In the second part of this blog series on PostgreSQL and YSQL date-time data types, the focus shifts to representing durations, or how long things last. Assuming the reader has read the first part and downloaded the companion code kit, this post explores the relevant data types, including interval, timestamp difference, and other related functions.
This is the first of a two-part blog post series that deals with the basic business of representing moments using time, date, and timestamp data types. YugabyteDB’s YSQL subsystem provides a similar experience to PostgreSQL, so the topic may still be enlightening for some YSQL users.
In this blog post, you’ll learn how to implement a timer for multi-statement operations using SQL with YugabyteDB or PostgreSQL. These tools are commonly used for various tasks, but their native timing feature only times individual SQL statements and doesn’t support end-to-end timing or output to spool files.
YugabyteDB is an open source, high-performance distributed SQL database built on a scalable and fault-tolerant design inspired by Google Spanner. YugabyteDB uses its own special distributed document store called DocDB. But it provides SQL and stored procedure functionality by re-using the “upper half” of the standard PostgreSQL source code. This is explained in the two part blog post “Distributed PostgreSQL on a Google Spanner Architecture”: (1) Storage Layer;
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YugabyteDB is an open source, high-performance distributed SQL database built on a scalable and fault-tolerant design inspired by Google Spanner. YugabyteDB uses its own special distributed document store called DocDB. But it provides SQL and stored procedure functionality by re-using the “upper half” of the standard PostgreSQL source code. This is explained in the two part blog post “Distributed PostgreSQL on a Google Spanner Architecture”: (1) Storage Layer;
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YugabyteDB is an open source, high-performance distributed SQL database built on a scalable and fault-tolerant design inspired by Google Spanner. YugabyteDB uses its own special distributed document store called DocDB. But it provides SQL and stored procedure functionality by re-using the “upper half” of the standard PostgreSQL source code. This is explained in the two part blog post “Distributed PostgreSQL on a Google Spanner Architecture”: (1) Storage Layer; and (2) Query Layer.
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YugabyteDB is an open source, high-performance distributed SQL database built on a scalable and fault-tolerant design inspired by Google Spanner. YugabyteDB uses its own special distributed document store called DocDB. But it provides SQL and stored procedure functionality by re-using the “upper half” of the standard PostgreSQL source code. This is explained in the two part blog post “Distributed PostgreSQL on a Google Spanner Architecture”: (1) Storage Layer; and (2) Query Layer.
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The term “polymorphism”, and its inflections, has various meanings in the context of programming languages in general. Roughly, it means “chameleon-like”. Of course, Wikipedia has something to say about it, here. PostgreSQL uses it with this meaning: “Parametric polymorphism: when one or more types are not specified by name but by… symbols that can represent any type”.
The term “variadic” has a universal meaning,
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I delivered the webinar “YugabyteDB: a distributed PostgreSQL database” last week. It was hosted by Postgres Conference in a series that they’d arranged so that the speakers who’d been selected to speak at their big annual conference in New York City in March could reach their audience. COVID-19 brought that event’s cancellation. This brief post tells you where you can find the edited videorecording and the PDF version of the slides that I used. It also gives me the chance to answer the couple of questions from attendees that shortness of time prevented me from answering during the webinar.
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YugabyteDB has two JSON data types, json and jsonb, to let you store documents in a column in a YSQL table and to do content-based queries with index support. YSQL is PostgreSQL compatible and it therefore supports every one of the rich set of about thirty five JSON-specific operators and functions that users of PostgreSQL’s JSON functionality might already know, and rely upon. These features let you handle semi-structured data,
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