A method for cluster based searching for a value range stored in a storage system, the method may include receiving a request to find a certain value range within a set of information elements that are stored in a storage system; wherein the set of information elements comprises subsets of information elements associated with subset hash based filters; wherein different subsets of information elements are associated with different subset hash based filters; determining a certain cluster value of a certain cluster that comprises the certain value range; applying one or more hush functions on the certain cluster value to provide one or more hash results; and determining whether one or more members of the certain cluster are possibly in a subset of information elements, based on the one or more hash results and on a subset hash based filter of the subset of information elements; and when determining that the one or more members of the certain cluster are possibly in the subset then searching, within the subset, a matching information element that matches the certain value range.

A method for a first set of processors and a second set of processors comprises, the first set of processors processing a set of queries, as a result of a change in utilization of the first set of processors, processing the set of queries using the second set of processors. The change in processors is independent of a change in storage resources, the storage resources shared by the first set of processors and the second set of processors.

A network storage volume stores first entries in a first-mode storage bucket and a second entries in a second-mode storage bucket. The first-mode storage bucket has first bucket metadata, and the second-mode storage bucket has second bucket metadata. A computer-implemented method includes comparing a utilized capacity of the network storage volume to a target capacity information of the network storage volume to obtain a comparison result. Based on the comparison result, at least one bucket is selected to be purged from the buckets of the network storage volume based at least in part on bucket metadata of the buckets. The method further includes causing a purge of the at least one selected bucket from the network storage volume.

A network storage volume stores first entries in a first-mode storage bucket and a second entries in a second-mode storage bucket. The first-mode storage bucket has first bucket metadata, and the second-mode storage bucket has second bucket metadata. A computer-implemented method includes comparing a utilized capacity of the network storage volume to a target capacity information of the network storage volume to obtain a comparison result. Based on the comparison result, at least one bucket is selected to be purged from the buckets of the network storage volume based at least in part on bucket metadata of the buckets. The method further includes causing a purge of the at least one selected bucket from the network storage volume.

A virtual machine that includes a plurality of processes executes on a computer processor. A record-replay file, trace annotations, and an application program interface request are received into the computer processor. The trace annotations and application program interface request are translated into record-replay commands. The record-replay commands capture data from the record-replay file, and the captured data can be accessed via a programmatic interface.

Implementations are described herein for leveraging digital media files retrieved and/or created by users to predict/determine topics of potential relevance to the users. In various implementations, digital media file(s) created and/or retrieved by a user with a client device may be applied as input across trained machine learning model(s), which in some cases are local to the client device, to generate output that indicates object(s) detected in the digital media file(s). Data indicative of the indicated object(s) may be provided to a remote computing system without providing the digital media file(s) themselves. In some implementations, information associated with the indicated object(s) may be retrieved and proactively output to the user. In some implementations, a frequency at which objects occur across a corpus of digital media files may be considered when determining a likelihood that a detected object is potentially relevant to a user.

A method of summarizing data files includes implementing, at a server, a storage event for a data file, analyzing the data file and creating a summary of the data file, and storing the summary linked to the data file.

Systems and methods are provided for analyzing data in one or more datasets, where the one or more datasets are embodied as local, embedded databases in a client-side application, such as a web browser or web browser tab. A client-side data analysis application or artifact may be used to interact, i.e., query, the local, embedded databases, and retrieve results to analyze data. Because the one or more datasets are localized, there is no need to access a remote database/datastore in order to analyze the data. Moreover, the client-side data analysis application or artifact can be executed as individual instances in the client-side application. The state of a local, embedded database may be stored as another file that can be used as a local, embedded database for another instance of the client-side data analysis application or artifact.

Provided are a data archiving method and apparatus capable of providing a remote near-line data archiving function by receiving remote function invoking from a target system in which data is stored, providing the target system with a first function for archiving, in a storage system, at least some of the data stored in the target system over a network in response to the remote function invoking, and providing the target system with a second function for the query of the data archived in the storage system over the network.

One example technique includes receiving a request for accessing a file from a container process. In response to receiving the request, the technique includes querying a mapping table corresponding to the container process to locate an entry corresponding to a file identifier of the requested file. The entry also includes data identifying a file location on the storage device from which the requested file is accessible. The technique further includes retrieving a copy of the requested file according to the file location identified by the data in the located entry in the mapping table and providing the retrieved copy of the requested file to the container process, thereby allowing the container process to access the requested file.