Datadog, Inc.

Charles-Philippe Masson

Homin Lee

Stephen Kappel

Jee Eun Rim

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 method for processing a data stream to identify a structure of the data stream includes receiving the data stream a sequence of characters, retrieving a set of rules for encoding characters into at least one token, and parsing the data stream. Parsing includes generating a plurality of tokens according to the set of rules. Each token represents a corresponding portion of the sequence of characters. Parsing includes forming a sequence of tokens from the plurality of tokens and assigning at least one attribute value describing the corresponding portion of the sequence of characters of the corresponding token to which the attribute value is assigned. The sequence of tokens are assigned to a cluster by determining that the sequence of tokens matches a pattern by which the cluster is characterized. The sequence of tokens is merged with the cluster. A representation of the cluster is output.

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Paris, France

Charles-Philippe Masson