Abstract: A format conversion engine for Apache Hadoop that converts data from its original format to a database-like format at certain time points for use by a low latency (LL) query engine. The format conversion engine comprises a daemon that is installed on each data node in a Hadoop cluster. The daemon comprises a scheduler and a converter. The scheduler determines when to perform the format conversion and notifies the converter when the time comes. The converter converts data on the data node from its original format to a database-like format for use by the low latency (LL) query engine.

Abstract: A format conversion engine for Apache Hadoop that converts data from its original format to a database-like format at certain time points for use by a low latency (LL) query engine. The format conversion engine comprises a daemon that is installed on each data node in a Hadoop cluster. The daemon comprises a scheduler and a converter. The scheduler determines when to perform the format conversion and notifies the converter when the time comes. The converter converts data on the data node from its original format to a database-like format for use by the low latency (LL) query engine.

Abstract: A format conversion engine for Apache Hadoop that converts data from its original format to a database-like format at certain time points for use by a low latency (LL) query engine. The format conversion engine comprises a daemon that is installed on each data node in a Hadoop cluster. The daemon comprises a scheduler and a converter. The scheduler determines when to perform the format conversion and notifies the converter when the time comes. The converter converts data on the data node from its original format to a database-like format for use by the low latency (LL) query engine.

Abstract: A format conversion engine for Apache Hadoop that converts data from its original format to a database-like format at certain time points for use by a low latency (LL) query engine. The format conversion engine comprises a daemon that is installed on each data node in a Hadoop cluster. The daemon comprises a scheduler and a converter. The scheduler determines when to perform the format conversion and notifies the converter when the time comes. The converter converts data on the data node from its original format to a database-like format for use by the low latency (LL) query engine.

Abstract: A format conversion engine for Apache Hadoop that converts data from its original format to a database-like format at certain time points for use by a low latency (LL) query engine. The format conversion engine comprises a daemon that is installed on each data node in a Hadoop cluster. The daemon comprises a scheduler and a converter. The scheduler determines when to perform the format conversion and notifies the converter when the time comes. The converter converts data on the data node from its original format to a database-like format for use by the low latency (LL) query engine.

Abstract: Methods for configuring a system to collect and aggregate datasets are disclosed. One embodiment includes, identifying a data source in the system from where dataset is to be collected, configuring a machine in the system that generates the dataset to be collected, to send the dataset to the data source, identifying an arrival location where the dataset that is collected is to be aggregated or written, and/or configuring an agent node by specifying a source for the agent node as the data source in the system and specifying a sink for the agent node as the arrival location.

Abstract: A low latency query engine for APACHE HADOOP™ that provides real-time or near real-time, ad hoc query capability, while completing batch-processing of MapReduce. In one embodiment, the low latency query engine comprises a daemon that is installed on data nodes in a HADOOP™ cluster for handling query requests and all internal requests related to query execution. In a further embodiment, the low latency query engine comprises a daemon for providing name service and metadata distribution. The low latency query engine receives a query request via client, turns the request into collections of plan fragments and coordinates parallel and optimized execution of the plan fragments on remote daemons to generate results at a much faster speed than existing batch-oriented processing frameworks.

Abstract: Systems and methods of collecting and aggregating log data with fault tolerance are disclosed. One embodiment includes, one or more devices that generate log data, the one or more machines each associated with an agent node to collect the log data, wherein, the agent node generates a batch comprising multiple messages from the log data and assigns a tag to the hatch. In one embodiment, the agent node further computes a checksum for the batch of multiple messages. The system may further include a collector device, the collector device being associated with a collector tier having a collector node to which the agent sends the log data; wherein, the collector determines the checksum for the hatch of multiple messages received from the agent node.

Abstract: Systems and methods of dynamically processing an event using an extensible data model are disclosed. One embodiment includes, specifying attributes of the event in a data model; the data model being extensible to add properties to the event as the dataset is streamed from the source to the sink.

Abstract: A low latency query engine for APACHE HADOOP™ that provides real-time or near real-time, ad hoc query capability, while completing batch-processing of MapReduce. In one embodiment, the low latency query engine comprises a daemon that is installed on data nodes in a HADOOP™ cluster for handling query requests and all internal requests related to query execution. In a further embodiment, the low latency query engine comprises a daemon for providing name service and metadata distribution. The low latency query engine receives a query request via client, turns the request into collections of plan fragments and coordinates parallel and optimized execution of the plan fragments on remote daemons to generate results at a much faster speed than existing batch-oriented processing frameworks.

Abstract: A format conversion engine for Apache Hadoop that converts data from its original format to a database-like format at certain time points for use by a low latency (LL) query engine. The format conversion engine comprises a daemon that is installed on each data node in a Hadoop cluster. The daemon comprises a scheduler and a converter. The scheduler determines when to perform the format conversion and notifies the converter when the time comes. The converter converts data on the data node from its original format to a database-like format for use by the low latency (LL) query engine.

Abstract: A format conversion engine for Apache Hadoop that converts data from its original format to a database-like format at certain time points for use by a low latency (LL) query engine. The format conversion engine comprises a daemon that is installed on each data node in a Hadoop cluster. The daemon comprises a scheduler and a converter. The scheduler determines when to perform the format conversion and notifies the converter when the time comes. The converter converts data on the data node from its original format to a database-like format for use by the low latency (LL) query engine.

Abstract: Systems and methods of collecting and aggregating log data with fault tolerance are disclosed. One embodiment includes, one or more devices that generate log data, the one or more machines each associated with an agent node to collect the log data, wherein, the agent node generates a batch comprising multiple messages from the log data and assigns a tag to the hatch. In one embodiment, the agent node further computes a checksum for the batch of multiple messages. The system may further include a collector device, the collector device being associated with a collector tier having a collector node to which the agent sends the log data; wherein, the collector determines the checksum for the hatch of multiple messages received from the agent node.

Abstract: Methods for configuring a system to collect and aggregate datasets are disclosed. One embodiment includes, identifying a data source in the system from where dataset is to be collected, configuring a machine in the system that generates the dataset to be collected, to send the dataset to the data source, identifying an arrival location where the dataset that is collected is to be aggregated or written, and/or configuring an agent node by specifying a source for the agent node as the data source in the system and specifying a sink for the agent node as the arrival location.

Abstract: Systems and methods of collecting and aggregating log data with fault tolerance are disclosed. One embodiment includes, one or more devices that generate log data, the one or more machines each associated with an agent node to collect the log data, wherein, the agent node generates a batch comprising multiple messages from the log data and assigns a tag to the batch. In one embodiment, the agent node further computes a checksum for the batch of multiple messages. The system may further include a collector device, the collector device being associated with a collector tier having a collector node to which the agent sends the log data; wherein, the collector determines the checksum for the batch of multiple messages received from the agent node.

Abstract: A low latency query engine for APACHE HADOOP™ that provides real-time or near real-time, ad hoc query capability, while completing batch-processing of MapReduce. In one embodiment, the low latency query engine comprises a daemon that is installed on data nodes in a HADOOP™ cluster for handling query requests and all internal requests related to query execution. In a further embodiment, the low latency query engine comprises a daemon for providing name service and metadata distribution. The low latency query engine receives a query request via client, turns the request into collections of plan fragments and coordinates parallel and optimized execution of the plan fragments on remote daemons to generate results at a much faster speed than existing batch-oriented processing frameworks.

Abstract: Methods for configuring a system to collect and aggregate datasets are disclosed. One embodiment includes, identifying a data source in the system from where dataset is to be collected, configuring a machine in the system that generates the dataset to be collected, to send the dataset to the data source, identifying an arrival location where the dataset that is collected is to be aggregated or written, and/or configuring an agent node by specifying a source for the agent node as the data source in the system and specifying a sink for the agent node as the arrival location.

Abstract: Systems and methods of dynamically processing an event using an extensible data model are disclosed. One embodiment includes, specifying attributes of the event in a data model; the data model being extensible to add properties to the event as the dataset is streamed from the source to the sink.

Abstract: Systems and methods of dynamically processing an event using an extensible data model are disclosed. One embodiment includes, specifying attributes of the event in a data model; the data model being extensible to add properties to the event as the dataset is streamed from the source to the sink.

Abstract: Systems and methods of collecting and aggregating log data with fault tolerance are disclosed. One embodiment includes, one or more devices that generate log data, the one or more machines each associated with an agent node to collect the log data, wherein, the agent node generates a batch comprising multiple messages from the log data and assigns a tag to the batch. In one embodiment, the agent node further computes a checksum for the batch of multiple messages. The system may further include a collector device, the collector device being associated with a collector tier having a collector node to which the agent sends the log data; wherein, the collector determines the checksum for the batch of multiple messages received from the agent node.