When indexing JSON documents in PostgreSQL, you can add indexes for the access patterns just like with relational table columns. We will walk through an example of this using the Pokémon GO Pokédex as a dataset, utilizing YugabyteDB, a distributed SQL database compatible with PostgreSQL.
DBAs often need to generate SQL statements using SQL queries and then execute them. PostgreSQL has many features that can help. Let’s walk through an example using YugabyteDB, which is Postgres-compatible and provides the same SQL language and catalog views.
The Orafce extension for PostgreSQL can be used to quickly and easily generate random strings to fill in a text column. Let’s look at an example on how to use Orafce, on either your PostgreSQL database or on a PostgreSQL-compatible database, like YugabyteDB.
There are many ways to handle ID generation in PostgreSQL. In this blog, we’ll demonstrate four ways to do so in Sequelize for PostgreSQL and YugabyteDB.
Let’s see how to capture pg_stat_statements from all the nodes in a persistent table to use for analysis. The data can then be stored in YugabyteDB tables, accessible from any node unless purged. Want to see how it can be done?
Every SQL execution in PostgreSQL and therefore in YugabyteDB YSQL takes time to process. A common way to identify how much is time spent on processing is to use the pg_stat_statements view in the database. However, the time visible in pg_stat_statements might differ from the time a database client registers for the execution. Where does this difference come from? Let’s take a look.
Finding one-to-one mapping of the differences between monolithic and cloud-native databases is difficult.
In this blog, we compare YugabyteDB and Oracle (RAC, Data Guard) via availability objectives.
This should help you understand how Oracle MAA options map to YugabyteDB’s intrinsic features when considering migration projects.
In Java development, garbage collection is a routine task. Applications generate garbage all the time. And that garbage is meticulously cleaned out by CMS, G1, Azul C4 and other types of collectors. Basically, our applications are born to bring value to this world, but nothing is perfect—including our applications that leave litter in the Java heap.
However, the story doesn’t end with the Java heap. In fact, it only starts there. Let’s take the example of a basic Java application that uses a relational database—such as PostgreSQL—and solid state drives (SSDs) as a storage device.
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In this blog, we’ll discover how to publish data to a Kafka topic and then read those messages to another sink, in this case a PostgreSQL database.
In this blog post, we will stream data to downstream databases leveraging YugabyteDB’s Change Data Capture (CDC) feature introduced in YugabyteDB 2.13. This will publish the changes to Kafka and then stream those changes to databases like MySQL, PostgreSQL, and Elasticsearch.