Distributed SQL offers greater scalability, availability, and geo-distribution of data compared to PostgreSQL, but it is important to understand the differences in behaviors between the two systems in order to make the most of your distributed SQL database.
This edition of the Distributed SQL Tips and Tricks blog looks how and why Yugabyte handles efficient row retrieval using a primary key, as well as the implications of not having one.
When considering replication options between YugabyteDB clusters, you have two options—xCluster async replication or setting up Kafka. Learn the when and how and why to both choices.
This edition of the Distributed SQL Tips and Tricks blog looks at YugabyteDB’s support for composite keys for tables (as well as secondary indexes).
In YugabyteDB you can use sharding and partitioning, so it can be confusing. In this blog post, we explore the differences (and similarities) between them and when each should be used.
This blog explores YugabyteDB’s ability to propagate client requests to surviving nodes when a node becomes unavailable. The behavior depends on factors such as client connection method, driver used, and connection pooling.
Setting the number of shards/tablets in your YugabyteDB cluster is dependent on your table size and activity, and while it is hard to gauge the exact number, here are some high-level suggestions.
YugabyteDB’s compatibility with Postgres and Cassandra allows seamless integration with popular third-party tools and integrations, such as visualization tools like pgAdmin, DBeaver, and ArcType, as well as Kafka, Spark, Flyway, and drivers and ORMS, due to the decision to reuse the Postgres query layer (YSQL) and recreate Cassandra using C++ (YCQL).
YugabyteDB’s implementation of DocDB, based on RocksDB, has made significant improvements to ensure node density and scalability, allowing hundreds of tablets per tserver without limiting the amount of data per node.
In a recent interview, Amit Patel, Principal Architect at Comcast, discussed the challenges of managing global data distribution with legacy databases, and how the company evaluates new technologies like distributed SQL to support modern workloads that align with their business goals and objectives.