en.search-data.min.fad7ab67520a5317f59227a6ce5b1a29cbc03aca32b2fb95a6dd703f1f8ed478.js

'use strict';(function(){const indexCfg={cache:true};indexCfg.doc={id:'id',field:['title','content'],store:['title','href','section'],};const index=FlexSearch.create('balance',indexCfg);window.bookSearchIndex=index;index.add({'id':0,'href':'/docs/references/aggregation/avg/','title':"Avg aggregation",'section':"Aggregation",'content':"Avg aggregation #  A single-value metrics aggregation that computes the average of numeric values that are extracted from the aggregated documents.\nExamples #  Assuming the data consists of documents representing exams grades (between 0 and 100) of students we can average their scores with:\nPOST /exams/_search { \u0026#34;aggs\u0026#34;: { \u0026#34;avg_grade\u0026#34;: { \u0026#34;avg\u0026#34;: { \u0026#34;field\u0026#34;: \u0026#34;grade\u0026#34; } } } } The above aggregation computes the average grade over all documents. The aggregation type is avg and the field setting defines the numeric field of the documents the average will be computed on. The above will return the following:\n{ ... \u0026#34;aggregations\u0026#34;: { \u0026#34;avg_grade\u0026#34;: { \u0026#34;value\u0026#34;: 75.0 } } } The name of the aggregation (avg_grade above) also serves as the key by which the aggregation result can be retrieved from the returned response.\nParameters for avg #   field\n(Required, string) Field you wish to aggregate.  "});index.add({'id':1,'href':'/docs/references/collection/create/','title':"Create a collection",'section':"Collection",'content':"Create a collection #  Creates a new collection.\nExamples #  The following request creates a new collection called my-collection in the namespace my-namespace:\nPUT /my-namespace:my-collection If creating a collection within the default namespace, it can be simplified as:\nPUT /my-collection Request #  PUT /[\u0026lt;namespace\u0026gt;:]\u0026lt;name\u0026gt; Path parameters #   \u0026lt;namespace\u0026gt;\n(Optional, string) The namespace which the collection belongs to. Namespace names must meet the following criteria:  Lowercase only Cannot include \\ /, *, ?, \u0026quot;, \u0026lt;, \u0026gt;, |,  , ,, # Cannot start with -, _, + Cannot be . or .. Cannot be longer than 255 bytes (note it is bytes, so multi-byte characters will count towards the 255 limit faster)   \u0026lt;name\u0026gt;\n(Required, string) Name of the collection you wish to create. Collection names must meet the same criteria as namespace names.  "});index.add({'id':2,'href':'/docs/references/document/create/','title':"Create a document",'section':"Document",'content':"Create a document #  Creates a new document.\nExamples #  Insert a JSON document into the my-collection collection:\nPOST /my-collection/_doc { \u0026#34;message\u0026#34;: \u0026#34;GET /search HTTP/1.1 200 1070000\u0026#34;, \u0026#34;org\u0026#34;: { \u0026#34;id\u0026#34;: \u0026#34;infini\u0026#34; } } The API returns the following result:\n{ \u0026#34;_id\u0026#34;: \u0026#34;0,0\u0026#34;, \u0026#34;_version\u0026#34;: 1, \u0026#34;_namespace\u0026#34;: \u0026#34;default\u0026#34;, \u0026#34;_collection\u0026#34;: \u0026#34;my-collection\u0026#34;, \u0026#34;result\u0026#34;: \u0026#34;created\u0026#34;, ... } The API supports passing a customized UUID as the document identify, eg:\nPOST /my-collection/_doc/news_001 { \u0026#34;message\u0026#34;: \u0026#34;GET /search HTTP/1.1 200 1070000\u0026#34;, \u0026#34;org\u0026#34;: { \u0026#34;id\u0026#34;: \u0026#34;infini\u0026#34; } } Request #  POST /\u0026lt;target\u0026gt;/_doc/[\u0026lt;doc_id\u0026gt;] {\u0026lt;fields\u0026gt;} Path parameters #   \u0026lt;target\u0026gt;\n(Required, string) Name of the collection to target.   \u0026lt;doc_id\u0026gt;\n(Optional, string) The unique identify of the document, auto generated if not specified.    Request body #   \u0026lt;fields\u0026gt;\n(Required, string) Request body contains the JSON source for the document data.  "});index.add({'id':3,'href':'/docs/references/namespace/create/','title':"Create a namespace",'section':"Namespace",'content':"Create a namespace #  Creates a new namespace.\nExamples #  If creating a website namespace, the following request creates a new namespace called website:\nPUT /_namespace/website Request #  PUT /_namespace/\u0026lt;name\u0026gt; Path parameters #   \u0026lt;name\u0026gt;\n(Required, string) The name of the namespace. Namespace names must meet the following criteria:  Lowercase only Cannot include \\ /, *, ?, \u0026quot;, \u0026lt;, \u0026gt;, |,  , ,, # Cannot start with -, _, + Cannot be . or .. Cannot be longer than 255 bytes (note it is bytes, so multi-byte characters will count towards the 255 limit faster)    "});index.add({'id':4,'href':'/docs/references/aggregation/date-histogram/','title':"Date histogram aggregation",'section':"Aggregation",'content':"Date histogram aggregation #  This multi-bucket aggregation is similar to the normal histogram, but it can only be used with date or date range values. Because dates are represented internally in Elasticsearch as long values, it is possible, but not as accurate, to use the normal histogram on dates as well. The main difference in the two APIs is that here the interval can be specified using date/time expressions. Time-based data requires special support because time-based intervals are not always a fixed length.\nExamples #  As an example, here is an aggregation requesting bucket intervals of a month in calendar time:\nPOST /sales/_search { \u0026#34;aggs\u0026#34;: { \u0026#34;sales_over_time\u0026#34;: { \u0026#34;date_histogram\u0026#34;: { \u0026#34;field\u0026#34;: \u0026#34;date\u0026#34;, \u0026#34;calendar_interval\u0026#34;: \u0026#34;1M\u0026#34; } } } } Response:\n{ ... \u0026#34;aggregations\u0026#34;: { \u0026#34;sales_over_time\u0026#34;: { \u0026#34;buckets\u0026#34;: [ { \u0026#34;key\u0026#34;: 1420070400000, \u0026#34;doc_count\u0026#34;: 3 }, { \u0026#34;key\u0026#34;: 1422748800000, \u0026#34;doc_count\u0026#34;: 2 }, { \u0026#34;key\u0026#34;: 1425168000000, \u0026#34;doc_count\u0026#34;: 2 } ] } } } Parameters for date_histogram #   field\n(Required, string) Field you wish to aggregate.  calendar_interval #  (Optional, string) Calendar-aware intervals are configured with the calendar_interval parameter. You can specify calendar intervals using the unit name, such as month, or as a single unit quantity, such as 1M. For example, day and 1d are equivalent. Multiple quantities, such as 2d, are not supported.\nThe accepted calendar intervals are:\n minute, 1m\nAll minutes begin at 00 seconds. One minute is the interval between 00 seconds of the first minute and 00 seconds of the following minute in the specified time zone, compensating for any intervening leap seconds, so that the number of minutes and seconds past the hour is the same at the start and end. hour, 1h\nAll hours begin at 00 minutes and 00 seconds. One hour (1h) is the interval between 00:00 minutes of the first hour and 00:00 minutes of the following hour in the specified time zone, compensating for any intervening leap seconds, so that the number of minutes and seconds past the hour is the same at the start and end. day, 1d\nAll days begin at the earliest possible time, which is usually 00:00:00 (midnight). One day (1d) is the interval between the start of the day and the start of the following day in the specified time zone, compensating for any intervening time changes. week, 1w\nOne week is the interval between the start day_of_week:hour:minute:second and the same day of the week and time of the following week in the specified time zone. month, 1M\nOne month is the interval between the start day of the month and time of day and the same day of the month and time of the following month in the specified time zone, so that the day of the month and time of day are the same at the start and end. quarter, 1q\nOne quarter is the interval between the start day of the month and time of day and the same day of the month and time of day three months later, so that the day of the month and time of day are the same at the start and end. year, 1y\nOne year is the interval between the start day of the month and time of day and the same day of the month and time of day the following year in the specified time zone, so that the date and time are the same at the start and end.  fixed_interval #  Fixed intervals are configured with the fixed_interval parameter.\nIn contrast to calendar-aware intervals, fixed intervals are a fixed number of SI units and never deviate, regardless of where they fall on the calendar. One second is always composed of 1000ms. This allows fixed intervals to be specified in any multiple of the supported units.\nHowever, it means fixed intervals cannot express other units such as months, since the duration of a month is not a fixed quantity. Attempting to specify a calendar interval like month or quarter will throw an exception.\nThe accepted units for fixed intervals are:\n milliseconds (ms)\nA single millisecond. This is a very, very small interval. seconds (s)\nDefined as 1000 milliseconds each. minutes (m)\nDefined as 60 seconds each (60,000 milliseconds). All minutes begin at 00 seconds. hours (h)\nDefined as 60 minutes each (3,600,000 milliseconds). All hours begin at 00 minutes and 00 seconds. days (d)\nDefined as 24 hours (86,400,000 milliseconds). All days begin at the earliest possible time, which is usually 00:00:00 (midnight).  "});index.add({'id':5,'href':'/docs/getting-started/installation/','title':"Installation",'section':"Getting started",'content':"Installation #  Pizza is compatible with all major operating systems. The package is compiled statically, and it does not require any external dependencies.\nAutomatic installation #  Use the following command to automatically download the latest version of INFINI Pizza for your platform and extract it into /opt/pizza:\ncurl -sSL http://get.infini.cloud | bash -s -- -p pizza  The optional parameters for the script are as follows:\n -v \u0026lt;version number\u0026gt; (default is the latest version) -d \u0026lt;installation directory\u0026gt; (default is /opt/pizza)   Manual installation #  Visit the URL below to download the package for your operating system:\nhttps://release.infinilabs.com/\nVerification of the installation #  Assuming Pizza is in your $PATH after installation, run the following command to ensure the package has been installed correctly:\n$ pizza --version PIZZA 0.1.0 Starting the server #  Start Pizza as follows with the configuration:\n$ pizza --config pizza.yaml ___ _____ __________ _ / _ \\\\_ \\/ _ / _ / /_\\  / /_)/ / /\\/\\// /\\// / //_\\\\ / ___/\\/ /_ / //\\/ //\\/ _ \\ \\/ \\____/ /____/____/\\_/ \\_/ [PIZZA] The Next-Gen Real-Time Hybrid Search \u0026amp; AI-Native Innovation Engine. ... Interaction with the server #  Assuming Pizza is listening on 127.0.0.1:9200, use the following command to create a collection named testing:\ncurl -XPUT http://127.0.0.1:9200/testing Refer to the reference page for more APIs.\nShutdown the server #  Press Ctrl+C to shut down Pizza, and the message below is displayed:\n... __ _ __ ____ __ _ __ __ / // |/ // __// // |/ // / / // || // _/ / // || // / /_//_/|_//_/ /_//_/|_//_/ ©INFINI.LTD, All Rights Reserved. "});index.add({'id':6,'href':'/docs/references/aggregation/max/','title':"Max aggregation",'section':"Aggregation",'content':"Max aggregation #  A single-value metrics aggregation that keeps track and returns the maximum value among the numeric values extracted from the aggregated documents.\nExamples #  Computing the max price value across all documents:\nPOST /sales/_search { \u0026#34;aggs\u0026#34;: { \u0026#34;max_price\u0026#34;: { \u0026#34;max\u0026#34;: { \u0026#34;field\u0026#34;: \u0026#34;price\u0026#34; } } } } Response:\n{ ... \u0026#34;aggregations\u0026#34;: { \u0026#34;max_price\u0026#34;: { \u0026#34;value\u0026#34;: 200.0 } } } As can be seen, the name of the aggregation (max_price above) also serves as the key by which the aggregation result can be retrieved from the returned response.\nParameters for avg #   field\n(Required, string) Field you wish to aggregate.  "});index.add({'id':7,'href':'/docs/references/aggregation/min/','title':"Min aggregation",'section':"Aggregation",'content':"Min aggregation #  A single-value metrics aggregation that keeps track and returns the minimum value among numeric values extracted from the aggregated documents.\nExamples #  Computing the min price value across all documents:\nPOST /sales/_search { \u0026#34;aggs\u0026#34;: { \u0026#34;min_price\u0026#34;: { \u0026#34;min\u0026#34;: { \u0026#34;field\u0026#34;: \u0026#34;price\u0026#34; } } } } Response:\n{ ... \u0026#34;aggregations\u0026#34;: { \u0026#34;min_price\u0026#34;: { \u0026#34;value\u0026#34;: 10.0 } } } As can be seen, the name of the aggregation (min_price above) also serves as the key by which the aggregation result can be retrieved from the returned response.\nParameters for avg #   field\n(Required, string) Field you wish to aggregate.  "});index.add({'id':8,'href':'/docs/references/namespace/','title':"Namespace",'section':"References",'content':"Namespace #  Pizza supports a multi-tenant architecture, allowing different sets of data for various scenarios to be stored within a single engine. Each set is referred to as a namespace, and different namespaces can have distinct topologies and access permissions configured.\nUsually, there\u0026rsquo;s no need to set up an additional namespace, and the default namespace is default.\nWithin a namespace, there are several types of data:\n Collection, Docs: Collections of documents. Data: Source data, stored in columns layout. Index: Indexed data, built based on Data, optional. View: View data, composite views of data across collections.  Namespace management #  Namespace APIs are used to manage individual Namespace and settings.\n  Create a namespace  Delete a namespace  "});index.add({'id':9,'href':'/docs/overview/','title':"Overview",'section':"Documentation",'content':"Overview #  Introduction #  INFINI Pizza is a distributed hybrid search database system. Our mission is to deliver real-time smart search experiences tailored for enterprises by fully harnessing the potential of modern hardware and the AI capability. We are committed to meeting the demands of high concurrency and high throughput in challenging environments, all while providing seamless and efficient search capabilities.\nFeatures #   The Next-Gen Real-Time Search \u0026amp; AI-Native Innovation Engine Written in Rust.\n Major Features of Pizza:\n True Real-Time, get search results instantly after insertion, no need to refresh anymore. Support partial update in place, no longer pull and push back the entire document again. High performance, lightning fast with high throughput and low latency, hardware reduced. High scalability, supports very large-scale clusters, beyond petabytes. Native integration with LLMs and ML, empowering AI-Native enterprise innovation. Design with storage and computation separation, and also storage and index separation.  Architecture #  -- Pizza is building on top of these awesome projects:\n  Rust  Apache Arrow  Arrow DataFusion  Why Pizza #  The name Pizza was taken from our unique sharding design.\nThe documents in Pizza are persisted as Parquet files in object storage. Native integration with other big data systems through object storage and the standard Parquet format.\nWhen to use Pizza #  Pizza is good fit when:\n You have latency-sensitive search applications that millisecond matters. You need fresh data, your data is mutable, and you need fast queries. You need to handle high concurrency with complex queries. You need to handle more than petabytes data for user-facing use cases. You need to handle JOIN for complex data relations. You need to keep thousands of fields, but only a handful are subject to change. You need to manage both structured and unstructured data in a cohesive manner.  Pizza is designed to address these problems at heart, to solve real critical business issues, serve your data-driven applications in realtime at very large scale. Enhance and enrich the data experiences of your end-users.\nDesign choices #  The philosophy of Pizza is that indexes should be designed per use case, and should not attempt to fit every use case with a single index. Therefore, we introduced Views, which allow combining different document sources into a single index or separating a document into different layers of indexes for different use cases.\nBy emphasizes the decoupling of storage and computation, as well as the separation of storage and indexing. Which enables efficient and scalable data processing by allowing independent management and optimization of storage resources, computational resources, and indexing strategies.\nNative integration with LLMs (Language Models) and ML (Machine Learning) technologies is a key aspect of Pizza, providing powerful capabilities for AI-Native enterprise innovation. By seamlessly integrating with LLMs and ML frameworks, Pizza enables advanced natural language processing, machine learning, and data analytics directly within the search and data retrieval pipeline.\nWe are in the process of building the next-generation search infrastructure, driven by our unwavering commitment to delivering real-time search experiences for enterprises, unlocking the potential of modern hardware, and catering to the demands of high concurrency and high throughput in the most challenging of environments\nNext step #   Install and configure Pizza.\n"});index.add({'id':10,'href':'/docs/references/aggregation/percentiles/','title':"Percentiles aggregation",'section':"Aggregation",'content':"Percentiles aggregation #  A multi-value metrics aggregation that calculates one or more percentiles over numeric values extracted from the aggregated documents.\nPercentiles show the point at which a certain percentage of observed values occur. For example, the 95th percentile is the value which is greater than 95% of the observed values.\nPercentiles are often used to find outliers. In normal distributions, the 0.13th and 99.87th percentiles represents three standard deviations from the mean. Any data which falls outside three standard deviations is often considered an anomaly.\nWhen a range of percentiles are retrieved, they can be used to estimate the data distribution and determine if the data is skewed, bimodal, etc.\nExamples #  Assume your data consists of website load times. The average and median load times are not overly useful to an administrator. The max may be interesting, but it can be easily skewed by a single slow response.\nLet\u0026rsquo;s look at a range of percentiles representing load time:\nPOST latency/_search { \u0026#34;aggs\u0026#34;: { \u0026#34;load_time_outlier\u0026#34;: { \u0026#34;percentiles\u0026#34;: { \u0026#34;field\u0026#34;: \u0026#34;load_time\u0026#34; } } } } By default, the percentile metric will generate a range of percentiles: [1, 5, 25, 50, 75, 95, 99]. The response will look like this:\n{ ... \u0026#34;aggregations\u0026#34;: { \u0026#34;load_time_outlier\u0026#34;: { \u0026#34;values\u0026#34;: { \u0026#34;1.0\u0026#34;: 10.0, \u0026#34;5.0\u0026#34;: 30.0, \u0026#34;25.0\u0026#34;: 170.0, \u0026#34;50.0\u0026#34;: 445.0, \u0026#34;75.0\u0026#34;: 720.0, \u0026#34;95.0\u0026#34;: 940.0, \u0026#34;99.0\u0026#34;: 980.0 } } } } As you can see, the aggregation will return a calculated value for each percentile in the default range. If we assume response times are in milliseconds, it is immediately obvious that the webpage normally loads in 10-725ms, but occasionally spikes to 945-985ms.\nOften, administrators are only interested in outliers — the extreme percentiles. We can specify just the percents we are interested in (requested percentiles must be a value between 0-100 inclusive):\nPOST latency/_search { \u0026#34;aggs\u0026#34;: { \u0026#34;load_time_outlier\u0026#34;: { \u0026#34;percentiles\u0026#34;: { \u0026#34;field\u0026#34;: \u0026#34;load_time\u0026#34;, \u0026#34;percents\u0026#34;: [95, 99, 99.9] } } } } Parameters for avg #   field\n(Required, string) Field you wish to aggregate. percents\n(Optional, array) A range of percentiles that are calculated. Default is [1, 5, 25, 50, 75, 95, 99].  keyed #  By default the keyed flag is set to true which associates a unique string key with each bucket and returns the ranges as a hash rather than an array. Setting the keyed flag to false will disable this behavior:\nPOST latency/_search { \u0026#34;aggs\u0026#34;: { \u0026#34;load_time_outlier\u0026#34;: { \u0026#34;percentiles\u0026#34;: { \u0026#34;field\u0026#34;: \u0026#34;load_time\u0026#34;, \u0026#34;keyed\u0026#34;: false } } } } Response:\n{ ... \u0026#34;aggregations\u0026#34;: { \u0026#34;load_time_outlier\u0026#34;: { \u0026#34;values\u0026#34;: [ { \u0026#34;key\u0026#34;: 1.0, \u0026#34;value\u0026#34;: 10.0 }, { \u0026#34;key\u0026#34;: 5.0, \u0026#34;value\u0026#34;: 30.0 }, { \u0026#34;key\u0026#34;: 25.0, \u0026#34;value\u0026#34;: 170.0 }, { \u0026#34;key\u0026#34;: 50.0, \u0026#34;value\u0026#34;: 445.0 }, { \u0026#34;key\u0026#34;: 75.0, \u0026#34;value\u0026#34;: 720.0 }, { \u0026#34;key\u0026#34;: 95.0, \u0026#34;value\u0026#34;: 940.0 }, { \u0026#34;key\u0026#34;: 99.0, \u0026#34;value\u0026#34;: 980.0 } ] } } } "});index.add({'id':11,'href':'/docs/references/search/prefix/','title':"Prefix query",'section':"Search",'content':"Prefix query #  Returns documents that contain a specific prefix in a provided field.\nExamples #  The following search returns documents where the org.id field contains a term that begins with inf.\nGET /_search { \u0026#34;query\u0026#34;: { \u0026#34;prefix\u0026#34;: { \u0026#34;org.id\u0026#34;: { \u0026#34;value\u0026#34;: \u0026#34;inf\u0026#34; } } } } Top-level parameters for prefix #   \u0026lt;field\u0026gt;\n(Required, object) Field you wish to search.  Parameters for \u0026lt;field\u0026gt; #   value\n(Required, string) Beginning characters of terms you wish to find in the provided \u0026lt;field\u0026gt;. case_insensitive\n(Optional, Boolean) Allows ASCII case insensitive matching of the value with the indexed field values when set to true. Default is false.  "});index.add({'id':12,'href':'/docs/references/search/range/','title':"Range query",'section':"Search",'content':"Range query #  Returns documents that contain terms within a provided range.\nExamples #  The following search returns documents where the age field contains a term between 10 and 20.\nGET /_search { \u0026#34;query\u0026#34;: { \u0026#34;range\u0026#34;: { \u0026#34;age\u0026#34;: { \u0026#34;gte\u0026#34;: 10, \u0026#34;lte\u0026#34;: 20 } } } } Top-level parameters for range #   \u0026lt;field\u0026gt;\n(Required, object) Field you wish to search.  Parameters for \u0026lt;field\u0026gt; #   gt\n(Optional) Greater than. gte\n(Optional) Greater than or equal to. lt\n(Optional) Less than. lte\n(Optional) Less than or equal to.  "});index.add({'id':13,'href':'/docs/references/search/regexp/','title':"Regexp query",'section':"Search",'content':"Regexp query #  Returns documents that contain terms matching a regular expression.\nA regular expression is a way to match patterns in data using placeholder characters, called operators. For a list of operators supported by the regexp query, see Regular expression syntax.\nExamples #  The following search returns documents where the org.id field contains any term that begins with in and ends with y. The .* operators match any characters of any length, including no characters. Matching terms can include ini, inni, and infini.\nGET /_search { \u0026#34;query\u0026#34;: { \u0026#34;regexp\u0026#34;: { \u0026#34;org.id\u0026#34;: { \u0026#34;value\u0026#34;: \u0026#34;in.*i\u0026#34;, \u0026#34;case_insensitive\u0026#34;: true } } } } Top-level parameters for range #   \u0026lt;field\u0026gt;\n(Required, object) Field you wish to search.  Parameters for \u0026lt;field\u0026gt; #   value\n(Required, string) Regular expression for terms you wish to find in the provided \u0026lt;field\u0026gt;. For a list of supported operators, see Regular expression syntax. case_insensitive\n(Optional, Boolean) Allows ASCII case insensitive matching of the value with the indexed field values when set to true. Default is false.  "});index.add({'id':14,'href':'/docs/references/aggregation/sum/','title':"Sum aggregation",'section':"Aggregation",'content':"Sum aggregation #  A single-value metrics aggregation that sums up numeric values that are extracted from the aggregated documents.\nExamples #  Assuming the data consists of documents representing sales records we can sum the sale price of all hats with:\nPOST /sales/_search { \u0026#34;query\u0026#34;: { \u0026#34;constant_score\u0026#34;: { \u0026#34;filter\u0026#34;: { \u0026#34;match\u0026#34;: { \u0026#34;type\u0026#34;: \u0026#34;hat\u0026#34; } } } }, \u0026#34;aggs\u0026#34;: { \u0026#34;hat_prices\u0026#34;: { \u0026#34;sum\u0026#34;: { \u0026#34;field\u0026#34;: \u0026#34;price\u0026#34; } } } } Resulting in:\n{ ... \u0026#34;aggregations\u0026#34;: { \u0026#34;hat_prices\u0026#34;: { \u0026#34;value\u0026#34;: 450.0 } } } The name of the aggregation (hat_prices above) also serves as the key by which the aggregation result can be retrieved from the returned response.\nParameters for avg #   field\n(Required, string) Field you wish to aggregate.  "});index.add({'id':15,'href':'/docs/references/search/term/','title':"Term query",'section':"Search",'content':"Term query #  Returns documents that contain an exact term in a provided field.\nYou can use the term query to find documents based on a precise value such as a price, a product ID, or a username.\nExamples #  GET /_search { \u0026#34;query\u0026#34;: { \u0026#34;term\u0026#34;: { \u0026#34;org.id\u0026#34;: { \u0026#34;value\u0026#34;: \u0026#34;infini\u0026#34; } } } } Top-level parameters for term #   \u0026lt;field\u0026gt;\n(Required, object) Field you wish to search.  Parameters for \u0026lt;field\u0026gt; #   value\n(Required, string) Term you wish to find in the provided \u0026lt;field\u0026gt;. To return a document, the term must exactly match the field value, including whitespace and capitalization. case_insensitive\n(Optional, Boolean) Allows ASCII case insensitive matching of the value with the indexed field values when set to true. Default is false.  "});index.add({'id':16,'href':'/docs/references/aggregation/terms/','title':"Terms aggregation",'section':"Aggregation",'content':"Terms aggregation #  A multi-bucket value source based aggregation where buckets are dynamically built - one per unique value.\nExamples #  POST /_search { \u0026#34;aggs\u0026#34;: { \u0026#34;genres\u0026#34;: { \u0026#34;terms\u0026#34;: { \u0026#34;field\u0026#34;: \u0026#34;genre\u0026#34; } } } } Response:\n{ ... \u0026#34;aggregations\u0026#34;: { \u0026#34;genres\u0026#34;: { \u0026#34;doc_count_error_upper_bound\u0026#34;: 0, \u0026#34;sum_other_doc_count\u0026#34;: 0, \u0026#34;buckets\u0026#34;: [ { \u0026#34;key\u0026#34;: \u0026#34;electronic\u0026#34;, \u0026#34;doc_count\u0026#34;: 6 }, { \u0026#34;key\u0026#34;: \u0026#34;rock\u0026#34;, \u0026#34;doc_count\u0026#34;: 3 }, { \u0026#34;key\u0026#34;: \u0026#34;jazz\u0026#34;, \u0026#34;doc_count\u0026#34;: 2 } ] } } } Parameters for terms #   field\n(Required, string) Field you wish to aggregate.  "});index.add({'id':17,'href':'/docs/references/aggregation/value-count/','title':"Value count aggregation",'section':"Aggregation",'content':"Value count aggregation #  A single-value metrics aggregation that counts the number of values that are extracted from the aggregated documents. Typically, this aggregator will be used in conjunction with other single-value aggregations. For example, when computing the avg one might be interested in the number of values the average is computed over.\nvalue_count does not de-duplicate values, so even if a field has duplicates each value will be counted individually.\nExamples #  Assuming the data consists of documents representing sales records we can sum the sale price of all hats with:\nPOST /sales/_search { \u0026#34;aggs\u0026#34; : { \u0026#34;types_count\u0026#34; : { \u0026#34;value_count\u0026#34; : { \u0026#34;field\u0026#34; : \u0026#34;type\u0026#34; } } } } Response:\n{ ... \u0026#34;aggregations\u0026#34;: { \u0026#34;types_count\u0026#34;: { \u0026#34;value\u0026#34;: 7 } } } The name of the aggregation (types_count above) also serves as the key by which the aggregation result can be retrieved from the returned response.\nParameters for avg #   field\n(Required, string) Field you wish to aggregate.  "});index.add({'id':18,'href':'/docs/references/collection/','title':"Collection",'section':"References",'content':"Collection #  A \u0026ldquo;Collection\u0026rdquo; typically refers to a grouping or container for related data items in a database or similar data storage system. It can hold various types of data, such as documents, records, or other structured data elements. In the context of the previous discussion about namespaces and data types, a collection could contain documents, each representing a specific piece of information or record.\nCollection management #  Collection APIs are used to manage individual collections and settings.\n  Create a collection  Delete a collection  "});index.add({'id':19,'href':'/docs/getting-started/configuration/','title':"Configuration",'section':"Getting started",'content':"Configuration #  Pizza supports several methods to overwrite the default configuration.\nCommand lines #  ➜ ./bin/pizza --help A Distributed Real-Time Search \u0026amp; AI-Native Innovation Engine. Usage: pizza [OPTIONS] [COMMAND] Commands: service Builtin service management (install, uninstall, start, stop) help Print this message or the help of the given subcommand(s) Options: -l, --log \u0026lt;LEVEL\u0026gt; Set the logging level, options: trace,debug,info,warn,error --debug Run in debug mode, panic immediately with full stack trace -c, --config \u0026lt;FILE\u0026gt; -p, --pid \u0026lt;FILE\u0026gt; Place pid to this file -E, --override \u0026lt;KEY=VALUE\u0026gt; -h, --help Print help -V, --version Print version Configuration file #  You can fully customize Pizza by utilizing the pizza.yaml configuration file:\n# ======================== INFINI Pizza Configuration ========================== # -------------------------------- Log ----------------------------------------- log: level: info # -------------------------------- API ----------------------------------------- api: logging: true network: binding: 127.0.0.1:9100 skip_occupied_port: true # -------------------------------- Cluster ------------------------------------- cluster: name: pizza node: name: my_node_1 network: binding: 127.0.0.1:8100 skip_occupied_port: true # -------------------------------- Storage ------------------------------------- storage: compression: ZSTD # -------------------------------- MemTable ------------------------------------ memtable: threshold: 1k max_num_of_instance: 2 allow_multi_instance: true Override configuration #  You can tweak the configuration by passing the command line option -E with KEY=VALUE style during Pizza start:\n./bin/pizza -E log.level=trace -E api.network.binding=127.0.0.1:12200 "});index.add({'id':20,'href':'/docs/references/collection/delete/','title':"Delete a collection",'section':"Collection",'content':"Delete a collection #  Delete a exists collection.\nExamples #  The following request delete the collection called my-collection:\nDELETE my-collection Request #  PUT /[\u0026lt;namespace\u0026gt;:]\u0026lt;name\u0026gt; Path Parameters #   \u0026lt;namespace\u0026gt;\n(Optional, string) The namespace which the collection belongs to. \u0026lt;name\u0026gt;\n(Required, string) Name of the collection you wish to create.  "});index.add({'id':21,'href':'/docs/references/namespace/delete/','title':"Delete a namespace",'section':"Namespace",'content':"Delete a namespace #  Delete a exists namespace.\nExamples #  The following request delete the namespace called website:\nDELETE /_namespace/website Request #  DELETE /_namespace/\u0026lt;name\u0026gt; Path parameters #   \u0026lt;name\u0026gt;\n(Optional, string) The name of the namespace that you want to delete.  "});index.add({'id':22,'href':'/docs/references/document/','title':"Document",'section':"References",'content':"Document #  In Pizza, a document is a data structure composed of field-and-value pairs. It\u0026rsquo;s roughly equivalent to a row in a relational database table, but with a dynamic schema. Documents are the basic unit of data storage in Pizza, and collections are groupings of documents.\nEach document in Pizza is represented in JSON format, which is a lightweight data interchange format that is easy for humans to read and write and easy for machines to parse and generate.\nDocument management #  Document APIs are used to manage documents.\n  Create a document  Fetch a document  Replace a document  Partial update a document  Delete a document  Batch document operation  "});index.add({'id':23,'href':'/docs/references/','title':"References",'section':"Documentation",'content':"References #  Data management #    Namespace APIs  Collection APIs  Document APIs  Search and analyze #    Search you data  Aggregations  "});index.add({'id':24,'href':'/docs/administration/','title':"Administration",'section':"Documentation",'content':"Administration #  Cluster management #  Search and analyze #  "});index.add({'id':25,'href':'/docs/release-notes/','title':"Release notes",'section':"Documentation",'content':"Release notes #  Information about release notes of INFINI Pizza is provided here.\n0.1.0 #  Breaking changes #  Features #  Bug fix #  Improvements #  "});index.add({'id':26,'href':'/docs/references/document/fetch/','title':"Fetch a document",'section':"Document",'content':"Fetch a document #  Retrieve an existing document by specifying its unique identifier.\nExamples #  Fetch a document from the my-collection collection with customized uuid news_001:\nGET /my-collection/_doc/news_001 The API returns the following result:\n{ \u0026#34;_id\u0026#34;: \u0026#34;0,0\u0026#34;, \u0026#34;_version\u0026#34;: 1, \u0026#34;_namespace\u0026#34;: \u0026#34;default\u0026#34;, \u0026#34;_collection\u0026#34;: \u0026#34;my-collection\u0026#34;, \u0026#34;_key\u0026#34; : \u0026#34;news_001\u0026#34;, \u0026#34;found\u0026#34;: true, \u0026#34;_source\u0026#34; : { \u0026#34;message\u0026#34;: \u0026#34;GET /search HTTP/1.1 200 1070000\u0026#34;, \u0026#34;org\u0026#34;: { \u0026#34;id\u0026#34;: \u0026#34;infini\u0026#34; } } } As you can see, the customized uuid are represented as _key within the document, and there is also a _id returned with value 0,0, this is the internal id generated by Pizza, and it is guaranteed to be unique, so you can also fetch this document by this value like this:\nGET /my-collection/_doc/0,0 Request #  POST /\u0026lt;target\u0026gt;/_doc/\u0026lt;doc_id\u0026gt; Path parameters #   \u0026lt;target\u0026gt;\n(Required, string) Name of the collection to target. \u0026lt;doc_id\u0026gt;\n(Required, string) The unique identify of this document, support both _key or _id.  "});index.add({'id':27,'href':'/docs/references/document/replace/','title':"Replace a document",'section':"Document",'content':"Replace a document #  Replace an existing document by specifying its unique identifier and the new content.\nExamples #  Replace a document news_001 of the collection my-collection with new content:\nPUT /my-collection/_doc/news_001 { \u0026#34;message\u0026#34;: \u0026#34;GET /search HTTP/1.1 200 1070000\u0026#34;, \u0026#34;org\u0026#34;: { \u0026#34;id\u0026#34;: \u0026#34;infinilabs\u0026#34; } } The API returns as following result:\n{\u0026#34;_id\u0026#34;:\u0026#34;0,0\u0026#34;, \u0026#34;_key\u0026#34;:\u0026#34;news_001\u0026#34;, \u0026#34;result\u0026#34;:\u0026#34;updated\u0026#34;} After the document modification, If you perform the fetch request:\nGET /my-collection/_doc/news_001 It returns an updated document like:\n{ \u0026#34;_id\u0026#34;: \u0026#34;0,0\u0026#34;, \u0026#34;_version\u0026#34;: 2, \u0026#34;_namespace\u0026#34;: \u0026#34;default\u0026#34;, \u0026#34;_collection\u0026#34;: \u0026#34;my-collection\u0026#34;, \u0026#34;_key\u0026#34; : \u0026#34;news_001\u0026#34;, \u0026#34;found\u0026#34;: true, \u0026#34;_source\u0026#34; : { \u0026#34;message\u0026#34;: \u0026#34;GET /search HTTP/1.1 200 1070000\u0026#34;, \u0026#34;org\u0026#34;: { \u0026#34;id\u0026#34;: \u0026#34;infinilabs\u0026#34; } } } Note that the document _version was increased to 2.\nPizza works by marking the old document as deleted and insert a new document under the hood.\nRequest #  POST /\u0026lt;target\u0026gt;/_doc/\u0026lt;doc_id\u0026gt; {\u0026lt;fields\u0026gt;} Path parameters #   \u0026lt;target\u0026gt;\n(Required, string) Name of the collection to target. \u0026lt;doc_id\u0026gt;\n(Required, string) The unique identify of this document, support both _key or _id.  Request body #   \u0026lt;fields\u0026gt;\n(Required, string) Request body contains the JSON source for the document data.  "});index.add({'id':28,'href':'/docs/references/document/partial_update/','title':"Partial update a document",'section':"Document",'content':"Partial update a document #  Sometimes we may only need to update a portion fields of the document.\nExamples #  Update the org.id field of the document news_001 in the collection my-collection:\nPUT /my-collection/_doc/news_001/_update { \u0026#34;sync\u0026#34;:{ \u0026#34;replace\u0026#34;:{ \u0026#34;org\u0026#34;: { \u0026#34;id\u0026#34;: \u0026#34;infinilabs\u0026#34; } } } } The API returns as following result:\n{\u0026#34;_id\u0026#34;:\u0026#34;0,0\u0026#34;, \u0026#34;_key\u0026#34;:\u0026#34;news_001\u0026#34;, \u0026#34;result\u0026#34;:\u0026#34;updated\u0026#34;} Pizza using the method of fetching a document, then merging partial updates and replacing it.\nRequest #  POST /\u0026lt;target\u0026gt;/_doc/\u0026lt;doc_id\u0026gt;/_update { \u0026#34;sync\u0026#34;:{ \u0026lt;operation\u0026gt;: {\u0026lt;fields\u0026gt;} } \u0026#34;async\u0026#34;:{ \u0026lt;operation\u0026gt;: {\u0026lt;fields\u0026gt;} } } Pizza support both sync and async way to perform the updates, in order to update in realtime, you need to use sync here.\nIn asynchronous mode, the update process is considered complete once the request is committed to the WAL. Background tasks independently consume and process updates asynchronously, making it suitable for scenarios prioritizing update efficiency.\nPath parameters #   \u0026lt;target\u0026gt;\n(Required, string) Name of the collection to target. \u0026lt;doc_id\u0026gt;\n(Required, string) The unique identify of this document, support both _key or _id.  Request body #   \u0026lt;operation\u0026gt; The operation supported by partial updates: add, replace, remove, array_append. \u0026lt;fields\u0026gt;\n(Required, string) The JSON format of the fields operation by partial updates.  "});index.add({'id':29,'href':'/docs/references/document/delete/','title':"Delete a document",'section':"Document",'content':"Delete a document #  Delete a specific document from the specified collection by specifying its unique identifier.\nExamples #  Delete the document 1 from collection my-collection:\nDELETE /my-collection/_doc/0,0 The API returns the following result:\n{ \u0026#34;_id\u0026#34;: \u0026#34;0,0\u0026#34;, \u0026#34;_version\u0026#34;: 2, \u0026#34;result\u0026#34;: \u0026#34;deleted\u0026#34;, ... } Request #  DELETE /\u0026lt;target\u0026gt;/_doc/\u0026lt;doc_id\u0026gt; Path parameters #   \u0026lt;target\u0026gt;\n(Required, string) Name of the collection to target. \u0026lt;doc_id\u0026gt;\n(Required, string) Unique identifier for the document, support both _key or _id.  "});index.add({'id':30,'href':'/docs/references/document/bulk/','title':"Batch document operation",'section':"Document",'content':"Batch document operation #  Provides a efficient way to perform multiple index, create, delete, and update operations in a single request.\nExamples #  POST /_bulk { \u0026#34;index\u0026#34; : { \u0026#34;_index\u0026#34; : \u0026#34;test\u0026#34;, \u0026#34;_id\u0026#34; : \u0026#34;1\u0026#34; } } { \u0026#34;field1\u0026#34; : \u0026#34;value1\u0026#34; } { \u0026#34;delete\u0026#34; : { \u0026#34;_index\u0026#34; : \u0026#34;test\u0026#34;, \u0026#34;_id\u0026#34; : \u0026#34;2\u0026#34; } } { \u0026#34;create\u0026#34; : { \u0026#34;_index\u0026#34; : \u0026#34;test\u0026#34;, \u0026#34;_id\u0026#34; : \u0026#34;3\u0026#34; } } { \u0026#34;field1\u0026#34; : \u0026#34;value3\u0026#34; } { \u0026#34;update\u0026#34; : {\u0026#34;_id\u0026#34; : \u0026#34;1\u0026#34;, \u0026#34;_index\u0026#34; : \u0026#34;test\u0026#34;} } { \u0026#34;doc\u0026#34; : {\u0026#34;field2\u0026#34; : \u0026#34;value2\u0026#34;} } The API returns the following result:\n{ \u0026#34;took\u0026#34;: 30, \u0026#34;errors\u0026#34;: false, \u0026#34;items\u0026#34;: [ { \u0026#34;index\u0026#34;: { \u0026#34;_namespace\u0026#34;: \u0026#34;default\u0026#34;, \u0026#34;_collection\u0026#34;: \u0026#34;test\u0026#34;, \u0026#34;result\u0026#34;: \u0026#34;created\u0026#34;, ... } }, { \u0026#34;delete\u0026#34;: { \u0026#34;_namespace\u0026#34;: \u0026#34;default\u0026#34;, \u0026#34;_collection\u0026#34;: \u0026#34;test\u0026#34;, \u0026#34;result\u0026#34;: \u0026#34;not_found\u0026#34;, ... } }, { \u0026#34;create\u0026#34;: { \u0026#34;_namespace\u0026#34;: \u0026#34;default\u0026#34;, \u0026#34;_collection\u0026#34;: \u0026#34;test\u0026#34;, \u0026#34;result\u0026#34;: \u0026#34;created\u0026#34;, ... } }, { \u0026#34;update\u0026#34;: { \u0026#34;_namespace\u0026#34;: \u0026#34;default\u0026#34;, \u0026#34;_collection\u0026#34;: \u0026#34;test\u0026#34;, \u0026#34;result\u0026#34;: \u0026#34;updated\u0026#34;, ... } } ] } Request #  POST /_bulk POST /\u0026lt;target\u0026gt;/_bulk Path parameters #   \u0026lt;target\u0026gt;\n(Required, string) Name of the collection to target.  Request body #  The actions are specified in the request body using a newline delimited JSON (NDJSON) structure:\naction_and_meta_data\\n optional_source\\n action_and_meta_data\\n optional_source\\n .... action_and_meta_data\\n optional_source\\n The index and create actions expect a source on the next line, and have the same semantics as the standard API: create fails if a document with the same ID already exists in the target, index adds or replaces a document as necessary.\nupdate expects that the partial doc, upsert, and script and its options are specified on the next line.\ndelete does not expect a source on the next line and has the same semantics as the standard delete API.\nBecause this format uses literal \\n\u0026rsquo;s as delimiters, make sure that the JSON actions and sources are not pretty printed.\nIf you provide a \u0026lt;target\u0026gt; in the request path, it is used for any actions that don’t explicitly specify an _index argument.\ncreate #  Indexes the specified document if it does not already exist. The following line must contain the source data to be indexed.\n _namespace\n(Optional, string) Name of the namespace to perform the action on. _collection\n(Optional, string) Name of the collection to perform the action on. This parameter is required if a \u0026lt;target\u0026gt; is not specified in the request path. _index\n(Optional, string) A shortcut to specify the namespace and collection in [\u0026lt;namespace\u0026gt;:]\u0026lt;collection\u0026gt; syntax. This parameter conflicts with \u0026lt;_namespace\u0026gt; and \u0026lt;_collection\u0026gt;. _id\n(Optional, string) The document ID. If no ID is specified, a document ID is automatically generated.  delete #  Removes the specified document from the index.\n _namespace\n(Optional, string) Name of the namespace to perform the action on. _collection\n(Optional, string) Name of the collection to perform the action on. This parameter is required if a \u0026lt;target\u0026gt; is not specified in the request path. _index\n(Optional, string) A shortcut to specify the namespace and collection in [\u0026lt;namespace\u0026gt;:]\u0026lt;collection\u0026gt; syntax. This parameter conflicts with \u0026lt;_namespace\u0026gt; and \u0026lt;_collection\u0026gt;. _id\n(Required, string) The document ID. If no ID is specified, a document ID is automatically generated.  index #  Indexes the specified document. If the document exists, replaces the document and increments the version. The following line must contain the source data to be indexed.\n _namespace\n(Optional, string) Name of the namespace to perform the action on. _collection\n(Optional, string) Name of the collection to perform the action on. This parameter is required if a \u0026lt;target\u0026gt; is not specified in the request path. _index\n(Optional, string) A shortcut to specify the namespace and collection in [\u0026lt;namespace\u0026gt;:]\u0026lt;collection\u0026gt; syntax. This parameter conflicts with \u0026lt;_namespace\u0026gt; and \u0026lt;_collection\u0026gt;. _id\n(Optional, string) The document ID. If no ID is specified, a document ID is automatically generated.  delete #  Removes the specified document from the index.\n _namespace\n(Optional, string) Name of the namespace to perform the action on. _collection\n(Optional, string) Name of the collection to perform the action on. This parameter is required if a \u0026lt;target\u0026gt; is not specified in the request path. _index\n(Optional, string) A shortcut to specify the namespace and collection in [\u0026lt;namespace\u0026gt;:]\u0026lt;collection\u0026gt; syntax. This parameter conflicts with \u0026lt;_namespace\u0026gt; and \u0026lt;_collection\u0026gt;. _id\n(Required, string) The document ID. If no ID is specified, a document ID is automatically generated.  doc #  The partial document to index. Required for update operations.\n\u0026lt;fields\u0026gt; #  The document source to index. Required for create and index operations.\n"});index.add({'id':31,'href':'/docs/references/search/','title':"Search",'section':"References",'content':"Search #  A search query, or query, is a request for information about documents in Pizza collections.\nA search consists of one or more queries that are combined and sent to Pizza. Documents that match a search\u0026rsquo;s queries are returned in the hits, or search results, of the response.\nA search may also contain additional information used to better process its queries. For example, a search may be limited to a specific collection or only return a specific number of results.\nExamples #  Search all the collections under the default namespace whose names are ended with -logs, fetch the documents whose field year has value 2024:\nPOST /default.*-logs/_search { \u0026quot;query\u0026quot;: { \u0026quot;term\u0026quot;: { \u0026quot;year\u0026quot;: \u0026quot;2024\u0026quot; } } } Requests #  POST /\u0026lt;targets\u0026gt;/_search Path parameters #   targets\n(Optional, String) Comma-separated, names of the collection to search (wildcard supported)  Query parameters #    from\n(Optional, integer) How many documents to skip, should be non-negative and defaults to 0.\n  size\n(Optional, integer) The maximun number of documents to be returned in hits, defaults to 20.\n  Term-level queries #    prefix query\nReturns documents that contain a specific prefix in a provided field.  range query\nReturns documents that contain terms within a provided range.  regexp query\nReturns documents that contain terms matching a regular expression.  term query\nReturns documents that contain an exact term in a provided field.  "});index.add({'id':32,'href':'/docs/references/aggregation/','title':"Aggregation",'section':"References",'content':"Aggregation #  An aggregation summarizes your data as metrics, statistics, or other analytics.\nPizza organizes aggregations into the following categories:\n  Metric aggregations that calculate metrics, such as a sum or average, from field values.  Bucket aggregations that group documents into buckets, also called bins, based on field values, ranges, or other criteria.  Metric aggregations #    avg aggregation\nA single-value metrics aggregation that computes the average of numeric values that are extracted from the aggregated documents.  max aggregation\nA single-value metrics aggregation that keeps track and returns the maximum value among the numeric values extracted from the aggregated documents.  min aggregation\nA single-value metrics aggregation that keeps track and returns the minimum value among numeric values extracted from the aggregated documents.  percentiles aggregation\nA multi-value metrics aggregation that calculates one or more percentiles over numeric values extracted from the aggregated documents.  sum aggregation\nA single-value metrics aggregation that sums up numeric values that are extracted from the aggregated documents.  value_count aggregation\nA single-value metrics aggregation that counts the number of values that are extracted from the aggregated documents.  Bucket aggregations #    date_histogram aggregation\nA histogram aggregation that can only be used with date or date range values.  terms aggregation\nA multi-bucket value source based aggregation where buckets are dynamically built - one per unique value.  "});index.add({'id':33,'href':'/docs/community/','title':"Community",'section':"Documentation",'content':"Community hall of fame #  The following acknowledges the Maintainers for the Pizza project, credits to those who have Contributed to this repository (via bug reports, code, design, ideas, project management, translation, testing, etc.), proactive advocates of pizza as Evangelists, and any other References utilized.\nMaintainers #   Medcl(@medcl), SteveLauC, Loi Chyan(@loichyan)\nContributors #  Evangelists #  Adopters #  Pizza community of adopters is growing! Innovative organizations of all sizes and across industry sectors are committed to accelerating the adoption of commercial-grade, production-ready open source technologies developed by the Pizza community.\nDo you use INFINI Pizza? Show your support for open source by adding your logo to this page.\nPlease create an issue to add your logo below.\nReferences #  "});})();