Systems and methods are described for exporting bucket data from one or more buckets to one or more worker nodes. The system can identify data from different bucket data from buckets stored in a data intake and query system that is to be processed by one or more worker nodes. The system can allocate one or more execution resources, such as a processing pipeline, to process and export the bucket data from the buckets. The system can assign bucket data corresponding to individual buckets to the execution resource based on a bucket distribution policy. The indexer can export the bucket data to the worker nodes for further processing based on the bucket data-execution resource assignment.

An example operation may include one or more of identifying a plurality of instances of a blockchain storage request that have been independently submitted by a plurality of clients, respectively, verifying whether execution content of the plurality of instances of the blockchain storage request match, and in response to verifying the match, storing the blockchain storage request within a block among a hash-linked chain of blocks.

The present teaching relates to providing a query suggestion. In one example, a request is received for query suggestions with respect to a query prefix input by a user. A plurality of query suggestions is determined based on the query prefix and a preceding query input by the user. A degree of popularity of the preceding query is determined. One or more query suggestions are selected from the plurality of query suggestions based on the degree of popularity of the preceding query. The one or more query suggestions are provided as a response to the request.

A computer-implemented method for transmitting and/or submitting digital content such as feedback for an entity to a blockchain is disclosed. This may be, for example, the Bitcoin blockchain. The method, which is implemented at a first node, includes: submitting, to the blockchain, a first transaction for committing a first quantity of tokens to transfer to a second node associated with the entity, the first transaction having a first output; generating a first content for submission to the blockchain; and submitting, to the blockchain, a second transaction that returns a second quantity of the committed first quantity of tokens to an address associated with the first node, the second transaction having the first output of the first transaction as an input, wherein the first content is used to unlock the first output of the first transaction.

The subject technology receives information associated with a machine learning model. The subject technology determines a set of metrics based at least in part on the information associated with the machine learning model, where the set of metrics corresponds to respective indicators of performance of the machine learning model based on input data from a data set, the set of metrics further including a number of errors produced by the machine learning model when applied to the input data from the data set. Further, the subject technology displays a user interface based at least in part on the set of metrics, where the user interface includes a set of graphical elements, and the set of graphical elements further includes representations of the set of metrics, and representations of the input data from the data set utilized by the machine learning model.

A software voting or prediction system iteratively solicits participant preferences between members of a set, with a binary tree built used to minimize the number of iterations required. As each member of the set is considered, it is pairwise-compared with select members represented by nodes already in the binary tree, with iterations beginning at a root node of the tree and continuing to a leaf node. The newly considered member is placed as a new leaf node, and the tree is height-rebalanced as appropriate. Red-black tree coloring and tree rotation rules are optionally used for this purpose. Yes/no preference tallies are kept for each member of the set throughout the tree-building process and are ultimately used for scoring. Height-rebalancing of the tree helps minimize the number of iterations needed to precisely score each member of the set relative to its alternatives.

Joining data using a disjunctive operator using a lookup table is described. An example computer-implemented method can include receiving a query with a set of conjunctive predicates and a set of disjunctive predicates. The method may also include generating a lookup table for each predicate in the sets of conjunctive predicates and disjunctive predicates. The method, for each row in a probe-side table, may also further include looking up a value associated with that row in each of the lookup tables and adding the row to a results set when there is a match. Additionally, the method may also include returning the results set.

Techniques for generated regular expressions are disclosed. In some embodiments, a regular expression generator may receive input data comprising one or more character sequences. The regular expression generator may convert character sequences into a sets of regular expression codes and/or span data structures. The regular expression generator may identify a longest common subsequence shared by the sets of regular expression codes and/or spans, and may generate a regular expression based upon the longest common subsequence. Alignment of span data structures may be performed when generating the regular expression.

Methods, non-transitory computer readable media, and computing devices for three-dimensional searching that precisely targets retrieval within diverse types of content includes retrieving web content in response to a received request from a client device. A destination page, an address within the page, and an offset distance from a starting point at the address to a correlated responsive portion of the web content to the received request is identified. The responsive web content with the identified destination page, the address, and the offset distance is provided to the requesting client device.

Methods are presented for providing dynamic search filter suggestions that are updated and ranked based on the user filter selections. One method includes detecting a query received in a user interface (UI), calculating, by a search-candidate model, first search results, and calculating, by a suggestions model, first filter suggestions for filter categories to filter responses to the query. The suggestions model is obtained by training a machine-learning algorithm utilizing pairwise learning-to-rank modeling. The first search results and the first filter suggestions are presented in the UI. When a selection in the UI of a filter suggestion is detected, the search-candidate model calculates second search results for the filter categories based on the query and the selected filter suggestion, and the suggestions model calculates second first filter suggestions based on the query and the selected filter suggestion. The second search results and the second filter suggestions are presented in the UI.