Abstract: A method and system for comparing at least two structured data files. A computer polls at least one network device for data files. The computer determines a structural difference between a first data file and a second data file and creates at least one patch file based on the structural difference. The computer extracts at least one span from the at least one patch file, wherein the at least one span is defined by a starting point byte and a length measured in bytes and creates an ordered listing. The computer performs a lexer operation using the at least one span as a limiting criterion to limit an output of the lexer to statements that include the at least one span. The computer outputs the statements indicating changes in a configuration between said first and second data files.

Abstract: Indexing objects in a datastore, where the objects are characterized by a set of object properties. For objects from a datastore: a Bloom filter for the object properties, based on a predetermined Bloom filter configuration, is created; the Hamming value of the Bloom filter is determined; a binary logarithm of the Bloom filter is calculated; and the Bloom filter, the Hamming value, the binary logarithm, and a representation of the object is stored in the datastore.

Abstract: Computer-implemented methods are provided for storing records in and retrieving records from a sharded table of a sharded database. The sharded database comprises a plurality of database shards. The database shards are each configured to store a subset of records of the sharded table. The database shards are also each associated with a respective Bloom filter. Records are inserted into the sharded table by generating a respective representation of the sharding key of the record to be stored for use with each of a plurality of the respective Bloom filters. Hamming distances between current values of each of the plurality of Bloom filters and the respective representation of the sharding key for use with that Bloom filter are then calculated and a database shard for storing the record is selected from amongst those database shards based on the calculated Hamming distances.

Abstract: Computer program products and systems are provided for storing records in and retrieving records from a sharded table of a sharded database. The sharded database comprises a plurality of database shards. The database shards are each configured to store a subset of records of the sharded table. The database shards are also each associated with a respective Bloom filter. Records are inserted into the sharded table by generating a respective representation of the sharding key of the record to be stored for use with each of a plurality of the respective Bloom filters. Hamming distances between current values of each of the plurality of Bloom filters and the respective representation of the sharding key for use with that Bloom filter are then calculated and a database shard for storing the record is selected from amongst those database shards based on the calculated Hamming distances.

Abstract: A method and system for comparing at least two structured data files. A computer polls at least one network device for data files. The computer determines a structural difference between a first data file and a second data file and creates at least one patch file based on the structural difference. The computer extracts at least one span from the at least one patch file, wherein the at least one span is defined by a starting point byte and a length measured in bytes and creates an ordered listing. The computer performs a lexer operation using the at least one span as a limiting criterion to limit an output of the lexer to statements that include the at least one span. The computer outputs the statements indicating changes in a configuration between said first and second data files.

Abstract: A method and system for comparing at least two structured data files. A computer polls at least one network device for data files. The computer determines a structural difference between a first data file and a second data file and creates at least one patch file based on the structural difference. The computer extracts at least one span from the at least one patch file, wherein the at least one span is defined by a starting point byte and a length measured in bytes and creates an ordered listing. The computer performs a lexer operation using the at least one span as a limiting criterion to limit an output of the lexer to statements that include the at least one span. The computer outputs the statements indicating changes in a configuration between said first and second data files.

Abstract: A method and system for comparing at least two structured data files. A computer polls at least one network device for data files. The computer determines a structural difference between a first data file and a second data file and creates at least one patch file based on the structural difference. The computer extracts at least one span from the at least one patch file, wherein the at least one span is defined by a starting point byte and a length measured in bytes and creates an ordered listing. The computer performs a lexer operation using the at least one span as a limiting criterion to limit an output of the lexer to statements that include the at least one span. The computer outputs the statements indicating changes in a configuration between said first and second data files.

Abstract: Identifying objects in a datastore with specified object properties, where each object is characterized by a Bloom filter, a Hamming value of the Bloom filter, and a binary logarithm of the Bloom filter. A set of object properties is received. A search Bloom filter is created from the object properties. A Hamming value of the search Bloom filter is determined. A binary logarithm of the search Bloom filter is calculated. Objects in the datastore which have respective Hamming values greater than or equal to the Hamming value of the search Bloom filter and respective binary logarithms that are greater than or equal to the binary logarithm of the search Bloom filter are identified.

Abstract: Computer program products and systems are provided for storing records in and retrieving records from a sharded table of a sharded database. The sharded database comprises a plurality of database shards. The database shards are each configured to store a subset of records of the sharded table. The database shards are also each associated with a respective Bloom filter. Records are inserted into the sharded table by generating a respective representation of the sharding key of the record to be stored for use with each of a plurality of the respective Bloom filters. Hamming distances between current values of each of the plurality of Bloom filters and the respective representation of the sharding key for use with that Bloom filter are then calculated and a database shard for storing the record is selected from amongst those database shards based on the calculated Hamming distances.

Abstract: Computer-implemented methods are provided for storing records in and retrieving records from a sharded table of a sharded database. The sharded database comprises a plurality of database shards. The database shards are each configured to store a subset of records of the sharded table. The database shards are also each associated with a respective Bloom filter. Records are inserted into the sharded table by generating a respective representation of the sharding key of the record to be stored for use with each of a plurality of the respective Bloom filters. Hamming distances between current values of each of the plurality of Bloom filters and the respective representation of the sharding key for use with that Bloom filter are then calculated and a database shard for storing the record is selected from amongst those database shards based on the calculated Hamming distances.

Abstract: Indexing objects in a datastore, where the objects are characterized by a set of object properties. For objects from a datastore: a Bloom filter for the object properties, based on a predetermined Bloom filter configuration, is created; the Hamming value of the Bloom filter is determined; a binary logarithm of the Bloom filter is calculated; and the Bloom filter, the Hamming value, the binary logarithm, and a representation of the object is stored in the datastore.

Abstract: Indexing objects in a datastore, where the objects are characterized by a set of object properties. For objects from a datastore: a Bloom filter for the object properties, based on a predetermined Bloom filter configuration, is created; the Hamming value of the Bloom filter is determined; a binary logarithm of the Bloom filter is calculated; and the Bloom filter, the Hamming value, the binary logarithm, and a representation of the object is stored in the datastore.

Abstract: A Bloom filter index is implemented as a multiway tree that stores Bloom filters having a predefined number of N-bit sequences. Nodes are labeled with portions of the N-bit sequences and non-leaf tree nodes may have up to 2N children. All children of a given node have labels that are the same length. Bloom filters are inserted recursively, starting at a first non-leaf node, based on a node label matching an initial N-bit sequence of the Bloom filter. If a given node is full, its child nodes are split, resulting in fewer than 2N new child nodes, each labeled with different initial N-bit sequence of the original child node, which becomes a child node of a new node with label the remaining bits in the label of the original child node. The recursive insert procedure is then performed to insert the Bloom filter in the given node.

Abstract: A Bloom filter index is implemented as a multiway tree that stores Bloom filters having a predefined number of N-bit sequences. Nodes are labeled with portions of the N-bit sequences and non-leaf tree nodes may have up to 2N children. All children of a given node have labels that are the same length. Bloom filters are inserted recursively, starting at a first non-leaf node, based on a node label matching an initial N-bit sequence of the Bloom filter. If a given node is full, its child nodes are split, resulting in fewer than 2N new child nodes, each labeled with different initial N-bit sequence of the original child node, which becomes a child node of a new node with label the remaining bits in the label of the original child node. The recursive insert procedure is then performed to insert the Bloom filter in the given node.

Abstract: A Bloom filter index is implemented as a multiway tree that stores Bloom filters having a predefined number of N-bit sequences. Nodes are labeled with portions of the N-bit sequences and non-leaf tree nodes may have up to 2N children. All children of a given node have labels that are the same length. Bloom filters are inserted recursively, starting at a first non-leaf node, based on a node label matching an initial N-bit sequence of the Bloom filter. If a given node is full, its child nodes are split, resulting in fewer than 2N new child nodes, each labeled with different initial N-bit sequence of the original child node, which becomes a child node of a new node with label the remaining bits in the label of the original child node. The recursive insert procedure is then performed to insert the Bloom filter in the given node.

Abstract: A Bloom filter index is implemented as a multiway tree that stores Bloom filters having a predefined number of N-bit sequences. Nodes are labeled with portions of the N-bit sequences and non-leaf tree nodes may have up to 2N children. All children of a given node have labels that are the same length. Bloom filters are inserted recursively, starting at a first non-leaf node, based on a node label matching an initial N-bit sequence of the Bloom filter. If a given node is full, its child nodes are split, resulting in fewer than 2N new child nodes, each labeled with different initial N-bit sequence of the original child node, which becomes a child node of a new node with label the remaining bits in the label of the original child node. The recursive insert procedure is then performed to insert the Bloom filter in the given node.

Abstract: Implementing a Bloom filter index as a hierarchical data structure. Bloom filters are received and their bit vectors are decomposed into successive bit sequences. For each bit sequence except the last one, memory for at least storing a pointer to a memory location on the next level is allocated on the level corresponding to the bit sequence. The pointer storage is labeled by the value of the next bit sequence. A pointer to the allocated memory is stored in the pointer storage on the previous level that was labeled by the binary value of the current bit sequence. For the last bit sequence, memory for storing Bloom filters is allocated on the last level. A pointer to the allocated memory is stored in the pointer storage on the second-to-last level that was labeled by the value of the last bit sequence. The Bloom filter is stored in the allocated memory.

Abstract: Indexing objects in a datastore, where the objects are characterized by a set of object properties. For objects from a datastore: a Bloom filter for the object properties, based on a predetermined Bloom filter configuration, is created; the Hamming value of the Bloom filter is determined; a binary logarithm of the Bloom filter is calculated; and the Bloom filter, the Hamming value, the binary logarithm, and a representation of the object is stored in the datastore.

Abstract: Identifying objects in a datastore with specified object properties, where each object is characterized by a Bloom filter, a Hamming value of the Bloom filter, and a binary logarithm of the Bloom filter. A set of object properties is received. A search Bloom filter is created from the object properties. A Hamming value of the search Bloom filter is determined. A binary logarithm of the search Bloom filter is calculated. Objects in the datastore which have respective Hamming values greater than or equal to the Hamming value of the search Bloom filter and respective binary logarithms that are greater than or equal to the binary logarithm of the search Bloom filter are identified.

Abstract: Identifying network devices having specified traits using a multi-level hierarchical data structure. Bloom filters representing traits of network devices are received and their bit vectors are decomposed into successive bytes. For each byte except the last one, memory for storing a pointer to memory on the next level is allocated on the level corresponding to the byte. The pointer storage is labeled by the value of the next byte. A pointer to the allocated memory is stored in the pointer storage on the previous level that was labeled by the value of the current byte. For the last byte, memory for storing references to network devices is allocated on the last level. A pointer to the allocated memory is stored in the pointer storage on the second-to-last level that was labeled by the value of the last byte. A reference to the network device is stored in the allocated memory.