YugabyteDB High Availability and Transactions for PostgreSQL and MongoDB Developers
In this post, we comparethe high availability and transactions architecture of PostgreSQL and MongoDB to that of YugabyteDB.
In this post, we comparethe high availability and transactions architecture of PostgreSQL and MongoDB to that of YugabyteDB.
In this post, we reviewed the building blocks of the YugabyteDB and mapped them to the corresponding concepts (if any) in PostgreSQL and MongoDB.
As described in our previous post “How We Built a High Performance Document Store on RocksDB?”, YugabyteDB’s distributed document store (DocDB) uses RocksDB as its per-node storage engine. We made multiple performance and data density related enhancements to RocksDB in the course of embedding it into DocDB’s document storage layer (figure below). These enhancements are distributed as part of the YugabyteDB open source project. The goal of this post is to deep dive into these enhancements for the benefit of engineering teams interested in leveraging RocksDB beyond its original design intent of a fast monolithic key-value store.
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FoundationDB enjoys a unique spot in the transactional NoSQL space given its positioning as a basic key-value database that can be used to build new, more application-friendly databases. Given that many of the guarantees provided by its core engine (such as multi-shard ACID transactions and high fault tolerance) are similar to those provided by the YugabyteDB database, our users often ask us for a comparison. These users are essentially trying to understand whether they should build their app directly using one of the three YugabyteDB APIs or should they explore/build a new database layer on FoundationDB first.
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Our post Getting Started with Distributed Backups in YugabyteDB details the core architecture powering distributed backups in YugabyteDB. It also highlights a few backup/restore operations in a single region, multi-AZ cluster. In this post, we perform distributed backups in a multi-region YugabyteDB cluster and verify that we achieve performance characteristics similar to those observed in a single region cluster.
We configured a 9 node cluster with 3 availability zones across 2 regions and repeated the benchmark introduced in the post.
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YugabyteDB is a distributed database with a Google Spanner-inspired strongly consistent replication architecture that is purpose-built for high availability and high performance. This architecture allows administrators to place replicas in independent fault domains, which can be either availability zones or racks in a single region or different regions altogether. These types of multi-AZ or multi-region deployments have the immediate advantage of guaranteeing organizations a higher order of resilience in the event of a zone or region failure.
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This post describes how you can run Presto queries on YCQL API as well as join data across the YCQL and YSQL APIs.
MongoDB’s “schemaless” JSON data modeling was initially attractive to web app developers looking to escape the constraints of traditional relational databases, but issues with data durability and ACID transactions have been a consistent challenge. While the recent MongoDB 4.0 release includes multi-document transaction support, this post explores where the platform falls short for transactional, high performance apps.
In this post, we will look at the architecture of YSQL, the PostgreSQL-compatible distributed SQL API in YugabyteDB. We will also touch on the current state of the project and the next steps in progress. Here is a quick overview:
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YugaByte has introduced YSQL, a PostgreSQL-compatible distributed SQL API in YugabyteDB’s 1.1 release. This architecture enables developer agility, operational simplicity, and scalability, addressing the needs of fast-growing online services and cloud-native deployments.