Databricks Inc.

Michael Paul Armbrust

Shixiong Zhu

Matei Zaharia

Burak Yavuz

Mukul Murthy

Shi Xin

Tathagata Das

Andreas Neumann

Jonathan Mio

Ali Ghodsi

Ramesh Chandra

Xiaotong Sun

Yannis Papakonstantinou

Vuk Ercegovac

Nitin Sharma

Alexander Balikov

Boyang Peng

Paul Lappas

Xi Liang

Joseph Torres

John Sismanis

Min Yang

A database system performs pipelined execution of queries that process batches of streaming data. The database system compiles a database query to generate an execution plan and determines a set of stages based on the execution plan. The database query processes streaming data comprising batches. A scheduler schedules pipelined execution stages of the database query. Accordingly, the database system performs execution of a particular stage processing a batch of the streaming data in parallel with subsequent stages of the database query processing previous batches of the streaming data. The system further maintains watermarks for different stages of the database query.

Pipelined execution of database queries processing streaming data

A system receives ETL specification for processing stream data, including a transform operation represented using a database query specification. The system generates a dataflow graph of a sequence of database queries by decomposing the database query into a first database query that generates an intermediate results table, and a second database query that receives as input the intermediate results table and outputs data used for performing the transform operation. The system executes the sequence of database queries for performing the transform operation on stream data received from the source. When receiving an incremental data set, the system determines an output change set based on the received incremental data set by traversing an execution plan and processing each operator in the execution plan, and computing a change set of a particular operator from the change sets output by the one or more other operators based on the incremental data set.

Incremental execution of extract, transform, load process using microtechniques architecture

A system includes an interface and a processor. The interface is configured to receive a table indication of a data table and to receive a transaction indication to perform a transaction. The processor is configured to determine a current position N in a transaction log, determine a current state of the metadata; determine a read set associated with a transaction; attempt to write an update to the transaction log associated with a next position N+1; in response to a transaction determination that a simultaneous transaction associated with the next position N+1 already exists, determine a set of updated files; and in response to a determination that there is not an overlap between the read set associated with the current transaction and the set of updated files associated with the simultaneous transaction, attempt to write the update to the transaction to the transaction log associated with a further position N+2.

Update and query of a large collection of files that represent a single dataset stored on a blob store

The present application discloses a method, system, and computer system for providing access to data. The method includes receiving, by a data manager service from a data requesting service, a request using an identifier for a high-level data object to access a set of data associated with the high-level data object, determining, by the data manager service, low-level data object(s) corresponding to the set of data based on the identifier for the high-level data object, determining whether a user associated with the request has permission to access at least a subset of the low-level data object(s), and in response to determining that the user associated has permission to access the at least the subset of the low-level data object(s), generating, by the data manager service, a uniform resource locator (URL) via which the at least the subset of the one or more low-level data objects is accessible by the user.

Data sharing for network connected systems

A system for executing a streaming query includes an interface and a processor. The interface is configured to receive a logical query plan. The processor is configured to determine a physical query plan based at least in part on the logical query plan. The physical query plan comprises an ordered set of operators. Each operator of the ordered set of operators comprises an operator input mode and an operator output mode. The processor is further configured to execute the physical query plan using the operator input mode and the operator output mode for each operator of the query.

Structured cluster execution for data streams

A system for dataflow graph processing comprises a communication interface and a processor. The communication interface is configured receive an indication to generate a dataflow graph, wherein the indication includes a set of queries and/or commands. The processor is coupled to the communication interface and configured to: determine dependencies of each query in the set of queries on another query; determine a DAG of nodes based at least in part on the dependencies; determine the dataflow graph by determining in-line expressions for tables of the dataflow graph aggregating calculations associated with a subset of dataflow graph nodes designated as view nodes; and provide the dataflow graph.

Dataflow graph processing

A system for dataflow graph processing comprises a communication interface and a processor. The communication interface is configured receive an indication to generate a dataflow graph, wherein the indication includes a set of queries. The processor is coupled to the communication interface and is configured to: determine dependencies of each query in the set of queries on another query; determine a DAG of nodes based at least in part on the dependencies; insert a node in the DAG of nodes to generate an updated DAG to enforce an expectation; determine a dataflow graph based on the updated DAG; and provide the dataflow graph.

Dataflow graph processing with expectations

A system includes an interface and a processor. The interface is configured to receive a table indication of a data table and to receive a transaction indication to perform a transaction. The processor is configured to determine a current position N in a transaction log; determine a current state of the metadata; determine a read set associated with a transaction; attempt to write an update to the transaction log associated with a next position N+1; in response to a transaction determination that a simultaneous transaction associated with the next position N+1 already exists, determine a set of updated files; and in response to a determination that there is not an overlap between the read set associated with the current transaction and the set of updated files associated with the simultaneous transaction, attempt to write the update to the transaction to the transaction log associated with a further position N+2.

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Berkeley, CA, United States of America

Michael Paul Armbrust