International Business Machines Corporation

Lanjun Wang

Zhao Cao

Chen Wang

Lin Xu

Shuo Zhang

Tao Li

Ju Wei Shi

Yuan Feng

Hai Shan Wu

Kang Ye

Limei Jiao

Jing Wang

Yi Yang

Yan Wu

Di Xu

Yao Dong Qi

Li Wen

Methods and a system are provided. A method includes automatically detecting, by an attribute structural variation detector, attribute structural variations in record schemas of a database lacking schema enforcement. The automatically detecting step includes encoding, by an encoder, nodes in the record schemas based on respective updated node labels to obtain a plurality of codes. The encoding step comprises, for each of the plurality of codes: recording, by a label frequency counter, a frequency fof the record schemas that include a given label; and recording, by a label frequency counter, a frequency fof the record schemas that use a given one of the plurality of codes to update a label of a next level. An attribute variation is detected and retrieved responsive to the frequency f&#x2260;0, and the frequency f&#x3e;(two times the frequency f).

Automatic attribute structural variation detection for not only structured query language database

Generation of a schema of a NoSQL type database, where a set of entry(ies) of a NoSQL type database can be determined, and the record schema is a treelike relationship between key hierarchies with a key as a node in the entry, wherein the key corresponds to an attribute name. For at least one record schema, a node in which there is an attribute name variation is determined based on a similarity of sub-nodes in the treelike relationship, and the record schema is updated according to the determined result. The schema of the NoSQL type database is generated based on the updated record schema.

Generating a schema of a Not-only-Structured-Query-Language database

An information processing system, a computer readable storage medium, and a method of managing a query to find a set of JSON documents in a multi-schema JSON document store. A query engine receives a first query to find at least one JSON document in a plurality of sets of JSON documents stored in the JSON document store, each set of JSON documents being organized in a unique JSON schema version related to a unique JSON schema version of each other set of JSON documents by at least one schema change. The first query is organized in a first unique JSON schema version. A query translator translates the first query into a plurality of queries based on the plurality of schema changes. The first and the plurality of queries are executed to provide a collective set of query results.

System and method of query processing with schema change in JSON document store

Data analytics is performed on a distributed document storage database by receiving a request for initiating a data analytics job; collecting statistics from the database in response to the request; using the statistics to estimate a first cost for merging an incremental data update for the job into a first resilient distributed dataset; using the statistics to estimate a second cost for newly creating a second resilient distributed dataset for the job; when the first cost is less than the second cost, reading data updates from the database and merging the data updates into the first resilient distributed dataset; and when the first cost is not less than the second cost, newly creating the second resilient distributed dataset by reading all documents from the database.

Data analytics on distributed databases

Methods and a system are provided. A method includes automatically detecting, by an attribute structural variation detector, attribute structural variations in record schemas of a database lacking schema enforcement. The automatically detecting step includes encoding, by an encoder, nodes in the record schemas based on respective updated node labels to obtain a plurality of codes. The automatically detecting step further includes clustering, by a clusterer, the plurality of codes into one of a used category and an unused category. The used category and the unused category respectively consist of any of the plurality of codes used or unused to update a child label. The automatically detecting step also includes, when an intersection set formed from the used and unused categories has at least one member, retrieving, by the attribute structural variation detector, the at least one member as a structural variation between at least two of the record schemas.

A method of operating a computer system including collecting, from the computer system, data indicative of variations in throughput and response time over a period of time, calculating processing power of the computer system over the period of time, recording a maximal power, calculating a standard deviation of the response time (RT-StdDev), recording the standard deviation of the response time corresponding to a time of the maximal power (RT-StdDevMaxPower), and generating a notification that the computer system is in a bottleneck state using a comparison of a current processing power to the maximal power and a comparison of the RT-StdDev to the RT-StdDevMaxPower.

Application abnormality detection

Generating a schema of a not-only-structured-query-language database

Automatically moving NoSQL data store to a relational database system. Based on discovered data structure schema of a NoSQL file and query plans, attribute usage and association relationships may be determined. Trunk tables may be defined based on trunk part of the data structure schema determined based on the attribute usage. Trunk tables may be validated and relational database tables are generated that correspond to the trunk tables. NoSQL trunk template is generated based on the trunk tables. The relational database tables are loaded with data filtered from the NoSQL file according to the NoSQL trunk template.

NoSQL relational database (RDB) data movement

An information processing system, a computer readable storage medium, and a method of managing a query to find a set of JSON documents in a multi-schema JSON document store. A query engine receives a first query to find at least one JSON document in a plurality of sets of JSON documents stored in the JSON document store, each set of JSON documents being organized in a unique JSON schema version related to a unique JSON schema version of each other set of JSON documents by at least one schema change. The first query is organized in a first unique JSON schema version. A query translator translates the first query into a second query based on the at least one schema change. The first and second queries are executed to provide first and second query results which are collectively returned.

A system for determining a root cause of a performance issue of a software application executing in a distributed computer system. The system includes constructing a hierarchical graph of metrics of a component of the distributed computer system based on search traces of the metrics of different users. A parent-child edge between a first metric and a second metric in the hierarchical graph is constructed when there is a sequential association between the search traces. A sibling edge between the first metric and the second metric in the hierarchical graph is constructed when there is no sequential association between the search traces. A metric investigation recommendation is provided to address the performance issue based on traversal of the hierarchical graph of the metrics.

Automatic knowledge base generation for root cause in application performance management

Automatically moving NoSQL data store to a relational database system. Based on discovered data structure schema of a NoSQL file and query plans, attribute usage and association relationships may be determined. Trunk tables may be defined based on trunk part of the data structure schema determined based on the attribute usage. Trunk tables are validated and relational database tables are generated that correspond to the trunk tables. NoSQL trunk template is generated based on the trunk tables. The relational database tables are loaded with data filtered from the NoSQL file according to the NoSQL trunk template.

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Beijing, China

Lanjun Wang