Transactional databases used in applications are optimized for fast reads and writes with relatively simple queries on a small number of records. Data warehouses are optimized for batched writes and complex analytical queries. Between those use cases there are varying levels of support for fast reads on quickly changing data. To address that need more completely the team at Materialize has created an engine that allows for building queryable views of your data as it is continually updated from the stream of changes being generated by your applications. In this episode Frank McSherry, chief scientist of Materialize, explains why it was created, what use cases it enables, and how it works to provide fast queries on continually updated data.
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- Your host is Tobias Macey and today I’m interviewing Frank McSherry about Materialize, an engine for maintaining materialized views on incrementally updated data from change data captures
- How did you get involved in the area of data management?
- Can you start by describing what Materialize is and the problems that you are aiming to solve with it?
- What was your motivation for creating it?
- What use cases does Materialize enable?
- What are some of the existing tools or systems that you have seen employed to address those needs which can be replaced by Materialize?
- How does it fit into the broader ecosystem of data tools and platforms?
- What are some of the use cases that Materialize is uniquely able to support?
- How is Materialize architected and how has the design evolved since you first began working on it?
- Materialize is based on your timely-dataflow project, which itself is based on the work you did on Naiad. What was your reasoning for using Rust as the implementation target and what benefits has it provided?
- What are some of the components or primitives that were missing in the Rust ecosystem as compared to what is available in Java or C/C++, which have been the dominant languages for distributed data systems?
- In the list of features, you highlight full support for ANSI SQL 92. What were some of the edge cases that you faced in complying with that standard given the distributed execution context for Materialize?
- A majority of SQL oriented platforms define custom extensions or built-in functions that are specific to their problem domain. What are some of the existing or planned additions for Materialize?
- Can you talk through the lifecycle of data as it flows from the source database and through the Materialize engine?
- What are the considerations and constraints on maintaining the full history of the source data within Materialize?
- For someone who wants to use Materialize, what is involved in getting it set up and integrated with their data sources?
- What is the workflow for defining and maintaining a set of views?
- What are some of the complexities that users might face in ensuring the ongoing functionality of those views?
- For someone who is unfamiliar with the semantics of streaming SQL, what are some of the conceptual shifts that they should be aware of?
- The Materialize product is currently pre-release. What are the remaining steps before launching it?
- What do you have planned for the future of the product and company?
- From your perspective, what is the biggest gap in the tooling or technology for data management today?
- Thank you for listening! Don’t forget to check out our other show, Podcast.__init__ to learn about the Python language, its community, and the innovative ways it is being used.
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- Timely Dataflow
- Dryad: Distributed Data-Parallel Programs from SequentialBuilding Blocks
- [Naiad](Programs from SequentialBuilding Blocks): A Timely Dataflow System
- Differential Privacy
- Data Council Presentation on Materialize
- Change Data Capture
- Apache Spark
- Go language
- Rust Borrow Checker
- GDB (GNU Debugger)
- Apache Calcite
- ANSI SQL 92
- Correlated Subqueries
- OOM (Out Of Memory) Killer
- Log-Structured Merge Tree
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