Abstract: A query of time series data stored in a database is received that specifies at least one value. The database includes (i) an index table specifying groups of segments of compressed time series data with corresponding ranges each having a lowest value and a highest value, and (ii) a segments table specifying individual segments of compressed time series data. Thereafter, using the index table, at least one group for which the specified at least one value falls within the corresponding range is identified. The segments table is then queried for the segments corresponding to the identified at least one group to generate a new segments table specifying at least one segment. Next, the at least one segment specified by the new segments table is decompressed. Data responsive to the query within the decompressed at least one segment is then identified using the specified at least one value.

Abstract: In an example embodiment, a method of handling duplicate records in a computer database is provided. A record is received via an API. An item type associated with the record is identified. A duplicate check schema corresponding to the item type is obtained. A string is generated for the record by extracting values for each of a plurality of fields, defined in the duplicate check schema, located in the record and concatenating the values. Then a hash algorithm is applied to the string to create a hash for the record. The hash for the record is compared to hashes from previous records stored in the computer database to identify whether the hash is a duplicate. In response to a determination that the hash is a duplicate, one or more processing rules from the duplicate check schema are executed.

Abstract: Systems and methods of independent processing streams for event data are disclosed. In some example embodiments, a computer-implemented method comprises receiving transaction data items, persisting the transaction data items in a first database, persisting a copy of the transaction data items as auxiliary data items in a second database different from the first database, detecting a manipulation of the transaction data items, updating the auxiliary data items in the second database based on the detecting of the manipulation, performing at least one online analytical processing operation using the auxiliary data items in the second database, and accessing the transaction data items in the first database and generating one or more documents using the accessed transaction data items subsequent to and independently from the performing of the at least one online analytical processing operation, with the generating of the document(s) being one of a plurality of periodic document generation operations.

Abstract: In an example embodiment, a method of handling duplicate records in a computer database is provided. A record is received via an API. An item type associated with the record is identified. A duplicate check schema corresponding to the item type is obtained. A string is generated for the record by extracting values for each of a plurality of fields, defined in the duplicate check schema, located in the record and concatenating the values. Then a hash algorithm is applied to the string to create a hash for the record. The hash for the record is compared to hashes from previous records stored in the computer database to identify whether the hash is a duplicate. In response to a determination that the hash is a duplicate, one or more processing rules from the duplicate check schema are executed.

Abstract: A query of time series data stored in a database is received that specifies at least one value. The database includes (i) an index table specifying groups of segments of compressed time series data with corresponding ranges each having a lowest value and a highest value, and (ii) a segments table specifying individual segments of compressed time series data. Thereafter, using the index table, at least one group for which the specified at least one value falls within the corresponding range is identified. The segments table is then queried for the segments corresponding to the identified at least one group to generate a new segments table specifying at least one segment. Next, the at least one segment specified by the new segments table is decompressed. Data responsive to the query within the decompressed at least one segment is then identified using the specified at least one value.

Abstract: A query of time series data stored in a database is received that specifies at least one value. The database includes (i) an index table specifying groups of segments of compressed time series data with corresponding ranges each having a lowest value and a highest value, and (ii) a segments table specifying individual segments of compressed time series data. Thereafter, using the index table, at least one group for which the specified at least one value falls within the corresponding range is identified. The segments table is then queried for the segments corresponding to the identified at least one group to generate a new segments table specifying at least one segment. Next, the at least one segment specified by the new segments table is decompressed. Data responsive to the query within the decompressed at least one segment is then identified using the specified at least one value.

Abstract: A query of time series data stored in a database is received that specifies at least one value. The database includes (i) an index table specifying groups of segments of compressed time series data with corresponding ranges each having a lowest value and a highest value, and (ii) a segments table specifying individual segments of compressed time series data. Thereafter, using the index table, at least one group for which the specified at least one value falls within the corresponding range is identified. The segments table is then queried for the segments corresponding to the identified at least one group to generate a new segments table specifying at least one segment. Next, the at least one segment specified by the new segments table is decompressed. Data responsive to the query within the decompressed at least one segment is then identified using the specified at least one value.