Distributed memory column type database compilation actuator architecture

Abstract

The invention discloses a distributed memory column type database compilation actuator architecture, which solves the problem of execution speed defect on a DAG operator execution mode in a distributed memory column type database. A compiling actuator system on a single machine comprises a matching and scheduling device, an IR / Bitcode generator, a local IR code cache manager, a compiler, an executable code cache manager and an actuator. The local IR code cache manager comprises a local IR code cache unit, and the executable code cache manager comprises an executable code cache unit. The architecture also comprises a matching and scheduling device, an IR / Bitcode generator and a local IR code caching unit. The device is used for coordinating the local IR code cache manager and the executablecode cache manager, managing mapping information, carrying out matching code execution on an DAG task of an external request or generating a code, and responding to the external request. The IR / Bitcode generator is used for generating IR codes for the DAG task which are not matched with the executable code. The local IR code caching unit is used for performing caching management on the IR code generated by the IR / Bitcode generator.

Description

technical field [0001] The invention relates to the technical field of executors for distributed memory columnar databases, in particular to a framework for compiling executors for distributed memory columnar databases. Background technique [0002] In the massive data analysis scenario of the big data era, distributed in-memory columnar databases have become irreplaceable and important products relying on their fast query and analysis functions. Unlike traditional databases such as MySQL and Oracle, which store data on a single machine, distributed in-memory columnar databases divide data into columns, and further divide into blocks and store them in multiple nodes. [0003] In a distributed in-memory columnar database, the SQL query statement entered by the user first undergoes lexical analysis to form a syntax tree, and is further processed by a logical optimizer and a physical optimizer to form an execution plan represented by DAG. During execution, each node of the DAG...

AI Technical Summary

Problems solved by technology

[0007] 2. Operators cannot be dynamically generated during operation because they are selected, written and compiled into the system by developers

One is that it is impossible to generate more efficient execution code based on the information in the SQL parsing and optimization phase; the other is that it is impossible to generate new functional operators according to the environment such as heterogeneous scenarios, or support user-defined functions (UserDefined Function, UDF)

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