Abstract: A system is provided for storing, in a database, a plurality of timeseries represented by a plurality of respective documents events in a columnar format. The system further is adapted compress at least one of the values within the plurality of documents. According to some embodiments, the system stores the compressed values as a Simple-8b block and calculates the optimal Simple-8b selector. According to some embodiments, the system is adapted to determine a secondary index based on values within the bucket.

Abstract: A system is provided for storing, in a database, a plurality of timeseries represented by a plurality of respective documents events in a columnar format. The system further is adapted compress at least one of the values within the plurality of documents. According to some embodiments, the system stores the compressed values as a Simple-8b block and calculates the optimal Simple-8b selector. According to some embodiments, the system is adapted to determine a secondary index based on values within the bucket.

Abstract: A system is provided for storing, in a database, a plurality of timeseries represented by a plurality of respective documents events in a columnar format. The system further is adapted compress at least one of the values within the plurality of documents. According to some embodiments, the system stores the compressed values as a Simple-8b block and calculates the optimal Simple-8b selector. According to some embodiments, the system is adapted to determine a secondary index based on values within the bucket.

Abstract: In some implementations, events measured at various points in time may be organized in a data structure that defines an event represented by a document. In particular, events can be organized in columns of documents referred to as buckets. These buckets may be indexed using B-trees by addressing metadata values or value ranges. Buckets may be defined by periods of time. Documents may also be geoindexed and stored in one or more locations in a distributed computer network. One or more secondary indexes may be created based on time and/or metadata values within documents.

Abstract: In some implementations, events measured at various points in time may be organized in a data structure that defines an event represented by a document. In particular, events can be organized in columns of documents referred to as buckets. These buckets may be indexed using B-trees by addressing metadata values or value ranges. Buckets may be defined by periods of time. Documents may also be geoindexed and stored in one or more locations in a distributed computer network. One or more secondary indexes may be created based on time and/or metadata values within documents.

Abstract: In some implementations, events measured at various points in time may be organized in a data structure that defines an event represented by a document. In particular, events can be organized in columns of documents referred to as buckets. These buckets may be indexed using B-trees by addressing metadata values or value ranges. Buckets may be defined by periods of time. Documents may also be geoindexed and stored in one or more locations in a distributed computer network. One or more secondary indexes may be created based on time and/or metadata values within documents.

Abstract: According to one aspect, methods and systems are provided for selectively employing storage engines in a distributed database environment. The methods and systems can include a processor configured to execute a plurality of system components, wherein the system components comprise an operation prediction component configured to determine an expected set of operations to be performed on a portion of the database; a data format selection component configured to select, based on at least one characteristic of the expected set of operations, a data format for the portion of the database; and at least one storage engine for writing the portion of the database in the selected data format.

Abstract: Systems and methods are provided to enable control and placement of data repositories. In some embodiments, the system segments data into zones. A website, for example, may need to segment data according to location. In this example, a zone may be created for North America and another zone may be created for Europe. Data related to operations executed in North America, for example, can be placed in the North America zone and data related to transactions in Europe can be placed in the Europe zone. According to some embodiments, the system may use zones to accommodate a range of deployment scenarios.

Abstract: Systems and methods are provided to enable control and placement of data repositories. In some embodiments, the system segments data into zones. A website, for example, may need to segment data according to location. In this example, a zone may be created for North America and another zone may be created for Europe. Data related to operations executed in North America, for example, can be placed in the North America zone and data related to transactions in Europe can be placed in the Europe zone. According to some embodiments, the system may use zones to accommodate a range of deployment scenarios.

Abstract: According to one aspect, methods and systems are provided for selectively employing storage engines in a distributed database environment. The methods and systems can include a processor configured to execute a plurality of system components, wherein the system components comprise an operation prediction component configured to determine an expected set of operations to be performed on a portion of the database; a data format selection component configured to select, based on at least one characteristic of the expected set of operations, a data format for the portion of the database; and at least one storage engine for writing the portion of the database in the selected data format.

Abstract: According to one embodiment, a translation component is configured to operate on document encoded data to translate the document encoded data into a canonical format comprising a plurality of canonical types that fold together into a byte stream. The translation component is configured to accept any storage format of data (e.g., column store, row store, LSM tree, etc. and/or data from any storage engine, WIREDTIGER, MMAP, AR tree, Radix tree, etc.) and translate that data into a byte stream to enable efficient comparison. When executing searches and using the translated data to provide comparisons there is necessarily a trade-off based on the cost of translating the data and how much the translated data can be leveraged to increase comparison efficiency.

Abstract: According to one embodiment, a translation component is configured to operate on document encoded data to translate the document encoded data into a canonical format comprising a plurality of canonical types that fold together into a byte stream. The translation component is configured to accept any storage format of data (e.g., column store, row store, LSM tree, etc. and/or data from any storage engine, WIREDTIGER, MMAP, AR tree, Radix tree, etc.) and translate that data into a byte stream to enable efficient comparison. When executing searches and using the translated data to provide comparisons there is necessarily a trade-off based on the cost of translating the data and how much the translated data can be leveraged to increase comparison efficiency.

Abstract: According to one embodiment, a translation component is configured to operate on document encoded data to translate the document encoded data into a canonical format comprising a plurality of canonical types that fold together into a byte stream. The translation component is configured to accept any storage format of data (e.g., column store, row store, LSM tree, etc. and/or data from any storage engine, WIREDTIGER, MMAP, AR tree, Radix tree, etc.) and translate that data into a byte stream to enable efficient comparison. When executing searches and using the translated data to provide comparisons there is necessarily a trade-off based on the cost of translating the data and how much the translated data can be leveraged to increase comparison efficiency.

Abstract: According to one aspect, methods and systems are provided for selectively employing storage engines in a distributed database environment. The methods and systems can include a processor configured to execute a plurality of system components, wherein the system components comprise an operation prediction component configured to determine an expected set of operations to be performed on a portion of the database; a data format selection component configured to select, based on at least one characteristic of the expected set of operations, a data format for the portion of the database; and at least one storage engine for writing the portion of the database in the selected data format.

Abstract: Systems and methods are provided to enable control and placement of data repositories. In some embodiments, the system segments data into zones. A website, for example, may need to segment data according to location. In this example, a zone may be created for North America and another zone may be created for Europe. Data related to operations executed in North America, for example, can be placed in the North America zone and data related to transactions in Europe can be placed in the Europe zone. According to some embodiments, the system may use zones to accommodate a range of deployment scenarios.

Abstract: Systems and methods are provided to enable control and placement of data repositories. In some embodiments, the system segments data into zones. A website, for example, may need to segment data according to location. In this example, a zone may be created for North America and another zone may be created for Europe. Data related to operations executed in North America, for example, can be placed in the North America zone and data related to transactions in Europe can be placed in the Europe zone. According to some embodiments, the system may use zones to accommodate a range of deployment scenarios.

Abstract: According to one embodiment, a translation component is configured to operate on document encoded data to translate the document encoded data into a canonical format comprising a plurality of canonical types that fold together into a byte stream. The translation component is configured to accept any storage format of data (e.g., column store, row store, LSM tree, etc. and/or data from any storage engine, WIREDTIGER, MMAP, AR tree, Radix tree, etc.) and translate that data into a byte stream to enable efficient comparison. When executing searches and using the translated data to provide comparisons there is necessarily a trade-off based on the cost of translating the data and how much the translated data can be leveraged to increase comparison efficiency.

Abstract: According to one embodiment, a translation component is configured to operate on document encoded data to translate the document encoded data into a canonical format comprising a plurality of canonical types that fold together into a byte stream. The translation component is configured to accept any storage format of data (e.g., column store, row store, LSM tree, etc. and/or data from any storage engine, WIREDTIGER, MMAP, AR tree, Radix tree, etc.) and translate that data into a byte stream to enable efficient comparison. When executing searches and using the translated data to provide comparisons there is necessarily a trade-off based on the cost of translating the data and how much the translated data can be leveraged to increase comparison efficiency.

Abstract: According to one embodiment, a translation component is configured to operate on document encoded data to translate the document encoded data into a canonical format comprising a plurality of canonical types that fold together into a byte stream. The translation component is configured to accept any storage format of data (e.g., column store, row store, LSM tree, etc. and/or data from any storage engine, WIREDTIGER, MMAP, AR tree, Radix tree, etc.) and translate that data into a byte stream to enable efficient comparison. When executing searches and using the translated data to provide comparisons there is necessarily a trade-off based on the cost of translating the data and how much the translated data can be leveraged to increase comparison efficiency.

Abstract: According to one aspect, methods and systems are provided for selectively employing storage engines in a distributed database environment. The methods and systems can include a processor configured to execute a plurality of system components, wherein the system components comprise an operation prediction component configured to determine an expected set of operations to be performed on a portion of the database; a data format selection component configured to select, based on at least one characteristic of the expected set of operations, a data format for the portion of the database; and at least one storage engine for writing the portion of the database in the selected data format.