Open Issues Need Help
View All on GitHubAI Summary: This GitHub issue is a highly ambiguous request regarding Elasticsearch's `significant_text` aggregation. It provides only a title and a link to the official documentation, lacking any specific input, expected output, or context, making it unclear what problem the user is trying to solve or what functionality they are requesting.
Automatically add output types to your Elasticsearch queries.
Automatically add output types to your Elasticsearch queries.
Automatically add output types to your Elasticsearch queries.
Automatically add output types to your Elasticsearch queries.
Automatically add output types to your Elasticsearch queries.
Automatically add output types to your Elasticsearch queries.
Automatically add output types to your Elasticsearch queries.
Automatically add output types to your Elasticsearch queries.
Automatically add output types to your Elasticsearch queries.
Automatically add output types to your Elasticsearch queries.
AI Summary: This GitHub issue requests a 'Cartesian-centroid aggregation,' referencing Elasticsearch documentation for context. Despite the title, the issue provides no specific input, expected output, or additional context, making the exact user request unclear.
Automatically add output types to your Elasticsearch queries.
Automatically add output types to your Elasticsearch queries.
Automatically add output types to your Elasticsearch queries.
AI Summary: This GitHub issue is a feature request to add support for a function or method named `asyncSearch.submit`. No further details or context regarding its purpose, scope, or implementation requirements are provided in the issue description.
Automatically add output types to your Elasticsearch queries.
Automatically add output types to your Elasticsearch queries.
Automatically add output types to your Elasticsearch queries.
AI Summary: This GitHub issue is titled 'Matrix stats aggregation' but provides no details regarding input, expected output, or additional context. It only includes a link to the Elasticsearch documentation for this aggregation type, making its purpose (e.g., bug report, feature request, question) entirely unclear.
Automatically add output types to your Elasticsearch queries.
AI Summary: This GitHub issue requests a "Weighted avg aggregation" and provides a link to the Elasticsearch documentation for this feature. However, it lacks any specific input, expected output, or additional context, making the exact nature of the request (e.g., bug report, feature request, general question) and its scope entirely unclear.
Automatically add output types to your Elasticsearch queries.
Automatically add output types to your Elasticsearch queries.
Automatically add output types to your Elasticsearch queries.
AI Summary: This GitHub issue is a placeholder or an incomplete request regarding the Boxplot aggregation feature from Elasticsearch. It lacks any specific details about the desired input, expected output, or additional context, making it impossible to act upon without further clarification.
Automatically add output types to your Elasticsearch queries.
Automatically add output types to your Elasticsearch queries.
AI Summary: This GitHub issue proposes a new `ip_prefix` aggregation for Elasticsearch. It demonstrates how this aggregation would group documents by IP address prefixes (e.g., /24 for IPv4), providing the aggregated prefix, document count, and netmask in the output. This feature would allow users to analyze network traffic or other IP-related data based on subnet ranges.
Automatically add output types to your Elasticsearch queries.
AI Summary: The user is requesting support for or demonstrating the expected behavior of an IP range aggregation. They provide an Elasticsearch query example using `ip_range` aggregation on an 'ip' field with specific 'to' and 'from' ranges, along with the desired output structure including bucket keys, range values, and document counts. The issue references the official Elasticsearch documentation for this aggregation type.
Automatically add output types to your Elasticsearch queries.
AI Summary: The issue proposes refactoring the test suite to eliminate extensive copy-pasting in test files, particularly for Elasticsearch aggregation tests. The goal is to create a shared utility function that can generate or validate test cases for various aggregation types, including valid, invalid field, and sub-aggregation scenarios, significantly improving test maintainability.
Automatically add output types to your Elasticsearch queries.
AI Summary: This GitHub issue is a feature request to implement a Geohex grid aggregation. It details the required input query structure, specifying the location field and desired precision, and illustrates the expected output format as buckets containing Geohex keys and their corresponding document counts, similar to Elasticsearch's `geohex_grid` aggregation.
Automatically add output types to your Elasticsearch queries.
Automatically add output types to your Elasticsearch queries.
Automatically add output types to your Elasticsearch queries.
Automatically add output types to your Elasticsearch queries.
Automatically add output types to your Elasticsearch queries.
AI Summary: This GitHub issue proposes adding a new 'string_stats' aggregation. This aggregation would calculate various statistics for string fields, including the count, minimum, maximum, and average string length, as well as the entropy of the string values. The issue provides an example request and the expected JSON output, referencing existing Elasticsearch documentation for this feature.
Automatically add output types to your Elasticsearch queries.
Automatically add output types to your Elasticsearch queries.
AI Summary: This GitHub issue demonstrates the usage of the `percentile_ranks` aggregation in Elasticsearch. It provides a clear example of an input query to calculate percentile ranks for specific values (500 and 600) on a `load_time` field, along with the corresponding expected output format. The issue serves as a straightforward illustration of this aggregation's functionality.
Automatically add output types to your Elasticsearch queries.
AI Summary: This GitHub issue requests the implementation or demonstration of a percentiles aggregation feature, similar to Elasticsearch. It provides an example input query for calculating percentiles on a 'load_time' field and shows the expected output format with various percentile values.
Automatically add output types to your Elasticsearch queries.
AI Summary: This GitHub issue requests the addition of a `geo_line` aggregation. It would take a geographic point field and a sort field (e.g., timestamp) as input, then output a GeoJSON LineString representing the path of points ordered by the specified sort field. This feature is inspired by an existing Elasticsearch aggregation.
Automatically add output types to your Elasticsearch queries.
Automatically add output types to your Elasticsearch queries.
Automatically add output types to your Elasticsearch queries.
AI Summary: The task is to extend the TypeScript type definitions in the `@vahor/typed-es` library to correctly handle the `keyed: true` parameter within the `date_histogram` aggregation in Elasticsearch queries. This involves updating the types to reflect the presence of the `key_as_string` field in the aggregation's output when `keyed` is true, as documented in the Elasticsearch documentation.
Automatically add output types to your Elasticsearch queries.
AI Summary: The task is to enhance the TypeScript type generation in the `typed-es` library to correctly handle Elasticsearch field types with variants like `.keyword`. Currently, if a field has a `.keyword` suffix in the query's `_source` parameter, the corresponding type is missing from the generated output type. The solution should either include the field with type `unknown` or, if appropriate, with a union type including the parent type (e.g., `string | unknown`). This ensures type safety when accessing fields with keyword variants.
Automatically add output types to your Elasticsearch queries.
AI Summary: The task is to modify the `@vahor/typed-es` library to ensure that the `fields` and `docvalue_fields` parameters only return leaf types in Elasticsearch queries, aligning with Elasticsearch's behavior of not returning nested objects. This involves updating the type definitions and potentially the query construction logic to prevent nested fields from being included in the response when these parameters are used.
Automatically add output types to your Elasticsearch queries.
AI Summary: Implement a `date_range` aggregation function within the `@vahor/typed-es` library, allowing users to perform date range aggregations in their Elasticsearch queries while maintaining type safety. This involves adding the necessary type definitions and logic to handle the `date_range` aggregation parameters and results, ensuring compatibility with the existing type system and error handling.
Automatically add output types to your Elasticsearch queries.
AI Summary: Implement support for the Elasticsearch histogram aggregation within the `@vahor/typed-es` library. This involves adding the necessary type definitions and handling for histogram aggregations, ensuring type safety and correct output typing as with other aggregations. The implementation should follow the existing pattern of type-safe query building and output handling, addressing any potential limitations or edge cases.
Automatically add output types to your Elasticsearch queries.
AI Summary: Implement support for the Elasticsearch `filters` aggregation within the `@vahor/typed-es` library. This involves extending the existing type definitions to accommodate the `filters` aggregation structure and ensuring that the generated types accurately reflect the output of queries using this aggregation. The implementation should handle various filter types and maintain type safety.
Automatically add output types to your Elasticsearch queries.
AI Summary: Implement a 'range' aggregation function within the `@vahor/typed-es` library, allowing users to perform range-based aggregations in their Elasticsearch queries while maintaining type safety. This involves adding support for the Elasticsearch range aggregation's structure and ensuring proper type inference for the resulting aggregation output.
Automatically add output types to your Elasticsearch queries.
AI Summary: Extend the `@vahor/typed-es` library to support the `scripted_metric` aggregation in Elasticsearch queries, ensuring that the output type `value` is correctly inferred and typed, handling the `Record<string, unknown>` input type.
Automatically add output types to your Elasticsearch queries.
AI Summary: Implement support for the Elasticsearch `top_metrics` aggregation in the `@vahor/typed-es` library. This involves adding type definitions for the input and output structures of the `top_metrics` aggregation, ensuring type safety when using it with the typed client, and handling potential edge cases.
Automatically add output types to your Elasticsearch queries.
Automatically add output types to your Elasticsearch queries.
Automatically add output types to your Elasticsearch queries.
AI Summary: Enhance the TypeScript types in the `typed-es` library to correctly infer the type of the `key` property within `buckets` when using composite aggregations in Elasticsearch queries. Currently, the type is too generic (`Record<string, unknown>`), but it should be narrowed down based on the `sources` defined in the composite aggregation, allowing for more precise type safety.
Automatically add output types to your Elasticsearch queries.
AI Summary: Improve the error message clarity in the `typed-es` library when an invalid field is specified in the `_source` parameter of an Elasticsearch query. The current error message is misleading, referencing a field from a different index. The task involves modifying the type checking logic to provide a more accurate and helpful error message indicating that the specified field is not present in the selected index.
Automatically add output types to your Elasticsearch queries.
AI Summary: Update the README for the `typed-es` project to include a new "Features" section detailing the following: wildcard support, automatic type determination based on query options (e.g., returning total hits), and automatic output type inference based on the `_source` and aggregations used in the Elasticsearch query.
Automatically add output types to your Elasticsearch queries.
AI Summary: The task is to extend the `@vahor/typed-es` library to support the `SearchSourceFilter` interface for Elasticsearch queries. This involves modifying the type definitions and logic to correctly handle `excludes` and `includes` properties within the `_source` parameter, ensuring type safety and accurate field inclusion/exclusion in query results.
Automatically add output types to your Elasticsearch queries.
AI Summary: The task is to modify the `typed-es` library to correctly handle the `rest_total_hits_as_int` option in Elasticsearch queries. Currently, the library returns the total hits as an object or long regardless of this option's setting. The fix involves adjusting the code to respect the option and return the appropriate type.
Automatically add output types to your Elasticsearch queries.
AI Summary: Enhance the TypeScript types in the `@vahor/typed-es` library to support more flexible field names in Elasticsearch queries. Currently, only exact field names are allowed; the improvement should allow fields with suffixes like `fieldname.keyword` as found in Elasticsearch mappings.
Automatically add output types to your Elasticsearch queries.
AI Summary: Modify the `@vahor/typed-es` library to ensure that the `_source` property in the Elasticsearch search response is not undefined when the query includes a `_source` parameter that is not explicitly set to `false` or an empty array. This involves handling cases where `_source` is omitted from the query, and potentially improving error handling for cases where the Elasticsearch mapping explicitly disables `_source`.
Automatically add output types to your Elasticsearch queries.
AI Summary: The task is to modify the `typedEs` function in the `@vahor/typed-es` library to support both `aggs` and `aggregations` properties consistently for Elasticsearch query type inference. Currently, only `aggs` is correctly handled, leading to type errors when using `aggregations`.
Automatically add output types to your Elasticsearch queries.