Abstract: The present invention is generally directed to systems and methods of determining and provisioning peak memory requirements in Structured Query Language Processing engines. More specifically, methods may include determining or obtaining a query execution plan; gathering statistics associated with each database table; breaking the query execution plan into one or more subtasks: calculating an estimated memory usage for each subtask using the statistics; determining or obtaining a dependency graph of the one or more subtasks; based at least in part on the dependency graph, determining which subtasks can execute concurrently on a single worker node; and totaling the amount of estimated memory for each subtask that can execute concurrently on a single worker node and setting this amount of estimated memory as the estimated peak memory requirement for the specefic database query.

Abstract: Systems and methods for automatically scaling a big data system. Methods include determining, at a first time, a first number of nodes for a cluster to process a request; assigning an amount of nodes equal to the first number of nodes to the cluster; determining a rate of progress of the request; determining, at a second time based on the rate of progress a second number of nodes; and modifying the amount of nodes to equal the second number of nodes. Systems include a cluster manager, to add and/or remove any nodes; the big data system, to process requests that utilize the cluster and nodes, and an automatic scaling cluster manager including a big data interface for communicating with the big data system; a cluster manager interface for communicating with the cluster manager; and a cluster state machine.

Abstract: The present invention is generally directed to systems and methods of providing automatic scaling pure-spot clusters. Such dusters may be dynamically rebalanced for further costs savings. In accordance with some methods of the present invention may include a method of utilizing a cluster in a big data cloud computing environment where instances may include reserved on-demand instances for a set price and on-demand spot instances that may be bid on by a user, the method including: creating one or more stable nodes, comprising spot instances with a bid price above a price for an equivalent on-demand instance; creating one or more volatile nodes, comprising spot instances with a bid price below a price for an equivalent on-demand instance; using one or more of the stable nodes as a master node; and using the volatile nodes as slave nodes.

Abstract: The invention is generally directed to systems and methods of automatically tuning big data workloads across various cloud platforms, the system being in communication with a cloud platform and a user, the cloud platform including data storage and a data engine. The system may include: a system information module in communication with the cloud platform; a static tuner in communication with the system information module; a cloud tuner in communication with the static tuner and the user; and an automation module in communication with the cloud tuner. Methods may include extracting information impacting or associated with the performance of the big data workload from the cloud platform; determining recommendations based at least in part on the information extracted; iterating through different hardware configurations to determine optimal hardware and data engine configuration; and applying the determined configuration to the data engine.

Abstract: The invention is directed to systems and methods for scheduling interactive database queries from multiple tenants onto distributed query processing clusters with service level agreements (SLAs). SLAs may be provided through a combination of estimation of resources per query followed by scheduling of that query onto a cluster if enough resources are available or triggering proactive autoscaling to spawn new clusters if they are not. In some embodiments systems may include a workflow manager; a resource estimator cluster; one or more execution clusters; and one or more metastores. A workflow manager may include an active node and a passive node configured to send a query to the resource estimator cluster and receive a resource estimate. A resource estimator cluster may be in communication with the workflow manager. One or more execution clusters may be scaled by the workflow manager as part of a schedule or autoscale based on workload.

Abstract: The present invention is generally directed to a caching framework that provides a common abstraction across one or more big data engines, comprising a cache filesystem including a cache filesystem interface used by applications to access cloud storage through a cache subsystem, the cache filesystem interface in communication with a big data engine extension and a cache manager; the big data engine extension, providing cluster information to the cache filesystem and working with the cache filesystem interface to determine which nodes cache which part of a file; and a cache manager for maintaining metadata about the cache, the metadata comprising the status of blocks for each file. The invention may provide common abstraction across big data engines that does not require changes to the setup of infrastructure or user workloads, allows sharing of cached data and caching only the parts of files that are required, can process columnar format.

Abstract: In general, the invention is directed to systems and methods of distributing tasks amongst servers or nodes in a cluster in a cloud-based big data environment, including: establishing a high_server_threshold; dividing active servers/nodes into at least three (3) categories of high usage servers, comprising servers on which usage is greater than the high_server_threshold; medium usage servers, comprising servers on which usage is less than the high_server_threshold, but is greater than zero; and low usage servers, comprising servers that are currently not utilized; receiving one or more tasks to be performed; scheduling the tasks by: first requesting that medium usage servers take tasks; if tasks remain that are not scheduled on the medium usage servers, schedule remaining tasks on low usage servers; if any tasks remain that are not scheduled on medium usage servers or low usage servers, scheduling remaining tasks on high usage servers.

Abstract: The present invention is generally directed to systems and methods of provisioning and using heterogeneous clusters in a cloud-based big data system, the heterogeneous clusters made up of primary instance types and different types of instances, the method including: determining if there are composition requirements of any heterogeneous cluster, the composition requirements defining instance types permitted for use; determining if any of the permitted different types of instances are required or advantageous for use; determining an amount of different types of instances to utilize, this determination based at least in part on an instance weight; provisioning the heterogeneous cluster comprising both primary instances and permitted different types of instances.

Abstract: The present invention is generally directed to a distributed computing system comprising a plurality of computational clusters, each computational cluster comprising a plurality of compute optimized instances, each instance comprising local instance data storage and in communication with reserved disk storage, wherein processing hierarchy provides priority to local instance data storage before providing priority to reserved disk storage.