A method and system for executing database queries in parallel using a shared metadata store. The metadata store may reside on a master node, where the master node is the root node in a tree. The master node may distribute query plans and query metadata to other nodes in the cluster. These additional nodes may request additional metadata from each other or the master nodes as necessary.

In some examples, a system receives function descriptors for different types of functions to be used when processing database queries, each function descriptor of the function descriptors comprising information relating to a respective function of the different types of functions. The system computes, based on a first function descriptor for a first function of the different types of functions, an estimate of a runtime metric associated with execution of the first function for processing a database query.

Systems and methods for managing a highly available distributed hybrid database comprising: a memory storing instructions; and one or more processors configured to execute the instructions to: receive a query from a user device to retrieve data from a distributed database comprising a source node, a first plurality of replica nodes, and a second plurality of replica nodes, wherein the source node and the first plurality of replica nodes form a transactional cluster, and wherein the second plurality of replica nodes forms an analytical cluster; determine whether to process the query using the transactional cluster or the analytical cluster based on one or more rules; translate the query into a first protocol that the determined cluster comprehends; select a replica node corresponding to the determined cluster; process the query using the selected replica node; and send data associated with results from processing the query to the user device.

A database system operates by facilitating execution of a query, where each of a plurality of sequential operator execution steps includes: determining whether each operator of a plurality of operators of a query operator execution flow is currently executable; generating a plurality of priority values by calculating a priority value for each operator based on whether each operator is determined to be currently executable, and based on a position value of each operator; identifying one operator of with a most favorable priority value; facilitating execution of the one operator on a queued set of data blocks to generate at least one output data block; identifying a next operator serially positioned consecutively after the one operator; and appending the at least one output data block to another queued set of data blocks of the next operator.

Methods and systems disclosed herein relate generally to temporally prioritizing queries of queue-task partitions based on distributions of flags assigned to bits corresponding to access rights.

Certain implementations of the disclosed technology may include methods and computing systems for performing high-density data compression, particularly on numerical data that demonstrates various patterns, and patterns of patters. According to an example implementation, a method is provided. The method may include extracting a data sample from a data set, compressing the data sample using a first compression filter configuration, and calculating a compression ratio associated with the first compression filter configuration. The method may also include compressing the data sample using a second compression filter configuration and calculating a compression ratio associated with the second compression filter configuration. A particular compression filter configuration to utilize in compressing the entire data set may be selected based on a comparison of the compression ratio associated with the first compression filter configuration and a compression ratio associated with the second compression filter configuration.

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.

Techniques and systems for reducing data stored on a block processing storage system are described. A losslessly reduced representation of a data block can include references to one or more prime data element blocks, and optionally a description of a reconstitution program which, when applied to the one or more prime data element blocks, results in the data block.

Methods, systems, and computer-readable media for query language interoperability in a graph database are disclosed. Data elements are inserted into a graph database using one or more of a plurality of graph database query languages. The graph database query languages comprise a first graph database query language associated with a first data model and a second graph database query language associated with a second data model. The data elements are stored in the graph database using an internal data model that differs from the first and second data models. One or more of the data elements are retrieved from the graph database based at least in part on a query. The query is expressed using a different graph database query language than the graph database query language used to insert the one or more retrieved data elements.

Implementations described herein relate to systems and methods to provide platform agnostic query acceleration. In some implementations, a method includes receiving, at a processor associated with a query acceleration service, a request from an client/application, wherein the request conforms to a particular wire protocol of a plurality of supported wire protocols, and wherein the request includes header data and body content data, analyzing the request to identify at least one of a query and a command in the body content data, determining an optimal matched model of the one or more query acceleration models, rewriting the query based on the optimal matched model, transmitting the rewritten query to the query processing platform, receiving a response to the rewritten query or the query from the query processing platform, and transmitting the received response to the application, wherein the transmission is configured based on the particular wire protocol.