Datadog, Inc.

Homin Lee

Charles-Philippe Masson

Jee Eun Rim

Stephen Kappel

Alex Ustian

The technology provides a consistent hashing approach that can be used with many types of hash functions. This approach, called flip hashing, enables dynamic adjustment of a hash table while satisfying balance and monotonicity requirements. Flip hashing is particularly applicable to database and load rebalancing applications due to its low computational cost and ease of implementation. As computing resources are added to a system, keys are remapped evenly across the newly added resources, e.g., by one or more load-balancing or routing servers. This enables upscaling of the system to minimize hotspot issues. The computational cost for a flip hash approach is effectively constant and regardless of the number of resources. This can provide fast response times to queries and avoid overloading of routing servers.

Consistent hashing with sequential indexing

The technology involves sampling space-saving set sketches, which relates to tracking and evaluating information associated with large data sets. This can include a number of distributed data streams or other data sets that may come from different sources such as Internet traffic. The sets each have corresponding labels and distinct items. One approach includes performing a query for each distributed data set to determine whether a given label associated with that distributed data set is in a corresponding sketch. When that label is in the corresponding sketch, the distinct item associated with that label is inserted into the corresponding sketch. When the label is not in the corresponding sketch, then depending on whether a number of labels in the sketch is less than a specified size, the approach may add the label to a particular sketch and/or insert the distinct item into the particular sketch according to certain defined criteria.

Sampling space-saving set sketches

A compact data structure generation engine can be used to generate a compact data structure that represents performance data for high-scale networks. The compact data structure representing the performance data can be used to monitor the operation performed on or by a computer system to identify potentially anomalous conditions. In response, a corrective action can be taken to address the issue. This can be useful, for example, in improving the efficiency, effectiveness, and reliability of the computer system during operation.

Generating compact data structures for monitoring data processing performance across high scale network infrastructures

A data structure is provided that identifies relationships between entities of an infrastructure of a computing system and that is configured to update in response to changes in the infrastructure of the computing system. The data structure includes vertices and edges, where each vertex of the data structure represents an entity of the infrastructure, and where each edge of the data structure represents a relationship between entities of the infrastructure. When usage data are received, the usage data are analyzed to determine a correlation between a first operation specifying a first entity and a second operation specifying a second entity. An edge between the first entity specified by the first operation and the second entity specified by the second operation is generated. Event data comprising usage data specifying either the first entity or the second entity is generated.

Generating data structures representing relationships among entities of a high-scale network infrastructure

An anomaly detection platform can be used to monitor the operation performed on or by a computer system to identify potentially anomalous conditions. In response, a corrective action can be taken to address the issue. This can be useful, for example, in improving the efficiency, effectiveness, and reliability of the computer system during operation.

Systems and techniques for adaptive identification and prediction of data anomalies, and forecasting data trends across high-scale network infrastructures

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Jersey City, NJ, United States of America

Homin Lee