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.

Described are methods, systems and computer readable media for dynamic updating of query result displays.

The subject technology determines, using a connection to an external data source, a set of shards stored in an external data source, the connection to the external data source being established using an external integration, the external integration including security and configuration information. The subject technology determines a set of offsets of each shard of the set of shards. The subject technology generates a query plan indicating a degree of parallelism based at least in part on a size of the set of offsets. The subject technology, based on the set of shards and the set of offsets, performs an operation on the external data source by performing, using the connection to the external data source, a write operation from a query statement on the external data source, the external data source being different than a storage platform associated with the system.

A data intake and query system establishes a network connection with an instrumented target system. The instrumented target system collects metrics in accordance with an instrumentation platform, whereby the instrumentation platform defines an instrumentation platform query. The metrics are stored in the data intake and query system. A data intake and query system query for a subset of metrics is transmitted. The data intake and query system query replicates the instrumentation platform query of the instrumentation platform. From the data store, log data for the instrumented target system, and correlated with the metrics to obtain correlated results.

Described are methods, systems and computer readable media for performance logging of complex query operations.

Described are methods, systems and computer readable media for data source refreshing.

Systems and methods are described for processing ingested data in an asynchronous manner as the data is being ingested to detect potential anomalies. For example, one or more streaming data processors can convert data as the data is ingested into a comparable data structure, determine whether the comparable data structure should be assigned to an existing data pattern or a new data pattern, and optionally update a characteristic of the data pattern to which the comparable data structure is assigned. The streaming data processor(s) can perform these operations automatically in real-time or in periodic batches. Once one or more comparable data structures have been assigned to one or more data patterns, the streaming data processor(s) can analyze the comparable data structures assigned to a particular data pattern to determine whether any of the comparable data structures appear to be anomalous.

Methods, systems, apparatuses, and computer program products are described herein for the generation and traversal of a hierarchical index structure. The structure indexes search keys from data ingested from different data sources and enables efficient retrieval of the keys. As data is ingested, index nodes are generated at the lowest level of the structure. The nodes are analyzed to determine whether such nodes comprise duplicate keys. Responsive to doing so, a new index node is generated located at a higher level of the structure. This process results in a DAG comprising orphan nodes including different search keys. When processing a query for search keys, the orphan index nodes are initially analyzed for the keys. Upon finding a search key, its child nodes are recursively searched until location information specifying the location of ingested data in which the search key is located is found.

Methods, systems, and computer-readable storage media for receiving a capture file, the capture file holding data representative of a workload executed in a source database system, processing the capture file to provide a replay file, the replay file being in a format that is executable by a replayer to replay the workload in a target database system, the workload including a set of requests represented within the replay file, providing a set of tags associated with the replay file, the set of tags including one or more tags, each tag associated with a request in the set of requests, and during replay of the workload in the target database system: pausing replay of the workload in response to a tag, executing a request associated with the tag, providing replay results specific to the request, and selectively resuming replay of the workload in the target database system.

A technique for data sharing among multiple filers that share a volume in a private or public cloud object store is implemented. In this approach, a mechanism is provided to enable a local filer to determine whether other filers that are sharing the volume have a consistent view of new data being written to the cloud object store by the local filer. The begins by associating together a collection of one or more files in a “push class.” On demand, a push operation for the push class is initiated on the local filer. Preferably, the push is managed according to one or more push criteria associated with the push class. Typically, the push operation pushes file data and metadata associated with the one or more files of the push class in respective phases, with the file data being pushed to the cloud during a first phase and the metadata associated with that file data being pushed during a second phase that follows the first phase. After the push operation completes, a determination is made whether the new version of the file is available at one or more other filers that share the volume.