are used to working with.

Keep in mind that queries that include regular expressions can take a while since they require a relatively large amount of processing by Elasticsearch. Depending on your query, there may be some effect on performance and so, if possible, try and use a long prefix before the actual regex begins to help narrow down the analyzed data set.

Ranges

Ranges are extremely useful for numeric fields. While you can search for a specific numeric value using a basic field-level search, usually you will want to look for a range of values.

Using Apache access logs again as an example, let's say we want to look for a range of response error codes:

  • response: [400 TO 500] - searches for all response errors ranging between code 400 and 500, with the specified values included in results.
  • response : [400 TO 500} - searches for all response errors ranging between code 400 and 500, with 500 excluded from the results.
  • response:[400 TO *] - searches for all response errors ranging from code 400 and above.
  • response:>400 - searches for all response errors ranging from code 400 and above, excluding 400 from the results.
  • response:<400 - searches for all response errors ranging from code 400 and below, excluding 400 from the results.
  • response:>=400 searches for all response errors ranging from code 400 and above, including 400 in the results.
  • response:<=400 searches for all response errors ranging from code 400 and below, including 400 in the results.

Bonus — Non-Existing Fields

To wrap up this article, I thought I'd mention two methods to quickly look for documents that either contains a field or does not contain a field. This can be useful if you're acquainted with the structure of your logs and want to narrow down results quickly to specific log types.

  • _missing_ searches for all documents that DO NOT contain a specific field, or that contain the field but with a null value.
  • _exists_ searches for all documents that DO contain a specific field with a non-null value.

As always with learning a new language - mastering Kibana advanced searches is a matter of trial and error and exploring the different ways you can slice and dice your data in Kibana with queries.

I'll end this article with two tips. First, the better your logs are structured and parsed, the easier the searching will be. Second, before you start using advanced queries, I also recommend understanding how Elasticsearch indexes data and specifically - analyzers and tokenizers.

There are so many different ways of querying data in Kibana. If there is an additional query method you use and find useful, please feel free to share it in the comments below.

Happy querying!

Sours: https://dzone.com/articles/getting-started-with-kibana-advanced-searches

How to filter records containing a particular string in a field value for a kibana visualization

Hi, analyzed text mapping is for fulltext search. For example, if you had a lot of biography data, and you needed to find the biographies that have certain words, phrases, topics, etc - you would want analyzed text. You don't want analyzed text mapping for doing log analysis.

The mapping of the logs should be plain , not multi-field. If the field has and , then it is multi-field.

https://www.elastic.co/guide/en/elasticsearch/reference/current/multi-fields.html#multi-fields

Trying to do wildcard search on pageUrl-as-analyzed text is not going to work, because the Elasticsearch tokenizer breaks up the string for full-text search, which is not what you want in your data.

When you filter , I'm not entirely sure if that references the keyword part by default. The multi-fields mapping will lead to other instances of confusion for Kibana, too.

Sours: https://discuss.elastic.co/t/how-to-filter-records-containing-a-particular-string-in-a-field-value-for-a-kibana-visualization/174532
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Searching logs in Kibana

Kibana provides a front-end to Elasticsearch. Quoting the introduction from Kibana's User Guide,

Kibana allows to search, view and interact with the logs, as well as perform data analysis and visualize the logs in a variety of charts, tables and maps.

Viewing logs in Kibana is a straightforward two-step process.

Step 1: create an index pattern

Open Kibana at . Select the section in the left pane menu, then . Then, depending on Kibana's version, either click or .

index pattern

Enter the index pattern, and uncheck . As soon as Kibana checks the index pattern against Elasticsearch and the result is positive, the button at the bottom will activate and display .

create index pattern

Click to configure the index pattern.

Step 2: view the logs

Navigate to the section in the left pane menu. On the left of the page, just under the search bar, select the index pattern just created and all the logs matching the index will be displayed.

discover logs

Every log entry can be inspected by clicking the small triangular bullet just besides it on the left. Each entry can be viewed as either table or JSON.

discover log entry

Search results can be filtered, using the following buttons

filter options

to respectively filter for value, filter out value, toggle column view in the table, and filter for field present.

However, the search bar is the best place for querying and filtering the logs, using the Lucene query syntax or the full JSON-based Elasticsearch Query DSL. More details on searching data, managing searches, etc. are here.

Sours: https://deep-log-inspection.readthedocs.io/en/latest/user/kibana-logs/

Kibana is an extremely versatile analysis tool that allows you to perform a wide variety of search queries to find the data you’re interested in and build beautiful visualizations and dashboards on top of these queries.

In a previous article, we covered some basic querying types supported in Kibana, such as free-text searches, field-level searches and using operators. In some scenarios however, and with specific data sets, basic queries will not be enough. They might result in a disappointing “No results found” message or they might result in a huge dataset that is just as frustrating.

This is where additional query types come in handy.

While often defined as advanced, they are not difficult to master and often involve using a specific character and understanding the syntax. In this article, we’ll be describing some of these searches — wildcards, fuzzy searches, proximity searches, ranges, regex and boosting.

Wildcards

In some cases, you might not be sure how a term is spelled or you might be looking for documents containing variants of a specific term. In these cases, wildcards can come in handy because they allow you to catch a wider range of results.

There are two wildcard expressions you can use in Kibana – asterisk (*) and question mark (?). * matches any character sequence (including the empty one) and ? matches single characters.

For example, I am shipping AWS ELB access logs which contain a field called loadbalancer. A production instance is spelled incorrectly as ‘producation’ and searching for it directly would not return any results. Instead, I will use a wildcard query as follows:

type:elb AND loadbalancer:prod*

I could also use the ? to replace individual characters:

type:elb AND loadbalancer:prod?c?tion

Since these queries are performed across a large number of terms, they can be extremely slow. Never start your query with * or ? and try and be as specific as possible.  

Fuzzy searches

Fuzzy queries searches for terms that are within a defined edit distance that you specify in the query. The default edit distance is 2, but an edit distance of 1 should be enough for catching most spelling mistakes. Similar to why you would use wildcards, fuzzy queries will help you out when you’re not sure what a specific term looks like.

Fuzzy queries in Kibana are used with a tilde (~) after which you specify the edit distance. In the same example above, we can use a fuzzy search to catch the spelling mistake made in our production ELB instance.

Again, without using fuzziness, the query below would come up short:

type:elb AND loadbalancer:productio

But using an edit distance of 2, we can bridge the gap and get some results:

type:elb AND loadbalancer:productio~2

Proximity searches

Whereas fuzzy queries allow us to specify an edit distance for characters in a word, proximity queries allow us to define an edit distance for words appearing in a different order in a specific phrase.

Proximity queries in Kibana are also executed with a tilde (~) following the words you are looking for in quotation marks. As with fuzzy queries, you define the edit distance after the ~.

For example, say you’re looking for a database error but are not sure what the exact message looks like. Using a free-text query will most likely come up empty or display a wide range of irrelevant results, and so a proximity search can come in handy in filtering down results:

"database error"~5

Kibana

Boosting

Boosting in queries allows you to make specific search terms rank higher in importance compared to other terms.

To boost your queries in Kibana, use the ^ character. The default boost value is 1, where 0 and 1 reduce the importance, or weight, you want to apply to search results. You can play around with this value for better results.

error^2 database api^6

Regular expressions

If you’re comfortable with regular expressions, they can be quite an effective tool to use in queries. They can be used, for example, for partial and case-insensitive matching or searching for terms containing special characters.

To embed regular expressions in a Kibana query, you need to wrap them in forward-slashes (“/”).

In this example, I’m looking for IPs in the message field:

message:/[0-9]{1,3}\.[0-9]{1,3}\.[0-9]{1,3}\.[0-9]{1,3}/

Below, I’m searching apache access logs for requests containing a specific search URL:

request:/\/search\/.*/

I recommend reading up on the syntax and the allowed characters in the documentation. Elasticsearch uses its own regex flavor that might be a bit different from what you are used to working with.  

Keep in mind that queries that include regular expressions can take a while since they require a relatively large amount of processing by Elasticsearch. Depending on your query, there may be some effect on performance and so, if possible, try and use a long prefix before the actual regex begins to help narrow down the analyzed data set.

Ranges

Ranges are extremely useful for numeric fields. While you can search for a specific numeric value using a basic field-level search, usually you will want to look for a range of values.

Using Apache access logs again as an example, let’s say we want to look for a range of response error codes:

  • response: [400 TO 500] – searches for all response errors ranging between code 400 and 500, with the specified values included in results.
  • response : [400 TO 500}– searches for all response errors ranging between code 400 and 500, with 500 excluded from the results.
  • response:[400 TO *]– searches for all response errors ranging from code 400 and above.
  • response:>400 – searches for all response errors ranging from code 400 and above, excluding 400 from the results.
  • response:<400 – searches for all response errors ranging from code 400 and below, excluding 400 from the results.
  • response:>=400 searches for all response errors ranging from code 400 and above, including 400 in the results.
  • response:<=400 searches for all response errors ranging from code 400 and below, including 400 in the results.

Bonus – non-existing fields

To wrap up this article, I thought I’d mention two methods to quickly look for documents that either contain a field or do not contain a field. This can be useful if you’re acquainted with the structure of your logs and want to narrow down results quickly to specific log types.

  • _missing_ – searches for all documents that DO NOT contain a specific field, or that contain the field but with a null value.
  • _exists_ – searches for all documents that DO contain a specific field with a non-null value.

As always with learning a new language — mastering Kibana advanced searches is a matter of trial and error and exploring the different ways you can slice and dice your data in Kibana with queries.

I’ll end this article with two tips. First, the better your logs are structured and parsed, the easier the searching will be. Second, before you start using advanced queries, I also recommend understanding how Elasticsearch indexes data and specifically — analyzers and tokenizers.

There are so many different ways of querying data in Kibana — if there is an additional query method you use and find useful, please feel free to share it in the comments below.

Happy querying!

Sours: https://logz.io/blog/kibana-advanced/

Contains kibana query

Kibana Search Cheatsheet (KQL & Lucene)

This article is a cheatsheet about searching in Kibana. You can find a more detailed explanation about searching in Kibana in this blog post.

KQL or Lucene

KQL (Kibana Query Language) is a query language available in Kibana, that will be handled by Kibana and converted into Elasticsearch Query DSL. Lucene is a query language directly handled by Elasticsearch. In nearly all places in Kibana, where you can provide a query you can see which one is used by the label on the right of the search box. Clicking on it allows you to disable KQL and switch to Lucene.

KQL

  • Supports auto completion of fields and values
  • Supports searching on scripted fields
  • Supports wildcard on field names
  • Supports querying for nested fields
  • Simpler syntax for some operators
  • More resilient in where you can use spaces (see below)

Lucene

  • Supports regular expressions
  • Supports fuzzy search
  • Supports boosting

Which one should you use? Start with KQL — which is also the default in recent Kibana versions — and just fall back to Lucene if you need specific features not available in KQL.

Spaces in queries

Lucene is rather sensitive to where spaces in the query can be, e.g. (using here to represent a space) , and are all equivalent, while and are actually searching for different documents. Thus when using Lucene, I’d always recommend to not put any spaces around the operators to be safe. KQL is more resilient to spaces and it doesn’t matter where around the operator you’ll put spaces.

Finding values

Find documents where any field matches any of the words/terms listed. The term must appear as it is in the document, e.g. this query won’t match documents containing the word “darker”. Read the detailed search post for more details into how fields will be analyzed.

Use and/or and parentheses to define that multiple terms need to appear. This query would find all documents that have the term “orange” and either “dark” or “light” (or both) in it.

ℹ️ Use quotes to search for the word "and"/"or"

⚡ AND/OR must be written uppercase

To find values only in specific fields you can put the field name before the value e.g. this query will only find “orange” in the color field.

⚡ Spaces need to be escaped

Putting quotes around values makes sure they are found in that specific order (“match a phrase”) e.g. if you want to make sure to only find documents containing “our planet” and not “planet our” you’d need the following query:

ℹ️ No escaping of spaces in phrases

Wildcards

You can use the wildcard to match just parts of a term/word, e.g. this query will find anything beginning with “dark” like “darker”, “darkest”, “darkness”, etc.

Wildcards can be used anywhere in a term/word. ⚡ Using a wildcard in front of a word can be rather slow and resource intensive for your Elasticsearch — use with care.

You can use the wildcard also for searching over multiple fields in KQL e.g. this query will search “fakestreet” in all fields beginning with “user.address.”.

Not supported

Wildcards cannot be used when searching for “phrases” i.e. will not retrieve results containing “our planet”.

Comparing values

Compare numbers or dates. Those operators also work on text/keyword fields, but might behave not very intuitive and thus I’d recommend avoiding usage with text/keyword fields.

⚡ No quotes around the date in Lucene

Lucene supports a special range operator to search for a range (besides using comparator operators shown above).

Not supported

ℹ️ Excluding sides of the range using curly braces

ℹ️ Use a wildcard for having an open sided interval

Special queries

Find documents in which a specific field exists (i.e. that does have a non value for that field).

Querying nested fields is only supported in KQL. The syntax is a bit more complex given the complexity of nested queries. Thus I’d recommend reading the official documentation.

Not supported

Lucene has the ability to search for regular expressions. ⚡ This can be rather slow and resource intensive for your Elasticsearch — use with care.

Not (yet) supported (see #46855)

Fuzzy search allows searching for strings, that are very similar to the given query.

Not (yet) supported (see #54343)
Sours: https://www.timroes.de/kibana-search-cheatsheet
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This page contains information about the query type. For information about running a search query in Elasticsearch, see Search your data.

Returns documents based on a provided query string, using a parser with a strict syntax.

This query uses a syntax to parse and split the provided query string based on operators, such as or . The query then analyzes each split text independently before returning matching documents.

You can use the query to create a complex search that includes wildcard characters, searches across multiple fields, and more. While versatile, the query is strict and returns an error if the query string includes any invalid syntax.

Because it returns an error for any invalid syntax, we don’t recommend using the query for search boxes.

If you don’t need to support a query syntax, consider using the query. If you need the features of a query syntax, use the query, which is less strict.

When running the following search, the query splits into two parts: and . The field’s analyzer then independently converts each part into tokens before returning matching documents. Because the query syntax does not use whitespace as an operator, is passed as-is to the analyzer.

GET /_search { "query": { "query_string": { "query": "(new york city) OR (big apple)", "default_field": "content" } } }

Top-level parameters for edit

(Required, string) Query string you wish to parse and use for search. See Query string syntax.

(Optional, string) Default field you wish to search if no field is provided in the query string.

Defaults to the index setting, which has a default value of . The value extracts all fields that are eligible for term queries and filters the metadata fields. All extracted fields are then combined to build a query if no is specified.

Searching across all eligible fields does not include nested documents. Use a query to search those documents.

For mappings with a large number of fields, searching across all eligible fields could be expensive.

There is a limit on the number of fields that can be queried at once. It is defined by the search setting, which defaults to 1024.

(Optional, Boolean) If , the wildcard characters and are allowed as the first character of the query string. Defaults to .
(Optional, Boolean) If , the query attempts to analyze wildcard terms in the query string. Defaults to .
(Optional, string) Analyzer used to convert text in the query string into tokens. Defaults to the index-time analyzer mapped for the . If no analyzer is mapped, the index’s default analyzer is used.
(Optional, Boolean) If , match phrase queries are automatically created for multi-term synonyms. Defaults to . See Synonyms and the query for an example.

(Optional, float) Floating point number used to decrease or increase the relevance scores of the query. Defaults to .

Boost values are relative to the default value of . A boost value between and decreases the relevance score. A value greater than increases the relevance score.

(Optional, string) Default boolean logic used to interpret text in the query string if no operators are specified. Valid values are:

(Default)
For example, a query string of is interpreted as .
For example, a query string of is interpreted as .
(Optional, Boolean) If , enable position increments in queries constructed from a search. Defaults to .

(Optional, array of strings) Array of fields you wish to search.

You can use this parameter query to search across multiple fields. See Search multiple fields.

(Optional, string) Maximum edit distance allowed for fuzzy matching. For fuzzy syntax, see Fuzziness.
(Optional, integer) Maximum number of terms to which the query expands for fuzzy matching. Defaults to .
(Optional, integer) Number of beginning characters left unchanged for fuzzy matching. Defaults to .
(Optional, Boolean) If , edits for fuzzy matching include transpositions of two adjacent characters (ab → ba). Defaults to .
(Optional, Boolean) If , format-based errors, such as providing a text value for a numeric field, are ignored. Defaults to .

(Optional, integer) Maximum number of automaton states required for the query. Default is .

Elasticsearch uses Apache Lucene internally to parse regular expressions. Lucene converts each regular expression to a finite automaton containing a number of determinized states.

You can use this parameter to prevent that conversion from unintentionally consuming too many resources. You may need to increase this limit to run complex regular expressions.

(Optional, string) Minimum number of clauses that must match for a document to be returned. See the parameter for valid values and more information. See How works for an example.

(Optional, string) Analyzer used to convert quoted text in the query string into tokens. Defaults to the mapped for the .

For quoted text, this parameter overrides the analyzer specified in the parameter.

(Optional, integer) Maximum number of positions allowed between matching tokens for phrases. Defaults to . If , exact phrase matches are required. Transposed terms have a slop of .

(Optional, string) Suffix appended to quoted text in the query string.

You can use this suffix to use a different analysis method for exact matches. See Mixing exact search with stemming.

(Optional, string) Method used to rewrite the query. For valid values and more information, see the parameter.

(Optional, string) Coordinated Universal Time (UTC) offset or IANA time zone used to convert values in the query string to UTC.

Valid values are ISO 8601 UTC offsets, such as or -, and IANA time zone IDs, such as .

The parameter does not affect the date math value of . is always the current system time in UTC. However, the parameter does convert dates calculated using and date math rounding. For example, the parameter will convert a value of .

The query string “mini-language” is used by the Query string and by the query string parameter in the API.

The query string is parsed into a series of terms and operators. A term can be a single word —  or  — or a phrase, surrounded by double quotes —  — which searches for all the words in the phrase, in the same order.

Operators allow you to customize the search — the available options are explained below.

You can specify fields to search in the query syntax:

  • where the field contains

    status:active
  • where the field contains or

    title:(quick OR brown)
  • where the field contains the exact phrase

    author:"John Smith"
  • where the field contains (note how we need to escape the space with a backslash)

    first\ name:Alice
  • where any of the fields , or contains or (note how we need to escape the with a backslash):

    book.\*:(quick OR brown)
  • where the field has any non-null value:

    _exists_:title

Wildcard searches can be run on individual terms, using to replace a single character, and to replace zero or more characters:

qu?ck bro*

Be aware that wildcard queries can use an enormous amount of memory and perform very badly — just think how many terms need to be queried to match the query string .

Pure wildcards are rewritten to queries for efficiency. As a consequence, the wildcard would match documents with an empty value like the following:

{ "field": "" }

... and would not match if the field is missing or set with an explicit null value like the following:

{ "field": null }

Allowing a wildcard at the beginning of a word (eg ) is particularly heavy, because all terms in the index need to be examined, just in case they match. Leading wildcards can be disabled by setting to .

Only parts of the analysis chain that operate at the character level are applied. So for instance, if the analyzer performs both lowercasing and stemming, only the lowercasing will be applied: it would be wrong to perform stemming on a word that is missing some of its letters.

By setting to true, queries that end with a will be analyzed and a boolean query will be built out of the different tokens, by ensuring exact matches on the first N-1 tokens, and prefix match on the last token.

Regular expression patterns can be embedded in the query string by wrapping them in forward-slashes ():

name:/joh?n(ath[oa]n)/

The supported regular expression syntax is explained in Regular expression syntax.

The parameter does not have any control over regular expressions. A query string such as the following would force Elasticsearch to visit every term in the index:

/.*n/

Use with caution!

You can run queries using the operator:

quikc~ brwn~ foks~

For these queries, the query string is normalized. If present, only certain filters from the analyzer are applied. For a list of applicable filters, see Normalizers.

The query uses the Damerau-Levenshtein distance to find all terms with a maximum of two changes, where a change is the insertion, deletion or substitution of a single character, or transposition of two adjacent characters.

The default edit distance is , but an edit distance of should be sufficient to catch 80% of all human misspellings. It can be specified as:

quikc~1

Avoid mixing fuzziness with wildcards

Mixing fuzzy and wildcard operators is not supported. When mixed, one of the operators is not applied. For example, you can search for (fuzzy) or (wildcard), but searches for do not apply the fuzzy operator ().

While a phrase query (eg ) expects all of the terms in exactly the same order, a proximity query allows the specified words to be further apart or in a different order. In the same way that fuzzy queries can specify a maximum edit distance for characters in a word, a proximity search allows us to specify a maximum edit distance of words in a phrase:

"fox quick"~5

The closer the text in a field is to the original order specified in the query string, the more relevant that document is considered to be. When compared to the above example query, the phrase would be considered more relevant than .

Ranges can be specified for date, numeric or string fields. Inclusive ranges are specified with square brackets and exclusive ranges with curly brackets .

  • All days in 2012:

    date:[2012-01-01 TO 2012-12-31]
  • Numbers 1..5

    count:[1 TO 5]
  • Tags between and , excluding and :

    tag:{alpha TO omega}
  • Numbers from 10 upwards

    count:[10 TO *]
  • Dates before 2012

    date:{* TO 2012-01-01}

Curly and square brackets can be combined:

  • Numbers from 1 up to but not including 5

    count:[1 TO 5}

Ranges with one side unbounded can use the following syntax:

age:>10 age:>=10 age:<10 age:<=10

To combine an upper and lower bound with the simplified syntax, you would need to join two clauses with an operator:

age:(>=10 AND <20) age:(+>=10 +<20)

The parsing of ranges in query strings can be complex and error prone. It is much more reliable to use an explicit query.

Use the boost operator to make one term more relevant than another. For instance, if we want to find all documents about foxes, but we are especially interested in quick foxes:

quick^2 fox

The default value is 1, but can be any positive floating point number. Boosts between 0 and 1 reduce relevance.

Boosts can also be applied to phrases or to groups:

"john smith"^2 (foo bar)^4

By default, all terms are optional, as long as one term matches. A search for will find any document that contains one or more of or or . We have already discussed the above which allows you to force all terms to be required, but there are also boolean operators which can be used in the query string itself to provide more control.

The preferred operators are (this term must be present) and (this term must not be present). All other terms are optional. For example, this query:

quick brown +fox -news

states that:

  • must be present
  • must not be present
  • and are optional — their presence increases the relevance

The familiar boolean operators , and (also written , and ) are also supported but beware that they do not honor the usual precedence rules, so parentheses should be used whenever multiple operators are used together. For instance the previous query could be rewritten as:

This form now replicates the logic from the original query correctly, but the relevance scoring bears little resemblance to the original.

In contrast, the same query rewritten using the query would look like this:

{ "bool": { "must": { "match": "fox" }, "should": { "match": "quick brown" }, "must_not": { "match": "news" } } }

Multiple terms or clauses can be grouped together with parentheses, to form sub-queries:

(quick OR brown) AND fox

Groups can be used to target a particular field, or to boost the result of a sub-query:

status:(active OR pending) title:(full text search)^2

If you need to use any of the characters which function as operators in your query itself (and not as operators), then you should escape them with a leading backslash. For instance, to search for , you would need to write your query as . When using JSON for the request body, two preceding backslashes () are required; the backslash is a reserved escaping character in JSON strings.

GET /my-index-000001/_search { "query" : { "query_string" : { "query" : "kimchy\\!", "fields" : ["user.id"] } } }

The reserved characters are:

Failing to escape these special characters correctly could lead to a syntax error which prevents your query from running.

and can’t be escaped at all. The only way to prevent them from attempting to create a range query is to remove them from the query string entirely.

Whitespaces and empty queriesedit

Whitespace is not considered an operator.

If the query string is empty or only contains whitespaces the query will yield an empty result set.

Avoid using the query for nested documentsedit

searches do not return nested documents. To search nested documents, use the query.

Search multiple fieldsedit

You can use the parameter to perform a search across multiple fields.

The idea of running the query against multiple fields is to expand each query term to an OR clause like this:

field1:query_term OR field2:query_term | ...

For example, the following query

GET /_search { "query": { "query_string": { "fields": [ "content", "name" ], "query": "this AND that" } } }

matches the same words as

GET /_search { "query": { "query_string": { "query": "(content:this OR name:this) AND (content:that OR name:that)" } } }

Since several queries are generated from the individual search terms, combining them is automatically done using a query with a . For example (the is boosted by 5 using notation):

GET /_search { "query": { "query_string" : { "fields" : ["content", "name^5"], "query" : "this AND that OR thus", "tie_breaker" : 0 } } }

Simple wildcard can also be used to search "within" specific inner elements of the document. For example, if we have a object with several fields (or inner object with fields) in it, we can automatically search on all "city" fields:

GET /_search { "query": { "query_string" : { "fields" : ["city.*"], "query" : "this AND that OR thus" } } }

Another option is to provide the wildcard fields search in the query string itself (properly escaping the sign), for example: :

GET /_search { "query": { "query_string" : { "query" : "city.\\*:(this AND that OR thus)" } } }

Since (backslash) is a special character in json strings, it needs to be escaped, hence the two backslashes in the above .

The fields parameter can also include pattern based field names, allowing to automatically expand to the relevant fields (dynamically introduced fields included). For example:

GET /_search { "query": { "query_string" : { "fields" : ["content", "name.*^5"], "query" : "this AND that OR thus" } } }

Additional parameters for multiple field searchesedit

When running the query against multiple fields, the following additional parameters are supported.

(Optional, string) Determines how the query matches and scores documents. Valid values are:

(Default)
Finds documents which match any field and uses the highest from any matching field. See .
Creates a query on each field and combines the from each field. See .
Treats fields with the same as though they were one big field. Looks for each word in any field. See .
Finds documents which match any field and combines the from each field. See .
Runs a query on each field and uses the from the best field. See and .
Runs a query on each field and uses the from the best field. See and .

NOTE: Additional top-level parameters may be available based on the value.

Synonyms and the queryedit

The query supports multi-terms synonym expansion with the synonym_graph token filter. When this filter is used, the parser creates a phrase query for each multi-terms synonyms. For example, the following synonym: would produce:

It is also possible to match multi terms synonyms with conjunctions instead:

GET /_search { "query": { "query_string" : { "default_field": "title", "query" : "ny city", "auto_generate_synonyms_phrase_query" : false } } }

The example above creates a boolean query:

that matches documents with the term or the conjunction . By default the parameter is set to .

How worksedit

The splits the query around each operator to create a boolean query for the entire input. You can use to control how many "should" clauses in the resulting query should match.

GET /_search { "query": { "query_string": { "fields": [ "title" ], "query": "this that thus", "minimum_should_match": 2 } } }

The example above creates a boolean query:

that matches documents with at least two of the terms , or in the single field .

How works for multiple fieldsedit

GET /_search { "query": { "query_string": { "fields": [ "title", "content" ], "query": "this that thus", "minimum_should_match": 2 } } }

The example above creates a boolean query:

that matches documents with the disjunction max over the fields and . Here the parameter can’t be applied.

GET /_search { "query": { "query_string": { "fields": [ "title", "content" ], "query": "this OR that OR thus", "minimum_should_match": 2 } } }

Adding explicit operators forces each term to be considered as a separate clause.

The example above creates a boolean query:

that matches documents with at least two of the three "should" clauses, each of them made of the disjunction max over the fields for each term.

How works for cross-field searchesedit

A value in the field indicates fields with the same analyzer are grouped together when the input is analyzed.

GET /_search { "query": { "query_string": { "fields": [ "title", "content" ], "query": "this OR that OR thus", "type": "cross_fields", "minimum_should_match": 2 } } }

The example above creates a boolean query:

that matches documents with at least two of the three per-term blended queries.

Allow expensive queriesedit

Query string query can be internally be transformed to a which means that if the prefix queries are disabled as explained here the query will not be executed and an exception will be thrown.

Sours: https://www.elastic.co/guide/en/elasticsearch/reference/current/query-dsl-query-string-query.html

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Future son-in-law. My mother, as if ordered, also finished with us, Irishka brought her with her tongue and the girl herself moaned as if experiencing an orgasm. But probably as a sign of solidarity.



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